an examination of assessed valuation to income for...

AN EXAMINATION OF ASSESSED VALUATION TO INCOME

FOR FUNDING PUBLIC EDUCATION IN FLORIDA

By

SHARDA JACKSON SMITH

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF EDUCATION

UNIVERSITY OF FLORIDA

2017

To my father, Arnette Jackson

4

ACKNOWLEDGMENTS

I am indebted to the University of Florida and former President James Bernard Machen

for providing an opportunity to those of a modest background, which inspired me to pursue

degrees in the field of education and ultimately select the topic of this dissertation. I’d like to

thank my Dissertation Chair, Dr. R. Craig Wood, for his direct personality and the hours of

limitless wisdom he has afforded me. Without his guidance, my understanding of education

finance would be surface-level. I would also like to thank the remainder of my committee: Dr.

Thomas Dana for offering perspective, Dr. David Therriault for offering advice, and Dr. Linda

Eldridge for her command of educational practice and lifelong learning. In addition, I would also

like to acknowledge Dr. Rose Pringle for inspiring me to go further in my education, Dr. Jann

MacInnes for sharing her expertise involving methodology throughout the years, and Dr. Robert

Tauber, from Penn State, for presenting a real-world view of education theory and beyond.

I admire my extended family for their support and desire to see me succeed. I thank my

extraordinary mother for her foresight, poise, and belief in my purpose, my sister for

continuously reminding me of my capabilities, and my daughter for giving me life and

motivation. I thank my husband for his patience, intelligence, and everlasting love. Lastly, I

praise Almighty God for seeing me through to the end.

.

5

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF TABLES ...........................................................................................................................8

LIST OF FIGURES .........................................................................................................................9

LIST OF ABBREVIATIONS ........................................................................................................20

ABSTRACT ...................................................................................................................................21

CHAPTER

1 DEFINITION OF PROBLEM ................................................................................................23

Background .............................................................................................................................23 Problem Statement ..................................................................................................................28 Purpose Statement ..................................................................................................................28

Significance of the Study ........................................................................................................29

Methodology ...........................................................................................................................30 Research Questions .........................................................................................................30 Research Design ..............................................................................................................30

Definition of Terms ................................................................................................................32

Organization of the Study .......................................................................................................33

Summary .................................................................................................................................33

2 REVIEW OF LITERATURE .................................................................................................36

Introduction .............................................................................................................................37 Part I: Education Finance Programs .......................................................................................38

Education Funding Litigation .................................................................................................38

Fiscal Revenue and Capacity ..................................................................................................43 Federal Revenue ..............................................................................................................43 State Revenue ..................................................................................................................44 Local Revenue .................................................................................................................45

Local Fiscal Capacity ......................................................................................................46

Florida Education Funding Program ......................................................................................47 Florida’s Education Funding Responsibilities.................................................................47 Florida’s Tax Structure and Education ............................................................................49

Part II. The Property Tax ........................................................................................................51 Property Tax Climate ..............................................................................................................52

Property Rate and Tax Limitations .........................................................................................54

Housing Market ......................................................................................................................56 Assessment Equity ..................................................................................................................61 Part III: Property Value, Income, and Save Our Homes ........................................................62

6

Property Value and Income in Florida ....................................................................................62 Property Value .................................................................................................................62 Income .............................................................................................................................63

Florida’s Save Our Homes Assessment Limitation ................................................................64 Effects of Florida’s Save Our Homes .....................................................................................67 Part IV: Similar Studies and Topics .......................................................................................69 Summary .................................................................................................................................76

3 METHODS .............................................................................................................................87

Methodological Approaches ...................................................................................................87 Property Assessed Valuation (PAV) ...............................................................................87 Median Household Income (MHI) ..................................................................................88

Purpose of the Study ...............................................................................................................90 Research Design .....................................................................................................................91

Research Questions .........................................................................................................91 Research Design ..............................................................................................................91 Description of Measure ...................................................................................................92 Validity and Reliability of the Measure ..........................................................................94 Description of Analysis ...................................................................................................95

Setting and Participants ..........................................................................................................95

Data Sources and Organization...............................................................................................96 Property Assessed Valuation ...........................................................................................96 Median Household Income..............................................................................................97

Data Processing and Analysis .................................................................................................98 Summary .................................................................................................................................99

4 PRESENTATION OF RESULTS ........................................................................................100

Purpose of Study ...................................................................................................................100 Demographics .......................................................................................................................100 2006 Correlation Results ......................................................................................................100

Results for 2006 .............................................................................................................100

Interpretation of Results 2006 .......................................................................................101 2007 Correlation Results ......................................................................................................101

Results for 2007 .............................................................................................................101 Interpretation of Results 2007 .......................................................................................101

2008 Correlation Results ......................................................................................................101


2009 Correlation Results ......................................................................................................102 Results for 2009 .............................................................................................................102 Interpretation of Results 2009 .......................................................................................102


Results for 2010 .............................................................................................................103

Interpretation of Results 2010 .......................................................................................103 2011 Correlation Results ......................................................................................................103

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2013 Correlation Results ......................................................................................................104 Results for 2013 .............................................................................................................104 Interpretation of Results 2013 .......................................................................................104



2015 Correlation Results ......................................................................................................105 Results for 2015 .............................................................................................................105

Interpretation of Results 2015 .......................................................................................105 Correlation Results of 2006-2015 .........................................................................................106

Correlation Coefficient Results for 2006-2015 .............................................................106 Interpretation of Results 2006-2015 ..............................................................................106

Summary ...............................................................................................................................108

5 DISCUSSION AND RECOMMENDATIONS ...................................................................116

Introduction ...........................................................................................................................116 Summary of Findings ...........................................................................................................117 Implications for Practice .......................................................................................................118 Recommendations for Research ...........................................................................................120 Conclusion ............................................................................................................................123

APPENDIX

A PROPERTY TAX LIMITATIONS ACROSS THE UNITED STATES .............................128

B SAVE OUR HOMES VALUE HISTORY 2005-2015 ........................................................129

C SPSS OUTPUT RESULTS ..................................................................................................135

D INTERNAL REVENUE SERVICE ZIP CODE DATA DOCUMENTATION GUIDE ....225

BIBLIOGRAPHY ........................................................................................................................230

BIOGRAPHICAL SKETCH .......................................................................................................241

8

LIST OF TABLES

Table page

1-1 Florida Demographic Statistics: Population, Housing, Income, Poverty, and Land .........35

2-1 State Funding Formulas .....................................................................................................78

2-2 Florida School Districts Schedule of Millage Rates ..........................................................79

2-3 Florida Education Finance Program Formula ....................................................................80

2-4 Gross State and Local FEFP Components .........................................................................81

2-5 2008 Constitutional Amendment Impact (2009-2015) ......................................................82

2-6 2016 Statewide Just, Assessed, Exemption, and Taxable Values, by Property Type........83

2-7 Annual Homestead Portability Impact ...............................................................................84

4-1 List of Counties / School Districts Used in the Study .....................................................109

4-2 Table of Primary Descriptive Statistics, by Year.............................................................110

4-3 Table of Descriptive Statistics without Outliers, by Year ...............................................111

5-1 Save Our Homes Annual Increases, 2006-2016 ..............................................................127

A-1 Property Tax Limitations Across the United States .........................................................128

B-1 Save Our Homes Value History (2005-2008) ..................................................................129



9

LIST OF FIGURES

Figure page

2-1 The Income Effect..............................................................................................................85

2-2 Florida Average Annual Wages as a Percent of the United States ....................................86

4-1 Graphical Representation of the PPMCC Fluctuation, 2006-2015 ..................................112

4-2 Graphical Representation of the P-Value Fluctuation, 2006-2015 ..................................113

4-3 Graphical Representation of the PPMCC Fluctuation (without Outliers), 2006-2015 ....114

4-4 Graphical Representation of the P-Value Fluctuation (without Outliers), 2006-2015 ....115

C-1 2006 Descriptive Statistics for Median Household Income and Property Assessed

Valuation. .........................................................................................................................135

C-2 2006 Correlations for Median Household Income and Property Assessed Valuation.....135

C-3 Scatterplot Results for 2006. ............................................................................................135

C-4 Histogram Results for 2006 Median Household Income. ................................................136

C-5 Histogram Results for 2006 Property Assessed Valuation. .............................................136


Valuation. .........................................................................................................................137






Valuation. .........................................................................................................................139





10


Valuation. .........................................................................................................................141






Valuation. .........................................................................................................................143






Valuation. .........................................................................................................................145






Valuation. .........................................................................................................................147






Valuation. .........................................................................................................................149


11





Valuation. .........................................................................................................................151


C-43 Scatterplot results for 2014. .............................................................................................151




Valuation. .........................................................................................................................153





C-51 2006 Correlations without Outliers for Median Household Income and Property

Assessed Valuation. .........................................................................................................155

C-52 Scatterplot without Outliers Results for 2006. .................................................................155










12



C-60 Scatterplot without Outliers results for 2010. ..................................................................159















C-70 Scatterplot without Outliers results for 2015. ..................................................................164

C-71 2006 Test for Normal Distribution for Median Household Income (Descriptive

Statistics). .........................................................................................................................165

C-72 2006 Test for Normal Distribution for Median Household Income (Kolmogorov-

Smirnov Statistic and Shapiro-Wilk Statistic). ................................................................165

C-73 2006 Normal Q-Q Plot for Median Household Income...................................................166

C-74 2006 Test for Normal Distribution for Property Assessed Valuation (Descriptive

Statistics). .........................................................................................................................166

C-75 2006 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov-


C-76 2006 Normal Q-Q Plot for Property Assessed Valuation. ...............................................167

C-77 2006 Test for Normal Distribution without Outliers for Median Household Income

(Descriptive Statistics). ....................................................................................................168

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(Kolmogorov-Smirnov Statistic and Shapiro-Wilk Statistic). .........................................168

C-79 2006 Normal Q-Q Plot without Outliers for Median Household Income. ......................169

C-80 2006 Test for Normal Distribution without Outliers for Property Assessed Valuation




C-82 2006 Normal Q-Q Plot without Outliers for Property Assessed Valuation. ....................170


Statistics). .........................................................................................................................171





Statistics). .........................................................................................................................172















Statistics). .........................................................................................................................177

14





Statistics). .........................................................................................................................178















Statistics). .........................................................................................................................183





Statistics). .........................................................................................................................184






15










Statistics). .........................................................................................................................189





Statistics). .........................................................................................................................190















Statistics). .........................................................................................................................195

16





Statistics). .........................................................................................................................196















Statistics). .........................................................................................................................201





Statistics). .........................................................................................................................202






17










Statistics). .........................................................................................................................207





Statistics). .........................................................................................................................208















Statistics). .........................................................................................................................213

18





Statistics). .........................................................................................................................214















Statistics). .........................................................................................................................219





Statistics). .........................................................................................................................220






19









D-1 Internal Revenue Service Zip Code Data Documentation Guide ....................................229

20

LIST OF ABBREVIATIONS

ACS

CPI

American Community Survey

Consumer Price Index

DOE Department of Education

DOR Department of Revenue

F.S. Florida Statutes

FDOE Florida Department of Education

FDOR Florida Department of Revenue

FEFP

FTE

Florida Education Finance Program

Full Time Equivalent

HE Homestead Exemption

IBM International Bureau Machines

IRS Internal Revenue Service

MHI

OEDR

Median Household Income

Office of Economic and Demographic Research

PAV Property Assessed Valuation

PPMCC Pearson Product-Moment Correlation Coefficient

PT Portability Transfer

RLE Required Local Effort

SOH Florida’s “Save Our Homes” Property Assessment Limitation

SPSS Statistical Package for the Social Sciences

USCB

USPAP

VAB

United States Census Bureau

Uniform Standards of Professional Appraisal Practices

Value Adjustment Board

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Abstract of Dissertation Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Doctor of Education

AN EXAMINATION OF ASSESSED VALUATION TO INCOME

FOR FUNDING PUBLIC EDUCATION IN FLORIDA

By

Sharda Jackson Smith

August 2017

Chair: R. Craig Wood

Major: Educational Leadership

The purpose of this examination was to determine whether the state of Florida’s assessed

valuation and income were correlated because the Commissioner’s Required Local Effort

calculation uses school district property assessed valuation as the measure of wealth (i.e.,

funding capacity). This study retrospectively determined the degree of the relationship of the

variables over time and considered that the state assessment differential policy, Save Our Homes,

interfered with the degree of robustness in using property assessed valuation as the sole wealth

indicator.

This study concluded that wealth, a measure of fiscal capacity that is based on tangible

assets, is comprehensive and should weigh both property assessed valuation and income. The

results of the study determined that the association between property assessed valuation and

median household income was exceptionally weak and although the Pearson Product-Moment

Correlation Coefficient was always positive, it was not identical year to year. More convincingly,

the results were not statistically significant and likely due to chance for the past decade. The

outcome of this study provided the education finance field with further research that property

assessed valuation is not a complete gauge of wealth for the state of Florida and highly suggests

22

an income factor be added to the state education funding formula if it seeks to provide an

equitable education despite economic and geographic differences.

23

CHAPTER 1

DEFINITION OF PROBLEM

Background

A writer for the New York Times, a source of popular literature, concluded, “The inequity

of education finance in the United States is a feature of the system, not a bug, stemming from its

great degree of decentralization and its reliance on local property taxes.”1 Ironically, forty years

earlier, the Florida Legislature established the intent of the state education finance program and

promised to guarantee facilities that would provide “each student in the public education system

the availability of an educational environment appropriate to his or her educational needs…

equal to that available to any similar student, notwithstanding geographic differences and

varying local economic factors….”2 Today, the field of education still seeks to ensure that public

education has exercised an equitable system.

Economically, the United States Census Bureau’s 2015 Annual Survey of State

Government Tax Collections revealed that Florida’s state tax burden was low; yet, local

taxpayers paid more than half of all government income.3 The Florida Tax Watch Research

Institute concluded that the state “[relied] more heavily on local governments to fund public

services that any other state; [and] 55 percent of all government revenues in Florida [were] raised

1 Eduardo Porter, “In Public Education, Edge Still Goes to Rich,” New York Times, November 5,

2013, accessed April 15, 2017, http://www.nytimes.com/2013/11/06/business/a-rich-childs-edge-

in-public-education.html.

2 FLA. STAT. § 235.002 (1)(a) (2001).

3 Florida Tax Watch Research Institute, How Florida Compares Taxes: State and Local Tax

Rankings for Florida and the Nation (Florida Tax Watch Research Institute, 2015), 2-3, accessed

April 15, 2017, http://www.floridataxwatch.org/resources/pdf/2015_HFCTaxes_Final.pdf;

“STC005: State Government Tax Collections,” United States Department of Commerce,

accessed April 15, 2017, https://www.census.gov/govs/statetax.

http://www.nytimes.com/2013/11/06/business/a-rich-childs-edge-in-public-education.html

http://www.nytimes.com/2013/11/06/business/a-rich-childs-edge-in-public-education.html

http://www.floridataxwatch.org/resources/pdf/2015_HFCTaxes_Final.pdf

24

by local governments which was the highest percentage in the nation.”4 Furthermore, although

Florida homeownership5 was greater than the national average, the median household income

was lower than the national average ($47,507 to $53,889 respectively).6 The special economic

and demographic structure of Florida requires stakeholders in the field of education to

continually evaluate whether education and tax policies satisfy their intended motive. Table 1-1

summarizes the state of Florida’s demographic statistics as reported by the United States Census

Bureau.

The Florida tax system attempts to regulate the vast amount of population differences and

needs that are present within the state through exemptions and assessment differentials. Save Our

Homes, a petition-initiated amendment, “limited increases in the assessment of homestead

property to three percent per year or the percent change in the Consumer Price Index [CPI],

whichever is lower.”7 Presently, widowed, senior citizen, blind, disabled, and veteran

populations are granted estate exemptions to relieve property taxation, in addition to the Save

4 Ibid., 3.

5 The United States Census Bureau, American Community Survey defines a housing unit as

owner-occupied if the owner or co-owner lives in the unit, even if it is mortgaged or not fully

paid for. The homeownership rate is computed by dividing the number of owner-occupied

housing units by the number of occupied housing units or households.

6 “State and County QuickFacts,” United States Department of Commerce, accessed April 15,

2017, https://www.census.gov/quickfacts/table/PST045215/12; Data derived from Population

Estimates, American Community Survey, Census of Population and Housing, State and County

Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census,

Survey of Business Owners, Building Permits.

7 Budget Subcommittee on Finance and Tax, Property Tax Update, Fla. S. Rep. No. 2012-207, at

7 (2011), accessed April 15, 2017,

https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012-207ft.pdf; FLA.

STAT. § 193.155 (2016).

https://www.census.gov/quickfacts/table/PST045215/12

https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012-207ft.pdf

25

Our Homes assessment differential.8 With exceptions,9 the income of these populations are not

directly factored into exemption qualification, but many researchers believe that their status has

an inherent negative effect on their household income. Because exemptions are largely

voluntary, unlike taxes, the financial abilities of these populations become increasingly

complicated to decipher through taxable property assessed valuation. Yet, assessed valuation is

the gauge of local district wealth in the state of Florida, especially pertaining to education

finance.

Just as the Florida property tax system compensates for population differences for

individuals, its public education funding formula also attempts to counterbalance variation

among districts. The Florida Legislature mandated, “[e]ach district’s share of the state total

required local effort [be] determined by a statutory procedure that is initiated by certification of

the property tax valuation of each district by the Florida Department of Revenue.”10 The

8 See “Homestead and Other Exemptions,” Florida Department of Revenue, accessed April 15,

2017, floridarevenue.com/dor/property/taxpayers/exemptions.html for a complete list of

individual, family, fallen heroes, and other property tax exemptions; FLA. STAT. § 196 (2016).

9 Those claiming “Total and Permanently Disabled” and “Two Additional Homestead

Exemptions for Persons 65 and Older” are subject to income limitations (FLA. STAT. § 196

(2016) and FLA. CONST. art. VII, § 6); “Cost of Living Adjustments” were based on preceding

year’s CPI; See “Florida Property Tax Valuation and Income Limitation Rates,” Florida

Department of Revenue, accessed April 15, 2017,

floridarevenue.com/dor/property/resources/limitations.html.

10 Florida Department of Education, 2015-2016 Funding for Florida School Districts: Statistical

Report (Florida Department of Education, 2015), 2, accessed April 15, 2017,

http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf.

http://floridarevenue.com/dor/property/taxpayers/exemptions.html

http://floridarevenue.com/dor/property/resources/limitations.html

http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf

26

Commissioner,11 School Board,12 and Voter Referendum13 has the authority to adjust millage

rates in a manner that is unique to each district. However, with a 10-millage maximum14 for

operations, Florida school districts are confined to specific allocation mandates that aim to

satisfy state statutes but also meet the needs of each district.

The state of Florida’s public education funding formula observes property assessed

valuation as an appropriate measure of district financial capability. Millage rates, assessment

differentials, and exemptions, which are all rarely based on income, disrupt the premise that

property assessed valuation is interchangeable with both income or property taxes. The central

issue is that income, from which taxes are paid, may or may not correlate to property assessed

valuation but may cause distribution of taxpayer dollars to become lost in aggregation. If income

were equally proportional throughout the state, via property assessed valuation, the higher the

correlation and the more likely the education finance formula will satisfy the goal of educational

and financial equity.

The Florida Department of Revenue reported the 2016 just ($2,431.2 billion), assessed

($2,055.2 billion), exemptions ($447.7 billion) and taxable ($1,607.2 billion) values by property

type.15 The Save Our Homes assessment differential totaled $231.7 billion.16 Assessed as a

11 FLA. STAT.§ 1011.62(4) (2016), FLA. STAT.§ 1011.62(4)(e) (2016).

12 FLA. STAT.§ 1011.71(1) (2016), FLA. STAT.§ 1011.71(2) (2016), FLA. STAT.§

1011.71(3)(a) (2016).

13 FLA. STAT.§ 1011.73(1) (2011), FLA. STAT.§ 1011.73(2) (2011), FLA. STAT.§

200.001(3)(e) (2016), FLA. CONST. art. VII, § 12.

14 FLA. STAT.§ 200.065(5) (2016).

15 “Florida Property Tax Data Portal,” Florida Department of Revenue, accessed April 15, 2017,

http://floridarevenue.com/dor/property/resources/data.html.

16 Ibid.

http://floridarevenue.com/dor/property/resources/data.html

27

percentage of just value was 84.5 percent. Exemptions as a percentage of assessed value was

21.7 percent. Taxable as a percentage of just value was 66.2 and of assessed value was 78.2

percent. This amount of precision per county and state allows taxpayers to differentiate the

amount of collection and frames the trillions of dollars that circulate through the state and local

government.

Stakeholders dispute whether equity exists in not only education spending, but in revenue

dispersion. Recent literature has debated whether the property tax has been regressive or

progressive for particular communities, whether changing millage rates to offset changes in the

tax base is beneficial for all, and how to measure the amount of tax burden that has been placed

on school districts. It has also argued how much of education funding should be placed on the

property tax, the relationship between income mobility and quality of education, and whether

property value equates to property taxes.17

Stakeholders for education finance must consider the possibility that the housing bubble

of the Great Recession18 may have changed the economic climate in a manner that equates to a

funding formula that requires an adjustment. Because the property tax is the link that connects

17 E.g., Marcus T. Allen and William H. Dare, “Identifying Determinants of Horizontal Property

Tax Inequity: Evidence from Florida,” Journal of Real Estate Research 24, no. 2 (2002); James

Alm, Robert D. Buschman, and David L. Sjoquist, “Rethinking Local Government Reliance on

the Property Tax,” Regional Science and Urban Economics 41, no. 4 (2011): 320-31; Keith R.

Ihlanfeldt, “The Property Tax is a Bad Tax, but It Need Not Be,” Cityscape 15, no. 1 (2013):

255-59; Mark Skidmore, Laura Reese, and Sung Hoon Kang, “Regional Analysis of Property

Taxation, Education Finance Reform, and Property Value Growth,” Regional Science and Urban

Economics 42, no. 1-2 (2012): 351-63, accessed April 15, 2017,

http://dx.doi.org/10.1016/j.regsciurbeco.2011.10.008; Dean Stansel, Gary Jackson, and J.

Howard Finch, “Housing Tenure and Mobility with an Acquisition-Based Property Tax: The

Case of Florida,” Journal of Housing Research 16, no. 2 (2007).

18 United States Department of Labor, Bureau of Labor Statistics, BLS Spotlight on Statistics:

Recession of 2007-2009 (United States Department of Labor, 2012), accessed April 15, 2017,

http://www.bls.gov/spotlight/2012/recession/pdf/recession_bls_spotlight.pdf.

http://www.bls.gov/spotlight/2012/recession/pdf/recession_bls_spotlight.pdf

28

property assessed valuation and household income, the argument considered the vast amount of

population differences, and possibly assessment differentials exercised, in the state of Florida

that create an unbalanced arrangement of taxation and, consequently, education funding. If

property were the means in which wealth were measured in public education, property assessed

valuation abstractly yields a positive relationship that parallels median household income,

another form of wealth measurement. This study essentially determined if equity were present in

terms of the primary variable that dictates the funding formula and taxpayer ability to exert the

required local effort.

Problem Statement

The root of the problem rests in education finance methodology and tax code.

Controversy encompasses the property tax as a percent of personal income. Literature is limited

when it comes to the relationship of these variables through a correlational design. Also,

although the available previous research has focused on property value and income, recent

research has not focused greatly enough on the correlation of property valuation and median

household income after the implementation of an assessment differential for educational funding

purposes, especially post-Great Recession and specifically for the state of Florida. This research

related to the current literature by focusing the discussion of the impact of assessment

limitations, income and property on education funding. The research problem is to investigate

the extent to which property assessed valuation post-assessment limitation is the most equitable

measurement of school district wealth in Florida overtime.

Purpose Statement

Glomm, Ravikumar, and Schiopu stated, “if the provision of public education is a form of

redistribution between groups (e.g., between rich and poor, between old and young), the political

decisions have to aggregate conflicting preferences regarding taxation, redistribution, or income

29

inequality.”19 As a response, this study openly addressed existing theory and evidence that

predicated the premise that property assessed valuation without regard for income was the most

faultless measure of district wealth in public school finance. Generally, although the proportion

of income may be similar, the amount of property taxes a lower-income household pays has a

greater impact on their total earnings than a higher-income household due to the increasingly

limited amount of disposable income. In addition, it is not uncommon for households to have

high income but low property value or households to have low income but high property value.

The same theory stands for school districts. If property assessed valuation and median household

income were consistently correlated in Florida, this testified that property valuation was an

authentic measure of district wealth. If property assessed valuation and median household

income were not consistent in Florida, using assessed valuation as a measure of ability was less

valid.

Significance of the Study

Some believe that meaningful reform not only requires restoration of the public education

system but the tax system from which it is funded. However, what is more likely than a

dismantling of a state tax system is an education funding formula adjustment. Providing state aid

in a manner that ignores income neglects the probable extent to which property assessed

valuation may differ from median household income. Public education has the duty of providing

a proper funding structure for all students in the wake of the Great Recession, being mindful of

shifting environmental circumstances and socioeconomic status.

19 Gerhard Glomm, B. Ravikumar, and Iona Schiopu, “Chapter 9: The Political Economy of

Education Funding,” in Handbook of the Economics of Education, ed. Eric A. Hanushek,

Stephen J. Machin, Ludger Woessmann (Waltham: Elsevier, 2011), 617, accessed April 15,

2017, http://dx.doi.org/10.1016/B978-0-444-53444-6.00009-2.

http://dx.doi.org/10.1016/B978-0-444-53444-6.00009-2

30

Florida has a distinct population with a series of implications based on its demographics

that directly affect local government funding. This study added to existing literature considering

the interchangeability of property assessed valuation and median household income within the

state of Florida. It considered that although ad valorem property taxes have been an acceptable

foundation for local revenue, it may fall short as an absolute measure of income for public

education purposes. Otherwise, the probable extent to which income may differ from assessed

valuation and the unmeasured burden it places on populations when various exemptions and

assessment differentials are exercised is continually ignored. This study contemplated the

possible funds that taxpayers are capable of yielding, regardless of their income. At its

conclusion, policy-makers learned whether to consider an adjustment added to the public

education funding formula that weighed whether a median household income measure was

needed to uphold Florida’s constitutional promise.

Methodology

Research Questions

1. Is there a correlation between property assessed valuation and median household income

among school districts in the state of Florida over a 10-year span?

H0: 𝑟 = 0

HA: 𝑟 ≠ 0

2. How consistent is the correlation between property assessed valuation and median

household income amongst school districts in the state of Florida over a 10-year span?

Research Design

This study sought to determine how property assessed valuation and median household

income correlated amongst school districts in the state of Florida over the last decade of

available data (2006-2015). A bivariate correlational test was used to examine the relationship

between two variables per district, with a significance level (p-value) of .05. The variables of

31

interest were Property Assessed Valuation (PAV) and Median Household Income (MHI). One

PAV item and one MHI item were entered into the Statistical Package for the Social Sciences

(SPSS), a predictive analytic software, for each available school district in the state of Florida for

each year. The Pearson Product-Moment Correlation Coefficient was used to determine the

direction and strength of association between each variable due to the interval scales of

measurement for both variables. Median income was used, as opposed to mean income, because

it is a more robust measure of central tendency that is efficient and has little bias. It was expected

that there would be a strong, consistent correlation between PAV and MHI, which would be

statistically significant. A significant, strong correlation shows that PAV and MHI are related but

not that one variable caused changes in another variable. Nevertheless, the researcher focused on

the consistency of the correlation between the variables over a 10-year span.

In determining the strength of the association between the variables, the correlation

coefficient best depicted the relationship between PAV and MHI because it standardized the

variables. PAV was secured from the Florida Department of Revenue (FDOR).20 Property

assessed value, which was measured in real, personal, and centrally assessed, was based on the

annual property appraisal reported to FDOR. MHI was secured from the United States Census

Bureau. Demographic data were derived from the United States Census Bureau American

Community Survey and the Decennial Census Long Form.




32

Definition of Terms

Equity the outcome of practices that result in the same outcomes for members of a

group.21

Florida

Education

Finance Program

(FEFP)

Florida’s state policy on equalized funding.22

Median

Household

Income (MHI)

based on the United States Census Bureau, American Community Survey

1-Year Estimate; based on the distribution of the total number of

households and families including those with no income; based on

individuals 15 years old and over with income; computed on the basis of a

standard distribution.23

Millage Rate the amount per $1,000 used to calculate taxes on property; one one-

thousandth of a United States dollar. “Millage” may apply to a single levy

of taxes or to the cumulative of all levies.24

Property Tax the local government tax on real estate. “Ad valorem tax” means a tax

based upon the assessed value of property. The term “property tax” may be

used interchangeably with the term “ad valorem tax”.25

Property

Assessed

Valuation (PAV)

the difference of market value and assessment differentials (i.e., Save Our

Homes); an annual determination of: (a) The just or fair market value of an

item or property; (b) The value of property as limited by Article VII of the

Florida Constitution; or (c) The value of property in a classified use or at a

fractional value if the property is assessed solely on the basis of character

or use or at a specified percentage of its value under Article VII of the

Florida Constitution.26

21 Randall Lindsey, Kikanza Nuri Robins, and Raymond D. Terrell, Cultural Proficiency: A

Manual for School Leaders, 3rd ed. (Thousand Oaks: Corwin of Sage Publications, 2009), 166.




23 “Median Household Income,” United States Department of Commerce, United States Census

Bureau, accessed April 15, 2017, http://quickfacts.census.gov/qfd/meta/long_INC110213.htm.

24 FLA. STAT.§ 192.001(10) (2016).

25 FLA. STAT. § 192.001(1) (2016).

26 FLA. STAT. § 192.001(2) (2016); The Department of Revenue reports other exceptions that

make up the difference of Just Value and Assessed Valuations. They include a ten percent Non-


http://quickfacts.census.gov/qfd/meta/long_INC110213.htm

33

School Tax the product of taxable property assessed valuation and millage rate; total

tax liability.27

Organization of the Study

The first chapter of this dissertation presented a purpose of the study while previewing

the literature, methodology, and significance of the study. The next chapter overviewed relevant

literature and provided connections to the study. Chapter 3 of this study identified the design,

participants, setting, instruments, procedures, and the process used to analyze these data. The

fourth chapter included data presentation, analyses, and interpretation. The last chapter of this

dissertation reported the findings in context while presenting implications and recommendations.

Summary

Researchers agree, “[t]ax systems are hugely complex and interrelated, and, generally

speaking, efforts to make them fairer and more equitable usually result in making them more

complicated and more difficult for laypersons to understand – hence – to accept.”28 Under these

circumstances, Florida has managed to adopt an extensive funding formula. Yet, the debate

persists involving a more equitable and adequate education funding structure that is sensitive to

economic factors and current legislation. This argument deserves education-based academic

attention if taxable property assessed valuation will continue to be the single measure of wealth

in the state of Florida. Researchers have continued to address income as a legitimate factor in

Homestead Assessment Increase Cap, Agricultural Classification, Pollution Control Devices,

Conservation Lands and Working Waterfronts. The Save Our Homes assessment differential

makes up the greatest difference.

27 “Information for Taxpayers,” Florida Department of Revenue, accessed April 15, 2017,

http://dor.myflorida.com/dor/property/taxpayers.

28 David C. Thompson, Faith E. Crampton, and R. Craig Wood, Money and Schools, 5th ed.

(New York: Routledge, 2012), 101.

http://dor.myflorida.com/dor/property/taxpayers

34

determining state education funding capacities for districts.29 The goal is for Florida legislators to

consider whether the state education funding formula deserves an adjustment because of

advancing population differences, a changing economic environment, and the implementation of

dynamic policy that only an income factor can alleviate.

Determining the extent to which property assessed valuation and median household

income were correlated contributed to the field of education by adding to the body of knowledge

that connects taxpayers to the quality of education that students receive. The correlation between

the variables confronted whether the means in which the local government funds schools was

geographically and economically judicious. This study, specific to Florida, examined existing

policy that claimed to presently be sensitive to the wealth of households by using the

measurement of property assessed valuation as the sole contributor to local funding of education.

This study sought to confirm that concept through the observation of fiscal capacity. The next

chapter provided background information to help illustrate the climate of the education field as it

pertains to education finance, income, property, and assessment limitations.

29 E.g., Roe L. Johns, Edgar Morphet, and Kern Alexander, The Economics and Financing of

Education, 4th ed. (Englewood Cliffs: Prentice Hall, 1983); Ellwood Cubberly, The History of

Education (Boston: Houghton Mifflin, 1920); George D. Strayer and Robert M. Haig, The

Financing of Education in the State of New York (New York: Macmillan, 1923); Paul Mort, State

Support for the Public Schools (New York: Teachers College Press, Columbia University, 1926);

Percy Burrup, Financing Education in a Climate of Change. (Boston: Allyn and Bacon, 1974);

Michael Griffith, Lawrence O. Picus, Allan Odden, and Anabel Aportela, “Policies that Address

the Needs of High Property-Wealth School Districts with Low-Income Households,” (paper

presented to the Maine Legislature’s Joint Standing Committee on Education and Cultural

Affairs, ME, August 2013), accessed April 15, 2017,

http://www.maine.gov/legis/opla/MaineFiscalCapacityMeasuresPaper73013.pdf; Vern Brimley,

Deborah A. Verstegen, and Rulon R. Garfield, Financing Education in a Climate of Change, 12th

ed. (Boston: Pearson, 2016).

http://www.maine.gov/legis/opla/MaineFiscalCapacityMeasuresPaper73013.pdf

35

Table 1-1. Florida Demographic Statistics: Population, Housing, Income, Poverty, and Land

Category Value

Population estimates, July 1, 2016, (V2016) 20261439

Population estimates base, April 1, 2010, (V2016) 18804592

Population, Census, April 1, 2010 18801310

Persons under 5 years, percent, July 1, 2015, (V2015) 5.4

Persons under 18 years, percent, July 1, 2015, (V2015) 20.3

Persons 65 years and over, percent, July 1, 2015, (V2015) 19.4

Veterans, 2011-2015 1507738

Housing units, July 1, 2015, (V2015) 9209857

Housing units, April 1, 2010 8989580

Owner-occupied housing unit rate, 2011-2015 65.3

Median value of owner-occupied housing units, 2011-2015 $159000

Median selected monthly owner costs -with a mortgage, 2011-2015 $1438

Median selected monthly owner costs -without a mortgage, 2011-2015 $463

Median gross rent, 2011-2015 $1002

Median household income (in 2015 dollars), 2011-2015 $47507

Per capita income in past 12 months (in 2015 dollars), 2011-2015 $26829

Persons in poverty, percent 15.7

Population per square mile, 2010 350.6

Land area in square miles, 2010 53624.76

Source: Information adapted from “Quick Facts: Florida,” United States Census Bureau,

accessed April 15, 2017, http://www.census.gov/quickfacts/table/PST045215/12.

Note: The vintage year (e.g., V2015) refers to the final year of the series (2011 thru 2015).

http://www.census.gov/quickfacts/table/PST045215/12

36

CHAPTER 2

REVIEW OF LITERATURE

Scholars discuss a variety of factors that interfere with the most genuine assessed value of

property, leading to what may be defined as less than uniform assessment. While the Florida

education finance distribution uses property assessed value to determine a school district’s

financial capacity, taxation impacts the discretionary income of households resulting in a

compromised proportionality of assessed value to income ratio. What currently exists is an

education system that bases district financial ability on one historically reliable but yet indirect

variable: Property Assessed Valuation. If there were evidence to support that the variable used

was consistently or inconsistently associated with the most untouched form of Florida taxpayer

wealth (i.e., income), greater support can be had for an equitable education finance formula. This

study sought to determine if property assessed value were correlated to income, despite recent

policy that limits property assessment in the state of Florida.

The purpose of the literature review was to provide justification of the study through an

examination of the field. The information noted was secured from scholarly sources. Those

whom were identified as popular literature were noted as such and were occasionally provided to

document ubiquitous discourse. The literature review was organized into four parts. First, this

chapter discussed school finance programs and how it influenced the research question. It then

provided a brief analysis of property tax limitations and assessment equity. Afterward, the

current state of assessed value and income, and the inherent effect of the assessment limitation in

Florida were discussed. Last, the review evaluated approaches to similar topics.

37

Introduction

Florida’s Amendment 10, also known as the Save Our Homes1 (SOH) assessment

differential, requires that a particular homestead’s assessment not exceed three percent of its

assessed value or the percentage change in the Consumer Price Index (CPI) of the prior year,

whichever is lower. 2015’s annual SOH value totaled over $184 billion, peaked at over $427

billion and fell to as little as $56 billion within the last decade of reported data.2 This study

acknowledged that Florida’s public school funding is directly affected by this policy’s

implementation.

Concerning education finance, the purpose of a school district’s Required Local Effort

(RLE) is to appraise its share toward the Florida Education Finance Program (FEFP) calculation,

all the while sensitive to the abilities of the school district. School taxable value, a Department of

Revenue (DOR) computation used to determine RLE, is based on the SOH-influenced value of

property. With assessed value being the current FEFP measure of wealth, Florida’s education

stakeholders are left to determine how sensitive a district’s RLE is to the changes that take place

at the assessment level relative to tangible affluence.

Measuring wealth from a wielded figure that varies from household to household

jeopardizes the idea of equity for any one student, household, or district. With equity in mind,

this study made an observation overtime of the correlation between two variables that are

commonly used to determine wealth. It sought to discover whether there was a consistent

1 FLA. STAT.§ 193.155 (2016) and FLA. ADMIN. CODE R. 12D-8.0062 (1995); Beginning in

2009, assessment increases for non-homestead property were limited to 10 percent, for purposes

of non-school taxation.

2 “Florida Property Tax Data Portal,” Florida Department of Revenue Property Tax Oversight,

Research and Analysis, accessed April 15, 2017,



38

relationship between property assessed valuation and median household income, despite the

fluctuating assessment differential. It considered that tax policy possibly weakened the

relationship as time continued. If the correlation between assessed valuation and income were

not significant and consistent over time, there is a need to discuss whether the Florida Legislature

should collect more precise records at the household level to compensate for authentic financial

prosperity and to accurately measure local ability within the state education finance distribution

formula.

Part I: Education Finance Programs

Crampton, Wood and Thompson stated, “schools compete at all government levels for

tax revenues because there are practical limits on the amount of tax dollars that can be generated

– and those same dollars must be apportioned among the many worthy programs that serve the

public good.”3 Acknowledging this contingency, the next subsection outlined education funding

programs within the United States. It discussed the motivation and avenues from which revenue

for the federal, state, and local governments are collected and ended with a description of the

state of Florida’s education finance program.

Education Funding Litigation

The United States has the daunting task of creating a multidimensional education funding

system that seeks to provide an equitable opportunity for all students. The Every Student

Succeeds Act,4 which became public law in December of 2015, reauthorized the “50-year-old

Elementary and Secondary Education Act,5 the nation’s national education law and longstanding

3 Faith E. Crampton, R. Craig Wood, and David C. Thompson, Money and Schools, 6th ed. (New

York: Routledge, 2015), 85.

4 Every Student Succeeds Act, Pub. L. No. 114-95, 129 Stat. 1802 (2015).

5 Elementary and Secondary Education Act, Pub. L. No. 89-10, 27 Stat. 79 (1965).

39

commitment to equal opportunity for all students.”6 Although a federal law, its policy affects

both state and local government. State7 policy-makers are seeking to meet these goals through

education finance programs that exercise the concept of equity and adequacy.

Verstegen and Knoeppel summarized that states fund education finance structures in the

form of “flat grants, full state funding, foundation programs, district power equalization

6 “Every Student Succeeds Act (ESSA),” United States Department of Education, accessed April

15, 2017, http://www.ed.gov/essa.

7 State Department of Education Websites: Alabama (https://www.alsde.edu/);

Alaska (https://education.alaska.gov/); Arizona (http://www.azed.gov/);

Arkansas (http://www.arkansased.gov/); California (http://www.cde.ca.gov/)

Colorado (http://www.cde.state.co.us/); Connecticut (http://www.sde.ct.gov/sde/site/default.asp);

Delaware (http://www.doe.k12.de.us/site/default.aspx?PageID=1);

Florida (http://www.fldoe.org/) Georgia (http://www.gadoe.org/Pages/Home.aspx);

Hawaii (http://www.hawaiipublicschools.org/Pages/Home.aspx);

Idaho (http://sde.idaho.gov/Illinois (http://www.isbe.net/); Indiana (http://www.doe.in.gov/);

Iowa (https://www.educateiowa.gov/); Kansas (http://www.ksde.org/);

Kentucky (http://education.ky.gov/Pages/default.aspx);

Louisiana (http://www.louisianabelieves.com/); Maine (http://www.maine.gov/doe/);

Maryland (http://www.marylandpublicschools.org/); Massachusetts (http://www.doe.mass.edu/)

Michigan (https://www.michigan.gov/mde);

Minnesota (http://education.state.mn.us/mde/index.html)

Mississippi (http://www.mde.k12.ms.us/); Missouri (https://dese.mo.gov/); Montana

(http://opi.mt.gov/)Nebraska (https://www.education.ne.gov/); Nevada (http://www.doe.nv.gov/);

New Hampshire (http://education.nh.gov/); New Jersey (http://www.state.nj.us/education/); New

Mexico (http://ped.state.nm.us/ped/index.html); New York (http://schools.nyc.gov/default.htm)

North Carolina (http://www.dpi.state.nc.us/); North Dakota (https://www.nd.gov/dpi)

Ohio (http://education.ohio.gov/); Oklahoma (http://sde.ok.gov/sde/);

Oregon (http://www.ode.state.or.us/home/); Pennsylvania

(http://www.education.pa.gov/Pages/default.aspx#.VvCxrmQrIfE); Rhode

Island (http://www.ride.ri.gov/); South Carolina (http://ed.sc.gov/); South

Dakota (http://doe.sd.gov/); Tennessee (https://www.tn.gov/education);

Texas (http://tea.texas.gov/); Utah (http://www.schools.utah.gov/main/); Vermont

(http://education.vermont.gov/) ; Virginia (http://www.doe.virginia.gov/);

Washington (http://www.k12.wa.us/); West Virginia (https://wvde.state.wv.us/);

Wisconsin (http://dpi.wi.gov/); Wyoming (http://edu.wyoming.gov/).

http://www.ed.gov/essa

http://state.1keydata.com/alabama.php

http://state.1keydata.com/alaska.php

http://state.1keydata.com/arizona.php

http://state.1keydata.com/arkansas.php

http://state.1keydata.com/california.php

http://state.1keydata.com/colorado.php

http://state.1keydata.com/connecticut.php

http://state.1keydata.com/delaware.php

http://state.1keydata.com/florida.php

http://state.1keydata.com/georgia.php

http://state.1keydata.com/hawaii.php

http://state.1keydata.com/idaho.php

http://state.1keydata.com/illinois.php

http://state.1keydata.com/indiana.php

http://state.1keydata.com/iowa.php

http://state.1keydata.com/kansas.php

http://state.1keydata.com/kentucky.php

http://state.1keydata.com/louisiana.php

http://state.1keydata.com/maine.php

http://state.1keydata.com/maryland.php

http://state.1keydata.com/massachusetts.php

http://state.1keydata.com/michigan.php

http://state.1keydata.com/minnesota.php

http://state.1keydata.com/mississippi.php

http://state.1keydata.com/missouri.php

http://state.1keydata.com/montana.php

http://state.1keydata.com/nebraska.php

http://state.1keydata.com/nevada.php

http://state.1keydata.com/new-hampshire.php

http://state.1keydata.com/new-jersey.php

http://state.1keydata.com/new-mexico.php


http://state.1keydata.com/new-york.php

http://state.1keydata.com/north-carolina.php

http://state.1keydata.com/north-dakota.php

http://state.1keydata.com/ohio.php

http://state.1keydata.com/oklahoma.php

http://state.1keydata.com/oregon.php

http://state.1keydata.com/pennsylvania.php

http://state.1keydata.com/rhode-island.php


http://state.1keydata.com/south-carolina.php

http://state.1keydata.com/south-dakota.php


http://state.1keydata.com/tennessee.php

http://state.1keydata.com/texas.php

http://state.1keydata.com/utah.php

http://state.1keydata.com/vermont.php

http://state.1keydata.com/virginia.php

http://state.1keydata.com/washington.php

http://state.1keydata.com/west-virginia.php

http://state.1keydata.com/wisconsin.php

http://state.1keydata.com/wyoming.php

40

systems, and combination approaches.”8 Foundation programs provide “a uniform state

guarantee per pupil, with state and local district funding.”9 Flat grants are funds that are absolute

per unit, “paid to districts without concern for a local share or local ability to pay,” while

equalization grants increase state aid to local districts with the least fiscal capacity.10 Full state

funding grants change the “portion of the local property tax dedicated to school support to a state

tax so that it can be pooled at the state level and redistributed as aid to schools without regard to

local property wealth.”11 Multi-tiered grants are a combination of plans. In addition, state

educational doctrine also seek to provide equity via vertical adjustments through statutes.

Currently, there are thirty-seven state legislative policies that provide foundational funding, two

that have adopted district power equalization systems, one that uses flat grants, one that uses full

state funding, and nine that have adopted the multi-tiered approach to funding schools.12 Table 2-

1 summarizes the types of funding formulas that each state has adopted.

What makes equity throughout the states rigorous and fluid is that it seeks to coagulate

education finance through taxation. History has shown how arduous it is to balance economic

8 Deborah A. Verstegen and Robert C. Knoeppel, “From Statehouse to Schoolhouse: Education

Finance Apportionment Systems in the United States,” Journal of Education Finance 38, no. 2

(2012): 164.

9 Deborah A. Verstegen, “Policy Brief: How Do States Pay for Schools? An Update of a 50-State

Survey of Finance Policies and Programs,” (paper presented at the Association for Education

Finance Policy Annual Conference, San Antonio, TX, March 2014), 2, accessed April 15, 2017,

https://schoolfinancesdav.files.wordpress.com/2014/04/aefp-50-stateaidsystems.pdf.

10 Faith E. Crampton, R. Craig Wood, and David C. Thompson, Money and Schools, 6th ed.

(New York: Routledge, 2015), 89 and 93.

11 Ibid., 96.

12 Deborah A. Verstegen, “Policy Brief: How Do States Pay for Schools? An Update of a 50-

State Survey of Finance Policies and Programs,” (paper presented at the Association for

Education Finance Policy Annual Conference, San Antonio, TX, March 2014), 2, accessed April

15, 2017, https://schoolfinancesdav.files.wordpress.com/2014/04/aefp-50-stateaidsystems.pdf.

https://schoolfinancesdav.files.wordpress.com/2014/04/aefp-50-stateaidsystems.pdf


41

theory that supports the belief that individuals with similar income and assets should pay the

same amount in taxes and the theory that supports taxes paid should progressively increase with

the amount of earned income. Scholars refer to these concepts as vertical and horizontal equity.

Horizontal equity suggests that “similarly situated individuals face similar tax burdens.”13

Vertical equity suggests that taxpayers “with the greater ability to pay should pay more tax.”14

Dishman and Redish declared, “Following [San Antonio Independent School District v.]

Rodriguez,15 finance litigation cases moved to state courts, initially advancing under a state

constitutional ‘equity’ theory challenging the disparate and allegedly discriminatory method in

which states chose to disburse state educational dollars.”16 More recently, Verstegen claimed,

“States are moving to weighted systems to tailor funding streams to individual student needs and

characteristics and providing additional funding for remote schools/districts.”17

13 David Elkins, “Horizontal Equity as a Principle of Tax Theory,” Yale Law and Policy Review

24, no. 1 (2006): 43, accessed April 15, 2017,

http://digitalcommons.law.yale.edu/ylpr/vol24/iss1/3.

14 American Institute of Certified Public Accountants, Tax Policy Concept Statement – Guiding

Principles of Good Tax Policy: A Framework for Evaluating Tax Proposals (Tax Division of the

American Institute of Certified Public Accountants, 2017), 10, accessed April 15, 2017,

http://www.aicpa.org/advocacy/tax/downloadabledocuments/tax-policy-concept-statement-no-1-

global.pdf.

15 San Antonio Independent School District v. Rodriguez, 411 U.S. 1 (1973).

16 Mike Dishman and Traci Redish, “Education Adequacy Litigation and the Quest for Equal

Educational Opportunity,” Peabody Journal of Education 85, no. 1 (2010): 16.

17 Deborah A. Verstegen, “Policy Brief: How Do States Pay for Schools? An Update of a 50-

State Survey of Finance Policies and Programs,” (paper presented at the Association for

Education Finance Policy Annual Conference, San Antonio, TX, March 2014), 1, accessed April

15, 2017, https://schoolfinancesdav.files.wordpress.com/2014/04/aefp-50-stateaidsystems.pdf.

http://digitalcommons.law.yale.edu/ylpr/vol24/iss1/3

http://www.aicpa.org/advocacy/tax/downloadabledocuments/tax-policy-concept-statement-no-1-global.pdf

http://www.aicpa.org/advocacy/tax/downloadabledocuments/tax-policy-concept-statement-no-1-global.pdf

https://en.wikipedia.org/wiki/Case_citation


42

Adequacy is another concept that is embraced by the states. Adequacy is present when

“students in every school district receive an education that meets some minimum standard,”18

often set by federal and state education laws. Odden believed that adequacy grants “resources to

schools that will enable them to make substantial improvements in student performance over

[time] as progress toward ensuring that all, or almost all, students meet their state’s performance

standards in the longer term.”19 Yinger stated that stakeholders agree that the “foundation plan

with a foundation level based on a generous notion of educational adequacy, a required

minimum tax rate, and some kind of educational cost adjustment that provides extra funds for

high-need districts [form] the core of an acceptable reform of state education finance.”20

With this in mind, state legislators still struggle to arrive at a consensus on the best way

to provide and measure an equitable and adequate education finance program within a state.

Evidence within judicial and educational literature show that state legislation require an

education finance formula that is tailored for its specific economic and demographic conditions,

likely resulting in different definitions of equity and adequacy for the circumstance. Wood

considered the complexity of “statistically similar school districts serving statistically similar

students [that] produce significantly differing results within a state that exhibits a high degree of

18 John Yinger, ed., Helping Children Left Behind: State Aid and the Pursuit of Educational

Equity (Cambridge: MIT Press, 2004), 9.

19 Allan R. Odden, Lawrence O. Picus, and Michael E. Goetz, “A 50-State Strategy to Achieve

School Finance Adequacy,” Educational Policy 24, no. 4 (2010): 630,

http:doi.org/10.1177/0895904809335107.

20 John Yinger, ed., Helping Children Left Behind: State Aid and the Pursuit of Educational

Equity (Cambridge: MIT Press, 2004), 46.

http://doi.org/10.1177/0895904809335107

43

statistical education finance equity.”21 Across America, school and tax systems work

simultaneously to derive a solution of financial support to satisfy equity and adequacy for all.

Fiscal Revenue and Capacity

Federal Revenue

Controversy accompanying California’s Serrano v. Priest22 led to the country’s

consciousness of state and district-wide facilitation of adequate education funding. On these

terms, the federal, state, and local governments have intertwined roles. School districts receive

funds from the federal government directly and through the state as an administering agency;

districts receive federal funds from various departments such as the Department of Education,

Veterans Administration, Department of Interior, Department of Labor, Department of Defense

and Department of Agriculture.23

Federal funding aids state programs associated with a number of legislation. Support

programs are often associated with the No Child Left Behind Act,24 the Individuals with

Disabilities Education Act,25 the Workforce Investment Act,26 and the Carl D. Perkins Vocational

21 R. Craig Wood, “Justiciability, Adequacy, Advocacy, and the ‘American Dream,’” The

Kentucky Law Journal 98, no. 4 (2010): 776.

22 Serrano v. Priest, 487 P.2d 1241, 5 Cal. 3d 584 (1971); Serrano v. Priest, 557 P.2d 929, 18

Cal. 3d 728 (1976); Serrano v. Priest, 569 P.2d 1303, 20 Cal. 3d 25 (1977).




24 No Child Left Behind Act, Pub. L. No. 107–110, 115 Stat. 1425 (2001).

25 Individuals with Disabilities Education Act, Pub. L. No. 101-476, 1103 Stat. 104 (1990).

26 Workforce Investment Act, Pub. L. No. 105-220, 112 Stat. 936 (1998).


44

and Technical Education Act.27 The American Recovery and Reinvestment Act of 2009 (ARRA)28

provided approximately “$100 billion for education, creating a historic opportunity to save

hundreds of thousands of jobs, support states and school districts, and advance reforms and

improvements that [seek to] create long-lasting results for students and the nation including early

learning, K-12, and post-secondary education.”29 Initiatives like Pell grants, work-study,

independent living services, teacher incentives, teacher quality, education for homeless youth,

and statewide data systems are supported by the federal government.30

State Revenue

In education finance, the state serves as the liaison between the local and federal

governments, as needed. Although state legislation has the option of determining the manner in

which to provide education finance to school districts, they are still under the jurisdiction of

federal legislation. State legislators have the liberty of constructing funding formulas that are

then implemented by local governments. State legislators often have the goal to provide revenue

methods that are malleable, sensitive to regional conditions, and that widen the dissemination of

the tax burden to consumers.

27 Carl D. Perkins Vocational and Technical Education Act, Pub. L. No. 109-270, 120 Stat. 683

(2006).

28 American Recovery and Reinvestment Act, Pub. L. No. 111–5, 115 Stat. 123 (2009).

29 “The American Recovery and Reinvestment Act of 2009: Saving and Creating Jobs and

Reforming Education,” United States Department of Education, March 7, 2009, accessed April

15, 2017, http://www2.ed.gov/policy/gen/leg/recovery/implementation.html.

30 United States Department of Education, Fiscal Year 2017 Budget Summary and Background

Information (United States Department of Education, 2016), accessed April 15, 2017,

https://www2.ed.gov/about/overview/budget/budget17/summary/17summary.pdf.

http://www2.ed.gov/policy/gen/leg/recovery/implementation.html

https://www2.ed.gov/about/overview/budget/budget17/summary/17summary.pdf

45

Local Revenue

Local government is the frontline of education funding. For instance, the Florida

Department of Revenue (FDOR) acknowledged, “Roughly, 50 percent of Florida’s public

education funding and 30 percent of its local government revenues come from property taxes.”31

Crampton, Wood, and Thompson state, “At the local level, tax systems derive a large percentage

of its revenues from real property taxation, and a significant portion is used by school districts –

a reality that has caused the property tax to be seen (incorrectly) as the ‘school tax.’”32

Customarily, property taxes are derived from assessed valuation. The Florida Administrative

Code defines assessed value as:

The price at which a property, if offered for sale in the open market, with a

reasonable time for the seller to find a purchaser, would transfer for cash or its

equivalent, under prevailing market conditions between parties who have

knowledge of the uses to which the property may be put, both seeking to

maximize their gains and neither being in a position to take advantage of the

exigencies of the other.33

Most local school districts across America extract revenue based on millage rates

imposed on local districts. A mill is defined as one one-thousandth of a dollar.34 The millage rate

is established through taxing authorities (in terms of Florida’s education finance, the school

district/state commissioner) to meet the needs of fiscal conditions and projections. Not all states

grant school districts with taxing authority. In the state of Florida, “Budgeted revenues from

31 Florida Department of Revenue, Property Tax Oversight (Florida Department of Revenue), 1,

accessed April 15, 2017,

http://floridarevenue.com/dor/property/taxpayers/pdf/ptoinfographic.pdf.

32 Faith E. Crampton, R. Craig Wood, and David C. Thompson, Money and Schools, 6th ed.

(New York: Routledge, 2015), 85.

33 FLA. ADMIN. CODE R. 12D-1.002[2] (1996).

34 FLA. STAT.§ 192.001 (2016).

http://floridarevenue.com/dor/property/taxpayers/pdf/ptoinfographic.pdf

46

local taxes are determined by applying millage levies to 96 percent of the school taxable value of

property. School board adoption of millage levies is governed by the advertising and public

meeting requirements of Chapter 200, F.S.”35 Table 2-2 illustrates the types of millage rates

imposed on Florida’s school districts, the statute that grants the authority, and for what those

funds can be distributed.

Local Fiscal Capacity

State legislators vary the measurement of local ability. Yet, there are themes present.

State statutes use pupil/population, property, income, sales, motor, excise or a combination of

such (most of which equalized) to determine how much school districts are able to supply for

education funding.36 Measuring local ability across the United States has quite a degree of

diversification pending the circumstance, all of which contingent on the value of property.

State statutes may require the collection of local revenue from particular taxes, it does not

mean that local fiscal capacity is measured using those same taxes. Although several state

constitutions prohibit collecting state income taxes, some states base its fiscal capacity on

income taxes in addition to property taxes. In the state of Florida, local fiscal capacity (or RLE)

is based on the taxable assessed valuation of property for school purposes. The FDOE reports:

The Florida Department of Revenue provides the Commissioner with its most

recent determination of the assessment level of the prior year’s assessment roll for

each district and for the state. A millage rate is computed based on the positive or

negative variation of each district from the state average assessment level. The

millage rate resulting from application of this equalization factor is added to the




36 “Education Finance Statistics Center,” National Center for Education Statistics, Education

Finance Statistics Center, accessed April 15, 2017, http://nces.ed.gov/edfin/state_financing.asp;

Website offers descriptions of funding systems arranged by state. Taxes that are not named may

also be used.


http://nces.ed.gov/edfin/state_financing.asp

47

state average required local effort millage. The sum of these two rates becomes

each district’s certified required local effort millage.37

Florida Education Funding Program

The purpose of Florida’s education system is to provide an educational opportunity that is

sensitive to local property tax bases, education program costs, costs of living, and costs for

equivalent educational programs due to sparsity and dispersion of student population.38 Over

fifty years ago, the Florida Legislature enacted the FEFP and established the state policy on

equalized funding to guarantee facilities to “each student in the public education system the

availability of an educational environment appropriate to his or her educational needs… equal to

that available to any similar student, notwithstanding geographic differences and varying local

economic factors….”39

The state of Florida’s education finance program attempts to compensate for the

uniqueness of the population therein through adjustments. Table 2-3 illustrates the different

components of the entire FEFP. The FEFP levies the RLE from taxable value for school

purposes. The Full Time Equivalent (FTE) student is the primary method in which the FEFP

determines need and eventually disperses the appropriate funds.

Florida’s Education Funding Responsibilities

Florida funding for school districts is elaborate, unifying a combination of federal, state,

and local funding. The FDOE reported that school funding consisted of “41.71 percent of

financial support from state sources, 45.93 percent from local sources (including the RLE portion




38 Ibid., 1.

39 Ibid; FLA. STAT.§ 235.002(1)(a) (2001).


48

of the FEFP) and 12.36 percent from federal sources.”40 The FTE, calculated five times per year

before arriving at the ultimate allotment, is the state education finance program base that makes

it primarily foundational.

For the 2015-2016 school year, the state legislature earmarked $7,758,617,37441 for the

FEFP.42 These funds, accrued from the General Revenue Fund, Educational Enhancement Trust

Fund, and the State School Trust fund, were mostly obtained from the state sales tax on goods

and services. More specifically, the Florida Legislature set the amount of $7,605,422,57243 as the

adjusted local fiscal capacity for the state. The statewide district millage set by the

Commissioner determined each district’s share of local contribution that is needed to fund K-12,

a part of the larger FEFP calculation.

The FEFP’s different components make the finance distribution more equitable. The

Florida Department of Education reports that “[f]unds for state support to school districts are

provided primarily by legislative appropriations” and “[l]ocal revenue for school support which

is derived almost entirely from property taxes levied by Florida’s school districts.”44 However,

there are portions of school district millage rates that are relatively unrestricted.45 Millage types

that are established by voter referendum have undefined application, although limited to year-

40 Ibid., 1.

41 Ibid; This amount consisted of $7,488,209,041 from the General Revenue Fund, $219,369,431

from the Educational Enhancement Trust Fund and $51,038,902 from the State School Trust

Fund; Florida Department of Education.

42 Ibid., 2.

43 Ibid., 1; 2015-2016 School Year.

44 Ibid., 2.

45 FLA. STAT § 1011.73 (2011) outlines portions of the schedule of millage rates that are subject

to Voter Referendum rather than the School Board or Commissioner of Education.

49

based time frames.46 Table 2-4 defines the different components that when joined together make

up the Gross State and Local FEFP dollars.

Florida’s Tax Structure and Education

Florida’s constitution has not only provided protection for those who receive education, it

has also provided security for those who pay for education. The history of millage rate

limitations47 within the state of Florida is fairly extensive. The state constitution does not allow

school districts to collect an income tax or an income tax surcharge; It also prohibits a state

income tax and state property tax.48 Local governments, however, have greater access to tax

structures. School districts receive funding from several different avenues. For instance, Florida

school boards are authorized to levy a sales surtax of 0.5 percent for capital outlay purposes, if

46 E.g., Debt service is established by voter referendum and is limited to debt service.

47 E.g., The late 1960s state of Florida’s Constitution, “limited millage rates to 10 mills for

county purposes, 10 mills for municipal purposes, and 10 mills for school purposes. These rates

could be exceeded for not more than two years if approved by the voters, or to repay bonds

authorized by the voters.” In 1980, “an immediate $25,000 exemption for school taxes, and a

phased increase in the homestead exemption for other taxes, contingent on compliance with fair

market assessment in the county where the property is located.” Also in 1980, “Truth in Millage

(TRIM) legislation was intended to provide information to taxpayers that would shift taxpayer

concern over the level of taxes away from the assessment process and toward the local budgetary

processes where millage rates were set. Under this legislation, proposed tax rates are compared

to a tax rate which will, if applied to the same tax base, provide the same amount of property tax

revenue for each taxing authority as was levied during the prior tax year. This is referred to as

the rolled-back rate. A millage rate higher than the rolled-back rate must be advertised as a tax

increase, even if the actual level is lower.” In 2007, the Maximum Millage Limitation established

that, “the first-year maximum levies required reductions in taxes levied for most jurisdictions;

going forward the maximum is based on the rolled-back rate and the change in per capita Florida

income. The maximum levy may be exceeded by a super-majority vote or referendum.” [Budget

Subcommittee on Finance and Tax, Property Tax Update, Fla. S. Rep. No. 2012-207, at 2 (2011),


https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012-207ft.pdf].

48 FLA. STAT.§ 220.02 (2016).


50

approved by referendum.49 A portion of state motor vehicle license tag proceeds is dedicated to

school board debt service or capital outlay.50 As discussed prior, the Commissioner, School

Board, and Voter Referendum allow adjustments to millage rates. The state constitution provides

for a homestead exemption of $25,000 on the assessed value of residential property for school

purposes.51 Gross revenue from the sale of lottery tickets and other earned revenues are

deposited into the Educational Enhancement Trust Fund.52 Also, districts may derive revenue

from the collection of the gross receipts tax on utilities.53 Florida school districts are restricted to

supplying no more than 90 percent of funding from local revenue.54 For the 2015-2016 School

Year the following was reported by the Florida Department of Education pertaining to millage

rate that satisfies this constraint:

Based on the 2015 tax roll provided by the Florida Department of Revenue, the

Commissioner certified the required millage of each district on July 14, 2015. The

state average millage was set at 4.984 and certifications for the 67 school districts

varied from 5.132 mills (Gulf) to 1.802 mills (Monroe) due to the assessment

ratio adjustment and the 90 percent limitation. The 90 percent limitation reduced

the required local effort of seven districts. The districts and their adjusted millage

rates were: Collier (3.229), Franklin (3.551), Martin (4.848), Monroe (1.802),

Sarasota (4.504), Sumter (3.791) and Walton (2.707).55

49 FLA. STAT.§ 211.055(6) (2016).

50 FLA. CONST. art. XII, § 9(d).

51 FLA. CONST. art. VII, § 6.

52 FLA. STAT. § 1010.70 (2011); FLA. STAT.§ 24.121 (2016).

53 FLA. CONST. art. XII, § 9(a)(2).

54 FLA. STAT.§ 1011.66 (2016).





51

Florida’s property taxes are based on annual assessed property values, exemptions, and

millage rates. Its Homestead Exemption (HE) is a portion of the value of the home that is

exempted from local school property taxes. It also exempts business inventories from local

school taxes. Other than the HE and SOH, homesteaders can receive exemptions for qualification

as a widow(er), blind person, totally and permanently disabled person, senior citizen, veteran,

and more.56 Yet, overtime, some57 have adopted the view that exemptions and assessment

differentials have created a false perception of home value and distribution of tax effort. This

concept directly effects education funding in a formula that uses property value as the sole basis

of determining local ability to support education.

Part II. The Property Tax

Blankenau and Skidmore agreed, “evaluating the effects of education finance reform

without jointly considering tax and expenditures limitations could lead to biased estimates of the

effects of education finance reform.”58 Thusly, this subsection began with a description of the

property tax and the argument of the general consensus of its need. Appropriately, the idea of the

portability of property tax limitations and its implications were reviewed. Afterward, the

discussion shifted toward the housing market, acknowledging the income effect and how it

influences assessed value and income. Last, assessment equity was discussed.

56 FLA. STAT. § 196 (2016).

57 Research Committee International Association of Assessing Officers, “Assessed Value Cap

Overview,” Journal of Property Tax Assessment & Administration 7, no. 1 (2010); It is the belief

that one group of property owners has to pay an increased tax burden if another group of

property owners is allowed to pay less than they would have had to pay if there were no cap in

place.

58 William Blankenau and Mark Skidmore, “School Finance Litigation, Tax and Expenditure

Limitations, and Education Spending,” Contemporary Economic Policy 22, no. 1 (2004): 128.

52

Property Tax Climate

Property taxes have been an attractive source to fund education. The property tax is

accommodating of the elasticity of income when compared to other taxes. Kenyon conceded,

“Researchers agree the property tax is not generally regressive, and to the extent that it is a tax

on capital, can be progressive. Furthermore, the property tax is more progressive than the sales

tax.”59 Alm also reported, “There is some evidence that the property tax has at least a

proportional and often a progressive effect on the distribution of income.”60 Because education

funding is ongoing, support through local residents is desirable not only because residents are

able to have a direct impact on the funding its youngest citizens receive but because property is

an everlasting revenue source.

Reliance upon property not only aids the individual consumer but it also benefits the local

government, particularly in times of economic hardship such as a recession or depression. Alm

stated, “despite the overall decline in property values in the United States attributable to the

bursting of the housing bubble before the start of the Great Recession, the experiences of local

governments were quite varied.”61 He further supported this claim by stating, “local government

reliance on the property tax rather than on more elastic revenue sources like income, sales, and

excise taxes has…helped local governments to avoid some of the more severe difficulties

59 Daphne Kenyon, The Property Tax, School Funding Dilemma (Cambridge: Lincoln Institute

of Land Policy, 2007), 3.

60 James Alm, “A Convenient Truth: Property Taxes and Revenue Stability,” Cityscape: A

Journal of Policy Development and Research 15, no. 1 (2013): 243.

61 Ibid., 244. See James Alm, Robert D. Buschman, and David L. Sjoquist, “Rethinking Local

Government Reliance on the Property Tax,” Regional Science & Urban Economics 41, no. 4

(2011).

53

experienced by many other governments in the ‘Great Recession’….”62 Lutz, Molloy, and Shan

believed that this stability was not temporary stating, “it [was] unlikely that property tax

revenues [would] fall sharply in coming years.”63 Such firm characteristics of the property tax

makes assessed value a likely factor in a thorough funding formula.

Despite the stability of the property tax, many researchers believe that property taxes

create disparities in the quality of education of particular school districts. Kenyon believed that

this disparity is improperly measured proclaiming that “[p]roperty tax rates are not a good

measure of property tax burden because high tax rates can reflect a high level of local

government services or restrictive zoning practices rather than low fiscal capacity; high tax rates

can also reduce house prices, which partially compensates new homeowners for high taxes.”64

In 2008, Florida’s Save our Homes Amendment 165 impacted government revenue. It

added an additional $25,000 homestead exemption for non-school taxes, a $25,000 tangible

personal property (TPP) exemption for business owners, a 10 percent non-homestead assessment

increase limitation, and as it pertains to this study, homestead portability. Since then, the FDOR

reports the effect of the constitutional amendment on a yearly basis following its implementation.

Table 2-5 outlined the effect of Amendment 1 between the years of 2009 and 2015. This

legislation effected Florida school district education funding in that the portability transfer

62 Ibid.

63 Byron Lutz, Raven Molloy, and Hui Shan, “The Housing Crisis and State and Local

Government Tax Revenue: Five Channels,” Regional Science & Urban Economics 41, no. 4

(2011): 318, accessed April 15, 2017, http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.009.



65 Modified FLA. CONST. art. VII, § 3, FLA. CONST. art. VII, § 4, and FLA. CONST. art. VII,

§ 6; FLA. CONST. art. XII, § 27.

http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.009

54

portion lowered the assessment of property in a school district, once again possibly distorting the

perception of fiscal capability and thusly capacity.

Property Rate and Tax Limitations

Assessed value limitations restrict the amount that an assessed value can increase in a

year, “often expressed as a percentage increase limit referencing the previous year.”66 Sirmans

and Sirmans claim, “Tax and expenditure limitations67 most often appeal to homeowners who


Overview,” Journal of Property Tax Assessment & Administration 7, no. 1 (2010): 57.

67 In 1978, California’s Proposition 13 (People's Initiative to Limit Property Taxation) allowed

property owners to be able to estimate future property taxes by placing a limit on the amount of

rates at one percent of full cash value at the time of acquisition and allowed assessments to rise

by no more than two percent per year until the property was resold. [Cali. Const. art. XIII § 1(a)];

O’Sullivan, Sexton, and Sheffrin used property tax records and income tax returns for

homeowners in California to analyze the differential impacts of Proposition 13 resulting from the

cap on increases in assessed values. [Arthur O’Sullivan, Terri Sexton, and Steven Sheffrin,

“Differential Burdens from the Assessment Provisions of Proposition 13,” National Tax Journal

47, no. 4 (1994): 721–31.]

As with many high-profile pieces of legislation, there were amendments to make the law more

comprehensive. Sonstelie and Richardson acknowledged that, “In approving that initiative,

voters began a process that effectively shifted control over the property tax (and school revenues)

from the local to the state level.” [Jon Sonstelie and Peter Richardson, eds., School Finance and

California's Master Plan for Education (San Francisco: Public Policy Institute of California,

2001), 127] [Right to Vote on Taxes Act, Cali. Const. art. XIII § (c) (1996) and Cali. Const. art.

XIII § (d) (1996); also known as Proposition 218]; Another popular limitation was the

Massachusetts’ Proposition 2½ of 1982. It sought to limit property tax rates [Mass. Gen. L. c. 59,

§ 21C]; Wallin and Zabel found that, “Cuts in state aid [had] a disproportionate impact on poorer

towns [which were] faced with reducing expenditures (e.g., teacher layoffs) or passing overrides

to increase revenues.” [Bruce Wallin and Jeffrey Zabel, “Property Tax Limitations and Local

Fiscal Conditions: The Impact of Proposition 2½ in Massachusetts,” Regional Science and

Urban Economics 41, no. 4 (2011): 383, accessed April 15, 2017,

http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.008].

https://en.wikipedia.org/wiki/General_Laws_of_Massachusetts

http://www.malegislature.gov/Laws/GeneralLaws/Chapter59/Section21C

http://www.malegislature.gov/Laws/GeneralLaws/Chapter59/Section21C


55

feel overtaxed and underserved or who feel that local governments are not efficient in providing

services.”68 A chart of property tax limitations nation-wide is provided in Appendix A. 69

68 G. Stacy Sirmans and C. Stace Sirmans, “Property Tax Initiatives in the United States,”

Journal of Housing Research 21, no. 1 (2012): 1.

69 As a whole, some state statutes impose different rates on different jurisdictions. State

legislation also has the option of imposing an overall property tax rate limitation. Mikhailov and

Kolman state that property tax rate limitations are, “potentially binding if coupled with a limit on

assessment increases; Otherwise, these limits can be circumvented by altering assessment

practices (or through interfund transfers for specific services [for specific property tax rate

limits]).” [Nikolai Mikhailov and Jason Kolman, Types of Property Tax and Assessment

Limitations and Tax Relief Programs (Lincoln Institute of Land Policy, 1998), 3, accessed April

15, 2017, https://www.leg.state.nv.us/73rd/otherDocuments/PTax/lincoln institute - property tax

relief.pdf].

At the turn of the century, states exercised variations of property limitations. Appendix A

summarized the types of property tax limitations imposed in different states across the United

States. State Department of Revenue Websites: Alabama (http://www.ador.alabama.gov/);

Alaska (http://dor.alaska.gov/); Arizona (https://www.azdor.gov/);

Arkansas (http://www.dfa.arkansas.gov/Pages/default.aspx)

California (http://www.taxes.ca.gov/); Colorado (https://www.colorado.gov/revenue)

Connecticut (http://www.ct.gov/drs/site/default.asp); Delaware (http://revenue.delaware.gov/)

Florida (http://dor.myflorida.com/Pages/default.aspx); Georgia (https://dor.georgia.gov/);

Hawaii (http://tax.hawaii.gov/); Idaho (http://tax.idaho.gov/); Illinois

(http://www.revenue.state.il.us/#&panel1-1); Indiana (http://www.in.gov/dor/);

Iowa (https://tax.iowa.gov/)

Kansas (http://www.ksrevenue.org/); Kentucky (http://revenue.ky.gov/);

Louisiana (http://www.rev.state.la.us/); Maine (http://www.maine.gov/revenue/)

Maryland (http://dat.maryland.gov/Pages/default.aspx);

Massachusetts (https://www.mass.gov/dor/)

Michigan (http://www.michigan.gov/treasury/0,4679,7-121--8483--,00.html);

Minnesota (http://www.revenue.state.mn.us/Pages/default.aspx);

Mississippi (http://www.dor.ms.gov/Pages/default.aspx); Missouri (http://dor.mo.gov/) Montana

(https://revenue.mt.gov/); Nebraska (http://www.revenue.nebraska.gov/);

Nevada (http://tax.nv.gov/); New Hampshire (http://revenue.nh.gov/); New

Jersey (http://www.state.nj.us/treasury/taxation/); New

Mexico (http://www.tax.newmexico.gov/) New York (https://www.tax.ny.gov/); North

Carolina (http://www.dornc.com/); North Dakota (https://www.nd.gov/tax/);

Ohio (http://www.tax.ohio.gov/); Oklahoma (https://www.ok.gov/tax/)

Oregon (http://www.oregon.gov/dor/Pages/index.aspx); Pennsylvania

(http://www.revenue.pa.gov/Pages/default.aspx#.VvLK0GQrIfE); Rhode

Island (http://www.tax.ri.gov/)

South Carolina (https://dor.sc.gov/); South Dakota (http://dor.sd.gov/);

Tennessee (https://www.tn.gov/revenue); Texas (http://comptroller.texas.gov/taxinfo/sales/);

https://www.leg.state.nv.us/73rd/otherDocuments/PTax/lincoln%20institute%20-%20property%20tax%20relief.pdf

















































56

Florida’s Save Our Homes, enacted over twenty years ago, sought to limit homestead

property assessed valuation, as well. Today, Florida’s tax structure70 provides several exemptions

and limitations on property taxes. SOH, a property rate limitation, is specific to jurisdictions and

requires a popular vote in order to be lifted. Moore claimed, “[H]orizontal equity and vertical

equity deteriorated in Florida between 1995 and 2004, and a simulation using actual data

indicated that a constitutional amendment71 approved by voters in January 2008 resulted in even

greater inequity.”72

Housing Market

Understanding the housing market is essential in preparing an education funding formula

that will withstand the test of time. The past two decades of the housing market has undoubtedly

been subject to the oscillation of the economy. Scopelliti depicted the cause of the most recent

economic crisis by stating, “As the 2000s unfolded, economic growth and public policies

designed to increase homeownership led to a housing boom. By 2006, the ‘housing bubble’

Utah (http://tax.utah.gov/); Vermont (http://tax.vermont.gov/)

Virginia (tax.virginia.gov); Washington (http://dor.wa.gov/); West

Virginia (http://www.wvrevenue.gov/)

Wisconsin (https://www.revenue.wi.gov/); Wyoming (http://revenue.wyo.gov/)

By including the property tax rate levied by other local governments (counties, school districts),

Wu and Hendrick found that “tax competition exists for property tax among neighboring

municipalities (horizontal) as well as between municipalities and other local governments

(vertical).” [Yonghong Wu and Rebecca Hendrick, “Horizontal and Vertical Tax Competition in

Florida Local Governments,” Public Finance Review 37, no. 3 (2009): 289,

http://dx.doi.org/10.1177/1091142109332054].

70 FLA. CONST. art. VII.

71 The amendment extended the homestead exemption to $50,000, rather than $25,000. The

second $25,000 does not apply to school taxes.

72 J. Wayne Moore, “Property Tax Equity Implications of Assessment Capping and Homestead

Exemptions for Owner-Occupied Single-Family Housing,” Journal of Property Tax Assessment

& Administration 5, no. 3 (2008): 55.









http://dx.doi.org/10.1177/1091142109332054

57

began to burst. In late 2007, the economy fell into recession. The housing market continued to

soften, people began to lose their jobs, and the banking industry was in crisis.”73 The housing

marked effected the value of property and discretionary income. Although the income effect74 is

not regarded as a property-to-income-specific model, it is an economic theory that supports the

side effect that people who have income typically purchase within their price range and to the

point where they are more likely to purchase items that are of a greater quality than an inferior

quality. The income effect is, “the change in demand for a good whose price has altered which

would have resulted if prices had stayed the same, but incomes had risen or fallen sufficiently to

bring the consumer to the same level of welfare as after the price change.”75 Figure 2-1 shows a

graphical representation of this theory.

The price-to-income ratio76 has helped measure the wellbeing of the housing market. This

measure compares the price of a homestead to the median annual income of a given area.77

73 Demetrio M. Scopelliti, “Housing: Before, During, and After the Great Recession,” United

States Department of Labor, Bureau of Labor Statistics, accessed April 15, 2017,

http://www.bls.gov/spotlight/2014/housing/home.htm.

74 Popular literature defines the income effect as “the change in an individual's or economy's

income and how that change will impact the quantity demanded of a good or service; The

relationship between income and the quantity demanded is a positive one, as income increases,

so does the quantity of goods and services demanded.” [“Income Effect,” Investopedia, accessed

April 15, 2017, http://www.investopedia.com/terms/i/incomeeffect.asp].

75 John Black, Nigar Hashimzade, and Gareth Myles, A Dictionary of Economics (Oxford

University Press, 2012), 198.

76 The price-to-income ratio is sometimes referred to as “measure of affordability;” See Shelly

Dreiman, Using the Price to Income Ratio to Determine the Presence of Housing Price Bubbles

(Federal Housing Finance Agency, 2000), accessed April 15, 2017,

http://www.fhfa.gov/DataTools/Downloads/Documents/HPI_Focus_Pieces/2000Q4_HPIFocus_

N508.pdf.

77 Forbes (popular literature) stated that “historically median home in the U.S. cost 2.6 times as

much as the median annual income.” [“High Home Price-to-Income Ratios Hiding Behind Low

Mortgage Rates,” Forbes, April 16, 2013, accessed April 15, 2017,

http://www.bls.gov/spotlight/2014/housing/home.htm

http://www.investopedia.com/terms/q/quantitydemanded.asp

http://www.investopedia.com/terms/i/incomeeffect.asp

http://www.fhfa.gov/DataTools/Downloads/Documents/HPI_Focus_Pieces/2000Q4_HPIFocus_N508.pdf


58

Researchers have discussed the connectedness of income to house prices, household

vulnerability to income effects, and income inequality to house prices. Gallin contrasted the

literature that supports that housing prices are co-integrated with income. He argued that the

evidence does not support a long-run equilibrium relationship and that “the levels regressions

found in the literature are likely spurious and the associated error-correction models may be

inappropriate.”78

Studies acknowledge the relatedness of the housing market and income. Guo and Hardin

maintained that wealth composition is a significant determinant of consumption. Their study

found, “Households with the highest percentage of net worth in financial assets have much lower

income effects, have substantially higher marginal79 effects associated with stock holdings and

have housing equity effects that differ noticeably from other households.”80 Further, “[i]ncome

effects for groups with the smallest amounts of relative financial wealth are dramatically higher

than for households with greater financial wealth.”81 This suggests that the housing market

impacts the purchasing power of those with lower financial wealth and that the disposable

income for those with lower financial wealth is significantly different than households with

http://www.forbes.com/sites/zillow/2013/04/16/high-home-price-to-income-ratios-hiding-

behind-low-mortgage-rates/ - 73dc3c99378d.]

78 Joshua Gallin, “The Long‐Run Relationship Between House Prices and Income: Evidence

from Local Housing Markets,” Real Estate Economics 34, no. 3 (2006): 417, accessed April 15,

2017, http://dx.doi.org/10.1111/j.1540-6229.2006.00172.x.

79 A marginal effect is sometimes referred to as “instantaneous rate of change;” Richard

Williams, Marginal Effects for Continuous Variables (University of Notre Dame, 2016),

accessed April 15, 2017, https://www3.nd.edu/~rwilliam/stats3/Margins02.pdf.

80 Sheng Guo and William G. Hardin III, “Wealth, Composition, Housing, Income and

Consumption,” Journal of Real Estate Finance and Economics 48, no. 2 (2014): 221, accessed

April 15, 2017, http:dx.doi.org/10.1007/s11146-012-9390-z.

81 Ibid.

http://www.forbes.com/sites/zillow/2013/04/16/high-home-price-to-income-ratios-hiding-behind-low-mortgage-rates/#73dc3c99378d

http://www.forbes.com/sites/zillow/2013/04/16/high-home-price-to-income-ratios-hiding-behind-low-mortgage-rates/#73dc3c99378d

http://dx.doi.org/10.1111/j.1540-6229.2006.00172.x

https://www3.nd.edu/~rwilliam/stats3/Margins02.pdf

http://dx.doi.org/10.1007/s11146-012-9390-z

59

higher financial wealth. Maattanen and Tervio’s research “provide[d] a framework for analyzing

how income differences get capitalized into house prices.”82 Their nine-year study observed that

increased income inequality has a “negative impact on average house prices in six U.S.

metropolitan areas.”83

House prices are directly related to the assessed valuation of property so acknowledging

its connection to income is appropriate, especially in terms of a cap. Researchers agree that:

Properties that increase in value due to external market forces at a rate greater

than the assessed value limit or cap rate received favorable treatment from the

cap, while properties that increased in value due to external market forces at a rate

equal to or less than the assessed value limit or tax cap received unfavorable

treatment.84

Epple, Romano, and Sieg discussed how the market, income, and education effects

taxpayer mobility by explaining, “Since the demand for public education and the willingness to

support high quality education at the ballot box is at least partially determined by income,

households with higher income tend to locate in communities with higher expenditures and

housing prices.”85 As it pertains to assessment differentials, researchers agree, “the enactment of

the SOH amendment has raised issues of tax burden equity across households in different income

groups occupying different property types.”86

82 Niku Maattanen and Marko Tervio, “Income Distribution and Housing Prices: An Assignment

Model Approach,” Journal of Economic Theory 151 (2014): 403, accessed April 15, 2017,

http://dx.doi.org/10.1016/j.jet.2014.01.003.

83 Ibid., 381; See Jesse M. Abraham and Patric H. Hendershott, “Bubbles in Metropolitan

Housing Markets,” Journal of Housing Research 7, no. 2 (1996): 191-208.



85 Dennis Epple, Richard Romano, and Holger Sieg, “The Intergenerational Conflict Over the

Provision of Public Education,” Journal of Public Economics 96 (2012): 255.

86Wayne R. Archer, Brian Buckles, David A. Denslow, Jr., James F. Dewey, Dean H. Gatzlaff,

Lynne Holt, Tracy L. Johns, Babak Lotfinia, David A. Macpherson, Gabriel Montes-Rojas,

http://dx.doi.org/10.1016/j.jet.2014.01.003

60

With respect to taxation, alternative housing scenarios exist that must be considered when

discussing property and income. For instance, some property owners have multiple homes. In

some Florida districts (i.e., counties), properties that are used as vacation or rental homes are

assessed differently. Restrictions are based on how often the home is inhabited and how much

the owner or their tenants use the property. Also, some properties are tax-delinquent, vacant,

or/and foreclosed. Each district is unique in the degree of property types and delinquency, all of

which impacting property assessed valuation and therefore levied taxes. Some districts have a

great degree of polarization within the scope of property value or income. So, although the

property value for the district may be indicative of wealth (for funding schools) in some school

districts, the income of the district may contrast that value. Additionally, while a district may

have significant income, it does not mean that individual households will have an income that is

relatively equivalent. These reasons serve as the basis of the factors that make up the FEFP but

still fall short as the most comprehensive of district financial capacity.

Recently, the Florida Legislature presented information, reported via the Office of

Economic and Demographic Research, that recognized that the homeownership rate was below

normal. In Florida, “[t]he 2015 percentage of 64.8 [was] the lowest since 1989, and [was] below

the long-term average for Florida. Second-quarter data for 2016 [showed] a further decline to

63.8 percent. If this level [held] for the year, it [would] be the lowest level for Florida in the

Stefan C. Norrbin, Donald E. Schlagenhauf, Michael J. Scicchitano, G. Stacy Sirmans, Robert C.

Stroh, Sr., Anne R. Williamson, Analytical Services Relating to Property Taxation Part 1:

Assessment Component (Bureau of Economic and Business Research, 2007), 17, accessed April

15, 2017, http://edr.state.fl.us/Content/special-research-projects/property-tax-study/Report-

Assessment.pdf.

http://edr.state.fl.us/Content/special-research-projects/property-tax-study/Report-Assessment.pdf


61

thirty-two-year history of the series.”87 This type of market effected both house prices and

assessment values.

Assessment Equity

Assessed valuation is dependent on property appraisal. Scholars recognize how important

accuracy of assessed valuation is for the Department of Revenue. Sirmans, Gatzlaff, and

MacPherson projected, “Inequity in property taxes is conceptualized by the relationship between

assessed value and market value and is, to some extent, a problem of basic econometrics relative

to errors in variables measurement.”88 Payton stated, “Since assessment is the foundation of the

property tax system, valuation becomes the root from which all other components of the property

tax can be accurately evaluated.”89 Assessment equity is significant because “property taxes

affect the property owner’s tax burden.”90

Zhu and Pace found, “[E]xperienced and licensed appraisers provide materially more

accurate valuations. Unlicensed, inexperienced appraisers have an error rate approximately four

times worse than licensed, experienced appraisers.”91 In 2008, Florida enacted legislation92 that

87 Florida Legislature, Office of Economic and Demographic Research, Florida: Economic

Overview (Florida Legislature, 2016), accessed April 15, 2017,

http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_8-24-16.pdf.

88 G. Stacy Sirmans, Dean Gatzlaff, and David MacPherson, “Horizontal and Vertical Inequity in

Real Property Taxation,” Journal of Real Estate Literature 16, no. 2 (2008): 168.

89 Seth Payton, “A Spatial Analytic Approach to Examining Property Tax Equity After

Assessment Reform in Indiana,” Journal of Regional Analysis and Policy 36, no. 2 (2006): 182.

90 Ibid., 192.

91 Shuang Zhu and Kelley Pace, “Distressed Properties: Valuation Bias and Accuracy,” Journal

of Real Estate Finance and Economics 44 (2012): 153, accessed April 15, 2017,

http:dx.doi.org/10.1007/s11146-010-9290-z.

92 In 2008, the legislature “[r]equired the Department of Revenue to develop a uniform policies

and procedures manual and to provide training for special magistrates; changed the make-up of

VABs (Value Adjustment Board) to include 2 citizen members; imposed several conditions on

http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_8-24-16.pdf

http://dx.doi.org/10.1007/s11146-010-9290-z

62

sought to further transparency within the appraisal process partly because critics view property

assessed valuation as subjective.

Part III: Property Value, Income, and Save Our Homes

When considering that consumers absorb taxes of all kinds, the wealth of the taxpayer is

a significant factor when determining the least intrusive but adequate amount of funding to

delegate toward public education. Floridians unremittingly support the lack of a state income tax,

showing their apprehension toward the measure, at least in the way that it may affect their

discretionary income. The following subsection began with a discussion of the state of property

value and income in Florida. It then discussed the effects of the Florida’s Save Our Homes

(SOH) assessment differential. Last, this section discussed wealth as a method of ensuring

equity.

Property Value and Income in Florida

Property Value

In general, the assessed value of property is the difference of its just value and assessment

limitations while taxable value is the difference of assessed value and tax exemptions. The total

tax obligation of a taxpayer is the product of taxable value and the millage rate established by the

taxing authority. Ultimately, the sum of the total tax liabilities is the amount for which any

the qualifications for special magistrates and board counsel; and expressed the intent of the

Legislature that a taxpayer shall never have the burden of proving that the property appraiser’s

assessment is not supported by any reasonable hypothesis.” The next year (2009), the legislature

“[c]hanged the burden of proof in challenging the property appraiser’s assessment of value.

Provides that the property appraiser’s assessment is presumed correct, if the appraiser can prove

by a preponderance of the evidence that the assessment was arrived at by complying with s.

193.011, F.S. However, a taxpayer who challenges an assessment is entitled to a determination

by the VAB or the court, as to the appropriateness of the appraisal methodology used.” [Budget

Subcommittee on Finance and Tax, Property Tax Update, Fla. S. Rep. No. 2012-207, at 3 (2011),


https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012-207ft.pdf].


63

property owner is legally responsible. If the legislature of a given state believes that certain

populations are in need of relief from particular taxes, property exemptions or circuit breakers

are instituted. Exemptions may be specific to school taxes, non-school taxes, or to both and

possibly in different amounts. Millage rates and legally supported exemptions are what separate

school taxes from non-school taxes and the amount at which each taxpayer is responsible post-

property assessment.93

The FDOR reported the 2016 statewide just, assessed, exemption, and taxable values by

property type. Table 2-6 illustrates the data reported for real, personal, and centrally assessed

property types.

Income

Income in the state of Florida varies from year to year but trends are present. Unlike the

relatively stagnant nature of property value, income is generally more yielding. In 2017, the

Florida Legislature’s Office of Economic and Demographic Research (OEDR) reported the

elasticity of personal income:

Florida’s pace for the 2015 calendar year was stronger than 2014…. Florida grew

above the national average of 4.4%, recording growth of 5.2% and ranking 6th in

the country for the percent change from the prior year. However, the state’s per

capita income was below the nation as a whole and ranked Florida 28th in the

United States. Newly released Florida data for the third quarter of 2016 showed a

slight weakening relative to the second quarter, dropping Florida to a ranking of

22nd in the country.94

93 “Information for Taxpayers,” Florida Department of Revenue, accessed April 15, 2017,

http://dor.myflorida.com/dor/property/taxpayers.




http://dor.myflorida.com/dor/property/taxpayers


64

Also, in 2016, OEDR reported that Florida’s average annual wage had “typically been

below the US average… [and that] data for 2014 showed that it further declined to 87.2 percent

of the US average.95 Although Florida’s wage level increased over the prior year, the US average

annual wage increased more.”96 Figure 2-2 illustrates the average annual wage as a percent of the

United States from the year 2001 to 2015.

Florida’s Save Our Homes Assessment Limitation

The outcome of assessment limitations on local property tax revenues in an area is chiefly

dependent on “the size of the gap between the rate of appreciation and any binding assessment

cap; the percentage of properties that are homesteaded in a community; the frequency of sales

‘turnover’ in the taxing jurisdiction; new construction activity; and the millage rate which is

unconstrained by the amendment.”97

95 In 2013, the average was 87.6 percent (lowest percentage since 2001).




97 Wayne R. Archer, Brian Buckles, David A. Denslow, Jr., James F. Dewey, Dean H. Gatzlaff,






Assessment.pdf.

About a decade ago, the Department of Revenue analyzed the impact of SOH on public school

property taxes. Their study, which compared the amount of tax roll that would be collected with

and without the assessment limitation, found that, “[c]ounties in which the elimination of the

SOH assessment limitation result[ed] in a change in taxable value greater than the statewide

average would experience an increase in the RLE dollars levied and counties with a roll change

less than the statewide average would see a decrease in the RLE contribution.” [Florida

Department of Revenue, Florida's Property Tax Structure: An Analysis of save out Homes and

Truth in Millage, Pursuant to 2006-311, L.O.F. (Florida Department of Revenue, 2007): 34,


http://dor.myflorida.com/dor/property/trim/ptsreport/pdf/ptaxstructure.pdf] Because of the 90/10




http://dor.myflorida.com/dor/property/trim/ptsreport/pdf/ptaxstructure.pdf

65

Considering that limitations were created to have positive effects, its nature greatly

influences budget-making decisions which will continue to require inspection as time continues.

Researchers found that limitations, “reduce the growth of local revenues and expenditures,

though this is partially offset by corresponding increases in state aid to local governments.”98

Allen and Dare’s research concluded that Florida’s SOH amendment “reduced the degree of

progressivity in the state’s property tax system such that the owners of lower value home

properties are shouldering an increasing proportion of the property tax burden relative to the

owners of higher value homestead properties.”99

In addition to Florida’s SOH assessment limitation, legislation later allowed a Portability

Transfer100 for those who desired to relocate within the state of Florida. Cheung and

Cunningham stated, “Support for portability is higher when a city has many out-of-state and thus

Rule [FLA. STAT.§ 1011.66 (2016)], school districts that would have to lower their millage rate,

“would see no change in the total property tax revenue contributed to the FEFP, but would see a

reduction in the millage required due to the fact that the tax roll is now higher.” [Ibid]. The report

directly addressed RLE, the measurement of district fiscal capacity for education funding. The

significance of this study suggested that there was an anticipation of some effect of SOH on

education funding, which validates a portion of this study’s conceptual framework.

98 William Blankenau and Mark Skidmore, “School Finance Litigation, Tax and Expenditure

Limitations, and Education Spending,” Contemporary Economic Policy 22, no. 1 (2004): 128.

99 Marcus T. Allen and William H. Dare, “Changes in Property Tax Progressivity for Florida

Homeowners after the ‘Save Our Homes Amendment,’” Journal of Real Estate Research 31, no.

1 (2009): 81.

100 FLA. STAT.§ 193.155(8) (2016); This statute allows homestead property owners to transfer

up to $500,000 of Save Our Homes assessment differential to a new homestead if the property

owner had received a homestead exemption within either of the 2 years immediately preceding

the establishment of the new homestead.

66

‘exemption-less’ immigrants and support is lower when mobility in the rest of the tax jurisdiction

is high.”101 They argue that voters alter assessment rules to minimize their tax share.

Some say that property tax limitations create a lock-in effect. Researchers have studied

tenure as a result of portability litigation. Stansel, Jackson, and Finch102 examined housing tenure

at two points in time to see whether housing tenure has changed in the state of Florida as a result

of assessment limitations. Their research studied the percentage difference between the just

value and assessed value between twenty counties whose geographical and demographic

composition varied. The results of their study rejected the notion that acquisition-based property

tax systems increase house tenure. The researchers note limitations103 to their study that could

have influenced their results such as that the study used only residential properties that received

the Homestead Exemption, less than a third of the state’s counties, and data from only two points

in time.

Researchers found that the SOH differential “created significant differences in the

property tax burdens of individual homeowners with properties having similar market values

[and] that these occurrences were due to differences in individual house price appreciation and

length of tenure.”104 Archer et al., concluded, “The Save Our Homes initiative is found to have

101 Ron Cheung and Chris Cunningham, “Who Supports Portable Assessment Caps: The Role of

Lock-In, Mobility and Tax Share,” Regional Science and Urban Economics 41, no. 3 (2011):

173.

102 Dean Stansel, Gary Jackson, and J. Howard Finch, “Housing Tenure and Mobility with an

Acquisition-Based Property Tax: The Case of Florida,” Journal of Housing Research 16, no. 2

(2007): 117-29.

103 Ibid.





67

had a minimal effect on a property selling at relatively low SOH savings levels. However, the

effect is non-linear. As the SOH saving grows, the deterrent effect becomes progressively

stronger.”105 The implementation of assessment limitations across the country has prompted the

education field to study the effects of these limitations on education finance.106

Effects of Florida’s Save Our Homes

SOH legislation was created to provide tax relief for Florida citizens. Yet, Thomas

warned, “The Florida Legislature and Florida voters must see through the immediate

gratification of appeasing the masses by way of a proposal of supposed ‘tax relief’….”107 The

researcher believed that Floridians should, “examine the effect that cutting local tax revenues

will have on the ability of counties, cities, and municipalities to provide basic infrastructure

services such as water, sewer, law enforcement, rescue services, schools, and parks and

recreation.”108



Assessment.pdf.

105 Ibid., 10.

106 E.g., Snyder studied the durability of property tax cuts proposed by the state of Kansas’

government on education spending. The study found that limitations cuts are not viable as time

progresses because of “court and federal mandates that require additional spending on education,

economic fluctuations that reduce the ability of state budgets to maintain a given share of

education spending, and demands for local control to allow school districts to spend more or less

than state-mandated levels.” [Nancy McCarthy Snyder, “The Property Tax and Public Education:

Are State-Initiated Tax Cuts Sustainable?” Journal of Public Budgeting, Accounting & Financial

Management 15, no. 4 (2003): 593].

107 Josephine Thomas, “Increasing the Homestead Tax Exemption: ‘Tax Relief’ or Burden on

Florida Homeowners and Local Governments,” Stetson Law Review 35, no. 2 (2006): 516.

108 Ibid.



68

In the state of Florida, the effects of SOH across the counties were fairly diverse.109 As

one could predict, “the evidence reveal[ed] that following the housing boom, the state average

ratio of property taxes to assessed values fell.”110 Researchers found, “The impact of SOH

varie[d] by county and region depending on the real property value appreciation that occurred in

the last decade.”111 They concluded, “The dollar amount of values protected by SOH [was]

certainly impressive in some coastal counties, especially Brevard, Broward, Miami-Dade,

Martin, Pinellas and Palm Beach. At the other extreme, it [had] a very small impact in the central

and northern counties.”112 Sonnier and Lassar stated, “Since 2006, property values [had]

dramatically declined in Florida causing a substantial loss in the economic fortunes of many

individuals and businesses and resulting in significant decreases in the property tax base of

governmental entities.”113

Sirmans and Sirmans state that, by definition, “because [an assessment differential] calls

for homestead properties to be reassessed at market value after any change in ownership,

109 Appendix B for specific Save Our Homes data extracted from the Florida Department of

Revenue organized by county for the year of 2005 to 2015; Adapted from “Florida Property Tax

Data Portal,” Florida Department of Revenue Property Tax Oversight, Research and Analysis,

accessed April 15, 2017, http://floridarevenue.com/dor/property/resources/data.html.


Tracy L. Johns, David A. Macpherson, Stefan C. Norrbin, Donald E. Schlagenhauf, Michael J.

Scicchitano, Stacy Sirmans, Robert C. Stroh, Sr., Anne R. Williamson, Analytical Services

Relating to Property Taxation Part 2: Revenue Component (Bureau of Economic and Business

Research, 2007), 100, accessed April 15, 2017, http://edr.state.fl.us/Content/special-research-

projects/property-tax-study/Report-Revenue-Revised.pdf.

111 Ibid., 99.

112 Ibid.

113 Blaise M. Sonnier and Sharon S. Lassar, “Florida Adds Portability to its Save Our Homes Tax

Relief Measure and Inflation Protection for Non-Homestead Real Property,” Journal of State

Taxation 26, no. 6 (2008): 45.


http://edr.state.fl.us/Content/special-research-projects/property-tax-study/Report-Revenue-Revised.pdf


69

differences can occur in the assessment equity among comparable homestead properties.”114

Sirmans, Gatzlaff, and MacPherson agree that as a result, “the ratio of assessed value to market

value is not constant across different value ranges.”115 The Florida Senate conceded that SOH

acquired an unintended impression on the market:

While SOH allowed long term residents with a fixed income to be able to afford

to stay in their homes without being hit by large tax increases as their property

value increases, it had consequences that may not have been fully anticipated by

its proponents, and many of these consequences were aggravated by changes in

the residential real estate market during the early years of the new century.116

Amendment 1 sought to alleviate the unintended strain. Table 2-7 lists the effect in

dollars of the Portability Transfer for the past five years. Theory charges that SOH is only

beneficial if the just value of a property transcends the taxable value. SOH could potentially

create a greater gap between measured income and housing, making property value a less

accurate measure of fiscal capacity for any household or district.

Part IV: Similar Studies and Topics

Baker claimed the Recession’s impact on state education finance programs included a

“loss of [income] in many states, thus a greater loss to state general fund revenues, a [collapse]

of housing [markets] or at least leveling of growth of taxable property wealth, but also involved a

substantive infusion of federal ‘fiscal stabilization’ aid….”117 This subsection discussed the

114 G. Stacy Sirmans and C. Stace Sirmans, “Property Tax Initiatives in the United States,”

Journal of Housing Research 21, no. 1 (2012): 5.

115 G. Stacy Sirmans, Dean Gatzlaff, and David MacPherson, “Horizontal and Vertical Inequity

in Real Property Taxation,” Journal of Real Estate Literature 16, no. 2 (2008): 168.

116 Budget Subcommittee on Finance and Tax, Property Tax Update, Fla. S. Rep. No. 2012-207,

at 8 (2011), accessed April 15, 2017,

https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012-207ft.pdf.

117 Bruce D. Baker, “Evaluating the Recession’s Impact on State School Finance Systems,”

Education Policy Analysis Archives 22, no. 91 (2014): 1, accessed April 15, 2017,


70

appropriateness of studying the relationship of assessed valuation and income, the research that

has been conducted on similar terms, and the variables in question as they relate to this study’s

research question.

For well over a century scholars have debated whether property is demonstrative of the

capacity to pay. Early developers118 of this concept in education finance debated the most proper

way to define district fiscal capacity, and therefore fiscal capacity, for decades. With Florida’s

current measure of school district capacity being based on property assessed valuation, scholars

are forced to evaluate whether it is symbolic of a district’s ability to pay, despite tax policy.

Income, being a common measure of financial prominence, is often compared to property

assessed valuation, a common measure of fiscal capacity.

The philosophical basis of this study rests on an approach to fiscal capability that

“utilize[s] economic indicators, particularly measures of income from which taxes can be paid,

and involved comparisons of state or local taxing jurisdictions119 on the basis of such

indicators.”120 The argument for and against income as a wealth indicator in public school

http://dx.doi.org/10.14507/epaa.v22n91.2014.

118 E.g., Roe L. Johns, Edgar Morphet, Cornell Francis, Herbert Meyer, Puff Clinton, George

Strayer, Robert Haig, Paul Mort.

119 Although the United States Department of Treasury (Internal Revenue Service) measures and

levies income for federal income tax purposes, the state of Florida does not levy a state income

tax.

120 Charles Dziuban, Richard Rossmiller, and James Hale, “Fiscal Capacity and Educational

Finance: Some Further Variations,” (paper presented at the American Educational Research

Association, Chicago, IL, April 1974), 1.

http://dx.doi.org/10.14507/epaa.v22n91.2014

71

finance has prevailed.121 Perspectives of proper financing have been reviewed,122 rewritten,123

and restated124 by the leaders of education finance, including discussion about fiscal capacity and

capability.125 Critics of the “economic-indicator approach”126 claim that it is a theoretical flaw to

determine fiscal capability via income for states like Florida, a state that does not collect income

taxes. They argue that school district fiscal capability should be measured only by

constitutionally unrestricted tax structures and believe that it alternatively places an improper

121 E.g., Ellwood Cubberly, School Funds and their Apportionment (New York: Teachers

College Press, Columbia University, 1906) and The History of Education (Boston: Houghton

Mifflin, 1920); George D. Strayer and Robert M. Haig, The Financing of Education in the State

of New York (New York: Macmillan, 1923); Paul Mort, State Support for the Public Schools

(New York: Teachers College Press, Columbia University, 1926).

122 E.g., Roe L. Johns, Edgar Morphet, and Kern Alexander, The Economics and Financing of

Education, 4th ed. (Englewood Cliffs: Prentice Hall, 1983); Wood, R. Craig, review of The

Economics and Financing of Education, 4th ed. by Roe L. Johns, Edgar L. Morphet, Kern

Alexander, Journal of Education Finance 9, no. 1 (1983): 133-6, accessed April 15, 2017,

http://www.jstor.org/stable/40703400.

123 E.g., Percy Burrup, Financing Education in a Climate of Change (Boston: Allyn and Bacon,

1974; Vern Brimley, Deborah A. Verstegen, and Rulon R. Garfield, Financing Education in a

Climate of Change, 12th ed. (Boston: Pearson, 2016).

124 E.g., Kern Alexander, Richard G. Salmon, and F. King Alexander, Financing Public Schools:

Theory, Policy, and Practice (New York: Routledge, 2015); Faith E. Crampton, R. Craig Wood,

and David C. Thompson, Money and Schools, 6th ed. (New York: Routledge, 2015); Allan

Odden and Larry Picus, School Finance: A Policy Perspective, 5th ed. (New York: McGraw-Hill,

2014); Bruce D. Baker, Preston C. Green, and Craig E. Richards. Financing Education Systems

(Upper Saddle River: Pearson/Merrill/Prentice Hall, 2008).

125 See Allan Odden, “Alternative Measures of School District Wealth,” Journal of Education

Finance 2, no. 3 (1977): 356-79 and Roe L. Johns, “Response of Roe L. Johns to: Alternative

Measures of School District Wealth,” Journal of Education Finance 3, no. 1 (1977): 98-100.

126 Kern Alexander, Richard G. Salmon, and F. King Alexander, Financing Public Schools:

Theory, Policy, and Practice (New York: Routledge, 2015), 156-7. Currently, states typically

hold the view of the critics of the “economic-indicator approach” by measuring ability through

accessible tax sources.

http://www.jstor.org/stable/40703400

72

burden on taxpayers.127 Advocates argue that fiscal capability does not have to be based on a

usable tax base. They explain that tax liability is satisfied from a taxpayer’s income, regardless

of the tax base upon which they are legally accountable.128

Scholars persist in presenting both sides of the argument, resting on equity for both

student and taxpayer. Researchers continue to advance ideas to further define fiscal capacity as

they pertain to state funding structures. Griffith et al., claimed, “A school funding model that

does not take income into account in determining a school district’s ability to fund educational

services, is more likely to result in low-income, high property wealth districts being treated as if

they have a greater tax capacity than the local community believes it can afford.”129

Scholars and practitioners must accept that assessed value, income, and education are

interconnected via funding formulas based on data from the Department of Education and the

Department of Revenue, in spite of state taxation practices. The Research Committee

International Association of Assessing Officers addressed wealth, equalization, and assessment

limitations as it pertains to school funding:

Because of requirements to provide an adequate level of education for all citizens,

regardless of whether they live in property-rich or -poor school districts, states

typically equalize school funding, adding state funds when sufficient local funds

are not available. Assessment limits artificially distort this system. If constrained

value is used to equalize school funding, districts with large market value

increases may appear poor and may receive larger shares of state funds, despite

more market value wealth. If full market value is used to equalize school funding,

127 Ibid.

128 Ibid.

129 Michael Griffith, Lawrence O. Picus, Allan Odden, and Anabel Aportela, “Policies that

Address the Needs of High Property-Wealth School Districts with Low-Income Households,”

(paper presented to the Maine Legislature’s Joint Standing Committee on Education and Cultural

Affairs, ME, August 2013), 2, accessed April 15, 2017,

http://www.maine.gov/legis/opla/MaineFiscalCapacityMeasuresPaper73013.pdf.


73

school property tax rates may be disparate, giving the impression of unequal

treatment.130

In a variety of situations, similar studies have sought to discuss or determine the

relationship between property and income,131 tax burden and tax effort,132 school funding and

income,133 income and school quality,134 property tax and millage rate,135 and income tax and

property tax.136 These studies can often be characterized by age, location, and the intended

audience by academic discipline.137



131 E.g., William A. Fischel, “Chapter 21: The Courts and Public School Finance: Judge-Made

Centralization and Economic Research,” 2nd ed, in Handbook of the Economics of Education, ed.

Eric A. Hanushek and Fenis Welsh (Amsterdam: North Holland, 2006); Rhys Davies, Michael

Orton, and Dereck Bossworth, “Local Taxation and the Relationship Between Income and

Property Values,” Environment and Planning C: Government and Policy 25, no. 5 (2007);

Joshua Gallin, “The Long-Run Relationship between House Prices and Income: Evidence from

Local Housing Markets,” Real Estate Economics 34, no. 3 (2003).

132 E.g., Meagan M. Jordan, David Chapman, and Sharon L. Wrobel, “Rich Districts, Poor

Districts: The Property Tax Equity Impact of Arkansas School Finance Equalization,” Public

Finance and Management 14, no. 4 (2014).

133 E.g., James Alm, Robert D. Buschman, and David L. Sjoquist, “Economic Conditions and

State and Local Education Revenue,” Public Budgeting & Finance 29, no. 3 (2009).

134 E.g., Barry Thornton and Gordon Arbogast, “Factors Affecting School Quality in Florida,”

Contemporary Issues in Education Research 7, no. 2 (2014).

135 E.g., Patrick Colabella, “The Effect of Public School Districts’ Property Value on State

Educational Funds,” (PhD diss., St. John’s University, 2008), accessed April 15, 2017.

136 E.g., Casey J. Muhm, “Exploring the Relationship Between Income and Property Taxation at

the Municipal Level,” (master’s thesis, Iowa State University, 2008), accessed April 15, 2017;

Timothy Goodspeed, “The Relationship Between State Income Taxes and Local Property Taxes:

Education Finance in New Jersey,” National Tax Journal 51, no. 2 (1998).

137 Studies within the last few decades attempt to address the relationship between the variables.

Although many of their methods did not use Pearson’s correlation of total property assessed

valuation and median household income to inform education finance, they still provide insight

into the association of the variables. For instance, Gallin sought to determine the relationship of

house prices, income, and population in 95 metropolitans across the United States, six of whom

74

were Florida cities [Joshua Gallin, “The Long‐Run Relationship Between House Prices and

Income: Evidence from Local Housing Markets,” Real Estate Economics 34, no. 3 (2006): 434,

accessed April 15, 2017, http://dx.doi.org/10.1111/j.1540-6229.2006.00172.x]. Regression

analysis revealed the lack of “cointegration” between the cities across the country. Cohn

measured wealth per pupil valuation of property [Elchanan Cohn, “Revenue and Formula Effects

of School Finance Reform on Wealth Neutrality,” Applied Economics 19, no. 12 (1987): 1690]

and Goodspeed found that in New Jersey there was no relationship between property and income

taxes, in addition to a correlation coefficient that was not significant [Timothy Goodspeed, “The

Relationship Between State Income Taxes and Local Property Taxes: Education Finance in New

Jersey,” National Tax Journal 51, no. 2 (1998)].

More current economics studies continue to support this phenomenon. Jordan, Chapman, and

Wrobel defined tax effort (the total local property tax receipts divided by capacity, where

capacity is equal to assessed value multiplied by millage; revenue collected relative to property

taxes levied) and tax burden (the amount the districts taxpayers paid in property taxes relative to

wealth; revenue collected relative to wealth or assessed value) in the context of property and

wealth. [Meagan M. Jordan, David Chapman, and Sharon L. Wrobel, “Rich Districts, Poor

Districts: The Property Tax Equity Impact of Arkansas School Finance Equalization,” Public

Finance and Management 14, no. 4 (2014): 146]. Their study used the Mann-Whitney U analysis

to measure data that was distributed in quintiles. Thornton and Arbogast used regression analysis

to distinguish several factors that affect the variability in school quality. [Barry Thornton and

Gordon Arbogast, “Factors Affecting School Quality in Florida,” Contemporary Issues in

Education Research 7, no. 2 (2014)] Of those qualities, lie income, property, and tax, all of

which yielding a low coefficient but statistically significant value. Some studies provide insight

on the relationship between property value and taxes. For instance, trends support that property

values and taxes are not positively simultaneous. Through quasi-experimental design, Gallagher,

Kurban, Persky, considered the significance of small home taxation and public school funding.

Although their study had a different purpose, they found, “when benefits are reasonably

controlled for, property taxes are found to be negatively, and quite strongly, capitalized into

property values.” [Ryan M. Gallagher, Haydar Kurban, and Joseph J. Persky, “Small Homes,

Public Schools, and Property Tax Capitalization,” Regional Science & Urban Economics 43, no.

2 (2013): 426, accessed April 15, 2017, http://dx.doi.org/10.1016/j.regsciurbeco.2013.01.001].

Studies outside of the United States attempted to discuss property value and income that provide

insight into the relationship between the variables. Orton and Davies studied the relationship

between household income and property value for owner-occupiers and whether there was

evidence of people living in high value properties that have low incomes. The researchers were

unable to secure data that, “provide[d] for a direct analysis of the relationship between household

income and property value, due to a lack of information about current property values” and

instead used multivariate analysis for two of three measures of property values that were based

on surveys that “inflated recorded house purchase prices.” [Michael Orton and Rhys Davies,

“‘Wealth Rich but Income Poor’ Council Tax and the Relationship between Household Income

and Property Value,” Warwick Institute for Employment Research 75 (2004): 4] They stated,

“Searching for a ‘perfect’ relationship between household income and property value is

misplaced, because it ignores the complexity of individual choice and circumstance.” [Ibid., 1].

http://dx.doi.org/10.1111/j.1540-6229.2006.00172.x


75

Within the past decade education finance studies have addressed property and income

through equity ideology. Kenyon stated, “Some households pay an extraordinarily high amount

of property taxes in relation to their income.”138 Furthermore, “Communities with low per-pupil

property values may be high-income communities just as communities with high per-pupil

property values can be low-income.”139

Despite the similarities of the studies previously mentioned, very little research in the

past decade have specifically questioned the relationship between property assessed valuation

and median household income through a correlational design, with a Florida education funding

focus, and in response to economics-based public policy that effects wealth. Nevertheless,

determining, understanding, and applying this association from education theory is essential to

Another study by Orton and Davies, alongside Bossworth, further emphasized the relationship of

the variables. Their results charged that, “there [was] a strong positive relationship between

property values and income amongst higher income households,” which starkly contrasts studies

by American-based scholars. [Rhys Davies, Michael Orton, and Dereck Bossworth, “Local

Taxation and the Relationship Between Income and Property Values,” Environment and

Planning C: Government and Policy 25, no. 5 (2007): 756] Their study also argued “the

elasticity of property prices with respect to income is not constant, but follows a bell-shaped

distribution which is skewed to the right.” [Ibid.] They used a variety of measures for the value

of property. One measure involved an average of over 50,000 households but another measure

inflated prices to a desired year and included about 55 percent of the original sample population.

Davies, Orton, and Bossworth agreed that there was an insufficient amount of empirical evidence

for a relationship.

A decade ago, in a policy analysis, Fischel warned stakeholders about the possibility of a lack of

representation of particular populations in revamped funding formulas. He claimed that property

value does not necessarily equate to income, as well. He reported that, “the correlation between

‘property rich’ and ‘income rich’ is essentially zero, largely because low-income communities

are more willing to tolerate the nonresidential uses that lower their tax price.” [William A.

Fischel, “Chapter 21: The Courts and Public School Finance: Judge-Made Centralization and

Economic Research,” 2nd ed, in Handbook of the Economics of Education, ed. Eric A. Hanushek

and Fenis Welsh (Amsterdam: North Holland, 2006), 1280].



139 Ibid., 3.

76

practice. In Florida, property value is the present measure of wealth for education funding

purposes and this practice presents the assumption that property value encompasses preference

and financial condition. With a changing economy and as education funding formulas seek to

become more equitable, this research is fundamental.

Summary

The Florida Senate explained, “Property taxes as a percent of Florida income is a reliable

measure of how much of the state’s economic output is transferred from property owners to

counties, municipalities, special districts, and school districts.”140 All the same, upon

examination of various factors (i.e., assessment limitations) there is reason to believe that this

may not be present to the degree expected because of assessment differentials that are tailored

for particular populations within a district. By recognizing this disconnectedness and striving to

create a more inclusive state education finance distribution, education has the possibility of

receiving more equitable and adequate funding regardless of economic factors, as promised. If

there were a consistently positive correlation between the two variables over time, more support

for property tax equity would be established in terms of local education funding in the state of

Florida. If not, the promise of an equalized educational opportunity that guarantees to each

student “educational needs that are substantially equal to those available to any similar

student….”141 becomes unlikely.

The next chapter outlined the methodology of this study. It defined the setting,

participants, instrumentation, procedures, and the process used to analyze these data. It examined

140 Budget Subcommittee on Finance and Tax, Property Tax Update, Fla. S. Rep. No. 2012-207,

at 5 (2011), accessed April 15, 2017,


141 FLA. STAT.§ 235.002(1)(a) (2001).


77

the trend based on the implementation of particular policy to inform decisions that are likely be

made in the future.

78

Table 2-1. State Funding Formulas State Type of Funding Formula State Type of Funding Formula

Alabama Foundation Montana Combination / Tiered System

Alaska Foundation Nebraska Foundation

Arizona Foundation Nevada Foundation

Arkansas Foundation New Hampshire Foundation

California Foundation New Jersey Foundation

Colorado Foundation New Mexico Foundation

Connecticut Foundation New York Foundation

Delaware Foundation North Carolina Flat Grant

Florida Foundation North Dakota Foundation

Georgia Combination / Tiered System Ohio Foundation

Hawaii Full State Funding Oklahoma Combination / Tiered System

Idaho Foundation Oregon Foundation

Illinois Combination / Tiered System Pennsylvania Foundation

Indiana Foundation Rhode Island Foundation

Iowa Foundation South Carolina Foundation

Kansas Foundation South Dakota Foundation

Kentucky Combination / Tiered System Tennessee Foundation

Louisiana Combination / Tiered System Texas Combination / Tiered System

Maine Foundation Utah Combination / Tiered System

Maryland Combination / Tiered System Vermont District Power Equalizing

Massachusetts Foundation Virginia Foundation

Michigan Foundation Washington Foundation

Minnesota Foundation West Virginia Foundation

Mississippi Foundation Wisconsin District Power Equalizing

Missouri Foundation Wyoming Foundation

Source: Information adapted from Deborah A. Verstegen, “Policy Brief: How Do States Pay for

Schools? An Update of a 50-State Survey of Finance Policies and Programs,” (paper presented at

the Association for Education Finance Policy Annual Conference, San Antonio, TX, March

2014), 2, accessed April 15, 2017, https://schoolfinancesdav.files.wordpress.com/2014/04/aefp-

50-stateaidsystems.pdf.



79

Table 2-2. Florida School Districts Schedule of Millage Rates Type of Millage Statutory Authority Established By Uses

Required Local Effort

(RLE)

FLA. STAT. § 1011.62(4) Commissioner Operating

Prior Period

Adjustment Formula

FLA. STAT. § 1011.62(4)(e) Commissioner

Operating

Current Operating

Discretionary –

Maximum 0.748

Mills

FLA. STAT. § 1011.71(1)

School Board

Operating

Local Capital

Improvement –

Maximum 1.50 Mills

FLA. STAT. § 1011.71(2)

School Board

Capital improvements

Capital Improvement

Discretionary –

Maximum 0.25 Mills

FLA. STAT. § 1011.71(3)

School Board

Lease-purchase

payments or to meet

other critical fixed

capital outlay needs in

lieu of operating

discretionary millage

Operating or Capital

(Not to Exceed Two

Years)

FLA. STAT. § 1011.73(1)

Voter Referendum

Not specified

Additional Millage

(Not to Exceed Four

Years)

FLA. STAT. § 1011.73(2)

Voter Referendum

Not specified

Debt Service FLA. STAT. § 200.001(3)(e);

FLA. CONST. art. VII, § 12

Voter Referendum

Debt service

Source: Information adapted from Florida Department of Education, 2015-2016 Funding for

Florida School Districts: Statistical Report (Florida Department of Education, 2015), 2, accessed

April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf.


80

Table 2-3. Florida Education Finance Program Formula Category Formula

Weighted FTE Students (FTE Students) X (Program Cost Factors)

Base Funding (Weighted FTE Students) X (Base Student Allocation [BSA]) X

(District Cost Differential [DCD])

Gross State and Local FEFP

Dollars

(Base Funding) + (DJJ Supplement) + (Declining Enrollment) +

(Sparsity Supplement) + (State-Funded Discretionary Contribution)

+ (0.748 Discretionary Compression) + (Safe Schools) + (Reading

Program) + (Supplemental Academic Instruction) + (ESE

Guaranteed Allocation) + (Instruction Materials) + (Teachers

Classroom Supply Assistance) + (Student Transportation) +

(Virtual Education Contribution) + (Digital Classrooms Allocation)

Net State FEFP Allocation (Gross State FEFP) + (Adjustments)

Total State Funding (Net State FEFP Allocation) + (Categorical Program Funds)


Florida School Districts: Statistical Report (Florida Department of Education, 2015), 8-9,

accessed April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf.

Note: FTE (Full Time Equivalent [student]), DJJ (Department of Juvenile Justice), ESE

(Exceptional Student Education)


81

Table 2-4. Gross State and Local FEFP Components FEFP Categorization Definition

Base Funding Product of the weighted FTE students multiplied by the Base Student Allocation

and the District Cost Differential; FLA. STAT § 1011.62; FTE (Full Time

Equivalent), the basis of the funding formula, is the quantification of one student

who is enrolled in at least one FEFP program for a given school year. Program Cost

Factors defines a category for each FTE student. The sum of the weighted FTE, the

Base Student Allocation and the District Cost Differential equals the Base Funding.

Department of Juvenile

Justice (DJJ) Supplement

The total K-12 weighted FTE student membership in juvenile justice education

program in each school district shall be multiplied by the amount of the state

average class-size reduction factor multiplied by the district’s cost differential.

Declining Student

Supplement

Compares the unweighted FTE for the current year to the unweighted FTE of the

prior year.

Sparsity Supplement Divides the FTE of the district by the number of permanent senior high school

centers.

State Funded Discretionary

Contribution

FLA. STAT § 1002.32(9), FLA. STAT § 1011.71(1)

0.748 Mills Discretionary

Compression

FLA. STAT § 1011.62(5)

Safe Schools Base funding appropriated to each district; Of the remaining funds, 67 percent shall

be allocated based on the latest official Florida Crime Index provided by the

Florida Department of Law Enforcement and 33 percent shall be allocated based on

each district’s share of the state’s total unweighted student enrollment.

Reading Program K-12 comprehensive, district-wide system of research based reading instruction;

FLA. STAT § 1008.22(3), 1011.62(9); FLA. STAT § 1008.32

Supplemental Academic

Instruction

Districts with one or more of the 300 lowest performing elementary schools based

on the statewide, standardized English Language Arts assessment provide an

additional hour of instruction beyond the normal school day for each day of the

entire school year for intensive reading instruction for the students in each of these

schools

ESE Guaranteed

Allocation

ESE services for students whose level of service is less than Support Levels 4 and 5

Instructional Materials Instructional content, as well as electronic devices and technology equipment and

infrastructure.

Teachers Classroom

Supply Assistance

Allocation to each school district based on the prorated total of each school

district’s share of the total grades K-12 unweighted FTE student enrollment; FLA.

STAT § 1012.71.

Student Transportation Equitable distribution of funds for safe and efficient transportation services in

school districts in support of student learning,

Virtual Education

Contribution

FLA. STAT § 1011.62(11)

Digital Classrooms

Allocation

to support school and district efforts and strategies to improve outcomes related to

student performance by integrating technology in classroom teaching and learning.

Federally Connected

Student Supplement

for school districts to support the education of students connected with federally

owned military installations, National Aeronautics and Space Administration

property, and Indian lands.


Florida School Districts: Statistical Report (Florida Department of Education, 2015), 17-20,

accessed April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf.


82

Table 2-5. 2008 Constitutional Amendment Impact (2009-2015) 2009 2010 2011 2012 2013 2014 2015

Total 109,763,958

,199

104,767,650

,017

102,234,039

,946

104,038,479

,741

111,756,447

,044

135,598,622

,449

165,119,972

,954

Add.

$25K

Ex.

91,832,647,

069

87,962,853,

837

84,198,498,

206

81,251,969,

966

80,691,798,

384

81,369,346,

256

82,764,644,

264

TPP

$25K

Ex.

8,448,822,5

19

8,098,463,3

00

7,765,836,6

67

7,705,343,1

93

7,716,750,5

91

7,772,966,3

67

7,829,244,3

43

10%

Cap

7,205,266,0

94

7,671,415,6

27

9,707,982,0

64

14,615,646,

273

22,840,323,

397

45,566,414,

883

72,786,742,

187

Portabi

lity

2,277,222,5

17

1,034,917,2

53

561,723,009 465,520,309 507,574,672 889,894,943 1,739,342,1

60

Source: Data from “Florida Ad Valorem Valuation and Tax Data,” Florida Department of

Revenue, accessed April 15, 2017, http://dor.myflorida.com/dor/property/resources/data.html.

Note: All values are measured in dollars. In this table, “Add” is the abbreviation for

“Additional”, “Ex.” is the abbreviation for “Exemption”, and “TPP” is the abbreviation for

“Tangible Personal Property (for business owners).”

http://dor.myflorida.com/dor/property/resources/data.html

83

Table 2-6. 2016 Statewide Just, Assessed, Exemption, and Taxable Values, by Property Type Property

Type

Number of

Parcels /

Accounts

Just Value

(JV) in Dollars

Assessed Value

(AV) in Dollars

Exemptions

(E) in Dollars

Taxable Value

(TV = AV - E) in

Dollars

Real 10,198,467 2,265,383,628,563 1,895,186,823,046 399,235,061,006 1,495,951,762,040

Personal 1,213,937 164,180,260,401 158,386,019,619 47,623,330,592 110,762,689,027

Centrally

Assessed

1,641,927,080 1,639,613,248 69,335,318 1,570,277,930

Total 11,412,404 2,431,205,816,044 2,055,212,455,913 446,927,726,916 1,608,284,728,997

Property

Type

% of Total

Number of

Parcels

JV as percent of

Total JV

AV as percentage

of JV

E as percentage of

AV

TV as percentage

of JV

Real 89.4 93.2 83.7 21.1 66.0

Personal 10.6 6.8 96.5 30.1 67.5

Centrally

Assessed

0.1 99.9 4.2 95.6

Total 100.0 84.5 21.7 66.2

Source: Data from “Statewide Just, Assessed, Exemption and Taxable Values by Property

Type,” Florida Department of Revenue, accessed April 15, 2017,

http://dor.myflorida.com/dor/property/resources/data.html.

http://dor.myflorida.com/dor/property/resources/data.html

84

Table 2-7. Annual Homestead Portability Impact Year Homestead Portability Impact

(in dollars)

Annual Percent Increase

2015 1739342160 95.5 (2014-2015)

2014 889894943 75.3(2013-2014)

2013 507574672 9 (2012-2013)

2012 465529309 -17.1(2011-2012)

2011 561723009 -45.7 (2010-2011)

Source: Data from “Florida Property Tax Data Portal,” Florida Department of Revenue, accessed

March 4, 2017, http://floridarevenue.com/dor/property/resources/data.html.


85

Figure 2-1. The Income Effect.

Source: Image from Income Effect 2009. 3rd ed. Oxford University.

86

Figure 2-2. Florida Average Annual Wages as a Percent of the United States

Source: Florida Legislature, Office of Economic and Demographic Research, Florida: Economic



Note: Blue – Average Annual Wage; Red – Average Percentage 2001-2015


87

CHAPTER 3

METHODS

One could logically project that overtime policy that affects property valuation could

disproportionately impact measurement of fiscal capacity. Property tax limitations, like the Save

Our Homes policy, by statutory definition, become less equitable as time progresses if

uncompensated for in later legislation and policy. It is likely that it will become increasingly

difficult for education stakeholders to measure and control which populations the limitation

impacts and to what degree within the education finance program formula. The duty is to

accurately gauge financial prosperity to dictate fiscal accountability. Therefore, measuring the

relationship of Median Household Income (MHI) and Property Assessed Valuation (PAV),

despite SOH implementation over time, will constitute using the variables interchangeably.

Methodological Approaches

The approach of this study is to use correlation to imply lack of proportionality between

property assessed valuation and income based on the concept that assessment limitations can

create economic inequity before taxable value is determined.

Property Assessed Valuation (PAV)

This study used property assessed valuation data secured from the Florida Department of

Revenue (FDOR). Property assessed valuation was chosen because the office of the

Commissioner of Education uses the derivative of the exact measure to help establish the

Required Local Effort for each school district’s contribution to the Florida Education Finance

Program (FEFP). Critics view property assessed valuation as subjective, a claim that is outside

the scope of this study. Nevertheless, scholars recognize how important accuracy is for the

Department of Revenue. Uniform Standards of Professional Appraisal Practice (USPAP),

authorized by Congress as the source of appraisal standards and qualifications, The Appraisal

88

Foundation defines value as the “monetary relationship between properties and those who buy,

sell, or use those properties; value expresses an economic concept [that] is never a fact but

always an opinion of the worth of a property at a given time in accordance with a specific

definition of value.”1 However, the standard is that the appraiser will “perform valuation services

competently and in a manner that is independent, impartial, and objective.”2

Median Household Income (MHI)

The United States Census Bureau (USCB) provided a complete measure of median

household income for most districts for each state.3 The USCB reported that Florida’s average

median household income was $47,507.4 This source was chosen to minimize manipulation of

data on behalf of the researcher, for ease of duplication for other researchers, and because its data

are reliable.5 Many other researchers have secured their quantification of household income from

indirect sources such as the Federal School Lunch Program enrollment, etc.

Median household income was also chosen as the measure of fiscal wealth is because of

its degree of robustness. The paradox of using a measure of central tendency is that although a

district may have a caliber of income, it does not mean that this calculation is representative of

any one household (and vice versa). However, the goal is to establish what the status of a district

1 The Appraisal Foundation, 2016-17 Uniform Standards of Professional Appraisal Practice

(Washington D.C.: Appraisal Foundation, 2015), 365.

2 Ibid., 1.

3 Not all median household income statistics for each county in a state is represented.


2017, https://www.census.gov/quickfacts/table/PST045215/12.

5 “Economic Census: Reliability of Data,” United States Department of Commerce, United States

Census Bureau, accessed April 15, 2017,

http://www.census.gov/econ/census/help/methodology_disclosure/reliability_of_data.html.


http://www.census.gov/econ/census/help/methodology_disclosure/reliability_of_data.html

89

is in terms of the average individual student, taxpayer, etc. With a focus on equity, the researcher

chose the value of median because it is the widely accepted measure of central tendency.

Chiripanhura explained the appeal of median income by stating:

Median income is the income available to the household in the middle of the

income distribution; thus it represents the standard of living of the ‘typical’

household. It has been shown that in most instances, median income is lower than

mean income, and that rising inequality causes median income to lag behind mean

income. The latter is influenced by high values at the top of the income

distribution, thus giving an impression of high living standards even though this

may not be the case.6

Scholars have used median household income to evaluate economic trends to justify

factors impacting school quality in Florida. For instance, Thornton and Arbogast7 used median

per capita income for each Florida county and Jordon, Chapman, and Wrobel8 measured the

“median” tax burden and tax effort to determine whether there was a decrease in burden and

effort post reform.

Others have proposed Nash’s Geometric Mean9 as an alternative robust measure of

central tendency. Geometric means are often used to observe nonlinear data and is usually

exercised when data possesses different ranges or times, comparatively speaking. Although

possible to interpret through the Statistical Package for Social Sciences (SPSS) and the National

6 Blessing M. Chiripanhura, “Median and Mean Income Analyses - Their Implications for

Material Living Standards and National Well-Being,” Economic and Labour Market Review 5,

no. 2 (2011): 16, accessed April 15, 2017, http:dx.doi.org/10.1057/elmr.2011.17.

7 Barry Thornton and Gordon Arbogast, “Factors Affecting School Quality in Florida,”

Contemporary Issues in Education Research 7, no. 2 (2014).

8 Meagan M. Jordan, David Chapman, and Sharon L. Wrobel, “Rich Districts, Poor Districts:

The Property Tax Equity Impact of Arkansas School Finance Equalization,” Public Finance and

Management 14, no. 4 (2014): 408.

9 Jana Nemcova, Mihaly Petreczky, and Jan van Schuppen, “Realization Theory of Nash’s

Systems,” Siam Journal on Control and Optimization 51, no. 5 (2013): 3386-414, accessed April

15, 2017, http:dx.doi.org/10.1137/110847482.

http://dx.doi.org/10.1057/elmr.2011.17

http://dx.doi.org/10.1137/110847482

90

Center for Education Statistics, the USCB has not supplied this particular measure of raw data.

Perhaps more importantly, in terms of the methodology, this study’s primary purpose was not to

evaluate how the median has changed over time between one variable but rather to determine

how the correlation of PAV to MHI has changed over time.

The researcher considered that PAV represents the entire population of the variable

whereas MHI represents a portion of the population of the variable, the drawback being that the

MHI variable lacks representation of non-residential property. The limitations of the design are

largely due to policy-created ambiguity that seeks to authorize moral value via fiscal equity.

Florida’s state education finance program establishes that regardless of income or property value,

all students are due quality education. Because taxpayers are accountable to property taxes

through their income and state education legislation places value on equity, the statistical design

of this examination attempts to consider these complexities through variables that quantify

magnitude and central tendency.

Purpose of the Study

The objective of this study was to use a bivariate correlational design to determine the

extent to which PAV and MHI were correlated amongst school districts in the state of Florida as

time progressed. Its purpose was to observe the relationship between the variables as they occur

in the population.10 By design, Pearson correlational tests cannot be used to imply causation or to

develop conclusions that are beyond the scope of the data. Income, a relative measurement of

wealth from which taxes are paid, and property assessed valuation, a measurement used to

determine school district wealth from which all taxes are derived, were analyzed amongst each

other to determine if there were a significant level of interconnectedness to justify the validity of

10 Barry Cohen, Explaining Psychological Statistics (Hoboken: John Wiley & Sons, 2008), 276.

91

using assessed value to measure wealth for school district fiscal aid in current Florida

educational policy.

Research Design

Research Questions

1. Is there a correlation between property assessed valuation and median household income

among school districts in the state of Florida over a 10-year span?

H0: 𝑟 = 0

HA: 𝑟 ≠ 0

2. How consistent is the correlation between property assessed valuation and median

household income amongst school districts in the state of Florida over a 10-year span?

Research Design

Cohen explained that correlation is used to measure the “degree of association between

two variables that are not obviously related but are predicated by some theory or past research to

have an important connection.”11 The Pearson correlation is one method of performing a basic

bivariate analysis and is often used for more sophisticated statistical analyses. Pearson’s Product-

Moment Correlation Coefficient (PPMCC), often referred to as Pearson’s 𝑟, established whether

there was a relationship between the variables as well as the extent to which they were

correlated. Fouladi and Steiger confirm Pearson’s 𝑟 has been “based on independent units of

observation, has been a tool in the analysis of observational data.”12 The unit of measurement for

both variables of interest were comparatively appropriate because the weight of the dollar was

equal between the variables within the year for each year. The data were not altered to

11 Ibid., 275.

12 Rachel T. Fouladi and James H. Steiger, “The Fisher Transform of the Pearson Product

Moment Correlation Coefficient and Its Square: Cumulants, Moments, and Applications,”

Communications in Statistics - Simulation and Computation 37, no. 5 (2008): 928, accessed

April 15, 2017, http://dx.doi.org/10.1080/03610910801943735.

http://dx.doi.org/10.1080/03610910801943735

92

accommodate the current weight of the dollar. Each pair of variables were independent of other

pairs. The study’s variables were measured on an interval scale and inclusive of all available

comparable data supplied by the FDOR and USCB, with very little limitations.

A scatterplot, often used to illustrate the characteristics and the limitations of the

correlation coefficient, is a graph in which one of the variables is plotted on the x axis and the

other variable is plotted on the y axis.13 Typically, for the sake of validity of the statistical

measure used, researchers draft the variables on a scatterplot to determine if the variables

independently reveal signs of normal distribution, linearity and homoscedasticity (which are the

assumptions of the PPMCC). The significance level was provided to rule out the

misinterpretation of statistical results by researchers.

Description of Measure

The PPMCC helped obtain an objective analysis that uncovered the magnitude and

significance of the relationship between the variables, PAV and MHI. This value was calculated

by multiplying the z scores of each variable by one another to get the product and then

calculating the average or mean value, which is called a moment of those products.

Conceptually, the Pearson correlation coefficient is the ratio of the joint covariability of x

and y, to the variability of x and y separately. Young explained that the formula “uses the sum of

products as the measure of covariability, and the square root of the product of the sum of squares

for x and the sum of squares for y as the measure of separate variability.”14 The following

13 Barry Cohen, Explaining Psychological Statistics (Hoboken: John Wiley & Sons, 2008), 258-

59.

14 Forrest Young, “Correlation: The Relationship of Two Variables,” University of North

Carolina, accessed April 15, 2017, http://forrest.psych.unc.edu/research/vista-

frames/help/lecturenotes/lecture11/overview.html.

http://forrest.psych.unc.edu/research/vista-frames/help/lecturenotes/lecture11/overview.html

http://forrest.psych.unc.edu/research/vista-frames/help/lecturenotes/lecture11/overview.html

93

equation is representative of the PPMCC formula where n is equal to the number of pairs of

scores, Σ𝑥𝑦 is equal to the sum of the products of paired scores, Σ𝑥 is equal to the the sum of x

scores, Σ𝑦 is equal to the sum of the y scores; Σ𝑥2 is equal to the sum of squared 𝑥 scores, and

Σ𝑦2 is equal to the sum of squared 𝑦 scores:

PPMCC = Pearson’s 𝑟 =𝑛(Σ𝑥𝑦)−(Σ𝑥)(Σ𝑦)

√[𝑛Σ𝑥2−(Σ𝑥)2][𝑛Σ𝑦2−(Σ𝑦)2] (3-1)

Pearson’s 𝑟 was used because both variables are continuous and this technique provides

the smallest standard of error. One variable, MHI, is based on an estimate, constituting the use of

𝑟 rather than , which is customarily used with sample correlation statistical tests. Also, even

though all data available are being used to test the relationship between the variables, only a

portion of the district MHI has been supplied by the USCB and thusly the entire “population”

cannot be argued. Neither variable was deemed independent or dependent because of the nature

of the statistical test.

The range of the PPMCCs was calculated, as well. The range of the data set was

determined to illustrate the amount of correlation coefficient deviation that took place in the last

decade of the relationship between property assessed valuation and median household income.

Range measures the amount of variation between a given set of numbers. The fluctuation

amongst the coefficients on a year-to-year basis is not reflected in the range, lowering the degree

of robustness. Yet, range does help create an approximate account of consistency. The range is

determined by subtracting the lowest number, in this case, the correlation coefficient, from the

highest number. The greater the range, the greater variation there is between the lowest and

highest number in a set. The lesser the range, the smaller the variation between the lowest and

highest number in a set. In Chapter 4, a correlation coefficient table was presented alongside

other descriptive statistics.

94

Validity and Reliability of the Measure

As with any statistical measure, there were constraints. Cohen explained, “Because the

measurement of correlation depends on pairs of numbers, correlation is especially sensitive to

bivariate outliers.”15 Outliers, depending on its degree, can greatly influence measures of central

tendency. Another limitation in using this particular design are its theoretical parameters.

Pearson’s 𝑟 only measures the tendency for the pairs of variables to fall on the same straight

line.16 Thus, other relationships, like a curvilinear relationship, are possible but may display a

weak Pearson correlation coefficient. The scatterplot was inspected to help interpret the

correlation because Pearson’s 𝑟 can be “raised or lowered in magnitude, or even reversed in

direction by a truncated range or a single outlier.”17 Also, when analyzing the results, the

researcher considered, “correlation is based not on absolute numbers, but on relative numbers

(i.e., z scores) within a particular group.”18

Pearson’s Product-Moment Correlation was appropriate for answering the research

question and for the population of interest. Because the full population of that which was

available by the USCB was used, validity and reliability of the results are as absolute as possible.

On the grounds that this study’s design was correlational, internal validity is not applicable. As

stated prior, it was neither the correlational test nor the study’s goal to determine cause and

effect. The goal was merely to make an observation of trend during a designated scope of time.

15 Barry Cohen, Explaining Psychological Statistics (Hoboken: John Wiley & Sons, 2008), 262.

16 Ibid., 260.

17 Ibid., 263.

18 Ibid., 261.

95

In terms of external validity, this study was exclusive to Florida’s geographic and financial

situation. It did not intend to project the outcome of another location or population.

Description of Analysis

SPSS was used to evaluate these data through simple correlational analysis. PAV data,

via the FDOR, was entered into Microsoft Excel and later imported into SPSS. MHI data, via the

USCB, was entered directly from Microsoft Excel into SPSS. There were no missing data to be

coded or defined. The Shapiro-Wilk Test Statistic was used to check for normal distribution of

variables. The SPSS Descriptives, Tests of Normality, and Normal Q-Q Plot Graphs were

provided for each year in Appendix C. The researcher also evaluated school district population,

property assessed valuation, and median household income via ranking and considered the

impact it may have had on the correlation results.

Setting and Participants

This study’s parameters were geographically placed in the state of Florida, boasting a

population of more than 20 million. In Florida there are 67 school districts formed along county

lines. Data for forty school districts, in which both property assessed valuation and median

household income, were provided and thusly used in the study. The state extends 53,625 square

miles,19 many of them rural. The 2015 average median household income of the state was $47,

507.20 The state of Florida’s 2016 just value totaled $2,431.21 billion, assessed value totaled

$2,055.21 billion, and taxable value totaled $1,608.28 billion.21



20 Ibid.





96

Baker, Bradford, Calhoun, DeSoto, Dixie, Franklin, Gadsden, Gilchrist, Glades, Gulf,

Hamilton, Hardee, Hendry, Holmes, Jackson, Jefferson, Lafayette, Levy, Liberty, Madison,

Okeechobee, Suwannee, Taylor, Union, Wakulla, Walton, and Washington county/school

districts were not used in the study because median household income data were not provided by

the United States Census Bureau for the prospective years. These school districts had the

smallest population in relation to those that were included in the study. The populations of these

counties were less than 70,000 and some counties as few as about 8,000.22 There were seven

school districts that could never be used in the study because of the abstract existence.

Data Sources and Organization

PPMCC was used to determine the direction and strength of association between each

variable due to the interval scales of measurement for both variables. The types of data involved

in the correlational test, PAV and MHI, were numerical (measured in dollars). Each variable was

collected for year 2006 to year 2015 for each available district.

Property Assessed Valuation

Assessed valuation is dependent on the just value or market value of a property, which is

calculated by the property appraiser for each county/district. Market value conditions are based

on “the relationship, knowledge, and motivation of the parties (i.e., seller and buyer); the terms

of sale (e.g., cash, cash equivalent, or other terms); and the conditions of sale (e.g., exposure in a

competitive market for a reasonable time prior to sale).”23



23 The Appraisal Foundation, 2016-2017 Uniform Standards of Professional Appraisal Practice

(Washington D.C.: Appraisal Foundation, 2015), 3-4.


97

Generally, PAV is calculated by subtracting assessment limitations from the just value of

a property. In an effort to use primary data sources, the researcher used property assessed

valuation data secured from the FDOR. These data were accumulated from Microsoft Excel

Spreadsheets for the year 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, and 2015.

PAV are net values for real, personal, and centrally assessed property.

Median Household Income

Median is a widely-used, robust measure of central tendency that calibrates the center of

a distribution of a given set of numbers. When compared to the mean, an average of a given set

of numbers, it is not as influenced by outliers. The data were aggregated from for the year 2006,

2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, and 2015. The value presented as MHI is

reported as an absolute number with an accompanying margin of error supporting the estimated

range of values. The USCB used the American Community Survey (ACS) to produce

population, demographic and housing unit estimates. They report that the ACS is based on the

USCB’s “Population Estimates Program that produces and disseminates the official estimates of

the population for the nation, states, counties, cities and towns and estimates of housing units for

states and counties.”24

Data are based on a sample and are subject to sampling variability. The degree of

uncertainty for an estimate arising from sampling variability is represented

through the use of a margin of error. The value [reported] is the 90 percent margin

of error. The margin of error can be interpreted roughly as providing a 90 percent

probability that the interval defined by the estimate minus the margin of error and

the estimate plus the margin of error (the lower and upper confidence bounds)

contains the true value. In addition to sampling variability, the ACS estimates are

24 “Population Estimates Program,” United States Department of Commerce, United States

Census Bureau, accessed April 15, 2017,

https://factfinder.census.gov/faces/nav/jsf/pages/programs.xhtml?program=pep.

https://factfinder.census.gov/faces/nav/jsf/pages/programs.xhtml?program=pep

98

subject to nonsampling error. The effect of nonsampling error is not represented

in [the] tables.25

The USCB reports yearly MHI as “inflation-adjusted.” The Board of Governors of the

Federal Reserve acknowledge that “inflation is an increase in the overall price level of goods and

services in the economy.”26 At first it appeared as if there would be a discrepancy between the

FDOE Commissioner-derived RLE, which is based on current FDOR reported PAV, and the

researcher using the seemingly derived MHI value reported by the USCB. Comparing the

monetary value of a variable over a long range of time against another variable that is not

reported as “inflation-adjusted” would have constituted a different research design. However,

USCB reports income data in “current” dollars and “inflated” dollars. Per USCB, “current”

dollars represent a particular year’s dollars as adjusted to the current year while “inflated” dollars

represent a particular year’s dollars in the year of reference. Thusly, in terms of the methodology

of this study, although MHI is reported in inflation-adjusted dollars, the adjustment is only

relevant to the year in which it was reported making its comparison to PAV statistically sound.

Data Processing and Analysis

Variable data were acquired directly from the USCB and FDOR. Free from manipulation,

median household income and property assessed valuation were organized chronologically by

year and county/school district. Data were analyzed by detecting a pattern between each county

from year to year for a series of ten years. Data were interpreted by viewing the extent to which

25 “American Community Survey: Methodology,” United States Department of Commerce,

United States Census Bureau, accessed April 15, 2017, http://www.census.gov/programs-

surveys/acs/methodology.html.

26 “What is Inflation and How Does the Federal Reserve Evaluate Changes in the Rate of

Inflation?” Board of the Governors of the Federal Reserve System, accessed April 15, 2017,

https://www.federalreserve.gov/faqs/economy_14419.htm.

http://www.census.gov/programs-surveys/acs/methodology.html.

http://www.census.gov/programs-surveys/acs/methodology.html.

https://www.federalreserve.gov/faqs/economy_14419.htm.

99

each correlation coefficient compared to relative years. Ultimately, the range of coefficients

observed in the study helped discover the possible impact of the SOH assessment differential on

Florida counties over time.

Summary

The next chapter presented the results based on the selected research design. In total, ten

analyses were performed, one for each fiscal year. Correlational results were presented for each

year with interpretation. Generalizations were made for the entire study and were used to draw

conclusions presented in the fifth chapter.

100

CHAPTER 4

PRESENTATION OF RESULTS

Purpose of Study

The research question sought to discover if there were a correlation and how consistent

the correlation of Property Assessed Valuation (PAV) and Median Household Income (MHI)

were over a 10-year span. These variables were measured amongst each other for strength and

direction of association each year from 2006 to 2015. This chapter provided the results for

preliminary scatterplot results and for the Pearson Product-Moment Correlation Coefficients

(PPMCC) for each year. Each section includes a subsection for the statistical results and for the

interpretation of those results. The end of the chapter displays graphical depictions of the

Statistical Package of the Statistical Sciences (SPSS) output results (see Appendix C).

Demographics

Table 4-1 lists the school districts (i.e., counties) that were used in the study. Seven

school districts in Florida were not measured because they do not have a geographical location,

and accordingly an income. They included: Florida Agricultural and Mechanical University

Laboratory School, Florida Atlantic University Laboratory School, Florida State University

Laboratory School, University of Florida Laboratory School, Florida School for the Deaf and

Blind, Florida Virtual School, and the Okeechobee Youth Development Center.

2006 Correlation Results

Results for 2006

The Pearson Correlational statistical test was conducted to evaluate the relationship

between PAV and MHI for 2006. SPSS results concluded that there was a weak, non-significant,

positive association between PAV (M=52553716620.00, SD=62435336680.000) and MHI

(M=45691.23, SD=6696.057), r (38) = .168, p = .301 (see Appendix C: SPSS Output Results).

101

Interpretation of Results 2006

The strength of the PPMCC was very weak at .168. The sign of the coefficient indicated

the direction of the relationship was positive (i.e., as one PAV item increased, so did the

accompanying MHI item). There was insufficient evidence to conclude that there was a

significant linear relationship at the .05 α level between PAV and MHI because the PPMCC was

not very different from 0. The researcher could not rule out the correlation was not due to

chance.


Results for 2007






The strength of the PPMCC was profoundly weak at .179. The sign of the coefficient

indicated the direction of the relationship was positive (i.e., as one PAV item increased, so did

the accompanying MHI item). There was insufficient evidence to conclude that there was a



chance.


Results for 2008



102




The strength of the PPMCC was essentially zero at .138. The sign of the coefficient





chance.


Results for 2009











chance.

103


Results for 2010






The strength of the PPMCC was virtually zero at .073. The sign of the coefficient





chance.


Results for 2011










104


chance.


Results for 2012











chance.


Results for 2013






The strength of the PPMCC was profoundly weak at .198. The sign of the coefficient


105


significant linear relationship at the α level between PAV and MHI because the PPMCC was not

very different from 0. The researcher could not rule out the correlation was not due to chance.


Results for 2014









significant linear relationship at the α level between PAV and MHI because the PPMCC was not

very different from 0. The researcher could not rule out the correlation was not due to chance.


Results for 2015








106




chance.

Correlation Results of 2006-2015

Table 4-2 lists the Pearson Product-Moment Correlation Coefficient and significance

level for each year.

Correlation Coefficient Results for 2006-2015

Table 4-3 lists the Pearson Product-Moment Correlation Coefficient and significance

level for each year when the three greatest outliers were removed.

Interpretation of Results 2006-2015

The research question investigated the consistency of the correlation coefficient in

retrospect. Rather than solely focusing on the degree of strength and direction of each coefficient

for each year, the researcher observed the descriptive statistics as they relate to one another. For

each year, the researcher provided the mean and standard deviation. The Shapiro-Wilk statistics

test revealed that the median household income was normally distributed, while the property

assessed valuation violated the assumption for correlation with and without outliers, each year.

The range of the correlation coefficients was less than 0.125 (with outliers), which

signified that the results from year to year were quite consistent. Although the correlation

between property assessed valuation and median household was minimal and not significant with

a p-value range of .434, the association was fairly persistent. This led the researcher to conclude

that the assessment limitation, Save Our Homes, had not had a significantly different effect over

time amongst Florida districts, yet. Refer to Figure 4-1 for a graphical representation of the

PPMCC (with outliers), Figure 4-2 of the p-value (with outliers), Figure 4-3 for the graphical

107

representation of the PPMCC fluctuation (without Outliers), and Figure 4-4 for the graphical

representation of the p-value fluctuation (without Outliers), 2006-2015.

Upon close inspection, the outliers of the study were large population counties whose

geographical size were not large, comparatively speaking, as other districts in the state. Broward,

Miami-Dade, and Palm Beach were the three school districts that consistently skewed these data

via property assessed valuation. These school districts have extremely high property value due to

population but income that was consistent with the rest of the state. The assessment ratio millage

adjustment and the 90/10 limitation applied to seven counties for the 2015-2016 school year (five

of them were included in this study [Collier, Martin, Monroe, Sarasota, Sumter]). There is the

possibility that having data for MHI for rural counties could have smoothed the distribution of

data points. The researcher considered that the economic climate, however, increased the price of

real estate in metropoles.

When viewing these data, the stakeholders must consider a few important details that

were not illustrated through the results. All data must be analyzed with caution because of the

economic recession, the economic recovery period, and relevant policy changes. The year 2007,

2008, and 2009 were all subject to the effects of the Great Recession1 Housing Bust. Thirty-

seven geographic districts were not included because the median household income data were

not available, possibly compromising statistical power. These counties had the smallest

populations and property assessed valuations but median household incomes were diverse. These

limitations could have strengthened or weakened the relationship of the variables, established a

statistically significant result, and thus provided a more comprehensive picture.

1 United States Department of Labor, Bureau of Labor Statistics, BLS Spotlight on Statistics:

Recession of 2007-2009 (United States Department of Labor, 2012), accessed April 15, 2017,



108

Summary

The Florida Education Finance Program (FEFP) seeks to accommodate for the lack of

funding due to property value or degree of rural student population. Despite these

acknowledgements, the weak correlation between PAV and MHI for each year, and more

importantly, the lack of statistical significance between years exposes a major fault in fiscal

accountability that may or may not yet be absolutely attributable to SOH but certainly the

framework itself in how fiscal capacity is defined. The formula that the Florida Education

Finance Program uses is sophisticated and encompasses many important segments that attempt to

protect districts that may have a financial disadvantage. Yet, this study urged a closer

examination of the accountability of the taxpayer by the Florida Department of Education. In that

respect, more refinement is achievable and necessary to accurately ensure equity on behalf of the

means required by each district. Chapter Five discussed the results of the study, provides

implications for practice and presents recommendations to future researchers.

109

Table 4-1. List of Counties / School Districts Used in the Study County/District County/District County/District County/District

1 Alachua 11 Duval 21 Manatee 31 Pinellas

2

3

4

5

6

7

8

9

10

Bay

Brevard

Broward

Charlotte

Citrus

Clay

Collier

Columbia

Miami-Dade

12

13

14

15

16

17

18

19

20

Escambia

Flagler

Hernando

Highlands

Hillsborough

Indian River

Lake

Lee

Leon

22

23

24

25

26

27

28

29

30

Marion

Martin

Monroe

Nassau

Okaloosa

Orange

Osceola

Palm Beach

Pasco

32

33

34

35

36

37

38

39

40

Polk

Putnam

Saint Johns

Saint Lucie

Santa Rosa

Sarasota

Seminole

Sumter

Volusia

110

Table 4-2. Table of Primary Descriptive Statistics, by Year Year PPMCC (Pearson’s r) P-Value

2006 .168 .301

2007 .179 .270

2008

2009

2010

2011

2012

2013

2014

2015

.138

.119

.073

.109

.098

.198

.097

.121

.397

.464

.654

.502

.548

.220

.551

.457

111

Table 4-3. Table of Descriptive Statistics without Outliers, by Year Year PPMCC (Pearson’s r)

P-Value

2006 .234 .164

2007 .285 .088

2008

2009

2010

2011

2012

2013

2014

2015

.263

.204

.131

.171

.142

.134

.158

.224

.116

.225

.438

.312

.401

.430

.351

.182

112

Figure 4-1. Graphical Representation of the PPMCC Fluctuation, 2006-2015

113

Figure 4-2. Graphical Representation of the P-Value Fluctuation, 2006-2015

114

Figure 4-3. Graphical Representation of the PPMCC Fluctuation (without Outliers), 2006-2015

115

Figure 4-4. Graphical Representation of the P-Value Fluctuation (without Outliers), 2006-2015

116

CHAPTER 5

DISCUSSION AND RECOMMENDATIONS

Introduction

In 1995, when Save Our Homes (SOH)1 was implemented, Florida’s economy was

relatively stable. Yet, about a decade later, America’s Great Recession created income and

property disparities, prompting well-intentioned legislation that stimulated both deliberate and

unintended outcomes. The Portability Transfer (PT), enacted in 2008, helped further widen the

spectrum of property value in communities across the state. These approaches to relieving

citizens of property taxes, when paired with the Florida Education Funding Program (FEFP),

compromised the allegiance of an equitable education funded and based on a district’s fiscal

capability.

This study recognized a statute-based theoretical caveat in Florida education funding:

Income and assessed valuation, although treated synonymously, are dissimilar. The researcher

suggests that policies like the Save Our Homes assessment differential create variance in the

perceived value of property. The study argues that by the time the Florida Department of

Education (FDOE) imposes the district-specific millage rate, the statewide assessment

differential has adjusted the true value of property, the measure of wealth, from household to

household and thusly district to district. Consequently, the education policy issue is that wealth is

derived from an indirect, intricately flexible value. In an effort to close the gap of funding

inequity, the intention of the examination is to verify prosperity through some feasible measure

that is the most reflective of wealth despite tax policy.

1 FLA. STAT. §193.155 (2016).

117

Summary of Findings

Data showed a consistently weak positive correlation for each year of observable data

between Property Assessed Valuation (PAV) and Median Household Income (MHI). The

scatterplots helped illustrate the high degree of unpredictability between PAV and MHI, district

to district and year to year, which yielded the appropriate Pearson Product-Moment Correlation

Coefficient (PPMCC). The years with the least statewide SOH savings loosely imitated a decline

in the PPMCC for statistical analyses that did not include the three major outliers.

In addition, the researcher evaluated the latest data by ranking the school districts in

descending order by population, PAV, and MHI. The tendencies observed yielded the results

discovered in the statistical analyses. The researcher found that districts with the highest MHI

were located in the center of the PAV continuum. Some districts that were located in the middle

of the PAV distribution were ranked high in MHI. Alternatively, some school districts that were

ranked high in PAV, were ranked in the middle of the MHI continuum. School districts that were

in the top ten in population may have ranked toward the bottom in MHI. Two of the outliers

were in the top ten of population, PAV, and MHI, while the other consistent outlier had high

PAV but one of the lowest MHI’s in the state.

When study districts and all Florida districts were ranked by PAV, the position of each

district was within one position. For instance, if a district that were included in the study ranked

30 amongst study districts, when that same district was ranked against all Florida school districts,

it was either positioned at 29, 30, or 31. Although not as closely mirrored, when population was

compared, the rankings were strikingly similar to PAV, showing that population has an

advantage in Florida’s definition of fiscal capacity. MHI, however, varied greatly district to

district. The 90/10 Limitation school districts had above average MHI with a low to average

PAV. The outliers had average MHI, very high population and exceptionally high PAV. The

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higher the PAV, the more likely the district could serve as an outlier. These behaviors manifested

the outcome of the examination.

This study confirmed the principles and research of several scholars’ views of the

association between property value and income but in a manner that was time-sensitive, specific

to Florida, through correlational methods, and in response to current tax policy.

Implications for Practice

Although each school district was assessed at less than $2 billion in PAV, on average,

school taxable value was about 85 percent of PAV, potentially resulting in billions of unrealized

dollars of revenue statewide. This study recognized that the intricacies of this phenomenon could

be the difference of the quality of education a student receives. Thus, the objective of this study

was to determine the relationship between PAV and MHI because the FEFP uses taxable PAV to

determine district wealth. The SOH policy (and its PT) immediately skew the measure of wealth

of households within a population before school taxable value is derived. If an entire district’s

financial capacity is based on a distorted value, equity is threatened.

Florida property tax exemptions are typically imposed in fixed dollars, regardless of the

value of a home (especially for properties valued over $50,000) and often for the duration of the

homestead. Changes in income limitations are minimal, uniform, and typically only applicable in

the tax system for seniors and totally/permentently disabled persons.2 Pronounced fluctuation

within a homestead's property tax exemptions from year to year, is unlikely, usually changing

only if the quality of life of a taxpayer has changed. Fluctuation within a homestead’s property

2 “Florida Property Tax Valuation and Income Limitations,” Florida Department of Revenue,

accessed March 6, 2017, http://floridarevenue.com/dor/property/resources/limitations.html; FLA.

STAT. § 196 (2016) and FLA. CONST. art. VII, § 6.


119

taxes are more frequent, however, often due to changes at the assessment level or levied taxes. In

lieu, assessment differentials are flexible, changing as frequently as the housing market and

inflation rises or falls. Moreover, the implementation of the PT makes deciphering the property

value, before exemptions, increasingly difficult to determine. These concepts serve as the moral

fiber of assessment cap policy but inadvertently interrupts education funding.

The target of this study was based on the equity of two measures of prosperity that should

be comparable to determine whether the average taxpayer is properly represented in each school

district. This study did not seek to measure a district’s PAV or MHI between years. Rather, the

examination compared the districts’ PAV to MHI amongst one another, statewide, from year to

year. A strong correlation would have yielded a certain level of predictability with proper

statistical analyses of PAV based on MHI or vice versa of any particular district, which based on

the results of the study are nonexistent.

PPMCC fluctuation was likely due to changes in the housing market, income, assessed

valuation, and more, all responsive to a changing economy. Because of this cyclical nature,

changes in PPMCC could be also be due to differing percentages from year to year of the

Consumer Price Index (CPI) and thusly, the implementation of SOH. Table 5-1 outlined the

percentages from year to year for both definitions. Because the CPI is reflective of the economy

through the view of the United States Bureau of Labor Statistics, the table helped to illustrate the

fluidity of the general market in conjunction with housing. Appendix B outlined the application

of SOH in Florida (and accordingly the dramatic variation of the housing market) throughout the

past decade.

The assessment growth rate for all property types varied greatly within the last decade.

From year 2008 to 2009, the lowest school district (i.e., county) growth rate was -25.33 while

120

from year 2014 to 2015 there were only three districts with a negative percent increase.3 Percent

increases amongst the districts in the state varied greatly within a year as well. For instance,

between year 2013 and 2014 the growth rate ranged from -2.65 to 13.19, when just four years

earlier the growth rate ranged from -12.93 to 83.66 between counties, signaling economic

disarray. 4

This study did not seek to define legitimacy of the SOH policy overtime but rather to

raise doubt about the strength and consistency of the PAV measure in lieu of a taxpayer’s

income. When analyzing the results, the researcher concluded that although changes in SOH

implementation directly affected property assessment, what is likely is that the reason for these

changes, the economy, has affected MHI as well but not at an equally proportional rate.

Supposing state education funding equity is effected, recreating tax policy so that it is attentive to

education funding is improbable. Presently, wealth is determined and taxes are levied from

property tax data. In the future, policy-makers may see fit that it is appropriate to continue

levying taxes from property valuation but that there is also room to determine wealth by an

additional, more precise measure, such as a federal income tax measure. This method will satisfy

the need to closely, and therefore accurately, identify the financial prosperity of a school district

at the most basic level and the need for policy that honors the absence of a statewide income tax.

Recommendations for Research

The researcher proposes five recommendations for future investigators. Foremost, this

study should be replicated in its entirety as more recent data becomes available. As taxpayers



4 Ibid.


121

migrate within and between districts, and as the economy and demographic-makeup of Florida

shifts, what may be equitable at one point in time may not be at another. The SOH will continue

to effect district PAV and the PPMCC will likely continue to vary year to year.

Also, it is suggested that future researchers focus on how just value is correlated to

median household income. Data can be compared to the association of PAV and MHI. This

research will add to what scholars know about how incomplete property assessed value (and in

turn, taxable value) as a measure of wealth may be and provide more evidence of how the two

are weakly correlated. Just as this study observes relationship over time, so should the named

recommendation because the relationship between the variables at any one time does not

encompass the full status of threatened equity.

Additionally, the research observed the variables against one another in terms of an

estimated median household income. MHI data obtained from the United States Census Bureau

(USCB) also provided margins of error for each county/district. Another PPMCC could be

derived based on the same values for property assessed valuation from the FDOR and both

values of the proposed margin of error for median household income. Although it is likely that

the outcome will verify the results of this study, until the research is conducted, this assumption

cannot be confirmed.

This study is replicable in other states. Each state has its own DOR that is responsible for

recording and reporting property assessed valuation data and the USCB provides MHI statistics

for all states. Replicating the study across state lines, across years can solidify research that

observes the impact of assessment differentials and its impact on education finance. If state

legislators do not constitute assessment differentials, data could provide a baseline of the PAV

and MHI association. An investigator can choose to replicate the study for all states for the same

122

year as one statistical test. This could likely boost the statistical significance and create a more

robust study. However, the investigation would no longer be state specific. Because each state

education finance formula is distinct, the conclusions of that study would be limited, possibly

only providing support for not using the variables interchangeably in a general sense, which

would still require states to pursue more tailored data.

Last, researchers have the option of measuring median property assessed valuation

against median household income or total property assessed valuation against total household

income or total income of each district. A reevaluation of the research question would be

necessary as the results would be fairly predictable. Essentially, the investigator would not be

measuring equity for the state of Florida. State legislators use total property assessed valuation,

which is contingent on the number of parcels per district. If total median household income or

total income were measured, that too would be based on the number of taxpayers. Both are

measures of magnitude, which would likely be consistent with the ranking of districts based on

PAV and population. Of course, this information would provide insight of whether there is a

simple association between assessed value and income in the chosen quantity but falls short of

drawing conclusions about what that means for the average taxpayer against the currently

established measure of wealth for public school funding.

All investigators must keep in mind the tax climate when discussing the results of their

study. For instance, SOH cannot prevent an increase or decrease in taxation, rather that is an

indirect goal. So, when discussing the results of the study, researchers must consider that

although SOH decreases the assessment of a homestead, it does not exclusively prevent an

increase in property taxes due to the possibility of an increased millage rate via taxing

123

authorities, which is why observing taxable value year to year is not as valid as it relates to

education funding in the state of Florida.

Conclusion

In conclusion, if wealth were the accumulation of all assets (minus debts), property value

and income must be factored into a comprehensive funding formula with conviction. Because the

variables were only slightly correlated and that the measured correlations could greatly be due to

chance, there should be a more certain manner in which the FEFP identifies financial prosperity.

The degree of non-significance from year to year further supports the invalid nature of using the

variables interchangeably without compensation for the other.

Several states, including Connecticut, Maryland, Massachusetts, New Jersey, New York,

Rhode Island, and Virginia use a combination of property value and income to determine fiscal

capacity. Methods range from ranking property and income for each district within a state,

creating a ratio that weighs property and income, comparing district income to the state average,

and implementing an income factor based on the state’s income tax returns, all of which hinging

on equalization.

The severely debilitated nature of the correlational relationship does not cast anticipation

of an association in the future, granted the current arrangement of tax policy and the state of the

economy. The education finance system, however, is more pliable, especially if the lack of

equity is argued, which instills hope for those populations that are lost in aggregation. A major

concern of including an income factor by critics is the availability of data outlining the income of

school districts, especially in Florida, a state that does not collect state income taxes. Yet, the

United States Department of Treasury’s Internal Revenue Service (IRS) reported that in 2015

124

over 9.4 million Florida federal individual income tax forms were filed.5 This information makes

it more than reasonable and realistic to prepare district data. The IRS reports Statistics of Income

that outlines tax data by zip code:

The Statistics of Income (SOI) division bases its ZIP code data on administrative

records of individual income tax returns (Forms 1040) from the Internal Revenue

Service (IRS) Individual Master File (IMF) system. Included in these data are

returns filed during the 12-month period, January 1, 2015 to December 31, 2015.

While the bulk of returns filed during the 12-month period are primarily for Tax

Year 2014, the IRS received a limited number of returns for tax years before 2014

and these have been included within the ZIP code data.6

The Internal Revenue Service Zip Code Data Documentation Guide, which frames 127 variable

names with descriptions, is noted in Appendix D. The future of income data is promising,

helping to ease the responsibility of including such measurements in an education funding

formula for a state that does not tax income.

Based on the results of this study, conversation must continue regarding the way in which

property and income is used methodologically and how it is used in policy. The issue is that the

median of a given set of numbers measures central tendency, what this study used for income,

while the total (or sum) measures magnitude, what this study used for property. Even though

researchers have often been limited in their access, data are increasingly becoming publicly

available. Policy writers risk modifying a formula that is less equitable because it does not fully

encompass the funding abilities of the district.7 Median is more robust and representative of the

5 United States Department of Treasury, Internal Revenue Service, The Internal Revenue Service

Data Book: 2015 (United States Department of Treasury, 2015), 4, accessed April 15, 2017,

https://www.irs.gov/pub/irs-soi/15databk.pdf.

6 “SOI Tax Stats - Individual Income Tax Statistics - 2014 ZIP Code Data (SOI),” United States

Department of Treasury, Internal Revenue Service, accessed April 15, 2017,

https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi.

7 Some scholars suggest that the income factor be used as a multiplier to property; E.g., Michael

Griffith, “Who Pays the Tab for K-12 Education: How States Allocate Their Share of Education


https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi.

125

normal population while the sum represents the entire population but without regard of the

variability within the population. There is a need for using both measures to describe the base in

research and policy. Although comparing median household income and median property

assessed valuation or total income and total property assessed valuation may add merit to the

elementary understanding of the relationship between the two variables, it does not weigh

legislative principle. Regardless, total relevant property is used to fund education, not those that

are centered in the distribution of homes and the concern of this study and education scholars is

that the taxpayer’s ability to pay is accurately represented. Education policy discourse within the

states must continue to discuss multiple measures of both variables to fully comprehend the

condition of a state. Still, because current Florida education finance policy does not yet recognize

income in its school funding formula, that is the present argument.

Policy-makers have the task to ensure that their method of implementing an income

factor actually changes district dispersion so that it is useful and certainly not a disadvantage.

Regardless of legally accessible tax structures, the compensation of communities with high

property taxes (and low income) and communities with low property taxes (and high income)

should be an explicit focus of an education funding formula that utilizes one tax structure to

measure wealth. The Florida Legislature pledges to provide every student “an educational

environment appropriate to his or her educational needs… equal to that available to any similar

student, notwithstanding geographic differences and varying local economic factors….”8

constituting a reevaluation of the association between two important variables that merit our

Costs,” Education Commission of the States, 14, no. 4 (2013): 4-5, accessed April 15, 2017,

http://www.ecs.org/clearinghouse/01/08/47/10847.pdf.

8 FLA. STAT. § 235.002 (1)(a) (2001).

http://www.ecs.org/clearinghouse/01/08/47/10847.pdf.

126

funding formula through the conversation of fiscal capacity. Synthesizing a formula that

acknowledges the difference between two distinct measurements of financial prominence is

critical. Adding an income factor to the FEFP formula is the most practical method in achieving

greater equity in education funding formulas through income. Because its purpose is to quantify

capacity through a reliable measure, the federal income tax may provide a clearer picture of

fiscal ability.

127

Table 5-1. Save Our Homes Annual Increases, 2006-2016 Year Consumer Price

Index Change

Cap Year Consumer Price

Index Change

Cap

2006 3.4 3.0 2012 3.0 3.0

2007 2.5 2.5 2013 1.7 1.7

2008 2.7 2.7 2014 1.5 1.5

2009 0.1 0.1 2015 0.8 0.8

2010 2.7 2.7 2016 0.7 0.7

2011 1.5 1.5

Source: Data adapted from “Florida Property Tax Valuation and Income Limitations,” Florida

Department of Revenue, accessed March 6, 2017,

http://floridarevenue.com/dor/property/resources/limitations.html.


128

APPENDIX A

PROPERTY TAX LIMITATIONS ACROSS THE UNITED STATES

Table A-1. Property Tax Limitations Across the United States State Overall Counties Municipalities School Districts

Alabama* x x x x

Alaska x

Arizona* x

Arkansas* x x

California* x

Colorado x x x

Florida* x x x

Georgia* x

Idaho x x x

Illinois* x x x

Iowa* x x x

Kansas x

Kentucky x x x

Louisiana x x x

Massachusetts x

Michigan* x x

Missouri x x x

Montana x x x

Nevada* x x x

New Mexico x x x x

New York* x x x North Carolina x x

North Dakota x x x

Ohio x

Oklahoma* x

Oregon* x x

Pennsylvania x x x

South Dakota x x x

Texas* x x x

Utah x x x

Washington* x x x x

West Virginia x x x x

Wisconsin x

Wyoming x x x

Source: Information from Nikolai Mikhailov and Jason Kolman, Types of Property Tax and

Assessment Limitations and Tax Relief Programs (Lincoln Institute of Land Policy, 1998), 3-4,


https://www.leg.state.nv.us/73rd/otherDocuments/PTax/lincoln%20institute%20-

%20property%20tax%20relief.pdf.

Note: “x” signifies that the state satisfies a particular type of assessment limitation; States with

an asterick (*) impose limitations on assessment increases. (In addition to those listed in the

chart, Maryland, New Jersey, South Carolina are also included.)



129

APPENDIX B

SAVE OUR HOMES VALUE HISTORY 2005-2015

Table B-1. Save Our Homes Value History (2005-2008) School District 2005 2006 2007 2008

Alachua 1,350,504,930 1,877,892,110 2,355,631,830 2,295,775,910

Baker 87,599,027 128,062,975 197,164,279 185,253,367

Bay 1,137,956,907 3,061,880,357 3,106,897,710 2,565,356,894

Bradford 56,187,634 132,343,752 184,133,860 167,373,395

Brevard 10,765,177,610 14,595,888,600 11,170,739,060 8,286,353,750

Broward 34,025,806,342 52,417,665,175 59,326,069,219 40,527,231,354

Calhoun 10,367,796 29,692,558 61,891,418 57,030,785

Charlotte 2,874,384,298 5,183,994,390 3,748,624,181 1,697,507,958

Citrus 1,299,786,120 2,495,309,113 2,344,557,000 1,615,763,301

Clay 1,218,956,255 2,246,196,635 2,695,873,393 2,049,796,428

Collier 8,821,177,526 15,661,507,972 14,963,377,810 10,522,473,867

Columbia 167,281,933 349,414,109 455,863,881 414,861,126

Miami-Dade 38,586,357,410 57,656,530,522 74,022,145,510 65,907,689,639

DeSoto 109,253,537 330,549,045 353,098,173 298,617,373

Dixie 93,517,899 78,840,613 67,487,706 71,397,492

Duval 7,188,475,624 9,664,706,456 13,390,801,942 11,698,499,856

Escambia 1,430,437,710 3,189,831,900 2,604,582,400 2,059,776,345

Flagler 1,092,445,576 1,717,916,865 1,847,523,684 1,237,240,752

Franklin 348,112,974 509,824,705 468,724,954 372,154,994

Gadsden 99,261,113 181,764,050 258,176,099 233,860,091

Gilchrist 34,690,987 101,010,034 152,855,164 140,498,004

Glades 32,789,434 80,198,994 92,474,478 82,835,308

Gulf 320,901,115 301,790,123 256,746,819 221,989,383

Hamilton 14,976,938 39,160,351 60,005,596 58,417,029

Hardee 31,854,043 95,848,002 150,025,850 137,286,632

Hendry 153,963,850 349,344,810 393,743,360 253,079,170

Hernando 1,374,292,010 2,290,345,963 2,491,811,311 1,712,878,172

Highlands 580,985,745 1,256,217,849 1,530,718,220 1,198,806,097

Hillsborough 12,276,878,890 20,187,341,354 20,271,133,053 13,373,861,957

Holmes 19,155,601 39,766,487 48,183,257 44,797,646

Indian River 2,504,791,190 3,816,745,990 2,964,387,610 2,158,769,950

Jackson 95,977,919 99,663,878 179,298,520 164,394,945

Jefferson 37,524,715 55,477,981 112,446,040 115,094,736

Lafayette 16,796,913 39,254,903 44,711,467 48,153,746

Lake 1,136,486,359 2,947,837,079 3,353,941,454 2,789,048,643

Lee 8,566,335,740 16,482,984,230 15,768,779,230 9,174,880,770

Leon 1,753,656,572 2,666,448,320 3,100,683,337 2,758,365,522

Levy 239,338,198 493,437,579 517,942,518 458,580,742

Liberty 13,104,462 29,767,649 34,000,397 31,996,860

Madison 32,349,731 64,960,885 103,863,261 122,663,594

Manatee 4,433,835,996 6,833,181,670 7,368,335,726 4,372,214,555

Marion 1,479,859,024 3,330,706,677 5,333,715,889 4,367,852,536

Martin 4,652,475,470 6,909,655,668 6,066,454,257 3,797,110,345

Monroe 4,363,418,573 6,224,777,715 5,578,491,528 4,101,252,787

Nassau 811,552,259 1,149,989,059 1,332,092,887 1,150,374,395

Okaloosa 1,929,494,340 3,783,910,660 3,582,676,899 2,621,443,608

Okeechobee 200,098,617 328,970,547 411,059,047 282,664,756

130

Table B-1. Continued. School District 2005 2006 2007 2008

Orange

Osceola

7,248,637,307

1,078,216,373

15,113,273,007

2,585,936,541

19,553,574,834

3,643,195,787

13,841,116,831

2,711,704,415

Palm Beach 29,014,276,021 47,852,430,832 41,073,586,244 28,975,426,228

Pasco 3,590,739,466 6,749,056,418 7,016,844,123 4,529,764,567

Pinellas 15,657,412,902 24,626,947,671 23,713,326,637 16,431,372,239

Polk 2,597,453,712 5,559,904,889 6,991,167,460 5,740,095,417

Putnam 353,349,308 634,064,762 781,844,927 722,964,115

Saint Johns 3,113,357,349 4,806,192,905 5,370,122,738 4,165,151,300

Saint Lucie 3,088,222,988 4,942,999,073 4,233,796,452 2,069,631,163

Santa Rosa 954,414,699 1,930,805,460 1,484,495,597 1,178,464,678

Sarasota 9,728,947,032 16,369,486,988 14,252,363,423 7,995,560,591

Seminole 4,167,971,093 8,434,527,410 9,946,459,205 7,166,833,405

Sumter 507,549,291 722,731,621 1,072,240,736 920,563,225

Suwannee 163,958,068 312,835,824 367,217,078 320,531,683

Taylor 62,730,059 83,052,522 90,901,497 92,123,462

Union 21,761,676 23,007,926 57,022,864 50,630,697

Volusia 6,261,249,349 11,080,033,140 11,465,446,498 7,757,968,999

Wakulla 218,020,127 282,195,117 249,173,475 217,373,770

Walton 741,241,947 1,098,989,709 1,102,796,069 862,202,930

Washington 20,527,263 58,004,467 64,460,645 63,935,501

Statewide 246,460,668,942 404,775,082,641 427,453,977,573 313,816,741,781

131

Table B-2. Save Our Homes Value History (2009-2012) School District 2009 2010 2011 2012

Alachua 1,731,568,060 1,116,226,290 697,574,500 457,186,880

Baker 155,144,281 76,732,622 56,369,065 33,997,124

Bay 1,946,172,739 1,501,386,992 1,118,614,926 893,348,656

Bradford 150,413,388 109,654,096 74,558,023 43,165,571

Brevard 4,334,844,770 1,852,160,030 653,825,480 518,966,500

Broward 20,449,406,705 9,353,745,860 10,149,832,510 8,898,832,000

Calhoun 56,527,904 45,471,427 41,687,490 31,317,731

Charlotte 948,671,433 437,117,411 377,839,693 300,881,443

Citrus 981,287,954 500,759,129 315,327,773 175,048,468

Clay 1,382,644,003 729,519,531 432,844,076 290,365,037

Collier 6,130,051,844 3,454,997,992 2,618,618,427 2,720,176,985

Columbia 283,456,416 196,076,643 124,352,068 79,048,904

Miami-Dade 36,876,679,881 15,861,968,602 14,229,201,683 13,507,068,668

DeSoto 208,090,779 42,811,358 26,115,981 13,657,857

Dixie 80,566,557 72,086,376 62,455,910 57,862,316

Duval 8,588,538,129 5,640,021,435 3,607,745,483 2,371,836,631

Escambia 1,620,999,001 1,151,372,873 791,333,986 553,807,099

Flagler 643,186,161 225,634,139 89,102,267 49,585,832

Franklin 253,935,879 146,698,964 111,370,025 82,204,815

Gadsden 182,488,692 144,284,986 121,770,411 71,236,162

Gilchrist 102,910,548 69,426,681 46,129,992 27,817,034

Glades 54,484,813 29,652,653 13,877,724 7,556,248

Gulf 147,695,383 95,419,490 67,077,764 45,099,747

Hamilton 49,765,546 36,333,612 10,232,208 5,496,593

Hardee 110,231,456 65,105,863 16,596,801 12,463,502

Hendry 138,418,170 57,153,020 18,438,720 11,007,720

Hernando 800,298,843 223,925,263 91,930,667 48,801,146

Highlands 787,782,221 337,613,527 199,534,692 88,582,736

Hillsborough 5,731,649,834 3,092,702,961 2,114,307,279 1,426,673,599

Holmes 45,591,917 37,468,820 33,213,472 20,480,545

Indian River 1,336,187,590 848,269,100 580,534,330 425,024,930

Jackson 142,668,952 111,214,836 86,900,670 61,493,176

Jefferson 111,865,937 95,466,819 85,096,129 64,390,814

Lafayette 41,464,492 14,671,669 10,823,096 11,908,344

Lake 1,667,261,291 864,078,189 473,350,692 264,229,729

Lee 3,493,941,770 1,681,563,652 2,051,648,363 2,524,580,364

Leon 1,791,129,666 1,466,641,405 1,084,169,939 665,880,436

Levy 304,219,368 220,588,342 103,842,571 37,207,062

Liberty 31,741,567 28,866,544 26,113,947 23,813,360

Madison 95,588,791 61,500,979 40,840,465 25,644,990

Manatee 2,298,236,465 960,145,445 663,634,770 455,355,804

Marion 2,451,668,546 1,208,354,435 620,573,828 308,866,813

Martin 2,508,262,978 1,534,191,400 1,135,456,233 849,524,736

Monroe 2,574,985,439 1,554,766,723 1,393,501,623 1,349,412,058

Nassau 936,890,827 572,668,048 453,386,353 262,688,703

Okaloosa 1,707,060,981 1,041,011,032 778,153,059 529,439,432

Okeechobee 123,786,938 40,674,555 23,021,062 11,393,645

Orange 5,872,339,457 2,353,717,180 1,553,851,044 1,156,041,250

132

Table B-2. Continued. School District 2009 2010 2011 2012

Osceola 763,770,929 213,634,161 119,994,281 110,836,236

Palm Beach 14,645,705,987 7,647,612,537 7,609,083,898 6,656,283,738

Pasco 1,682,650,841 766,174,184 618,091,607 340,888,187

Pinellas 8,853,202,550 4,325,300,985 3,028,915,738 2,130,772,036

Polk 3,033,610,661 1,038,395,011 536,759,357 277,804,985

Putnam 645,247,086 515,649,922 321,189,673 196,001,707

Saint Johns 2,440,847,274 1,437,005,748 1,065,071,839 809,385,360

Saint Lucie 632,447,977 344,739,021 279,295,932 210,761,197

Santa Rosa 524,062,950 274,644,293 192,388,372 115,512,195

Sarasota 3,936,116,297 2,213,458,402 1,556,743,344 1,457,221,417

Seminole 3,411,781,417 1,711,940,706 834,895,908 484,344,536

Sumter 766,703,976 429,979,825 374,372,273 284,179,380

Suwannee 235,282,297 126,893,674 112,181,004 116,049,055

Taylor 83,769,738 69,216,519 65,171,459 45,533,863

Union 41,826,249 32,499,942 19,745,335 14,799,069

Volusia 3,212,838,106 1,391,659,876 901,596,270 841,387,527

Wakulla 177,261,272 121,442,700 92,931,011 71,281,184

Walton 562,558,631 320,000,249 258,334,689 221,571,283

Washington 59,913,234 48,029,828 32,622,608 18,274,372

Statewide 168,172,401,834 84,390,196,582 67,496,161,868 56,273,356,522

133

Table B-3. Save Our Homes Value History (2013-2015) School District 2013 2014 2015

Alachua 357,875,800 343,572,690 671,844,670

Baker 29,272,858 52,433,806 53,172,570

Bay 761,718,501 657,464,020 586,165,028

Bradford 30,770,372 34,694,907 38,605,926

Brevard 1,687,370,700 3,250,275,480 4,925,213,190

Broward 11,298,007,980 19,530,951,300 26,263,235,270

Calhoun 26,293,358 17,524,701 16,206,390

Charlotte 630,898,309 1,177,532,206 1,560,035,298

Citrus 137,056,896 175,334,882 326,482,730

Clay 354,664,973 597,036,867 834,843,069

Collier 3,674,400,812 5,618,096,591 8,382,048,906

Columbia 73,108,086 61,551,885 64,051,267

Miami-Dade 14,730,822,254 25,646,467,119 36,718,444,904

DeSoto 11,611,034 26,410,572 35,173,249

Dixie 55,114,280 51,792,420 48,238,257

Duval 1,938,219,844 3,479,592,663 4,859,002,810

Escambia 503,816,603 929,478,571 1,147,402,104

Flagler 99,919,535 378,261,573 601,360,324

Franklin 71,443,914 71,697,448 72,110,221

Gadsden 61,641,643 57,821,352 47,224179

Gilchrist 22,452,851 19,274,625 18,711,660

Glades 6,207,568 3,828,348 3,514,563

Gulf 41,841,282 40,183,465 38,396,534

Hamilton 4,980,198 4,345,685 3,790,113

Hardee 10,554,063 10,142,235 23,134,214

Hendry 15,140,470 26,443,770 41,971,420

Hernando 61,038,862 217,902,689 404,770,206

Highlands 61,984,394 56,831,521 101,546,651

Hillsborough 3,895,597,007 6,648,819,714 8,548,286,690

Holmes 17,006,661 14,690,273 13,477,107

Indian River 523,833,310 852,917,230 1,703,834,560

Jackson 51,240,922 45,356,301 44,443,685

Jefferson 56,102,218 49,303,469 41,467,981

Lafayette 10,294,960 8,945,611 10,220,344

Lake 317,323,118 647,043,873 1,042,202,844

Lee 3,748,599,666 6,110,623,884 7,417,092,555

Leon 563,440,574 696,410,936 883,442,956

Levy 24,724,459 23,385,057 40,499,641

Liberty 22,260,627 21,174,769 20,253,952

Madison 19,800,259 17,584,974 17,429,156

Manatee 824,555,642 1,645,767,156 3,128,489,783

Marion 356,119,875 665,623,086 948,681,269

Martin 943,161,820 1,388,146,458 1,910,214,321

Monroe 1,502,014,032 1,934,328,606 2,248,013,394

Nassau 263,859,517 425,238,355 629,717,935

Okaloosa 521,917,522 677,954,269 893,723,920

Okeechobee 21,758,405 23,840,583 60,502,260

Orange 1,906,287,367 5,160,147,868 8,670,531,665

134

Table B-3. Continued. School District 2013 2014 2015

Osceola 339,131,441 932,440,659 1,321,834,059

Palm Beach 9,026,775,494 16,517,810,277 23,545,729,962

Pasco 424,150,039 1,302,933,452 1,880,196,089

Pinellas 3,506,581,458 7,194,153,640 10,492,346,086

Polk 1,093,475,268 2,132,196,031 2,285,133,907

Putnam 174,731,638 161,753,322 149,274,415

Saint Johns 962,031,509 1,385,132,861 2,245,492,980

Saint Lucie 234,534,427 631,858,229 1,228,903,587

Santa Rosa 141,831,288 353,288,462 350,900,388

Sarasota 2,733,402,805 4,458,069,236 5,878,953,265

Seminole 865,685,988 2,055,693,008 2,733,909,602

Sumter 425,704,470 986,940,440 1,172,672,330

Suwannee 102,405,638 88,631,321 86,461,220

Taylor 40,816,083 38,067,016 40,009,406

Union 12,811,633 10,858,188 9,773,715

Volusia 1,230,801,669 2,520,576,930 3,749,925,205

Wakulla 51,742,452 50,298,953 70,885,906

Walton 242,453,589 346,552,999 495,943,118

Washington 14,318,246 10,303,931 10,572,190

Statewide 73,971,510,536 130,771,804,818 184,448,139,171

Source: Data adapted from “Florida Property Tax Data Portal,” Florida Department of Revenue

Property Tax Oversight, Research and Analysis, accessed April 15, 2017,



135

APPENDIX C

SPSS OUTPUT RESULTS

Descriptive Statistics, Correlation, and Scatterplot 2006

2006 Descriptive Statistics

Mean Std. Deviation N

2006 Median Household Income 45691.23 6696.057 40

2006 Property Assessed Valuation 52553716620.00 62435336680.000 40

Figure C-1. 2006 Descriptive Statistics for Median Household Income and Property Assessed

Valuation.

2006 Correlations

2006 Median

Household

Income

2006 Property

Assessed

Valuation

2006 Median Household Income Pearson Correlation 1 .168

Sig. (2-tailed) .301

N 40 40

2006 Property Assessed Valuation Pearson Correlation .168 1


N 40 40

Figure C-2. 2006 Correlations for Median Household Income and Property Assessed Valuation.

Figure C-3. Scatterplot Results for 2006.

136

Figure C-4. Histogram Results for 2006 Median Household Income.

Figure C-5. Histogram Results for 2006 Property Assessed Valuation.

137







Valuation.

2007 Correlations

2007 Median

Household

Income

2007 Property

Assessed

Valuation



N 40 40



N 40 40



138



139







Valuation.

2008 Correlations

2008 Median

Household

Income

2008 Property

Assessed

Valuation



N 40 40



N 40 40



140



141







Valuation.

2009 Correlations

2009 Median

Household

Income

2009 Property

Assessed

Valuation



N 40 40



N 40 40



142



143







Valuation.

2010 Correlations

2010 Median

Household

Income

2010 Property

Assessed

Valuation



N 40 40



N 40 40



144



145







Valuation.

2011 Correlations

2011 Median

Household

Income

2011 Property

Assessed

Valuation



N 40 40



N 40 40



146



147







Valuation.

2012 Correlations

2012 Median

Household

Income

2012 Property

Assessed

Valuation



N 40 40



N 40 40



148



149







Valuation.

2013 Correlations

2013 Median

Household

Income

2013 Property

Assessed

Valuation



N 40 40



N 40 40



150



151







Valuation.

2014 Correlations

2014 Median

Household

Income

2014 Property

Assessed

Valuation



N 40 40



N 40 40


Figure C-43. Scatterplot results for 2014.

152



153







Valuation.

2015 Correlations

2015 Median

Household

Income

2015 Property

Assessed

Valuation



N 40 40



N 40 40



154



155

Correlation and Scatterplot 2006 to 2015 without Outliers

2006 Correlations

2006 Median

Household

Income

2006 Property

Assessed

Valuation



N 37 37



N 37 37

Figure C-51. 2006 Correlations without Outliers for Median Household Income and Property

Assessed Valuation.

Figure C-52. Scatterplot without Outliers Results for 2006.

156

2007 Correlations

2007 Median

Household

Income

2007 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


157

2008 Correlations

2008 Median

Household

Income

2008 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


158

2009 Correlations

2009 Median

Household

Income

2009 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


159

2010 Correlations

2010 Median

Household

Income

2010 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.

Figure C-60. Scatterplot without Outliers results for 2010.

160

2011 Correlations

2011 Median

Household

Income

2011 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


161

2012 Correlations

2012 Median

Household

Income

2012 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


162

2013 Correlations

2013 Median

Household

Income

2013 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


163

2014 Correlations

2014 Median

Household

Income

2014 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.


164

2015 Correlations

2015 Median

Household

Income

2015 Property

Assessed

Valuation



N 37 37



N 37 37


Assessed Valuation.

Figure C-70. Scatterplot without Outliers results for 2015.

165

Tests for Normality

2006 Test for Normal Distribution

Statistic Std. Error

2006 Median Household

Income

Mean 45691.23 1058.740

95% Confidence Interval for

Mean

Lower Bound 43549.72

Upper Bound 47832.73

5% Trimmed Mean 45743.81

Median 44962.50

Variance 44837174.740

Std. Deviation 6696.057

Minimum 30771

Maximum 60450

Range 29679

Interquartile Range 9522

Skewness .110 .374

Kurtosis -.152 .733

Figure C-71. 2006 Test for Normal Distribution for Median Household Income (Descriptive

Statistics).

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.


Income

.100 40 .200* .976 40 .560

*. This is a lower bound of the true significance.

a. Lilliefors Significance Correction

Figure C-72. 2006 Test for Normal Distribution for Median Household Income (Kolmogorov-

Smirnov Statistic and Shapiro-Wilk Statistic).

166

Figure C-73. 2006 Normal Q-Q Plot for Median Household Income.


2006 Property Assessed

Valuation

Mean 52553716620.00 9871893520.000


Mean

Lower Bound 32585927230.00

Upper Bound 72521506000.00

5% Trimmed Mean 44306532810.00

Median 28807501900.00

Variance 38981712670000

00000000.000


Minimum 3076767588

Maximum 277787622200

Range 274710854700


Skewness 2.257 .374

Kurtosis 5.035 .733

Figure C-74. 2006 Test for Normal Distribution for Property Assessed Valuation (Descriptive

Statistics).

167




Valuation

.274 40 .000 .698 40 .000


Figure C-75. 2006 Test for Normal Distribution for Property Assessed Valuation

(Kolmogorov-Smirnov Statistic and Shapiro-Wilk Statistic).

Figure C-76. 2006 Normal Q-Q Plot for Property Assessed Valuation.

168

2006 without Outliers Test for Normal Distribution



Income

Mean 45519.89 1119.302


Mean




Median 44951.00

Variance 46354982.100


Minimum 30771

Maximum 60450

Range 29679


Skewness .171 .388

Kurtosis -.110 .759

Figure C-77. 2006 Test for Normal Distribution without Outliers for Median Household Income

(Descriptive Statistics).




Income

.109 37 .200* .971 37 .442





169

Figure C-79. 2006 Normal Q-Q Plot without Outliers for Median Household Income.



Valuation

Mean 37458217810.00 5292845762.000


Mean

Lower Bound 26723829070.00

Upper Bound 48192606550.00

5% Trimmed Mean 35213612970.00

Median 25640003330.00

Variance 10365260020000

00000000.000


Minimum 3076767588

Maximum 112499924100

Range 109423156500


Skewness 1.302 .388

Kurtosis .579 .759

Figure C-80. 2006 Test for Normal Distribution without Outliers for Property Assessed

Valuation (Descriptive Statistics).

170




Valuation

.215 37 .000 .816 37 .000



Valuation (Kolmogorov-Smirnov Statistic and Shapiro-Wilk Statistic).

Figure C-82. 2006 Normal Q-Q Plot without Outliers for Property Assessed Valuation.

171




Income

Mean 47765.00 1127.217


Mean




Median 48656.00

Variance 50824722.820


Minimum 32621

Maximum 62677

Range 30056


Skewness -.093 .374

Kurtosis -.205 .733


Statistics).




Income

.070 40 .200* .987 40 .911





172




Valuation

Mean 57424690700.00 11002865480.000


Mean

Lower Bound 35169294600.00

Upper Bound 79680086810.00

5% Trimmed Mean 47646708040.00

Median 30702688930.00

Variance 48425219520000

00000000.000


Minimum 3507192106

Maximum 326881066000

Range 323373873900


Skewness 2.414 .374

Kurtosis 6.145 .733


Statistics).

173




Valuation

.263 40 .000 .689 40 .000


Figure C-87. 2007 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov-



174

2007 without Outliers Test for Normal Distribution Statistic Std. Error


Income

Mean 47589.92 1196.744


Mean




Median 48332.00

Variance 52991233.910


Minimum 32621

Maximum 62677

Range 30056


Skewness -.036 .388

Kurtosis -.229 .759






Income

.099 37 .200* .982 37 .801





175




Valuation

Mean 40654591400.00 5740912009.000


Mean

Lower Bound 29011482190.00

Upper Bound 52297700600.00

5% Trimmed Mean 37971873290.00

Median 29958431060.00

Variance 12194486160000

00000000.000


Minimum 3507192106

Maximum 132796482700

Range 129289290600


Skewness 1.355 .388

Kurtosis .835 .759



176




Valuation

.221 37 .000 .822 37 .000





177

2008 Test for Normal Distribution Statistic Std. Error


Income

Mean 47956.30 1147.634


Mean




Median 46509.50

Variance 52682578.680


Minimum 31971

Maximum 67056

Range 35085


Skewness .415 .374

Kurtosis .486 .733


Statistics).




Income

.114 40 .200* .977 40 .593





178




Valuation

Mean 54859468790.00 10790726810.000


Mean

Lower Bound 33033163650.00

Upper Bound 76685773940.00

5% Trimmed Mean 44982878230.00

Median 28569541490.00

Variance 46575914050000

00000000.000


Minimum 3657284884

Maximum 336523769600

Range 332866484800


Skewness 2.612 .374

Kurtosis 7.586 .733


Statistics).

179




Valuation

.255 40 .000 .670 40 .000





180



Income

Mean 47834.08 1226.277


Mean




Median 46410.00

Variance 55638934.080


Minimum 31971

Maximum 67056

Range 35085


Skewness .461 .388

Kurtosis .426 .759






Income

.126 37 .147 .972 37 .465




181




Valuation

Mean 38440166780.00 5409947662.000


Mean

Lower Bound 27468284380.00

Upper Bound 49412049180.00

5% Trimmed Mean 35670324280.00

Median 27043369870.00

Variance 10828987470000

00000000.000


Minimum 3657284884

Maximum 132471028800

Range 128813743900


Skewness 1.422 .388

Kurtosis 1.165 .759



182




Valuation

.233 37 .000 .821 37 .000





183



Income

Mean 45024.38 1000.722


Mean




Median 45040.00

Variance 40057794.910


Minimum 30278

Maximum 60900

Range 30622


Skewness .278 .374

Kurtosis .471 .733


Statistics).




Income

.083 40 .200* .983 40 .796

*. This is a lower bound of the true significance. a. Lilliefors Significance Correction



184




Valuation

Mean 46624591530.00 9040119216.000


Mean

Lower Bound 28339224480.00

Upper Bound 64909958590.00

5% Trimmed Mean 38364790080.00

Median 24965680610.00

Variance 32689502170000

00000000.000


Minimum 3526904486

Maximum 283668006100

Range 280141101700


Skewness 2.632 .374

Kurtosis 7.710 .733


Statistics).

185




Valuation

.268 40 .000 .666 40 .000





186



Income

Mean 44892.38 1066.159


Mean




Median 44739.00

Variance 42057677.580


Minimum 30278

Maximum 60900

Range 30622


Skewness .334 .388

Kurtosis .445 .759






Income

.093 37 .200* .981 37 .768





187




Valuation

Mean 32862883790.00 4511918131.000


Mean

Lower Bound 23712289690.00

Upper Bound 42013477880.00

5% Trimmed Mean 30500827840.00

Median 23496816050.00

Variance 75322399320000

0000000.000


Minimum 3526904486

Maximum 113719332400

Range 110192428000


Skewness 1.459 .388

Kurtosis 1.334 .759



188




Valuation

.243 37 .000 .816 37 .000





189



Income

Mean 44846.13 1066.075


Mean




Median 44389.00

Variance 45460626.010


Minimum 32488

Maximum 60729

Range 28241


Skewness .535 .374

Kurtosis .210 .733


Statistics).




Income

.117 40 .182 .961 40 .186




190




Valuation

Mean 42855724210.00 7795469852.000


Mean

Lower Bound 27087898120.00

Upper Bound 58623550290.00

5% Trimmed Mean 36129328350.00

Median 23131434150.00

Variance 24307740080000

00000000.000


Minimum 3688292661

Maximum 237508768600

Range 233820475900


Skewness 2.409 .374

Kurtosis 6.264 .733


Statistics).

191




Valuation

.275 40 .000 .693 40 .000





192



Income

Mean 44748.22 1134.870


Mean




Median 43993.00

Variance 47653370.170


Minimum 32488

Maximum 60729

Range 28241


Skewness .581 .388

Kurtosis .201 .759






Income

.143 37 .054 .952 37 .110




193




Valuation

Mean 31195254510.00 4225387683.000


Mean

Lower Bound 22625771100.00

Upper Bound 39764737920.00

5% Trimmed Mean 28870726380.00

Median 22943329380.00

Variance 66059433960000

0000000.000


Minimum 3688292661

Maximum 109419141000

Range 105730848400


Skewness 1.506 .388

Kurtosis 1.631 .759



194




Valuation

.237 37 .000 .817 37 .000





195




Income

Mean 44421.10 1027.810


Mean




Median 44520.50

Variance 42255772.090


Minimum 30440

Maximum 62510

Range 32070


Skewness .280 .374

Kurtosis .518 .733


Statistics).




Income

.090 40 .200* .985 40 .862





196




Valuation

Mean 41460771510.00 7753561055.000


Mean

Lower Bound 25777713970.00

Upper Bound 57143829060.00

5% Trimmed Mean 34583504770.00

Median 22147068560.00

Variance 24047083610000

00000000.000


Minimum 3575927939

Maximum 239266976100

Range 235691048200


Skewness 2.515 .374

Kurtosis 6.885 .733


Statistics).

197




Valuation

.286 40 .000 .677 40 .000





198



Income

Mean 44282.68 1093.207


Mean




Median 44310.00

Variance 44218786.170


Minimum 30440

Maximum 62510

Range 32070


Skewness .338 .388

Kurtosis .515 .759






Income

.088 37 .200* .982 37 .809





199




Valuation

Mean 29766894590.00 4057512703.000


Mean

Lower Bound 21537877420.00

Upper Bound 37995911760.00

5% Trimmed Mean 27445681310.00

Median 21923969160.00

Variance 60914614540000

0000000.000


Minimum 3575927939

Maximum 107348126300

Range 103772198300


Skewness 1.580 .388

Kurtosis 1.984 .759



200




Valuation

.235 37 .000 .811 37 .000





201



Income

Mean 45565.90 1033.900


Mean




Median 45091.00

Variance 42757962.350


Minimum 34025

Maximum 61288

Range 27263


Skewness .543 .374

Kurtosis .117 .733


Statistics).




Income

.142 40 .040 .962 40 .191




202




Valuation

Mean 41120029180.00 7823032297.000


Mean

Lower Bound 25296452790.00

Upper Bound 56943605580.00

5% Trimmed Mean 34116940290.00

Median 21821211910.00

Variance 24479933730000

00000000.000


Minimum 3483005489

Maximum 242277571000

Range 238794565500


Skewness 2.555 .374

Kurtosis 7.123 .733


Statistics).

203




Valuation

.287 40 .000 .671 40 .000





204

2012 without Outliers Test for Normal Distribution



Income

Mean 45409.86 1095.585


Mean




Median 45009.00

Variance 44411347.510


Minimum 34025

Maximum 61288

Range 27263


Skewness .613 .388

Kurtosis .185 .759






Income

.164 37 .013 .949 37 .092




205




Valuation

Mean 29284263050.00 4037314096.000


Mean

Lower Bound 21096210550.00

Upper Bound 37472315550.00

5% Trimmed Mean 26946243880.00

Median 21523727480.00

Variance 60309648890000

0000000.000


Minimum 3483005489

Maximum 107630488600

Range 104147483100


Skewness 1.607 .388

Kurtosis 2.112 .759



206




Valuation

.226 37 .000 .807 37 .000





207



Income

Mean 46767.53 1034.703


Mean




Median 46427.50

Variance 42824422.050


Minimum 32295

Maximum 64862

Range 32567


Skewness .247 .374

Kurtosis .587 .733


Statistics).




Income

.069 40 .200* .986 40 .904





208




Valuation

Mean 41880845740.00 7953384126.000


Mean

Lower Bound 25793607890.00

Upper Bound 57968083600.00

5% Trimmed Mean 34829852170.00

Median 22109831550.00

Variance 25302527620000

00000000.000


Minimum 3498142931

Maximum 243041113800

Range 239542970900


Skewness 2.501 .374

Kurtosis 6.720 .733


Statistics).

209




Valuation

.288 40 .000 .675 40 .000





210



Income

Mean 46459.65 1103.021


Mean




Median 46055.00

Variance 45016237.010


Minimum 32295

Maximum 64862

Range 32567


Skewness .376 .388

Kurtosis .587 .759






Income

.098 37 .200* .979 37 .686





211




Valuation

Mean 29845860550.00 4153770197.000


Mean

Lower Bound 21421624130.00

Upper Bound 38270096970.00

5% Trimmed Mean 27401463190.00

Median 21706151050.00

Variance 63839085340000

0000000.000


Minimum 3498142931

Maximum 110562335500

Range 107064192600


Skewness 1.613 .388

Kurtosis 2.139 .759



212




Valuation

.229 37 .000 .806 37 .000





213



Income

Mean 47170.63 1213.590


Mean




Median 46446.50

Variance 58911980.190


Minimum 30765

Maximum 65976

Range 35211


Skewness .229 .374

Kurtosis -.129 .733


Statistics).




Income

.079 40 .200* .988 40 .944





214




Valuation

Mean 44157754890.00 8513396344.000


Mean

Lower Bound 26937785400.00

Upper Bound 61377724370.00

5% Trimmed Mean 36576174670.00

Median 23047076290.00

Variance 28991166920000

00000000.000


Minimum 3521567064

Maximum 262149254400

Range 258627687300


Skewness 2.539 .374

Kurtosis 7.001 .733


Statistics).

215




Valuation

.288 40 .000 .670 40 .000





216



Income

Mean 47029.41 1295.000


Mean




Median 46238.00

Variance 62049927.800


Minimum 30765

Maximum 65976

Range 35211


Skewness .279 .388

Kurtosis -.176 .759






Income

.086 37 .200* .986 37 .919





217




Valuation

Mean 31282726280.00 4415270520.000


Mean

Lower Bound 22328142630.00

Upper Bound 40237309940.00

5% Trimmed Mean 28670057950.00

Median 22310003980.00

Variance 72130070920000

0000000.000


Minimum 3521567064

Maximum 117176235000

Range 113654668000


Skewness 1.621 .388

Kurtosis 2.150 .759



218




Valuation

.228 37 .000 .803 37 .000





219



Income

Mean 49623.53 1168.197


Mean




Median 49466.50

Variance 54587388.920


Minimum 31483

Maximum 70379

Range 38896


Skewness .192 .374

Kurtosis 1.183 .733


Statistics).



Median Household Income .124 40 .126 .976 40 .555




220




Valuation

Mean 46944039960.00 9189929726.000


Mean

Lower Bound 28355652550.00

Upper Bound 65532427370.00

5% Trimmed Mean 38783343810.00

Median 23823662670.00

Variance 33781923340000

00000000.000


Minimum 3554300136

Maximum 282475672100

Range 278921371900


Skewness 2.547 .374

Kurtosis 7.024 .733


Statistics).

221




Valuation

.291 40 .000 .667 40 .000





222



Income

Mean 49474.73 1233.276


Mean




Median 49379.00

Variance 56275908.590


Minimum 31483

Maximum 70379

Range 38896


Skewness .246 .388

Kurtosis 1.283 .759






Income

.152 37 .031 .968 37 .353




223




Valuation

Mean 33046844060.00 4766558717.000


Mean

Lower Bound 23379814920.00

Upper Bound 42713873200.00

5% Trimmed Mean 30127675630.00

Median 22770951430.00

Variance 84064303410000

0000000.000


Minimum 3554300136

Maximum 128754223100

Range 125199923000


Skewness 1.686 .388

Kurtosis 2.503 .759



224




Valuation

.226 37 .000 .797 37 .000





225

APPENDIX D

INTERNAL REVENUE SERVICE ZIP CODE DATA DOCUMENTATION GUIDE

Variable Name Description Value/Line Reference

STATEFIPS The State Federal Information Processing

System (FIPS) code

01-56

STATE The State associated with the ZIP code Two-digit State abbreviation code

ZIPCODE 5-digit Zip code

AGI_STUB Size of adjusted gross income 1 = $1 under $25,000

2 = $25,000 under $50,000

3 = $50,000 under $75,000

4 = $75,000 under $100,000

5 = $100,000 under $200,000

6 = $200,000 or more

N1 Number of returns

MARS1 Number of single returns Filing status is single

MARS2 Number of joint returns Filing status is married filing jointly

MARS4 Number of head of household returns Filing status is head of household

PREP Number of returns with paid preparer's signature

N2 Number of exemptions 1040:6d

NUMDEP Number of dependents 1040:6c

TOTAL_VITA

Total number of volunteer prepared returns [3]

VITA Number of volunteer income tax assistance

(VITA) prepared returns [3]

TCE Number of tax counseling for the elderly (TCE)

prepared returns [3]

A00100 Adjust gross income (AGI) [2] 1040:37 / 1040A:21 / 1040EZ:4

N02650 Number of returns with total income 1040:22 / 1040A:15 / 1040EZ:4

A02650 Total income amount 1040:22 / 1040A:15 / 1040EZ:4

N00200 Number of returns with salaries and wages 1040:7 / 1040A:7 / 1040EZ:1

A00200 Salaries and wages amount 1040:7 / 1040A:7 / 1040EZ:1

N00300 Number of returns with taxable interest 1040:8a / 1040A:8a / 1040EZ:2

A00300 Taxable interest amount 1040:8a / 1040A:8a / 1040EZ:2

N00600 Number of returns with ordinary dividends 1040:9a / 1040A:9a

A00600 Ordinary dividends amount 1040:9a / 1040A:9a

N00650 Number of returns with qualified dividends 1040:9b / 1040A:9b

226

A00650 Qualified dividends amount [4] 1040:9b / 1040A:9b

N00700 Number of returns with state and local income

tax refunds

1040:10

A00700 State and local income tax refunds amount 1040:10

N00900 Number of returns with business or professional

net income (less loss)

1040:12

A00900 Business or professional net income (less loss)

amount

1040:12

N01000 Number of returns with net capital gain (less

loss)

1040:13 1040A:10

A01000 Net capital gain (less loss) amount 1040:13 1040A:10

N01400 Number of returns with taxable individual

retirement arrangements distributions

1040:15b / 1040:11b

A01400 Taxable individual retirement arrangements

distributions amount

1040:15b / 1040:11b

N01700 Number of returns with taxable pensions and

annuities

1040:16b / 1040A:12b

A01700 Taxable pensions and annuities amount 1040:16b / 1040A:12b

SCHF Number of farm returns 1040:18

N02300 Number of returns with unemployment

compensation

1040:19 / 1040A:13 / 1040EZ:3

A02300 Unemployment compensation amount [5] 1040:19 / 1040A:13 / 1040EZ:3

N02500 Number of returns with taxable Social Security

benefits

1040:20b / 1040A:14b

A02500 Taxable Social Security benefits amount 1040:20b / 1040A:14b

N26270 Number of returns with partnership/S-corp net

income (less loss)

Schedule E:32

A26270 Partnership/S-corp net income (less loss) amount Schedule E:32

N02900 Number of returns with total statutory

adjustments

1040:36 / 1040A:20

A02900 Total statutory adjustments amount 1040:36 / 1040A:20

N03220 Number of returns with educator expenses 1040:23 / 1040A:16

A03220 Educator expenses amount 1040:23 / 1040A:16

N03300 Number of returns with self-employment

retirement plans

1040:28

A03300 Self-employment retirement plans amount 1040:28

N03270 Number of returns with self-employment health

insurance deduction

1040:29

A03270 Self-employment health insurance deduction

amount

1040:29

N03150 Number of returns with IRA payments 1040:32 / 1040A:17

A03150 IRA payments amount 1040:32 / 1040A:17

N03210 Number of returns with student loan interest

deduction

1040:33 / 1040A:18

227

A03210 Student loan interest deduction amount 1040:33 / 1040A:18

N03230 Number of returns with tuition and fees

deduction

1040:34 / 1040A:19

A03230 Tuition and fees deduction amount 1040:34 / 1040A:19

N03240 Returns with domestic production activities

deduction

1040:35

A03240 Domestic production activities deduction

amount

1040:35

N04470 Number of returns with itemized deductions 1040:40

A04470 Total itemized deductions amount 1040:40

A00101 Amount of AGI for itemized returns 1040:37

N18425 Number of returns with State and local income

taxes

Schedule A:5a

A18425 State and local income taxes amount Schedule A:5a

N18450 Number of returns with State and local general

sales tax

Schedule A:5b

A18450 State and local general sales tax amount Schedule A:5b

N18500 Number of returns with real estate taxes Schedule A:6

A18500 Real estate taxes amount Schedule A:6

N18300 Number of returns with taxes paid Schedule A:9

A18300 Taxes paid amount Schedule A:9

N19300 Number of returns with mortgage interest paid Schedule A:10

A19300 Mortgage interest paid amount Schedule A:10

N19700 Number of returns with contributions Schedule A:19

A19700 Contributions amount Schedule A:19

N04800 Number of returns with taxable income 1040:43 / 1040A:27 / 1040EZ:6

A04800 Taxable income amount 1040:43 / 1040A:27 / 1040EZ:6

N05800 Number of returns with income tax before

credits

1040:47 / 1040A:30 / 1040EZ:10

A05800 Income tax before credits amount 1040:47 / 1040A:30 / 1040EZ:10

N09600 Number of returns with alternative minimum tax 1040:45

A09600 Alternative minimum tax amount 1040:45

N05780 Number of returns with excess advance

premium tax credit repayment

1040:46/ 1040A:29

A05780 Excess advance premium tax credit repayment

amount

1040:46/ 1040A:29

N07100 Number of returns with total tax credits 1040:55 / 1040A:36

A07100 Total tax credits amount 1040:55 / 1040A:36

N07300 Number of returns with foreign tax credit 1040:48

228

A07300 Foreign tax credit amount 1040:48

N07180 Number of returns with child and dependent care

credit

1040:49 / 1040A:31

A07180 Child and dependent care credit amount 1040:49 / 1040A:31

N07230 Number of returns with nonrefundable education

credit

1040:50 / 1040A:33

A07230 Nonrefundable education credit amount 1040:50 / 1040A:33

N07240 Number of returns with retirement savings

contribution credit

1040:51 / 1040A:34

A07240 Retirement savings contribution credit amount 1040:51 / 1040A:34

N07220 Number of returns with child tax credit 1040:52 / 1040A:35

A07220 Child tax credit amount 1040:52 / 1040A:35

N07260 Number of returns with residential energy tax

credit

1040:53

A07260 Residential energy tax credit amount 1040:53

N09400 Number of returns with self-employment tax 1040:57

A09400 Self-employment tax amount 1040:57

N85770 Number of returns with total premium tax credit 8962:24

A85770 Total premium tax credit amount 8962:24

N85775 Number of returns with advance premium tax

credit

8962:25

A85775 Advance premium tax credit amount 8962:25

N09750 Number of returns with health care individual

responsibility payment

1040:61 / 1040A:38 / 1040EZ:11

A09750 Health care individual responsibility payment

amount

1040:61 / 1040A:38 / 1040EZ:11

N10600 Number of returns with total tax payments 1040:74 / 1040A:46 / 1040EZ:9

A10600 Total tax payments amount 1040:74 / 1040A:46 / 1040EZ:9

N59660 Number of returns with earned income credit 1040:66a / 1040A:42a / 1040EZ:8b

A59660 Earned income credit amount [6] 1040:66a / 1040A:42a / 1040EZ:8b

N59720 Number of returns with excess earned income

credit

1040:66a / 1040A:42a / 1040EZ:8b

A59720 Excess earned income credit (refundable)

amount [7]

1040:66a / 1040A:42a / 1040EZ:8b

N11070 Number of returns with additional child tax

credit

1040:67 / 1040A:43

A11070 Additional child tax credit amount 1040:67 / 1040A:43

N10960 Number of returns with refundable education

credit

1040:68 / 1040A:44

A10960 Refundable education credit amount 1040:68 / 1040A:44

229

N11560 Number of returns with net premium tax credit 1040:69 / 1040A:45

A11560 Net premium tax credit amount 1040:69 / 1040A:45

N06500 Number of returns with income tax 1040:56 / 1040A:37 / 1040EZ:10

A06500 Income tax amount [8] 1040:56 / 1040A:37 / 1040EZ:10

N10300 Number of returns with tax liability 1040:63 / 1040A:39 / 1040EZ: 10

A10300 Total tax liability amount [9] 1040:63 / 1040A:39 / 1040EZ: 10

N85530 Number of returns with additional Medicare tax 1040:62a

A85530 Additional Medicare tax amount 1040:62a

N85300 Number of returns with net investment income

tax

1040:62b

A85300 Net investment income tax amount 1040:62b

N11901 Number of returns with tax due at time of filing 1040:78 / 1040A:50 / 1040EZ:14

A11901 Tax due at time of filing amount [10] 1040:78 / 1040A:50 / 1040EZ:14

N11902 Number of returns with overpayments refunded 1040:75 / 1040A:47 / 1040EZ:13a

A11902 Overpayments refunded amount [11] 1040:75 / 1040A:47 / 1040EZ:13a

Figure D-1. Internal Revenue Service Zip Code Data Documentation Guide

Source: Information adapted from “SOI Tax Stats - Individual Income Tax Statistics - 2014 ZIP

Code Data (SOI),” United States Department of Treasury, Internal Revenue Service, accessed

April 15, 2017, https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-

code-data-soi.

https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi

https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi

230

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State Department of Education Websites:

Alabama (https://www.alsde.edu/)

Alaska (https://education.alaska.gov/)

Arizona (http://www.azed.gov/)

Arkansas (http://www.arkansased.gov/)

California (http://www.cde.ca.gov/)

Colorado (http://www.cde.state.co.us/)

Connecticut (http://www.sde.ct.gov/sde/site/default.asp)

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https://www3.nd.edu/~rwilliam/stats3/Margins02.pdf

http://www.jstor.org/stable/40703400

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https://www.alsde.edu/



http://www.arkansased.gov/






http://www.gadoe.org/Pages/Home.aspx

238

Hawaii (http://www.hawaiipublicschools.org/Pages/Home.aspx)

Idaho (http://sde.idaho.gov/)

Illinois (http://www.isbe.net/)

Indiana (http://www.doe.in.gov/)

Iowa (https://www.educateiowa.gov/);

Kansas (http://www.ksde.org/)

Kentucky (http://education.ky.gov/Pages/default.aspx)

Louisiana (http://www.louisianabelieves.com/)

Maine (http://www.maine.gov/doe/)

Maryland (http://www.marylandpublicschools.org/)

Massachusetts (http://www.doe.mass.edu/)

Michigan (https://www.michigan.gov/mde)

Minnesota (http://education.state.mn.us/mde/index.html)

Mississippi (http://www.mde.k12.ms.us/)

Missouri (https://dese.mo.gov/)

Montana (http://opi.mt.gov/)

Nebraska (https://www.education.ne.gov/)

Nevada (http://www.doe.nv.gov/);

New Hampshire (http://education.nh.gov/)

New Jersey (http://www.state.nj.us/education/)

New Mexico (http://ped.state.nm.us/ped/index.html)

New York (http://schools.nyc.gov/default.htm)

North Carolina (http://www.dpi.state.nc.us/)

North Dakota (https://www.nd.gov/dpi)

Ohio (http://education.ohio.gov/)

Oklahoma (http://sde.ok.gov/sde/)

Oregon (http://www.ode.state.or.us/home/)

Pennsylvania (http://www.education.pa.gov/Pages/default.aspx#.VvCxrmQrIfE)

Rhode Island (http://www.ride.ri.gov/)

South Carolina (http://ed.sc.gov/)

South Dakota (http://doe.sd.gov/)

Tennessee (https://www.tn.gov/education)

Texas (http://tea.texas.gov/)

Utah (http://www.schools.utah.gov/main/)

Vermont (http://education.vermont.gov/)

Virginia (http://www.doe.virginia.gov/)

Washington (http://www.k12.wa.us/)

West Virginia (https://wvde.state.wv.us/)

Wisconsin (http://dpi.wi.gov/)

Wyoming (http://edu.wyoming.gov/)

State Department of Revenue Websites:

Alabama (http://www.ador.alabama.gov/)

Alaska (http://dor.alaska.gov/)

Arizona (https://www.azdor.gov/)












































239

Arkansas (http://www.dfa.arkansas.gov/Pages/default.aspx)

California (http://www.taxes.ca.gov/)

Colorado (https://www.colorado.gov/revenue)

Connecticut (http://www.ct.gov/drs/site/default.asp)

Delaware (http://revenue.delaware.gov/)

Florida (http://dor.myflorida.com/Pages/default.aspx)

Georgia (https://dor.georgia.gov/);

Hawaii (http://tax.hawaii.gov/)

Idaho (http://tax.idaho.gov/)

Illinois (http://www.revenue.state.il.us/#&panel1-1)

Indiana (http://www.in.gov/dor/)

Iowa (https://tax.iowa.gov/)

Kansas (http://www.ksrevenue.org/)

Kentucky (http://revenue.ky.gov/)

Louisiana (http://www.rev.state.la.us/)

Maine (http://www.maine.gov/revenue/)

Maryland (http://dat.maryland.gov/Pages/default.aspx)

Massachusetts (https://www.mass.gov/dor/)

Michigan (http://www.michigan.gov/treasury/0,4679,7-121--8483--,00.html)

Minnesota (http://www.revenue.state.mn.us/Pages/default.aspx)

Mississippi (http://www.dor.ms.gov/Pages/default.aspx)

Missouri (http://dor.mo.gov/)

Montana (https://revenue.mt.gov/)

Nebraska (http://www.revenue.nebraska.gov/)

Nevada (http://tax.nv.gov/)

New Hampshire (http://revenue.nh.gov/)

New Jersey (http://www.state.nj.us/treasury/taxation/)

New Mexico (http://www.tax.newmexico.gov/)

New York (https://www.tax.ny.gov/)

North Carolina (http://www.dornc.com/)

North Dakota (https://www.nd.gov/tax/)

Ohio (http://www.tax.ohio.gov/)

Oklahoma (https://www.ok.gov/tax/)

Oregon (http://www.oregon.gov/dor/Pages/index.aspx)

Pennsylvania (http://www.revenue.pa.gov/Pages/default.aspx#.VvLK0GQrIfE)

Rhode Island (http://www.tax.ri.gov/)

South Carolina (https://dor.sc.gov/)

South Dakota (http://dor.sd.gov/)

Tennessee (https://www.tn.gov/revenue)

Texas (http://comptroller.texas.gov/taxinfo/sales/)

Utah (http://tax.utah.gov/)

Vermont (http://tax.vermont.gov/)

Virginia (tax.virginia.gov)

Washington (http://dor.wa.gov/)

West Virginia (http://www.wvrevenue.gov/)

Wisconsin (https://www.revenue.wi.gov/)








http://tax.hawaii.gov/







































240

Wyoming (http://revenue.wyo.gov/)


241

BIOGRAPHICAL SKETCH

Sharda Jackson Smith received her Bachelor of Arts degree in the spring of 2010 and

Master of Education degree at the University of Florida in the spring of 2011. While working on

her Doctor of Education degree in educational leadership, she worked full time as a classroom

teacher for Marion and Alachua County Public Schools.