evaluating smart growth

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Evaluating Smart GrowthState and Local Policy Outcomes

G r e G o r y K . I n G r a m a n d y u - H u n G H o n G

Policy Focus Report Lincoln Institute of Land Policy


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Evaluating Smart Growth:State and Local Policy Outcomes

Gregory K. Ingram and Yu-Hung Hong

Contents2 Executive Summary

4 Low-density Development and Smart Growth Policies

9 Growth Patterns and Trends

15 Natural Resources and Environmental Quality

19 Transportation

24 Affordable Housing

29 Fiscal Dimensions

33 Survey of Opinion Leaders

37 Conclusions and Recommendations

44 References

Polic Focs Report Series

The policy ocus report series is published by the Lincoln Institute o Land Policy to address

timely public policy issues relating to land use, land markets, and property taxation. Each report

is designed to bridge the gap between theory and practice by combining research ndings, case

studies, and contributions rom scholars in a variety o academic disciplines, and rom

proessional practitioners, local ocials, and citizens in diverse communities.

Abot the Athors

Gregory K. Ingram is president and CEO o the Lincoln Institute o Land Policy and cochair

o the Department o International Studies. Contact: [email protected]

Yu-Hung Hongis a ellow at the Lincoln Institute o Land Policy and a visiting assistant

proessor at Massachusetts Institute o Technology. Contact: [email protected]

Copyright 2009 by Lincoln Institute o Land Policy.

All rights reserved.

113 Brattle Street

Cambridge, MA 02138-3400, USA

Phone: 617-661-3016 x127 or 800-526-3873

Fax: 617-661-7235 or 800-526-3944

Email: [email protected]

Web: www.lincolninst.edu

ISBN 978-1-55844-193-4

Policy Focus Report/Code PF020


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i n g r a m & h o n g E v a l u at i n g s m a r t g r o w t h 1

. . . . . . . . . . . . . . . .

About this ReportPolic Evalatio

Growth Patterns and Trends: Gerrit Kaap

and Rebecca Lewis, University o Maryland

Natural Resources and Environmental Quality:

Terr Moore and Beth Goodma, ECONorth-

west, Oregon

Transportation: Tim Chapi and Keith Ihlaeldt,

Florida State University

Aordable Housing: Start Meck, Rutgers

Center or Government Services, and Timoth

MacKio, Monmouth University, New Jersey

Fiscal Dimensions: Robert W. Brchell and

William R. Dolphi, Rutgers Center or Urban

Policy Research

Survey o Opinion Leaders and Regulatory

Analysis: Alla Wallis and Tom Clark,

University o Colorado Denver

State Case Stdies

Florida: Tim Chapi and Keith Ihlaeldt,Florida State University

Maryland: Gerrit Kaap and Rebecca Lewis,

University o Maryland

New Jersey: Start Meck, Rutgers Center

or Government Services

Oregon: Terr Moore and Beth Goodma,

ECONorthwest

Colorado: Alla Wallis, University o Colorado

Denver

Indiana: Eric D. Kell, Ball State University

Texas: Robert G. Paterso, Rachael Rawlis,

Frederick Steier, and Mig Zhag, University

o Texas at Austin

Virginia: Case Dawkis, Virginia Tech

The Lincoln Institute initiated a research project

in late 2006 to evaluate the eectiveness o smart

growth policies rom 1990 to as ar past 2000 as

data allowed. The analysis ocused on our states

with well-established statewide smart growth

programs (Florida, Maryland, New Jersey, and

Oregon) and our states (Colorado, Indiana, Texas,

and Virginia) that oered a range o other land

management approaches.

This report summarizes the ndings and recom-

mendations o the complete evaluation, which

is published in the 2009 Lincoln Institute book,

Smart Growth Policies: An Evaluation of Programs

and Outcomes, edited by Gregory K. Ingram,

Armando Carbonell, Yu-Hung Hong, and Anthony

Flint. This book is the source o the data and

statistics cited here unless otherwise noted.

The goal o the evaluation was to examine the

eectiveness o various policies in achieving ve

commonly identied smart growth objectives:

promotecompactdevelopment;

protectnaturalresourcesand

environmentalquality;

provideandpromoteavarietyof

transportationoptions;

supplyaffordablehousing;and

createnetpositivescalimpacts.

Using 52 indicators based on U.S. Census Bureau

data and other state and local datasets, several

research teams compared dierences in peror-

mance among the selected states and between

the groups o smart growth and other states.

Another team surveyed opinion leaders on their

perceptions about the ecacy o smart growth

programs, and other researchers prepared case

studies on the political, environmental, and regu-

latory conditions in the eight selected states.

Contributors


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2 p o l i c y f o c u s r E p o r t L i n c o L n i n s t i t u t e o f L a n d P o L i c y

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Executive Summary

This evaluation o the eectivenesso smart growth policies in the

United States ocused on our states

with well-established statewide smart

growth programs (Florida, Maryland, New

Jersey, and Oregon) and our other states

(Colorado, Indiana, Texas, and Virginia)

that demonstrate a range o other land man-

agement approaches (Ingram et al. 2009).

The evaluation was objectives-based and

examined the extent to which ve specic

smart growth objectives were achieved,based on measureable and comparable

perormance indicators primarily during

the decade rom 1990 to 2000:

promote compact development;

protect natural resources and environ-

mental quality;

provide and promote a variety o

transportation options;

supply aordable housing; and create net positive scal impacts.

No state did well on all perormance mea-

sures, although individual states succeeded

in one or more o their priority policy areas.

Maryland was successul in protecting

natural resources through its land preser-

vation programs and state unding or the

purchase o armland conservation ease-

ments. New Jersey policies that responded

to state supreme court decisions led to an

aordable housing approach that slowed

house price escalation and encouraged

rental and multiamily housing production.

Oregons commitment to establishing

urban growth boundaries was able to reduce

development on armland in the Willamette

Valley. The state also perormed well in

reducing trac congestion growth by

Portlad, Orego


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. . . . . . . . . . . . . . . .

encouraging commuters to use transit and

by systematically planning or bicyclists and

pedestrians.

At the same time, some smart growthstates ailed to achieve objectives in policy

areas that were not given high priority

during the study period, such as providing

aordable housing in Oregon and Maryland,

and managing the spatial structure o urban

growth in Florida.

The message is clear: achieving smart

growth is possible, but states must remain

ocused on their key policy goals. No single

approach is right or all states. For example,

Colorado has no statewide smart growth

program, but it outperormed some states

with such policies by supporting local gov-

ernment actions to pursue eective land use

planning within a regional context. The more

successul states use a variety o regulatory

controls, market incentives, and institutional

policies to achieve their objectives.

RECOMMEnDAT IOnS

Program Structure and Transparency

Thedesignof smartgrowthprograms

and supporting regulations and incentives

should be guided by a vision o sustain-

able and desirable development outcomes.

Anytop-downorbottom-upsmart

growth policies must be coordinated at

the regional level to be able to achieve

their desired objectives.

Policymakersmustarticulatethemeans

o achieving smart growth objectives and

speciy implementation mechanisms,

rather than just declare objectives.

Functional Linkages for Policy Design

Thedesignof growthmanagementpolicies

should take account o interactions among

policies and coordination across relevant

agencies.

Smartgrowthpoliciesshouldmakeuse

o economic incentives, such as pricing

and tax policies, that have shown promise

in other countries. Smartgrowthprogramsneedtoconsider

the income distribution consequences

o their policies.

Sustainability and Monitoring

of Programs

Crediblecommitmentfromdifferent

levels o government is crucial or the

successul implementation o smart

growth programs.

Improvementsinmeasurementand

collection o data, particularly related to

environmental quality and public nance,

are needed to better monitor program

perormance.

More evidence is needed about the nature

o interactions among smart growth poli-

ciesparticularly those related to land use,

transportation, and housing aordability.

Clearer denition o perormance indi-

cators and measurement o their attain-

ment would acilitate the evaluation o

smart growth programs and contribute to

their technical and political sustainability.

Although this evaluation o smart growth

programs concentrates primarily on state-

wide perormance during the 1990s, the

ndings and recommendations will be use-

ul or ormulating growth management

policies in todays context o high energy

costs, historic housing market volatility, and

increasing pressures to reduce greenhouse

gas emissions. Many smart growth objectives

are precisely the outcomes posited to address

these current challenges acing state and

local policy makers.


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C H A P T E R 1

Low-Density Developmentand Smart Growth Policies

planning or compact urban growth and

transit-oriented development. These growth

management approaches have attracted

much public attention and research, but they

have received little systematic evaluation.

Several states have applied smart growth

policies or decades, and others are just

beginning to use them to address emerging

issues in the twenty-rst century.

An H IS TO R IC AL V IEW O F

GROWTH PATTERnS

Low-density development at the urban

ringe has been prevalent in the United

States since the 1940s. The average amount

o developed land per capita increased rom

0.32 acres in 1982 to 0.38 acres in 2002.

More important, the amount o newly devel-

Few public policy issues are as con-

tested as urban sprawl. Across the

United States, people are debating

the issue o low-density develop-

ment at the urban ringe as state and local

governments try to reconcile growing

demands or new housing and commercial

development with needs to protect open

space. The debate over sprawl oten pits

the public good against private sel-interest.

While most people agree that protecting

natural resources and open space is impor-

tant, many also value their property rights

and resist policies that may reduce the

value o their land holdings.

In response to this challenge, several U.S.

jurisdictions have implemented smart growth

principles since the early 1970s through

Baltimore, Marlad


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figure 1

Percet Growth i Persoal Icome Ieces Growth i Developed Ladad Poplatio, 19822002

oped land per added resident over this period

averaged about 0.6 acresnearly twice the

level o average land consumption. Growth

in population will increase overall landconsumption, and income growth can explain

much o the growth in area per person.

Figure 1 shows that between 1982 and

2002 the number o acres o developed

land increased by 46 percent, while the U.S.

population and personal income grew by 24

and 77 percent, respectively. This is consis-

tent with the nding that land consumption

will increase by about 30 percent i personal

income doubles. I the land area per person

remained constant, population growth

would be responsible or growth in land area

o 24 percent, leaving 22 percent o the 46

percent total area due to other actors. I

all o this additional 22 percent growth was

caused by the 77 percent increase in income,

each 10 percent increase in income would

have caused a 3 percent increase in land area

per person, a relationship similar to that

ound in estimates o the demand or lot

size (Glaeser, Kahn, and Rappaport 2008).

However, regional trends in population

density are certainly not uniorm. Figure 2

illustrates that the average density in the

Northeast was the highest among the our

regions in 1982, and more than twice that

in the Midwest and South. Between 1982and 2003, the incremental density was lower

than the average density in all regions, indi-

cating that all development was oriented

toward lower densities. The West experi-

enced rapid population growth, but man-

aged to keep its incremental density higher

than the other regions (see Fulton et al.

2001 or similar ndings).

TH E EV O L u T IO n O F S M AR T

G R O WTH PO L IC IES

In the ace o this decreasing density and

the spread o development at the ringe o

many urban areas, some states and localities

began to put policies in place to shape settle-

ment patterns. By the early 1990s, these

eorts came to be knows as smart growth

programs. What is now termed smart growth

has evolved rom a continuous process o

state land use policy development that has

coalesced around a set o objectives: pro-

mote compact development; protect natural

resources and environmental quality; create

Percent

1982 1987 1992 1997 2002

Personal

Income

Developed

Land

Population

80

100

120

140

160

180

200

Notes: Personal income is in

2005 dollars. Population and

developed land estimates do

not include Alaska.

Sources: U.S. Census Bureau

(1990c;2000c);U.S.Census

Bureau(1990d;2000d);U.S.

CensusBureau(2007);and

U.S. Department o Agriculture

(1982;1987a;1992;1997;

2003).


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. . . . . . . . . . . . . . . . . .

figure 2

Average ad Icremetal Desities Var Across u.S. Regios,19822003

Sources:U.S.CensusBureau(2007);U.S.DepartmentofAgriculture(2003).

transportation options and walkable neigh-

borhoods; supply aordable housing;

generate net positive scal impacts; encour-

age community collaboration and acilitate

transparent and eective development

decision making processes (Smart Growth

Network 2009).

The smart growth movement can be

divided into our waves. The rst three waves

were dened by John DeGrove (1984; 1992;

2005). We propose a ourth wave to charac-

terize the emerging eatures o the smart

growth movement in the twenty-rst century

(Ingram at el. 2009). The rst wave com-

menced in the 1970s with the enactment o

growth management programs to advance

environmental protection in Caliornia,

Colorado, Florida, Hawaii, North Carolina,

Oregon, and Vermont. These programs

were based on the regulation and control

o land development either throughout the

state or within specially designated zones

(DeGrove 1984).

The second wave, rom the 1980s into the

early 1990s, marked a shit rom regulating

and controlling growth to planning that was

aimed at promoting economic growth andprotecting natural resources. The deployment

o inrastructure also became more impor-

tant as a land use planning tool. The second-

wave smart growth states included Florida,

Georgia, Maine, New Jersey, Rhode Island,

Vermont, and Washington (DeGrove 1992).

The third wave, beginning in the mid-

1990s, was distinguished by the addition o

positive incentives to infuence growth and

by more growth-accommodating policies.

Some states shited rom land use regulation,

urban growth boundaries, and requirements

or local comprehensive plans to urban re-

vitalization, zoning reorm, and better coor-

dination o state agencies and their growth

policies. More emphasis was also placed at

the local, metropolitan, and regional levels,

and less on the hegemony o statewide

programs. The third wave brought several

additional states into the movement, includ-

ingMaryland,Minnesota,Pennsylvania,

Tennessee, and Utah (DeGrove 2005).

A ourth wave o smart growth policies is

still emerging. The need to respond to climate

change, environmental challenges, and soar-

ing energy costs, along with a new emphasis

on investments in public inrastructure, have

bolstered the demands being placed on smart

growth initiatives. Because automotive travel

produces a large share o greenhouse gas

emisions, support is increasing or land use

policies that oster more compact develop-

ment patterns, transit use, and walking.

New regional approaches are likely to

encourage development proposals that ad-

here to a smart growth ramework. Market

orces are also encouraging more compact,

mixed-use development as households

attempt to limit their travel costs and

achieve other energy savings.

West

Midwest

South

Northeast

3.52 3.46 2.67

1.06

2.72

1.39

6.16

1.10

Average Density 1982

(Persons per acre)

Incremental Density 19822003

(Added population per acre of

newly developed land)


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An O PPO R Tu n E T IM E FO R

EVALuAT IOn

Smart growth programs in some states are

now in their ourth decade, but new environ-

mental objectives have raised the stakes on

their success. With many years o experience

behind us, and the likelihood that reliance

on growth management policies will grow in

the uture, this is an opportune moment to

evaluate how eective smart growth policies

and programs have been at achieving their

goals and objectives.

This evaluation is based on a comparison

between our states that had statewide smart

growth policies in place by 2000 (Florida,

Maryland, New Jersey, and Oregon) and

our other states that did not (Colorado,

Indiana, Texas, and Virginia). Some o these

latter states did acilitate local and regional

smart growth initiatives by enabling local

governments to promulgate local options,

while others did little or nothing. These

eight states constitute a purposive and not

a random sample as part o the research

methodology (box 1).

The analysis revealed that the treatment

varied greatly across the our smart growth

states, producing a range o outcomes that

overlap with some o those in the other

selected states. Outcomes and policies were

thus ound to be more continuous across

the eight states rather than dichotomous

between the two groups o states.

This evaluation addresses two key ques-

tions. First, does the presence o state-level

smart growth programs result in objectively

measurable improvement in perormance?

Second, to the extent that smart growth

programs are successul, what underlies this

success? Conversely, i they ail, what are

the causes o their shortcomings?

Florida


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Box 1

Research Methodolog

The evaluation measured the achievement o ve smart growth objectives in each o the

eight states. The analytic methods used varied according to the data available, ranging

rom descriptive statistics to xed-eect regression models (Ingram et al. 2009, 1020). The

ocus was on changes in perormance indicators over time, given that current levels o many

measures (e.g., population density) refect the cumulative eects o past policies, technolo-

gies, and relative prices. The eects o recent policies are likely to be observed only in cur-

rent changes in perormance indicators. It is also likely that some smart growth objectives

reinorce each other, and others are antagonistic.

To make comparisons across states over multiyear periods, the evaluation developed a set

o perormance indicators that were dened consistently over time and available or all states.

These indicators relied heavily on data rom the U.S. Census Bureau and other nationally

collected datasets uniormly available at the state and county levels. The analysis generally

starts in 1990 and continues as ar past 2000 as data allow, but ocuses primarily on the

decade o the 1990s. In addition to the perormance indicators, the evaluation also analyzed

how opinion leaders perceive the eectiveness o smart growth programs, and how imple-

mentation has changed over time.


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C H A P T E R 2

Growth Patterns and Trends

Amajor objective o smart growth

policies is to alter the spatial dis-

tribution o population and employ-

ment, principally by increasing the

density and intensity o development, pro-

moting compactness, and slowing the sprawl-

ing o development to rural and undeveloped

areas. Four measures o growth patterns are

used to assess relative changes in spatial struc-

ture in the eight states: land use patterns,

spatial concentration, urbanization, and

centralization (Ingram et al. 2009, 2245).

A baseline review o the size and growth

o the eight case study states in 2000 shows

that they range widely in population (rom

more than 20 million in Texas to 3.4 million

in Oregon); size (rom 265,000 square miles

in Texas to 7,500 square miles in New Jersey);

and population density (rom 1,115 persons

per square mile in New Jersey to 35 in

Oregon). Their population and employment

growth rates over time vary much less, how-

ever. As gure 3 shows, on average, popula-

tion increased more slowly in the smart

growth states (15.9 percent) than in the other

selected states (19.4 percent) rom 1990 to

2000, as did employment (22.5 percent

versus 24.8 percent) rom 1994 to 2004.

LAnD uSE PATTERnS

Land uses vary considerably across the

states. For example, about hal o state area

is rangeland in Texas, cropland in Indiana,

orestland in Virginia, and ederal land in

Oregon. The area o developed land, which

is most relevant to smart growth policy,

increased in each o the eight states in every

ve-year interval rom 1982 through 1997.

Fort Worth, Texas


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The largest proportional increase in devel-

oped land occurred in Florida, ollowed by

Virginia; the smallest increase was in Indi-

ana. The average proportional increase was26 percent or the our smart growth states

and 21 percent or the our other states.

Because the states dier so widely in land

use and growth, these proportional increases

can be made comparable by relating the

increase in each states developed land area

to the increase in population over similar

periods. Figure 4 shows the ratio (marginal

land consumption) as the increase in square

miles o developed land per 1,000 new resi-

dents. While the average or smart growth

states is lower than or the other selected

states, the best perormersOregon and

Coloradoare rom the two dierent

groups (box 2).

SPAT IAL COnCEnTRAT IOn

The distribution o population over space

can be measured by the Gini coecient,

which is an index o inequality based on the

Lorenz curve measuring how evenly a vari-

able is spread. When activities are uniormly

figure 3

Emplomet Growth Geerall Exceeded Poplatio Growth

Note: Employment counts are derived rom the U.S. Census Bureaus Zip Code Business Patterns

in 1994 and 2004, with 1994 the earliest year or which data were available. Counts do not in-

clude government workers, arm workers, or part-time or sel -employed persons.Sources:U.S.CensusBureau(1990b;1996;2000b;2006).

figure 4

Developed Lad Geerall Icreased Less i Smart Growth States tha i Other Selected States

Florida Maryland New Jersey Oregon Colorado Indiana Texas Virginia

Smart Growth States

Other Selected States

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

MarginalLandConsumption

PercentCha

ngeinEmployment,1994

2004

40

35

30

25

20

15

10

5

0

0 5 10 15 20 25 30 35 40

Percent Change in Population, 19902000

Florida

Maryland

New JerseyOregon

Colorado

Indiana

Texas

Virginia

Smart Growth State Average

Other Selected State Average

Notes: Averages are 0.61 or the smar t growth states and 0.71 or the other selected states. Growth in developed land is measured rom 1987 to 1997 in square

miles. Population growth is measured rom 1990 to 2000. Marginal land consumption is square miles per 1000 additional residents.

Source: U.S. Department o Agriculture (2000).


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. . . . . . . . . . . . . . . .

distributed, the Gini coecient is zero; when

activity is concentrated in one place, it is one.

Decreases in the spatial Gini coecient over

time thereore indicate that the development

pattern is becoming more dispersed. SpatialGini coecients vary rom 0.25 to 0.90 across

all U.S. states, and are usually high in states

with only one large city and low in states

with no large cities and dispersed populations.

Here the concern is more about the change

in concentration over time than its level,

which refects the legacy o the past more

than the eects o current policies. Increased

concentration (higher Gini coecients) would

generally be consistent with smart growth

policies.Figure 5 shows statewide Gini coecients

calculated rom census tract data or popula-

tion and rom zip code data or employment

over ten-year intervals. Oregon is the only

state where population concentration in-

creased and employment concentration did

not decrease. While employment was typi-

cally more concentrated than population,

its concentration declined more than that

o population over the decade. The average

reduction in Gini coecients or the smart

growth states was greater than or the other

states, or both population (-.007 versus-.002) and employment (-.021 versus -.011).

This outcome is generally counter to smart

growth objectives. While these dierences

have similar patterns or most states, they

are not statistically signicant across states.

Gini coecients or population and

employment also were calculated or each

large metropolitan area (with population

over one million) in the eight case study

states. The results are similar to the state-

wide outcomes, but with larger Gini co-ecients. Again, the average reduction in

Gini coecients in the smart growth states

was greater than that in the other selected

states or both population (-.019 versus

-.017) and employment (-.044 versus -.028).

Echoingthestateresults,Portlandwasthe

only metropolitan area where population

concentration did not decrease.

figure 5

Poplatio ad Emplomet i Most States Became Less Cocetrated

Sources:U.S.CensusBureau(1990b;1996;2000b;2006);GeoLytics(2002).

0.5

0.6

0.7

0.8

0.9

1.0


Other Selected StatesSmart Growth States

1990 Population 2000 Population 2004 Employment1994 Employment

GiniCoefcient


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Box 2

urba Growth Maagemet i Orego ad Colorado

F

rom 1982 to 1997, Oregon and Colorado distin-

guished themselves by having smaller increases inmarginal land consumption than any o the other case

study states. Oregon experienced a decline in the amount

o developed land per capita over the 1990s, and in Colo-

rado 88 percent o the population lived in only 5 percent

o the census tracts during the same period.

Oregon has one o the rst and best-known statewide

smart growth programs in the country (Ingram et al.

2009, 188198). The 1960s development boom in the

state consumed productive armland and raised ears

about pollution, deteriorating quality o lie, and loss o

the states economic base. In response, Oregon passed

the 1973 Senate Bills 100 and 101 that emphasized

the need to protect its agricultural and orestry lands

by establishing an urban growth boundary (UGB).

The UGB identies and separates land that can be urban-

ized rom land that must remain rural, and encourages

compact development. Oregons urbanization goal re-

quires all cities to dene, adopt, and plan development

within growth boundaries that provide enough land to

accommodate projected residential and employment

growth over 20 years. Some communities also identiy urban

reserved areas intended to accommodate growth over a longertime horizon.

The state also provides incentives in the orm o grants and

technical assistance to jurisdictions undertaking planning unc-

tions. In the 1980s, the state and ederal governments provid-

ed $24 million in planning grants to local governments, or

nearly 63 percent o the planning budget during that period

(Rohse 1987). Between 1997 and 2007, these planning

grants declined to $12 million. Oregon also deers taxes on

armland and orestland. A recent study estimates the total

amount o taxes deerred between 1974 and 2004 at more

than $4.8 billion (Richmond 2007).

Colorados approach to managing urban growth in the early

1970s was to enable local governments to engage in planning

and to implement land use controls, but not to have a mandat-

ed statewide growth control program (Ingram et al. 2009, 200

208). The Colorado Constitution and Local Government Land

Use Control and Enabling Act granted counties and municipali-

ties the authority to plan and regulate land use. In principle,

the Land Use Commission and the Department o Local Aairs

could override local planning permission or land development

Portlad, Orego


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. . . . . . . . . . . . . . . .

in preserved areas. In practice, however, the state

role has been to assist, not direct, local govern-

ments to identiy and adopt guidelines or matters

o state interest.

During the 1990s, the Great Outdoors Colorado

Trust Fund was established to receive hal o the

proceeds o the Colorado Lottery. A portion o the

trusts unds can be awarded or open space plan-

ning grants. Consistent with general citizen sup-

port or preserving open space, Colorado ranked

rst in the nation between 1999 and 2004 in the

dollar value o voter-approved open space bonds

and tax measures (Trust or Public Land 2005).

Colorado also provides several examples o volun-

tary regional collaborations designed to manage

growth. The establishment o the Denver Region

Council o Governments Metro Vision 2020 is

most noteworthy. It includes the Mile High Com-

pact and has created a voluntary growth boundary

covering the six-county area along the Front Rangeo the Rocky Mountains where more than 60

percent o the states population resides. Other

elements o Vision 2020 call or reinorcing spines

o development along transit corridors. Passage

o the $4.7 million FasTracks bond initiative in

2004 also provided signicant support or the

implementation o Vision 2020.

uRBAnIZAT IOn

Smart growth programs seek to encourage

inll development in urbanized areas and

reduce the spread o development to adjoin-ing rural areas. To assess perormance on

these objectives, population growth was

classied by three locations: areas denoted

as urban in 1990; those newly urban between

1990 and 2000; and those rural in both 1990

and 2000. The smart growth states had a

larger share o new residents settle in urban

and newly urbanized areas, and a smaller

share in rural areas (gure 6).

Oregon had the highest share o popula-

tion growth in already urbanized areas at 49percent; New Jersey was second at 45 percent;

and Colorado was third at 38 percent. Indi-

ana had the lowest share at 6 percent. Using

this same classication or large metropolitan

areas, the results were similar to those at the

statelevel.Portlandhadthehighestshare

o additional residents settle in already

urbanized areas in the 1990s (59 percent),

while MiamiFt. Lauderdale had the

second highest (54 percent).

C En TR AL IZ AT IO n

To measure changes in the centralization

o population and employment over time,

each large metropolitan area in the eight

figure 6

Smart Growth States Kept More PoplatioGrowth i urbaized Areas

Sources:U.S.CensusBureau(1990b;2000b);GeoLytics(2002).

0

10

20

30

40

50

Urban in 1990 Urbanized

after 1990

Percentof1990

200

0

Population

Growth

Rural in 1990

and 2000

Smart Growth States


ver, Colorado


16/48


. . . . . . . . . . . . . . . . . .

case study states was divided into concentric

rings with radii o 5, 10, 20, and 30 miles.

The shares o population and employment

located within each ring were calculated at

the beginning and end o a ten-year period.

As might be expected, employment was more

centralized than population. The shares

o population and employment within the5-, 10-, and 20-mile rings decreased or re-

mained the same in all metropolitan areas.

Employment decentralized more than popu-

lation. Figure 7 shows that there was less

decentralization in the metropolitan areas

o the smart growth states, a result consis-

tent with the analysis o urbanization above.

S u M M AR y

Overall, the changes in growth patterns in

the smart growth states show some consis-tency with smart growth objectives. In these

figure 7

Metropolita Area Poplatio ad Emplomet Decetralized Lessi Smart Growth States

Sources:U.S.CensusBureau(1996;2006).

our states, marginal land consumption per

new resident was lower, the share o new

population locating in urban areas was

higher, and population and employment

decentralization was lower than in the our

other selected states. Smart growth states

also added a smaller share o new popula-

tion in rural areas. At the same time,however, the concentration o population

and employment declined more in the smart

growth states than in the other states.

When ranked in terms o overall peror-

mance, the top three states were Oregon,

Colorado, and New Jersey, with Florida

ranked eighth. Oregon perormed well

across most measures including land use,

urbanization, and concentration. Colorados

strong showing indicates that smart growth

outcomes can be attained without a man-datory statewide smart growth policy.

0

1

2

3

4

5

6

7

8

9


17/48


. . . . . . . . . . . . . . . .

C H A P T E R 3

Natural Resourcesand Environmental Quality

While smart growth policies are

intended to improve the envi-

ronment and protect natural

resources, the strength o the

linkages between specic programs and objec-

tives varies. Smart growth policies oten

relate directly to land use, including land

conservation, but only indirectly to air and

water quality through impacts on transpor-

tation and development patterns.

Data are reasonably accessible or landuse and land conservation measures that

are consistent over time and across states,

but it was impossible to obtain comparable

data or air and water quality. As a result,

all natural resource and environmental

perormance measures in this evaluation

pertain to the use and conservation o

land (Ingram et al. 2009, 4657).

LAnD COnSERVAT IOn

While all o the case study states support

land trusts and related conservation ease-

ments, state policies dier in many details.

Maryland and Oregon have programs to

protect open space and environmentally

sensitive land and to preserve agricultural

land. Maryland provides state unding to

purchase conservation easements on arm-

land. New Jersey preserves agricultural lands

by purchasing development rights, and pro-tects environmentally sensitive lands through

regional planning. Florida has purchased

over 2.5 million acres o environmentally

sensitive land, but has yet to und its pro-

gram or protecting agricultural lands.

Among the other selected states, Colo-

rado oers state tax credits or private con-

servation easements, purchases conservation

Virgiia


18/48


. . . . . . . . . . . . . . . . . .

land, and uses lottery proceeds to und park

and conservation programs. Indiana has a

modest land trust program unded rom sales

o anity license plates. Texas has threeprograms aimed at the preservation o orest-

land, one o which involves state purchase

o conservation easements. Virginia has

no statewide program, but acilitates local

eorts to adopt conservation policies.

R ES O u R C E L An D

AnD FARMLAnD

The analysis relied on two comprehensive

datasets available at ve-year intervals. The

rst, the National Resource Inventory, pro-

vides inormation on several land use cate-

gories, ve o which were aggregated into a

single measure termed resource land: crop-

land, pastureland, rangeland, orestland,

and conservation reserve program land. The

second dataset, available rom the National

Agricultural Statistics Service, collects inor-

mation on arm acreage through a census

o arms. Changes in the amounts o resource

land and armland were related to the

change in population in each o the eight

state cases.

figure 8

Smart Growth States Lost Less Lad per new Residettha Other Selected States

Sources:U.S.DepartmentofAgriculture(1987b;2000;2002).

As gure 8 indicates, the smart growth

states experienced smaller losses per new

resident in both land categories. Maryland

lost the least amount o resource land pernew resident (0.38 acres), while Indiana

(0.90 acres) and Oregon (0.88 acres) lost the

most. Virginia lost the least amount o arm-

land per new resident (0.04 acres), while

Colorado (2.36 acres), Indiana (1.63 acres),

and Oregon (0.89 acres) lost the most.

Oregons loss is surprising given the states

goal o protecting armland. Further anal-

ysis revealed, however, that the loss was

primarily in the sparsely populated eastern

part o the state. The densely settled Willa-

metteValleyregionaroundPortlandactu -

ally increased the amount o armland

per added resident by 0.05 acres.

L An D TR u S TS An D

COnSERVAT IOn PROGRAMS

All eight case study states support the place-

ment o private land in land trusts and o

armland in conservation programs. During

the two overlapping 15-year periods shown

in gure 9, smart growth states perormed

less well than the other selected states, but

the within-group perormance varied widely.

In 2005, New Jersey (3.6 percent) and Mary-

land (2.5 percent) had the highest percent-

ages o their areas in conservation easements

held by land trusts, while Oregon (0.1 per-

cent) and Florida (0.2 percent) had among

the lowest.

In terms o armland in conservation

programs, Colorado (5.6 percent) perormed

best in 2002, ollowed by Maryland and

Oregon (both at 2.8 percent). The share o

area in land trusts is thus a poor predictor

o the share o armland in conservation pro-

grams, except in Maryland where both are

reasonably high.0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Resource Land 1982-1997 Farmland 19872002

Acres

LostperNewR

esident

Smart Growth States Other Selected States


19/48


. . . . . . . . . . . . . . . .

STATE PARKL AnD

A nal indicator related to natural resources

is the amount o land devoted to state parks.

Acres o parkland per 1,000 persons, the

level o service measure used by the National

RecreationandParkAssociation,haschanged

over time in the two groups o states. Asgure 10 shows, the smart growth states were

slightly ahead o the other selected states

on this indicator in 1990, but the dierence

between the two groups was negligible by

2006. In that year, Colorado had the most

parkland per 1,000 persons (42.5 acres),

ollowed by New Jersey (38.5 acres). Virginia

had the lowest service level (8.1 acres) and

Indiana the next lowest (10.3 acres).

S u M M AR yThe evidence on natural resource and

environmental quality measures is mixed,

with neither group o states clearly outper-

orming the other in terms o protecting

undeveloped areas. At the individual state

figure 9

Smart Growth States Protected Smaller Shares o Their Area i Lad Trstsad Farmlad Coservatio Programs

Notes: Land trust data excludes land owned by The Nature Conservancy. Farmland data includes land enrolled in

Conservation Reserve and Wetlands Reserve programs.

Sources:LandTrustAlliance(n.d.;2005);U.S.DepartmentofAgriculture(1987b;2002).

figure 10

Smart Growth States Lost Their Lead i State ParkladAter the 1990s

Source: National Association o State Park Directors (n.d.).

level, Maryland had the highest averageranking across all measures (box 3), with

New Jersey and Colorado tied or second.

Indiana had the lowest average ranking.

Colorado again perormed well despite its

lack o a statewide smart growth program.

1990 2005 1987 2002

State Area in Land Trusts Farmland Area in Conservation

PercentArea

3.00

2.50

2.00

1.50

1.00

0.50

0.00

Smart Growth States


1990 2006

Acres

per1000

Persons

0.00

5.00

10.00

15.00

20.00

25.00

Smart Growth States



20/48


. . . . . . . . . . . . . . . . . .

Box 3

Lad Coservatio Programs i Marlad

M

aryland is the gatekeeper to the largest and

most productive estuary in the United States, the

Chesapeake Bay. Because most o Maryland lies within

the watershed, the health o the bay has been a major

driver o the states land use and environmental policies

or many years.

This evaluation revealed that Maryland had the largest

percentage increase in acres o armland enrolled in land

preservation programs among the eight selected states

(Ingram et al. 2009, 166175). About 2.5 percent o

Marylands land was privately conserved in 2005, the

second highest level among the selected states. In addi-

tion, Maryland preserved 343,000 acresabout 5 per-

cent o its total land areathrough state and county

transer o development rights and purchase o devel-

opment rights programs (Lynch et al. 2007).

Beginning in the 1960s, the Maryland General Assembly

and various governors proposed and enacted a series o

land use laws designed to protect the environment. These

laws were intended to help the state acquire parkland,

protect orests and wetlands, reduce soil erosion, preserve

armland, and regulate storm water runo. Much o the

ocus turned to the Chesapeake Bay ollowing passage

o the Chesapeake Bay Agreement in 1983.

The emphasis on land use grew in the 1990s, beginning

with the Economic Growth, Resource Protection, and Plan-

ning Act o 1992 (the Growth Act) and the Smart Growth

Areas Act o 1997, which took an inside/outside approach

in an eort to direct growth to Priority Funding Areas

(PFAs) while preserving undeveloped areas through the

Rural Legacy Program.

The centerpiece o the Smart Growth Areas Act attempted

to infuence development decisions by restricting growth-

related state spending to specic areas. County govern-

ments were required to designate certain areas as PFAs,

whichincludedallincorporatedmunicipalities;heavily

developed areas inside the circumerential highways

around Baltimore and the Maryland suburbs o Washing-

ton,DC;andotherareasmeetingspecicstatecriteria.

The counties were required to map their PFAs and submit

their plans to the Maryland Department o Planning (MDP)

or review and comment. The goal was to use the power

o the state budget as an incentive or smarter growth.

State programs were geared either to support develop-

ment within the PFAs or to protect undeveloped land

outside them.

Chesapeake Ba, Marlad


21/48


. . . . . . . . . . . . . . . .

C H A P T E R 4

Transportation

Smart growth proponents view trans-

portation as a major determinant

o land use patterns and an impor-

tant maniestation o the success

o smart growth policies. They argue that

expanding transport options, altering trans-

port pricing, and ostering pedestrian-riendly

settings yield less single-occupant car travel,

less congestion growth, and more trips by

transit, biking, and walking. These patternsare associated with more compact, mixed-

use, and dense urban orms. This evaluation

thereore looked at perormance indicators

related to mode choice and trac conges-

tion to assess how they are associated with

smart growth programs, which refect dierent

policy approaches (Ingram et al. 2009, 5875).

Transportation data that are dened con-

sistently across states are reasonably available,

including census data that record commute

mode and travel time. Data rom the Texas

Transport Institute, which estimates annual

delay per peak-period traveler and a peak-

period travel time index, have been used to

examine levels and changes in congestion.

Census data are available every 10 years

or all states and municipalities, and the

Texas Transport Institute data are availableannually or 85 U.S. metropolitan areas. Con-

gestion data were used or cities with popula-

tions o one to three million, and seven o

the eight case study states (all but New Jersey)

had at least one such metropolitan area with

congestion data. Vehicle miles traveled

(VMT) was one explanatory variable used

to analyze the change in congestion.

Dallas, Texas


22/48


. . . . . . . . . . . . . . . . . .

C O M M u TE M O D ES

Data on mode choice or work trips were

tabulated or cities and counties with an

average density o at least 50 persons persquare mile. Counties were sorted into three

density categoriesvery high (more than

500 persons per square mile), high (101 to

500), and medium (50 to 100)to control

or the level o urbanization.

Figure 11 shows that the transit share o

work trips varied greatly with population

density in both the smart growth and other

selected states. The transit share o work

trips across the country during the 1990s

was 6 percent in very high density counties,1 percent in high density counties, and 0.5

percent in medium density counties.

Work trip transit shares in all our smart

growth states exceeded U.S. averages, as

did those in Colorado. In addition, average

work trip transit shares increased rom 1990

to 2000 in the smart growth states in all

density categories, but generally declined in

the other selected states except Colorado.

Given that smart growth programs

typically provide bike lanes, bike racks,

sidewalks, and priced parking, they should

figure 11

Work Trip Trasit Shares Started Higher ad Rose i Smart Growth States

Sources:U.S.CensusBureau(1990e;2000e).

increase the share o bike/walk commutes

or at least retard its decline. But as gure

12 indicates, while the bike/walk share was

generally higher in the smart growth states,its share declined over time and was essen-

tially unrelated to population density. The

exception to this pattern is Oregon, where

the bike/walk share increased rom 1990

to 2000 by more than 10 percentlikely

refecting the state requirement that local

governments produce bike and pedestrian

plans as part o their transportation plans

(box 4).

C O n G ES T IO n

The relationship between the orm o

development and trac congestion is much

debated. Some analysts believe that dense,

compact development promotes transit use

and shorter automobile trips, while others

contend that decentralization reduces the

distances rom home to work and spreads

car travel more widely over existing trans-

port capacity. Since smart growth programs

typically seek to reduce congestion, assessing

the change in travel delays over time is

essential.

1990 2000 1990 2000 1990 2000

Very High Density Counties High Density Counties Medium Density Counties

Smart Growth States


9

8

7

6

5

4

3

2

1

0

Percent


23/48


. . . . . . . . . . . . . . . .

Figure 13 shows average annual increases

in peak-period hours o delay, including

data or Florida and Maryland beore and

ater their respective programs were initi-

ated. Oregons start date preceded the avail-

able data, and no data were available or New

Jersey. The results are mixed. The average

annual increase in congestion in the smart

growth states (1.73 hours) exceeds that in

the other selected states (1.31 hours). Yet

the initiation o smart growth programs in

Florida and Maryland reduced the annual

increase in trac delays, thus providing

some evidence o program success.

The results rom a sample o six smart

growth states indicate that smart growth

figure 12

The Bike/Walk Share Geerall Started Higher i Smart Growth States,bt Declied Drig the 1990s

Sources:U.S.CensusBureau(1990e;2000e)

figure 13

Aal Icreases i Trafc Delas i Smart Growth States Declied AterSmart Growth Programs Were Itrodced

Source: Ingram et al. (2009, 65).

Percent

1990 2000 1990 2000 1990 2000

Very High Density Counties High Density Counties Medium Density Counties

Smart Growth States


6

5

4

3

2

1

0

AnnualIncrease

in

Hou

rs

ofDelay,

1982

2003

2.50

2.00

1.50

1.00

0.50

0.00


Florida

Before

Florida

After

Maryland

Before

Maryland

After

Oregon Colorado Indiana Texas Virginia


24/48


. . . . . . . . . . . . . . . . . .

Box 4

Trasportatio Plaig i Orego

O

regons integration o transpor tation planning with

land use management is among the most advanced

in the country (Ingram et al. 2009, 188198). The states

transportation plans must be based on an inventory o

local needs and consider all modes (cars, reight, bicycle,

pedestrian, public transit). In 1993 the state created the

Oregon Transportation and Growth Management Program

(TGM), a partnership between the Oregon Department

o Land Conservation and Development and the Oregon

Department o Transportation. One o the primary goals

o TGM is to make walking, biking, and using transit sae

and convenient. The program also aims to enhance the

states road system as a whole and to improve the abilityo commercial enterprises to move goods and services

along the highways (Oregon Transportation and Growth

Management Program 2009).

TGMs unding rom the Federal Highway Administration

enables the state to leverage its own investment with ed-

eral dollars. TGM recognizes that scattered development

without good connections between local destinations in-

creases the need or driving and that well-planned devel-

opment with good street and walkway connections im-

proves transportation options (such as walking and

biking) and can reduce automobile usage.

Thus TGM promotes planning concepts including mixed-

use,compactdevelopment;goodconnectivitybetween

localdestinations;revitalizingdowntownandmainstreets

wheregoodtransportationoptionsarealreadyavailable;

transit-orienteddevelopment;andbicycleandpedestrian

networks. From 2007 to 2009, TGM granted $3.8 million

in nancial and technical assistance to 60 local transpor-

tation projects throughout Oregon.

TGM also sponsors community workshops, lecture series,

and other events to improve public understanding o land

use and transportation planning

concepts. Through its Quick

Response and Transportation

System Plan Assessments pro-

grams, TGM oers direct design

assistance and assessmentso transportation needs to

communities. These unctions

aid local governments in their

search or grants to carry out

necessary projects. Its Code

Assistance program helps local

governments revise zoning

and development codes.


25/48


. . . . . . . . . . . . . . . .

programs have a statistically signicant and

behaviorally meaningul eect on conges-

tion. The travel delay regression indicates

that smart growth programs reduced theannual increase in delay by 2.2 hours or

every year that the program was in place.

For example, i the travel delay had been

increasing by 4.0 hours per year, smart

growth programs would reduce that number

to 1.8 hours per year.

An attempt was also made to relate the

congestion reduction rom smart growth

programs to three underlying causal actors

increased population density, increased

transit ridership, and changes in VMT. Taken

together, these variables were ound to explain

no more than 20 percent o the eect o

state smart growth programs on congestion.

SuMMARy

Analysis o the transportation indicators,

especially work-trip transit ridership and

changes in congestion, provides reasonablystrong evidence that smart growth programs

are associated with desirable outcomes.

While the evidence on bike/walk commutes

was less compelling than that on transit,

smart growth states had somewhat higher

shares o work trips by these modes.

When perormance across the three

major indicators was aggregated or each

state, Oregon ranked at the top. That state

did very well in transit and bike/walk com-

mutes, and was the top smart growth statein terms o congestion. Indiana and Texas

were at the bottom o the overall rankings.

It is noteworthy that the our states that

perormed best in the rankings on trans-

portation (Oregon, Virginia, Colorado,

and New Jersey) also perormed best on

growth patterns and trends.


26/48


. . . . . . . . . . . . . . . . . .

C H A P T E R 5

Aordable Housing

While improving the aordabil-

ity o housing is a common

goal o smart growth programs,

the emphasis placed on this

objective varies across the case study states.

New Jersey has the strongest state-level

program, which stems rom theMt. Laurel I

and IIstate supreme court decisions requiring

municipalities to provide realistic opportuni-

ties or low- and moderate-income housingon a regional air-share basis.

Florida requires that local plans include a

housing element. Oregon has a requirement

or provision o needed housing, while

Maryland has no specic state-level housing

mandate. None o the other selected states

had a state-level aordable housing require-

ment during the 1990s, although Virginia

added such a requirement in 2003 (Ingram

et al. 2009, 7687).

HOuSInG VALuES

The rst measure used to assess housing

aordability was the change in median

housing values rom 1990 to 2000. Figure

14 shows that median values rose in all eight

states, with the largest percentage increase

(118 percent) in Oregon. Although New Jer-sey posted the smallest percentage increase,

it had the highest median housing value o

the eight case study states in both 1990 and

2000. Colorado had the second highest

median value in 2000, ollowed by Oregon

and Maryland. Housing in the smart growth

states was clearly more expensive than in

the other selected states.

Bolder, Co


27/48


. . . . . . . . . . . . . . . .

Even so, the average increase in median

prices was lower in the smart growth states

(31 percent) than in the other selected states

(58 percent). As a result, the dierence inmedian house values between the two

groups shrank rom $34,000 in 1990 to

$25,000 in 2000. The source o the price

increases in the smart growth states could

have been on the demand side (greater amen-

ities), the supply side (higher regulatory

costs), or both.

MuLTIFAMILy AnD

REnTAL HOuS InG

States that successully promote aordable

housing are likely to produce more multi-amily and rental units. As gure 15 shows,

all eight case study states added varied

shares o rental units during the 1990s.

Multiamily units made up a larger average

share o new housing in the smart growth

states (21 percent) than in the other selected

states (13 percent). Smart growth states were

figure 14

Percetage Icreases i Media Hose Vale Varied Widel Amog the Case Std States

Note: Includes all owner-occupied units.

Sources:U.S.CensusBureau(1990a,tableH061A;2000a,tableH85).

figure 15

Smart Growth States Added a Larger Share o Mltiamil uits Drig the 1990s

Sources:U.S.CensusBureau(1990a;2000a).

1990 2000

22%

24%

4%

118%

94%

73%

32%

31% 42%

Florida Maryland Jersey Oregon Colorado Indiana Texas Virginia U.S.

MedianHouseValue

$180,000

160,000

140,000

120,000

100,000

80,000

60,000

40,000

20,000

0

Smart growth state average increase = 31 percent. Other selected state average increase = 58 percent. Average

Rental Multifamily

PercentofUnits

Ad

ded

35

30

25

20

15

10

5

0Florida Mar yland Jersey Oregon Colorado Indiana Texas Virginia



28/48


. . . . . . . . . . . . . . . . . .

Box 5

The Cocil o Aordable Hosig i new Jerse

The Council on Aordable Housing (COAH) is a key

planning agency that administers the New Jersey Fair

Housing Act (Ingram et al. 2009, 177187). The enact-

ment o this legislation was a response to a series o

state supreme court decisions, known as the Mt. Laurel

cases, which dealt with aordable housing and exclusion-

ary zoning. In the rst case, Southern Burlington County

NAACP v. Township of Mount Laurel, 67 N.J. 151, the court

ruled in 1975 that developing municipalities have a con-

stitutional obligation to provide a realistic opportunity or

the construction o low- and moderate-income housing.

In the second case, Mt. Laurel II (92 N.J. 158) in 1983,

the court held that all municipalities should share the

obligation to provide the opportunity or the development

o aordable housing, and provided specic judicial guide-

lines or municipalities to ollow, so as to ulll their con-

stitutional obligation. Municipalities that enacted zoning

had to provide realistic opportunities to meet their air share

o low- and moderate-income housing in their regions.

Under the Fair Housing Act, COAHs responsibilities in-

cludedeninghousingregions;estimatingmoderateand

low-incomehousingneeds;settingcriteriaandguidelines

or municipalities to determine and address their air

sharenumbers;andreviewingandapprovinghous-

ing elements/air-share plans and regional contribution

agreements or municipalities. Once its housing element

and air share plans are approved, the municipality has

a degree o protection rom Mt. Laureltype lawsuits.

During the 1990s, the council used an allocation ormula

to establish goals or all municipalities in the state. As

o 2008, COAH had begun round three and moved to a

growth share ormula that bases the aordable housing

determination or a municipality on the actual growth o

market-rate units and nonresidential development.

Crabr, new Jerse


29/48


. . . . . . . . . . . . . . . .

figure 16

Hosig Cost Brdes Were Geerall Higher i the Smart GrowthStates, Especiall or Reters

Note: Cost-burdened owners and renters are dened as those paying 30 percent or more

o income or housing.

Source:U.S.CensusBureau(1990a;2000a).

figure 17

The Share o Cost-brdeed Owers Rose i the Smart GrowthStates i the 1990s

Note: Cost-burdened owners and renters are dened as those paying 30 percent or more

o income or housing.

Source:U.S.CensusBureau(1990a;2000a).

also less likely to add housing in rural

areas. New Jersey had the highest shares

o both rental and multiamily units o

the eight case study states (box 5), whileMaryland had the lowest rental share

and Colorado had the lowest multi-

amily share.

H O u S In G C O S T B u R D En

Aordability is determined by both

housing prices and household incomes.

The housing cost burden is the percent

o income spent on housing, commonly

measured by the ratio o median house

prices and rents to median household

income. Figure 16 indicates that renter

and owner housing cost burdens were

slightly higher in the smart growth states

than in the other selected states. The

share o income spent on housing changed

little rom 1989 to 1999, except that

owners in smart growth states saw their

cost burdens edge up rom 22 percent

to 23 percent.

In both groups o states, the housing

cost burden or renters was consistently

higher than or owners. The renter bur-

den ell slightly in all states except Oregon

(up 5.5 percent) and Colorado (up 1.1

percent). The owner cost burden rose

the most in Indiana (15.6 percent) and

Oregon (13.7 percent), and decreased

the most in Texas (-3.8 percent) and

Virginia (-2.3 percent).

A generally accepted standard o

aordability is that housing costs should

be less than 30 percent o household

income. Accordingly, a specic indicator

o aordability (or its lack) is the share

o households whose housing cost bur-

den exceeds 30 percent o income. As

shown in gure 17, the share o cost-

burdened owners rose between 1989

and 1999 in both groups o states, while

the share o cost-burdened renters ell or was

unchanged.

The shares o both cost-burdened owners

and renters were higher in the smart growth

states than in the other selected states. Oregon

posted the largest increase in the share o

cost-burdened owners (5.8 percent), ollowed

by Maryland (3.8 percent). Texas was the only

30

25

20

15

10

5

0

HousingCostas

PercentofIncome

1989 1999 1989 1999

Owners Renters


40

35

30

25

20

15

10

5

0PercentofCost-burd

ened

Households

1989 1999 1989 1999

Owners Renters

Smart Growth States


l i i


30/48


. . . . . . . . . . . . . . . . . .

state to show a decline. The share o cost-

burdened renters increased only in Oregon

(2.5 percent) and Maryland (0.1 percent),

and ell the most in Texas (-3.9 percent).Note that these changes in housing cost

burdens do not account or any osetting

cost reductions, such as or transportation,

that may be associated with smart growth

programs.

Statistical regressions were used to anal-

yze the determinants o the change in the

shares o cost-burdened owners and renters

across the case study states. Regressions that

hypothesized a uniorm eect rom smart

growth programs ound a statistically signi-

cant relationship. Smart growth programs

were associated with increased shares o

cost-burdened households.

Additional regressions that allowed each

state to have an independent eect ound

that the shares o cost-burdened renters and

owners increased the most in Oregon and

the least in Texas. But New Jersey and

Floridasmart growth states that require

aordable housing elements in local plans

perormed better than Oregon and Mary-

land or owners, and better than Oregon,

Maryland, Virginia, and Colorado or

renters.

SuMMARy

These results indicate that smart growth

programs that lack an aordable housing

element have been associated with increasesin housing cost burdens, especially or owners.

While smart growth states experienced a

smaller increase in median housing prices

and added a greater share o multiamily

units in the 1990s, they also had higher shares

o cost-burdened owners and renters than

the other selected states.

Regression results indicate that the

smart growth states had greater increases in

the share o cost-burdened owners than o

renters. New Jersey, with its court-mandatedaordable housing requirement, was rst

overall in a composite ranking across all

housing aordability indicators. Oregon

ranked last, having experienced the largest

increases in housing values, housing cost

burdens, and shares o cost-burdened

households.


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. . . . . . . . . . . . . . . .

C H A P T E R 6

Fiscal Dimensions

Smart growth programs seek to

concentrate economic activity in

areas that are already developed

and to control growth in undevel-

oped and rural areas. Fiscal revenues and

expenditures have a role to play in these

eorts. Some central questions are the extent

to which revenues rom developed areas are

sucient to pay or expenditures, and how

the balance o revenues and expenditures

compares between smart growth and

other states.

To examine these issues, counties in the

eight case study states were separated into

two groups according to their population

densitiesrural/undeveloped, and urban/

suburban or otherwise developed (Ingram et

al. 2009, 88115). The densities that dened

the two categories varied with the average

population density o each state. The anal-

ysis looked at ten variables related to econo-

mic development or which comparable data

were available or the decades rom 1980

to 1990 and rom 1990 to 2000: population

growth and density, households, employment,

personal income, retail sales, tax base, hous-

ing values, multiamily units, and journey-

to-work travel times.

The shares o incremental growth in

each variable that occurred in each states

urban/suburban and rural/undeveloped

counties were calculated or both decades.

The proportion in the 1980s was then sub-

tracted rom that in the 1990s to provide

a simple summary statistic measuring the

change in the distribution o economic acti-

vity. For example, i rural counties received

8 percent o a states population growth in

Idiaapolis, Idiaa


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. . . . . . . . . . . . . . . . . .

figure 18

Smart Growth States Had Less Growth i Rral/udeveloped Areas i the 1990stha i the 1980s

Source:U.S.CensusBureau(1980;1990c;2000c);WoodsandPooleEconomics,Inc.(2005).

the 1980s and 13 percent in the 1990s, the

net increment or rural counties would be

5 percent.

Figure 18 shows how much the shares ostatewide growth in rural/undeveloped coun-

ties changed rom the 1980s to the 1990s on

eight o the ten variables. The only activity

where that share decreased was multiamily

housing in the smart growth states. In all

other cases, the rural/undeveloped share o

growth was equal to or larger in the 1990s

than in the 1980s. It is striking that the rural

growth rate or all activities in the smart

growth states was less than or equal to that

in the other selected states. This indicatesthat smart growth states were more success-

ul in ostering density in urban/suburban

areas and in moderating the growth o devel-

opment in rural/undeveloped areas.

The analysis o scal impacts was based

on public nance data drawn rom the U.S.

Census o Governments. However, the 2002

census had two serious data problems that

made it necessary to impute values or many

missing variables beore expenditures and

revenues could be analyzed. First, a signi-

cant amount o inormation or New Jerseyand Texas was missing, because local juris-

dictions did not report it. Second, some

expenditures and revenues are not counted,

especially i they are nonrecurring or in the

orm o intrajurisdictional transers (table 1).

Figure 19 shows the ratios o the respec-

tive growth o expenditures and revenues

(in constant dollars) in the 1990s and in the

1980s. A ratio over 1.0 indicates that growth

accelerated; that is, its absolute magnitude

in the 1990s exceeded that in the 1980s. Thisgure reveals three trends: (1) both expendi-

tures and revenues grew aster in rural areas

than in urban/suburban areas; (2) revenues

grew aster than expenditures in all areas;

and (3) smart growth states had lower growth

in both expenditures and revenues than the

other selected states. The aster growth in

rural areas refects the increase in develop-

Smart Growth States


Average

Population

Households

Employment

Income

Retail Sales

Tax Base

House Value

MultifamilyUnits

-4 -2 0 2 4 6 8 10 12 14 16

Percentage Point Change in the Rural Share of Growth in the 1990s Relative to the 1980s.


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. . . . . . . . . . . . . . . .

TaBle 1

Expeditres ad Revees i the Evalatio

Expeditres Revees

Iclded Exclded Iclded Exclded

Local public inrastructure

and services investments

Transers between the same

or dierent levels o government

Property, sales, income ranchise,

lodging, uel, and other taxes

Impact ees or capital

improvements

Salaries/wages and other

expenditures

Public services provided

by community associations

Fees collected rom inspections,

building permits, and ordinance lings

Debt service to support

large-scale capital project

Educational expenditures in

noneducational budgets o selected

municipalities and counties

Trac and parking nes

Purchases o computers,

oce urniture, and regular

vehicles

Municipal subsidies to charter schools Service charges or contracted solid

waste removal, animal control, and

special assessments or improvements

New components o expendituressuch as start-up computer costs

Intergovernmental transers

New spending due to the addition o

new government divisions or annexation

ment as documented in gure 18, while

the other patterns may refect prudent scal

management in the smart growth states.

Fiscal impact, a common metric used to

evaluate scal perormance, is the ratio o

figure 19

Aggregate Expeditres ad Revees Icreased Less i Smart Growth Statestha i Other Selected States

Source:U.S.CensusBureau(1982;1992;2002).

the change in revenues to the change in

expenditures. When this ratio is greater than

1.0, revenues are growing aster than expen-

ditures. The analysis indicates that the smart

growth states had a more avorable scal

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Urban/Suburban Rural/Undeveloped Urban/Suburban Rural/Undeveloped

Expenditures Revenues

Ratio

of1990s

Growth

to

1980s

Gr

owth

Smart Growth States




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. . . . . . . . . . . . . . . . . .

balance (1.2) in the 1990s than the other

selected states (1.0) in urban/suburban areas.

What explains this outcome? Analysis o

the change in tax bases and tax rates revealsthat the smart growth states increased taxes

somewhat more than the other selected

states to strengthen their scal positions.

S u M M AR y

Overall, smart growth states did better than

the other selected states in controlling the

growth o economic development activities

in rural areas, and in achieving a avorable

balance o incremental revenues over incre-

mental expenditures. It is noteworthy that

the more avorable scal balances in the

smart growth states result rom larger tax

increases. This suggests that these states aregenerally more supportive o the public

sector than the other selected states, in terms

o both the regulatory structure underlying

smart growth programs and the provision o

nancial resources to the public sector. New

Jersey perormed best overall on measures

o the distribution o growth and scal

impact, ollowed by Florida and Maryland.

The lowest ranking states were Indiana

and Virginia.


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. . . . . . . . . . . . . . . .

This evaluation also included a sur-

vey o 117 state and local opinion

leadersabout 15 in each o the

eight case study states. The ques-

tionnaire covered the period rom 2000 to

2007 and addressed ve major topics related

to smart growth programs: eectiveness in

achieving goals; eectiveness o sanctions

and incentives; public participation; costs o

regulatory compliance; and the governmentsrole in guiding land use decisions. The survey

was careul to dierentiate between state- and

local-level eorts and activities because sev-

eral o the other selected states enable local

governments to apply smart growth policies

(Ingram et al. 2009, 116133).

Respondents in the smart growth states

were two to three times more likely to believe

C H A P T E R 7

Survey of Opinion Leaders

that the costs o smart growth policies and

the time required to complete the review

process had become a lot higher than

respondents in the other selected states.

Opinion leaders generally had similar views

about the role o government in smart growth

policies, except that those rom the other

selected states were more likely to believe

that state governments should deer to

local governments on such issues.Responses on the eectiveness o achiev-

ing goals, o public participation, and o

sanctions are summarized in gure 20. Opin-

ion leaders elt that smart growth states had

been more eective than the other selected

states at the state level, but that the other

selected states had been nearly as eective

at the local level. These views rearm

Baltimore, Marlad


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. . . . . . . . . . . . . . . . . .

figure 20

Srve Respodets Viewed State-level Programs i the Other Selected States asLeast Eective


figure 21

Perceptios o State-level Eectiveess i Achievig Smart Growth Goals VarMore tha Those o Local-level Eectiveess Across All States


Smart Growth States, State Level

Smart Growth States, Local Level

Other States, State Level

Other States, Local Level

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Achieving Goals Public Participation Effective Sanctions

Average

Score

(5=highest)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

AverageScore(5=MostEffective)


State Level

Local Level



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. . . . . . . . . . . . . . . .

earlier ndings in this report that states with-

out statewide programs, such as Colorado,

have succeeded in achieving smart growth

objectives through locally implementedpolicies.

This point is reinorced in gure 21, which

shows by state the opinion leaders views o

the eectiveness o state and local govern-

ments in achieving smart growth objectives.

The lack o a statewide smart growth prog-

ram apparently contributes to increased

local activism in the other selected states

that may exceed the perceived eectiveness

o programs in smart growth states.

The strength o state regulatory regimes

was assessed as a composite o ve attributes:

state requirements or local planning; state

specication o the size o communities that

must plan; and state requirements or inter-

nal consistency, vertical consistency, and hori-

zontal consistency. The Wharton Residential

Land Use Regulatory Index was used to

measure the strength o local housing devel-

opment regulation (Gyourko, Saiz, and

Summers 2006).

Figure 22 shows that both state and local

regulations are strong in the our smart growth

states. Colorado, which does well on many

smart growth perormance indicators, also

has relatively strong local regulations, sug-

gesting that i they are reasonably consistent

within a state they can produce smart growth

outcomes similar to those in states with

strong state-level regulation (box 6).

In addition, whether a state is subject to

Dillons Rule (a legal doctrine holding that

localities have only those powers specically

delegated by state law) has little relation to

the presence o strong state or local regula-

tions. For example, Maryland is a Dillons

Rule state and Oregon is not, yet both have

strong state and local regulations (Richard-

son and Gough 2003).

figure 22

Smart Growth States Have Stroger State ad LocalResidetial Reglatios

Note: SD=Standard deviation.


IN TX VA

CO

OR

MDFL

NJ

MEAN+1.0 SD

High

Strong 20

Weak 0

10

Average Score=12

-1.0 SDLow

SCORE

i i

i

State Smart Growth

Requirements

Wharton

Residential Land

Use Regulatory

Index



38/48


. . . . . . . . . . . . . . . . . .

Box 6

Local Govermet Plaig Roles i Colorado

Colorado currently has no statewide mandated growth

management program. Instead, the states approach

has been largely to create a toolbox o planning powers

that local governments can adopt. The ew mandatory

requirements oten refect ederal requirements devolved

by the state to local or regional jurisdictions. State agen-

cies oer airly modest technical support or planning

(Ingram et al. 2009, 200208).

Although there is no mandatory state planning require-

ment, there are many locally initiated regulations. Under

Colorado Revised Statutes, counties and municipalitieswith a certain population level or growth rate must pre-

pare and adopt a master plan. The Department o Local

Aairs annually calculates which local governments meet

certain growth thresholds, and receives plans or advi

evaluating smart growth

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