spillovers from tax increment financing districts: implications for housing price appreciation

commercial and residential parcels increases the appreciation of nearby houses. We discuss some of the

earmark growth in property tax revenues resulting from appreciation to finance economicdevelopment within the district. The original assessed valuation of the properties in the district is

Regional Science and Urban Economics 37 (2007) 259281www.elsevier.com/locate/regec Corresponding author. Tel.: +1 312 355 0307; fax: +1 312 413 2314.policy implications of our findings in the conclusion. 2006 Elsevier B.V. All rights reserved.

Jel classification: R50Keywords: Tax increment financing; Gentrification; Economic development; Urban policy

1. Introduction

From the arsenal of contemporary urban development programs, tax increment financing (TIF)has been singled out for encouraging the rapid appreciation of property in targetedneighborhoods. TIF allows municipalities to designate an area for improvement and thenSpillovers from tax increment financing districts:Implications for housing price appreciation

Rachel Weber a,,1, Saurav Dev Bhatta a,1, David Merriman b,1

a Urban Planning and Policy Program, University of Illinois at Chicago, 412 South Peoria MC 348,Chicago, IL 60607, United States

b School of Business Administration, Loyola University Chicago, United States

Accepted 6 November 2006Available online 17 January 2007

Abstract

Tax increment financing (TIF) has been both applauded and castigated for causing rapid appreciation ofnearby residential properties. We study the spillover effects of TIF on the appreciation of single-familyChicago homes that sold multiple times between 1993 and 1999. After controlling for structuralcharacteristics of the home, neighborhood conditions, and information about the nature and scale ofactivities within TIF districts, we find that proximity to industrial TIF districts is actually associated with adecrease in the rate of appreciation. However, proximity to mixed-use TIF districts that contain bothE-mail address: [email protected] (R. Weber).1 Each co-author contributed equally to the final manuscript.

0166-0462/$ - see front matter 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.regsciurbeco.2006.11.003

held constant for the duration of the district's lifetime (20-plus years in most states), and taxes onthis base are allocated to taxing bodies with jurisdiction over area properties. Meanwhile, taxesderived from incremental increases in property values are reinvested in the area. TIF funds areused to reimburse developers for eligible costs, such as land assembly, site clearance, andinfrastructure construction, or to service bonds that are issued to finance public improvementswithin the district. When the TIF district expires, taxes on the increment revert to the overlappingjurisdictions.

Previous research has shown that the establishment of a TIF district may affect the value ofparcels within its borders due to the elimination of blight and incentives for development(Weber et al., 2003). Development stimulated by TIF also may have important spillover effects onnearby properties, particularly residential ones. If a TIF district protects incompatible land uses orincreases noise or pollution, it could have a negative impact on the value of nearby houses. On theother hand, residential parcels could appreciate to reflect the enhanced value of TIF-suppliedamenities, such as new retail development. Even this may have negative consequences: fear ofrapid residential appreciation and ensuing displacement has prompted popular protest against TIFacross the country. In one Chicago neighborhood, low-income tenants blamed a nearby TIFdistrict for annual rent increases of 20% (Hardy, 1999). Mexican immigrants fought against aproposed industrial TIF district in another Chicago neighborhood, fearing they would be priced-out of the neighborhood (Lutton, 1997).

TIF has been used extensively in many cities including Minneapolis, Kansas City, and LosAngeles, but Chicago has made more extensive use of this form of off-balance sheet financingthan any other major the city's. As of June 2002, Chicago was home to 121 TIF districts thatcovered 38,550 acres and 16% of the city's property tax base (Neighborhood Capital BudgetGroup, 2002). Some of these TIF districts encompass large swaths of the city's most valuable realestate: the incremental value of parcels in these districts generates more revenue than their basevalue. Chicago has used TIF revenues to fund a variety of projects, from the expansion ofmanufacturing facilities to downtown mixed-use (commercial and residential) development andbeautification efforts.

We use econometric analysis to investigate whether Chicago TIF districts affect housingappreciation. Our dataset includes observations on owner-occupied homes within the city ofChicago that sold more than once during the period 1993 to 1999. Data have been derived frommandatory filings of real estate transfer forms and Multiple Listing Service (MLS) records as wellas other data sources. After controlling for the characteristics of the TIF district (e.g., commercialversus non-commercial), property (e.g., low-value versus high-value housing), and neighborhood(e.g., declining versus appreciating), we measure spillovers on nearby residential property values.

Using several alternative specifications of our model, we find that proximity to industrial TIFdistricts suppresses the general rate of appreciation. However, proximity to mixed-use TIFdistricts is associated with an increase in the rate of appreciation. The spillover effects associatedwith TIF, while relatively small, appear to be related to the land use within each district.

2. Background and review of literature

2.1. TIF and residential property appreciation

How might TIF affect residential property appreciation? If potential buyers and sellers of real

260 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281estate foresee future market conditions, initial sale prices will reflect their expectations, and therewill be no systematic real appreciation (Archer et al., 1996; Case and Shiller, 1989). In contrast,

the ad hoc and often secretive nature of public decision-making may lead to unforeseen policiesthat are not capitalized into initial housing prices. Unanticipated interventionse.g., the use ofeminent domain to acquire land for new development or a change in the zoning codemay resultin abrupt shocks in relative prices and significant variation in appreciation rates across an urbanarea.

TIF has been both applauded and castigated for causing the rapid appreciation of nearbyresidential properties. This is not an unreasonable accusation; the underlying source of financingfor TIF is the difference between the value of the property in its undeveloped state and the value ofthe same property after it has been redeveloped (i.e., the value differential or increment) (Weber(2003) provides more detail about the mechanics of TIF). In Illinois and most other states, TIFdistrict designation requires that a sufficient number of the properties in the area be blighted. Ifvacant land, deteriorated buildings, and abandoned structures are converted to productive uses,nearby properties will be favorably influenced (Ellen et al., 2001).

We might expect the infrastructure and financial incentives available from TIF to cause higherrates of appreciation of nearby housing if TIF draws jobs and new investment to targeted areas.Even if new business activity within the TIF comes at the expense of other areas within the samemunicipalityas Dye and Merriman (2000) suggestresidential properties near the TIF districtwill appreciate more rapidly than those that are more distant from TIF districts. TIF-fundedinvestment in infrastructure also can enhance the productivity of businesses already locatedwithin these enclaves, raising per capita output and income. Previous empirical studies havedemonstrated a positive relationship between infrastructure (primarily transportation-related),local business growth, and housing prices (see Bartik, 1991 for a summary; see also Mills andHamilton, 1994; Boarnet, 1998; Voith, 1993; McDonald and Osuji, 1995).

Eliminating blight and investing in infrastructure within TIF districts could improve thequality-of-life of local households. Households may benefit from increased opportunities forleisure, a heightened sense of security, and the aesthetic value of the improvements (Haughwout,2002). Moreover, new retail development in a TIF district may have a positive impact onresidential land values because households are willing to pay for convenience shopping.Residential TIF districts may be viewed as more attractive places to live, and houses in thesurrounding areas will face more competition. From a fiscal perspective, increasing municipalreliance on TIF may cause public spending in other, non-TIF portions of the municipality todecline, leaving them with fewer public goods and making them less attractive.

Finally, even if no new development actually occurs in the TIF district, the market maycapitalize the potential for future investment into the sale price of the properties within and nearTIF districts. In such cases, TIF designation acts as a signal, and land prices may be bid up inexpectation of future development.

Man and Rosentraub (1998) find evidence that, after an initial period of two years, TIF had apositive effect on the median value of owner-occupied housing of municipalities in Indiana. TIF,they argue, was responsible for increasing the median value of owner-occupied housing in theirsample by 11.4% between 1980 and 1990. Man (1999) also finds statistically significant positiverelationships between TIF and employment growth across 53 Indiana municipalities, growth thatshould have a positive impact on housing values.

If business and employment growth occurs within TIF boundaries, it is also possible that thevalue of nearby properties could be suppressed. By subsidizing new or existing commercial andindustrial parcels, TIF could precipitate negative externalities associated with incompatible land

261R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281uses. Traffic congestion, pollution, noise, and visual intrusions make living near commercialproperty less desirable than living near other residential properties (Crone, 1983). The positive

spillovers from TIF districts may be offset by countervailing negative externalities so that the neteffect of a TIF district on nearby property values is zero or negative. Since it is difficult to predictthe net effects of TIF, an empirical investigation is necessary to determine whether spillovereffects exist and whether they are positive or negative.

262 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 2592812.2. Residential appreciation in Chicago

The nominal median sales price of a single-family home in Chicago increased by 56% between1993 and 2002 (Chicago Area Realtors, 2004). Although TIF may have had some effect on thegeneral rate of appreciation, other factors were certainly at work and therefore should be taken intoaccount in any model measuring price effects. Fueled by an increase in population and growinghousehold income, the city enjoyed its largest residential construction boom, some say, since theaftermath of the Great Chicago Fire of 1871. The surge in new construction was most pronouncedin areas close to the central business district (CBD), but even in less central neighborhoods,thousands of units of infill housing were developed. Many older homes were demolished to makeway for multi-unit buildings whose high sales prices reflected the increasing desirability of cityliving and the growing value of urban land. Other older homes were rehabilitated, as householdswere helped along by the low interest rates that prevailed during this period.

The number of high-poverty neighborhoods where the poverty rate is 40% or higher-dramatically declined during this decade (Jargowsky, 2003). Homeownership rates among low-income households, particularly Latinos and African Americans, increased significantly duringthe 1990s (Immergluck and Smith, 2001). Several industrial and commercial corridors were eitherbuilt or improved in the city (Szatan, 2000). Some national chain stores opened their first urbanbranches in Chicago during the 1990s, developing both automobile-oriented strip centers and freestanding big boxes in the heart and the perimeter of the city. Industrial facilities weremodernized and recruited in portions of the city set-aside for these uses. Much of this newdevelopment did receive financial assistance through the City's TIF program.

Within Chicago, however, residential real estate appreciation was not uniform; different kindsof housing stock and distinct housing submarkets experienced different rates of appreciation.McMillen (2003a,b) finds that, between 1990 and 1996, annual appreciation rates were higher inneighborhoods close to the CBD with large minority populations, high concentrations of poverty,and many vacant lots in 1990. He posits that increased demand for housing near the city centerwas fueled by the growth in high-paying service sector employment in the CBD in the 1990s.

In Chicago, the lowest income neighborhoods experienced more than twice the rate ofappreciation of higher income neighborhoods between 1987 and 1998 (Case and Marynchencko,2001).2 These differential rates of appreciation may be due to the relative expansion of low-income demand often fueled by immigration (Case and Shiller, 1989), a glut of high-endhousing, or gentrification, i.e., demand for low-priced home and lower-income neighborhoods byin-movers of a higher socio-economic status. As we discuss in the following sections, we controlfor factors such as the median household income and initial price of the house in order to isolatethe effect of TIF on residential appreciation.

2 Similarly Li and Rosenblatt (1997) find that older, less expensive areas experienced greater appreciation than otherareas in California in the early 1990s. However, Quercia et al. (2000) find evidence that appreciation rates are just as highalthough not higher in poor, minority neighborhoods as in wealthier ones. Using American Housing Survey data,

Pollakowski et al. (1991) find that between 1974 and 1983 appreciation rates for lower-valued housing were about equalto those for higher-valued housing.

3. Theoretical framework

The standard economic model of urban spatial structure predicts that the economicdevelopment caused by a TIF will generate appreciation of homes near the TIF district(DiPasquale and Wheaton, 1996; McCann, 2001). We model the present value of a typicalstructure as:

PVN rD sD sp k hp Oi 1

where i is the interest rate at which future benefits are discounted, rD is the rent paid for land onthe neighborhood fringe, is the cost per unit of distance traveled, D is the distance from theneighborhood fringe to the neighborhood center, is the distance from the structure to theneighborhood center, k is the value of the services delivered by the structure, () is the value ofamenities at site , and O is an index of other variables (such as proximity to the CBD) thataffect the structure's value.

If the neighborhood center is designated as a TIF district, buyers and sellers may anticipatemore rapid growth in the value of amenities and may expect more demand for the neighborhoodso that D will grow. If demand will grow in the future, benefits from owning the land will growand potential buyers will discount those future benefits less heavily. We model the present valueof a structure when the neighborhood center is declared a TIF (PVTIF) as:

PVTIF rDi

sDi h

sp

i

k

i

hp

i h

Oi

PVN hii h

sD hp 2

where h is the increase in growth in attractiveness of the neighborhood center and amenities insurrounding areas as a result of the TIF.

The change in value as a result of a TIF is therefore a function of h (which can be thought of asthe level of activity because of TIF designation), travel costs (), activity at the neighborhoodcenter without TIF designation, the number of miles from the structure to the TIF () and theinterest rate (i).

In the empirical model, we treat the location of TIF districts as exogenous i.e. we assumethat the expected appreciation of the homes in our sample does not affect the probability that a TIFdistrict is established in that area. Anderson (1990) argued that municipal governments have anincentive to establish TIF districts in areas where they expect rapid appreciation so as to capturerevenue that otherwise would go to overlying taxing districts (e.g., schools). Thus, Andersonargued, a finding that property values grow more rapidly within a TIF district than in similar non-TIF areas does not necessarily prove that TIF caused property value growth. Empirical researchon this question has been mixed. While Anderson (1990) and Man and Rosentraub (1998) findevidence of endogenous TIF formation in Michigan and Indiana, neither Dye and Merriman(2000) nor Weber et al. (2003) find evidence of endogeneity in Chicago.

We assume that distance to the nearest TIF district is exogenous and believe this assumption isjustified for several reasons. The endogeneity hypothesized by Anderson and others concernsappreciation within the TIF district. Municipalities may have an incentive to declare a TIF district

263R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281in areas where appreciation is expected, but they have no particular reason to locate TIF districtsadjacent to rapidly (or slowly) appreciating properties. Since none of the properties in our sample

fall within the boundaries of TIF districts, it seems highly unlikely that their potential for futureappreciation influenced whether or not an adjacent TIF district was established. Furthermore,because we are using data on the appreciation of individual units rather than average values ofappreciation, our analysis is even less likely to be tainted by endogeneity. The potentialappreciation of each individual unit in our sample would have essentially no influence on whethera TIF district were established even in the unlikely event that the municipality considered theappreciation of adjacent areas when deciding whether to establish a TIF district.3 For thesereasons, we believe that our analyses can appropriately treat distance to TIF district as anexogenous variable that helps to determine the appreciation of individual housing units.

264 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 2592814. Empirical implementation

4.1. Description of data and model

Our theoretical discussion implies that the sale price of single-family homes should depend onthe housing services embodied in the structure, accessibility to amenities, and level ofneighborhood improvement expected over time. We hypothesize that, all else equal, residentialappreciation varies with proximity to a TIF district. We test this hypothesis by studying the rate ofappreciation in different samples of single-family homes in Chicago that sold more than onceduring the period 1993 to 1999.

Our basic empirical model is:

lnPj f lnPk; dTIF;TIF; SC; NC; k; j; ni 3

where Pj is the price of a house at the time of its last observed sale (j), Pk is its price at the firstobserved sale, dTIF is the distance of the ith structure to the nearest TIF district, TIF is a vector ofvariables describing the type of activities occurring within the TIF district, SC is a vector ofstructural characteristics of the home, NC is a vector of neighborhood characteristics, k and j arethe years in which the first and last sales of the house take place, and ni is the number of salesduring intervening years. We use several different regression specificiations to estimate therelationship between the dependent and independent variables. Before discussing our empiricalresults, we provide some background on the data.

The Illinois Department of Revenue provided transactions data for single-family home sales inthe city of Chicago between January 1993 and December 1999. Sales data were obtained fromaffidavits of real property sales and submitted to the county recorder when recording a deed orcontract for the transfer of real estate.4 We merged this dataset with data from the Office of the

3 Consider the following analogy. Textbooks explain that it is not possible to estimate the elasticity of demand (forcigarettes, for example) by regressing aggregate quantity sold on price since one cannot separately identify movements ofthe demand and supply curves. However, if the analyst has individual data on purchases of cigarettes and the price paidfor those cigarettes (which might vary due to differences in taxes), he or she can estimate a demand curve since eachindividual purchaser has what amounts to no influence over price.4 Although these datasets are organized according to each house's unique Property Identification Number (PIN) and

identify the address of the property owner, they do not provide the address of the actual building. If the PIN and propertyowner address were not located in the same quarter section (a square with an area of a quarter square mile), we assumedthat the building address was not the same as the owner's address. We dropped any observation in which the owner did

not live in the same quarter section as the property. This dataset has been used to great effect in previous research (see, forexample, McMillen, 2004).

Cook County Assessor and with Multiple Listing Service data compiled by professional realtorsto provide additional information about the characteristics of each house.

To generate the samples for our analysis, we first extracted all detached single-familyresidences that sold at least twice between 1993 and 1999 (5970 addresses). Although the repeatsales approach may be biased toward properties and neighborhoods likely to sell more frequently,we believe that transactions data provides an accurate picture of housing value change. We test forbias by expanding our sample, as we discuss later in this section.

Our dependent variable is the natural log of the price at last sale for all resales. We include the

265R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281natural log of the price at first sale as a control variable in all regressions. Because of this, weinterpret coefficients on other variables as the percentage change in appreciation resulting from aone-unit change in the independent variable associated with the coefficient.

There is a large literature that uses data on repeat sales to derive housing price indices (seeZabel, 1999; Meese and Wallace, 1997; McMillen, 2003a,b; Case and Shiller, 1989). Ourpurpose here is simply to control for average housing price appreciation while explaining thevariance across housing units. In order to do this, we must account for appreciation takingplace in the overall market so that we can isolate any extraordinary appreciation associatedwith nearby TIF districts. The most straightforward control for average housing price appre-ciation is to simply add a variable that measures the elapsed time between the first and last sale.If the rate of appreciation is constant over the period studied, this procedure is adequate.However, if the rate of appreciation changes over time, more sophisticated controls are re-quired. We improved the fit of our regression specifications (as measured by the adjusted R-squared) by including a series of dummy variables to control for both the date of first sale anddate of last sale (see Case and Shiller, 1989; Bailey et al., 1963). These dummy variables allowfor the possibility that the general rate of housing appreciation varied erratically over the timeperiod studied.5

We estimate a standard hedonic regression controlling for property features and neighborhoodamenities to predict appreciation (Rosen, 1974). The novelty in our analysis is that we includecontrols for initial price and for proximity to TIF district. As explained in the previous section, wewant to allow for the possibility that development within a TIF influences appreciation.

Since we are interested in whether there is greater appreciation in the vicinity of TIF districts,we include measures of proximity to TIF in all of our regression specifications. We employvariables that measure four dimensions of TIF district characteristics: the distance of eachproperty to the closest TIF district, the predominant land use in the TIF district, whether TIFdesignation occurred between the first and final sale, and the magnitude of new investment in theTIF district.

We obtained an electronic map of the TIF districts in Chicago from the City's Department ofPlanning and Development and used mapping software to plot the location of each observation

5 It is theoretically possible to control for within-year variation in general rates of appreciation, but this requires asubstantial increase in the number of independent variables. For example, to control for year of first sale, we need adummy variable for six of the seven years from 1993 to 1999 and another six dummy variables to control for year of finalsale. In order to control for month and year of first sale we would need a dummy variable for 83 of the 84 monthyearcombinations between January 1993 and December 1999. We would need 83 additional dummies for the monthyear offinal sale and would thus require a total of 186 dummy variables. Given our large sample sizes and the large number ofother independent variables, we believe that controls for year of initial and final sale are adequate. In most of our samples,

no unit sold twice in 1999, so we do not need to include a dummy variable for first sale in 1999 and thus include a total ofonly 11 dummy variables.

Table 1Appreciation by 0.5-mile rings measuring distance to closest TIF district for small sample

266 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281against the TIF boundaries. The state of Illinois classifies TIF districts according to the dominantexisting land use and the type of development expected to occur in each district.6 In 1999, 25 ofthe city's 79 TIF districts were designated industrial, 10 were designated commercial, and 40were mixed-use. In our study period, a very small number of residential re-sales fell within TIFboundaries, probably because TIF was being used primarily as a tool to stimulate commercial andindustrial development and because only four residential TIF districts existed at the time. Becausesome residential TIF districts were designated in order to demolish and redevelop deterioratedhigh-rise public housing, development there is not typical of that found in most TIF districts. We,therefore, excluded residential TIF districts from our analysis, eliminating the 28 observations forwhich the closest TIF district was residential. We measured the distance of each re-sale address tothe closest industrial, mixed-use, and commercial TIF districts. A negative and significantcoefficient on distance to closest TIF would indicate that location near a TIF district raises themarket value of housing compared to similar properties further away from TIF districts. Thehouses in our full sample were an average distance of three-quarters of a mile from the closest TIFdistrict with a standard deviation of about seven-tenths of a mile. They were 3.2 miles from theclosest commercial TIF district, 1.7 miles from the closest mixed-use TIF district, and 2.2 milesfrom the closest industrial TIF district.

To confirm the robustness of this measure of distance and better isolate any spillover effects,we remove the continuous variable, distance to closest TIF, and substitute dummy variables that

Distance toclosest TIF

Percent increase in sale price between first and final sale

N Mean Std. Dev Min Max

0.5 mi 370 31.58 57.24 12.24 668.29.51 mi 335 24.84 34.93 8.80 383.4911.5 mi 162 19.64 21.95 37.25 192.941.52 mi 46 15.95 12.36 10.78 48.9822.5 mi 26 19.77 14.86 1.23 58.822.53.0 mi 25 19.01 25.24 2.71 114.00N=3 mi 27 11.71 9.54 2.56 36.31measure whether the closest TIF district fell within discrete rings of 0.5 miles around eachobservation. Table 1 shows that the average rate of appreciation decreases as distance to closestTIF increases (for the small sample; samples will discussed later in this section). Houses withinone-half mile of the closest TIF district appreciated an average of 32%, which is more than the20% appreciation of houses where the closest TIF district was between 11.5 miles away. Ofcourse, we have not corrected this difference in appreciation for other characteristics of the houseand neighborhood. Hence we can not conclude that the difference was caused by proximity to aTIF district.

We considered three measures of the amount of development activity in each TIF district. Theage of the closest TIF district (in months) at the time of the unit's final sale measures both thepotential development in the TIF district and the duration of a house's exposure to TIF. Mixed-use

6 The six classifications are: Industrial, Central Business District, Commercial, Mixed-Use, Housing, and None/Multiple Classifications. There are no Chicago TIF districts classified as Central Business District or None/MultipleClassifications.

267R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281TIF districts were relatively old (the closest mixed-use TIF district was, on average, appro-ximately five years old) while commercial and industrial TIF districts had been established morerecently. Since we expect the aggregate amount of new investment to increase over time, thisvariable is a proxy for economic activity in the TIF district.

If, however, the benefits of TIF status are capitalized as soon as city officials signal that theywill favor development in the area in the future, land values should rise upon the shock of TIF

Fig. 1. Map of single-family home resales and TIF Districts in Chicago.

Table 2Descriptive statistics for three samples

Variable Large sample (5852 obs) Medium ple (2494 obs) Small sample (990 obs)

Mean Std.Dev.

Min Max Mean SD .

Min Max Mean Std.Dev.

Min Max

Natural log of price at last sale divided by natural log ofprice at first sale

0.29 0.35 2.77 3.40 0.24 0 1.00 2.04 0.20 0.20 0.47 2.04

Price at first sale (nominal $000s) 131.51 95.07 3.00 2250.00 149.65 1 32 50.00 2250.00 162.31 132.46 50.00 2250.00Distance to closest TIF (miles) 0.75 0.68 0.00 4.00 0.78 0 0.00 3.79 0.83 0.73 0.00 3.73Distance to closest commercial TIF (miles) 3.21 3.13 0.00 18.36 3.06 3 0.03 18.21 3.13 3.06 0.07 18.21Distance to closest mixed-use TIF (miles) 1.72 1.23 0.00 6.48 1.68 1 0.00 6.48 1.76 1.25 0.03 6.33Distance to closest industrial TIF (miles) 2.20 1.67 0.00 9.87 2.22 1 0.00 9.87 2.35 1.72 0.00 9.87Age of closest TIF at date of last sale (months) 48.55 37.73 1.00 157.00 49.92 3 9 1.00 157.00 49.31 37.51 1.00 156.00Age of closest mixed-use TIF at date of last sale (months) 65.13 45.05 1.00 157.00 63.21 4 1 1.00 157.00 63.82 45.62 1.00 156.00Age of closest commercial TIF at date of last sale (months) 28.94 20.63 1.00 121.00 26.85 1 5 1.00 107.00 26.43 17.99 1.00 107.00Age of closest industrial TIF at date of last sale (months) 33.02 22.85 1.00 101.00 33.76 2 7 1.00 100.00 32.95 22.45 1.00 84.00EAVof closest TIF (millions of $s) 20.38 24.32 0.47 188.04 20.54 2 3 0.47 188.04 20.51 24.69 0.47 188.04EAVof closest mixed-use TIF (millions of $s) 29.25 69.10 0.00 1458.07 28.54 5 3 2.78 1458.07 29.38 67.52 2.78 1458.07EAVof closest commercial TIF (millions of $s) 11.57 8.95 1.45 53.24 11.06 8 1.45 53.24 11.41 8.24 1.45 53.24EAVof closest industrial TIF (millions of $s) 27.07 42.98 0.68 188.04 28.51 4 8 0.68 188.04 27.04 43.78 0.68 188.04Distance to closest TIF (miles) First sale price (nominal $s) 98.07 112.72 0.00 1389.60 113.55 1 35 0.06 1389.60 130.52 139.09 0.06 1389.60Distance to closest mixed-use TIF (miles) First sale price(nominal $s)

207.03 182.92 0.12 2652.28 229.08 1 31 0.12 2652.28 260.58 222.35 4.50 2652.28

Distance to closest industrial TIF (miles) First sale price(nominal $s)

451.27 606.42 0.00 8812.80 499.18 6 51 2.03 8812.80 557.29 782.01 13.81 8812.80

Distance to closest commercial TIF (miles) First sale price(nominal $s)

292.84 294.57 0.06 2325.56 335.43 3 17 0.06 2325.56 376.75 340.94 0.06 2325.56

Structure age at last sale (years) 66.72 26.52 0.00 132.00 67.98 2 7 1.00 131.00 65.51 25.26 4.00 131.00Number of square feet of land in the 3916 1328 585 18,204 4049 1 945 15,124 4126 1303 960 15,124Equalized assessed value 1989 in quarter-section(millions of $s)

25.00 23.78 1.84 814.23 26.60 2 4 1.84 814.23 26.32 18.05 1.84 168.91

Percent change in equalized assessed value 1989 to 1999 17.47 16.71 0.78 319.41 18.41 1 3 0.78 319.41 18.44 15.25 0.78 97.53Industrial equalized assessed value 1989 (millions of $s) 1.58 2.92 0.00 31.01 1.62 2 0.00 31.01 1.55 2.75 0.00 23.44

268R.Weber

etal.

/Regional

Scienceand

Urban

Econom

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(2007)259281sam

td.ev

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10..69.00.21.657.65.77.62.84.43.8.096.021.93.

71.

13.

4.81883.5

5.9.90

Change in industrial equalized assessed value 1989 to 1999(millions of $s)

0.48 1.21 14.94 9.62 0.50 1.25 14.94 9.62 0.49 1.16 1.60 9.26

Commercial equalized assessed value 1989 (millions of $s) 4.19 9.87 0.00 476.41 4.60 10.44 0.00 476.41 4.31 4.77 0.00 59.13Change in commercial equalized assessed value 1989 to 1999(millions of $s)

2.79 5.16 3.47 203.49 2.99 5.42 3.47 203.49 2.83 3.90 3.47 31.06

Median household income 1989 (thousands of $s) 33.26 8.73 5.00 76.68 34.08 7.75 9.07 76.68 35.04 8.38 11.39 65.10Percent change in median household income 1989 to 1999 51.57 89.66 15.61 1458.74 47.57 28.91 1.81 332.00 47.03 28.31 3.30 315.24Percent residents that live in owner-occupied units 1989 62.66 22.73 0.94 97.26 63.73 22.65 0.94 97.26 65.16 23.21 2.50 97.26Percent change owner-occupied 1989 to 1999 0.52 5.98 34.97 65.96 0.42 4.22 16.82 36.94 0.54 4.25 14.68 36.94Percent black 1990 13.37 29.90 0.00 100.00 5.16 16.73 0.00 100.00 5.65 17.98 0.00 99.87Percent change black 1990 to 2000 3.24 10.39 47.10 52.78 3.64 10.56 29.93 52.78 3.89 11.04 23.98 52.78Percent Hispanic 1990 16.08 19.10 0.00 97.38 15.72 16.59 0.00 92.58 15.03 16.61 0.00 90.70Percent change Hispanic 1990 to 2000 11.61 16.78 39.54 57.32 13.34 17.37 39.54 57.32 11.33 16.35 39.54 57.32Distance to nearest CTA station (miles) 1.55 1.14 0.02 6.74 1.48 1.03 0.02 6.41 1.53 1.06 0.02 5.48Distance to nearest train line (miles) 0.82 0.51 0.02 2.93 0.83 0.50 0.02 2.93 0.80 0.49 0.02 2.93Distance to nearest highway on ramp (miles) 1.72 1.10 0.02 4.63 1.83 1.17 0.02 4.63 1.79 1.22 0.02 4.63Distance to central business district (miles) 8.48 2.79 1.03 16.75 8.40 2.42 1.36 16.43 8.56 2.53 2.31 14.56Distance to lake (miles) 5.40 2.59 0.08 0.83 5.53 2.57 0.08 10.83 5.54 2.63 0.20 10.79Elapsed months between first and last sale 38.36 19.04 1.00 82.00 40.14 17.87 3.00 82.00 37.73 17.71 3.00 81.00Number of sales between first and last sale 0.05 0.23 0.00 2.00 0.06 0.26 0.00 2.00 0.07 0.26 0.00 2.00Dummy=1 if closest TIF created between first and last sale 0.47 0.50 0.00 1.00 0.47 0.50 0.00 1.00 0.47 0.50 0.00 1.00Dummy=1 if closest commercial TIF created between firstand last sale

0.73 0.45 0.00 1.00 0.78 0.41 0.00 1.00 0.76 0.43 0.00 1.00

Dummy=1 if closest mixed TIF created between first and last sale 0.34 0.47 0.00 1.00 0.37 0.48 0.00 1.00 0.36 0.48 0.00 1.00Dummy=1 if closest industrial TIF created between first andlast sale

0.57 0.50 0.00 1.00 0.58 0.49 0.00 1.00 0.58 0.49 0.00 1.00

Number of rooms na na na na 6.70 1.65 0 17.00 6.69 1.50 3 15Number of bedrooms na na na na 3.15 0.92 0 9.00 3.14 0.86 1 8Number of bathrooms na na na na 1.86 0.82 0 10.00 1.94 0.81 1 5

Multiplied by.

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designation but should not increase rapidly thereafter. In some specifications, we delete age ofTIF at date of last sale and substitute a binary variable that measures whether the closest TIFdistrict was created between the first and last sale. In specifications not reported here, we also runthe models with a sample of houses that only includes those units where the closest TIF districtwas created between the initial and final sale.

Because changes in the physical features of the house (e.g., addition or elimination ofbedrooms) complicate the determinants of appreciation, we divide our universe of re-sales intothree samples. The first and most carefully controlled sample (small) includes only those homesthat experienced no change in their main physical characteristics between sales. In order todetermine whether any kind of modification had taken place, we matched the address and date ofeach repeat sale to housing profiles from the Multiple Listing Service (MLS) and eliminatedhouses with changes in the number of bathrooms, bedrooms, or total rooms. Additionally, theelimination of 15 houses with very low initial values (sales prices of under $50,000), eightobservations located within TIF districts, and one observation with missing data for a key variabledecreased our sample size to 990 observations.

The second sample (medium) includes all re-sales that we matched to the MLS. This sample,therefore, is comprised, not only of the homes that were unchanged between sales (i.e., firstsample), but also includes those that experienced a change in the number of bathrooms,bedrooms, or total rooms. The change in structure brought on by renovation activity could be animportant indicator of TIF spillovers, which is why we retain these observations in this sample(Helms, 2003). Omitting the few observations that were located within TIF districts or had verylow-values reduced this sample to 2494 re-sales.

At 5852 observations, the third sample (large) is the largest of the three and the leastcontrolled. It contains the entire universe of re-sales that occurred in Chicago during our studyperiod and so includes observations from the medium sample in addition to re-sales that could notbe matched to the MLS. Foregoing the matching requirement has the advantage of eliminatingpotential spatial and income bias; the MLS includes only those homes that were listed andultimately sold by licensed real estate brokers. However, without MLS data we cannot control forstructural characteristics of the unit. Low-income neighborhoods and low-valued homes, wheresales may take place through less formal channels, may be underrepresented in the first twosamples which is why, in this sample, we also retain the very low-value sales.

Fig. 1 is a map of Chicago TIF districts overlaid with observations in our samples. The maphighlights our smallest sample in dark triangles as a subset of the larger sample (in emptytriangles). Use of the three samples provides adequate geographic coverage of the city, although itis clear from the map that areas on the west and south sides experienced few repeat sales. Theseneighborhoods are either predominantly industrial or have a short supply of owner-occupiedsingle-family homes (because of a lack of demand, very low-incomes, or poor housing quality).

Table 2 gives basic descriptive statistics about variables across our three samples. All of thefirst sales of our samples took place after January 1993, and all of the last sales took place beforeDecember 1999. We expect that those houses that sold more than twice (between first and finalsales) would appreciate more quickly and therefore control for the total number of sales. Most ofthe units in our large sample (95%) sold only twice. However, a small share (4.8%) sold threetimes, and an even smaller share (.2%) sold more than three times. Regardless of how many timesa unit sold, we use data from the first sale (as an independent variable) and the last sale for thedependent variable. The average time between the first and last sale was 38 months.

270 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281Appreciation is the log of the ratio of the final and first sale prices of the unit. The meanappreciation in the large sample is 29% with a standard deviation of 35%. Properties in the

271R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281medium and small samples were more expensive at first sale, had longer elapsed time on themarket, were in neighborhoods with fewer African Americans, and appreciated less betweensales. However, in most other respects, they were similar to the entire sample.

All of the units in our smallest sub-sample have structural characteristics that are identical at thefirst and last observed sale because we deleted units with changes for this sample. Our analyses of thissample nevertheless includes controls for the number of rooms (average of 6.7), bedrooms (average of3.1), and bathrooms (average of 1.9) since it is possible that the rate of appreciation varies withstructure type.We also include the initial numbers of rooms for themedium sample inwhich structuralfeatures change between sales. For all the samples,we include the age of the building as of the last sale(average of 67 years for the large sample). Despite the boom in new construction in the 1990s, the bulkof the housing stock within the city limits was built in the first half of the twentieth century.

We also include an assortment of variables to control for economic, demographic, andtransportation conditions in the neighborhood of the units we study. We measured equalizedassessed value (EAV) in 1989 and its rate of change between 1989 and 1997 in the one-half mileby one-half mile quarter-section in which the unit is located.7 Since quarter-sections are of auniform size, EAV measures the capital intensity of development.

We linked each house to 1990 Census data to account for the level of relevant census tractcharacteristics. We included economic (median household income and percent owner-occupiedhousing units) and demographic variables (percent black and percent Hispanic) that may affectdemand for the neighborhood.

Geocoding the parcels allowed us to measure their distance from other geographic featureslikely to influence their value, such as the distance to the central business district (CBD) and LakeMichigan, and important transportation interchanges (nearest mass transit stop and highwayinterchange). We define miles to CBD as the distance from the parcel to the intersection of Stateand Madison Streets, the base point for Chicago's street numbering system. Traditional models ofurban structure predict that the residential bidrent function decreases monotonically withdistance from the CBD. In our full sample, the average house is located about 8.5 miles from theCBD. The miles to ramp variable measures the distance to the nearest highway interchange, andis inversely associated with commuting costs. However, convenient access to the highway alsomay be associated with negative spillovers, such as noise and pollution. We include the miles totrain and miles to CTA variables (which measure distances to the nearest commuter rail andChicago Transit Authority stations, respectively) to incorporate the advantages and disadvantagesof public transit access. We also measure the distance from each unit to Lake Michigan sinceaccess to the lake is an important amenity for many homebuyers.

4.2. Regression results

Table 3 presents regression estimates based on Eq. (3) using our smallest and most controlledsample (i.e., no change in structural characteristics between the first and final sale). We presentfour alternative specifications to allow for the possibility that different types of TIF districts havea different impact on residential appreciation. In specification (1) we do not differentiate by thetype of TIF district. We focus on commercial, mixed-use, and industrial TIF districts, respectively,in specifications (2), (3), and (4).

We first discuss results generated by our small sample (Table 3) and then note when resultsfrom the other two samples do or do not conform. Our qualitative findings are robust across the7 Property value data by quarter-section were provided by the Office of the Cook County Clerk.

Table 3Full regression results for small sample

Dependent variable: Natural log of price at final sale

Any TIF Commercial TIF Mixed-use Industrial TIF

(1) (2) (3) (4)

Number of sales between first and last sale 0.10218 0.09654 0.09404 0.09413(5.18) (4.95) (4.76) (4.85)

Log of price at first sale 0.63271 0.73393 0.60952 0.62907(26.17) (28.63) (22.02) (25.24)

Distance to closest TIF(miles) Log of first sale price 0.03920 0.02314 0.03092 0.00192(2.04) (4.92) (2.78) (0.25)

Distance to closest TIF (miles) 0.20791 0.12681 0.19385 0.06271(2.16) (5.58) (3.42) (1.58)

Square of distance to closest TIF (miles) 0.01037 0.00039 0.00813 0.00373(1.61) (1.02) (2.92) (2.52)

Equalized assessed value 1989 in quarter-section(millions of $s)

0.00167 0.00115 0.00152 0.00177(3.05) (2.14) (2.79) (3.31)

Change in equalized assessed value 1989 to 1999 0.00662 0.00606 0.00708 0.00725(7.31) (6.64) (7.67) (8.27)

Industrial equalized assessed value 1989 (millions of $s) 0.00499 0.00731 0.00564 0.00691(1.73) (2.58) (1.99) (2.47)

Change in industrial equalized assessed value1989 to 1999 (millions of $s)

0.00929 0.01710 0.01065 0.01195(1.40) (2.61) (1.61) (1.85)

Commercial equalized assessed value 1989 (millions of $s) 0.00095 0.00031 0.00067 0.00056(0.56) (0.18) (0.40) (0.34)

Change in commercial equalized assessed value1989 to 1999 (millions of $s)

0.00718 0.00657 0.00733 0.00640(3.18) (2.94) (3.24) (2.88)

Number of rooms 0.00078 0.00048 0.00078 0.00099(0.13) (0.08) (0.13) (0.17)

Number of bedrooms 0.00208 0.00107 0.00114 0.00585(0.22) (0.12) (0.12) (0.63)

Number of bathrooms 0.07195 0.07231 0.07093 0.06956(7.71) (7.86) (7.62) (7.61)

Structure age at last sale (years) 0.00079 0.00078 0.00092 0.00100(3.28) (3.29) (3.79) (4.18)

Number of square feet of land in the parcel 0.00002 0.00002 0.00002 0.00002(3.52) (3.64) (3.58) (4.02)

Median household income 1989 (thousands of) 0.00606 0.00624 0.00624 0.00541(5.41) (5.70) (5.60) (4.91)

Percent residents that live in owner-occupied units 1989 0.00382 0.00376 0.00371 0.00284(7.12) (7.15) (6.94) (5.21)

Percent black 1990 0.00176 0.00156 0.00179 0.00148(5.22) (4.64) (5.40) (4.42)

Percent Hispanic 1990 0.00085 0.00078 0.00049 0.00044(1.59) (1.52) (0.94) (0.84)

Distance to nearest CTA station (miles) 0.02240 0.02098 0.02932 0.01229(2.58) (2.51) (3.49) (1.44)

Distance to nearest train line (miles) 0.01340 0.02857 0.01440 0.02598(0.97) (2.18) (1.11) (2.04)

Distance to nearest highway on ramp (miles) 0.01130 0.00861 0.01248 0.00015(2.05) (1.68) (2.41) (0.03)

Distance to central business district (miles) 0.00666 0.00471 0.01237 0.01176(1.28) (0.95) (2.58) (1.99)

Distance to Lake Michigan (miles) 0.01850 0.01396 0.01780 0.01260

272 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281

Table 3 (continued )

Dependent variable: Natural log of price at final sale


(1) (2) (3) (4)

(5.45) (4.23) (5.11) (3.69)First sale occurred 1994 0.00390 0.00435 0.00714 0.00643


273R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281different samples. All of the specifications using the smallest sample explain about 92% of thevariation in the log of final sale prices although the adjusted R-squares decrease slightly with thelarger samples.

The positive coefficient on number of sales implies each additional sale of a unit increasesappreciation by about ten percentage points. We control for area-wide residential appreciationby including a series of dummy variables for the date of first sale and date of last sale using themethodology of Bailey et al. (1963). To avoid perfect multicollinearity, we do not include adummy variable indicating 1993 as the year of first or last sale. With minor exceptions, thepattern of coefficients on the included dummy variables confirm the intuitive result that,conditional on the characteristics of the unit and the date of last sale, the more recent the first




(1.69) (1.73) (1.68) (1.75)Last sale occurred 1994 0.14819 0.14662 0.16862 0.14852






(0.45) (0.56) (0.30) (0.88)Age of closest TIF at date of last sale (months) 0.00013 0.00060 0.00003 0.00090

(0.78) (1.58) (0.26) (3.08)Constant 1.97642 1.44225 2.09430 1.91061

(9.57) (7.07) (9.77) (9.45)

Number of Cases 990 990 990 990Adjusted R-square 0.917 0.919 0.918 0.920F statistic for the hypothesis that coefficienton distance to closest TIF=distance toclosest TIF squared=Distance to closest

3.58 11.72 5.54 13.92

P-value for F statistic above 0.0135 0.0000 0.0009 0.0000

Value of t statistics in parentheses.Significant at 10%; significant at 5%; significant at 1%.See text for data sources and methods.

sale, the less the unit appreciated. Similarly, the more recent the date of the last sale, the morethe unit appreciated.8

In each specification, many of the structural and neighborhood conditions for which we control

274 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281are statistically significant. We find that houses appreciate more if they are newer, larger, and havemore bathrooms. We also find that houses in neighborhoods with initially high householdincomes and smaller shares of African American residents appreciated more rapidly. Moreover,proximity to Lake Michigan was associated with increased appreciation. Such findings are nottypically associated with gentrifying areas, where distressed properties are purchased andrenovated by in-movers of a higher socio-economic status. On the other hand, the coefficients onsome of the variables provide evidence of such neighborhood transitions. Houses located in areaswith initially low levels of owner-occupancy appreciated rapidly. In some specifications,appreciation also increased with proximity to public transit (CTA) stations. This could indicatethat home owners moved into transit-accessible neighborhoods that were previously inhabited byrenters.

Not surprisingly, units located in neighborhoods that had high levels of growth in propertyvalues (total EAV) after 1989 and initially low levels of EAV appreciated more rapidly.Appreciation also increased with the level of industrial and commercial EAV in the quarter-section in 1989 and fell as the rate of growth of industrial and commercial EAV increased. Theseresults likely reflect the fact that neighborhoods with high industrial and commercial EAV in 1989had the greatest growth potential as land uses were converted to new residential purposes. Theinclusion of these variables controls for the actual land use mix near each observation so that ourTIF-related variables will not spuriously reflect the value of similar or dissimilar land uses.

Each of our specifications includes a variable that measures the log of the price at the first sale.If we had no other independent variables in our regression, we might expect that a 1% increase ininitial sale price would cause a 1% increase in last sale price-resulting in a coefficient of one onthe log of the price at first sale. However, since we include numerous variables that control for thecharacteristics of neighborhoods and structures, we expect a coefficient of much less than one onthis variable. A unit that initially sold for an unexpectedly high price, given structural andneighborhood conditions, should be expected to appreciate less than a similar unit that initiallysold for a lower price. Our estimated coefficients indicate that, at the TIF border (i.e., wheredistance to TIF is zero), each 1% increase in initial sale price causes a one-half to seven-tenths of1% increase in the final sale price, holding all other variables constant. The estimated coefficientsare generally significantly less than one, as we would expect.

Our key hypothesis is that proximity to a TIF district affects the rate of appreciation. All of ourspecifications include a number of variables to gauge the influence of TIF districts onappreciation. These variables include the age of the TIF (in months) at the time of the last sale, thedistance to the closest TIF district, the square of that distance, and distance to the TIF districtmultiplied by the log of the first sale price of the unit.

We include a variable that measures the age of the TIF at the time of the last sale to proxy forthe level of economic activity in the TIF district. This variable is statistically significant in thespecification that measures distance to the closest industrial TIF district, which leads us toconclude that the effect of TIF on appreciation depends on the age and predominant land use of

8 In our small sample only one unit had a last sale date of 1993. This was undoubtedly an unusual unit that had a high

rate of appreciation. As a result, all of the coefficients on the dummy variables for last sale year have a somewhatsurprising negative sign. However, the pattern of coefficients shows that appreciation increases with year of last sale.

275R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281the district.9 In the case of industrial (and commercial TIF districts using the medium and largesamples) TIF districts, proximity to older TIF districts resulted in slower appreciation. In the largesample, proximity to older mixed-use TIF districts resulted in greater appreciation. In preliminaryspecifications not reported here, we substituted total equalized assessed value (EAV) of propertieswithin the closest TIF district for the age of the TIF district.10 In these specifications, we obtainedresults similar to those reported here for the TIF age variable.

As mentioned above, we exploit the timing of the TIF designation in other ways. For example,we ran another set of specifications substituting a dummy variable that measured whether theclosest TIF district was designated between the first and final sales for the age of TIF districtvariable. This specification tested the idea that the shock of an unexpected policy interventionsuch as TIF increases appreciation rates. This dummy variable was not statistically significant

Fig. 2. Last price of mean-priced house as a function of distance to TIF-continuous distance (small sample).when we used the smallest sample. In the larger samples, this variable had a positive effect forindustrial TIF districts and a negative effect for mixed-use TIF districts due, in part, to itsnegative correlation with TIF age. We also reran the models using only those repeat sales wherethe closest TIF district was designated between the first and final sales. Such a move did notsubstantially alter the magnitude or significance of any of the coefficients.

The coefficients on variables measuring distance to the closest TIF district and distance to the TIFdistrict multiplied by the log of first sale price of the house are statistically significant in specification(1), and these variables are jointly statistically significant as shown in the last row of Table 3 (for adiscussion of joint significance tests, see Goldberger, 1991). We conclude that proximity to any TIFdistrict does have a statistically significant influence on the rate of appreciation in this sample.

9 We used EAVof the closest TIF in 1999 as an alternative measure of TIF size although we lack the data necessary todetermine what share of the EAV growth took place subsequent to its initial designation. We interpret the coefficient onthis variable as a measure of development within the TIF (at the time of last sale) on the rate of housing priceappreciation. The qualitative conclusions reported in the text are essentially unchanged by substituting this alternativemeasure of TIF size.10 Industrial and mixed-use TIF districts tend to be relatively large with an average EAV of almost $3 million whilecommercial TIF districts have an average EAVof about $1.2 million. Mixed-use TIF districts in the CBD account for over30% of the property value located within TIF districts in Chicago although they comprise less than four-fifths of 1% ofthe total land area with TIF districts (Neighborhood Capital Budget Group, 2002).

Table 4Selected regression results for medium sample

Dependent variable: Natural log of price at last sale


(1) (2) (3) (4)

Log of price at first sale (nominal $000s) 0.68244 0.78828 0.67821 0.67984(38.14) (42.26) (33.86) (35.60)

Distance to closest TIF(miles) Log offirst sale price(nominal $)

0.03565 0.02601 0.01473 0.00648(2.35) (7.78) (1.72) (1.04)


Square of distance to closestTIF (miles)

0.00770 0.00038 0.00848 0.00317(1.55) (1.29) (4.11) (2.68)

Age of closest TIF at date of last sale(months)

0.00011 0.00089 0.00005 0.00112(0.95) (3.16) (0.60) (5.56)

Constant 1.69765 1.16823 1.72851 1.65279(8.38) (5.87) (8.40) (8.25)

Number of cases 2494 2494 2494 2494Adjusted R-square 0.883 0.887 0.884 0.887F statistic for the hypothesis that coefficient on 3.57 26.50 9.94 25.19

276 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281The distance variables in the commercial and mixed-use TIF specifications (2) and (3) arealso statistically significant in both regressions. The distance to the closest industrial TIFdistrict variable is not significant, although its square is. The distance variables are jointlystatistically significant in all four regressions. Because our specifications are nonlinear indistance to the TIF district, the results are difficult to interpret from the regression coefficientsalone. In particular, the appreciation of a particular house will depend upon its initial price aswell as its proximity to TIF. We wish to study the change in appreciation resulting fromchanges in distance to TIF of a typical house. To do this we find the average first price and theaverage last price of houses in the 40th to 60th percentile of the initial price distribution. Theseprices are $132,000 and $157,000 respectively. Fig. 2 plots the estimated relationship betweenthe continuous distance to TIF district variable and the predicted last price for a house that hadcharacteristics such that it would have appreciated from $132,000 to $157,000 had it beenlocated at the border of a TIF. Fig. 2 then illustrates how the last price would have changed hadwe taken such a house and moved it (and all of its neighborhood characteristics) further andfurther away from the TIF border.

Fig. 2 shows that proximity to commercial and industrial TIF districts reduces predictedappreciation for our typical house in the small sample. Final sales prices increase with distancefrom these two kinds of TIF districts. Our typical house would have a final price of $157,000 (fora capital gain of $25,000 or about 18%) if it were located at the border of an industrial TIF district.We predict that a similar house would increase to $162,000 (for a capital gain of $30,000 about

distance to closest TIF=distance to closestTIF squared= distance to closest TIFlog offirst sale price


Value of t statistics in parentheses.Significant at 10%; significant at 5%; significant at 1%.All regressions also include all of the control variables shown in Table 3.See text for data sources and methods.

Table 5Selected regression results for large sample

Dependent variable: Natural log of price at last sale

Any TIF CommercialTIF

Mixed-use Industrial TIF

(1) (2) (3) (4)

Log of price at first sale (nominal $000s) 0.49118 0.53767 0.54008 0.48285(45.51) (47.18) (44.07) (43.66)

Distance to closest TIF (miles) Log first sale price 0.04635 0.00789 0.01486 0.00482(4.17) (3.37) (2.75) (1.34)


Square of distance to closest TIF (miles) 0.00688 0.00072 0.00810 0.00427(1.42) (2.64) (4.27) (3.88)

Age of closest TIF at date of last sale (months) 0.00040 0.00099 0.00019 0.00130(3.90) (4.57) (2.24) (7.20)

277R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281Constant 2.45879 2.23076 2.23351 2.42856(20.21) (18.07) (18.12) (20.13)

Number of cases 5852 5852 5852 5852Adjusted R-square 0.794 0.795 0.794 0.80120%) if it were located a half-mile away. The effect of a nearby commercial TIF district is not asdeleterious, reflected in its less steep slope in Fig. 2. In both cases, however, the effect of TIFproximity is small compared to total appreciation.

F statistic for the hypothesis that coefficient on distance toclosest TIF=distance to closest TIF squared=distance toclosest TIFlog of first sale price

9.79 20.42 14.67 70.91


Value of t statistics in parentheses.Significant at 10%; significant at 5%; significant at 1%.All regressions also include all of the control variables shown in Table 3.See text for data sources and methods.

Fig. 3. Last price of mean-priced house as a function of distance to TIF-distance bands (small sample).

For a mixed-use TIF district, in contrast, we do find evidence that proximity to the districtstimulates appreciation.We again consider a $132,000-priced home that would appreciate to $157,000if it were located at the TIF border. We compare this house with a similar house located a half-mileaway from the border of the mixed-use TIF. We find that moving the house in this manner causes itspredicted final price to fall to $151,000, resulting in an appreciation rate of only about 13%. The closera typical house is to a mixed-use TIF district, the more we predict that it will appreciate.

We check the robustness of these results in two ways. First, we re-estimated our regressionsusing the two larger samples discussed above. Recall that in the medium sample, we increase oursample size to 2494 by including homes that had changes in structural conditions between sales.The size of the large sample increases to 5852 when we include all homes that sold more thanonce, even those that we could not match to the MLS data for information about the homes'structural characteristics. In particular, our findings with respect to the large sample are important,since, as noted earlier, the MLS data may underrepresent poor and minority Chicagoneighborhoods. We report regression results for our key hypothesis variables in Tables 4 and 5.

In both the case of the medium and large samples, the results are consistent with what we foundusing the smallest and most controlled sample. In other words, proximity to commercial or mixed-use TIF districts significantly affects appreciation rates in both of the larger samples. This result alsoholds when we do not differentiate by type of district but instead include a variable that measuresproximity to any TIF district. Plotting the results for both samples yields graphs resembling Fig. 2. Inthe larger two samples, we also find stronger evidence that proximity to industrial TIF districtsreduces appreciation. While this result holds true for proximity to commercial TIF districts, themagnitude of the effect for these districts is much smaller and the slope is nearly flat. In contrast,appreciation increases with proximity to mixed-use TIF districts in the medium and large samples.

Our second method for checking robustness is to estimate an alternative specification in whichwe substitute binary variables (whether the closest TIF district was within discrete 0.5-mile ringsaround each observation) for our continuous distance to closest TIF district variables. The 0.5-mile size of our rings is relatively arbitrary but should be both large and small enough to pick uplocal spillover effects for TIF districts that ranged in size from 3 to 1200 acres during our studyperiod. We do not include a dummy variable for the closest ring whether the closest TIF districtwas within 0.5 miles from the observation to avoid perfect multicollinearity. We include dummyvariables for rings at .5, 1, 1.5, 2, 2.5, and over 3 miles in the regression specification. We interpretthe coefficients on the more distant rings as the relative price differential of a house located withinthat ring compared to the ring most proximate to the TIF district. We also include variables thatinteract the dummy ring variables with the log of the price at first sale.

Again, the coefficients are difficult to interpret since the specification is nonlinear so we plotthem for a typical house in Fig. 3. We confirm our previous findings with regard to the effect ofcommercial and industrial TIF districts. If the closest commercial or industrial TIF district islocated in any of the more distant rings (i.e., 0.51, 11.5, and 1.52 miles from the house),appreciation is more than it would be if the closest TIF district were located within 00.5 miles.The final sales price increases with distance from these kinds of TIF districts.

Using our small sample and this alternative specification, the mixed-use TIF regressions providesomewhat weaker support for our previous conclusions. Although we find that the average-pricedhouse appreciates less in the ring 1.52miles from the closest mixed-use TIF district than it does in thering immediately adjacent to the TIF district, Fig. 3 shows that in the closer two rings, the results arereversed: appreciation grows with distance. These contradictory results do not significantly detract

278 R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281from the strength of our original findings. First, coefficients on the distance ring variables are notsignificant until the 1.52mile ring. Second, running themodels with the two larger samples generates

more consistent and intuitive results: proximity to a mixed-use TIF district inflates final sale prices.

279R. Weber et al. / Regional Science and Urban Economics 37 (2007) 259281There is also some evidence that proximity to any kind of TIF district increases appreciation.

5. Concluding analysis

Our central research question is whether TIF has had spillover effects on nearby residentialproperties. As with urban renewal in the 1960s and 1970s, the municipal use of TIF is mired incontroversy because of its perceived impact on existing residents and neighborhood character.Indeed, the visual signs of new development in and around Chicago's TIF districts are hard tomiss: expensive condominiums, large-format retail outlets, and the ubiquitous trendy coffee shopsin what were previously modest neighborhoods.

Regardless of whether TIF is responsible for the development of amenities that provide realeconomic benefits or whether this public policy merely signals the possibility of new devel-opment, we would expect the effect of this economic development policy to be capitalized into thevalue of nearby properties. Our empirical findings imply that TIF does have a significantinfluence on the appreciation of neighboring houses. However, the nature and magnitude of thateffect challenges the arguments of both TIF advocates and opponents.

We found that the predominant land use within TIF districts is critical to its effect on residentialproperties in the vicinity. In general, any kind of TIF district has a mild appreciative effect onnearby houses. But, when we distinguished between different kinds of TIF districts, results for theclosest commercial, industrial and mixed-use TIF district were varied in interesting ways. In allthree samples, houses near commercial and industrial TIF districts appreciated less than thosefurther away. These negative spillovers might be explained by the fact that commercial TIFdistricts in Chicago have been favored locations for big box retailers (Neighborhood CapitalBudget Group, 2005). Traffic congestion in and around these areas and their lack of pedestrianaccess may generate negative externalities that are capitalized in housing prices. Industrial TIFdistricts may attract development that is noisy, polluting, aesthetically unappealing, and, likecommercial development, conflicts with residential land uses. Older TIF districts were presum-ably home to more such development activity, which may explain why the age of the closestindustrial and commercial TIF districts had a negative effect on appreciation.

In contrast, houses located near mixed-use districts appreciated more than those further away.Houses near these TIF districts may benefit from the construction of new high-end residentialunits with ground-floor convenience retail a form of development that has become prevalent inthis kind of Chicago TIF district.11 In order to ensure that our regression results reflect thespillover effects of this policy tool and not simply the compatibility of land uses (i.e., residentialdevelopment will naturally have a positive effect on residential property values), we controlled forthe level and rate of change in property devoted to commercial and industrial land uses in theneighborhoods surrounding each observation.

The relatively rapid appreciation of existing single-family houses in the vicinity ofmixed-use TIFdistricts may be justification for policies that protect tenants from higher rents and low-income homeowners unable to bear the additional tax burden. Mechanisms to discourage residential displacementinclude property tax deferrals or reverse mortgages for long-term owners and low-interestmortgagesfor eligible renters. Reforms to Illinois' TIF enabling legislation in 1999 require municipalities toconduct a housing impact study to identify the effect of new TIF districts on existing housing units

11 See, for example, developments within the Howard-Paulina, Chinatown Basin, Lawrence/Broadway, and Lincoln/Belmont/Ashland TIF districts.

and to submit a plan for relocating residents directly and negatively affected by new development.Such policiesmaymollify potential community opposition and are justified on the basis that, to someextent, it is government action that caused the rapid appreciation in the first place.

Even though our study suggests that mixed-use TIF districts promote the appreciation ofnearby housing in more active markets and TIF, in general, is an easy target for opponents ofgentrification, we do not find compelling evidence that TIF has been the leading cause ofneighborhood transition in Chicago. First, most of the other independent variables that we foundwere associated with housing appreciation are not indicative of the kinds of neighborhoodtransition usually associated with gentrification. These factors include a large number ofbathrooms, relatively new construction, proximity to Lake Michigan, and an initially high level ofneighborhood household income. However, we did find that appreciation was greater in areaswith an initially larger share of renters that were located close to public transit stations. This couldindicate that buyers paid a premium for urban neighborhoods accessible by public transit andwhere rental units could be converted to owner-occupied condominiums.

Second, the magnitude of the positive spillover effects is relatively small. We expect that thedegree of housing appreciation necessary to displace large numbers of lower-income householdswould need to be much greater. Even then, the degree to which rapid appreciation of real estatevalues causes displacement depends on the elasticity of housing supply, rates of home ownershipamong the existing residents, moving costs, and the degree to which residents can substitute othergoods for higher housing costs (Vigdor, 2002).

Third,whenTIF districts are designated to encourage industrial, and to a lesser extent commercial,uses, they suppress the appreciation rates of nearby homes.

These findings suggest that concerns about TIF causing gentrification ultimately depend on thenature of development expected and occurring within each individual district.

Acknowledgment

The authors thank Dan McMillen and the Center for Urban Research and Learning at LoyolaUniversity Chicago for providing critical data for this study. Amy Ellen Schwartz, Ingrid GouldEllen, Ioan Voicu, and two anonymous reviewers offered excellent advice. Diane McCarthyprovided invaluable research assistance, and Sarah Newby provided assistance with formatting.Weber gratefully acknowledges financial support from the U.S. Department of Housing andUrban Development Urban Scholars Fellowship.

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Spillovers from tax increment financing districts: Implications for housing price appreciationIntroductionBackground and review of literatureTIF and residential property appreciationResidential appreciation in Chicago

Theoretical frameworkEmpirical implementationDescription of data and modelRegression results

Concluding analysisAcknowledgmentReferences

spillovers from tax increment financing districts: implications for housing price appreciation

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