1 Introduction
Some neighborhoods in the USA were hit hard, but unevenly, by foreclosures in the aftermath of the 2008 housing market crash.
1 The prevailing view has been that the uneven distribution of foreclosures is largely responsible for the uneven price effects observed across neighborhoods, as greater concentrations of foreclosures increase marginal price effects of additional foreclosures. But this perspective ignores the possibility that foreclosure price effects on surrounding properties may depend not only on the localized concentration of foreclosures, but also on the physical and structural characteristics of neighborhoods. The nature of the built environment may matter; the age and uniformity of the neighborhood and the physical proximity to neglected or vacant foreclosed properties are determined by structural density and street configuration. The questions addressed here are straightforward: Does the surrounding built environment influence foreclosure price effects? And, if so, to what extent?
Most reviews of empirical studies on foreclosure effects suggest that neighboring property prices tend to be anywhere between one to two percent lower in neighborhoods with foreclosures.
2 The literature recognizes two mechanisms driving neighboring property prices lower. One is the negative foreclosure externality arising from poorly maintained or vacant foreclosed property.
3 The second is the supply effect arising from the fact that foreclosures increase the supply of housing for sale (Anenberg and Kung
2014; Hartley
2014; Mian et al.
2015; Gerardi et al.
2015).
4 Both mechanisms lead to lower neighborhood property values, the source of distress to residents and local governments.
The prevailing focus on foreclosure externality and supply effects on prices of surrounding houses overlooks a possibly mediating influence within the neighborhood. For instance, supply of new construction provides positive physical externalities to the neighborhood (Ioannides
2002; Rosenthal
2008; Helms
2012; Zahirovic-Herbert and Gibler
2014; Coulson et al.
2019; Gonzalez-Pampillon
2022). Buyers may interpret new construction as a signal of improved neighborhood quality, or the arrival of more affluent residents (Gonzalez-Pampillon
2022). In any case, new construction, whether rebuilding teardowns or new infill development, occurs unevenly in the interior of the urban area.
5 We empirically control for differences in the supply of new construction as a source of systematic variation in observed foreclosure price effects across neighborhoods.
It is worth emphasizing at this point that the foreclosure externality argument itself also suggests possible variation in foreclosure effects across neighborhoods. The negative externality arises because foreclosures induce vacancy and underinvestment in maintenance, creating negative physical externalities that reduce neighborhood attractiveness (Harding et al.
2009,
2012; Daneshvary and Clauretie
2012) as well as external social costs in the form of reduced social interaction and community involvement (Harding et al.
2009) or increased crime (Immergluck and Smith
2006). Foreclosures may be the source of negative shopping externalities as well; potential buyers may interpret the presence of foreclosures as a signal of a greater risk of neighborhood instability or secular decline, which prompts them to focus their search efforts elsewhere. All of these factors reduce property values of surrounding non-distressed property. Not all neighborhoods, however, are equally vulnerable to these risks (Anenberg and Kung
2014; Ihlanfeldt and Mayock
2016). This study focuses on how foreclosure price effects vary by neighborhood configuration. To do so, we use geocoded administrative data to allow more explicit models of how foreclosure effects vary across neighborhoods.
This paper makes two contributions to the empirical housing market literature. First, we propose a simple framework for sorting neighborhood property price effects into foreclosure externality, new construction externality, and supply or pecuniary externality effects. Most previous work struggles with how to empirically separate these externality and supply effects and so tends to avoid distinguishing them explicitly. Studies that do distinguish externality and supply effects use supply measures that count numbers of foreclosures at greater distance (Harding et al.
2009), nearby foreclosures of different property types (Hartley
2014; Fisher et al.
2015), or precise timing of distressed and non-distressed properties listings (Anenberg and Kung
2014). It seems appropriate to identify supply effects using measures of competing or substitute properties that are on the market at the same time as the subject property. To that end, we incorporate measures of both foreclosed and similar open market (non-foreclosed) properties for sale in the surrounding neighborhood at the same time as the subject property.
Second, we examine whether foreclosure price effects vary with neighborhood configuration. Foreclosure effects may vary across neighborhoods due to differences in underlying economic vitality (Rosenthal
2008), housing market segmentation (Gerardi et al.
2015; Cheung et al.
2014; Zhang and Leonard
2014) as well as the neighborhood structure (Schuetz et al.
2008). Ellen and O'Regan (
2010) argue that neighborhood configuration matters for market outcomes in general. Applying that notion to this case, a foreclosure may be more visible or physically closer to more surrounding properties in some neighborhoods than in other neighborhoods, and as a result, generate stronger price responses. To take these neighborhood differences into account, we also investigate foreclosure effects across built environments in terms of their urban density, neighborhood mix of homes, development period, vacancy rate, and whether the subdivision is gated or non-gated.
The geographic concentration of foreclosures in certain neighborhoods suggests that, for some neighborhood configurations, the foreclosure effect might be nonlinear in the number of foreclosures (e.g., in the case of rather uniform neighborhoods with many similar properties). If so, even a few neighboring foreclosures relative to non-distressed sales will have significant value effects on surrounding non-distressed transactions. Hanson et al. (
2012) show that households tend to spatially sort by credit quality, creating conditions ripe for geographic concentration of foreclosures. The question remains whether the resultant concentrations of foreclosures in certain neighborhoods lead to increasing marginal price effects, exhibiting deeper and more enduring neighborhood price effects than would otherwise be expected if foreclosures were instead more evenly distributed across the market area. Schuetz et al. (
2008) and Harding et al. (
2009) both find no evidence of nonlinear foreclosure effects. In contrast with the samples used in those studies, our sample covers a period of intense foreclosure activity in one of the most active foreclosure markets in the US, which allows us to probe more deeply into how an unprecedented level of foreclosures affects property prices.
The remainder of the paper is organized as follows. Section
2 explains the empirical framework to sort out the channels through which foreclosures may influence surrounding property values. Section
3 describes the data. Section
4 reports the empirical results. We first report on foreclosure spillover effects and then consider whether these spillover effects vary with neighborhood configuration. Section
5 concludes.
2 Empirical framework
The empirical framework maps local foreclosures, open market sales and new construction onto neighboring house prices. In estimating these local effects we face the following challenge. Neighborhood effects such as foreclosure externalities (Campbell et al.
2011; Towe and Lawley
2013), crime risk (Linden and Rockoff
2008) or externalities associated with new construction (Ioannides
2002; Helms
2012) may be associated with endogenous social effects (Manski
1993; Rossi-Hansberg et al.
2010; Ross
2011; Ioannides
2011). These social effects arise when a household’s foreclosure or new construction decision spillover to neighbors. Ideally, one would like to control for these effects using instrumental variables that are correlated with the local measure but not with price.
6 We take the alternative approach used by Linden and Rockoff (
2008) and Campbell et al. (
2011), which relies on the impact of extremely local neighborhood measures and very small time windows, and compares the relevant coefficients pertaining to neighborhood measures before and after each property sale to control for the state of the local economy in the micro-neighborhood.
We estimate separate localized externality effects of foreclosures, supply or pecuniary externality effects, and new housing construction
7 on other non-distressed property sales. We control for neighborhood market externalities by including number of foreclosures, number of open market sales and, number of newly constructed houses that are on the market at the same time as the subject property.
The model we estimate specifies the log of price of house transaction
i in neighborhood
r and year
t as a linear function of property characteristics and neighborhood market conditions:
$$\begin{aligned} {\text{ln}}P_{irt} =\; & \beta_{X} X_{it} + \beta_{{{\text{FS}}}}^{{{\text{before}}}} {\text{FS}}_{it}^{{{\text{before}}}} + \beta_{{{\text{FS}}}}^{{{\text{after}}}} {\text{FS}}_{it}^{{{\text{after}}}} + \beta_{{{\text{MS}}}}^{{{\text{before}}}} {\text{MS}}_{it}^{{{\text{before}}}} + \beta_{{{\text{MS}}}}^{{{\text{after}}}} {\text{MS}}_{it}^{{{\text{after}}}} \\ & + \beta_{{{\text{NC}}}}^{{{\text{before}}}} {\text{NC}}_{it}^{{{\text{before}}}} + \beta_{{{\text{NC}}}}^{{{\text{after}}}} {\text{NC}}_{it}^{{{\text{after}}}} + \mu_{t} + \eta_{r} + \varepsilon_{irt} ,\\ \end{aligned}$$
(1)
where
Pirt is the selling price;
Xit the vector of house characteristics; FS
it the number of nearby foreclosures
, MS
it the number of nearby open market sales, and NC
it is the number of nearby newly constructed houses, all within distance
d and within timeframe τ
before or
after the subject property transaction. Equation (
1) also includes location and (interaction) time fixed effects to reduce unobserved heterogeneity and omitted variable bias.
8 Further,
\({\varepsilon }_{irt}\) is the error term clustered at the location fixed effects-level. Davis (
2004) argues that using location fixed effects and clustered errors together also serves as nonparametric spatial correlation control.
Cast this way, the FS
, MS
, and NC variables control for nearby foreclosed houses, open market (non-foreclosure) houses, and new construction in the neighborhood at the same time the subject property is for sale. The timeframe windows
before and
after are included to capture other properties on the market at the same time as the subject property, some of which sell before and some after the subject sells.
9 The estimated coefficients for local market conditions variables provide important information regarding the extent of possible foreclosure and new construction externalities. FS captures the effect of foreclosures increasing the supply of existing houses on the market plus the negative foreclosure externality. MS captures the increasing supply associated with open (non-foreclosure) market sales of new construction and existing houses. The coefficient on the NC variable measures any externality associated with new construction (holding the total supply of houses constant).
These variables may include endogenous responses to neighborhood market conditions. Our identification strategy hinges on comparing short-run changes in values within very small areas arising from changes in the local or neighborhood housing market context. Following the procedure in Campbell et al. (
2011), the externality effect estimate is the difference between the coefficient for foreclosures before the subject property transaction and the coefficient for foreclosures after the transaction,
\({[\beta }_{\mathrm{FS}}^{\mathrm{before}}-{\beta }_{\mathrm{FS}}^{\mathrm{after}}]\). The standard error of the estimate can be calculated using the delta method. If foreclosure spillovers are present, then we should observe a negative impact as the largest negative impacts of foreclosure on property values are prior to the sale. The same identification strategy applies to the new construction and open market sales variables. To the extent that local economic shocks impact house prices, we include in one of the models also past and future foreclosures slightly farther away (between one-tenth and a quarter of a mile) as controls (Linden and Rockoff
2008; Campbell et al.
2011).
Second, because foreclosures generate both negative externality and supply effects (or pecuniary externality), we follow Anenberg and Kung (
2014) and Hartley (
2014), and use the difference in estimates relative to market sales to remove the supply effects of foreclosures on price, which in our application reduces to
10$$\mathrm{Foreclosure\, externality}={[\beta }_{\mathrm{FS}}^{\mathrm{before}}-{\beta }_{\mathrm{FS}}^{\mathrm{after}}]- {[\beta }_{\mathrm{MS}}^{\mathrm{before}}-{\beta }_{\mathrm{MS}}^{\mathrm{after}}].$$
(2)
The above calculation measures the combined effect of increasing nearby foreclosures by one, while decreasing the number of non-distressed properties on the market by one, to arrive at the net externality effect of an additional foreclosure.
Finally, to the extent that new construction creates a positive spillover in the neighborhood, the difference in the before and after coefficient estimates for the new construction variables should reflect the new construction externality:
$$\mathrm{New\, construction\, externality} ={[\beta }_{\mathrm{NC}}^{\mathrm{before}}-{\beta }_{\mathrm{NC}}^{\mathrm{after}}].$$
(3)
In this case, there is no need to remove a supply effect because the number of non-distressed properties for sale (which includes new houses) is already being captured by the market sales variables (which includes new houses). The estimated new construction effect reflects the effect of increasing the number of new houses for sale by one, while simultaneously decreasing the number of existing houses for sale by one.
If the (negative) FS coefficient is algebraically less than the MS coefficient, then increasing the number of foreclosures while holding neighborhood supply constant reduces the prices of surrounding properties. This result is consistent with a negative real externality effect from neighboring foreclosures. If, on the other hand, the FS and MS coefficients are not significantly different, neighboring foreclosures have no real externality effect on surrounding properties. Finally, if the (negative) MS coefficient is less than the FS coefficient, then increasing the number of foreclosures while holding neighborhood market supply constant increases the prices of surrounding properties. Although it may seem counter-intuitive at first blush, this outcome is nonetheless consistent with foreclosed properties generating a stronger shopping externality for the neighborhood than open market properties. In this case, the presence of nearby foreclosures for sale is a stronger draw for potential buyers than open market properties; the resultant increases in buyer arrival rates increase the probabilities of higher priced matches for nearby sellers, including sellers of open market properties. It is important to remember that the coefficients on the new construction variables (NC) do not capture supply effects and instead solely pick up the neighborhood quality signaling or the housing investment externality effect arising from new construction.
To provide evidence on the extent of variation in foreclosure effects by neighborhood configuration, we estimate separate models by the built structure of the neighborhood. The neighborhood configuration is often defined in terms of streets, lots, and buildings.
11 Along with physical elements, the social aspects of urban morphology have also been deemed important (Nedovic-Budic et al.
2016). We summarize neighborhood configuration using a set of simple dimensions of urban form: urban density, neighborhood mix of homes, development period, vacancy rate, and whether the property is in a gated or non-gated neighborhood subdivision.
3 Data
The data are drawn from property assessment records of Orange County, Florida, covering all of the 426,021 parcels in the county as of August 24, 2012. Orange County is part of the Orlando-Kissimmee-Sanford Metropolitan Statistical Area (MSA), and has been experiencing long-term population growth from 896,344 (2000 Census) to 1,145,956 (2010 Census). Orange County is an interesting case study to explore whether foreclosure price effects vary across neighborhoods. Orange County is one of the epicenters of the foreclosure crisis. While so, foreclosures were not evenly distributed across neighborhoods. We focus on how foreclosure price effects vary by neighborhood configuration.
Local assessor records in Florida have been used as the primary data source in a number of studies and have several advantages (Ihlanfeldt and Mayock
2012; Turnbull and Van der Vlist
2023). One advantage of local assessor records over multiple listing service (MLS) data for broker-assisted transactions is that tax records provide information on the entire stock of existing properties, not just those that sell. Another is that MLS data do not cover all public transactions and, most important for the question addressed here, likely underreport foreclosure transactions (Daneshvary and Clauretie
2012; Chinloy et al.
2017)—which may be critically important in this sample, as increasing numbers of foreclosed properties in Orange County were sold directly to investment firms and other organized investors without first being offered to individual buyers through traditional channels like the MLS.
Conversely, a disadvantage of tax assessment records is that they provide no direct information about liquidity or time-on-the market for sold properties (although most existing foreclosure studies using MLS data also have not exploited liquidity data). Krainer (
2001) shows that changes in buyer willingness-to-pay is reflected in both selling price and liquidity in search markets; recent empirical studies provide evidence of price-liquidity capitalization for both individual property and neighborhood characteristics (Turnbull et al.
2013; Turnbull and Zahirovic-Herbert
2011). Therefore, the absence of marketing time measures in this study means that the price effects of foreclosures identified here, as well as in most of the foreclosure literature, may reflect only one dimension of the possible capitalization effects.
The tax records yield detailed information on property characteristics as well as addresses, transaction prices, transaction dates, and deed types (which allows us to identify foreclosed properties).
12 We focus on built-up areas of the county, excluding the sparsely settled northwestern and eastern parts of the jurisdiction. The single family detached house (SFD) data cover 266,897 separate properties during the sample period of January 2007 through August 2012.
The data include transaction price, transaction date, and deed type. The structure of the data source allows us to observe the transaction history over 2007-to mid-2012. This sample period captures the declining market over 2008–2010 and the weak recovery starting in early 2012. The dependent variable is the transaction price. The control variables measure property characteristics and location and time fixed effects. The analysis focuses on single family detached houses (SFD). Living area indicates the square feet of air-conditioned/heated area. Other property characteristics include number of bedrooms and bathrooms, presence of a private swimming pool, house age, and type of exterior walls. Total land acreage is the measure of parcel size and encompasses both upland and any submerged area lying within the parcel legal boundary. The data allow us to construct GIS-based neighborhood housing market conditions indicators based on the number of FS, MS, and NC in the neighborhood within distance d (one-tenth, a quarter of a mile) taking place within time frame τ before and after transaction time t (90, 180 days) of each subject property.
We define neighborhood configuration using a set of simple dimensions of urban form. First, we measure urban density, development period, and vacancy at the neighborhood census blockgroup-level, using the 2000 American Community Survey (ACS). Next, at a more granular level, we measure neighborhood mix of homes (variation in development period, and size of living area) at the census block-level, using the administrative parcel-level data for the year 2000. Last, we identify for each transaction whether a property lies within a private gated or publicly accessible non-gated subdivision, using administrative information on subdivisions.
13 We use these measures to examine how foreclosure effects vary with neighborhood configuration.
The sample consists of open market transactions transferring warranty deeds, and excludes all legal administrator’s deed, tax deed, and quit claim deeds (all for administrative non-arm’s length transaction purposes), including all transactions for $100, the usual indicator of a non-market transfer of property interest. Following Daneshvary et al. (
2011), we trim the lower and upper 1 percent of the distribution of price and living area to control for outliers. Furthermore, we define the maximum spatial extent of the surrounding neighborhood for each property as one mile, so while observations within one mile of the county boundary are used to construct instruments for total number of properties, foreclosed sales, market sales, and new construction, they are not otherwise included in the price equation sample. Similarly, observations in the first six-month time frame are excluded from the model estimation to construct the proper burn-in period for our instruments. The number of observations in the sample for estimating the price equation is 39,913 open market transactions.
14 Variable definitions are given in Appendix
1.
Table
1 reports the descriptive statistics. The table indicates a mean price of almost $235,000 and a median of $185,000, thus reflecting a distribution skewed to the right. We therefore use the natural logarithm of transaction price in the empirical analysis. Structural property characteristics indicate the type of building construction material (63% have walls made of stucco covered concrete block versus wood frame construction), number of bedrooms (3.45 average), living area (2,040 square feet average), number of bathrooms (2.32 average), presence of a private pool (27%), lot size (40,079 square feet average), and actual age of the house (22.6 years). Location controls include the (quadratic) distance to the Orlando CBD (8.98 miles linear distance average) and zip code fixed effects. Over 70 percent of the transactions lie within the City of Orlando, the largest and most populous municipality in Orange County.
Table 1
Descriptive statistics
Property controls |
Price | 234,425 | 176,698 |
CBD distance (miles) | 8.979 | 4.207 |
Walls concrete stucco (1 = yes) | 0.632 | |
Number of bedrooms | 3.448 | 0.790 |
Living area (in sq. ft.) | 2,040 | 830.2 |
Age of property (in years) | 22.61 | 20.50 |
Number of bathrooms | 231.6 | 79.05 |
Pool (1 = yes) | 0.265 | |
Parcel size (in sq. ft.) | 40,079 | 39,637 |
Neighborhood configuration |
Low density | 0.258 | |
High density | 0.274 | |
Low vacancy | 0.231 | |
High vacancy | 0.283 | |
Old neighborhood | 0.251 | |
New neighborhood | 0.235 | |
Homogeneous in age SFD | 0.244 | |
Homogeneous in age SFD | 0.239 | |
Heterogeneous in living area SFD | 0.253 | |
Homogeneous in living area SFD | 0.247 | |
Local housing market controls |
Number of foreclosure sales (near, before) | 0.235 | 0.595 |
Number of foreclosure sales (near, after) | 0.205 | 0.543 |
Number of foreclosure sales (far, before) | 0.626 | 1.183 |
Number of foreclosure sales (far, after) | 0.628 | 1.144 |
Number of foreclosure sales = 1 (near, before) | 0.131 | |
Number of foreclosure sales = 2 (near, before) | 0.031 | |
Number of foreclosure sales > 2 (near, before) | 0.012 | |
Number of market sales (near, before) | 1.633 | 2.474 |
Number of market sales (near, after) | 1.096 | 1.410 |
Number of market sales (far, before) | 3.915 | 4.373 |
Number of market sales (far, after) | 2.971 | 2.910 |
Number of new construction (near, before) | 0.542 | 2.428 |
Number of new construction (near, after) | 0.806 | 3.650 |
Number of new construction (far, before) | 0.273 | 1.325 |
Number of new construction (far, after) | 0.441 | 1.902 |
A further decomposition of descriptive statistics by neighborhood configuration is found in Appendix
2. The descriptive statistics report some structural differences in average property characteristics across type of neighborhood. Low density neighborhoods have lower mean house prices ($206,809) than high density neighborhoods ($281,254). Also, older neighborhoods (in terms of mean building age) have lower mean house prices ($165,234) relative to newer neighborhoods ($311,275). The typical property in these older neighborhoods is smaller in terms of living area, and of less quality in terms of construction materials, but they generally have larger parcels. Note further that house prices are higher in neighborhoods with more heterogeneous housing stock measured in terms of property age or property size.
The lower panel of Table
1 provides summary statistics for the constructed variables measuring neighborhood market conditions including the number of foreclosures (FS), the number of market transactions (MS), and the number of newly constructed properties (NC). The data reveal that a large majority of sales occur in areas with no surrounding foreclosures. This is consistent with Campbell et al. (
2011) although the overall incidence of foreclosures in Orange County is greater. In total, 82.6 percent of sellers do not have any foreclosure within one-tenth mile and 180 days. Multiple nearby foreclosures are even less frequent. The data reveal that 95.7 percent of the sellers do not observe two or more foreclosures, while 98.8 percent of the sellers do not observe three or more foreclosures, all within one-tenth mile and 180 days.
15
The mean number of nearby foreclosures is 0.235 but varies across neighborhood types. Likewise, one observes differences in the number of market sales and in the number of newly constructed properties. Looking at the number of newly constructed properties within the indicated geographic area and time frame, the average new construction (0.542 for the pooled sample) varies between 0.117 and 4.522 across neighborhood types. These transactions can be interpreted relative to the density or mean total property which varies across neighborhood types between 19.59 and 36.76. Overall, the FS, MS, and NC measures all show substantial variation across neighborhood configuration.
5 Conclusion
Foreclosures influence nearby property values, but the marginal effects vary significantly across neighborhoods. Our approach estimates the foreclosure effects across neighborhoods while controlling for the increasing supply of housing for sale as erstwhile foreclosed owners are removed from the market. We also introduce new construction into the empirical framework to control for possible externality effects arising when buyers interpret new construction as a signal of neighborhood stability or future growth, removing these possibly confounding influences on the estimated foreclosure externality effects across neighborhood types.
Data from an epicenter of the US foreclosure experience, Orange County, Florida, reveal that nearby foreclosures appear to reduce property prices by 1.43 percent overall. Removing the supply effect, these estimates imply a foreclosure externality of − 1.27 percent. The new construction externality is 0.5 percent.
Turning to the main point of this paper, we find that foreclosure spillover effects systematically vary across types of neighborhoods, exhibiting almost tenfold variation in some cases. For example, the marginal foreclosure externality ranges from − 0.38 percent for newer neighborhoods to − 3.59 percent for older ones. Overall, the strongest foreclosure effects are found in low density neighborhoods, structurally homogeneous neighborhoods, and non-gated subdivisions. We also find that nonlinear foreclosure effects vary across types of neighborhoods, with older, structurally homogeneous neighborhoods with high vacancy rates most in jeopardy in this regard.
Admittedly, urban morphology includes more dimensions than the ones we have considered, including street connectivity and accessibility. Likewise, as foreclosures force affected households to move, foreclosures may well have long-term implications for the composition and social stability of urban neighborhoods. Hawley and Turnbull (
2019) show that the built environment affects household behavior, but at the same time households choose neighborhoods with built environments conducive to pursuing lifestyles they prefer. Taking this type of endogenous household sorting into account, while we find that neighborhood built environment does matter for foreclosure price effects on surrounding properties, the Hawley and Turnbull (
2019) conclusions suggest a need for future work to identify the extent to which these effects are specifically related to the built environment or to the mix of residents attracted to neighborhoods with those characteristics.
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