As an urban planning concept, brownfield sites are lands formerly used for industrial or commercial purposes, but the subsequent redevelopment and expansion of these properties may be difficult due to potential contamination by hazardous substances. For instance, gas stations and scrap yards emit high concentrations of subsurface pollutants. If their operations close, the lots that these facilities previously occupied could lie unused for decades as brownfields. Once cleaned up, such zones can accommodate new businesses or serve as green spaces for recreation. Thus, the Environmental Protection Agency (EPA) has sought to empower local governments and community stakeholders to evaluate and remediate brownfields.
Over the past 20 years, shifting market influences have dramatically impaired the City of Durham’s leading manufacturing industries. The collapse and flight to city edges of these industries gave rise to many brownfields—abandoned plots that historically accommodated manufacturing buildings, chemical facilities, railroad property, and automobile repair shops. In 2009 the EPA designated Durham, NC, as a recipient for two brownfields assessment grants: $200,000 for hazardous substances and $200,000 for petroleum. Throughout its EPA-funded reclamation efforts, Durham focused on properties in Northeast Central Durham (NECD), the primary locale for both vacant and currently functioning industrial facilities within the city. Since brownfields can be detrimental for human and ecosystem health, their presence likely has an adverse effect on the property values of neighboring houses.
This paper delves into the predicted impact of brownfields on nearby residential housing prices in Durham. The analysis reveals that, with exception of three brownfield sites, mean sales price tends to be higher for single-family houses located farther away from a brownfield. In the case of eight out of 12 brownfields assessed in this study, average housing prices are 9% to 38% higher in the outlying, surrounding region than within 2,000 feet of the site. The trends in these data seem to correspond with earlier research that establishes brownfields’ negative effects on nearby property values. From a policy perspective, quantifying houses’ lost value due to proximity to brownfields relative to the costs of reclamation could make a strong economic case for granting additional funding toward remediation.
II. Literature Survey
Past studies, employing different hedonic model specifications, have gauged the effect of brownfields on adjacent residential property values in certain regions. For example, Mihaescu and vom Hofe (2012) use ordinary least squares (OLS), spatial autoregressive, and spatial error models to compute the impact of 87 brownfields on the values of nearby single-family homes in Cincinnati, OH, finding that a $100,000 house situated 100 feet from a brownfield loses approximately $9,000 in property value. However, Mihaescu and vom Hofe also estimate that brownfields’ depreciating influence becomes insignificant 2,000 feet past the sites. At this rate, Cincinnati effectively loses more than $2.2 million in annual tax revenue from aggregate decreased property values related to brownfields. Although other studies have previously shown negative impact on housing prices (Bromberg and Spiesman, 2006), Mihaescu and vom Hofe distinguish their approach from existing literature by accounting for trends of spatial dependence among assessed property values.
Haninger, Ma, and Timmins (2014) determine brownfields’ impact from a different angle, by measuring the value of brownfield redevelopment as encapsulated in nearby housing prices. A simple comparison of regions with untouched brownfields and remediated brownfields can cause problems because the Brownfields Program gives cleanup grants based on a competitive procedure; therefore, communities that obtain funding may differ systematically from those that do not receive it. Haninger et al. overcome this issue by means of several fixed effects and ‘difference-in-differences’ (DID) specifications, which all generate a consistent solution—homes can undergo large observed rises in property values associated with brownfield remediation, ranging from 4.9% to 24.8%. Yet, this analysis has limitations in that its models cannot capture cleanup-related health benefits that local residents are unaware of and thereby are not reflected in house buying choices and prices. These benefits are best quantified in more environmentally-focused studies that demonstrate how remediating a brownfield helps to decrease harmful effects of the area’s soil, air, and groundwater pollution on human health as well as ecological systems (Alberini et al., 2005). Meanwhile, other analyses by Barnett (2006) and Amekudzi et al. (2003) show remediation’s positive economic impacts in form of increased local employment and greater tax revenues, in addition to higher property values.
To study the extent of association between brownfields’ presence and housing prices, I use publically available data provided by Concurrent Technologies Corporation (CTC), a company that, among other services, offers brownfields redevelopment consulting. The CTC listings give locations (either street addresses or approximate intersections for larger land parcels), acreage, property descriptions, and histories of the brownfields. Specifically, I identify 12 properties in NECD and the Pettigrew Street Corridor currently under assessment for possible participation in the EPA grant system. These properties range from small vacant lots to multistory buildings. As an example, one of the brownfield sites, located on 200 East Umstead Street, formerly served as the building for J.A. Whitted Junior High School. Although an abandoned school is a disamenity for reasons unrelated to environmental hazards, J.A. Whitted is nevertheless included in the CTC listings due to its high difficulty of redevelopment. The three-story, 73,500 square foot facility has been sitting vacant since 2004; a glance on Google Maps reveals fading school letters and boarded windows.
For each of these 12 potential brownfields, I collect sale price information on a total of 204 neighboring houses from Zillow, an online real estate databank. The analysis includes prices of homes currently for sale, along with potential sale prices of properties that may be emerging on the market shortly but do not appear yet on multiple listings service.
Using satellite imagery, I begin by pinpointing the center of a Durham brownfield site, which by itself can span almost the entirety of one block. After finding the midpoint, I mark a circular zone with a 2,000 feet radius on the Zillow map, making sure to scale accurately. Properties within this first zone lie within 0.4 miles of the brownfield. I also trace a larger circle with a radius of 3,000 feet; all properties in the second zone sit within 0.6 miles from the site. I then use these demarcations to compare property values of (1) houses within 2,000 feet of the brownfield and (2) houses situated 2,000 to 3,000 feet away. I calculate the mean sales price of all properties within each zone and subsequently use the two averages to compute the percentage change obtained by moving from the inner circle to the outer one. Both intuition and prior studies suggest that properties in the closer zone will be more affected by the brownfield; my rationale behind selecting 2,000 feet as the comparison boundary stems from the aforementioned study by Mihaescu and vom Hofe, which estimates that brownfields’ impacts become negligible beyond 2,000 feet.
When observing differences in residential property values, it is important to consider potential biases due to spatial dependence among housing prices. While a hedonic pricing approach can address this issue through certain econometric techniques (e.g., spatial autoregressive models), I attempt to minimize spatial externalities via the grouping design. Since most properties in the analysis lie within a 0.6 mile radius of each other and are thus situated in the same part of town, they largely encompass similar economic conditions and housing quality levels. By assuming spatial characteristics remain fairly constant within a small area, this analysis offers groundwork for more complex future research.
Data findings indicate that, with the exception of three Durham brownfield assessment sites, average sales price tends to be consistently higher for residential properties situated 2,000 to 3,000 feet away from a brownfield. For eight of the 12 brownfields evaluated in this study, mean housing prices are 9% to 38% higher in the outer zone than within 2,000 feet of the site. The patterns in these data seem to be in line with prior studies demonstrating brownfields’ negative impacts on nearby property values. However, this type of study cannot isolate brownfields’ effects from other potential causes of the difference in average housing prices. For example, if the local government prefers to invest in land redevelopment in areas that seem to have high promise of new business development, the fact that these brownfield sites have not already been remediated could indicate lower neighborhood quality.
Table 1: Proximity-Based Discrepancies in Average Housing Values
Moreover, a closer examination of observations that deviate from expected outcomes can be insightful as well. For instance, houses farther from the ‘Eliot Square Apartments’ brownfield surprisingly cost 23% less on average than those adjacent to the site. However, a satellite image of the site reveals that it simply looks like a large, overgrown grass field—pleasant overall, although strangely vacant compared to all the nearby land occupied with buildings. Previous research has demonstrated that property values can only capture brownfields’ negative externalities to the extent that local inhabitants are aware of them and can thus incorporate these externalities into their willingness to sell or buy a property at a given price (Haninger, Ma, and Timmins, 2014); in this light, the trend reversal at Eliot Square Apartments makes sense, especially given that the First Baptist Church and Durham County Library are located one block away from the seemingly innocuous cleared grassy lot. On a similar note, the Ridgeway Avenue Property (percentage change: -2.5%) appears from Google Maps street view to be a small, paved vacant area next to a convenience store. Since this brownfield does not look like an environmental disamenity to passersby, its presence hardly factors into nearby residential property values.
On the other extreme, houses on sale near former J.A. Whitted Junior High School are on average almost 40% less expensive than homes farther from the site. As mentioned, the abandoned multistory building is conspicuous and visibly rundown; its appearance is consistent with the earlier explanation that, when residents are cognizant of the brownfield, property values are more affected. Nevertheless, a discrepancy of 40% still seems very high, resembling an outlier. Further scrutiny shines light on a potential influence: a newly renovated neighborhood three blocks away on Chestnut Street (i.e., 2,000 to 3,000 feet from the site) currently has pre-construction sales on four well-built houses, each posted at a price from $180,000 to $299,000.
This study reveals a negative association between the presence of brownfields and adjacent residential property prices in Durham, hence corroborating earlier research on this significant urban planning issue (Schwarz et al., 2013; Watkins, 2010; Attoh-Okine and Gibbons, 2001). However, rather than establishing a cause and effect relationship, the analysis is intended to serve more as a broad introductory survey of patterns in housing values surrounding Durham’s brownfields.
Though most sample zones in the study share consistent socioeconomic conditions and housing qualities within the group, a main limitation involves the lingering possibility of spatial correlation. Real estate data commonly contain spatially dependent property values, meaning that high-priced homes tend to group together, as do low-priced homes. Conventional real estate wisdom discloses that expectations on price often develop based on neighboring housing values (Wang and Ready, 2005); even a single neighborhood block could experience spatially correlated prices among its row of houses. Furthermore, since this study is a descriptive survey on brownfields and housing prices in Durham, any underlying factors that affect property value cannot be fully captured by comparison of averages. Therefore, future research could reduce bias and increase predictive power by employing spatial hedonic pricing models—which can be suitably applied to variations in housing prices that reflect the value of a local environmental amenity, or in this case, disamenity. Later studies can also improve upon the analysis to distinguish between different stages of brownfield remediation at various Durham sites.
Investigations of this nature can have immensely beneficial policy implications. The ability to effectively demonstrate brownfields’ negative impact on surrounding communities could raise governmental and private sector incentives to invest in revitalizing ecologically and financially troubled areas.
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