Wednesday, May 15, 2013

The Impact of Brownfields on Residential Property Values in Cincinnati, Ohio: A Spatial Hedonic Approach

This paper empirically assesses the impact brownfield sites have on market values of single-family residential properties. Using three different hedonic model specifications, namely Ordinary Least Squares, Spatial Autoregressive, and Spatial Error Model, this study shows that properties located less than 1,000 feet from a brownfield site experience a significant depreciation in property values. As expected, spatial dependence among real estate property values is present meaning that the two spatial hedonic model specifications are superior to the Ordinary Least Squares specification. The study contributes to the relevant real estate and urban economics literature by determining the negative impact brownfield sites have on the value of single-family residential properties in our study region in Cincinnati, by explicitly accounting for the phenomenon of spatial dependence in the dependent variable, and by addressing the declining influence of brownfield sites on property values in a nonlinear relationship.
Most of the variables in each of the three model specifications are statistically significant at the 99% level of confidence.... The interpretation of the spatial dependence in the Spatial Autoregressive (SAR) specification is a structural process, based on the assumption of omitted variables....  As such, the SAR approach does account to some extent for ... latent influences, such as noise and crime issues and other excluded variables. The total SAR impacts ... show that one additional bedroom adds 8.67% to the market value of a house, while a full bath and a half bath add 16.18% and 11.37% respectively. Having a central air conditioning system increases the property value by 35.72%, while a heating system adds 27.03% and a garage adds 12.86%.... An increase in the age of the house by one year causes a decrease in the property value by only 0.68%; a 1% increase of the population aged 25 or older holding a bachelor's degree or higher by ZIP code increases the property values by 1.37%. Also of note is that properties in very poor and unlivable condition are devalued by 81.12%.
The total estimated impact with respect to the distance of a property to its closest brownfield is -0.23076. ... Decreasing the maximum distance allowed to a brownfield site, i.e., from 1,000 to 0 feet ... translates into a maximum possible reduction in property value of or 19.96%. In other words, ... the largest change in property value is 19.96%. ...

The Spatial Error Model (SEM) addresses spatial dependence through spatially structured random effects in the disturbance process.... The SEM results indicate that one additional bedroom, full bath, and a half bath add 7.05%, 14.03%, and 10.35% respectively to property values. An increase in property value of estimated 28.28%, 31.98%, and 12.19% is associated with adding an air conditioner, a heating system, and a garage, while age and very poor condition reduce property values by 0.64% and 86.72%. Again, a 1% increase in the population with at least a college degree increases the property values by 1.35%.

With an estimated coefficient of -0.2536, brownfields have in the SEM specification a somewhat higher influence on residential property prices. Here, a maximum possible reduction in property value is calculated as 21.93%, related to reducing the distance to the closest brownfield site from 1,000 to 0 feet.
Reducing the distance from 500 to 0 feet under the SEM model results in a decrease of residential property values of 16.03%, while reducing the distances from 1,000 to 500 feet and from 500 to 100 feet results in property reductions of 5.90% and 11.43% respectively. The non-linear relationship implies that brownfield sites have a larger impact on prices of properties that are closer than of those that are further. More specifically, reducing the distance from 100 to 0 feet in the SEM specification results devaluates a property by 4.60%, while reducing the distance from 1,000 to 900 feet devalues a property by 0.76%.
Using the results from the Spatial Autoregressive (SAR) or the Spatial Error Model (SEM) specification respectively, the authors estimated that brownfield sites in the City of Cincinnati, Ohio devalue housing prices by as much as 19.96% to 21.93% for those properties that are adjacent to brownfield sites. For the average priced house of $94,595 in our sample that means a devaluation of $18,881 to $20,745.
by Oana Mihaescu 1 and Rainer vom Hofe 2
1. HUI Research
2. University of Cincinnati
Date of creation: 24 Apr 2013
The Swedish Retail Institute (HUI), Regeringsgatan 60, 103 29 Stockholm, Sweden; Phone: +46 (0)8 762 72 80, Fax: +46 (0)8 679 76 06; email:
via Research Papers in Economics
Review of the Relevant Literature of Brownfield Impacts on Residential Property Values
There is unanimous agreement in the brownfield literature that unremediated sites constitute a nuisance to people, neighborhoods, and local governments. From their classification as an environmental disamenity, it follows that these brownfields are expected to have negative impacts on residential property values within the hedonic pricing framework. More recently, a body of literature has emerged attempting to quantify in terms of property values the negative impact of being in close proximity to unremediated brownfields and if/how this impact changes with remediation efforts.

In an earlier study, Gunterman (1995) explains potential decreases in property values through existing on-site contamination of nearby landfills. The author finds that solid waste landfills.

Negatively impact the selling price of nearby industrially zoned land and, of course, that of the landfills themselves. Once these solid waste landfills are closed, they do not adversely affect land values as they did before. Longo and Alberini (2006) confirm these findings for industrial properties in Baltimore, Maryland. In addition, they find that commercial properties are affected differently by proximity to a site with a history of contamination. More specifically, the authors find that commercial properties suffer an external cost due to their proximity to a contaminated site, a cost that is not cleared once the site has been cleaned up or has been pronounced harmless. Simons et al. (1997, 1999) focused their studies on the impact of underground storage tanks known to have leaked and residential property sales prices. They concluded that sales prices are prone to an approximate and statistically significant reduction of 15% if it is known that they have been contaminated by neighboring leaking underground storage tanks.

Jenkins-Smith et al. (2002) argue that the public release of information on the locations and/or the type and degree of contamination in itself devalues surrounding property values. The authors show, based on a probit model, that the mere disclosure of information about contamination, cleanup, and subsequent legal action caused as many as 30% of potential buyers of contaminated sites to not pursue the purchase. In addition they find that the buyer's willingness to pay for the typical home in the area drops by approximately $11,000 and that nearly half of the sellers accepted a $12,000 loss in the purchase price from the sale of properties situated in the proximity of contaminated sites. Kaufman and Cloutier (2006) studied the combined impacts of two brownfields and one neighborhood park on residential property values in Kenosha using a semi-log model and OLS estimation technique. Distance measures were included from each house to the two brownfields and the park. Confirming prior expectations, the authors show that brownfields exert a negative impact on residential property values, while the park has the opposite effect. They further determine that residential property values tend to increase once the brownfields are remedied. Lastly, the conversion of former brownfields into greenspaces further significantly increases the value of the neighboring residential properties.

Green Leigh and Coffin (2005) estimate a hedonic model applying OLS technique to property values in Atlanta, Georgia, and Cleveland, Ohio. They find that contamination has a negative impact not only on the value of nearby properties, but also on the value of the brownfield sites themselves. However, the magnitude of the negative impact on the neighboring properties varies with proximity to the contaminated site and whether the respective site has been remediated or not. Simons and Saginor (2006) performed a metadata OLS regression using results from 75 previous studies. Their results generally confirm that environmental contamination of sites (not particularly brownfields) does discount property values and that this discounting effect decreases with increasing distance from the source of contamination. In addition, the loss in property value differs by region, by the type of contamination, by undertaken remediation efforts, and other factors, such as legal liability.

Of much interest for the presented research are the studies that also account for spatial dependence in the data. Ding et al. (2000) use a hedonic price model that includes a spatially lagged dependent variable (SAR) to determine the infill development effect on nearby property values. Allowing for two separate types of residential property investments, they find that the impact of new construction is more spatially extensive than that of rehabilitation (300 feet as compared to 150 feet). Further, new construction also produces a higher average increase in housing values: $4,500 more per house as compared to $2,000 per house following rehabilitation. However, these effects vary across neighborhoods, being greater in low-income and predominantly white areas. Ihlanfeldt and Taylor (2002), compare the hedonic modeling results from the standard nonspatial OLS model with the results from the Spatial Error Model (SEM) for commercial and industrial properties located in the vicinity of hazardous waste sites in Fulton County, Georgia. Both sets of results indicate a statistically significant reduction in the value of commercial and industrial properties surrounding the contaminated sites. By including space in the analysis, the SEM results are preferred as this allows the authors to establish the perimeter of the negative impact of contaminated sites, and, in return, to estimate total property value losses for the entire county at as high as $1 billion. Further, the density effect (i.e., the number of contaminated sites within a certain distance of each property) is less important than a property's proximity to the closest contaminated site.

Svetlik (2007) evaluates the determinants of land prices in Monongalia County, West Virginia, with a specific focus on the impacts of local brownfields on residential property values. More specifically, he examines the influence of structural characteristics (i.e., square footage and age), locational amenities (i.e., elevation and distance from the analyzed Metropolitan Statistical Area (MSA)), and recreational amenities (i.e., streams) on the price per acre of land. Brownfields are identified as sites that either participate in the West Virginia Voluntary Remediation Program (VRP) or are known open dump sites.

Svetlik accounts for spatial dependence in land prices using both standard spatial approaches, namely the SAR and the SEM frameworks, and concludes that the SAR and SEM results are more robust than the OLS results estimation as the spatial approaches produce unbiased and consistent coefficient estimates. He determines that price per acre is positively associated with square footage, structural amenities, and locational amenities, such as access to sewer and location in the Morgantown MSA, and negatively related with the age of the structure, the lot size, elevation, and increasing distances from Morgantown and from recreational amenities such as streams. Not surprisingly, the coefficient on the distance from brownfield sites is significant and positive, showing a positive relationship between distance from brownfield sites and property values.

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