Monday, May 20, 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, Ordinary Least Squares and Spatial Autoregressive (SAR) and Spatial Error Models (SEM), 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, Ohio, 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.
[The SAR model finds] 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%, and 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 from a property to its closest brownfield is -0.2308. ... The largest change possible in a residential property value is 19.96%.... Reducing the distance from 500 to 0 feet results in a decrease of residential property values of 14.59%. Analogously, reducing the distances from 1,000 to 500 feet, from 500 to 100 feet, and from 100 to 0 feet result in property value reductions of 5.37%, 10.40%, and 4.18%, respectively.  The largest change in property values occurs in close proximity to the brownfield sites, while reductions in property values level off when moving beyond the 1,000 feet cut-off distance used in our study. For instance, reducing the distance from 100 to 0 feet results in a property reduction of 4.18%, while reducing the distance from 1,000 to 900 feet results in a property reduction of a mere 0.69%.
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 ... 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 nonlinear relationship implies that brownfield sites have a larger impact on the prices of properties that are closer than of those that are further away. 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, we estimated that brownfield sites in the City of Cincinnati, Ohio, devalue housing prices by as much as 19.96% to 21.93%, respectively, 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 and Rainer vom Hofe, both of the School of Planning, University of Cincinnati
Journal of Real Estate Analysis & Policy
Volume 42, Issue 3, Pages 223-236

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