Using Spatial Regression to Estimate Property Tax Discounts from Proximity to Brownfields: A Tool for Local Policy-Making

This paper assesses the discount in property values due to proximity to brownfields using a spatial hedonic price model. Using two Bayesian hedonic pricing models, namely the spatial lag of X (SLX) model and the spatial Durbin error model (SDEM), this study identifies a significant decrease in property values for properties located within 2,000 feet of a brownfield. The loss in property value and the subsequent decrease in tax revenue for the City of Cincinnati, Ohio, are then calculated based on these results. Using logarithmic transformations of the property value and the distance to the nearest brownfield variables, we calculate that a 1% increase in the average distance to the closest brownfield leads to a 0.0893% increase in market value. This translates into a $2,262,569 total annual revenue loss for the City of Cincinnati that could presumably be recovered following brownfield cleanup. In addition to accounting for the phenomenon of spatial dependence, this study contributes to the urban planning and environmental policy literature by providing a method for local policy-makers to identify and estimate the negative effects of brownfield sites on local tax revenue.