Spatial Dependence in Hedonic Property Models: Do Different Corrections For Spatial Dependence Result in Economically Significant Differences in Estimated Implicit Prices?

While data used in hedonic property models are inherently spatial in nature, to date the majority of past regression analyses have used OLS models that overlook possible spatial dependence in the data when estimating implicit prices for environmental hazards. This paper explicitly addresses spatial dependence in a hedonic property model. We use robust testing procedures to determine the existence and type of spatial dependence in our OLS model. After identifying the nature of the spatial dependence, OLS estimates of the implicit price of wildfire risk are compared to implicit prices obtained using a spatial error model with three different spatial weighting matrices. Spatially corrected estimates of implicit prices are often found to be nearly the same as those obtained using OLS. Our results indicate that the inefficiency of OLS in the presence of spatially correlated errors may not always be economically significant, suggesting nonspatial hedonic property models may provide results useful for policy analysis, and spatial and nonspatial hedonic property models may provide results useful for policy analysis, and spatial and nonspatial hedonic property models might be pooled in meta-analysis.