Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions

Abstract

We analyze the impacts of alternative submarket definitions when predicting house prices in a mass appraisal context, using both ordinary least squares (OLS) and geostatistical techniques. For this purpose, we use over 13,000 housing transactions for Louisville, Kentucky. We use districts defined by the local property tax assessment office as well as a classification of census tracts generated by principal components and cluster analysis. We also experiment with varying numbers of census tract groupings. Our results indicate that somewhat better results are obtained with more homogeneous submarkets. Also, the accuracy of house price predictions increases as the number of submarkets is increased, but then quickly levels off. Adding submarket variables to the OLS model yields price predictions that are as accurate as when geostatistical methods are used to account for spatial dependence in the error terms. However, using both dummy variables for submarkets and geostatistical methods leads to significant increases in accuracy.

Keywords: spatial dependence, hedonic price models, geostatistical models, mass appraisal, housing submarkets

JEL Classification: C21, R31