Data-driven regionalization of housing markets

Abstract

This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The out-of-sample prediction error of a hedonic pricing model is applied to determine a “near-optimal” number of spatially coherent and homogeneous submarkets. The usefulness of this method is demonstrated with a detailed data set for the Austrian housing market. The results provide evidence that submarkets must always be considered, however they are defined, and that the proposed submarket taxonomy on a regional level significantly improves predictive quality compared to (1) a traditional pooled model, (2) a model that uses an ad hoc submarket definition based on administrative units, and (3) a model incorporating an alternative submarket definition on the basis of aspatial k-means clustering. Moreover, it is concluded that the Austrian housing market is characterized by regional determinants and that geography is the most important component determining the house prices.