Data-Driven Regionalization of Housing Markets
Document Type
Article
Publication Date
7-4-2013
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. © 2013 Taylor and Francis Group, LLC.
Publication Source (Journal or Book title)
Annals of the Association of American Geographers
Number
552
First Page
871
Last Page
889
Recommended Citation
Helbich, M., Brunauer, W., Hagenauer, J., & Leitner, M. (2013). Data-Driven Regionalization of Housing Markets. Annals of the Association of American Geographers, 103 (4), 871-889. https://doi.org/10.1080/00045608.2012.707587