This paper develops a statistical method for defining housing submarkets. The method is applied using household survey data for Sydney and Melbourne, Australia, First, principal component analysis is used to extract a set of factors from the original variables for both local government area (LGA) data and a combined set of LGA and individual dwelling data. Second, factor scores are calculated and cluster analysis is used to determine the composition of housing submarkets. Third, hedonic price equations are estimated for each city as a whole, for a priori classifications of submarkets, and for submarkets defined by the cluster analysis. The weighted mean squared errors from the hedonic equations are used to compare alternative classifications of submarkets. In Melbourne, the classification derived from a K means clustering procedure on individual dwelling data is significantly better than classifications derived from all other methods of constructing housing submarkets.. In some other cases, the statistical analysis produces submarkets that are better than the a priori classification, but the improvement is not significant. (C) 1999 Academic Press.
|Number of pages||24|
|Journal||Journal of Housing Economics|
|Publication status||Published - 1999|