Forecasting Housing Prices under Different Market Segmentation Assumptions

Abstract

Three types of market segmentation strategies are available to estimate hedonic housing price models—i.e. no segmentation, segmentation by using statistical clustering methods and segmentation by using a priori information. This research tests the hypothesis of Tiebout theory that individual residential decision-making is determined by equilibrium provision of local public goods in accord with the tastes and preferences of residents, thereby sorting their housing locations into optimal sub-markets. Forecasting accuracies of eight sub-market segmentation strategies and two forecast-combining methods are examined by using housing sales data from Knox County, Tennessee, USA. The results provide empirical support for Tiebout theory of optimal housing sub-market location in that boundaries drawn using a priori information from local government jurisdictions, school districts and expert opinions are more closely aligned with the equilibrium provision of local public goods than boundaries drawn by statistical clustering methods. The advantage of forecast-combining is also demonstrated.