A Bayesian approach to hedonic price analysis*

Two important objectives in hedonic price analysis are to predict sale prices and delineate submarkets based on geographical and functional considerations. In this paper, we applied Bayesian models with spatially varying coefficients in an analysis of housing sale prices in the city of Toronto, Ontario to address these objectives. We evaluated model performance and identified patterns of submarkets indicated by the spatial coefficient processes. Our results show that Bayesian spatial process models predict housing sale prices well, provide useful inference regarding heterogeneity in prices within a market, and may be specified to include expert market opinions.