In this paper, the AA. apply Bayesian models with spatially varying coefficients in an analysis of housing sale prices in the city of Toronto, Ontario to address these objectives. They evaluate model performance and identified patterns of submarkets
indicated by the spatial coefficient processes. The 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