【摘要】Two important objectives in hedonic price analysis are to predict sale prices anddelineate submarkets based on geographical and functional considerations. In this paper, weapplied Bayesian models with spatially varying coefficients in an analysis of housing sale pricesin 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 showthat 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 marketopinions.

【关键词】Housing prices;hedonic analysis;spatial models;Bayesian analysis;elicited priors

【文献来源】David C. Wheeler;Antonio Paez;Jamie Spinney;Lance A. Waller Papers in Regional Studies.2014（3）