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A B ayesian approach to hedonic price analysis
Author(s) -
Wheeler David C.,
Páez Antonio,
Spinney Jamie,
Waller Lance A.
Publication year - 2014
Publication title -
papers in regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.937
H-Index - 64
eISSN - 1435-5957
pISSN - 1056-8190
DOI - 10.1111/pirs.12003
Subject(s) - econometrics , bayesian probability , economics , hedonic pricing , inference , bayesian inference , microeconomics , computer science , statistics , mathematics , artificial intelligence
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.