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A Bayesian approach to hedonic price analysis

Author

Listed:
  • Wheeler, David C.
  • Páez, Antonio
  • Spinney, Jamie
  • Waller, Lance A.

Abstract

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.

Suggested Citation

Handle: RePEc:eee:paresc:v:93:y:2014:i:3:p:663-684
DOI: 10.1111/pirs.12003
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JEL classification:

  • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
  • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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