A Bayesian approach to assessing the robustness of hedonic property value studies

Hedonic price models are widely employed to estimate implicit prices for bundled attributes. Residential property value studies dominate these applications. Using a representative cross-sectional property value data set, we employ Bayesian methods to translate a range of priors in covariate selection typical of hedonic property value studies into a range of posterior estimates. We also formulate priors regarding measurement error in individual covariates and compute the ranges of resulting posterior means. Finally, we empirically demonstrate that a greater and more systematic use of prior information drawn from one's own data and from other studies can break the collinearity deadlock in this data.