A Bayesian approach to assessing the robustness of hedonic property value studies
用贝叶斯方法处理特征价格模型中变量选择和测量误差的不确定性，展示如何利用先验信息打破共线性困境，对从事房地产或环境价值评估的研究者有用。
Abstract 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.