LEARNING IN A HEDONIC FRAMEWORK: VALUING BROWNFIELD REMEDIATION
研究了在房屋价值享乐模型中，购房者通过贝叶斯学习了解棕地污染信息如何影响其支付意愿，发现忽略学习过程会导致边际支付意愿估计出现显著偏差。
Abstract Incomplete information in property value hedonic models can bias estimates of marginal willingness to pay (MWTP). Using brownfield remediation as an application, this article recovers hedonic values from a dynamic neighborhood choice framework that allows households to learn about brownfield contamination in a Bayesian fashion before choosing where to live. I find that ignoring learning yields nontrivial biases to the MWTP estimate. This has important implications for hedonic valuation if agents are imperfectly informed. Estimates are used to calculate information's value had it been withheld from the public and to assess heterogeneity in information's value along site and homebuyer demographics.