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LEARNING IN A HEDONIC FRAMEWORK: VALUING BROWNFIELD REMEDIATION
Author(s) -
Ma Lala
Publication year - 2019
Publication title -
international economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.658
H-Index - 86
eISSN - 1468-2354
pISSN - 0020-6598
DOI - 10.1111/iere.12389
Subject(s) - brownfield , valuation (finance) , contingent valuation , property value , willingness to pay , value (mathematics) , hedonic pricing , economics , bayesian probability , econometrics , value of information , microeconomics , bayes' theorem , demographics , environmental economics , computer science , statistics , mathematics , redevelopment , real estate , mathematical economics , engineering , artificial intelligence , civil engineering , demography , finance , sociology
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.