LEARNING IN A HEDONIC FRAMEWORK: VALUING BROWNFIELD REMEDIATION

B-Tier
Journal: International Economic Review
Year: 2019
Volume: 60
Issue: 3
Pages: 1355-1387

Score contribution per author:

2.011 = (α=2.01 / 1 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:wly:iecrev:v:60:y:2019:i:3:p:1355-1387
Journal Field
General
Author Count
1
Added to Database
2026-01-25