Beyond implicit prices: recovering theoretically consistent and transferable values for noise avoidance from a hedonic property price model

Using a two-stage hedonic pricing methodology we estimate a system of structural demand equations for different sources of transport-related noise. In the first stage, we identify market segments using model-based clustering techniques and estimate separate hedonic price functions (HPFs) for each segment. In so doing, we show how a semiparametric spatial smoothing estimator outperforms other standard specifications of the HPF. In the second stage, we control for non-linearity of the budget constraint and identify demand relationships using techniques that account for problems of endogeneity and censoring of the dependent variable. Our estimated demand functions provide welfare estimates for peace and quiet that we believe to be the first derived from property market data in a theoretically consistent manner.