For many industrialised nations environmental noise is emerging as a local pollutant of major concern. Incorporating such concerns into policy guidance tools such as social cost benefit analysis requires estimates of the monetised benefits of noise reduction. Using a two-stage hedonic pricing methodology we estimate a system of structural demand equations for different sources of transport-related noise. Our application applies state-of-the-art econometric techniques. In the first stage, we identify market segments using model-based clustering techniques and estimate separate hedonic price functions for each segment. We spatially smooth the data to account for omitted spatial covariates and employ a semiparametric estimator to allow flexibility in functional form. In the second stage, we control for nonlinearity of the budget constraint and identify demand relationships using techniques that account for problems of endogeneity and censoring of the dependent variable. We report welfare estimates for peace and quiet that we believe to be the first derived from property market data in a theoretically consistent manner.
demand system hedonic pricing model-based clustering Noise valuation partial linear model simultaneous-equation Tobit spatial autocorrelation