Hedonic valuation of environmental amenities and issues of model selection

A large household-level data set is combined with the two-stage hedonic estimation technique to derive new estimates of willingness-to-pay for reduced air pollution. The estimates are found robust against functional-form specification. Marginal WTP estimates for a reduction in PM-10 are found to be quite comparable with some previous estimates. Benefits of non-marginal changes exhibit consistently higher monetary returns in the case of PM-10 than in the case of SO;Ellickson's random bidding model is compared with the standard hedonic model in estimating benefits due to marginal and non-marginal changes in housing attributes. The benefit estimates obtained using the two models are found to be very close. In addition, various categorizations based on household characteristics are tested in the case of the random bidding model. The effect of categorization on the overall benefit estimates are found to be negligible. Moreover, the results from the random bidding model reveal that increased income leads to greater demand for environmental quality.;The nested logit model is applied to the Chicago housing data set, with the goal of valuing environmental amenities. Four alternative hierarchical nestings based on dwelling, neighborhood, and city attributes are considered, and a sampling rule satisfying McFadden's uniform conditioning property is adopted. The benefit estimates of dwelling and city attributes are found to be less sensitive, while those of neighborhood attributes are found to be more sensitive to alternative nesting strategies. The effects of household demographics on parameter estimates generally conform to standard notions. A comparison of the model with the standard two-step hedonic model reveals that the benefit estimates are consistently lower in the case of the former.