We build on the intuitive (static) modeling framework of Rosen (1974) and specify a simple, forward-looking model of location choice. We use this model, along with a series of graphs, to describe the potential biases associated with the static model and relate these biases to the time series of the amenity of interest. We then derive an adjustment factor that allows the potentially biased static estimates to be converted into forwardlooking estimates. Finally, we illustrate these concepts with two empirical applications: the marginal willingness to pay to avoid violent crime and the marginal willingness to pay to avoid air pollution.

We use data from a housing-assistance experiment to estimate a model of neighborhood choice. The experimental variation effectively randomizes the rents which households face and helps identify a key structural parameter. Access to two randomly selected treatment groups and a control group allows for out-of-sample validation of the model. We simulate the effects of changing the subsidy-use constraints implemented in the actual experiment. We find that restricting subsidies to even lower poverty neighborhoods would substantially reduce take-up and actually increase average exposure to poverty. Furthermore, adding restrictions based on neighborhood racial composition would not change average exposure to either race or poverty. (JEL I32, I38, R23, R38)

The hedonic model, which has been used extensively in the Environmental, Urban, and Real Estate literatures, allows for the estimation of the implicit prices of housing and neighborhood attributes, as well as households' demand for these non-marketed amenities. A recognized drawback of the existing hedonic literature is that the models assume a myopic decision-maker. In this paper, we estimate a dynamic hedonic model and find that the average household is willing to pay $472 per year for a ten percent reduction in violent crime. In addition, we find that the traditional, myopic model suffers from a 21 percent negative bias.