This paper develops a dynamic model of neighborhood choice along with a computationally light multi-step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non-marketed amenitiesneighborhood air pollution, violent crime, and racial composition-in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.estimate P¯τ 0t separately for each year and type. Instead, for each type, we estimate a separate logit model including a linear time trend. 43 The closed form is given by ṽ¯τ j t = log( P¯τ j t ) − 1 J+1 J k=0 log( P¯τ k t ).

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.

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)

No abstract

This paper estimates a dynamic microeconometric model of housing supply. The model features forward-looking landowners who optimally choose both the timing and the nature of construction while taking into account expectations about future prices and costs. The model is estimated using a unique dataset describing individual landowners in the San Francisco Bay Area. Results indicate that geographic and time-series variation in costs are key to understanding where and when construction occurs. Pro-cyclical costs provide an incentive for some landowners to build before price peaks. Results also indicate that landowners actively “time” the market, which reduces the elasticity of supply. (JEL C51, D12, E32, R21, R23, R31)