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The authors develop a tractable model of neighborhood choice in a dynamic setting along with a computationally straightforward estimation approach. This approach uses information about neighborhood choices and the timing of moves to recover moving costs and preferences for dynamically-evolving housing and neighborhood attributes. The model and estimator are potentially applicable to the study of a wide range of dynamic phenomena in housing markets and cities. They focus here on estimating the marginal willingness to pay for non-marketed amenities - neighborhood racial composition, air pollution, and violent crime - using rich dynamic data.
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