A Dynamic Model of Demand for Houses and Neighborhoods
Patrick J. Bayer
Duke University - Department of Economics; National Bureau of Economic Research (NBER)
University of Toronto - Department of Economics
Washington University in St. Louis - Olin Business School
Duke University - Department of Economics
NBER Working Paper No. w17250
We 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. We 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. Consistent with the time-series properties of each amenity, we find that a static demand model understates willingness to pay to avoid pollution and crime but overstates willingness to pay to live near neighbors of one’s own race. These findings have important implications for the class of static housing demand models typically used to value urban amenities.
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Number of Pages in PDF File: 44working papers series
Date posted: July 29, 2011
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