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A Dynamic Model of Demand for Houses and Neighborhoods

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  • Patrick Bayer
  • Robert McMillan
  • Alvin Murphy
  • Christopher Timmins
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    Abstract

    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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    Bibliographic Info

    Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17250.

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    Date of creation: Jul 2011
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    Handle: RePEc:nbr:nberwo:17250

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    References

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    Cited by:
    1. Gregorio Caetano & Vikram Maheshri, 2013. "School Segregation and the Identification of Tipping Behavior," Working Papers 2013-252-50, Department of Economics, University of Houston.

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