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A Dynamic Model of Demand for Houses and Neighborhoods

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

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. The model is estimated 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- 2004. The results provide the first estimates of the marginal willingness to pay for several non-marketed amenities – neighborhood 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.

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File URL: http://www.nber.org/papers/w17250.pdf
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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
Note: EEE IO PE
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