A Dynamic Model of Demand for Houses and Neighborhoods

Patrick Bayer, Robert McMillan, Alvin Murphy and Christopher Timmins

No 17250, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: H0 H23 H41 H7 L85 R0 R14 R21 R31 R51 (search for similar items in EconPapers)
Date: 2011-07
New Economics Papers: this item is included in nep-ure
Note: EEE IO PE
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Citations: View citations in EconPapers (42)

Published as Patrick Bayer & Robert McMillan & Alvin Murphy & Christopher Timmins, 2016. "A Dynamic Model of Demand for Houses and Neighborhoods," Econometrica, Econometric Society, vol. 84, pages 893-942, 05.

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Related works:
Journal Article: A Dynamic Model of Demand for Houses and Neighborhoods (2016)
Working Paper: A Dynamic Model of Demand for Houses and Neighborhoods (2011)
Working Paper: A Dynamic Model of Demand for Houses and Neighborhoods (2011)
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