A Dynamic Model of Demand for Houses and Neighborhoods
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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Copy CitationPatrick Bayer, Robert McMillan, Alvin Murphy, and Christopher Timmins, "A Dynamic Model of Demand for Houses and Neighborhoods," NBER Working Paper 17250 (2011), https://doi.org/10.3386/w17250.Download Citation
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Published Versions
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. citation courtesy of 
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