A Dynamic Model of Demand for Houses and Neighborhoods

Patrick Bayer; Robert McMillan; Alvin Murphy; Christopher Timmins

doi:10.3982/ECTA10170

A Dynamic Model of Demand for Houses and Neighborhoods

Patrick Bayer, Robert McMillan, Alvin Murphy, Christopher Timmins

Economics

Research output: Contribution to journal › Article › peer-review

84 Scopus citations

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. We estimate the model 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 to 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.

Original language	English (US)
Pages (from-to)	893-942
Number of pages	50
Journal	Econometrica
Volume	84
Issue number	3
DOIs	https://doi.org/10.3982/ECTA10170
State	Published - May 1 2016

Keywords

Amenities
Dynamic discrete choice
Hedonic valuation
Housing demand
Neighborhood choice
Residential sorting
Unobserved heterogeneity

ASJC Scopus subject areas

Economics and Econometrics

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10.3982/ECTA10170

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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. We estimate the model 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 to 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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AU - McMillan, Robert

AU - Murphy, Alvin

AU - Timmins, Christopher

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AB - 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. We estimate the model 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 to 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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KW - Hedonic valuation

KW - Housing demand

KW - Neighborhood choice

KW - Residential sorting

KW - Unobserved heterogeneity

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A Dynamic Model of Demand for Houses and Neighborhoods

Abstract

Keywords

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