Working Paper: NBER ID: w17250
Authors: Patrick Bayer; Robert McMillan; Alvin Murphy; Christopher Timmins
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.
Keywords: dynamic model; neighborhood choice; housing demand; willingness to pay; nonmarketed amenities
JEL Codes: H0; H23; H41; H7; L85; R0; R14; R21; R31; R51
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| household location decisions (R20) | influenced by expectations about future utility streams (D84) |
| household location decisions (R20) | influenced by evolution of neighborhood attributes (R23) |
| changing neighborhood characteristics (R23) | affect household choices (D10) |
| current and lagged neighborhood characteristics (R23) | influence household location decisions (R20) |
| static models of demand (D12) | underestimate MWTP for low-income households (D11) |
| static models of demand (D12) | overestimate MWTP for high-income households (D11) |
| mean-reverting disamenities like crime (R11) | lead to underestimation of MWTP in static models (D11) |
| positively persistent attributes like neighborhood racial composition (R23) | lead to overestimation of MWTP in static models (D11) |
| neglecting dynamic considerations in demand estimation (D12) | results in biases in understanding household preferences (D11) |