Estimating Hedonic Models of Consumer Demand with an Application to Urban Sprawl

  • Patrick Bajari
  • Matthew E. Kahn

Hedonic regressions are one of the most commonly used techniques in applied microeconomics for the study differentiated product markets. Hedonic regressions date back at least 80 years (see Waugh 1928) and have been an active research area for several decades, with seminal contributions by Grillches (1971), Rosen (1974), Epple (1987) and Taylor (this Volume). In this chapter, we describe a flexible, but computationally simple approach for estimating structural models of consumer demand using hedonics. The framework is an application of Bajari and Benkard (2005) and Bajari and Kahn (2005), which builds on the classic Rosen hedonic two-step (Rosen 1974; Epple 1987). In a first stage estimation, a flexible home price regression is estimated using local linear regression. Second, using the results from the local linear regression, we recover the implicit price faced by each household in our data set and the marginal utility of each household for every product characteristic. This allows us to generate a nonparametric distribution of random coefficients for the various product characteristics in our data set. Third, we regress the random coefficients on consumer demographics in order to learn about the joint distribution of tastes and demographic characteristics.


Census Tract Central Business District Random Coefficient Hedonic Model Commute Time 
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Copyright information

© Springer Science + Business Media, LLC 2008

Authors and Affiliations

  • Patrick Bajari
    • 1
  • Matthew E. Kahn
    • 2
  1. 1.University of Minnesota and NBERMinneapolisUS
  2. 2.University of California, Los Angeles and NBERUS

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