The Journal of Real Estate Finance and Economics

, Volume 37, Issue 4, pp 317–333

Determinants of House Prices: A Quantile Regression Approach

  • Joachim Zietz
  • Emily Norman Zietz
  • G. Stacy Sirmans

DOI: 10.1007/s11146-007-9053-7

Cite this article as:
Zietz, J., Zietz, E.N. & Sirmans, G.S. J Real Estate Finance Econ (2008) 37: 317. doi:10.1007/s11146-007-9053-7


OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance, but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices. To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.


Hedonic price function Quantile regression Spatial lag 

JEL Classification

R31 C21 C29 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Joachim Zietz
    • 1
  • Emily Norman Zietz
    • 2
  • G. Stacy Sirmans
    • 3
  1. 1.Department of Economics and FinanceMiddle Tennessee State UniversityMurfreesboroUSA
  2. 2.Department of Economics and FinanceMiddle Tennessee State UniversityMurfreesboroUSA
  3. 3.Department of Insurance, Real Estate and Business LawThe Florida State UniversityTallahasseeUSA

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