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Determinants of House Prices: A Quantile Regression Approach

Author

Listed:
  • Joachim Zietz
  • Emily Zietz
  • G. Sirmans
Abstract
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.
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Suggested Citation

  • Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    • Handle: RePEc:kap:jrefec:v:37:y:2008:i:4:p:317-333
    DOI: 10.1007/s11146-007-9053-7
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    References listed on IDEAS

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    More about this item

    Keywords

    Hedonic price function; Quantile regression; Spatial lag; R31; C21; C29;
    All these keywords.

    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

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