Determinants of House Prices: A Quantile Regression Approach
AbstractOLS 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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Bibliographic InfoArticle provided by Springer in its journal The Journal of Real Estate Finance and Economics.
Volume (Year): 37 (2008)
Issue (Month): 4 (November)
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Web page: http://www.springerlink.com/link.asp?id=102945
Hedonic price function; Quantile regression; Spatial lag; R31; C21; C29;
Other versions of this item:
- Joachim Zietz & Emily N. Zietz & G. Stacy Sirmans., 2007. "Determinants of House Prices: A Quantile Regression Approach," Working Papers 200706, Middle Tennessee State University, Department of Economics and Finance.
- R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, 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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