Determinants of House Prices: A Quantile Regression Approach
Joachim Zietz,
Emily N. Zietz and
G. Stacy Sirmans
No 200706, Working Papers from Middle Tennessee State University, Department of Economics and Finance
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.
Keywords: hedonic price function; quantile regression; spatial lag (search for similar items in EconPapers)
JEL-codes: C21 C29 R31 (search for similar items in EconPapers)
Date: 2007-05
New Economics Papers: this item is included in nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)
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http://capone.mtsu.edu/berc/working/Quantile%20Reg%20working%20paper.pdf (application/pdf)
Related works:
Journal Article: Determinants of House Prices: A Quantile Regression Approach (2008) 
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Persistent link: https://EconPapers.repec.org/RePEc:mts:wpaper:200706
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