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Determinants of House Prices: A Quantile Regression Approach

Listed author(s):
  • Joachim Zietz
  • Emily N. Zietz
  • G. Stacy Sirmans.

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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File URL: http://capone.mtsu.edu/berc/working/Quantile%20Reg%20working%20paper.pdf
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Paper provided by Middle Tennessee State University, Department of Economics and Finance in its series Working Papers with number 200706.

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Date of creation: May 2007
Handle: RePEc:mts:wpaper:200706
Contact details of provider: Web page: http://www.mtsu.edu/~berc/working/Economics_Working_Papers.html
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  1. Tae-Hwan Kim & Christophe Muller, 2004. "Two-stage quantile regression when the first stage is based on quantile regression," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 218-231, 06.
  2. Bartik, Timothy J, 1987. "The Estimation of Demand Parameters in Hedonic Price Models," Journal of Political Economy, University of Chicago Press, vol. 95(1), pages 81-88, February.
  3. William Gould, 1993. "Quantile regression with bootstrapped standard errors," Stata Technical Bulletin, StataCorp LP, vol. 2(9).
  4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, December.
  5. Epple, Dennis, 1987. "Hedonic Prices and Implicit Markets: Estimating Demand and Supply Functions for Differentiated Products," Journal of Political Economy, University of Chicago Press, vol. 95(1), pages 59-80, February.
  6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  7. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-161, January.
  8. Joachim Zietz & Bobby Newsome, 2002. "Agency Representation and the Sale Price of Houses," Journal of Real Estate Research, American Real Estate Society, vol. 24(2), pages 165-192.
  9. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
  10. William Gould, 1998. "Interquartile and simultaneous-quantile regression," Stata Technical Bulletin, StataCorp LP, vol. 7(38).
  11. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
  12. Stephen Malpezzi, "undated". "Hedonic Pricing Models: A Selective and Applied Review," Wisconsin-Madison CULER working papers 02-05, University of Wisconsin Center for Urban Land Economic Research.
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