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Quantile Regression Estimates of Hong Kong Real Estate Prices


  • Stephen Mak

    (Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong,

  • Lennon Choy

    (Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong,

  • Winky Ho

    (Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong,


Linear regression is a statistical tool used to model the relation between a set of housing characteristics and real estate prices. It estimates the mean value of the response variable, given levels of the predictor variables. The quantile regression approach complements the least squares by identifying how differently real estate prices respond to a change in one unit of housing characteristic at different quantiles, rather than estimating the constant regression coefficient representing the change in the response variable produced by a one-unit change in the predictor variable associated with that coefficient. It estimates the implicit price for each characteristic across the distribution of prices and allows buyers of higher-priced properties to behave differently from buyers of lower-priced properties, even if they are within one single housing estate. Thus, it provides a better explanation of the real-world phenomenon and offers a more comprehensive picture of the relationship between housing characteristics and prices.

Suggested Citation

  • Stephen Mak & Lennon Choy & Winky Ho, 2010. "Quantile Regression Estimates of Hong Kong Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 47(11), pages 2461-2472, October.
  • Handle: RePEc:sae:urbstu:v:47:y:2010:i:11:p:2461-2472

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    References listed on IDEAS

    1. Epstein, Gil S., 2002. "Informational Cascades and Decision to Migrate," IZA Discussion Papers 445, Institute for the Study of Labor (IZA).
    2. Louis de Mesnard, 2004. "Biproportional Methods of Structural Change Analysis: A Typological Survey," Economic Systems Research, Taylor & Francis Journals, vol. 16(2), pages 205-230.
    3. Rouwendal, Jan, 1999. "Spatial job search and commuting distances," Regional Science and Urban Economics, Elsevier, vol. 29(4), pages 491-517, July.
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    Cited by:

    1. Zhang, Lei, 2016. "Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 12-19.
    2. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    3. Zahirovich-Herbert, Velma & Gibler, Karen M., 2014. "The effect of new residential construction on housing prices," Journal of Housing Economics, Elsevier, vol. 26(C), pages 1-18.
    4. Hyung-Gun Kim & Kwong-Chin Hung & Sung Park, 2015. "Determinants of Housing Prices in Hong Kong: A Box-Cox Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 50(2), pages 270-287, February.
    5. Zhang, Lei & Leonard, Tammy, 2014. "Neighborhood impact of foreclosure: A quantile regression approach," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 133-143.
    6. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    7. Sofie R. Waltl, 2015. "Variation across price segments and locations: A comprehensive quantile regression analysis of the Sydney housing market," Graz Economics Papers 2015-09, University of Graz, Department of Economics.

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