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Quantile Regression Estimates of Hong Kong Real Estate Prices

Listed author(s):
  • 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,

Registered author(s):

    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.

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    Article provided by Urban Studies Journal Limited in its journal Urban Studies.

    Volume (Year): 47 (2010)
    Issue (Month): 11 (October)
    Pages: 2461-2472

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    Handle: RePEc:sae:urbstu:v:47:y:2010:i:11:p:2461-2472
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