This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity.
The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon.
The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai.
The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition.
The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution.
This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses.
Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).
The release time support from the University of Dubai (UD) to the author to work on this paper is greatly appreciated. The author is grateful to Dr Geoffrey Gachino and Dr Alebel Bayru for their continued interest, stimulating discussions and criticism, and to REIDEN Dubai for providing the residential house sale price index. The Dubai Land Department (DLD) is also acknowledged for providing annual property transaction data. The author is indebted to the UDDBS faculty for their valuable comments during the weekly seminar at UD, notably Dr Mohammad Osman. Most importantly, the author is grateful to Mr Abdulmunem Alshehhi for placing the house locations on Google Maps.
Worku, G.B. (2017), "House price drivers in Dubai: nonlinearity and heterogeneity", International Journal of Housing Markets and Analysis, Vol. 10 No. 3, pp. 384-409. https://doi.org/10.1108/IJHMA-06-2016-0048
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