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Modeling Property Prices Using Neural Network Model for Hong Kong

  • Xin J. Ge

    ()

    (School of Built Environment, UNITEC New Zealand, Carrington Rd, Mt Albert, Private Bag 92025, Auckland, New Zealand)

  • G. Runeson

    ()

    (Faculty of Design, Architecture and Building, University of NSW, Australia, 22 Tarrawanna Rd, Corrimal 2518, NSW, Australia)

Registered author(s):

    This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

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    File URL: http://www.umac.mo/fba/irer/papers/past/Vol7_pdf/121-138.pdf
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    Article provided by Asian Real Estate Society in its journal International Real Estate Review.

    Volume (Year): 7 (2004)
    Issue (Month): 1 ()
    Pages: 121-138

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    Handle: RePEc:ire:issued:v:07:n:01:2004:p:121-138
    Contact details of provider: Postal: Asia Real Estate Society, 51 Monroe Street, Plaza E-6, Rockville, MD 20850, USA
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    1. Lennart Berg, 2005. "Price Indexes For Multi-dwelling Properties In Sweden," Journal of Real Estate Research, American Real Estate Society, vol. 27(1), pages 47-82.
    2. DiPasquale Denise & Wheaton William C., 1994. "Housing Market Dynamics and the Future of Housing Prices," Journal of Urban Economics, Elsevier, vol. 35(1), pages 1-27, January.
    3. Chan, Hing Lin & Lee, Shu Kam & Woo, Kai Yin, 2001. "Detecting rational bubbles in the residential housing markets of Hong Kong," Economic Modelling, Elsevier, vol. 18(1), pages 61-73, January.
    4. Jonathan Jingsheng Shi, 1999. "A neural network based system for predicting earthmoving production," Construction Management and Economics, Taylor & Francis Journals, vol. 17(4), pages 463-471.
    5. A. H. Boussabaine & A. P. Kaka, 1998. "A neural networks approach for cost flow forecasting," Construction Management and Economics, Taylor & Francis Journals, vol. 16(4), pages 471-479.
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