Modeling Property Prices Using Neural Network Model for Hong Kong

Xin J. Ge () and G. Runeson ()
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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

International Real Estate Review, 2004, vol. 7, issue 1, 121-138

Abstract: 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.

Keywords: residential property prices; artificial neural network (ANN); property price determinants; forecasting models; Hong Kong (search for similar items in EconPapers)
JEL-codes: L85 (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (2)

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