Modeling Property Prices Using Neural Network Model for Hong Kong

Ge, J; Runeson, G

Modeling Property Prices Using Neural Network Model for Hong Kong

Ge, J

Runeson, G

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Publisher:: Asian Real Estate Society
Publication Type:: Journal Article
Citation:: International Real Estate Review, 2004, 7 (1), pp. 121 - 138
Issue Date:: 2004-01

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Field	Value	Language
dc.contributor.author	Ge, J https://orcid.org/0000-0002-2252-7829	en_US
dc.contributor.author	Runeson, G	en_US
dc.date.issued	2004-01	en_US
dc.identifier.citation	International Real Estate Review, 2004, 7 (1), pp. 121 - 138	en_US
dc.identifier.issn	1029-6131	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10070
dc.description.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.	en_US
dc.publisher	Asian Real Estate Society	en_US
dc.relation.ispartof	International Real Estate Review	en_US
dc.title	Modeling Property Prices Using Neural Network Model for Hong Kong	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	7	en_US
utslib.for	1504 Commercial Services	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/e-Press
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	7	en_US

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/10070