Forecasting Hong Kong Property Prices: Multiple Regression Method vs An Artificial Neural Network Approach

Ge, J; Runeson, G; Lam, K; Chan, K

Forecasting Hong Kong Property Prices: Multiple Regression Method vs An Artificial Neural Network Approach

Ge, J

Runeson, G Lam, K Chan, K

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Publisher:: World of Construction Project Management
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 1st International Conference, World of Construction Project Management, 2004, NA pp. 79 - 86
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.contributor.author	Lam, K	en_US
dc.contributor.author	Chan, K	en_US
dc.contributor.editor	Poh, PSH	en_US
dc.contributor.editor	Mackenzie, AD	en_US
dc.contributor.editor	Katsanis, CJ	en_US
dc.date	2004-05-27	en_US
dc.date.issued	2004-01	en_US
dc.identifier.citation	Proceedings of the 1st International Conference, World of Construction Project Management, 2004, NA pp. 79 - 86	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11260
dc.description.abstract	Hong Kong's residential property market has experienced significant changes over the last two decades. It is an interesting challenge for modeling the volatility of property prices. The aim of the study is to build forecasting models for forecasting the private residential property prices using macro data in Hong Kong. Both artificial neural network approach and multiple regression analysis are employed for comparing the forecasting results. Variables such as lagged property prices, household income and transaction volume are derived to test the models. The results show that both methods are valid and that the artificial neural network demonstrates a good prediction power with low forecasting error.	en_US
dc.publisher	World of Construction Project Management	en_US
dc.relation.ispartof	Proceedings of the 1st International Conference, World of Construction Project Management	en_US
dc.relation.ispartof	World of Construction Project Management Conference	en_US
dc.title	Forecasting Hong Kong Property Prices: Multiple Regression Method vs An Artificial Neural Network Approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	NA	en_US
utslib.location	Toronto, Canada	en_US
utslib.location.activity	Toronto, Canada	en_US
utslib.for	1202 Building	en_US
dc.location.activity	Toronto, Canada	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.place-of-publication	Toronto, Canada	en_US
pubs.start-date	2004-05-27	en_US
pubs.volume	NA	en_US

Abstract:

Hong Kong's residential property market has experienced significant changes over the last two decades. It is an interesting challenge for modeling the volatility of property prices. The aim of the study is to build forecasting models for forecasting the private residential property prices using macro data in Hong Kong. Both artificial neural network approach and multiple regression analysis are employed for comparing the forecasting results. Variables such as lagged property prices, household income and transaction volume are derived to test the models. The results show that both methods are valid and that the artificial neural network demonstrates a good prediction power with low forecasting error.

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http://hdl.handle.net/10453/11260