Forecasting Hong Kong House Prices: An Artificial Neural Network vs Log-linear Regression Approach

Ge, J; Runeson, G; Lam, K

Forecasting Hong Kong House Prices: An Artificial Neural Network vs Log-linear Regression Approach

Ge, J

Runeson, G Lam, K

Permalink

Publisher:: Blackwell Publishing
Publication Type:: Conference Proceeding
Citation:: 2nd International Postgraduate Research Conference in the Built and Human Environment, 2002, pp. 81 - 95
Issue Date:: 2002-01

Closed Access

	Filename	Description	Size
	2006009448OK.pdf		2.31 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.editor	Sun, M	en_US
dc.contributor.editor	Aouad, G	en_US
dc.contributor.editor	Green, C	en_US
dc.contributor.editor	Omerod, M	en_US
dc.contributor.editor	Ruddock, L	en_US
dc.contributor.editor	Alexander, K	en_US
dc.date	2002-04-11	en_US
dc.date.issued	2002-01	en_US
dc.identifier.citation	2nd International Postgraduate Research Conference in the Built and Human Environment, 2002, pp. 81 - 95	en_US
dc.identifier.isbn	1-900491-70-2	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11276
dc.description.abstract	Modeling the volatility of property prices presents an interesting challenge for researchers. The purpose of the study is to compare an artificial neural network approach and log-linear regression model for predicting private residential property prices in Hong Kong using aggregate variables such real housing prices, real income, interest rate, demographic variables, and so on. The results show that the log-linear regression approach has less the standard error in forecasting. However, an artificial neural network (ANN) has an advantage in its ability to map complicated non-linear relationship between variables and it also has a good predict power.	en_US
dc.publisher	Blackwell Publishing	en_US
dc.relation.ispartof	2nd International Postgraduate Research Conference in the Built and Human Environment	en_US
dc.relation.ispartof	International Postgraduate Research Conference in the Built and Human Environment	en_US
dc.title	Forecasting Hong Kong House Prices: An Artificial Neural Network vs Log-linear Regression Approach	en_US
dc.type	Conference Proceeding
utslib.location	University of Salford, UK	en_US
utslib.location.activity	Salford, UK	en_US
utslib.for	1202 Building	en_US
utslib.for	120201 Building Construction Management and Project Planning	en_US
dc.location.activity	Salford, UK	en_US
dc.location.activity	Pasadena, CA
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	University of Salford, UK	en_US
pubs.start-date	2002-04-11	en_US

Abstract:

Modeling the volatility of property prices presents an interesting challenge for researchers. The purpose of the study is to compare an artificial neural network approach and log-linear regression model for predicting private residential property prices in Hong Kong using aggregate variables such real housing prices, real income, interest rate, demographic variables, and so on. The results show that the log-linear regression approach has less the standard error in forecasting. However, an artificial neural network (ANN) has an advantage in its ability to map complicated non-linear relationship between variables and it also has a good predict power.

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

http://hdl.handle.net/10453/11276