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

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dc.contributor.author Ge, J
dc.contributor.author Runeson, G
dc.contributor.author Lam, K
dc.contributor.editor Sun, M
dc.contributor.editor Aouad, G
dc.contributor.editor Green, C
dc.contributor.editor Omerod, M
dc.contributor.editor Ruddock, L
dc.contributor.editor Alexander, K
dc.date.accessioned 2010-05-28T10:03:15Z
dc.date.issued 2002-01
dc.identifier.citation 2nd International Postgraduate Research Conference in the Built and Human Environment, 2002, pp. 81 - 95
dc.identifier.isbn 1-900491-70-2
dc.identifier.other E1UNSUBMIT 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.
dc.publisher Blackwell Publishing
dc.subject artificial neural network, log-linear regression, property price forecating
dc.subject artificial neural network, log-linear regression, property price forecating
dc.title Forecasting Hong Kong House Prices: An Artificial Neural Network vs Log-linear Regression Approach
dc.type Conference Proceeding
dc.description.version Published
dc.parent 2nd International Postgraduate Research Conference in the Built and Human Environment
dc.journal.number en_US
dc.publocation University of Salford, UK en_US
dc.publocation University of Salford, UK
dc.identifier.startpage 81 en_US
dc.identifier.endpage 95 en_US
dc.cauo.name DAB.Faculty of Design, Architecture and Building en_US
dc.conference Verified OK en_US
dc.conference IEEE International Conference on Robotics and Automation
dc.conference International Postgraduate Research Conference in the Built and Human Environment
dc.for 1202 Building
dc.for 120201 Building Construction Management and Project Planning
dc.personcode 100820 en_US
dc.personcode 014057 en_US
dc.personcode 0000030397 en_US
dc.percentage 50 en_US
dc.classification.name Building en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Postgraduate Research Conference in the Built and Human Environment en_US
dc.date.activity 20020411 en_US
dc.date.activity 2008-05-19
dc.date.activity 2002-04-11
dc.location.activity Salford, UK en_US
dc.location.activity Pasadena, CA
dc.location.activity Salford, UK
dc.description.keywords artificial neural network, log-linear regression, property price forecating en_US
dc.description.keywords Science & Technology
dc.description.keywords Technology
dc.description.keywords Automation & Control Systems
dc.description.keywords Robotics
dc.description.keywords artificial neural network, log-linear regression, property price forecating
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
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


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