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

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dc.contributor.author Ge, Xin Janet en_US
dc.contributor.author Runeson, Karl en_US
dc.contributor.author Lam, Kai-Chi en_US
dc.contributor.editor Ming Sun, Ghassan Aouad, Catherine Green, Marcus Omerod, Les Ruddock, Keith Alexander en_US
dc.date.accessioned 2010-05-28T10:03:15Z
dc.date.available 2010-05-28T10:03:15Z
dc.date.issued 2002 en_US
dc.identifier 2006009448 en_US
dc.identifier.citation Ge Xin Janet, Runeson Karl, and Lam Kai-Chi 2002, 'Forecasting Hong Kong House Prices: An Artificial Neural Network vs Log-linear Regression Approach', Blackwell Publishing, University of Salford, UK, pp. 81-95. en_US
dc.identifier.issn 1-900491-70-2 en_US
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. en_US
dc.publisher Blackwell Publishing en_US
dc.relation.isbasedon en_US
dc.title Forecasting Hong Kong House Prices: An Artificial Neural Network vs Log-linear Regression Approach en_US
dc.parent 2nd International Postgraduate Research Conference in the Built and Human Environment en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation University of Salford, UK en_US
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.for 120200 en_US
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.location.activity Salford, UK en_US
dc.description.keywords artificial neural network, log-linear regression, property price forecating en_US


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