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Article: Using genetic algorithms and linear regression analysis for private housing demand forecast

TitleUsing genetic algorithms and linear regression analysis for private housing demand forecast
Authors
KeywordsDemand
Forecasting
Genetic algorithm
Housing
Models
Private sector
Supply
Issue Date2008
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/buildenv
Citation
Building And Environment, 2008, v. 43 n. 6, p. 1171-1184 How to Cite?
AbstractAn accurate prediction of prospective construction supply and demand, especially the private residential market, is paramount important to policy makers, as it could help formulate strategies to cultivate/stabilize the economy and satisfy the social needs (at macro level). Despite that, a realistic prediction of future private residential demand is never an easy task, as it is governed by a number of social and economic factors. In this paper, four leading indicator models are developed and compared for directly forecasting Hong Kong private sector residential demand. These comprise a (i) Linear Regression Analysis (LRA) model, (ii) Genetic Algorithms (GA) model, (iii) GA-LRA model, where LRA is used to select the indicator variables; and (iv) GA-LRA model with Adaptive Mutation Rate (AMR) to reduce the likelihood of local optima. The findings indicate that the GA-LRA model with AMR provides the most accurate forecasts and over a longer time horizon. In providing a range of possible forecasts, the model also provides an opportunity for the decision-maker to exercise judgment in selecting the most appropriate forecasts. © 2007 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/70679
ISSN
2015 Impact Factor: 3.394
2015 SCImago Journal Rankings: 2.121
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorThomas Ng, Sen_HK
dc.contributor.authorSkitmore, Men_HK
dc.contributor.authorWong, KFen_HK
dc.date.accessioned2010-09-06T06:25:08Z-
dc.date.available2010-09-06T06:25:08Z-
dc.date.issued2008en_HK
dc.identifier.citationBuilding And Environment, 2008, v. 43 n. 6, p. 1171-1184en_HK
dc.identifier.issn0360-1323en_HK
dc.identifier.urihttp://hdl.handle.net/10722/70679-
dc.description.abstractAn accurate prediction of prospective construction supply and demand, especially the private residential market, is paramount important to policy makers, as it could help formulate strategies to cultivate/stabilize the economy and satisfy the social needs (at macro level). Despite that, a realistic prediction of future private residential demand is never an easy task, as it is governed by a number of social and economic factors. In this paper, four leading indicator models are developed and compared for directly forecasting Hong Kong private sector residential demand. These comprise a (i) Linear Regression Analysis (LRA) model, (ii) Genetic Algorithms (GA) model, (iii) GA-LRA model, where LRA is used to select the indicator variables; and (iv) GA-LRA model with Adaptive Mutation Rate (AMR) to reduce the likelihood of local optima. The findings indicate that the GA-LRA model with AMR provides the most accurate forecasts and over a longer time horizon. In providing a range of possible forecasts, the model also provides an opportunity for the decision-maker to exercise judgment in selecting the most appropriate forecasts. © 2007 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/buildenven_HK
dc.relation.ispartofBuilding and Environmenten_HK
dc.subjectDemanden_HK
dc.subjectForecastingen_HK
dc.subjectGenetic algorithmen_HK
dc.subjectHousingen_HK
dc.subjectModelsen_HK
dc.subjectPrivate sectoren_HK
dc.subjectSupplyen_HK
dc.titleUsing genetic algorithms and linear regression analysis for private housing demand forecasten_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0360-1323&volume=43&issue=6&spage=1171&epage=1184&date=2008&atitle=Using+genetic+algorithms+and+linear+regression+analysis+for+private+housing+demand+forecasten_HK
dc.identifier.emailThomas Ng, S:tstng@hkucc.hku.hken_HK
dc.identifier.authorityThomas Ng, S=rp00158en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.buildenv.2007.02.017en_HK
dc.identifier.scopuseid_2-s2.0-38949215487en_HK
dc.identifier.hkuros142794en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-38949215487&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1171en_HK
dc.identifier.epage1184en_HK
dc.identifier.eissn1873-684X-
dc.identifier.isiWOS:000254216900022-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridThomas Ng, S=7403358853en_HK
dc.identifier.scopusauthoridSkitmore, M=7003387239en_HK
dc.identifier.scopusauthoridWong, KF=23490972600en_HK

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