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

Ng, S. Thomas, Skitmore, Martin, & Wong, Keung Fai (2008) Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment, 43(6), pp. 1171-1184.

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An 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.

Impact and interest:

35 citations in Scopus
17 citations in Web of Science®
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ID Code: 13416
Item Type: Journal Article
Refereed: Yes
Keywords: Construction safety, safety management system, safety performance evaluation
DOI: 10.1016/j.buildenv.2007.02.017
ISSN: 0360-1323
Subjects: Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > BUILDING (120200) > Building Construction Management and Project Planning (120201)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2008 Elsevier
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 01 May 2008 00:00
Last Modified: 21 Jun 2017 14:40

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