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

S. Thomas Ng*, Martin Skitmore, Keung Fai Wong

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

56 Citations (Scopus)
1 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1171-1184
Number of pages14
JournalBuilding and Environment
Issue number6
Publication statusPublished - Jun 2008
Externally publishedYes


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