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

69 Citations (Scopus)
66 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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