Forecasting spatial dynamics of the housing market using Support Vector Machine

Jieh Haur Chen, Chuan Fan Ong, Linzi Zheng, Shu-Chien Hsu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

49 Citations (Scopus)


This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.
Original languageEnglish
Pages (from-to)273-283
Number of pages11
JournalInternational Journal of Strategic Property Management
Issue number3
Publication statusPublished - 3 Jul 2017


  • Hedonic appraisal method
  • Housing price forecasting
  • Spatial dynamics
  • Supporting vector machine

ASJC Scopus subject areas

  • Strategy and Management


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