Forecasting spatial dynamics of the housing market using Support Vector Machine

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

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

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
Volume21
Issue number3
DOIs
StatePublished - 3 Jul 2017

Keywords

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

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