Forecasting spatial dynamics of the housing market using Support Vector Machine

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

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.

Keywords:

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

How to Cite

Chen, J.-H., Ong, C. F., Zheng, L., & Hsu, S.-C. (2017). Forecasting spatial dynamics of the housing market using Support Vector Machine. International Journal of Strategic Property Management, 21(3), 273-283. https://doi.org/10.3846/1648715X.2016.1259190

Share

Published in Issue
July 11, 2017
Abstract Views
1785

View article in other formats

CrossMark check

CrossMark logo

Published

2017-07-11

Issue

Section

Articles

How to Cite

Chen, J.-H., Ong, C. F., Zheng, L., & Hsu, S.-C. (2017). Forecasting spatial dynamics of the housing market using Support Vector Machine. International Journal of Strategic Property Management, 21(3), 273-283. https://doi.org/10.3846/1648715X.2016.1259190

Share