Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76001
Title: Forecasting spatial dynamics of the housing market using support vector machine
Authors: Chen, JH
Ong, CF
Zheng, LZ
Hsu, SC 
Keywords: Housing price forecasting
Spatial dynamics
Supporting vector machine
Hedonic appraisal method
Issue Date: 2017
Publisher: Taylor & Francis co-Published with Vilnius Gediminas Technical University
Source: International journal of strategic property management, 2017, v. 21, no. 3, p. 273-283 How to cite?
Journal: International journal of strategic property management 
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.
URI: http://hdl.handle.net/10397/76001
ISSN: 1648-715X
EISSN: 1648-9179
DOI: 10.3846/1648715X.2016.1259190
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