FORECASTING SPATIAL DYNAMICS OF THE HOUSING MARKET USING SUPPORT VECTOR MACHINE
| Autor: | Linzi Zheng, Jieh Haur Chen, Shu Chien Hsu, Chuan Fan Ong |
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| Rok vydání: | 2017 |
| Předmět: |
Housing price forecasting
Estimation 050208 finance Hedonic appraisal method Spatial dynamics Strategy and Management 05 social sciences Kernel density estimation Nonparametric statistics Supporting vector machine HD28-70 Power (physics) Support vector machine Set (abstract data type) Dynamics (music) HG1-9999 0502 economics and business Management. Industrial management Econometrics Economics 050207 economics Finance |
| Zdroj: | International Journal of Strategic Property Management; Vol 21 No 3 (2017); 273-283 International Journal of Strategic Property Management, Vol 21, Iss 3 (2017) |
| ISSN: | 1648-9179 1648-715X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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