Application of Machine Learning Approaches on Real-Time Apartment Prices in the Tokyo Metropolitan Area.
The widely applied hedonic regression approach for the relationship between property prices and housing attributes is subject to assumptions and specifications of models as well as the availability and content of second-hand official data. In a cross-disciplinary spirit, this study employs machine l...
| Publicado en: | Social Science Japan Journal Vol. 25; no. 1; pp. 3 - 29 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Winter2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=155087597&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 155087597 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 13691465 BJ9 jtl: Social Science Japan Journal issn: 13691465 maglogo: N pubinfo: dt: Winter2022 vid: 25 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 155087597 10.1093/ssjj/jyab029 ppf: 3 ppct: 26 formats: tig: atl: Application of Machine Learning Approaches on Real-Time Apartment Prices in the Tokyo Metropolitan Area. aug: au: Peng, Ti-Ching Wang, Chun-Chieh affil: Department of Real Estate and Built Environment at National Taipei University , New Taipei City , Taiwan Department of Computer Science at National Chengchi University , Taipei City , Taiwan su: Japan Metropolitan areas Machine learning Online databases Apartments Random forest algorithms sug: subj: Metropolitan areas Japan Machine learning Online databases Apartments Random forest algorithms keyword: apartment prices Hedonic price theory machine learning approaches online data apartment prices Hedonic price theory machine learning approaches online data ab: The widely applied hedonic regression approach for the relationship between property prices and housing attributes is subject to assumptions and specifications of models as well as the availability and content of second-hand official data. In a cross-disciplinary spirit, this study employs machine learning techniques to examine hedonic apartment prices in the Tokyo Metropolitan Area of Japan based on online sales data extracted by web-parsing technology. With 14,579 apartment observations, two machine learning regressions—decision tree (DT) and random forest (RF)—are compared to conventional ordinary least squares regression (OLS) for hedonic modelling. Empirical results demonstrated that RF regressions led to the highest accuracy in model prediction performance, followed by DT and OLS. The comparison with results across models revealed that the housing features that have consistent influences on apartment prices tend to be those associated with living quality (including management funds, repair fund fees, floor size, located floor, total floor of the building, and location in Tokyo). Other commonly appreciated features, such as southward orientation or corner-lot location, did not demonstrate importance, possibly due to changes in residents' preferences. In this big-data era, the adaptation of real-time data and machine learning approaches should add value to the variable selection process and model performance. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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