Exploring, modelling and predicting spatiotemporal variations in house prices

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• 作者：A. Stewart Fotheringham ; Ricardo Crespo ; Jing Yao
• 关键词：Hedonic price models ; GWR ; Spatiotemporal modelling ; GIS ; C3 ; R3
• 刊名：The Annals of Regional Science
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：54
• 期：2
• 页码：417-436
• 全文大小：1,978 KB
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• 作者单位：A. Stewart Fotheringham (1)
  Ricardo Crespo (2)
  Jing Yao (3)
  
  1. GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA
  2. Facultad de Ingeniería y Administración, Universidad Bernardo O’Higgins, Santiago, Chile
  3. Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, G12 8RZ, UK
  
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Regional Science
  Landscape, Regional and Urban Planning
  Microeconomics
  Environmental Economics
  Geography
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-0592

文摘

Hedonic price modelling has long been a powerful tool to estimate house prices in the real estate market. Increasingly, traditional global hedonic price models that largely ignore spatial effects are being superseded by models that deal with spatial dependency and spatial heterogeneity. In addition, many novel methods integrating spatial economics, statistics and geographical information science (GIScience) have been developed recently to incorporate temporal effects into hedonic house price modelling. Here, a local spatial modelling technique, geographically weighted regression (GWR), which accounts for spatial heterogeneity in housing utility functions is applied to a 19-year set of house price data in London (1980-998) in order to explore spatiotemporal variations in the determinants of house prices. Further, based on the local parameter estimates derived from GWR, a new method integrating GWR and time series (TS) forecasting techniques, GWR–TS, is proposed to predict future local parameters and thus future house prices. The results obtained from GWR demonstrate variations in local parameter estimates over both space and time. The forecasted future values of local estimates as well as house prices indicate that the proposed GWR–TS method is a useful addition to hedonic price modelling.