Exploring, modelling and predicting spatiotemporal variations in house prices

Stewart Fotheringham, Ricardo Crespo, Jing Yao

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


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 Increasingly, traditional global hedonic price models that largely ignore spatial effects are being superseded by models that deal with spatial dependency and spatial heterogeneity.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 (1980–1998) 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.

Original languageEnglish (US)
Pages (from-to)417-436
Number of pages20
JournalAnnals of Regional Science
Issue number2
StatePublished - Mar 4 2015


  • GIS
  • GWR
  • Hedonic price models
  • Spatiotemporal modelling

ASJC Scopus subject areas

  • General Environmental Science
  • General Social Sciences


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