caa a22 4500 606165878 CHVBK 20210128100655.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00168-015-0660-6 doi (NATIONALLICENCE)springer-10.1007/s00168-015-0660-6 Exploring, modelling and predicting spatiotemporal variations in house prices [Elektronische Daten] [A. Fotheringham, Ricardo Crespo, Jing Yao] 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 Increasingly, traditional global hedonic price models that largely ignore spatial effects are being superseded by models that deal with spatial dependency and spatial heterogeneity.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. Springer-Verlag Berlin Heidelberg, 2015 Hedonic price models nationallicence GWR nationallicence Spatiotemporal modelling nationallicence GIS nationallicence Fotheringham A. GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA aut Crespo Ricardo Facultad de Ingeniería y Administración, Universidad Bernardo O'Higgins, Santiago, Chile aut Yao Jing Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, G12 8RZ, Glasgow, UK aut The Annals of Regional Science Springer Berlin Heidelberg 54/2(2015-03-01), 417-436 0570-1864 54:2<417 2015 54 168 https://doi.org/10.1007/s00168-015-0660-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00168-015-0660-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fotheringham A. GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA aut NATIONALLICENCE 700 1- Crespo Ricardo Facultad de Ingeniería y Administración, Universidad Bernardo O'Higgins, Santiago, Chile aut NATIONALLICENCE 700 1- Yao Jing Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, G12 8RZ, Glasgow, UK aut NATIONALLICENCE 773 0- The Annals of Regional Science Springer Berlin Heidelberg 54/2(2015-03-01), 417-436 0570-1864 54:2<417 2015 54 168