Local spatiotemporal modeling of house prices: a mixed model approach

Yao, J. and Fotheringham, A. S. (2016) Local spatiotemporal modeling of house prices: a mixed model approach. Professional Geographer, 68(2), pp. 189-201. (doi:10.1080/00330124.2015.1033671)

Yao, J. and Fotheringham, A. S. (2016) Local spatiotemporal modeling of house prices: a mixed model approach. Professional Geographer, 68(2), pp. 189-201. (doi:10.1080/00330124.2015.1033671)

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Abstract

The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yao, Dr Jing
Authors: Yao, J., and Fotheringham, A. S.
Subjects:G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
H Social Sciences > HA Statistics
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Professional Geographer
Publisher:Routledge
ISSN:0033-0124
ISSN (Online):1467-9272
Published Online:08 June 2015

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