Residential Property Price Time Series Forecasting with Neural Networks

Ian Wilson, S. D. Paris, Andrew Ware, David Jenkins

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The residential property market accounts for a substantial proportion of UK economic activity. Professional valuers estimate property values based on current bid prices (open market values). However, there is no reliable forecasting service for residential values with current bid prices being taken as the best indicator of future price movement. This approach has failed to predict the periodic market crises or to produce estimates of long-term sustainable value (a recent European Directive could be leading mortgage lenders towards the use of sustainable valuations in preference to the open market value). In this paper, we present artificial neural networks, trained using national housing transaction time-series data, which forecasts future trends within the housing market.
Original languageEnglish
Title of host publicationApplications and Innovations in Intelligent Systems IX
Subtitle of host publicationProceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence
EditorsAnn Macintosh, Mike Moulton, Alun Preece
Number of pages18
ISBN (Electronic)978-1-4471-0149-9
ISBN (Print)978-1-85233-530-4
Publication statusPublished - 2001


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