Residential Property Price Time Series Forecasting with Neural Networks

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

doi:10.1007/978-1-4471-0149-9

Residential Property Price Time Series Forecasting with Neural Networks

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

Faculty of Computing, Engineering and Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	Applications and Innovations in Intelligent Systems IX
Subtitle of host publication	Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence
Editors	Ann Macintosh, Mike Moulton, Alun Preece
Publisher	Springer
Pages	17-28
Number of pages	18
ISBN (Electronic)	978-1-4471-0149-9
ISBN (Print)	978-1-85233-530-4
DOIs	https://doi.org/10.1007/978-1-4471-0149-9
Publication status	Published - 2001

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Wilson, I., Paris, S. D., Ware, A., & Jenkins, D. (2001). Residential Property Price Time Series Forecasting with Neural Networks. In A. Macintosh, M. Moulton, & A. Preece (Eds.), Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (pp. 17-28). Springer. https://doi.org/10.1007/978-1-4471-0149-9

Wilson, Ian ; Paris, S. D. ; Ware, Andrew et al. / Residential Property Price Time Series Forecasting with Neural Networks. Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence. editor / Ann Macintosh ; Mike Moulton ; Alun Preece. Springer, 2001. pp. 17-28

@inproceedings{f760e5f6b3914d5dae439baab3d9c388,

title = "Residential Property Price Time Series Forecasting with Neural Networks",

abstract = "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.",

author = "Ian Wilson and Paris, {S. D.} and Andrew Ware and David Jenkins",

year = "2001",

doi = "10.1007/978-1-4471-0149-9",

language = "English",

isbn = "978-1-85233-530-4",

pages = "17--28",

editor = "Ann Macintosh and Mike Moulton and Alun Preece",

booktitle = "Applications and Innovations in Intelligent Systems IX",

publisher = "Springer",

address = "Germany",

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Wilson, I, Paris, SD, Ware, A & Jenkins, D 2001, Residential Property Price Time Series Forecasting with Neural Networks. in A Macintosh, M Moulton & A Preece (eds), Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence. Springer, pp. 17-28. https://doi.org/10.1007/978-1-4471-0149-9

Residential Property Price Time Series Forecasting with Neural Networks. / Wilson, Ian; Paris, S. D.; Ware, Andrew et al.
Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence. ed. / Ann Macintosh; Mike Moulton; Alun Preece. Springer, 2001. p. 17-28.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Paris, S. D.

AU - Ware, Andrew

AU - Jenkins, David

PY - 2001

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N2 - 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.

AB - 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.

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BT - Applications and Innovations in Intelligent Systems IX

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Wilson I, Paris SD, Ware A, Jenkins D. Residential Property Price Time Series Forecasting with Neural Networks. In Macintosh A, Moulton M, Preece A, editors, Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence. Springer. 2001. p. 17-28 doi: 10.1007/978-1-4471-0149-9

Residential Property Price Time Series Forecasting with Neural Networks

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