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

Cyfadran Cyfrifiadureg, Peirianneg a Gwyddoniaeth

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

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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.

Iaith wreiddiol	Saesneg
Teitl	Applications and Innovations in Intelligent Systems IX
Is-deitl	Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence
Golygyddion	Ann Macintosh, Mike Moulton, Alun Preece
Cyhoeddwr	Springer
Tudalennau	17-28
Nifer y tudalennau	18
ISBN (Electronig)	978-1-4471-0149-9
ISBN (Argraffiad)	978-1-85233-530-4
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-1-4471-0149-9
Statws	Cyhoeddwyd - 2001

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10.1007/978-1-4471-0149-9Trwydded: Amhenodol

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Wilson, I., Paris, S. D., Ware, A., & Jenkins, D. (2001). Residential Property Price Time Series Forecasting with Neural Networks. Yn A. Macintosh, M. Moulton, & A. Preece (Gol.), Applications and Innovations in Intelligent Systems IX: Proceedings of ES2001, the Twenty-first SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (tt. 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. Gol. / Ann Macintosh ; Mike Moulton ; Alun Preece. Springer, 2001. tt. 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",

}

Wilson, I, Paris, SD, Ware, A & Jenkins, D 2001, Residential Property Price Time Series Forecasting with Neural Networks. yn A Macintosh, M Moulton & A Preece (gol.), 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, tt. 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. gol. / Ann Macintosh; Mike Moulton; Alun Preece. Springer, 2001. t. 17-28.

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

TY - GEN

T1 - Residential Property Price Time Series Forecasting with Neural Networks

AU - Wilson, Ian

AU - Paris, S. D.

AU - Ware, Andrew

AU - Jenkins, David

PY - 2001

Y1 - 2001

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.

U2 - 10.1007/978-1-4471-0149-9

DO - 10.1007/978-1-4471-0149-9

M3 - Conference contribution

SN - 978-1-85233-530-4

SP - 17

EP - 28

BT - Applications and Innovations in Intelligent Systems IX

A2 - Macintosh, Ann

A2 - Moulton, Mike

A2 - Preece, Alun

PB - Springer

ER -

Wilson I, Paris SD, Ware A, Jenkins D. Residential Property Price Time Series Forecasting with Neural Networks. Yn Macintosh A, Moulton M, Preece A, golygyddion, 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. t. 17-28 doi: 10.1007/978-1-4471-0149-9

Residential Property Price Time Series Forecasting with Neural Networks

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