Neural networks: the prediction of residential values

Stanley McGreal; Alastair Adair; Dylan McBurney; David Patterson

doi:10.1108/14635789810205128

Login

Welcome:
Guest

Search for:

Browse:

Product Information:

Resources:

Journal of Property Valuation and Investment

ISSN: 0960-2712
Currently published as: Journal of Property Investment & Finance
Incorporates: Journal of Property Finance

Online from: 1983

Subject Area: Built Environment

Content: Latest Issue | Latest Issue RSS | Previous Issues

Options: To add Favourites and Table of Contents Alerts please take a Emerald profile

Neural networks: the prediction of residential values

Document Information:
Title:	Neural networks: the prediction of residential values
Author(s):	Stanley McGreal, (Reader and Director, Centre for Research on Property and Planning at the School of the Built Environment, University of Ulster, Northern Ireland), Alastair Adair, (Chair of property investment and head of the School of the Built Environment, University of Ulster, Northern Ireland), Dylan McBurney, (Research assistant in the School of the Built Environment, University of Ulster, Northern Ireland), David Patterson, (Research officer in the Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Northern Ireland)
Citation:	Stanley McGreal, Alastair Adair, Dylan McBurney, David Patterson, (1998) "Neural networks: the prediction of residential values", Journal of Property Valuation and Investment, Vol. 16 Iss: 1, pp.57 - 70
Keywords:	Neural networks, Property, Valuation
Article type:	Research Paper
DOI:	10.1108/14635789810205128 (Permanent URL)
Publisher:	MCB UP Ltd
Abstract:	The potential application of data mining techniques in the extraction of information from property data sets is discussed. Particular interest is focused upon neural networks in the valuation of residential property with an evaluation of their ability to predict. Model testing infers a wide variation in the range of outputs with best results for stratified market subsets, using postal code as a locational delimiter. The paper questions whether predicted outcomes are within the range of valuation acceptability and examines issues relating to potential biasing and repeatability of results.