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.
%0 Journal Article
%1 Mcgreal1998Neural
%A Mcgreal, Stanley
%A Adair, Alastair
%A Mcburney, Dylan
%A Patterson, David
%D 1998
%I Emerald Group Publishing Limited
%J Journal of Property Valuation and Investment
%K neuralnetworks regression valuation
%P 57--70
%R http://dx.doi.org/10.1108/14635789810205128
%T Neural networks: the prediction of residential values
%U http://dx.doi.org/10.1108/14635789810205128
%X 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.
@article{Mcgreal1998Neural,
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.},
added-at = {2008-12-09T03:00:06.000+0100},
author = {Mcgreal, Stanley and Adair, Alastair and Mcburney, Dylan and Patterson, David},
biburl = {https://www.bibsonomy.org/bibtex/2a66ce11f7b8a1f2e2f343b6645b6979b/jamesh},
citeulike-article-id = {3738263},
doi = {http://dx.doi.org/10.1108/14635789810205128},
interhash = {611f29f619691f1f4d613a75884a4e91},
intrahash = {a66ce11f7b8a1f2e2f343b6645b6979b},
issn = {0960-2712},
journal = {Journal of Property Valuation and Investment},
keywords = {neuralnetworks regression valuation},
pages = {57--70},
posted-at = {2008-12-03 00:52:09},
priority = {2},
publisher = {Emerald Group Publishing Limited},
timestamp = {2008-12-09T09:59:02.000+0100},
title = {Neural networks: the prediction of residential values},
url = {http://dx.doi.org/10.1108/14635789810205128},
year = 1998
}