Reliable region predictions for automated valuation models

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Title: Reliable region predictions for automated valuation models
Authors: Bellotti, A
Item Type: Journal Article
Abstract: Accurate property valuation is important for property purchasers, investors and for mortgage-providers to assess credit risk in the mortgage market. Automated valuation models (AVM) are being developed to provide cheap, objective valuations that allow dynamic updating of property values over the term of a mortgage. A useful feature of automated valuations is to provide a region of plausible price estimates for each individual property, rather than just a single point estimate. This would allow buyers and sellers to understand uncertainty on pricing individual properties and mortgage providers to include conservatism in their credit risk assessment. In this study, Conformal Predictors (CP) are used to provide such region predictions, whilst strictly controlling for predictive accuracy. We show how an AVM can be constructed using a CP, based on an underlying k-nearest neighbours approach. Time trend in property prices is dealt with by assuming a systematic effect over time and adjusting prices in the training data accordingly. The AVM is tested on a large data set of London property prices. Region predictions are shown to be reliable and the efficiency, ie region width, of property price predictions is investigated. In particular, a regression model is constructed to model the uncertainty in price prediction linked to property characteristics.
Issue Date: 19-Jan-2017
Date of Acceptance: 1-Jan-2017
URI: http://hdl.handle.net/10044/1/45884
DOI: https://dx.doi.org/10.1007/s10472-016-9534-6
ISSN: 1012-2443
Publisher: Springer
Start Page: 71
End Page: 84
Journal / Book Title: Annals of Mathematics and Artificial Intelligence
Volume: 81
Issue: 1-2
Copyright Statement: © 2017 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Science & Technology
Technology
Physical Sciences
Computer Science, Artificial Intelligence
Mathematics, Applied
Computer Science
Mathematics
Automated valuation
Conformal predictor
Nearest neighbours
NEAREST
CLASSIFICATION
0801 Artificial Intelligence And Image Processing
0802 Computation Theory And Mathematics
0102 Applied Mathematics
Artificial Intelligence & Image Processing
Publication Status: Published
Open Access location: http://www.readcube.com/articles/10.1007/s10472-016-9534-6?author_access_token=z5LcjgS-2wIkjyk6qpwdF_e4RwlQNchNByi7wbcMAY6Q9vK9bMxMK90NyFP5Spu9nxJ9tsv69GN5fQLQT0ry37KPq0tm5_8DixhtmZ2zi-EU2LDGZvTjWUzgwyIEhPnMfKapbBNA7oPNnc8_uZapEQ==
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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