Crosby, H., Damoulas, T., Caton, A., Davis, P., Porto de Albuquerque, J. and Jarvis, S. A. (2018) Road distance and travel time for an improved house price Kriging predictor. Geo-Spatial Information Science, 21(3), pp. 185-194. (doi: 10.1080/10095020.2018.1503775)
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Abstract
The paper designs an automated valuation model to predict the price of residential property in Coventry, United Kingdom, and achieves this by means of geostatistical Kriging, a popularly employed distance-based learning method. Unlike traditional applications of distance-based learning, this papers implements non-Euclidean distance metrics by approximating road distance, travel time and a linear combination of both, which this paper hypothesizes to be more related to house prices than straight-line (Euclidean) distance. Given that – to undertake Kriging – a valid variogram must be produced, this paper exploits the conforming properties of the Minkowski distance function to approximate a road distance and travel time metric. A least squares approach is put forth for variogram parameter selection and an ordinary Kriging predictor is implemented for interpolation. The predictor is then validated with 10-fold cross-validation and a spatially aware checkerboard hold out method against the almost exclusively employed, Euclidean metric. Given a comparison of results for each distance metric, this paper witnesses a goodness of fit () result of 0.6901 ± 0.18 SD for real estate price prediction compared to the traditional (Euclidean) approach obtaining a suboptimal value of 0.66 ± 0.21 SD.
Item Type: | Articles |
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Additional Information: | This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Urban Science: [Grant Number EP/L016400/1] and Assured Property Group. This work was also supported by The Alan Turing Institute: [Grant Number EP/ N510129/1] and the Lloyd’s Register Foundation programme on Data Centric Engineering. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Porto de Albuquerque, Professor Joao |
Authors: | Crosby, H., Damoulas, T., Caton, A., Davis, P., Porto de Albuquerque, J., and Jarvis, S. A. |
College/School: | College of Social Sciences > School of Social and Political Sciences > Urban Studies |
Journal Name: | Geo-Spatial Information Science |
Publisher: | Taylor & Francis |
ISSN: | 1009-5020 |
ISSN (Online): | 1993-5153 |
Published Online: | 21 September 2018 |
Copyright Holders: | Copyright © 2018 Wuhan University |
First Published: | First published in Geo-Spatial Information Science 21(3): 185-194 |
Publisher Policy: | Reproduced under a Creative Commons License |
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