Road distance and travel time for an improved house price Kriging predictor. Issue 3 (3rd July 2018)

• Record Type: Journal Article
• Authors: Crosby, Henry; Damoulas, Theo; Caton, Alex; Davis, Paul; Porto de Albuquerque, João; Jarvis, Stephen A.
• Is Part Of: Geo-spatial information science. Volume 21:Issue 3(2018)
• Page Start: 185
• Page End: 194
• Publication Date: 2018-07-03
• Subjects: Kriging -- Minkowski -- travel time -- road distance -- real-estate valuation; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2018.1503775
• Languages: English
• ISSNs: 1009-5020

Abstract

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.