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 ...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 crossvalidation 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(r2)result of 0.6901±0.18 SD for real estate price prediction compared to the traditional(Euclidean)approach obtaining a suboptimal r2 value of 0.66±0.21 SD.展开更多
基金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 Groupsupported by The Alan Turing Institute:[Grant Number EP/N510129/1]and the Lloyd’s Register Foundation programme on Data Centric Engineering.
文摘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 crossvalidation 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(r2)result of 0.6901±0.18 SD for real estate price prediction compared to the traditional(Euclidean)approach obtaining a suboptimal r2 value of 0.66±0.21 SD.
