A spatio temporal, Gaussian process regression, real estate price predictor
This paper introduces a novel four stage methodology for real estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression kriging, random forests and an M5P decision tree. The output is integrated into a commercial real estate decision engine.
SIGSPATIAL/GIS