一个时空高斯过程回归，房地产价格预测器

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI:10.1145/2996913.2996960

Henry Crosby, Paul Davis, T. Damoulas, S. Jarvis

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引用次数: 18

摘要

本文介绍了一种新的四阶段房地产估价方法。本研究表明，空间、财产、经济、邻里和时间特征都是产生房价预测器的因素，其中验证表明高斯过程回归的准确率为96.6%，超过了回归-克里格、随机森林和m5p决策树。将输出集成到商业房地产决策引擎中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.

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Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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