Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data

作者：

Highlights：

• Housing price valuation is one of most important trading decisions.

• This study uses machine learning to develop housing price prediction models.

• This study analyzes the housing data of 5359 townhouses in Fairfax County, VA.

• The 10-fold cross-validation was applied to C4.5, RIPPER, Bayesian, and AdaBoost.

• RIPPER outperformed these other housing price prediction models in all tests.

摘要

•Housing price valuation is one of most important trading decisions.•This study uses machine learning to develop housing price prediction models.•This study analyzes the housing data of 5359 townhouses in Fairfax County, VA.•The 10-fold cross-validation was applied to C4.5, RIPPER, Bayesian, and AdaBoost.•RIPPER outperformed these other housing price prediction models in all tests.

论文关键词：Housing price index,Housing price prediction model,Machine learning algorithms,C4.5,RIPPER,Naïve Bayesian,AdaBoost

论文评审过程：Available online 26 November 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.11.040