基于随机森林模型的长租公寓租金定价评估研究

Research on Rent Pricing Evaluation of Long-term Rental Apartments Based on Random Forest Model

基于特征价格理论,选择区位特征、建筑特征、邻里特征、消费者心理特征和租约特征五类共计20项特征变量,构建出长租公寓租金定价评估的随机森林模型,并将成都市长租公寓市场作为研究对象进行实证研究。研究结果表明:随机森林模型MAPE(%)为6.9%,优于神经网络和传统多元回归模型。

Based on the feature price theory,this paper selectsa total of 20 feature variables from five categories of location feature,building feature,neighborhood feature,consumer psychological feature,and lease feature to construct a random forest model for long-term rental apartment rental pricing evaluation,and takes Chengdu mayor’s rental apartment market as the research object for empirical research.The research results show that the random forest model MAPE(%)is 6.9%,which is better than neural network and traditional multiple regression model.