Analysis of Beijing Second-Hand House Price Based on Random Forest
Abstract: With the development of economy and reducing of available land, the price of second-hand house is rising continuously. By the end of May 2016, average price of second-hand house in Beijing has been more than ¥60,000/m2. Evaluating the price of second-hand house will not only produce important influence on residents’ life, but also bring effective reference on the government’s macroeconomic regulation and control. Current mathematical model about housing price includes linear regression model, neural network model (NN) and support vector machine model (SVM). In linear regression model, the suppose of linear relationship may cause more error. NN and SVM are proved to have poor explanatory. Based on the price of 16,795 second-hand houses in Beijing, the random forest model was established to study the influence factors of house price and the forecast of house price. Method of variance explanatory changes shows lat (Residential latitude), long (Residential longitude) and cate (Residential area) are the three main significant prediction variables on housing price, while random forest model picks up cate, lat and long to be the most important. Through analysis of OOB (out-of bag) samples, random forest gets a precision of 0.69 in second-hand housing forecast. Finally, put price data into NN and SVM model and forecast, precision 5.15 and 1.10 were got respectively. The result shows that random forest forecast is the best, followed by SVM. NN prediction does not apply to the second-hand house data in this paper.
文章引用: 李晓童 , 郭 萱 , 王成杰 (2017) 基于随机森林方法的北京市二手房价格研究。 数据挖掘， 7， 37-45. doi: 10.12677/HJDM.2017.72004
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