Analysis and Evaluation of Housing Price for Chengdu Urban

House prices have received unusual attention in recent years, and evaluation of housing price has also become a research hotpot. Value of a house is considered as a mixture of various features, such as house type, orientation and location. Accurate assessment of houses thus faces a large number of challenges. Several methods have been applied to assess the value of a house, including geographically weighted regression and machine learning. However, every method has some limitations. The accuracy of traditional regression methods needs to be improved. Machine learning methods are weak in interpretability. In this paper, to analyze the housing price, we compared XBGoost, Random Forest, Support vector regression, back-propagation network and geographically weighted regression to model the second-hand housing price dataset, and analyzed the influenced of each feature. The result shows Random Forest reaches a lowest mean absolute error of 1517.29 and best r2 of 0.83. According to the constructed models, the importance values are given, and the area of house shows a strongest influence on housing price in the study area.