A New Appraisal Model of Second-Hand Housing Prices in China's First-Tier Cities Based on Machine Learning Algorithms

摘要

The accurate appraisal of second-hand housing prices plays an important role in second-hand housing transactions, mortgages and risk assessment. Machine learning technology, gradually applied to finance and economics, can also be used to upgrade the traditional appraisal methods of second-hand housing. A large number of appraisal indicators and price data on second-hand housing in Beijing, Shanghai, Guangzhou and Shenzhen, four first-tier cities in China, can be obtained by using crawler technology. Then, the geographical location information of second-hand housing can be visualized by GIS technology, and the descriptive text of second-hand housing can be processed by natural language processing. Finally, combined with other numerical and classification indicators, the second-hand housing appraisal model based on a two-tier stacking framework is constructed by using random forest, adaptive boosting, gradient boosting decision tree, light gradient boosting machine and extreme gradient boosting as base models and back propagation neural network as the meta-model. The result of model training shows that the machine learning models improve the accuracy significantly compared to linear multiple regression and spatial econometric models, and the performance of the stacking model is better than that of standalone machine learning models.