Multi-feature house price trend prediction based on machine learning

Abstract

As a pillar industry in China, real estate has provided great help for economic development. At the same time, buying a house has become a topic everyone cannot avoid. Until now, many scientific literatures and materials have proved that gradient boosting is a basic strategy. In this paper, the authors first clean the captured data and select features. Then, three methods eXtreme Gradient Boosting (XGBoost), Random Forest, and Bi-directional Long Short-Term Memory (BiLSTM), are used to predict housing prices in Beijing, one of the most representative cities in China, to investigate and compare the efficiency gradient methods of the three methods. The above three models process and predict 23 factors that affect housing prices by collecting information on houses sold and sold in Beijing in recent years. The score curve is obtained by fitting, finally, the best model of prediction is selected by adjusting the parameters. The influence of different characteristics on housing price prediction is studied. The experimental results show that compared with XGBoost and random forest, BiLSTM has a greater advantage in forecast speed and accuracy and is the most suitable model for predicting housing prices.