Research on prediction model of second-hand house price

With the rapid development of China's economy, the real estate industry has become an important topic of livelihood issues in China in recent years. This paper mainly takes the second-hand house price in Beijing as the data source, analyzes the status quo of the second-hand house price in each district, and studies the housing price prediction problem according to the properties of the second-hand house in the region, floor, year, layout and so on. In this paper, the housing price is predicted by linear regression model and decision tree regression model respectively, and the prediction results are scored by R2_Scroe scoring method, and the results are 0.66 and 0.79 respectively. Experimental results show that the prediction effect of decision tree regression model is better than that of linear regression model.