Abstract
Nowadays, there is an upsurge of research in the field of artificial intelligence around the world. As artificial intelligence slowly penetrates and is applied to all walks of life, the application of artificial intelligence to the field of real estate price batch evaluation has gradually become a reality. As the core area of artificial intelligence-machine learning, it has strong self-organization, self-adaptation and learning capabilities, and is very good at dealing with nonlinear mapping relations. It is a good choice to apply the ideas and methods of artificial intelligence to the field of real estate price batch evaluation.This paper uses Python web crawler technology to obtain Nanjing's second-hand housing transaction information data set from Shell.com, and cleans the second-hand housing transaction information data set to obtain a standardized data set. Then data processing is performed on the standardized data set, which mainly includes the quantitative processing of sub-type variables to obtain a standardized sample data set. Next, we build a real estate price batch evaluation model based on XGBoost, tune the main parameters of the regression model, and perform 5-fold cross-validation. Finally, we compare and analyze the XGboost regression model with the random forest regression model and the multiple regression model, and find that the XGBoost regression model has considerable advantages compared with the traditional multiple regression model, and is more suitable for building real estate price batch evaluation models; moreover, The real estate price batch evaluation model based on XGBoost is slightly better than the real estate price batch evaluation model based on random forest, and the regression prediction results are more robust.
Highlights
In recent years, as the expansion of urbanization has slowed and the market for new real estate has gradually become saturated, the real estate market has gradually shifted from an increase-based to a stock-based market, and the second-hand housing market has gradually become the main front of the real estate market
This is reflected in the XGBoost regression model’s r2 is about 60% higher than the multiple regression model, and reflected in the mean square of the XGBoost regression model is 40% lower than the multiple regression model, which shows that the application of XGBoost machine learning algorithm in the batch evaluation of real estate prices is feasible and has considerable advantages compared to the multiple regression model
We can know that the goodness of fit of the XGBoost regression model is higher than that of the Random Forest regression model, and the root mean square error of the XGBoost regression model is lower than that of the Random Forest regression model. From this we can know that the regression prediction effect of the real estate price batch evaluation model based on XGBoost is slightly better than the Random Forest regression model
Summary
As the expansion of urbanization has slowed and the market for new real estate has gradually become saturated, the real estate market has gradually shifted from an increase-based to a stock-based market, and the second-hand housing market has gradually become the main front of the real estate market. Taking Nanjing as an example, after a round of mandatory intervention measures in the real estate market (in 2016, first-tier cities strengthened purchase and loan restrictions, and many places have introduced restrictions on purchases and loans), the number of second-hand housing transactions in Nanjing has increased from 151,524 sets per year in 2016, and dropped to 72230 set s per year in 2018, a record low in 4 years. After a round of cooling in the second-hand housing market, the second-hand housing market in Nanjing began to pick up in 2019. As second-hand housing transactions become more active, the number of transactions continues to grow, and people’s demand for information and data such as second-hand housing transaction prices is growing. Efficient and accurate real estate price batch appraisal system to meet market demand As second-hand housing transactions become more active, the number of transactions continues to grow, and people’s demand for information and data such as second-hand housing transaction prices is growing. , Efficient and accurate real estate price batch appraisal system to meet market demand
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