Research On Batch Evaluation Of Real Estate Prices Based On Random Forest And XGBoost

In recent years,the real estate economy has become the mainstay of my country’s market economy and plays a very important role in my country’s economic development.However,with the prosperity and development of the real estate market,its degree of binding with the financial market has gradually deepened,and some price chaos has gradually appeared in the real estate market,such as ’price bubbles’,’price inversion’ and so on.Reasonable and accurate evaluation of real estate,especially accurate batch evaluation of a large number of existing second-hand houses in the real estate market,has become an urgent problem in the field of real estate finance research.Now,the world has set off a research boom in the field of artificial intelligence,and artificial intelligence has slowly penetrated into 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 problems.Applying the ideas and methods of artificial intelligence to the field of real estate price batch evaluation is a good choice.In this paper,the Python web crawler technology is used to obtain the second-hand housing transaction information datasets in Nanjing and Suzhou from Shell.com,and the two secondhand housing transaction datasets are cleaned and quantified to obtain two standardized sample datasets.Next,this paper conducts simple descriptive statistics on the sample data set,and builds a batch evaluation model of real estate prices based on multiple linear regression.Then,based on the machine learning algorithm theory of random forest and XGBoost,on the basis of two sample data sets,a real estate price batch evaluation model based on random forest and a real estate price batch evaluation model based on XGB oost are respectively constructed.Finally,this paper conducts a comprehensive analysis of several common real estate price batch evaluation models(multiple regression model,support vector regression,BP neural network regression,random forest regression model and XGBoost regression model),and obtains empirical research results.The empirical research results show that random forest and XGBoost have considerable advantages over traditional multiple regression,support vector regression and BP neural network,with better fitting effect and higher prediction accuracy,and are more suitable for building real estate price batches.Evaluate the model.