The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China

Abstract

House prices increase substantially in China from 1998. Because of expensive house prices, most Chinese people have only one chance to select suitable houses. Therefore, building a house price prediction model based on housing conditions is significant for customers to make decisions. This paper collects the estate market data of Jinan city from the HomeLink website and performs several feature selection algorithms to get critical features for house price prediction. The paper compares the classical machine learning methods for the problem, including Multiple Linear Regression, Random Forest, and Catboost. After cross-validation tests, the CatBoost, algorithm with the lowest Mean Square Error (MSE) is regarded as the most accurate algorithm to predict house prices. The analytic results show that the house price is dominated by the location features such as area and block.