Research on Prediction of Housing Prices Based on GA-PSO-BP Neural Network Model: Evidence from Chongqing, China

Since 2000, the real estate industry has experienced rapid development, and at the same time, it has driven the rapid growth of housing prices, and the trend of housing prices has attracted attention. This paper integrates genetic algorithm and particle swarm algorithm to optimize BP neural network, and establishes a housing price prediction model based on mixed genetic particle swarm BP neural network. The average data of housing prices in Chongqing, China from 2000 to 2020 and several main factors affecting the trend of housing prices were selected as experimental data. Through the training and simulation prediction based on the mixed particle swarm BP neural network, the error between the predicted value and the actual value was within 0.5%, the validity and accuracy of the model are proved. At the same time, this paper predicts the average price of residential commercial housing in Chongqing in 2021, which provides a reference for the government’s macro-control and sellers to carry out residential commercial housing.