Research on Housing Price Prediction Based on Machine Learning

Abstract

The stability and long-term growth of the real estate industry depend heavily on the capacity to forecast house price trends with accuracy. The purpose of this research is to evaluate the benefits and drawbacks of several machine learning models for housing price prediction. First, the factors influencing housing prices in various ways are explained. Next, the multiple linear regression, backpropagation neural network, and random forest model, respectively, are introduced. In the meantime, the paper analyzes their forecasting shortcomings and provides examples of other scholars' optimization experiments on the aforementioned three models. The findings indicate that these three types of optimized models can obtain more stable and accurate prediction results when housing prices are predicted. Housing price forecasting can lower investment risks for real estate developers by assisting them in creating more effective project development plans and sales tactics. The government can better grasp the real estate market's development trend, create pertinent regulations and control measures, and support the stable and sustainable growth of the real estate market with the aid of home price prediction. Finding an appropriate way to predict the price of housing is therefore imperative.