Predicting House Prices with a Linear Regression Model

House price forecasting is an important area of economic and social research. Among the many house price forecasting methods, the linear regression model is widely used because of its simplicity, easy interpretation, and high computational efficiency. This paper aims to investigate the effectiveness of linear regression models in house price forecasting. This paper will first introduce the basic theory of linear regression model, and discuss the factors that affect the housing price, then build and evaluate the housing price prediction model, and then verify the constructed data model through real data. Finally, we discuss the accuracy of the prediction, analyze the results of the passing model, and find that housing prices can be predicted more accurately for cases where the variables are relatively simple and differentiated, such as the ownership of specific facilities. However, linear regression prediction still has some defects, and there are more effective and general methods for housing price prediction to solve the problems that linear regression method cannot pay attention to, which will be the subject of future research.