The American real estate market has always been a subject of widespread interest, with its price fluctuations impacting not only the lives of millions of American households but also exerting profound effects on the nation's overall economic health. This paper focuses on analyzing which factors are more significant in influencing the American real estate market in order to gain a better understanding of the factors influencing this market and to offer insightful information for stakeholders, policymakers, and investors navigating this dynamic market. We apply linear regression model, lasso regression model and random forest regression model to United States real estate data to explore variables that influence the U.S. housing price and find that PPI, M3, and population are the critical variables impacting the housing price most. By comparing the performance of three regression models, we identify the random forest regression model as the optimal model for predicting real estate prices.