Urban Housing Price Prediction and Service Level Evaluation Based on Machine Learning

With the acceleration of urbanization, the sustainable development of cities and the improvement of residents' quality of life have become major challenges facing the world. Taking Wenzhou City and Hohhot City of China as examples, this paper uses machine learning method to build urban housing price prediction and service level evaluation models. First, the stochastic forest regression and ridge regression models are used to predict the housing price, and the forecasting effect of the two models is compared. The results show that the stochastic forest regression model has better performance in capturing nonlinear relationship and forecasting accuracy. Secondly, a multi-index comprehensive evaluation model is constructed to evaluate the urban service level, and the factors affecting the service level are determined by weighted summation method. The results show that optimizing the greening rate and property management fees can effectively enhance the service level and improve the quality of life of residents. The research results of this paper provide a scientific basis for urban planning and decision-making, and provide a reference for the sustainable development of other cities. Future studies can further explore other factors that affect housing prices and service levels, and incorporate more advanced machine learning models to improve the predictive accuracy and practicality of the models.