Simulation of Short-Term Prediction Model of Real Estate Price Index Based on Neural Network Algorithm

As a basic and leading industry, real estate plays an important role in modern social and economic life, and it is of great significance to promote the development of related industries and national economy. In the real estate market, real estate investors should conduct a comprehensive investigation, analysis and prediction of the real estate market before making investment decisions. The house price index objectively reflects the average price level of a certain type of real estate in the whole country and a certain area, and it is a necessary tool to effectively analyze the real estate market, and can describe the fluctuation track of the development of the real estate industry in a timely, accurate and intuitive way. In this paper, a short-term prediction model of real estate price index based on artificial neural network (ANN) algorithm is proposed, and the simulation comparison experiment is carried out to verify the effectiveness of the proposed prediction model. The results show that after many iterations, the error of short-term prediction algorithm of real estate price index gradually decreases and tends to be stable. This model not only overcomes the influence of data volatility on prediction accuracy, but also enhances the adaptability of prediction, which can provide reference for the research of real estate price index prediction.