Real estate evaluation model based on genetic algorithm optimized neural network

With the rapid development of society, the real estate economy, as an important part of Chinese economy, is showing a growing trend. But it is also the most likely to generate bubble economy, causing financial risks; it will trigger a series of social contradictions and cause social unrest in severe cases. Therefore, it is urgent to improve and optimize the real estate evaluation model. In this study, the real estate was evaluated based on the neural network model optimized by genetic algorithm. Through sorting out and summarizing the real estate data in a period of time, the corresponding model was established and the test data were obtained. The average relative error value of the genetic algorithm optimized neural network model was 3.552, which was smaller than that of the Back-Propagation (BP) neural network prediction model. The experimental conclusion that the new network model was better than the traditional model was obtained. This work opens up a new route of real estate evaluation. © 2019 The Author(s).