Prediction Of Housing Price Along The Urban Rail Transit Line Based On GA-BP Model And Accessibility

Abstract

In order to study the relationship between the accessibility along the urban rail transit line and the change in housing price, GA-BP model, which combining the genetic algorithm(GA) and back propagation neural network(BPNN), was used to predict the housing price along the urban rail transit line. The reliability of the model was verified by taking the price of residential areas along Nanjing Metro Line 10 as an example, and the performance between GA-BP model and the BP model was compared. It was found that the average absolute error rate of GA-BP model is 6.91%, which is 6.45% lower than that of BP model, and the mean square error of prediction results is significantly reduced. It can be seen that the price prediction algorithm based on GA-BP model for those residential areas along the urban rail transit line is reliable and can fully tap the potential relationship between the accessibility of urban transport network and the housing price.