Housing price index forecasting using neural tree model

Since the subprime crisis, the variance of housing price is receiving increasing attention especially because of its complexity and practical applications. This paper applies the flexible neural tree model for forecasting the housing price index (HPI). The optimal structure is developed using the modified breeder genetic programming (MBGP) and the free parameters encoded in the optimal tree are optimized by the particle swarm optimization (PSO), and a new fitness function based on error and Occam´s razor is used for for balancing of accuracy and parsimony of evolved structures. Based on the HPI of Shandong province, the performance and efficiency of the applied model are evaluated and compared with the classical multilayer feedforward network (MLFN) and support vector machine (SVM) models.