Investigation and Comparison between GM(1,1) and BPANN Forecast Models in Shanghai Low-Rent Housing Families

Based on the data of household income of Shanghai low-rent housing families, a GM(1,1) forecast model and a Back-Propagation Artificial Neural Network (BPANN) forecast model are established respectively to predict the average household income of low-rent housing families. The comparison between the GM(1,1) and the BPANN model showed that the BPANN model is better than the GM(1,1) model at the aspects of prediction accuracy and data adaptability. The BPANN model could be applied successfully to predict the average household income of Shanghai low-rent housing families in a short-term and it will provide scientific and effective basis for formulate policy on low-rent housing.