To examine how cross-strata neighboring behavior in a mixed-income community can influence the consumption choices of individuals in low-income groups, and to improve the prediction accuracy of the consumption choice model of low-income groups for small sample sizes, we developed a support vector machine (SVM) algorithm based on the influence of neighboring behavior. We substituted the predicted latent variables into the SVM classifier and constructed an SVM prediction model with latent variables based on reference group theory. We established the model parameters using cross-validation and used low-income residents from a mixed-income community in Shanghai as study objects to empirically test the model’s performance. The results show that the SVM selection model with latent variables has good prediction accuracy. The proposed model’s accuracy was improved by 1.29% on the basis of the particle swarm optimization (PSO)-SVM model without latent variables, and by 19.35% on the basis of the SVM model with latent variables. The proposed model can be employed to predict the consumption choices of individuals in low-income groups. This paper offers a theoretical reference for investigating neighboring behavior in a mixed-income community and the consumption choices of individuals in low-income groups and is practically important for urban community planning systems.

As is known to all, real estate prices are closely related to the national economy, whereas it is worth exploring whether there is a similar interactive relationship between their corresponding representatives, residential commodity prices, and gross domestic product. Based on painstakingly preconditioning monthly data of Harbin from January 2002 to June 2015, this paper in perspectives of theoretical analysis and empirical examination will introduce a bivariate linear regression model combined with an autoregressive moving average model and apply a vector autoregressive model to thoroughly analyze the nexus between commercial housing price and GDP. The study’s result demonstrates that, in the long run, property price’s impact on GDP is stronger than GDP having an influence upon residential price. Moreover, it also points out that one side changes remarkably facing the other’s positive shock in the short term, but as time elapses, impulse responses will gradually vanish. Hence, the government should actively formulate secular and sustainable policies in order to achieve goals of stabilizing housing prices and controlling GDP’s growth rate.