Motivated by the analysis of the crime heterogeneity in the communities within the United States, we investigated the effects of socio-economic information of the communities on crime rates. We aim to identify sub-groups of communities hidden within the United States with homogeneous effects of socio-economic information on crime rates. Moreover, we also identify disjoint groups of socio-economic features that similarly predict crimes within each community group. Identifying the homogeneous communities in terms of crimes is particularly important since this would help policymakers choose cluster-specific policy interventions in those areas. To achieve this, we employ the Multivariate Cluster-Weighted Disjoint Factor Analyzers (MCWDFA), enabling us to i) cluster communities based on their socio-economic features on crime rates; (ii) identify cluster-specific sub-groups of socio-economic characteristics with similar effects on the selected crime rates. Results confirm significant heterogeneity in crimes across United States communities and diverse effects of socio-economic information on crime rates within each community group.