The convergence of public data and statistical modeling has created opportunities for public safety officials to prioritize the deployment of scarce resources on the basis of predicted crime patterns. Current crime prediction methods are trained using observed crime and information describing various criminogenic factors. Researchers have favored global models (e.g., of entire cities) due to a lack of observations at finer resolutions (e.g., ZIP codes). These global models and their assumptions are at odds with evidence that the relationship between crime and criminogenic factors is not homogeneous across space. In response to this gap, we present area-specific crime prediction models based on hierarchical and multi-task statistical learning. Our models mitigate sparseness by sharing information across ZIP codes, yet they retain the advantages of localized models in addressing non-homogeneous crime patterns. Out-of-sample testing on real crime data indicates predictive advantages over multiple state-of-the-art global models.