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Abstract The quantification of residential water end uses is an important component of improving the sustainability of urban water infrastructure. Disaggregation and classification methods based on statistical learning are used in research and practice to extract meaningful insights from smart water meter data. These insights can also reflect individual behaviors within the built environment, enabling end-user activity detection from water consumption patterns. In this study, we present an initial framework for classifying residential water end uses and assisting with discerning between perceived typical and atypical water-use behavior in a permanent supportive housing context. Classification schemes, based on fine-resolution temporal flow data, incorporated baseline activity to inform what typical water use was for individuals while also considering general trends in specific end uses such as showers, toilet flushes, and leaks. We found that while atypical activity based on end-use duration and frequency might fall outside the normally-distributed expected value for a period of interest, it need not be the case for all atypical activity. Defining atypical activity based on prescriptive guidelines might not align with normative behavior for an occupant transitioning into housing. Additionally, exogenous variables can affect occupant behavior regarding water end uses and this impact should be accounted for in analytical frameworks. Our findings can specifically inform supportive services provided by stakeholders responsible for the well-being of individuals in their care via non-intrusive, privacy-respecting insights on occupant behavior.