As China’s urban development shifts toward stock-based optimisation, identifying inefficient residential land has become important for urban regeneration. Existing approaches often rely on subjective weighting, linear analytical structures, or homogeneous treatment of different residential types, which weakens robustness and transferability. This study proposes a data-driven framework that integrates mobile-phone signaling and other multi-source spatiotemporal big data in Shenzhen. Two dominant residential forms—formal residential communities and urban villages—are evaluated separately through a four-dimensional framework covering built form, activity vitality, economic efficiency, and environmental livability. Principal component analysis is used to estimate intrinsic dimensionality and initialize a parametric autoencoder. A spatially constrained Gaussian mixture model is then employed to identify inefficient residential clusters while preserving local coherence. The clustering results are interpreted using a random forest model and TreeSHAP, and externally validated by street-view imagery interpretation and limited field surveys. PCA retained five components for urban villages and six for formal residential communities, and the BIC selected six and five clusters for the two residential types, respectively. The results indicate that inefficient formal residential communities show scattered and island-like spatial patterns, whereas inefficient urban villages tend to form more continuous clusters along the edges of larger village agglomerations. Random forest and TreeSHAP further reveal that inefficient urban villages are more strongly associated with deficiencies in service accessibility and local socioeconomic conditions, whereas inefficient formal residential communities are more closely associated with lower residential vitality and relatively high development intensity. External validation indicates acceptable agreement with observed residential conditions.