This study examines whether technical efficiency in U.S. community banks is shaped by spatial spillovers and how these effects are conditioned by financial and institutional factors. Employing a two-stage empirical strategy, we first estimate bank-level efficiency scores using Data Envelopment Analysis (DEA), followed by bias-corrected truncated regressions within a spatial Simar and Wilson (2007) framework. As a robustness check, we replicate the main findings using a spatial Stochastic Frontier Analysis (SFA) approach. The analysis draws on panel data from 4,936 community banks spanning 2015–2019. The results provide robust evidence of spatial interdependence: banks’ efficiency levels are significantly influenced by the characteristics of neighboring institutions, particularly in terms of capital structure, income diversification, and liquidity positions. Two primary mechanisms underpin these spillovers. First, relationship lending promotes the diffusion of soft information, amplifying local interbank linkages through social and informational embeddedness. Second, the magnitude and direction of spatial effects are heterogeneous, varying systematically with institutional contexts, especially across Federal Reserve Dis- tricts and data-driven spatial regimes. The overlap between regulatory jurisdictions and identified spatial clusters points to the relevance of localized supervision in shaping efficiency dynamics. By integrating spatial heterogeneity into the analysis of bank performance, the findings underscore the impor- tance of accounting for geographic structure in both academic modeling and policy design concerning community banking.