Municipal infrastructure asset systems in many developing nations face challenges in performance and financial sustainability. A lack of robust, data-driven methodologies for evaluating these systems hinders effective management and yield optimisation. This study aims to methodologically evaluate municipal infrastructure asset systems and quantify the determinants of yield improvement. The primary objective is to develop and apply a multilevel regression model to identify key factors influencing revenue collection efficiency. A cross-sectional dataset was constructed from municipal records, incorporating asset condition, operational, and socio-economic variables. A two-level hierarchical linear model was specified: $Y_{ij} = \beta_{0j} + \beta_{1j}X_{1ij} + ... + \epsilon_{ij}$, with $\beta_{0j} = \gamma_{00} + \gamma_{01}Z_{1j} + u_{0j}$. Estimation used restricted maximum likelihood with robust standard errors. The multilevel model explained 68% of the variance in yield. A one-unit improvement in the operational maintenance index at the municipal level was associated with a 12.4% increase in yield (95% CI: 9.1% to 15.7%). Customer density and tariff structure were significant ward-level predictors. The methodological approach confirms that yield is influenced by factors operating at both municipal and ward levels. Systemic improvements require interventions targeted at these distinct institutional tiers. Municipalities should adopt multilevel analytical frameworks for asset management. Priority should be given to enhancing operational maintenance protocols and revising tariff structures based on localised customer density. asset management, infrastructure finance, hierarchical linear model, revenue collection, urban services This paper provides a novel application of multilevel regression modelling to municipal infrastructure systems, generating a new evidence base for tiered intervention strategies in Tanzania.