Municipal infrastructure asset management in many developing nations is hampered by a lack of robust, data-driven diagnostic tools for evaluating cost-effectiveness, leading to inefficient capital and operational expenditure. This short report presents a methodological evaluation of a novel diagnostic framework for municipal infrastructure systems, with the objective of quantifying cost-effectiveness drivers using multilevel regression analysis. A diagnostic framework was applied to asset management data from a sample of Kenyan municipalities. Cost-effectiveness was modelled using a two-level hierarchical linear model: $y_{ij} = \beta_{0j} + \beta_{1}x_{1ij} + ... + \epsilon_{ij}$, where $\beta_{0j} = \gamma_{00} + \gamma_{01}z_{1j} + u_{0j}$. Robust standard errors were used for inference. The multilevel analysis identified that institutional capacity at the municipal level explained approximately 40% of the variance in cost-effectiveness scores. A one-unit increase in a standardised capacity metric was associated with a 0.65 increase in the cost-effectiveness index (95% CI: 0.48 to 0.82). The methodological approach provides a statistically sound diagnostic for isolating municipality-level and asset-level determinants of infrastructure performance, moving beyond descriptive assessment. Municipalities should adopt structured diagnostic evaluations integrating multilevel modelling. Policy should prioritise interventions that build institutional capacity, as this is a key systemic driver of cost-effectiveness. asset management, infrastructure diagnostics, hierarchical linear model, institutional capacity, public works This report introduces and validates a novel multilevel modelling diagnostic framework for infrastructure asset systems, providing a replicable method for decomposing cost-effectiveness variance into asset-specific and managerial components.