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A significant portion of highway infrastructure life-cycle costs can be attributed to maintenance expenditures, particularly in the later years of an infrastructure’s life. These expenditures are a key input for agency functions including cost allocation, financial planning and needs assessment, and investment evaluation. Thus, agencies seek improved techniques for forecasting future maintenance expenditures. Evidence from empirical data suggests that maintenance expenditure data exhibits significant spatial and temporal variations. If this is not addressed, the analysis could be impaired by disturbance correlation and yield potentially inefficient model estimates. In addressing this issue this paper introduces and validates (for highway pavement sections) a random-effects panel-data methodology to account for variations in routine highway maintenance expenditures at different locations and years. The results suggest …