Understanding maintenance cost drivers in higher education: a multi-model econometric study

Abstract

Purpose This study aims to identify and quantify the principal determinants of maintenance costs in higher education buildings using a multi-model econometric approach. It integrates operational, structural and climatic variables to provide generalizable insights for cost-effective and climate-responsive facility management (FM) across institutions. Design/methodology/approach The analysis draws on 1.9 million maintenance work orders from 25 North American universities (2002–2021). A combination of mixed-effects modeling, quantile regression and panel data analysis is employed to examine how institutional, operational and environmental factors jointly influence maintenance expenditure. This multi-method econometric framework captures cost heterogeneity, handles nested data structures and distinguishes between cross-sectional and longitudinal effects. Findings Planned Preventive Maintenance significantly reduces costs by up to 57% in high-expenditure cases (75th percentile) compared with unplanned maintenance. Labor remains the largest cost driver, while better building conditions consistently lower expenses. Climatic variables show mixed impacts: precipitation amplifies costs during prolonged maintenance tasks, whereas temperature variability, humidity and wind chill often exert weak or negative effects. Originality/value The study extends the existing literature by applying a comprehensive econometric framework to a large, multi-institutional dataset, one of the most extensive of its kind in the higher education sector. By bridging quantitative modeling with FM practice, it offers empirical evidence to support predictive budgeting, resource optimization and climate-adaptive maintenance planning.