The management of heavy construction equipment is a difficult task. Equipment managers are often called upon to make complex economic decisions involving the machines in their charge. These decisions include those concerning acquisitions, maintenance, repairs, rebuilds, replacements, and retirements. The equipment manager must also be able to forecast internal rental rates for their machinery. Repair and maintenance expenditures can have significant impacts on these economic decisions and forecasts. The purpose of this research was to identify a regression model that can adequately represent repair costs in terms of machine age in cumulative hours of use. The study was conducted using field data on 270 heavy construction machines from four different companies. Nineteen different linear and transformed non-linear models were evaluated. A second-order polynomial expression was selected as the best. It was demonstrated how this expression could be incorporated in the Cumulative Cost Model developed by Vorster where it can be used to identify optimum economic decisions. It was also demonstrated how equipment managers could form their own regression equations using standard spreadsheet and database software.