The purpose of this honors thesis is to discover ways for a large humanitarian organization to more cost effectively manage its fleet of vehicles. The first phase of work involved cleaning the large data set provided by the organization. Next, we used the program STATA to run a Seemingly Unrelated Regression (SUR) to see which variables have the largest effect on the percentage of price decline and total mileage of each vehicle. The SUR model indicated that price decline is most influenced by cumulative minor repairs, total accessories, age, percentage of paved roads, and number of accidents. In addition, total mileage was most affected by percentage of paved roads, cumulative minor repairs, all wheel drive, and age. The final step of the project involved providing recommendations to the humanitarian organization based on the above results. We recommend several changes to their fleet management, including: driver training programs, increasing the amount of preventative maintenance performed on vehicles, and increasing the amount of accessories purchased for each vehicle. Implementing these changes could potentially save the organization millions of dollars due to the scope of its operation.