A New Method and Model for the Estimation of Residual Value of Agricultural Tractors

Abstract

The residual value of a tractor affects the cost of ownership. As there is not much transactional information available for used tractors, nor is there a history of new tractor prices, existing studies struggle to forecast the residual value of agricultural tractors. This is made even more challenging by the emission-regulation-related tractor price increase, low inflation in recent decades, and the complexity of the portfolio offerings from manufacturers. Using the new equivalent tractors, grouped by families of similar characteristics, bypasses these challenges and enables us to obtain larger data sets. These large data sets can be forecasted using transparent linear power regressions that offer the lowest root mean squared error (RMSE = 1.5574) and the highest combined, adjusted coefficient of determination (RSqAdj = 0.8457), outperforming all previously tested studies as well as the ensemble, Gaussian process regression, kernel, linear regression, neural network, support vector machine, and decision tree models. The accessibility of the public information required, as well as its processing using mainstream software through a model that is simple to use, yet robust, enables any stakeholder (manufacturers, sellers, financers, insurers, and, most of all, users) to reliably determine the residual value of an agricultural tractor, empowering them to make fact-based, cost-of-ownership-optimized decisions.