A Deep Multi-Task Approach for Residual Value Forecasting

Abstract

Residual value forecasting plays an important role in many areas, e.g., for vehicles to price leasing contracts. High forecasting accuracy is crucial as any overestimation will lead to lost sales due to customer dissatisfaction, while underestimation will lead to a direct loss in revenue when reselling the car at the end of the leasing contract. Current forecasting models mainly rely on the trend analysis of historical sales records. However, these models require extensive manual steps to filter and preprocess those records which in term limits the frequency at which these models can be updated with new data. To automate, improve and speed up residual value forecasting we propose a multi-task model that utilizes besides the residual value itself as the main target, the actual mileage that has been driven as a co-target. While combining off-the-shelf regression models with careful feature engineering yields already useful models, we show that for residual values further quantities such as the actual driven mileage contains further useful information. As this information is not available when contracts are made and the forecast is due, we model the problem as a multi-task model that uses actual mileage as a training co-target. Experiments on three Volkswagen car models show that the proposed model significantly outperforms the straight-forward modeling as a single-target regression problem.