Residual Value Prediction in Automotive: A Review of Methods and Data With Research Roadmap

Residual value prediction (RVP) has become a cornerstone of automotive economics, shaping leasing, resale, insurance, fleet management, and long-term brand positioning. Inaccurate predictions can lead to severe financial and strategic consequences: undervaluation undermines competitiveness, while overvaluation erodes profitability and trust. Artificial intelligence (AI) has opened new avenues for enhancing predictive accuracy, giving rise to diverse approaches that leverage heterogeneous data sources, including vehicle condition, ownership history, and macroeconomic factors, alongside advanced modelling techniques and varying levels of transparency and reliability. Given this diversity, a comprehensive synthesis of the field is needed to evaluate current practices and outline future research directions that integrate methodological rigour, multidimensional data inclusion, and principles of trustworthy RVP. This study offers a comprehensive synthesis of the literature by organising existing work into four themes: 1) data-related factors, 2) prediction and modelling methodologies, 3) evaluation approaches, and 4) mechanisms for transparency and trustworthiness. Our proposed research roadmap is guided by responsible AI principles, emphasising the importance of explanations that are both interpretable and reliable for RVP. By consolidating prior contributions and identifying open research opportunities, this study seeks to advance the development of RVP systems that are accurate, transparent, and reliable, thereby supporting more trustworthy decision-making across the automotive ecosystem.