A RISK EVALUATION FRAMEWORK FOR THE PROCUREMENT OF AUCTION-GRADE AND OFF-LEASE AUTOMOTIVE ASSETS
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Abstract
Purpose. This paper presents a comprehensive multi-factor risk evaluation model for the procurement of motor vehicles at North American insurance and off-lease auctions (such as Copart, IAAI and Manheim) for subsequent commercial operation within the P2P car-sharing segment (Turo platform) or resale. The research is aimed at addressing the problem of information asymmetry and minimizing financial losses for small-scale fleet operators under conditions of high secondary market volatility. Methodology. The methodology is based on a synthesis of VIN-based technical scoring, multivariate regression analysis for forecasting repair expenditures and financial modeling utilizing NPV, ROI and dynamic break-even point metrics. Results. The study provides a detailed analysis of legal aspects, including vehicle title branding (Salvage, Rebuilt) and the compliance requirements of sharing platforms, while also identifying specific risks such as electric vehicle battery degradation and the practice of “title washing”. Results from the model`s validation on a fleet of 50-100 vehicle units confirm the potential for reducing investment risks by 30-40%. Practical implications. The outcome of the work is a concept for an automated decision support system (SaaS), facilitating increased market transparency, the professionalization of small-scale actors and the implementation of circular economy principles within the automotive investment industry. Value / originality. This paper presents a comprehensive multi-factor risk evaluation model that integrates technical, financial and legal dimensions of vehicle procurement, addressing the problem of information asymmetry in secondary automotive markets.
How to Cite
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P2P car-sharing, Turo, ROI, financial modeling, information asymmetry, predictive analytics, US automotive market
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