A Comprehensive Approach to Residual Value Analysis in the Luxury Automotive Market

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-07-23 DOI:10.1109/ACCESS.2025.3591765

A. Ghibellini;A. Scioletti;M. Coletto;L. Bononi;M. Gabbrielli

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引用次数: 0

Abstract

Global automotive markets have introduced new complexities, from the surge in powertrain diversity to evolving consumer purchasing habits. In the luxury car segment, residual value (RV), the car’s actual value at the end of ownership, is particularly significant. A high RV translates into lower overall ownership costs, as the car retains more of its value over time, which can boost demand as well as leasing margin. For this reason, the analysis of RV offers key insights for strategic decision-making. The present study leverages a large-scale global dataset spanning a 10-year period, capturing both internal vehicle features and three available external market conditions (CPI, unemployment rate, and 10-year bond yield). Our approach employs machine learning techniques, particularly CatBoost, achieving a mean absolute percentage error of around 5%, deemed highly acceptable within the industry. Moreover, a novel method to enhance the reliability and interpretability of RV estimations is proposed by quantifying depreciation thresholds and mitigating distortions related to sample composition via a “Standard Vehicle” concept. The approach has been validated by Ferrari S.p.A., the provider of the data, serving as a robust tool for automotive industry stakeholders.

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豪华汽车市场残值分析的综合方法

从动力系统多样性的激增到消费者购买习惯的演变，全球汽车市场出现了新的复杂性。在豪华车领域，剩余价值（RV），即汽车在所有权结束时的实际价值，尤为重要。高房车意味着较低的总体拥有成本，因为随着时间的推移，汽车会保留更多的价值，这可以提振需求和租赁利润率。出于这个原因，对RV的分析为战略决策提供了关键的见解。本研究利用了一个跨越10年的大规模全球数据集，捕捉了汽车的内部特征和三个可用的外部市场条件（CPI、失业率和10年期债券收益率）。我们的方法采用机器学习技术，特别是CatBoost，实现了5%左右的平均绝对百分比误差，在业内被认为是高度可接受的。此外，提出了一种通过量化折旧阈值和通过“标准车辆”概念减轻与样本组成相关的扭曲来提高RV估计的可靠性和可解释性的新方法。该方法已得到数据提供商法拉利公司（Ferrari s.p.a.）的验证，可作为汽车行业利益相关者的强大工具。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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