Stella Domínguez

Throughout this article, an alternative depreciation method for electric vehicles (EVs) is presented, addressing the challenge of information asymmetry—a common issue in secondary markets. The proposed method is contrasted with traditional models, such as the Straight-Line Method (SLM), the Declining Balance Method, and the Sum-of-Years Digits (SYD) method, as these classic approaches fail to adequately consider key factors such as mileage and secondary aspects like battery degradation and rapid technological obsolescence, which critically impact the residual value of used EVs. The presented approach employs an adverse selection model that incorporates buyers’ and sellers’ perceptions of vehicle quality from the information recorded on e-commerce platforms, improving the depreciation estimation. The results show that the proposed method offers greater accuracy by leveraging asymmetric information extracted from web portals. Specifically, the method identifies a characteristic intersection point, marking the moment when the model aligns most closely with the data obtained through traditional methods in terms of precision. The analysis through the density of price estimations by vehicle model year indicates that, beyond 1.8 months, the proposed model provides more reliable results than traditional methods. The proposed model allows buyers to identify undervalued assets and sellers to obtain a fair market value, mitigating the risks associated with adverse selection, reducing uncertainty, and increasing market transparency and trust. It fosters equitable pricing between buyers and sellers by addressing the implications of adverse selection, where sellers—possessing more information about the vehicle’s condition than buyers—can dominate market transactions. This model restores balance by ensuring fairer valuation based on vehicle usage, primarily addressing the lack of critical data available on e-commerce platforms, such as battery certifications, among others.