Modern approaches to assessing the residual value of restored cars in the US dealer market.

This study aims to provide a comprehensive review of contemporary approaches to estimating the residual value of rebuilt vehicles in the U.S. dealer market. The relevance of this work is driven by the scale and dynamics of the auction segment for rebuilt and salvage vehicles, whose revenue rivals that of the midsize new-car market, and whose online segment is projected to grow at a compound annual rate of 13.7% through 2030. The novelty of the research lies in bringing together various types of data. This data comes from VIN parsing, operational logs from Copart, IAA, OpenLane, telematics streams coming from Geotab, and high-precision visual checks. It has been observed that the shift from expert visual appraisal to digital valuation methods has significantly reduced the mean absolute error in RV forecasts, while also shortening the days-to-sale and decreasing cosmetic damage arbitration rates. UVeye, Mitchell+PAVE, and Ravin AI systems will incorporate models with visual features, while conformal quantile regression will ensure guaranteed coverage, enabling automatic adjustments to financial terms. However, new risks have emerged: the increase in deepfake manipulations of photographic content, alongside regulatory requirements (SB-362), will impose very stringent demands on verification as well as the protection of personal and telematics data. This article will be of use to analysts, dealers, AI solution developers, and researchers in the fields of residual value estimation and risk management in the used-vehicle market.