Depreciation Dynamics of Electric vs. Internal Combustion Engine Vehicles in the US Car Market
Year: 2025
Type: dissertation
Abstract: The transition to light-duty electric vehicles (EVs), including battery electric vehicles (BEVs), plug-in hybrids (PHEVs), and hybrid electric vehicles (HEVs), is a critical strategy for decarbonizing the transportation sector and reducing greenhouse gas emissions. While new EVs typically have higher upfront costs compared to internal combustion engine vehicles (ICEVs), the growing used EV market offers a potential pathway to broader affordability. This study aims to quantify price depreciation patterns of EVs relative to ICEVs to better understand barriers to secondary market adoption. Using a dataset of approximately 150,000 vehicle listings scraped from Craigslist across 17 major U.S. cities, we developed a log-linear regression model to estimate vehicle retention rates, defined as the ratio of listed price to the manufacturer’s suggested retail price (MSRP). Key explanatory variables include vehicle age, mileage, powertrain type, vehicle class, seller type, MSRP, and geographic location. Additional models incorporate interaction terms and brand-specific effects to examine variation in depreciation behavior, with a focused comparison between Tesla and non-Tesla BEVs. Results show that BEVs generally exhibit lower retention rates than ICEVs, especially within the first 10 years. During this period, PHEVs and HEVs also exhibit better retention rates than BEVs. Moreover, Tesla electric vehicles start with higher initial retention but depreciate more quickly than their non-Tesla BEV counterparts. Additionally, regional variation was also observed. These findings suggest that while the used EV market is expanding, depreciation remains a significant economic factor affecting EV adoption. Understanding these trends can inform consumer decision-making, policy design, and manufacturer pricing strategies in support of a sustainable transition to electric mobility.
DOI: 10.7302/25396
Author: Das Green, Antara
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409846955