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LETTER
Battery-powered bargains? Assessing electric vehicle resale value
in the United States
Laura Roberson, Saurav Panthaand John Paul Helveston∗
Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC, United States of
America
∗Author to whom any correspondence should be addressed.
E-mail: jph@gwu.edu
Keywords: electric vehicles, plug-in electric vehicles, retention rate, depreciation, resale market, used market, affordability
Supplementary material for this article is available online
Abstract
The resale market will play a critical role in expanding plug-in electric vehicle (PEV) adoption to
middle- and lower-income households. Understanding PEV depreciation trends in comparison to
those of conventional gasoline vehicles (CVs) is critical for assessing PEV affordability and
informing relevant policy, such as subsidies for used PEVs. We deliver comprehensive,
high-resolution estimates of value retention rates at the make-model level in the United States for
battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles
(HEVs), and CVs using a database of 9 million used cars listed online between 2016 and 2022.
While BEVs and PHEVs have depreciated at faster rates than CVs, this trend is changing, with
newer model year BEVs and those with larger ranges have significantly higher retention rates than
older model years with smaller ranges. Tesla BEVs are a notable exception following the opposite
trend, with earlier model years holding their value better than newer model years. Subsidized BEVs
in the new market are associated with lower prices for the same model in the resale market, with
the $7500 federal subsidy translating to a 3% lower resale price on average. Finally, disruptions
from the COVID-19 pandemic have affected affordability across all vehicles, with mean listing
prices rising 37% and 39% for CVs and BEVs, respectively, from January 2020 to March 2022 in
inflation-adjusted 2019 dollars.
1. Introduction
Plug-in electric vehicle (PEV) adoption is a corner-
stone of plans to transition to a sustainable, low-
carbon future [1,2], but the success of PEVs as a
substitute for gasoline-powered conventional vehicles
(CVs) will depend on consumers’ willingness to pur-
chase them. While higher prices and limited driving
ranges are frequently cited as barriers to adoption
[3,4], researchers have found that uncertainty in
their resale value (or ‘resale anxiety’ [5,6]) remains
an important consideration when purchasing a tech-
nology with uncertain durability [7]. PEV afford-
ability in the resale market is also important for
expanding adoption beyond wealthier households,
which currently comprise the vast majority of PEV
owners [8]. Finally, accurate estimates of PEV depre-
ciation rates are important inputs for many mod-
eling efforts surrounding PEV development, such
as total cost of ownership models [9–12]. Given
the significance of understanding PEV resale value,
this study attempts to provide comprehensive estim-
ates of PEV value retention rates in the used mar-
ket, leveraging a detailed dataset of used vehicle
listings.
Vehicle resale value is affected by a variety of
factors, such as the vehicle make, model, trim, year,
and mileage, as well as features related to the mar-
ket, such as the location and overall supply of used
vehicles. For PEVs, resale value can be affected by
additional factors, such as the all-electric driving
range and battery condition [13], as well as PEV-
specific policies, such as tax incentives in the new
market [14]. Much of the prior research estimat-
ing PEV value retention rates has concluded that
while hybrid vehicles (HEVs) have depreciated at sim-
ilar rates as CVs, plug-in hybrid vehicles (PHEVs)
and battery electric vehicles (BEVs) have depreciated
© 2024 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
faster [15–18]. Table 1summarizes the prior research
on PEV resale value.
Although more rapid PEV depreciation is a con-
sistent finding in prior studies, this outcome is not
necessarily the same for every vehicle. Tesla BEVs, for
example, have been found to have among the highest
value retention rates of any vehicle [10,13], in part
due to their desirable features, such as high driving
ranges and well-established private charging infra-
structure, but also due to supply constraints they have
historically faced in the new market, leading to higher
used vehicle prices [22]. Furthermore, many of the
studies concluding that PEVs depreciate more quickly
than CVs are based on data from earlier PEV models
between 2011 and 2016, a time when most BEVs had
relatively low driving ranges and were first generation
vehicles [13,19,20]. More recent studies are finding
some evidence that newer model PEVs are holding
their value better [10].
In this study, we aim to improve upon prior
estimates of PEV value retention using listing prices
from a large, nationally representative dataset of used
cars listed online between 2016 and 2022 in the
United States. Whereas prior studies have used rel-
atively smaller samples of pre-processed data, such
as the Edmunds true market value [9,10,13], or
snapshots of available listed vehicles [12,19,20],
the data we use contains the raw, daily listing prices
from 66 641 dealerships. The detailed data enables an
unprecedented ability to quantify changes in value
retention due to vehicle features (e.g, mileage), envir-
onment features (e.g. number of days the vehicle was
listed), policy features (e.g. available subsidies), and
changes over time (e.g. newer versus older model
years), enabling greater insights into PEV value reten-
tion rates compared to prior studies. We combine the
listings data with other vehicle features to quantify the
retention rates of each car model in the dataset via
regression.
2. Method
2.1. Data
We use vehicle listing data licensed from mar-
ketcheck.com, a market research firm that collects
vehicle listing data from individual dealership web-
sites daily. The same dataset is used in a related
study on PEV mileage [23]. Since the PEV market
has a limited number of SUV models, we limit our
analyses to only car models. While the dataset cov-
ers vehicles listed between January 2016 and March
2022, we only use listings up until the end of 2019 to
avoid the pricing disruptions experienced due to sup-
ply shortages during and after the COVID-19 pan-
demic, which began in early 2020 (tables A5 and
A6 show results from vehicles listed between 2020
and 2022, and figure 5shows prices through March
2022 for different vehicle types for comparison). We
also limit our dataset to vehicle ages between 1 and
8 as fewer BEV listings are available outside of this
range (fewer vehicles are listed used within 1 year of
being new, and few used BEVs are older than 8 years
old as of December 2019). Finally, we only include
vehicle models that comprised at least 1% of the list-
ings within each powertrain as a compromise between
including a representative sample of the vehicle mar-
ket while remaining computationally reasonable. Of
all the vehicle listings in the dataset, this 1% cutoff
decision included 96% of the BEVs (13 models), 97%
of the PHEVs (7 models), 95% of the HEVs (19 mod-
els), and 59% of the CVs (25 models). The lower
inclusion rate (59%) of CV listings is because the
CV market contains many models with few sales,
such as exotic cars (the remaining 41% of listings
is comprised of 852 vehicle models). The final data-
set includes 9015 324 unique used car listings from
66 641 dealerships. Table 2summarizes the dataset
by powertrain, with Tesla and Non-Tesla BEVs sep-
arated given Tesla’s historically higher retention rates
and significant size in the U.S. market, and table A1 in
the supplementary information summarizes each car
model included in our analyses.
To control for other factors that can affect reten-
tion rates, other vehicle specifications were obtained
and joined onto the listings data. We use man-
ufacturer’s suggested retail prices (MSRPs) from
carsheet.io [24] and BEV and PHEV ranges as
well as all vehicle efficiencies (miles per gallon for
gasoline-powered vehicles and kWh per 100 miles for
electricity-powered vehicles) from fueleconomy.gov
[25], with a small number of missing values added
from carsheet.io. Both listing prices and MSRPs were
inflation-adjusted to constant 2019 dollars using the
consumer price index [26]. Vehicle operating cost
(in cents per mile) was computed for every vehicle
model using the vehicle efficiencies and monthly gas-
oline prices [27] and annual average electricity prices
[28] where the vehicle was listed from the US energy
information administration, using utilization factors
(0–1) from fueleconomy.gov to compute the elec-
tric and gas portions of operating costs for PHEVs.
Finally, we also include data on the federal and state
subsidies available for new PEVs at the time used
vehicles were listed. Data on these incentives between
2016 and 2018 were taken from Wee et al [29] and
manually updated through 2020 primarily from the
Alternative Fuels Data Center at https://afdc.energy.
gov/.
2.2. Modeling
To establish a comparable metric across different
vehicles from different price ranges, we compute a
value retention rate for every vehicle model, calcu-
lated as the listing price divided by the original MSRP
based on the vehicle make, model, trim, and model
year. While some prior studies subtract off available
federal and state subsidies from the MSRP before
computing retention rates [10,13], we chose not to
2
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Table 1. Summary of prior studies on PEV resale value.
Study Model years MSRP data
Resale value
data Resolution Sample size Main results
This study 2012–2018 EPA; carsheet.io Marketcheck Daily listings 9015 324 BEVs and PHEVs
depreciate quicker
than CV/HEV but is
improving with
more recent model
years and higher
ranges.
Rush et al [10] 2012–2019 Edmunds Edmunds TMV Monthly time
series
582 000aCVs and HEVs
consistent 3 yr
retention; PHEVs
and BEVs initially
lower but increasing
in retained value
Burnham et al [9] 2013–2019 EPA Edmunds TMV 1 TMV snapshot
(July 2020)
686aBEVs and PHEVs
depreciate more
quickly than HEVs
and CVs
Hamza et al [12] 2014–2019 KBB KBB Snapshot (2019) 72aPHEVs and CVs
hold value similarly;
BEVs 11% lower
retention over
5 years
Guo et al [13] 2010–2016 Wards Edmunds TMV Snapshot (Q4
2016)
1,400aPEV retention lower
than gasolines
equivalents. Tesla
major exception
with highest
retained value over
time.
Schoettle et al [19] 2011–2015 EPA KBB Snapshot (Jan.
2018)
200aPHEVs retained
resale value equally
as well as CVs (i.e.
0% average
difference), and
BEVs improved to
an average of −5.7%
difference in resale
value compared to
CVs
Tal et al [20] 2011–2015 New car buyers
survey/OEM
website
Self-reported
used car buyers
survey
Snapshot (2016) 160aPEVs models held
34% (2011 Nissan
Leaf) to 80% (2014
Toyota Prius
plug-in) of value in
2015 compared to
MSRP.
Zhou et al [21] Unknown NADA guides NADA guides Unknown Unknown Comparing the
adjusted retention
rates of PHEVs and
BEVs with those of
CVs indicates
2–3 year retention
rate is lower for
PEVs.
aSample sizes estimated based on descriptions of data in papers.
Abbreviations:
EPA =Environmental Protection Agency (fueleconomy.gov)
TMV =True Market Value (private party data)
KBB =Kelly Blue Book (private party data)
NADA =National Automobile Dealers Association.
3
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Table 2. Summary of used vehicle listings.
Key sample stats
Conventional
N=8395 000
Hybrid
N=464 560
PHEV
N=58 915
BEV Tesla
N=22 518
BEV other
N=74 331
Model Year
2012 764 383 61 590 6823 390 3943
(9.1%) (13%) (12%) (1.7%) (5.3%)
2013 1188 624 93 803 14 342 2797 11 245
(14%) (20%) (24%) (12%) (15%)
2014 1466 956 86 350 14 043 2969 13 665
(17%) (19%) (24%) (13%) (18%)
2015 1942 194 102 049 8217 6645 25 007
(23%) (22%) (14%) (30%) (34%)
2016 1598 340 54 178 4474 6608 15 193
(19%) (12%) (7.6%) (29%) (20%)
2017 1007 898 45 596 9905 1232 4075
(12%) (9.8%) (17%) (5.5%) (5.5%)
2018 426 605 20 994 1111 1877 1203
(5.1%) (4.5%) (1.9%) (8.3%) (1.6%)
Mileage (1 000)
Mean 42 45 42 34 24
Median 37 39 36 31 23
SD 26 28 25 19 14
Age (years)
Mean 3.40 3.70 3.86 3.82 3.66
Median 3.28 3.58 3.65 3.80 3.61
SD 1.49 1.51 1.40 1.28 1.13
Listing price (Used $USD)
Mean 16 192 16 954 16 684 51 314 14 109
Median 14 957 16 429 16 364 49 006 12 428
SD 6188 5040 4347 13 111 6068
MSRP (New $USD)
Mean 28 104 30 193 37 765 88 157 36 606
Median 27 308 28 923 36 645 89 320 34 409
SD 7819 4806 3284 8825 5134
Electric Range (miles)
Mean — — 35 238 87
Median — — 38 246 82
SD — — 14 47 27
Minimum — — 11 139 58
Maximum — — 53 335 238
make this adjustment because doing so is a non-
linear transformation of the retention rate that affects
both the intercept and slope terms of the resulting
depreciation. We believe this to be inconsistent with
the effect that a subsidy in the new market should
have on retained value in the resale market, which
is to have an immediate effect lowering the price (a
lower intercept term) but not necessarily affect the
rate by which the value drops from that price (the
slope term). In contrast, by subtracting the subsidy off
the MSRP when computing the retention rate, the res-
ulting depreciation curves will have larger intercept
terms and larger depreciation slopes, which implicitly
assumes that subsidized vehicles (BEVs and PHEVs)
should depreciate as faster rates and have higher ini-
tial retention rates than unsubsidized vehicles. Finally,
the subsidy available when a used vehicle was ini-
tially purchased is arguably less relevant to its resale
value; indeed, it is the subsidy available for the same
vehicle model in the new market at the time the used
vehicle is listed that should impact resale value. For
example, the maximum price a buyer should be will-
ing to pay for a used Nissan Leaf should be the price
for a new Nissan Leaf minus available subsidies, oth-
erwise the buyer could simply buy the new version for
the same price. Thus, in our models we include the
total subsidy (state plus federal) available for the equi-
valent new model at the time the used vehicle is listed
as a separate covariate to assess any potential effect
of new PEV subsidies on listing prices in the resale
market.
We develop an exponential decay model to
estimate the potential effects of various factors,
such as the powertrain, make, model, model year,
mileage, and available new market subsidies, on
the retention rate of different vehicles. The basic
exponential model follows that of other related
studies [9,10]:
r=αe(βx)
4
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
where ris the retention rate, xis a matrix of
model covariates, βis a vector of coefficients for the
covariates, and αrepresents the initial retention rate
upon immediate sale in the new market. Taking the
log of both sides of this equation yields a model that
can be estimated using linear regression:
logr=α+βx.
Interpreting the estimated
ˆ
βcoefficients in a
meaningful way (e.g. the change in rbased on a
change in any one covariate in x) requires the trans-
formation e
ˆ
β−1. We present the transformed coef-
ficients in tables 3and 4and the estimated (non-
transformed) coefficients in tables A2 and A3 in the
supplementary information.
3. Results
Figure 1compares the retention rates of CVs (gray
bands) with those of other powertrains (green bands),
including HEVs, PHEVs, and BEVs, where the
median (solid lines) and interquartile ranges (bands)
of retention rates were computed for all listings in
each month of age. In general, CVs and HEVs follow
similar retention rate patterns over time while PHEVs
initially retain slightly more value before dropping
at steeper rater compared to CVs. BEVs in general
depreciate faster than CVs, with the exception of Tesla
BEVs (blue bands) which actual retain higher values
in the first few years of age relative to CVs before fall-
ing to similar values with CVs. This aligns with prior
research in this area which finds Tesla BEVs follow a
different depreciation trend than other BEVs [13].
To quantify the differences in retention rates
by powertrain, we estimate an exponential decay
model of the log of retention rate on age interacted
with powertrain type. Since the estimated coefficients
(presented in table A2 in the supplementary informa-
tion) are less intuitive to interpret, we present a trans-
formation of those coefficients in table 3, interpreted
as the initial sale retention rate (exponentiation of the
intercept coefficients) and annual depreciation rates
(exponentiation of the slope coefficients minus one).
Models 1 and 2 are identical except for BEVs, which
are pooled in Model 1 and separated by Tesla or Non-
Tesla in Model 2.
Results are consistent with the trends shown in
figure 1: the annual depreciation rate (change in
retention rate with age) is approximately 9.7% and
10.0% for CVs and HEVs, respectively, and steeper for
PHEVs and Non-Tesla BEVs at approximately 14.5%
and 16.1%, respectively. Tesla BEVs depreciate at a
slightly lower rate of 13.2% per year but start at a
much higher intercept than any other vehicle type,
retaining 97.8% of their MSRP upon initial sale com-
pared to only 68.5% for Non-Tesla BEVs. CVs and
HEVs retain 79.3% and 80.4%, respectively, of their
value upon initial sale and PHEVs retain slightly more
at 85.3%. It is important to note the distinction here
between retention rate and prices; since BEVs tend
to have higher MSRPs than CVs, a lower retention
rate does not necessarily translate into lower relat-
ive prices. For example, 68.5% of $36600 (the mean
MSRP for Non-Tesla BEVs in our data) is $25 071; in
comparison, 79.3% of $28 100 (the mean MSRP for
Non-Tesla BEVs in our data) is $22 283. Thus, des-
pite the much lower initial sale retention rate for Non-
Tesla BEVs, they still often have higher prices than
used CVs.
To understand in greater detail the factors asso-
ciated with depreciation within each powertrain, we
estimate additional models on all cars within each
powertrain. Again, we present the transformed coef-
ficients in table 4, with the estimated coefficients
presented in table A3 in the supplementary inform-
ation. These models included interactions effects
between the vehicle age and each vehicle model to
allow for different depreciation intercepts and rates
by model, presented in table A4.
One of the notable observations from these mod-
els is that while Non-Tesla BEVs and PHEVs have
in general depreciated faster than HEVs and CVs,
this appears to be changing over time. Specifically,
we observe significantly higher initial sale retention
rates for these powertrains with each new model
year, with Non-Tesla BEVs increasing by 11.23% and
PHEVs increasing by 21.42% between 2018 and 2012
model years. The opposite trend has occurred for
CVs and HEVs, falling by 5.35% and 1.29%, respect-
ively, between 2018 and 2012 model years. Notably,
Tesla BEV retention rates have fallen steeply over this
period, with the 2018 model year initial sale retention
rate being 47.42% lower than that of the 2012 model
year. While this may seem surprising given Tesla’s rel-
ative popularity, it is important to note that all of the
pre-2018 models in this regression are from a single
vehicle model: the Model S. Throughout this period,
Tesla did not release any new car models (the Model X
is an SUV) and continued to cut new Model S prices;
as a result, used prices fell in anticipation of contin-
ued future lower prices. The pending release of the
Model 3 in 2018 could also have contributed to wan-
ing demand for the Model S.
These results suggest a more competitive market is
emerging across alternative powertrain vehicles, with
PHEVs and Non-Tesla BEVs performing increasingly
better in terms of value retention in the resale mar-
ket, though not yet quite as good as HEVs and CVs.
Figure 2illustrates this trend, comparing the pre-
dicted two-year old retention rates of a 2018 versus
2014 model year vehicle for every vehicle model in our
analyses. While not all vehicle models were available
in both years, for those that were we observe a steep
increase in the predicted retention rates of PHEVs and
Non-Tesla BEVs and a steep decline for Tesla BEVs.
Table 4also shows other important findings
regarding how retention rates vary with different
5
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Figure 1. Comparison of the median and interquartile ranges of computed retention rates by powertrain and age. The solid line
shows the median retention rate and the bands reflect the 25th and 75th percentiles. The same curve for CVs (in grey) is shown
for comparison in each sub-figure.
Table 3. Estimated initial sale retention rates and annual depreciation rates for different vehicle powertrains.
Powertrain
Initial sale
retention rate
exp(intercept)
Annual depreciation
rate exp(coefficient)—1
Model 1 Model 2 Model 1 Model 2
Conventional (CV) 79.31
(0.02)
9.69
(0)
Hybrid (HEV) 80.39
(0.07)
9.97
(0.02)
Plug-in hybrid (PHEV) 85.28
(0.24)
16.28
(0.06)
Battery electric (BEV) 71.24
(0.17)
— 14.46
(0.05)
—
Non-Tesla BEV — 68.45
(0.2)
— 16.14
(0.06)
Tesla BEV — 97.84
(0.47)
— 13.24
(0.1)
Number of observations: 9015 324 9015 324 9015 324 9015 324
Adjusted R-Squared: 0.325 0.331 0.325 0.331
vehicle characteristics. We observe that increased
mileage has a negative impact on retention rate across
all powertrains, with Tesla BEVs having the low-
est sensitivity to mileage at 3.9% for every addi-
tional 10 000 miles and Non-Telsa BEVs having the
highest sensitivity at 5%. We also observe lower reten-
tion rates for vehicles that remain on dealership lots
longer, suggesting that (as would be expected) deal-
erships reduce the prices of vehicles that take longer
to sell. CV prices fall at lower rates (0.08% for every
additional 10 d on the lot) relative to non-CVs, which
fall at rates between 0.13% (Tesla BEVs) and 0.37%
(Non-Tesla BEVs). This suggests dealers are willing
to lower prices much more aggressively over time for
used alternative fuel vehicles (including HEVs) than
used CVs.
Finally, we also find that less efficient vehicles
(those with higher operating costs) appear to hold
their value better than more efficient vehicles (lower
operating costs) across all powertrains. While this res-
ult may be counterintuitive, it is important to note
that more efficient vehicles tend to have higher prices;
as a result, higher demand for more affordable (but
less efficient) vehicles could lead to less rapid depreci-
ation compared to higher-priced (but more efficient)
vehicles.
4. Driving range and subsidy effects on
PEV value retention
Table 4shows several important effects specific to
PEVs. First, we find that BEVs with higher driving
ranges have higher retention rates, though this effect
is significantly higher for Non-Tesla BEVs, which tend
to have lower ranges in general than Tesla BEVs. For
every additional 10 miles of range, Non-Tesla BEVs
have 5.58% higher retention rates and Tesla BEVs
have 1.57% higher retention rates, all else being equal.
The range effect is not statistically significant for
PHEVs.
Figure 3shows this relationship, plotting the pre-
dicted two-year-old retention rate versus range for
multiple model years for select BEVs in our ana-
lysis. We only include vehicle models that have at
6
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Table 4. Estimated effects of vehicle model years and vehicle characteristics on retention rates, computed using estimated coefficients in
table A3. Vehicle model and age interaction coefficients are presented in table A4.
Non-Tesla BEV Tesla BEV PHEV HEV CV
Initial sale retention rate
exp(intercept) 23.95
(0.32)
102.27
(2.21)
72.68
(1.14)
89.1
(0.37)
104.37
(0.12)
Difference by model year
Reference Level: 2012
2013 4.33
(0.36)
10.56
(1.88)
−1.09
(1.3)
−1.88
(0.36)
0.83
(0.12)
2014 6.33
(0.41)
−11.58
(1.48)
9
(1.48)
−1.57
(0.36)
−1.82
(0.11)
2015 6.49
(0.41)
−14.64
(1.34)
8.66
(1.48)
−3.59
(0.35)
−5.28
(0.11)
2016 6.16
(0.42)
−22.66
(1.1)
18.46
(3.51)
0.74
(0.37)
−4.49
(0.11)
2017 7.73
(0.51)
−26.62
(1.04)
17.78
(3.46)
−0.41
(0.36)
−6.26
(0.11)
2018 11.23
(0.7)
−47.42
(0.75)
21.42
(3.58)
−1.29
(0.36)
−5.38
(0.11)
Percent Change in Retention Rate from Addition of…
…10 000 miles −5.07
(0.06)
−3.93
(0.08)
−4.49
(0.03)
−4.67
(0.01)
−4.64
(0)
…10 d on market −0.37
(0.02)
−0.13
(0.06)
−0.23
(0.02)
−0.28
(0.01)
−0.08
(0)
…1 cent per mile (operating cost) 0.13
(0.06)
0.38
(0.13)
0.35
(0.06)
1.13
(0.02)
0.08
(0)
…10 miles driving range 5.58
(0.14)
1.57
(0.04)
−0.8
(1.45)
…$7 500 subsidy in new market −3.25
(0.36)
−3.41
(0.31)
5.8
(0.26)
Number of observations: 74 331 22 518 58 915 464 560 8395 000
Adjusted R-Squared: 0.688 0.604 0.838 0.727 0.587
least three available model years between 2014 and
2018 as some models (such as the Tesla Model 3 and
Chevrolet Bolt EV) only came out in 2018. Prediction
lines are made using the estimated model coefficients
in table A4, and the plotted points are the mean two-
year-old retention rates across all listings for a given
model year-range pair. Here we see the rapid increase
in two-year-old retention rates for newer Non-Tesla
BEVs, which have larger electric driving ranges. In
contrast, the slope of line for the Tesla Model S is
negative because the negative model year effect is lar-
ger than the positive range effect, which is smaller in
magnitude than that of Non-Tesla BEVs. This sug-
gests that while range is important for value reten-
tion, there may be a limit to the higher value retention
obtained from increased driving range alone.
We also find an effect from subsidies available in
the new vehicle market. A $7500 subsidy for a new
BEV (the maximum federal subsidy for which most
BEVs in this time period qualified) is associated with
lower retention rates in the resale market by 3.3%
for Non-Tesla BEVs and 3.4% for Tesla BEVs. This is
expected as the retention rates (computed as the list-
ing price divided by the unsubsidized MSRP) should
be lower for used vehicles that have an equivalent sub-
sidized model available in the new market compared
to those that do not.
To put this subsidy effect in context, we estim-
ate that the total federal subsidies dispensed for
new BEVs sold between 2011 and 2019 resulted in
$255 million in indirect subsidies to the resale market
via lower resale prices (see figure 4). While relatively
small in magnitude compared to the $8.7 billion in
new subsidies, our estimate is still important to note
as it is an additional benefit to consumers that comes
at no additional government cost.
Although our data extends up until March of
2022, we chose to censor the data used for all mod-
eling to the end of 2019 due to the supply disrup-
tions in the automotive market that resulted from
the outbreak of the COVID-19 pandemic in early
2020. These disruptions led to significant increases
in vehicle prices in the new and used markets, with
prices for some used vehicles rising higher than their
new MSRPs since many new vehicles were unavail-
able for lengthy periods of time. This rise in pricing
made models that rely on assumptions of exponen-
tial decline unreliable, hence why we omitted the data
7
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Figure 2. Predicted two-year-old retention rates for all modeled vehicle models, comparing model years 2014 and 2018. Model
year 2018 HEVs, CVs, and Non-Tesla BEVs are experiencing lower retention rates relative to model year 2014 while PHEVs and
Non-Tesla BEVs are experience higher retention rates. Predictions are made using the estimated model coefficients in table A4.
8
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Figure 3. Predicted two-year-old retention rate versus range for select BEV models by model year (only vehicle models with at
least three model years are included). Predictions are made using the estimated model coefficients in table A4, and points are the
mean two-year-old retention rates across all listings for a given model year-range pair.
Figure 4. Annual total indirect subsidy to the resale market from lower prices due to federal subsidies for new PEVs.
9
Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
Figure 5. Summary of resale market prices for CVs (gray), Tesla BEVs (blue), and Non-Tesla BEVs (green). Solid lines are mean
prices and bands show the interquartile range of prices. The vertical red line marks March 2020, the start of the COVID19
pandemic.
from our models. Nonetheless, descriptions of the
post-pandemic listing prices are still informative.
Figure 5shows the mean listing prices for CVs,
Tesla BEVs, and Non-Tesla BEVs. As of March 2022,
mean inflation-adjusted prices are 37%, 39%, and 3%
higher compared to in January 2020 for CVs, Non-
Tesla BEVs, and Tesla BEVs, respectively. In combina-
tion with the overall trend of increased retention rates
for Non-Tesla BEVs, this result suggests that, at least
in the short term, affordability for all vehicles (includ-
ing BEVs) is worse than in pre-pandemic years, and
buyers may have a difficult time finding an affordable
BEV in the resale market, adding an additional barrier
to more equitable PEV adoption.
5. Discussion and conclusions
PEVs are a critical component in a low-carbon future,
and the resale market will play a critical role in
expanding PEV adoption beyond affluent demo-
graphics given its greater affordability [30] and size—
more than double the size of the new vehicle market in
annual sales [31]. In this study, we analyze the reten-
tion rates of different cars listed in the resale market,
comparing result across different powertrains.
We find in general that PEVs do not hold their
value as well as CVs as they age, which is consist-
ent with much of the prior literature on this topic
[9,10,12,13,21]. However, we also find evid-
ence that this is changing over time, with newer
PEV models holding higher retention rates that are
approaching those of many CVs. This trend is par-
ticularly significant given the rapid advancements in
PEV technology, notably in driving range. In our
dataset, the mean BEV range grew 76% from 86 to
151 miles from 2012 to 2018. This evolution not only
addresses range anxiety but also potentially mitig-
ates resale anxiety [7] as we find that higher-range
BEVs have significantly higher retention rates than
lower-range BEVs. Mileage continues to be a crit-
ical determinant of resale value across all powertrains,
with its impact slightly more pronounced in Non-
Tesla BEVs. This finding underscores the importance
of mileage as a proxy for battery health in BEVs, a
key consideration for potential buyers in the used
market.
Interestingly, our analysis reveals that subsidies
for BEVs in the new market are associated with lower
prices in the resale market, effectively passing through
an indirect subsidy to used BEVs. While this phe-
nomenon aids in making used BEVs more affordable,
it simultaneously could fuel resale anxiety among new
BEV purchasers. This dynamic presents a complex
scenario for policymakers, especially considering the
recent introduction of a used PEV tax credit. The
long-term effects of such incentives on PEVresale val-
ues warrant further investigation.
The COVID-19 pandemic has significantly
impacted the vehicle market by reducing affordability
across all powertrains. Recognizing and adapting
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Environ. Res. Lett. 19 (2024) 054053 L Roberson et al
to these shifts is imperative in maintaining PEV
affordability and, consequently, in promoting
broader adoption of the technology. The evolving
landscape of PEV retention rates, particularly among
Non-Tesla models, is noteworthy, but this also intro-
duces a new dynamic where the affordability of sub-
sequent vehicles diminishes due to elevated pricing
and retention rates. In the post-pandemic era, the
surge in used vehicle prices has somewhat eroded the
affordability that once characterized the used PEV
market.
Our study is not without limitations. The exclu-
sion of data post-2019 due to the COVID-19 pan-
demic’s impact on the market bounds our findings,
with 2018 model years being the most recent mod-
els we can analyze. This exclusion means we cannot
see how the resale market has evolved with the many
newer models that have since been released during
the pandemic and post-pandemic years. Additionally,
our reliance on listing prices as a proxy for transac-
tion prices may not fully capture the nuances of final
sale negotiations and discounts. If there are systematic
differences in listing and transaction prices by power-
train, then our comparative analyses across power-
trains may have slight discrepancies with respect to
true differences in retained value. Nonetheless, the
retention rates presented in this study should accur-
ately reflect what consumers would have observed at
dealerships prior to making a purchase decision. Our
study also focuses on the U.S. market, which may
limit the generalizability of our findings to global
markets where different economic conditions, con-
sumer preferences, and policy frameworks prevail.
Our analysis also does not extensively delve into the
impact of brand perception and consumer loyalty,
particularly concerning Tesla and other emerging
PEV manufacturers. The strength and reputation of
a brand can significantly influence resale values, and
as the market continues to evolve, shifts in consumer
perception could markedly alter the landscape of PEV
value retention.
Finally, the rapidly evolving nature of PEV tech-
nology, particularly advancements in battery effi-
ciency and lifespan, could alter the depreciation pat-
terns in ways not captured in our current dataset. As
newer models with improved technology enter the
resale market, their retention rates could differ signi-
ficantly from the trends observed in our study. For
these reasons, we caution modelers from extrapolat-
ing these trends too far into the future. Continuous
monitoring and analysis will be essential to fully
understand the longer-term trends and implications
for PEV adoption and sustainability in the transport-
ation sector.
Data availability statement
All of the code used to process the data, estim-
ate models, and produce all analyses and figures
are publicly available on GitHub at https://zenodo.
org/doi/10.5281/zenodo.11041585. The vehicle list-
ings data that support the findings of this study are
available from www.marketcheck.com, but restric-
tions apply to the availability of these data, which
were used under license for the current study and
so are not publicly available. A sample of the data is
included in the GitHub repository. The relevant vari-
ables in the full original database can be provided on
an individual bases for review purposes only to repro-
duce the study results by contacting the lead contact.
All other data used in the study are posted in the
repository.
Acknowledgments
The authors would like to thank the two anonymous
reviewers for their comments to improve this manu-
script as well as marketcheck.com for providing access
to the data used in this study. This study was suppor-
ted by a grant from the Department of Energy (Award
Number: DE-EE0010641).
Author contributions
Conceptualization, L R and J P H; Methodology, J P H
and L R; Software, L R, S P, and J P H; Validation, J P
H; Formal Analysis, L R, S P, and J P H; Investigation,
L R, S P, and J P H; Data Curation, S P and J P
H; Writing—Original Draft, L R; Writing—Review &
Editing, J P H; Visualization, L R and J P H.
Conflict of interests
The authors declare no competing interest.
ORCID iDs
Laura Roberson https://orcid.org/0000-0001-
7175-8888
Saurav Pantha https://orcid.org/0009-0002-4201-
1501
John Paul Helveston https://orcid.org/0000-0002-
2657-9191
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