rebrowser/copart-dataset

Copart Salvage Vehicle Auction Dataset

Salvage vehicle auction listings with damage assessments, condition grades, title status, and yard locations from Copart's nationwide network.

This dataset is a preview sample of the Copart dataset published by Rebrowser. If you're doing academic research, you may be eligible for free access to a much larger slice — see Free Datasets for Research.

This dataset contains 1 entity, each in its own folder: Auction Listings (auction-listings). See below for a full field breakdown, sample counts, and data distributions for each.

Found this useful? ❤️ Like this dataset on HuggingFace to help us keep publishing fresh data. Found an error? Let us know.

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Auction Listings

Daily sample of Copart salvage auction lots with damage types, condition codes, title status, mileage, repair costs, and yard locations across the US.

1,191,685 total records from 2025-11-16 to 2026-03-08, up to 30,000 rows in this sample (2.5% of full dataset). Exported as one file per day, up to 1,000 rows each, last 30 days retained.

Field	Type	Fill Rate	Description
_primaryKey	string	100%	Unique identifier for this record
_firstSeenAt	datetime	100%	First time this record was seen
_lastSeenAt	datetime	100%	Last time this record was updated
lotId	string	100%	Unique Copart lot number (auction identifier)
updatedAt	datetime	100%	Timestamp when Copart last updated the listing data
vin 🔒	string	100%	Vehicle Identification Number (17-character unique code)
yardNumber	string	100%	Copart yard/facility number
yardName	string	100%	Copart yard/facility name (e.g., "FL - MIAMI NORTH")
saleDate	datetime	89%	Scheduled auction sale date
saleDayOfWeek	string	89%	Day of week for the auction (e.g., TUESDAY, FRIDAY)
saleTime	string	89%	Auction start time in HHMM format (e.g., "1000" = 10:00 AM)
saleTimeZone	string	100%	Time zone for auction time (e.g., EST)
itemNumber	string	100%	Item sequence number within the auction
vehicleType	string	100%	Type code (V = Vehicle, C = Cycle/Motorcycle, K = Truck/Commercial)
year	float	100%	Vehicle model year
make	string	100%	Vehicle manufacturer (e.g., NISSAN, TOYOTA, MERCEDES-BENZ)
modelGroup	string	100%	Vehicle model group (e.g., SENTRA, TACOMA, GLE-CLASS)
modelDetail	string	100%	Detailed model name (e.g., SENTRA SV, TACOMA DOU, GLE COUPE)
bodyStyle	string	20%	Vehicle body style
exteriorColor	string	100%	Vehicle exterior color (e.g., WHITE, BLACK, GRAY)
damageDescription	string	100%	Primary damage description (e.g., FRONT END, REAR END, MINOR DENT/SCRATCHES)
secondaryDamage	string	43%	Secondary damage description (e.g., SIDE, REAR END)
saleTitleState	string	100%	State where the title is held (e.g., FL)
saleTitleType	string	100%	Title type code (SC = Salvage Certificate, CD = Certificate of Destruction, NR = Non-Repairable, DV = Dealer Vehicle, ST = Salvage Title, CT = Clear Title, RB = Rebuildable, AQ = Acquisition)
hasKeys	string	100%	Whether keys are available (YES/NO/EXM - Exempt)
lotCondCode	string	96%	Lot condition code (D = Drivable, E = Enhanced inspection, S = Stationary)
mileage	float	100%	Odometer reading in miles
odometerBrand	string	100%	Odometer status (A = Actual, N = Not Actual, E = Exempt)
estRetailValue 🔒	float	100%	Estimated retail value in USD
repairCost	float	100%	Estimated repair cost in USD
engine	string	97%	Engine description (e.g., "3.5L 6", "2.0L 4")
drivetrain	string	98%	Drivetrain type (All wheel drive, Front-wheel Drive, Rear-wheel drive)
transmission	string	98%	Transmission type (e.g., AUTOMATIC)
fuelType	string	99%	Fuel type (e.g., GAS)
cylinders	float	97%	Number of engine cylinders
runsDrives	string	96%	Run/drive status (Run & Drive Verified, Vehicle Starts, DEFAULT, null)
saleStatus	string	100%	Auction sale status (Pure Sale, On Minimum Bid)
highBid 🔒	float	100%	Current high bid amount in USD
specialNote	string	3%	Special notes (e.g., "ODOMETER IS IN KILOMETERS")
locationCity	string	100%	Vehicle storage location city
locationState	string	100%	Vehicle storage location state
locationZip	string	100%	Vehicle storage location ZIP code
locationCountry	string	100%	Vehicle storage location country (e.g., USA)
currencyCode	string	100%	Currency code for prices (e.g., USD)
imageThumbnail 🔒	string	100%	Thumbnail image URL
imageUrl 🔒	string	100%	Full-size image URL
gridRow	string	100%	Yard grid/row location (e.g., "A130", "SD006", "RACK", "*OFF" for offsite)
makeOfferEligible	bool	100%	Whether Make-an-Offer is available
buyItNowPrice 🔒	float	100%	Buy-It-Now price in USD (0 if not available)
trim	string	75%	Vehicle trim level (e.g., SV, EX, LUXE, AMG 53 4MATIC)
rentals	bool	100%	Whether vehicle was a former rental
wholesale	bool	100%	Whether listing is wholesale
sellerName 🔒	string	36%	Seller name (e.g., "State Farm Insurance", "GEICO")
offsiteAddress1	string	1%	Offsite pickup address line 1
offsiteState	string	1%	Offsite pickup state
offsiteCity	string	1%	Offsite pickup city
offsiteZip	string	1%	Offsite pickup ZIP code
saleLight	string	1%	Sale light indicator
autoGrade	float	2%	Auto grade rating (e.g., 3.0, 2.5)
announcements	string	1%	Auction announcements and special conditions
listingUrl 🔒	string	100%	Full URL to the Copart lot listing page

🔒 Premium fields are included in the data files but their values are replaced with [PREMIUM]. To access real values, use our website.

Field Distributions

Top Vehicle Makes (make)

Value	Count	Share
TOYOTA	153,745	████░░░░░░░░░░░░░░░░ 18.3%
FORD	135,347	███░░░░░░░░░░░░░░░░░ 16.1%
CHEVROLET	119,960	███░░░░░░░░░░░░░░░░░ 14.3%
HONDA	109,602	███░░░░░░░░░░░░░░░░░ 13.0%
NISSAN	93,816	██░░░░░░░░░░░░░░░░░░ 11.2%
HYUNDAI	65,882	██░░░░░░░░░░░░░░░░░░ 7.8%
KIA	52,248	█░░░░░░░░░░░░░░░░░░░ 6.2%
JEEP	42,982	█░░░░░░░░░░░░░░░░░░░ 5.1%
DODGE	35,620	█░░░░░░░░░░░░░░░░░░░ 4.2%
SUBARU	31,423	█░░░░░░░░░░░░░░░░░░░ 3.7%

Top Damage Types (damageDescription)

Value	Count	Share
FRONT END	643,008	███████████░░░░░░░░░ 56.2%
REAR END	174,784	███░░░░░░░░░░░░░░░░░ 15.3%
SIDE	152,800	███░░░░░░░░░░░░░░░░░ 13.4%
MINOR DENT/SCRATCHES	52,478	█░░░░░░░░░░░░░░░░░░░ 4.6%
MECHANICAL	31,676	█░░░░░░░░░░░░░░░░░░░ 2.8%
NORMAL WEAR	23,949	░░░░░░░░░░░░░░░░░░░░ 2.1%
ALL OVER	22,289	░░░░░░░░░░░░░░░░░░░░ 1.9%
ROLLOVER	16,335	░░░░░░░░░░░░░░░░░░░░ 1.4%
UNDERCARRIAGE	13,979	░░░░░░░░░░░░░░░░░░░░ 1.2%
VANDALISM	13,057	░░░░░░░░░░░░░░░░░░░░ 1.1%

Title Type Distribution (saleTitleType)

Value	Count	Share
SC	356,243	███████░░░░░░░░░░░░░ 33.2%
ST	323,894	██████░░░░░░░░░░░░░░ 30.2%
CT	175,467	███░░░░░░░░░░░░░░░░░ 16.3%
SV	67,415	█░░░░░░░░░░░░░░░░░░░ 6.3%
RB	46,512	█░░░░░░░░░░░░░░░░░░░ 4.3%
SM	25,772	░░░░░░░░░░░░░░░░░░░░ 2.4%
BS	22,880	░░░░░░░░░░░░░░░░░░░░ 2.1%
S1	20,884	░░░░░░░░░░░░░░░░░░░░ 1.9%
RS	18,840	░░░░░░░░░░░░░░░░░░░░ 1.8%
CD	15,652	░░░░░░░░░░░░░░░░░░░░ 1.5%

Listings by State (locationState)

Value	Count	Share
CA	110,284	████░░░░░░░░░░░░░░░░ 19.1%
TX	100,499	███░░░░░░░░░░░░░░░░░ 17.4%
FL	73,620	███░░░░░░░░░░░░░░░░░ 12.7%
IL	52,261	██░░░░░░░░░░░░░░░░░░ 9.0%
PA	51,392	██░░░░░░░░░░░░░░░░░░ 8.9%
GA	46,594	██░░░░░░░░░░░░░░░░░░ 8.1%
NY	36,942	█░░░░░░░░░░░░░░░░░░░ 6.4%
MI	36,621	█░░░░░░░░░░░░░░░░░░░ 6.3%
TN	35,880	█░░░░░░░░░░░░░░░░░░░ 6.2%
AL	34,583	█░░░░░░░░░░░░░░░░░░░ 6.0%

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Pre-built Views on Rebrowser

Rebrowser web viewer lets you filter, sort, and export any slice of this dataset interactively. These pre-built views are ready to open:

Auction Listings

Listings with Bid Over $1,000 — 210,597 records

↳ [{"field":"highBid","op":"gt","value":1000},{"sort":"highBid DESC"}]

Salvage Title Auctions — 286,288 records

↳ [{"field":"saleTitleType","op":"is","value":"ST"},{"sort":"saleDate ASC"}]

Run and Drive Vehicles — 685,918 records

↳ [{"field":"lotCondCode","op":"is","value":"D"},{"sort":"estRetailValue DESC"}]

Listings with Estimated Value Over $10,000 — 514,354 records

↳ [{"field":"estRetailValue","op":"gt","value":10000},{"sort":"estRetailValue DESC"}]

Make-an-Offer Eligible Lots — 117,597 records

↳ [{"field":"makeOfferEligible","op":"isTrue"},{"sort":"_lastSeenAt DESC"}]

See all 38 views →

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Code Examples

import pandas as pd
from pathlib import Path

# ── Auction Listings ─────────────────────────────────────────────────────────
# Load the last 7 days of auction listings
files = sorted(Path('rebrowser/copart-dataset/auction-listings/data').glob('*.parquet'))[-7:]
listings = pd.concat([pd.read_parquet(f) for f in files])

# Top 10 most common makes
print(listings['make'].value_counts().head(10).to_string())

# Average mileage by damage type
damage_mileage = listings.groupby('damageDescription')['mileage'].mean().sort_values(ascending=False)
print(damage_mileage.head(10).round(0).to_string())

# Count of listings by title type and condition code
print(pd.crosstab(listings['saleTitleType'], listings['lotCondCode']).to_string())

# Run-and-drive vehicles by state, sorted by volume
drivable = listings[listings['lotCondCode'] == 'D']
print(drivable['locationState'].value_counts().head(10).to_string())

# Average repair cost by damage type
repair_by_damage = listings.groupby('damageDescription')['repairCost'].mean().sort_values(ascending=False)
print(repair_by_damage.head(10).round(2).to_string())

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Use Cases

Salvage Value Modeling

Build predictive models for salvage vehicle pricing using damage type, condition grade, mileage, and repair cost data. Identify undervalued lots by comparing repair costs against market values.

Parts Sourcing Pipeline

Monitor incoming auction inventory by make, model, and damage type to source high-demand parts. Filter by yard location and condition code to optimize logistics and pickup costs.

Regional Market Analysis

Compare auction volume, vehicle mix, and damage patterns across states and Copart yards. Track seasonal trends in inventory and identify geographic arbitrage opportunities.

Title Status Research

Analyze the distribution of salvage certificates, clean titles, and rebuildable designations across vehicle types. Study how title classification varies by state and affects auction outcomes.

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Full Dataset on Rebrowser

This is a 1,000-row preview sample. The full dataset is at rebrowser.net/products/datasets/copart

Doing academic research? You may qualify for free access to a larger slice. See Free Datasets for Research.

On Rebrowser you can:

• Filter before you buy — use the web UI to apply filters on any field and sort by any column. Preview results before purchasing. You only pay for records that match your criteria.
• Export in your format — CSV, JSON, JSONL, or Parquet depending on your plan.
• Access via API — integrate dataset queries into your pipelines and workflows.
• Choose your freshness — plans range from a 14-day lag to real-time data with no delay.
• Select only the fields you need — keep exports lean. Premium fields with richer data are available on higher plans.

Pricing starts at $2 per 1,000 rows with volume discounts.

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License & Terms

Free for research and non-commercial use with attribution. See license terms and how to cite.

@misc{rebrowser_copart,
  author       = {Rebrowser},
  title        = {Copart Salvage Vehicle Auction Dataset},
  year         = {2026},
  howpublished = {\url{https://rebrowser.net/products/datasets/copart}},
  note         = {Accessed: YYYY-MM-DD}
}

Commercial use requires a paid license — see pricing. Use of this data is governed by the Rebrowser Terms of Use, which may be updated at any time independently of this dataset.

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Disclaimer

Rebrowser is an independent data provider and is not affiliated with, endorsed by, or sponsored by Copart. Any trademarks are the property of their respective owners. This dataset is compiled from publicly available information; we do not request or collect Copart user credentials. By using this dataset, you agree to comply with Copart's Terms of Service and all applicable laws and regulations. Images, logos, descriptions, and other materials included in this dataset remain the intellectual property of their respective owners and are provided solely for informational purposes. Rebrowser makes no warranties regarding the accuracy, completeness, or legality of the data and assumes no liability for how the data is used. You are solely responsible for ensuring that your use of this dataset does not infringe on the rights of any third party.

You can also find this data on GitHub, Kaggle, Zenodo.