rebrowser/autotrader-dataset

AutoTrader Vehicle Listings Dataset

New, used, and certified pre-owned vehicle listings from AutoTrader with pricing, KBB valuations, dealer info, drivetrain specs, and listing placement data.

This dataset is a preview sample of the AutoTrader 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: Car Listings (car-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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Car Listings

AutoTrader vehicle listings with make, model, trim, body style, mileage, MSRP, KBB fair price range, deal rating, drivetrain, fuel type, seller location, and listing priority.

1,423,175 total records from 2025-11-16 to 2026-03-29, up to 30,000 rows in this sample (2.1% 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
listingId	string	100%	Unique Autotrader listing ID
vin 🔒	string	100%	Vehicle Identification Number (17-character unique code)
stockNumber	string	99%	Dealer stock number
year	float	100%	Vehicle model year
makeCode	string	100%	Make code (e.g., BMW, TOYOTA, GENESIS)
makeName	string	100%	Make display name (e.g., BMW, Toyota, Genesis)
modelCode	string	100%	Model code (e.g., X3, CAMRY, GENGV80)
modelName	string	100%	Model display name (e.g., X3, Camry, GV80)
trim	string	96%	Vehicle trim level (e.g., xDrive30i, 3.5T Prestige)
bodyStyle	string	96%	Body style (e.g., SUV, Sedan, Truck)
listingType	string	100%	Listing type (New, Used, Certified)
listingTitle	string	100%	Full listing title (e.g., "Used 2025 BMW X3 xDrive30i w/ Convenience Package")
mileage	float	100%	Odometer reading in miles
salePrice 🔒	float	99%	Listed sale price in USD
msrp	float	7%	Manufacturer Suggested Retail Price in USD (for New vehicles)
dealIndicator	string	68%	KBB deal rating (Great, Good, Fair, High, Overpriced)
kbbFairPurchasePrice 🔒	float	98%	KBB Fair Purchase Price estimate in USD
kbbFairPriceLow	float	98%	KBB Fair Purchase Price low range in USD
kbbFairPriceHigh	float	98%	KBB Fair Purchase Price high range in USD
exteriorColor	string	99%	Exterior color name (e.g., Alpine White, Capri Blue)
exteriorColorSimple	string	99%	Simplified exterior color (e.g., WHITE, BLUE, BLACK)
interiorColor	string	96%	Interior color name (e.g., Espresso Brown, Blue)
interiorColorSimple	string	96%	Simplified interior color (e.g., BROWN, BLUE, BLACK)
engineCode	string	100%	Engine code (e.g., 4CLDR, 6CLDR)
engine	string	100%	Engine description (e.g., 4-Cylinder Turbo, 6-Cylinder Turbo)
drivetrain	string	93%	Drivetrain type (e.g., All wheel drive, Front-wheel drive)
transmissionCode	string	99%	Transmission code (e.g., AUT, MAN)
transmission	string	99%	Transmission type (e.g., 8-Speed Automatic)
transmissionGroup	string	99%	Transmission type group (Automatic, Manual)
fuelTypeCode	string	94%	Fuel type code (e.g., G, D, E)
fuelType	string	94%	Fuel type (e.g., Gasoline, Diesel, Electric)
fuelTypeGroup	string	94%	Fuel type group (Gas, Diesel, Electric, Hybrid)
mpgCity	float	80%	City fuel economy in MPG
mpgHighway	float	80%	Highway fuel economy in MPG
displacementUOM	float	100%	Engine displacement in liters (e.g., 2.0, 3.5)
hasLeatherSeats	bool	100%	Whether vehicle has leather seats
daysOnMarket	float	99%	Number of days listing has been on market
isHot	bool	100%	Hot listing flag (high interest)
isNewlyListed	bool	100%	Recently listed flag
isReducedPrice	bool	100%	Price recently reduced flag
isNoHagglePrice	bool	100%	No-haggle/fixed price flag
hasSpecialOffer	bool	100%	Has special offer/promotion
moneyBackGuarantee	bool	100%	Money-back guarantee offered
mainImageIsStock	bool	100%	Main image is stock photo (not actual vehicle)
priority	string	100%	Listing priority/tier (PREMIUM, STANDARD, BASIC)
sellerId	float	100%	Seller/dealer ID
sellerName 🔒	string	100%	Seller/dealer name
sellerPhone 🔒	string	96%	Seller phone number
sellerRating 🔒	float	95%	Seller KBB rating (1-5 scale)
sellerReviewCount	float	95%	Number of seller reviews
sellerWebsite 🔒	string	96%	Seller website URL
isPrivateSeller	bool	100%	Whether seller is a private seller (true) or dealer (false)
sellerContractLevel	string	96%	Seller contract level (PARTNER, STANDARD, etc.)
kbbVehicleId	float	95%	KBB vehicle ID for price lookup
kbbConsumerRatings	float	100%	KBB consumer rating (1-5 scale)
kbbConsumerReviewCount	float	100%	Number of KBB consumer reviews
safetyRecallCount	float	89%	Number of open safety recalls
vhrPreview	string	98%	Vehicle history report preview flags (JSON array: NO_SALVAGE_TITLE, NO_ACCIDENTS_REPORTED, ONE_OWNER, etc.)
options	string	98%	Vehicle options object with subcategories: exterior, interior, safety, mechanical, technology, other
optionsCount	float	98%	Total number of options across all categories
description	string	99%	Full marketing description text (HTML tags stripped except br)
images 🔒	string	99%	All listing image URLs as JSON array
imagesCount	float	99%	Number of listing images
sellerAddress	string	97%	Seller address line 1
sellerCity	string	100%	Seller city
sellerState	string	100%	Seller state
sellerZip	string	100%	Seller ZIP code
listingUrl 🔒	string	100%	Full URL to the Autotrader 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

Listing Type (New / Used / Certified) (listingType)

Value	Count	Share
Used	1,138,778	████████████████░░░░ 80.0%
Certified	177,429	██░░░░░░░░░░░░░░░░░░ 12.5%
New	106,968	██░░░░░░░░░░░░░░░░░░ 7.5%

Body Style Distribution (bodyStyle)

Value	Count	Share
SUV	670,714	██████████░░░░░░░░░░ 48.9%
TRUCKS	285,127	████░░░░░░░░░░░░░░░░ 20.8%
SEDAN	243,753	████░░░░░░░░░░░░░░░░ 17.8%
HATCH	50,387	█░░░░░░░░░░░░░░░░░░░ 3.7%
COUPE	49,692	█░░░░░░░░░░░░░░░░░░░ 3.6%
VANS	34,192	░░░░░░░░░░░░░░░░░░░░ 2.5%
CONVERT	30,090	░░░░░░░░░░░░░░░░░░░░ 2.2%
WAGON	6,458	░░░░░░░░░░░░░░░░░░░░ 0.5%

Fuel Type Distribution (fuelTypeGroup)

Value	Count	Share
Gas	1,216,781	██████████████████░░ 91.1%
Hybrid: Gas/Electric	56,156	█░░░░░░░░░░░░░░░░░░░ 4.2%
Electric	45,413	█░░░░░░░░░░░░░░░░░░░ 3.4%
Plug-in Hybrid: Gas/Electric	16,681	░░░░░░░░░░░░░░░░░░░░ 1.2%

Top States by Listing Count (sellerState)

Value	Count	Share
TX	180,064	████░░░░░░░░░░░░░░░░ 21.2%
CA	161,011	████░░░░░░░░░░░░░░░░ 19.0%
FL	147,844	███░░░░░░░░░░░░░░░░░ 17.4%
NC	60,235	█░░░░░░░░░░░░░░░░░░░ 7.1%
OH	59,251	█░░░░░░░░░░░░░░░░░░░ 7.0%
GA	57,018	█░░░░░░░░░░░░░░░░░░░ 6.7%
IL	48,436	█░░░░░░░░░░░░░░░░░░░ 5.7%
AZ	47,118	█░░░░░░░░░░░░░░░░░░░ 5.6%
MI	43,547	█░░░░░░░░░░░░░░░░░░░ 5.1%
PA	43,310	█░░░░░░░░░░░░░░░░░░░ 5.1%

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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:

Car Listings

Listings with Pricing Data — 1,423,174 records

↳ [{"field":"salePrice","op":"gt","value":0},{"sort":"salePrice DESC"}]

Certified Pre-Owned Listings — 177,429 records

↳ [{"field":"listingType","op":"is","value":"Certified"},{"sort":"salePrice DESC"}]

New Vehicle Listings — 106,968 records

↳ [{"field":"listingType","op":"is","value":"New"},{"sort":"msrp DESC"}]

Used Vehicle Listings — 1,138,777 records

↳ [{"field":"listingType","op":"is","value":"Used"},{"sort":"salePrice DESC"}]

Premium and Sponsored Listings — 1,283,053 records

↳ [{"field":"priority","op":"is","value":"PREMIUM"},{"sort":"salePrice DESC"}]

See all 34 views →

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Code Examples

import pandas as pd
from pathlib import Path

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

# Median KBB fair price midpoint by make for new vehicles
listings['kbbMidpoint'] = (listings['kbbFairPriceLow'] + listings['kbbFairPriceHigh']) / 2
new_cars = listings[listings['listingType'] == 'New']
print(new_cars.groupby('makeName')['kbbMidpoint'].median()
      .sort_values(ascending=False).head(15).to_string())

# Certified pre-owned listing count by body style
cpo = listings[listings['listingType'] == 'Certified']
print(cpo['bodyStyle'].value_counts().to_string())

# Average days on market by fuel type group for used vehicles
used = listings[listings['listingType'] == 'Used']
print(used.groupby('fuelTypeGroup')['daysOnMarket'].mean().sort_values().to_string())

# Listing volume and premium-placement rate by state
state_stats = listings.groupby('sellerState').agg(
    total=('listingId', 'count'),
    premium=('priority', lambda x: (x == 'PREMIUM').sum())
).assign(pct_premium=lambda d: (d['premium'] / d['total'] * 100).round(1))
print(state_stats.sort_values('total', ascending=False).head(15).to_string())

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Use Cases

Market Pricing Analysis

Compare MSRP and KBB fair price ranges across makes, models, and trim levels to identify over- and under-priced listings. Filter by body style and state to benchmark regional pricing gaps.

Inventory Trend Tracking

Monitor which makes and body styles dominate dealer inventory by state. Track days-on-market to identify slow-moving segments and spot seasonal inventory shifts.

EV & Fuel Type Research

Filter by fuelTypeGroup to analyze electric, hybrid, and diesel vehicle availability and regional adoption patterns relative to gasoline alternatives.

Deal Quality Scoring

Use KBB deal indicator ratings alongside KBB price ranges to rank listings by value. Identify which markets and vehicle types produce the most "Great Deal" inventory.

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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/autotrader

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_autotrader,
  author       = {Rebrowser},
  title        = {AutoTrader Vehicle Listings Dataset},
  year         = {2026},
  howpublished = {\url{https://rebrowser.net/products/datasets/autotrader}},
  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 AutoTrader. Any trademarks are the property of their respective owners. This dataset is compiled from publicly available information; we do not request or collect AutoTrader user credentials. By using this dataset, you agree to comply with AutoTrader'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.