We extract new and used inventory, pricing signals, VIN metadata, dealer intelligence, and consumer reviews from Cars.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Vehicle Listings objects from cars.com. All fields typed and schema-versioned.
"vin": "1G1RC6E49EU123456", "year": 2022, "make": "Chevrolet", "model": "Silverado 1500", "trim": "LTZ", "mileage": 24500, "price": 45990.0, "exterior_colour": "Silver Ice Metallic"
| # | vin | stock_number | year | make | model | trim |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Value objects from cars.com. All fields typed and schema-versioned.
"vin": "1G1RC6E49EU123456", "listed_price": 45990.0, "msrp": 48990.0, "price_drop_amount": 1500.0, "great_deal_badge": true, "fair_price_indicator": "Good Deal", "currency": "USD"
| # | vin | listed_price | msrp | price_drop_amount | price_drop_date | great_deal_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dealer Data objects from cars.com. All fields typed and schema-versioned.
"dealer_id": "DLR-84729", "dealership_name": "Smith Chevrolet Motors", "city": "Austin", "state": "TX", "average_rating": 4.7, "review_count": 842, "inventory_size": 314
| # | dealer_id | dealership_name | address | city | state | zip |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Consumer Reviews objects from cars.com. All fields typed and schema-versioned.
"review_id": "REV-993821", "dealer_id": "DLR-84729", "star_rating": 5, "review_date": "2023-11-14", "purchase_status": "Purchased a vehicle", "vehicle_purchased": "2022 Chevrolet Silverado 1500", "helpful_votes": 12
| # | review_id | dealer_id | reviewer_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Specs & History objects from cars.com. All fields typed and schema-versioned.
"vin": "1G1RC6E49EU123456", "fuel_type": "Gasoline", "mpg_city": 16, "mpg_highway": 21, "carfax_1_owner": true, "carfax_clean_title": true, "accidents_reported": 0
| # | vin | fuel_type | mpg_city | mpg_highway | doors | seating_capacity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Cars.com scraper navigates complex search filters, dealer subdomains, and dynamic pricing modules to extract structured vehicle data across all US zip codes.
Capture make, model, trim, VIN, mileage, and stock numbers for millions of new and used vehicles.
Monitor listed price, MSRP, price drops, and Cars.com specific Deal Badges (Great Deal, Good Deal).
Extract dealership names, addresses, inventory counts, and aggregate review scores per location.
Pull detailed specifications including engine type, transmission, drivetrain, exterior colour, and interior colour.
Capture surfaced vehicle history indicators like 1-owner status, clean title, and accident reports.
Extract full text, star ratings, and purchase status from dealership reviews paginated across the platform.
Bypass 50-page search limits by programmatically querying overlapping geographic radii across all US zip codes.
Track listing age and days-on-market metrics to calculate inventory turn rates and supply saturation.
Run continuous pipelines that only emit records with changed fields (price drops, sold status) since the last crawl.
Brief in. Clean data out.
Provide target makes, models, zip codes, or dealer IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and geographic bounding boxes for cars.com.
Schema validation, null-rate checks, price-outlier detection, and sample inventory before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Automotive marketplaces deploy strict rate limiting and complex pagination. Here is how we maintain steady extraction.
Cars.com utilizes advanced bot mitigation. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with normal consumer traffic.
Vehicle detail pages load pricing breakdowns, dealer fees, and financing estimates dynamically. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
Cars.com caps search results at 50 pages (1,500 vehicles). To extract national inventory for high-volume models, we programmatically subdivide searches using overlapping zip code radii and price brackets, deduplicating via VIN.
Automotive sites frequently test new UI layouts. Our selector strategy uses multiple fallback chains per field, including structured JSON-LD extraction, so a layout change does not break your data pipeline overnight.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing VINs, and coverage drops, responding before you notice.
Dealership groups monitor regional competitors to optimise their own pricing strategies and identify mispriced inventory.
Financial institutions track real-time retail asking prices at the VIN level to adjust loan-to-value models and depreciation curves.
Automotive groups track inventory turn rates, days-on-market, and stock levels across competing local dealerships.
Manufacturers monitor new car availability, trim-level distribution, and dealer pricing behaviour across their network.
B2B automotive software vendors identify high-volume dealerships based on inventory size and review velocity.
Machine learning teams use historical pricing, mileage, and trim datasets to train predictive vehicle valuation algorithms.
"Cars.com holds the most accurate pulse on US retail automotive pricing, but extracting national inventory requires bypassing strict pagination limits and bot protection."
Most data teams underestimate the investment required: reliable automotive scraping requires US residential proxies, dynamic geographic search splitting, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your analysts can focus on market trends, not infrastructure.
Everything supported by our cars.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, geographic query generation, and retry logic. Playwright handles JavaScript rendering and interaction flows for dynamic pricing data.
We maintain pools of US-based residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent IP bans from automotive marketplaces.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About cars.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Cars.com is generally permissible under applicable US law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated vehicle and dealer data. We do not extract personal user data or circumvent authentication walls.
Cars.com restricts search results to 1,500 vehicles. We bypass this by programmatically generating thousands of micro-searches based on overlapping zip code radii, specific makes/models, and narrow price brackets, then deduplicate the final dataset using the VIN.
Yes. We extract public dealership names, addresses, phone numbers, website URLs, and aggregate review scores from the dealer profile pages.
For targeted regional searches, pipelines can run hourly. Full national catalogue refreshes typically complete within a 12-24 hour window depending on specific filter criteria.
Yes. We calculate days on market by tracking the first time a VIN is observed. Price drops are captured natively if surfaced by the platform, or calculated historically by our change-detection engine.
Our smallest packages start at defined regional extractions or specific make/model tracking. For national inventory extraction, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a regional dealer analysis or a continuous national inventory feed across 1.5M vehicles - we scope, build, and operate the pipeline. Tell us what you need.
