We extract Kelley Blue Book valuations, local dealer inventory, detailed vehicle specifications, and consumer reviews. 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 Valuations objects from kbb.com. All fields typed and schema-versioned.
"vin": "1G1RC6E49EU123456", "zip_code": "90210", "condition": "Very Good", "mileage": 45200, "trade_in_typical": 18450, "fair_purchase_price": 21300, "valuation_date": "2026-05-12T09:14:00Z"
| # | vin | year | make | model | trim | zip_code |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Vehicle Specs objects from kbb.com. All fields typed and schema-versioned.
"year": 2023, "make": "Toyota", "model": "Camry", "trim": "XSE", "engine": "3.5L V6", "drivetrain": "FWD", "msrp_base": 34295, "fuel_economy_city": 22
| # | year | make | model | trim | body_style | engine |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Dealer Inventory objects from kbb.com. All fields typed and schema-versioned.
"vin": "JTDBR32K241234567", "dealer_name": "Downtown Toyota", "price": 24990, "kbb_price_rating": "Good Price", "mileage": 12450, "exterior_colour": "Midnight Black Metallic", "cpo_status": true, "distance_miles": 14.2
| # | vin | stock_number | year | make | model | dealer_name |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Consumer Reviews objects from kbb.com. All fields typed and schema-versioned.
"review_id": "REV-982347", "overall_rating": 4.8, "reliability_rating": 5.0, "review_title": "Best commuter car I have owned", "review_date": "2026-02-14", "recommend_to_friend": true, "reviewer_name": "John D."
| # | review_id | vehicle_id | reviewer_name | review_date | overall_rating | value_rating |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Cost to Own objects from kbb.com. All fields typed and schema-versioned.
"state": "CA", "total_cost_to_own": 42150, "depreciation_5yr": 18200, "fuel_cost_5yr": 8450, "maintenance_cost_5yr": 4100, "insurance_cost_5yr": 6200, "cost_per_mile": 0.56
| # | vehicle_id | state | depreciation_5yr | fuel_cost_5yr | insurance_cost_5yr | maintenance_cost_5yr |
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Our KBB scraper navigates ZIP-code localised pricing, complex trim configurations, and dynamic valuation widgets with proxy rotation and session management built in.
Extract exact valuation ranges based on condition, mileage, and trim configuration.
Capture Fair Purchase Price and Typical Listing Price specific to targeted ZIP codes.
Calculate value modifiers for specific factory options, packages, and drivetrain configurations.
Monitor local dealer stock, pricing, days on lot, and KBB price ratings.
Extract projected depreciation, fuel, maintenance, and insurance costs per vehicle.
Comprehensive technical data including engine codes, dimensions, fuel economy, and MSRP.
Aggregate owner ratings across reliability, comfort, and performance categories.
Track CPO pricing deltas compared to standard used inventory.
Input raw VINs to extract exact build configurations and associated market values.
Brief in. Clean data out.
Provide VIN lists, Year/Make/Model combinations, or ZIP codes. We design the extraction schema together.
We configure Playwright crawlers, US proxy rotation, session management, and CAPTCHA handling for kbb.com.
Schema validation, null-rate checks, valuation-outlier detection, and sample payloads before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Kelley Blue Book heavily guards its valuation engine. Here is how we maintain extraction stability.
KBB uses aggressive bot protection that blocks data centre IPs instantly. Our crawlers use US residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass perimeter defences.
Valuations are strictly bound to local markets. We manage cookie and session state to simulate user location accurately, ensuring the Fair Purchase Price reflects the requested ZIP code.
The KBB valuation engine relies heavily on client-side JavaScript. We run full Playwright browser sessions to execute scripts, trigger condition modifiers, and render the final pricing data.
Vehicle trim and option hierarchies change frequently. Our selector strategy uses structural mapping and fallback chains so a layout change does not break your pricing pipeline.
Extracting values via VIN requires navigating multi-step asynchronous forms. We automate these exact user flows, handling network delays and conditional option prompts.
Banks and credit unions use automated KBB valuations for precise Loan-to-Value (LTV) calculations and portfolio risk assessment.
Insurers extract Fair Market Range data to calculate total loss payouts and set accurate policy premiums.
Franchise and independent dealers align their inventory pricing with local KBB Fair Purchase Prices to optimise turn rates.
Rental and corporate fleets track residual values and depreciation curves to optimise vehicle disposal timing.
Analysts track shifting trade-in values and dealer inventory levels to measure consumer demand and supply constraints.
Vehicle listing platforms integrate KBB pricing guidance to help private sellers price their vehicles competitively.
"Kelley Blue Book sets the standard for North American vehicle pricing, but extracting those valuations programmatically requires navigating localised, bot-protected endpoints."
Most teams fail at KBB extraction because valuations are strictly bound to ZIP codes, complex trim configurations, and aggressive anti-bot perimeters. DataFlirt manages the residential proxies, JavaScript execution, and session state required to extract accurate Fair Market Ranges at scale.
Everything supported by our kbb.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 and deduplication. Playwright handles JavaScript rendering, cookie sessions, and multi-step valuation flows.
We maintain pools of US residential ISP proxies. Rotation happens per-session to maintain ZIP code localisation while bypassing bot detection.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About kbb.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and inventory information is generally permissible. DataFlirt targets only public, non-authenticated vehicle data. We do not extract personal data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use US residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and automated CAPTCHA solving. We manage cookie state carefully to maintain session validity during multi-step valuation flows.
Yes. We hydrate sessions with specific ZIP codes before extracting the Fair Purchase Price or Trade-in values to ensure accurate local market representation.
Dealer inventory can be tracked at daily or hourly cadences depending on your requirements. Valuation data is typically refreshed weekly or monthly as KBB updates its models.
Yes. You can provide a list of VINs, and our pipeline will navigate the KBB interface to resolve the exact year, make, model, trim, and associated valuations.
Our smallest packages start at a defined list of 5,000 VINs or specific Make/Model permutations. We price based on volume, complexity, and delivery frequency.
Yes. We provide a sample run of up to 500 vehicles as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off valuation dump or continuous inventory monitoring, we scope, build, and operate the pipeline. Tell us what you need.
