We extract upcoming auction lots, historical price realisations, equipment specifications, and condition reports from Ritchie Bros. 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 Upcoming Lots objects from ritchiebros.com. All fields typed and schema-versioned.
"lot_number": "142A", "make": "Caterpillar", "model": "D8T", "year": 2018, "meter_reading": "4,512 hours", "auction_location": "Orlando, FL", "current_bid": 125000.0
| # | lot_number | auction_id | auction_location | auction_date | make | model |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Auction Results objects from ritchiebros.com. All fields typed and schema-versioned.
"selling_price": 245000.0, "currency": "USD", "make": "Komatsu", "model": "PC210LC-11", "year": 2020, "auction_date": "2023-11-14", "buyer_location": "Texas, USA"
| # | result_id | lot_number | auction_date | selling_price | currency | buyer_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Equipment Specs objects from ritchiebros.com. All fields typed and schema-versioned.
"category": "Excavators", "operating_weight": "49,600 lb", "engine_make": "Cummins", "gross_power": "165 hp", "track_width": "31.5 in", "bucket_capacity": "1.5 yd3"
| # | item_id | category | sub_category | engine_make | engine_model | gross_power |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Condition Reports objects from ritchiebros.com. All fields typed and schema-versioned.
"overall_rating": 4.2, "engine_condition": "Good", "hydraulic_system": "Minor leaks detected", "ironclad_assurance": true, "undercarriage": "50% remaining", "exterior_damage": "Scratches on counterweight"
| # | report_id | item_id | overall_rating | engine_condition | transmission_condition | hydraulic_system |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Locations & Yards objects from ritchiebros.com. All fields typed and schema-versioned.
"facility_name": "Orlando Auction Site", "city": "Davenport", "state": "FL", "country": "USA", "latitude": 28.2435, "longitude": -81.6321, "upcoming_auctions_count": 3
| # | yard_id | facility_name | address | city | state | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Ritchie Bros scraper extracts complex auction data across thousands of equipment categories, normalising specifications, meter readings, and condition reports into structured formats.
Extract make, model, year, meter, and serial numbers across all heavy equipment categories.
Capture final auction results and price realisations to build residual value models.
Extract IronClad Assurance data, component ratings, and inspector notes.
Normalise hour meter and odometer readings for precise valuation modelling.
Monitor upcoming sales, yard locations, and lot additions in real-time.
Download high-resolution equipment photos and PDF inspection reports to S3.
Merge data from Ritchie Bros and IronPlanet listings into a single schema.
Extract and structure VINs and PINs for fleet tracking and history verification.
Run one-off bulk exports or configure continuous pipelines at daily cadences.
Brief in. Clean data out.
Provide target categories, auction locations, or specific makes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for ritchiebros.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Heavy equipment data is highly unstructured and gated by complex search interfaces. Here is how we maintain reliable extraction.
Ritchie Bros uses strict rate limiting and perimeter defenses. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management to maintain access.
Auction lot details and image galleries are heavily JavaScript-rendered. We run full Playwright browser sessions to capture dynamic content that headless HTTP clients miss.
Heavy machinery specifications vary wildly between a bulldozer and a crane. We normalise specs into a structured JSON schema, extracting distinct attributes like operating weight and engine power.
Large auctions contain thousands of lots spread across complex filter views. Our crawlers systematically traverse all pagination states and category trees to ensure zero dropped records.
For upcoming auctions, we maintain a hash index of last-seen values per lot. Subsequent runs only push diffs for bid updates or added lots, reducing compute cost.
Financial institutions use historical auction results to calculate depreciation curves and residual values for heavy machinery.
Construction firms monitor upcoming auctions for specific makes and models to optimise capital expenditure.
OEMs and dealerships track secondary market pricing for their equipment versus competitors.
Analysts track volume and price realisations across equipment categories to gauge construction sector health.
Appraisers use recent comparable sales from Ritchie Bros to value existing fleets for insurance or liquidation.
Dealers identify pricing disparities between regional auctions to buy low and transport to higher-demand markets.
"Ritchie Bros holds the definitive dataset for heavy equipment valuation, but extracting structured, historical pricing requires navigating complex, dynamic auction interfaces."
Most teams underestimate the investment required: reliable Ritchie Bros scraping requires residential proxies, full JavaScript rendering for condition reports, and complex schema normalisation across thousands of equipment types. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our ritchiebros.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 and interaction flows for complex lot pages.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to bypass rate limits.
Pipelines run on AWS Lambda and ECS. 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 ritchiebros.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available auction data is generally permissible under applicable law. DataFlirt targets only public, non-authenticated listings and specifications.
We use residential ISP proxies, Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour.
Yes, we extract publicly visible historical pricing and auction results, normalising the data for residual value modelling.
Yes, we parse structured condition reports, including component ratings, inspector notes, and IronClad Assurance flags.
Our schema normalises core attributes like make, model, year, and meter reading, while retaining category-specific specifications in a nested JSON payload.
Yes, we can extract image URLs or download high-resolution assets directly to your S3 bucket, linked by lot ID.
Our smallest packages start at scheduled extraction of specific auction events or categories. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical pricing dump for valuation models or continuous monitoring of upcoming auctions, we scope, build, and operate the pipeline. Tell us what you need.
