We extract active listings, sold price history, auction results, seller intelligence, feedback scores, and category data from eBay. 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 Active Listings objects from ebay.com. All fields typed and schema-versioned.
"item_id": "296418773291", "title": "Apple iPhone 15 Pro 256GB Natural Titanium Unlocked", "condition": "Used – Like New", "price": 72999.00, "currency": "INR", "listing_type": "BUY_IT_NOW", "watcher_count": 84, "seller_feedback_score": 4821, "seller_positive_pct": 99.2, "returns_accepted": true
| # | item_id | title | category | sub_category | condition | condition_description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sold Prices objects from ebay.com. All fields typed and schema-versioned.
"item_id": "296418773291", "title": "Apple iPhone 15 Pro 256GB Natural Titanium Unlocked", "sold_price": 71500.00, "listing_type": "AUCTION", "bid_count": 17, "sale_date": "2026-05-10", "buyer_country": "IN", "global_shipping": false
| # | item_id | title | category | condition | sold_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Intelligence objects from ebay.com. All fields typed and schema-versioned.
"seller_id": "techdeals_india", "feedback_score": 14829, "positive_pct": 99.4, "top_rated": true, "power_seller_status": "PLATINUM", "business_seller": true, "active_listings_count": 3417, "member_since": "2011-03-15"
| # | seller_id | username | feedback_score | positive_pct | reviews_count | member_since |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from ebay.com. All fields typed and schema-versioned.
"keyword": "iphone 15 pro unlocked", "position": 1, "item_id": "296418773291", "listing_type": "BUY_IT_NOW", "free_shipping": true, "condition": "Used – Like New", "price": 72999.00, "scraped_at": "2026-05-12T10:22:11Z"
| # | keyword | site | position | item_id | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our eBay scraper covers every layer of the marketplace: active listings, auction outcomes, sold price history, seller intelligence, and search rankings — with full JavaScript rendering and anti-bot circumvention built in.
Title, condition, description, item specifics, images, shipping options, returns policy, and every metadata field eBay surfaces — scraped at item-ID level.
Capture final sold prices, auction outcomes, bid counts, Buy It Now conversions, and Best Offer acceptance rates — timestamped per crawl.
Track watcher counts, bid velocity, and time-left signals to gauge real-time demand for any category or keyword.
Full feedback scores, positive percentages, Top Rated and PowerSeller status, store details, and active listing counts — for every seller.
Scrape entire eBay store inventories, category breakdowns, store policies, and promotional banners for competitor intelligence.
Monitor organic and promoted listings position for any keyword across eBay.com, eBay.co.uk, eBay.de and other regional sites.
ebay.com, ebay.co.uk, ebay.de, ebay.com.au, ebay.fr, ebay.it, ebay.es, ebay.ca — all from a unified schema with normalised pricing.
Vehicles, parts, and accessories with make, model, year, mileage, VIN, and condition specifics — ideal for automotive market intelligence.
One-off bulk exports or continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide item IDs, category URLs, keyword sets, or seller usernames. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for ebay.com.
Schema validation, null-rate checks, price-outlier detection, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
eBay's dynamic listings, auction timers, and bot-detection layers require specialised infrastructure. Here's how we stay resilient.
eBay's fraud detection operates on IP reputation, TLS fingerprints, and behavioural heuristics. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
eBay listing pages, auction timers, and seller feedback panels are JavaScript-rendered. We run full Playwright sessions with JavaScript execution and lazy-load triggering — capturing data that headless HTTP clients miss entirely.
eBay updates its DOM structure regularly. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, text-pattern matching, and structured data extraction (LD+JSON) — so layout changes don't break your pipeline.
Auction data requires time-sensitive scraping near listing end. Our pipeline schedules crawls around end-time windows to capture final bid counts, watcher spikes, and sold prices immediately after settlement.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Resellers monitor sold price history and active listing spreads to identify profitable arbitrage opportunities across conditions, categories, and geographies.
Finance teams and insurers use eBay sold prices as real-world secondary market valuations for electronics, collectibles, vehicles, and industrial equipment.
Marketplace sellers track competitor listings, pricing strategies, sell-through rates, and feedback trends to optimise their own eBay storefronts.
ML teams use eBay datasets — product descriptions, condition gradings, pricing pairs — to train condition-assessment models and price prediction engines.
Brand protection teams monitor eBay for unauthorised sellers, MAP violations, counterfeit listings, and suspicious seller patterns at scale.
Analysts track category velocity, average selling prices, and seller concentration metrics to assess marketplace liquidity and demand trends.
"eBay's sold price data is one of the richest secondary-market pricing signals available anywhere — but it's locked behind pagination, dynamic rendering, and aggressive bot detection."
Most teams underestimate what reliable eBay scraping requires: residential proxies, full JavaScript rendering, auction-window timing, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our ebay.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and auction-timer interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across IN/US/UK/DE regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 ebay.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from eBay is generally permissible under applicable law in India, the US, and the UK — reinforced by the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated listing, pricing, and seller data. We do not extract personal data, circumvent authentication walls, or violate GDPR. We recommend clients review eBay's ToS independently and consult legal counsel for specific use cases.
We use residential ISP proxies that appear as real consumer traffic, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline. We monitor for 503/CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
We support ebay.com, ebay.co.uk, ebay.de, ebay.com.au, ebay.fr, ebay.it, ebay.es, ebay.ca, ebay.nl, ebay.at, ebay.be, and ebay.ie — all from a unified schema with marketplace-normalised pricing.
Yes. eBay's completed listings and sold items pages expose historical sale prices for the past 90 days. We scrape these systematically and maintain a time-series table per item category or keyword — giving you a robust secondary-market pricing dataset.
We schedule crawls around auction end-time windows to capture final bid counts, watcher spikes, and sold prices immediately after settlement. For real-time auction monitoring on a defined item set, we can run sub-hourly polling with webhook delivery.
Our smallest packages start at a defined item list (typically 1,000–50,000 items) with weekly delivery. For larger catalogues, ongoing monitoring contracts, or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Yes — including vehicle-specific fields: make, model, year, mileage, VIN, transmission, fuel type, colour, and all item specifics. Parts and accessories are also supported with cross-reference compatibility data where eBay surfaces it.
Absolutely. We provide a sample run of up to 500 items or 50 search result pages as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off sold-price history dump or a continuous listing-monitoring feed across 1M items — we scope, build, and operate the pipeline. Tell us what you need.
