We extract classified listings, asking prices, seller intelligence, location signals, and ad metadata from OLX. 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 Classified Listings objects from olx.in. All fields typed and schema-versioned.
"ad_id": "OLX-IN-1847291034", "title": "Honda City 2019 Petrol Automatic", "category": "Cars", "asking_price": 750000, "currency": "INR", "negotiable": true, "location_city": "Bengaluru", "ad_posted_date": "2026-05-10", "condition": "used"
| # | ad_id | title | category | sub_category | condition | asking_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Vehicles objects from olx.in. All fields typed and schema-versioned.
"ad_id": "OLX-IN-1847291034", "make": "Honda", "model": "City", "year": 2019, "fuel_type": "Petrol", "km_driven": 42000, "ownership_count": 1, "asking_price": 750000, "rto_code": "KA-05"
| # | ad_id | title | make | model | year | fuel_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Real Estate objects from olx.in. All fields typed and schema-versioned.
"ad_id": "OLX-IN-9382741028", "property_type": "Apartment", "transaction_type": "Sale", "asking_price": 8500000, "area_sqft": 1200, "bedrooms": 3, "furnishing_status": "Semi-Furnished", "location_locality": "Whitefield"
| # | ad_id | title | property_type | transaction_type | asking_price | price_per_sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from olx.in. All fields typed and schema-versioned.
"seller_id": "OLX-USR-28401923", "seller_name": "Ravi Auto Sales", "seller_type": "dealer", "verified": true, "active_ad_count": 84, "response_rate": 94, "member_since": "2019-03-14"
| # | seller_id | seller_name | seller_type | verified | member_since | active_ad_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our OLX scraper handles every layer of the classifieds platform: vehicle listings, real estate ads, electronics, seller profiles, geo-coordinates, and ad freshness signals — with full JavaScript rendering built in.
Title, category, condition, asking price, description, image count, negotiable flag, ad age, and every metadata field OLX surfaces — at ad level.
Make, model, year, fuel type, transmission, kilometres driven, ownership count, RTO code, insurance validity, and colour — for every vehicle listing.
Property type, transaction type, price per sqft, area, bedrooms, furnishing status, floor, facing, society name, and locality — for all property ads.
City, state, PIN code, locality name, latitude, and longitude for every listing — enabling geo-clustered market analysis and hyperlocal price mapping.
Seller name, type (private vs dealer), verified flag, active ad count, historical listings, response rate, and member-since date.
Capture ad posted date, last refreshed date, and derived ad age — critical for demand-side analysis and de-listing lag modelling.
Scrape any category feed, keyword search result, or location-filtered listing page — with pagination across all result pages.
OLX India, OLX Poland, OLX Brazil, OLX Portugal, OLX UAE, and other regional OLX sites — unified schema with local currency.
One-off snapshots or continuous new-ad monitoring pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, search keywords, location filters, or specific ad IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and anti-bot handling tailored to OLX's regional infrastructure.
Schema validation, price sanity checks, geo-coordinate validation, and sample review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
OLX classifieds data is ephemeral — ads expire, get refreshed, and disappear. Here's how we track freshness and stay resilient.
OLX data is ephemeral — ads are posted, bumped, refreshed, and removed. Our pipeline tracks ad first-seen, last-seen, and refresh events, building a timeline that reveals true market velocity and demand-side signals not visible from a single snapshot.
OLX uses session fingerprinting and IP reputation scoring to throttle crawlers. We use residential ISP proxies matched to the relevant country market, with realistic browser fingerprints and randomised timing, to maintain consistent access across high-volume runs.
Seller contact panels, location widgets, and image galleries on OLX are JavaScript-rendered. We run full Playwright sessions to capture these — including deferred content loads triggered by user interaction events.
OLX location data is often imprecise in the raw DOM. We extract city, state, PIN, locality name, and where available latitude/longitude — normalising to a consistent geo schema for downstream spatial analysis.
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.
Auto dealers, fleet buyers, and financial services firms track asking prices, depreciation curves, make/model demand signals, and regional price differentials for the used vehicle market.
PropTech platforms and analysts track hyperlocal asking prices, supply velocity, and property attribute premiums to build AVM models and market indices.
Recommerce platforms and insurers track used device pricing, condition distributions, and demand velocity to power trade-in valuation models.
Brands and distributors monitor dealer ad activity, pricing compliance, inventory depth, and listing quality across regional OLX markets.
Research teams use classified listing volume, asking price trends, and ad age data as leading indicators for consumer demand and disposable income proxies.
Financial institutions and insurers cross-reference OLX listing data against declared asset values for vehicle and property loan origination risk models.
"OLX classifieds data is one of the richest real-world price signal datasets available — but its ephemeral nature means you need a continuous pipeline, not a one-off scrape."
Reliable OLX scraping requires tracking ad lifecycle events, handling location data normalisation, maintaining session continuity for contact panel access, and running daily selector maintenance. DataFlirt absorbs that complexity so your research and analytics team can focus on the insights.
Everything supported by our olx.in 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 dynamic panel interactions. Combined via scrapy-playwright middleware.
We maintain country-matched residential ISP proxy pools for OLX India, OLX Poland, OLX Brazil, and other market sites. Rotation is per-request with sticky sessions for multi-page ad traversal.
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 olx.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available classified listings from OLX is generally permissible under applicable law in India and other markets where OLX operates. DataFlirt targets only public, non-authenticated listing and seller data. We do not extract personal contact details gated behind login walls. We recommend clients review OLX's ToS and consult legal counsel for specific use cases.
Yes — ad lifecycle tracking is one of our core capabilities for classifieds. We record first-seen timestamp, all subsequent refresh events, last-seen timestamp, and infer de-listing when an ad disappears from crawls. This timeline is critical for demand velocity and market liquidity analysis.
We extract city, state, PIN code, and locality name from every listing, and capture latitude/longitude where OLX surfaces it. All geo fields are normalised to a consistent schema for spatial analysis.
Yes — the vehicle schema captures make, model, year, fuel type, transmission, kilometres driven, ownership count, RTO registration code, insurance validity, and colour from structured listing attributes.
For new-ad monitoring pipelines, we can achieve sub-4-hour latency for new listing detection on a defined category and location set. Full category refreshes at daily cadence complete within a 3–6 hour window.
Yes. We provide a sample run across a defined category and city as part of the pre-engagement scoping process — so you can validate schema fit 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 classifieds snapshot or a continuous new-listing monitor across categories and cities — we scope, build, and operate the pipeline. Tell us what you need.
