We extract residential listings, commercial spaces, pricing history, developer data, and agent intelligence from Cian.ru. Delivered as clean JSON, CSV, or Parquet to S3, ClickHouse, or Postgres 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 Residential Listings objects from cian.ru. All fields typed and schema-versioned.
"listing_id": "284910234", "property_type": "flat", "deal_type": "sale", "price": 14500000, "currency": "RUB", "total_area": 65.4, "floor": 4, "rooms": 2
| # | listing_id | property_type | deal_type | price | currency | price_per_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Commercial Real Estate objects from cian.ru. All fields typed and schema-versioned.
"listing_id": "391029384", "commercial_type": "office", "deal_type": "rent", "price": 250000, "price_per_sqm": 1200, "total_area": 208.5, "class_type": "B+", "parking_spaces": 4
| # | listing_id | commercial_type | deal_type | price | price_per_sqm | total_area |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Building Characteristics objects from cian.ru. All fields typed and schema-versioned.
"address": "Moscow, Presnenskaya Naberezhnaya, 12", "year_built": 2015, "building_type": "monolithic", "total_floors": 75, "passenger_lifts": 8, "parking_type": "underground", "developer": "Capital Group"
| # | building_id | address | year_built | building_type | total_floors | ceiling_height |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent & Agency Profiles objects from cian.ru. All fields typed and schema-versioned.
"agent_id": "948172", "name": "Ivan Ivanov", "agency_name": "Inkom Real Estate", "active_listings_count": 34, "rating": 4.8, "review_count": 112, "pro_status": true
| # | agent_id | name | agency_name | experience_years | active_listings_count | total_completed_deals |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Price History & Analytics objects from cian.ru. All fields typed and schema-versioned.
"listing_id": "284910234", "initial_price": 15000000, "current_price": 14500000, "days_on_market": 42, "view_count": 845, "favorite_count": 12, "price_diff_pct": -3.3
| # | listing_id | initial_price | current_price | price_changes | days_on_market | view_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Cian scraper handles every layer of the platform: residential listings, commercial spaces, dynamic pricing, and agent profiles - with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, areas, floor, address, images, and metadata fields scraped at listing level.
Automated JS interaction to click and reveal agent or owner phone numbers behind Cian's masked UI.
Capture price drops, price per square metre, and historical changes across specific districts.
Extract year built, wall material, lift counts, parking types, and exact coordinates.
Parse walking and driving times to nearest metro stations, bus stops, and major highways.
Track residential complex availability, completion phases, and developer portfolio metrics.
Extract broker profiles, active listing counts, review scores, and Pro status indicators.
Class types, ceiling heights, warehouse specifics, and B2B lease terms extracted accurately.
Feed bounding boxes or district IDs to extract all properties within specific municipal boundaries.
Run continuous pipelines at daily cadences with change-detection diffing for active market monitoring.
Brief in. Clean data out.
Provide region IDs, property types, or search URLs. We design the extraction schema together.
We configure Scrapy crawlers, Russian residential proxies, session management, and Qrator bypass for cian.ru.
Schema validation, null-rate checks, price-outlier detection, and sample listings before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, ClickHouse cluster, or Postgres database on agreed cadence.
Cian deploys strict regional blocks, Qrator anti-bot protection, and dynamic UI elements. Here is how we maintain extraction resilience.
Cian uses Qrator for traffic filtering. We use tailored TLS fingerprints, Russian residential IP pools, and header rotation to mimic legitimate local traffic.
Contact numbers are masked and require a click event to load via XHR. Our Playwright workers execute the required interaction flows to capture the raw digits.
Cian alters content based on regional cookies. We maintain isolated session pools for Moscow, St. Petersburg, and regional targets to ensure accurate local pricing.
Cian frequently updates its frontend framework. We rely on underlying JSON state objects embedded in the page source rather than brittle CSS selectors.
For large city catalogues, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs.
PropTech firms use historical pricing and building characteristics to train automated valuation models.
Real estate agencies track competitor listing volumes, time-on-market, and price reductions.
B2B service providers extract newly listed commercial properties to pitch fit-out, moving, or IT services.
Firms track new build absorption rates and district-level price per square metre to plan future developments.
Researchers map infrastructure proximity, building ages, and density metrics across major Russian cities.
Investors monitor under-priced listings or distressed assets with high yield potential in specific metro radii.
"Cian.ru holds the definitive record of the Russian real estate market, but extracting that data requires bypassing aggressive regional blocks and dynamic obfuscation."
Most teams underestimate the investment required: reliable Cian scraping requires Russian residential proxies, Qrator mitigation, headless browser interaction for phone reveals, and daily schema maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our cian.ru 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. Playwright handles JavaScript rendering and phone reveal interactions.
We maintain pools of RU/CIS residential proxies. Rotation happens per-request to avoid Qrator blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About cian.ru scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public property listings and agent data. We do not extract authenticated user data.
We deploy headless Playwright instances that trigger the specific UI interactions required to fire the backend XHR request, capturing the unmasked phone number.
Yes, we extract all property types including retail, office, warehouse, and land, along with specific commercial metrics like class type and ceiling height.
Daily pipelines capture new listings and price changes within 24 hours. Hourly pipelines can be configured for specific high-velocity districts.
Yes. We accept region IDs, metro station parameters, or custom polygon coordinates to bound the extraction scope.
Yes. We use localized Russian residential proxies, TLS fingerprint spoofing, and realistic request headers to navigate Cian's security layers.
Our smallest packages start at a defined region or category with weekly delivery. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off Moscow property dump or a continuous price-monitoring feed across Russia, we scope, build, and operate the pipeline. Tell us what you need.
