We extract property listings, price histories, building metadata, and agent profiles from StreetEasy. 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 Rentals objects from streeteasy.com. All fields typed and schema-versioned.
"listing_id": "4192841", "price": 4500, "beds": 2, "baths": 1, "neighbourhood": "Williamsburg", "broker_fee": false, "days_on_market": 12
| # | listing_id | url | price | beds | baths | sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Sales Listings objects from streeteasy.com. All fields typed and schema-versioned.
"listing_id": "3928174", "price": 1250000, "common_charges": 850, "monthly_taxes": 920, "property_type": "Condo", "price_per_sqft": 1150, "days_on_market": 45
| # | listing_id | url | price | common_charges | monthly_taxes | beds |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Building Profiles objects from streeteasy.com. All fields typed and schema-versioned.
"building_id": "B84729", "name": "The Austin", "address": "123 Main St", "units": 145, "year_built": 2018, "building_type": "Condo", "active_rentals": 4, "active_sales": 2
| # | building_id | name | address | neighbourhood | units | stories |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Price History objects from streeteasy.com. All fields typed and schema-versioned.
"listing_id": "4192841", "event_date": "2023-10-14", "event_type": "Price Drop", "price": 4300, "previous_price": 4500, "percentage_change": -4.4, "status": "Active"
| # | listing_id | event_date | event_type | price | previous_price | percentage_change |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Agent Intelligence objects from streeteasy.com. All fields typed and schema-versioned.
"agent_id": "A93821", "name": "Sarah Jenkins", "brokerage": "Compass", "active_listings_count": 14, "past_deals_count": 182, "neighbourhoods_served": "['Chelsea', 'West Village']", "phone": "212-555-0199"
| # | agent_id | name | brokerage | phone | license_number | active_listings_count |
|---|---|---|---|---|---|---|
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Our StreetEasy scraper bypasses aggressive anti-bot measures to extract clean property metadata, historical transaction logs, and building-level intelligence across all five boroughs.
Extract price, beds, baths, square footage, amenities, and broker fee status for every active NYC listing.
Capture price drops, delistings, and relistings with exact timestamps to track market sentiment.
Scrape unit counts, year built, developer info, and aggregated building transaction histories.
Map active inventory and past deal volume to specific agents and brokerages.
Extract nearest subway lines, distance to transit, and exact geocoordinates for spatial analysis.
Monitor upcoming open houses across neighbourhoods to gauge foot traffic and buyer interest.
Track exact listing duration to identify stale inventory and negotiation opportunities.
Extract monthly carrying costs, tax abatements, and maintenance fees for accurate cap rate modelling.
Run daily diff pipelines to capture new listings and status changes without rescraping the entire catalogue.
Brief in. Clean data out.
Provide target neighbourhoods, property types, or building IDs. We map the extraction schema.
We configure Scrapy crawlers, residential proxy rotation, and anti-bot bypass for streeteasy.com.
Schema validation, null-rate checks, and location accuracy verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or Snowflake warehouse on agreed cadence.
StreetEasy uses sophisticated bot mitigation to protect its proprietary NYC dataset. We handle the circumvention layer so you get clean data.
StreetEasy employs strict PerimeterX and DataDome protections. We use residential NY-based IPs, TLS fingerprinting, and human-like interaction delays to maintain access.
Instead of scraping DOM elements on the map view, we intercept the underlying GraphQL and REST API responses, yielding richer metadata and exact coordinates.
StreetEasy caps search results at 50 pages. We dynamically segment searches by micro-neighbourhoods and price bands to ensure 100% coverage of active inventory.
Extracting past sales requires traversing individual building pages. We maintain a master index of NYC building IDs to systematically scrape historical transactions.
We normalise inconsistent address formats, parse complex amenity strings, and calculate true price-per-square-foot where missing from the source.
Model cap rates and identify undervalued multi-family properties using real-time rental yields and tax data.
Power automated valuation models (AVMs) and market trend dashboards with structured transaction histories.
Monitor competitor inventory, track agent performance, and identify market share shifts across boroughs.
Access comprehensive past sales data for accurate comparative market analysis (CMA) and risk assessment.
Track neighbourhood rent fluctuations and concession trends to optimise pricing for managed portfolios.
Analyse housing supply, price elasticity, and transit proximity correlations across NYC districts.
"StreetEasy holds the definitive record of NYC real estate, but extracting it requires navigating some of the strictest bot mitigation in the industry."
Building a DIY scraper for StreetEasy usually results in blocked IPs within hours. DataFlirt manages the proxy rotation, session handling, and WAF bypass logic. Your data team receives structured property records without managing the extraction infrastructure.
Everything supported by our streeteasy.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.
Custom Playwright stealth plugins and TLS fingerprinting to navigate StreetEasy's aggressive bot mitigation layers.
Dynamic geographic bounding boxes to bypass pagination limits and ensure complete coverage of dense NYC neighbourhoods.
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 streeteasy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible under US law. DataFlirt extracts only public, non-authenticated property and agent data. We do not extract personal user data or bypass authentication walls.
We utilise NY-based residential proxies, TLS fingerprinting, and headless browser automation via Playwright. Our systems mimic human interaction patterns to avoid triggering WAF blocks.
Yes. We traverse individual building profiles to extract past sales and rental transactions, providing a comprehensive historical view of NYC real estate.
We typically run daily diff pipelines to capture new inventory, price drops, and status changes. Higher frequency runs are available upon request.
Yes. We extract 'No Fee' badges and specific broker fee percentages where explicitly listed in the property description or metadata.
StreetEasy restricts search results to 50 pages. We dynamically segment searches by micro-neighbourhoods, property types, and narrow price bands to ensure we capture every listing.
Yes. We offer a sample extraction of a specific NYC neighbourhood to validate our schema and data quality before you commit to a full pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of active rentals or a historical database of NYC building transactions - we build and operate the pipeline. Tell us what you need.
