We extract apartment listings, rent histories, availability dates, building amenities, and property manager details from Zumper. 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 Listings & Pricing objects from zumper.com. All fields typed and schema-versioned.
"listing_id": "84920184", "title": "Luxury 2B/2B in Downtown", "price": 3450.0, "currency": "USD", "beds": 2, "baths": 2, "sqft": 1105, "available_date": "2026-06-01"
| # | listing_id | title | price | currency | beds | baths |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Building & Amenities objects from zumper.com. All fields typed and schema-versioned.
"building_id": "b-94821", "building_name": "The Vertex Tower", "year_built": 2019, "unit_count": 240, "gym": true, "pool": true, "doorman": true, "parking": "garage"
| # | building_id | building_name | year_built | unit_count | parking | gym |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Location & Scores objects from zumper.com. All fields typed and schema-versioned.
"latitude": 40.7128, "longitude": -74.006, "neighbourhood": "Financial District", "walk_score": 98, "transit_score": 100, "bike_score": 85, "county": "New York"
| # | latitude | longitude | neighbourhood | walk_score | transit_score | bike_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Property Managers objects from zumper.com. All fields typed and schema-versioned.
"manager_id": "m-48291", "manager_name": "Sarah Jenkins", "agency": "Urban Living PM", "active_listings": 42, "verified_badge": true, "response_time": "within 1 hour", "rating": 4.7
| # | manager_id | manager_name | agency | phone | active_listings | |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Media & Floorplans objects from zumper.com. All fields typed and schema-versioned.
"listing_id": "84920184", "image_urls": "['https://img.zumper.com/1', 'https://img.zumper.com/2']", "3d_tour_url": "https://my.matterport.com/show/?m=xyz", "floorplan_image": "https://img.zumper.com/fp1", "unit_layout": "split_bedroom", "floor_number": 14
| # | listing_id | image_urls | 3d_tour_url | floorplan_image | video_tour | unit_layout |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Zumper scraper navigates map-based searches, dynamic pagination, and anti-bot perimeters to extract high-fidelity rental data across any market.
Extract rent, beds, baths, square footage, availability dates, and pet policies for every unit in a building.
Monitor daily price changes, concessions, and special offers to track market volatility and yield adjustments.
Capture comprehensive building features including gyms, pools, doormen, parking types, and in-unit laundry details.
Extract image URLs, floorplan layouts, and 3D virtual tour links for richer property analysis.
Track listing agents, property management companies, response times, and active inventory counts per manager.
Capture exact coordinates, neighbourhood assignments, Walk Scores, and Transit Scores for spatial analysis.
Run extractions across hundreds of zip codes or custom bounding boxes simultaneously with distributed crawlers.
Identify listings offering '1 month free', waived deposit fees, or reduced parking rates to gauge market softness.
Run daily market snapshots or configure continuous pipelines with change-detection diffing to monitor inventory turnover.
Brief in. Clean data out.
Provide zip codes, city names, or coordinate bounding boxes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for zumper.com.
Schema validation, null-rate checks, rent-outlier detection, and sample listings before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Rental platforms deploy aggressive rate limiting and IP blocks to protect their inventory. Here is how we maintain reliable extraction without triggering defences.
Zumper monitors request velocity and IP reputation. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and randomised request timing to simulate genuine renter behaviour.
Listings are often loaded via map-based API calls rather than static pagination. We intercept and replicate these spatial queries, iterating through coordinate grids to ensure 100% market coverage.
Platform layouts change frequently. Our selector strategy uses multiple fallback chains per field, combining CSS selectors, XPath, and JSON payload interception so structural changes do not break your data feed.
For large city markets, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute cost and downstream processing load. You get a clean changelog of new rents and availability.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, inventory drops, and schema drift, responding before you notice. SLA uptime is contractual.
Property managers track competitor pricing, concessions, and market days-on-market to optimise their own rent rolls.
Acquisition teams monitor yield trends, rent growth, and neighbourhood inventory metrics to identify undervalued assets.
Real estate platforms ingest normalised listing data to enrich their own user-facing search portals.
Consultancies track macro rental trends, amenity premiums, and urban migration patterns across major metropolitan areas.
Municipalities analyse housing affordability, transit-oriented development impact, and rental stock composition.
Brokerages track the active listing volume and market share of competing property management firms.
"Zumper contains the most accurate real-time rental inventory and concession data in North America - but accessing it requires navigating aggressive anti-bot perimeters."
Most teams underestimate the investment required: reliable Zumper scraping requires residential proxies, full JavaScript rendering for map-based search, 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 zumper.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 map interaction flows.
We maintain pools of residential ISP proxies across US 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 zumper.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available listing information is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated rental data. We do not extract personal user data or circumvent authentication walls.
We use US residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
Yes. We can target specific zip codes, city names, or custom coordinate bounding boxes to extract listings precisely where you need them.
Daily market snapshots complete within a 4-8 hour window depending on the target area size. Streaming pipelines can achieve sub-60-minute latency for specific high-priority markets.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per listing for rent price, concessions, and availability from the date your pipeline starts.
Absolutely. We provide a sample run of up to 1,000 listings as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off city dump or a continuous rent-monitoring feed across 500,000 listings - we scope, build, and operate the pipeline. Tell us what you need.
