We extract property listings, dynamic rent pricing, floor plan availability, and amenity data from Apartmentlist. 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 Property Details objects from apartmentlist.com. All fields typed and schema-versioned.
"property_id": "apt-98241", "name": "Lumina Apartments", "city": "Austin", "state": "TX", "zip_code": "78704", "latitude": 30.2514, "longitude": -97.7631, "management_company": "Greystar"
| # | property_id | name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Floor Plans objects from apartmentlist.com. All fields typed and schema-versioned.
"plan_id": "fp-1042", "property_id": "apt-98241", "beds": 2, "baths": 2, "sqft_min": 1050, "sqft_max": 1120, "rent_min": 2450.0, "rent_max": 2600.0
| # | plan_id | property_id | beds | baths | sqft_min | sqft_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Concessions objects from apartmentlist.com. All fields typed and schema-versioned.
"property_id": "apt-98241", "unit_id": "unit-412", "current_rent": 2450.0, "deposit_fee": 500.0, "application_fee": 75.0, "specials_text": "1 Month Free on 12 Month Lease", "concessions_value": 2450.0, "scraped_date": "2026-05-12T09:14:00Z"
| # | property_id | plan_id | unit_id | current_rent | market_rent | deposit_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities & Policies objects from apartmentlist.com. All fields typed and schema-versioned.
"property_id": "apt-98241", "parking_type": "Covered Garage", "parking_fee": 150.0, "pets_allowed": true, "pet_deposit": 300.0, "pet_rent": 25.0, "laundry_type": "In Unit", "building_amenities": "['Pool', 'Fitness Center', 'Dog Park']"
| # | property_id | unit_amenities | building_amenities | parking_type | parking_fee | pets_allowed |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Location & Scores objects from apartmentlist.com. All fields typed and schema-versioned.
"property_id": "apt-98241", "neighbourhood": "Zilker", "walk_score": 82, "transit_score": 54, "bike_score": 91, "district": "South Austin", "region": "Central Texas", "nearest_transit": "Barton Springs Station"
| # | property_id | neighbourhood | walk_score | transit_score | bike_score | nearby_schools |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Apartmentlist scraper handles dynamic map-based search, paginated property feeds, and complex floor plan matrices with JavaScript rendering and anti-bot circumvention built in.
Address, geo-coordinates, year built, management company, and contact details scraped at the property level.
Extract beds, baths, square footage ranges, and layout specifics across all available floor plan configurations.
Capture minimum and maximum rent ranges, unit-level pricing, and daily fluctuations timestamped per crawl.
Monitor future move-in dates and available unit counts to gauge supply constraints and occupancy rates.
Parse promotional text for free rent months, waived application fees, and reduced deposits.
Categorised lists of unit features, building amenities, parking configurations, and laundry types.
Extract pet rent, deposits, breed restrictions, and weight limits for dogs and cats.
Capture Walk Score, Transit Score, Bike Score, and specific neighbourhood designations.
Extract links to high-resolution property images, floor plan diagrams, and virtual 3D tours.
Brief in. Clean data out.
Provide target cities, zip codes, or bounding box coordinates. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and map traversal logic for apartmentlist.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate platforms invest heavily in scraping detection and dynamic map rendering. Here is how we stay resilient.
Apartmentlist employs sophisticated bot mitigation. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and automated TLS handshake normalisation to avoid blocking.
Standard pagination limits results to a few hundred properties per city. We calculate coordinate grids and traverse the map using granular bounding boxes to extract total market inventory without truncation.
Pricing and availability matrices are hydrated client-side via complex API calls. We run full Playwright browser sessions to trigger layout hydration and capture actual rendered prices.
DOM structures for property pages vary heavily based on management company integrations. Our selector strategy uses fallback chains to ensure data integrity across disparate listing templates.
For large national catalogues, we maintain a hash index of last-seen values per unit. Subsequent runs only push price and availability diffs, reducing downstream processing load.
Asset managers benchmark local rent velocities and concession trends to optimise their own pricing models.
Acquisition teams track supply constraints and rent growth at the neighbourhood level to identify investment targets.
Analysts aggregate floor plan availability and move-in dates to forecast macro housing supply and demand.
Aggregators and tenant-facing tools ingest property metadata to enrich their own real estate catalogues.
Valuation professionals use granular rent comparables and historic concession data to underwrite multifamily loans.
Municipalities track dynamic pricing and unit sizes to monitor housing affordability and policy impact.
"Apartmentlist contains granular multifamily pricing and concession data that dictates local market yields - but it requires complex map traversal to extract at scale."
Most teams underestimate the investment required: reliable Apartmentlist scraping requires residential proxies, bounding-box coordinate math for map pagination, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our apartmentlist.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, map grid math, and retry logic. Playwright handles JavaScript rendering and interaction flows for dynamic pricing tables.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where map state requires continuity.
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 apartmentlist.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property, pricing, and floor plan data. We do not extract personal user data or circumvent authentication walls. Clients should review platform terms and consult legal counsel for specific use cases.
Apartmentlist truncates search results to a few hundred properties per query. We bypass this by calculating granular lat/long bounding boxes and querying the map API sequentially across a grid, ensuring 100% market coverage.
Yes. We configure pipelines to poll specific properties or entire zip codes daily. Our change detection system records the exact delta in minimum/maximum rent or unit-level pricing.
Yes. We parse the promotional text fields associated with listings, which often contain critical yield information like waived deposits or free rent months.
Property management companies upload inconsistent data. Our schema normalises floor plans, filling gaps where possible and flagging null rates in our observability stack to ensure data quality.
Our smallest packages start at a defined MSA or zip code list with weekly delivery. For national catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 properties as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off city export or a continuous price-monitoring feed across national inventory - we scope, build, and operate the pipeline. Tell us what you need.
