We extract property listings, floor plans, historical pricing, unit availability, and neighbourhood metrics from apartments.com. 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 Listings objects from apartments.com. All fields typed and schema-versioned.
"property_id": "8xqk2l1", "title": "The Asher", "address": "220 Elm St", "city": "Austin", "state": "TX", "zip_code": "78701", "property_type": "Apartment", "year_built": 2018, "rating": 4.2
| # | property_id | title | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Floor Plans objects from apartments.com. All fields typed and schema-versioned.
"plan_id": "p_9m4x1", "plan_name": "A1 - One Bedroom", "beds": 1, "baths": 1.0, "sqft": 754, "rent_min": 1850, "rent_max": 2100, "availability_status": "Available Now"
| # | property_id | plan_id | plan_name | beds | baths | sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities & Policies objects from apartments.com. All fields typed and schema-versioned.
"property_id": "8xqk2l1", "parking_type": "Covered Garage", "parking_fee": 150.0, "pet_policy": "Dogs and Cats Allowed", "pet_fee": 300.0, "in_unit_laundry": true, "fitness_center": true
| # | property_id | parking_type | parking_fee | pet_policy | pet_fee | fitness_center |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Neighbourhood objects from apartments.com. All fields typed and schema-versioned.
"neighborhood_name": "Downtown Austin", "walk_score": 92, "transit_score": 68, "bike_score": 85, "elementary_school": "Mathews Elementary", "high_school": "Austin High", "school_rating": 8
| # | property_id | neighborhood_name | walk_score | transit_score | bike_score | elementary_school |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Management Info objects from apartments.com. All fields typed and schema-versioned.
"property_id": "8xqk2l1", "management_company": "Greystar Real Estate Partners", "contact_phone": "(512) 555-0198", "office_hours": "Mon-Fri 9AM-6PM", "broker_name": "Sarah Jenkins", "languages": "['English', 'Spanish']"
| # | property_id | management_company | contact_phone | office_hours | broker_name | broker_address |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our apartments.com scraper bypasses bot mitigation to extract accurate, unit-level pricing and availability across thousands of zip codes, delivering clean datasets for real estate analysis.
Extract building specifications, year built, total units, contact details, and descriptions for every property listing.
Track individual unit availability, square footage, and dynamic rent ranges per floor plan across the platform.
Parse structured arrays for community features, unit amenities, and pet policies into clean, queryable columns.
Capture Walk Score, Transit Score, and local school district ratings linked to each property.
Extract high-resolution image URLs, 3D tour links, and video walkthroughs for downstream integration.
Scrape property management company details, office hours, and contact numbers for lead generation.
Monitor daily changes in unit availability and pricing adjustments to track market velocity.
Extract hidden costs including application fees, parking rates, and pet deposits.
Run continuous extraction pipelines at daily or weekly cadences with hash-based change detection.
Brief in. Clean data out.
Provide zip codes, cities, or specific property URLs. We design the extraction schema together.
We configure Scrapy crawlers, Playwright instances, proxy rotation, and map-cluster handling.
Schema validation, null-rate checks, rent-outlier detection, and sample data review before launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on schedule.
Apartments.com relies heavily on dynamic maps and React components. Here is how we extract the data reliably.
Real estate portals aggressively rate-limit datacenter IPs. Our crawlers use US-based residential proxies with realistic browser fingerprints and randomised request timing to maintain access.
Apartments.com caps search results per geographic area. We use recursive bounding-box splitting to drill down into dense urban clusters, ensuring zero missed properties in high-density zip codes.
Pricing tables and unit availability are rendered dynamically via JavaScript. We run full Playwright browser sessions to execute scripts and hydrate components before extraction.
Property detail pages frequently change layout. We use multiple fallback chains per field, combining CSS selectors, XPath, and JSON-LD extraction to ensure pipeline stability.
For large market tracking, we maintain a hash index of last-seen values. Subsequent runs only push diffs for rent changes and unit availability, reducing your downstream processing load.
Real estate funds monitor local rent yields, concession trends, and market velocity.
Listing platforms enrich their own databases with floor plan and amenity data.
PE firms evaluate property management portfolios, occupancy rates, and fee structures.
Researchers analyse neighbourhood density, transit scores, and housing supply metrics.
Property managers track nearby building prices and amenities to optimise their own rent rolls.
Corporate mobility teams map commute times, school districts, and pet policies for employee moves.
"Apartments.com holds the most comprehensive rental inventory in the US, but extracting unit-level pricing across thousands of zip codes requires dedicated infrastructure."
Building a reliable scraper for apartments.com means handling map-based pagination, dynamic React hydration, and aggressive bot mitigation. DataFlirt manages the proxies, selectors, and orchestration so your data engineering team receives clean, normalised property records without the operational overhead.
Everything supported by our apartments.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 and deduplication. Playwright handles JavaScript rendering for dynamic pricing tables and React components.
We maintain pools of US residential ISP proxies. Rotation happens per-request to bypass aggressive rate limiting from real estate portals.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, bounding-box logic, and SLA alerting. State is stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About apartments.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property listings is generally permissible under US law. DataFlirt targets only public, non-authenticated real estate data. We do not extract personal renter data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for their specific use cases.
Apartments.com limits search results per view. We bypass this using recursive bounding-box splitting. Our crawler divides the map into smaller geographic grids until the result count for each grid falls below the limit, ensuring 100% coverage.
Yes. We use Playwright to execute the JavaScript required to render floor plan tables, capturing real-time rent ranges, unit availability status, and deposit amounts.
We can configure pipelines to run daily or weekly depending on your requirements. Daily runs are typical for tracking unit availability and price adjustments in volatile markets.
Yes. We capture all neighbourhood metrics displayed on the property page, including Walk Score, Transit Score, Bike Score, and assigned school ratings.
Our minimum engagement typically starts with a defined set of MSAs or zip codes. For national-level extraction covering all listings, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property dump for a single city or continuous price-monitoring across the US — we scope, build, and operate the pipeline. Tell us what you need.
