We extract urban rental listings, map coordinates, floorplan details, and dynamic availability from Hotpads. Delivered as clean JSON, CSV, or Parquet to your warehouse.
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 hotpads.com. All fields typed and schema-versioned.
"property_id": "8m2x9p", "address": "123 Main St", "latitude": 40.7128, "longitude": -74.006, "beds": 2, "baths": 2.0, "price": 3200, "listing_status": "Active"
| # | property_id | address | city | state | zip_code | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Unit Availability objects from hotpads.com. All fields typed and schema-versioned.
"unit_number": "4B", "price": 3200, "beds": 2, "baths": 2.0, "available_date": "2026-08-01", "specials": "1 month free", "floorplan_name": "The Madison"
| # | property_id | unit_number | floor | beds | baths | sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities & Policies objects from hotpads.com. All fields typed and schema-versioned.
"in_unit_laundry": true, "parking_type": "Garage", "pet_policy_dogs": true, "pet_fee": 50, "fitness_center": true, "pool": false
| # | property_id | in_unit_laundry | parking_type | parking_fee | pet_policy_cats | pet_policy_dogs |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Contact & Management objects from hotpads.com. All fields typed and schema-versioned.
"listing_agent": "Jane Doe", "property_management_company": "Urban Living LLC", "contact_phone": "555-019-8372", "tour_options": "['In-person', 'Virtual', 'Self-guided']", "office_hours": "Mon-Fri 9am-5pm", "brokerage": "City Realty"
| # | property_id | listing_agent | brokerage | property_management_company | contact_phone | contact_email |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Location & Neighborhood objects from hotpads.com. All fields typed and schema-versioned.
"neighborhood": "Downtown", "walk_score": 92, "transit_score": 88, "bike_score": 75, "nearby_schools": "['Lincoln Elementary', 'Central High']", "commute_times": "15 mins to CBD"
| # | property_id | neighborhood | walk_score | transit_score | bike_score | nearby_schools |
|---|---|---|---|---|---|---|
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Our Hotpads scraper navigates map-based search interfaces, extracts nested unit availability, and parses complex building amenities while handling strict anti-bot measures.
Extract listings using GeoJSON bounding boxes to capture exact neighborhood boundaries and spatial coordinates.
Track move-in dates, unit-level pricing, and active inventory status across large multi-family buildings.
Extract image URLs, square footage, and layout dimensions per unit type within a property.
Structure unstructured text into boolean amenity fields for easy filtering and comparative analysis.
Extract property management details, broker names, and phone numbers for lead generation.
Capture Walk Score, Transit Score, and local school ratings associated with each listing.
Monitor rent specials, waived fees, and promotional pricing offered by property managers.
Detail breed restrictions, weight limits, and monthly pet rent requirements per building.
Run daily pipelines to capture new listings, delisted properties, and price adjustments.
Brief in. Clean data out.
Provide zip codes, bounding boxes, or city names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for hotpads.com.
Schema validation, null-rate checks, location accuracy, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hotpads employs aggressive scraping countermeasures typical of major real estate portals. We handle the complexity so you receive clean data.
Real estate portals use advanced bot mitigation. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass perimeter defenses.
Hotpads relies heavily on map-based search. We deploy headless browsers to simulate viewport movements, triggering location-based API responses that static scrapers miss.
Multi-family buildings hide individual unit availability behind dynamic menus. Our crawlers interact with these components to extract specific floorplans, prices, and move-in dates.
DOM structures change frequently. Our selector strategy uses multiple fallback chains so a minor layout update does not break your daily data feed.
Every run emits structured logs. We monitor for null-rate spikes and inventory anomalies across target zip codes, ensuring high data fidelity.
Investors calculate cap rates by correlating Hotpads rental data with property acquisition costs.
Managers track competitor pricing, concessions, and availability to optimise their own rent rolls.
Municipalities analyse housing supply, affordability, and transit proximity across neighborhoods.
Real estate startups enrich their own applications with active rental inventory and floorplan data.
Corporate mobility teams source available urban apartments matching specific commute criteria.
Analysts monitor macro rental trends, days on market, and inventory absorption rates.
"Hotpads holds the definitive map of urban rental inventory, but extracting that spatial data at scale requires complex browser orchestration."
Scraping modern real estate maps requires more than simple HTTP requests. It demands residential proxies, viewport simulation, and deep pagination handling. DataFlirt manages this infrastructure entirely, delivering structured property records directly to your warehouse.
Everything supported by our hotpads.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 executes JavaScript for map interfaces and nested property menus.
We maintain pools of US residential ISP proxies. Rotation happens per-request to avoid perimeter blocks common on real estate portals.
PostGIS integration allows us to normalise coordinate data, process bounding box queries, and deduplicate overlapping map regions.
Data delivered to where your team already works — no new tooling required.
About hotpads.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available factual information, such as rent prices and addresses, is generally permissible. DataFlirt targets only public, non-authenticated listing data. Clients should review terms of service and consult legal counsel for specific use cases.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass perimeter defenses.
Yes. We accept zip codes, city names, or exact coordinate bounding boxes to define the extraction scope.
Yes. We parse the nested unit tables on multi-family property pages to extract specific floorplans, unit prices, and move-in dates.
Pipelines can run daily to capture new listings, delisted properties, and price changes across your target markets.
Yes. We extract all publicly listed contact details, including property management companies, listing agents, and brokerage information.
20-minute scoping call. Pilot dataset within the week. Production within two. Need urban apartment listings or building availability data? We build and maintain the infrastructure. Tell us your target markets.
