We extract residential and commercial properties, agent directories, MLS details, and pricing histories from remax.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 remax.com. All fields typed and schema-versioned.
"property_id": "RMX-92841", "mls_number": "TX-882910", "price": 450000, "bedrooms": 4, "bathrooms": 3, "square_feet": 2450, "city": "Austin", "state": "TX"
| # | property_id | mls_number | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent Profiles objects from remax.com. All fields typed and schema-versioned.
"agent_id": "A-4829", "full_name": "Sarah Jenkins", "phone_number": "512-555-0198", "office_name": "RE/MAX Austin Excellence", "active_listings_count": 14, "sold_listings_count": 87, "languages_spoken": "['English', 'Spanish']"
| # | agent_id | full_name | title | phone_number | office_name | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & History objects from remax.com. All fields typed and schema-versioned.
"property_id": "RMX-92841", "current_price": 450000, "original_price": 475000, "days_on_market": 42, "price_per_sqft": 183.67, "status": "Active", "annual_taxes": 5200
| # | property_id | current_price | original_price | days_on_market | price_per_sqft | hoa_fees |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Features objects from remax.com. All fields typed and schema-versioned.
"property_id": "RMX-92841", "cooling_type": "Central Air", "heating_type": "Forced Air", "parking_spaces": 2, "garage_type": "Attached", "appliances_included": "['Dishwasher', 'Oven', 'Refrigerator']"
| # | property_id | cooling_type | heating_type | parking_spaces | garage_type | basement |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Office Locations objects from remax.com. All fields typed and schema-versioned.
"office_id": "O-9921", "office_name": "RE/MAX Austin Excellence", "city": "Austin", "state": "TX", "agent_count": 45, "phone_number": "512-555-0000"
| # | office_id | office_name | franchise_name | street_address | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our RE/MAX scraper navigates map-based searches, dynamic pagination, and agent directories to extract structured real estate data directly into your warehouse.
Capture price, beds, baths, square footage, and MLS numbers for residential and commercial properties.
Extract agent names, contact details, office affiliations, and production metrics across all RE/MAX franchises.
Monitor price reductions, days on market, and status changes from active to pending or sold.
Map RE/MAX office locations, franchise ownership, and agent counts per branch.
Bypass standard pagination limits by iterating through map coordinate grids to ensure full geographic coverage.
Extract high-resolution image URLs, floor plans, and 3D virtual tour links for every listing.
Capture annual property taxes, tax years, and monthly HOA fees associated with each property.
Extract interior and exterior features, cooling/heating systems, and appliance inclusions.
Run daily diffs to capture new listings, sold properties, and price changes without re-scraping the entire database.
Brief in. Clean data out.
Provide zip codes, cities, or state-level targets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, map-grid iteration, and CAPTCHA handling for remax.com.
Schema validation, null-rate checks, and coordinate overlap deduplication before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate sites use map-based rendering and aggressive bot protection to prevent mass extraction. Here is how we maintain data flow.
RE/MAX caps list-view results to a few hundred properties. We programmatically split geographic areas into smaller bounding boxes, adjusting zoom levels dynamically to extract every listing without hitting display limits.
Real estate platforms block datacenter IPs aggressively. Our crawlers route through US-based residential ISP proxies, rotating per request to maintain high success rates and avoid IP bans.
Property coordinates, dynamic pricing widgets, and agent contact reveals require JavaScript execution. We run full Playwright browser sessions to hydrate the DOM before extraction.
Map grid overlaps cause duplicate listing captures. We maintain a Redis-backed deduplication layer using MLS numbers and normalised addresses to ensure clean output.
For market monitoring, we maintain a hash index of active listings. Subsequent runs only push status changes, price reductions, or new properties, reducing downstream processing load.
PropTech firms aggregate listing data to train automated valuation models (AVMs) and predict market trends.
Institutional investors monitor days on market and price reductions to identify distressed assets or motivated sellers.
Brokerages extract agent production metrics and contact details to target top-performing agents for recruitment.
Competitor brokerages track RE/MAX active listing volume and sold data to benchmark regional market share.
Lenders monitor new listings and status changes to time their outreach for pre-approval and financing services.
Home staging, moving, and inspection companies target newly listed properties and their representing agents.
"RE/MAX represents one of the largest global real estate franchises. Accessing their inventory data programmatically is critical for institutional market analysis."
Extracting real estate data at scale requires overcoming map-based pagination limits, dynamic JavaScript rendering, and strict anti-bot measures. DataFlirt manages the proxy rotation, bounding-box coordinate math, and schema maintenance so your data engineering team receives clean, normalised property records ready for immediate analysis.
Everything supported by our remax.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.
We use PostGIS and custom bounding-box algorithms to segment target regions into precise coordinate grids, ensuring zero missed properties across large metros.
Real estate sites use strict WAFs. We route requests through US residential ISP proxies with realistic browser fingerprints to maintain high throughput.
Pipelines run on AWS ECS with Airflow handling scheduling and dependency management. All state and deduplication keys are stored in managed Postgres and Redis.
Data delivered to where your team already works — no new tooling required.
About remax.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible under applicable law, reinforced by rulings like hiQ v. LinkedIn. DataFlirt extracts only public, non-authenticated property and agent data. We do not bypass login walls to access private MLS remarks. Clients should consult legal counsel for their specific use cases.
RE/MAX limits the number of properties displayed in a single search result. We programmatically divide target geographies into smaller latitude/longitude bounding boxes, zooming in until the property count falls below the display limit, ensuring 100% extraction coverage.
Yes. We extract agent names, phone numbers, office affiliations, and public email addresses from the RE/MAX agent directory and individual listing pages.
For active market monitoring, pipelines typically run daily to capture new listings, status changes, and price reductions. We can configure hourly runs for specific high-priority zip codes.
Yes, we extract the MLS number and the source MLS name where it is publicly displayed on the RE/MAX property listing page.
Map-grid scraping often results in overlapping boundaries. We maintain a Redis-backed deduplication layer using MLS numbers and normalised addresses to ensure each property is delivered only once per run.
We extract historical sold data that is publicly accessible on the platform. However, some states are non-disclosure states where sold prices are not publicly displayed on broker websites.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of a specific state or a daily feed of active listings nationwide — we scope, build, and operate the pipeline. Tell us what you need.
