We extract MLS listings, property metadata, pricing history, agent directories, and neighbourhood demographics from Realtor.ca. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Residential Listings objects from realtor.ca. All fields typed and schema-versioned.
"mls_number": "C5912345", "property_type": "Detached", "price": 1250000, "bedrooms": 4, "bathrooms": 3, "city": "Toronto", "province": "ON", "status": "Active"
| # | mls_number | title | property_type | price | currency | bedrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Commercial Properties objects from realtor.ca. All fields typed and schema-versioned.
"mls_number": "W1234567", "building_type": "Retail", "price": 2500000, "zoning": "Commercial", "total_area": 5000, "city": "Vancouver", "province": "BC", "lease_type": "NNN"
| # | mls_number | building_type | price | lease_rate | lease_type | zoning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent & Brokerage objects from realtor.ca. All fields typed and schema-versioned.
"agent_id": "A98765", "first_name": "Jane", "last_name": "Doe", "brokerage_name": "RE/MAX Hallmark", "phone_number": "416-555-0198", "languages": "['English', 'French']", "active_listings_count": 14
| # | agent_id | first_name | last_name | title | phone_number | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Neighbourhood Data objects from realtor.ca. All fields typed and schema-versioned.
"postal_code": "M4K", "neighbourhood_name": "Playter Estates", "population": 8500, "median_age": 41, "median_income": 145000, "walk_score": 88, "transit_score": 92
| # | postal_code | neighbourhood_name | population | median_age | median_income | household_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Property History objects from realtor.ca. All fields typed and schema-versioned.
"mls_number": "C5912345", "event_type": "Price Drop", "event_date": "2023-10-15", "previous_price": 1300000, "new_price": 1250000, "open_house_start": "2023-10-21T14:00:00Z", "open_house_end": "2023-10-21T16:00:00Z"
| # | mls_number | event_type | event_date | previous_price | new_price | open_house_start |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Realtor.ca scraper handles complex spatial pagination, API interception, and dynamic session management to deliver complete property datasets without missing listings.
Extract complete property details including MLS numbers, room dimensions, building amenities, and tax information.
Monitor price drops, delistings, and relistings across the Canadian market to identify pricing trends.
Scrape contact information, active listing counts, and brokerage affiliations for real estate professionals.
Capture neighbourhood statistics, Walk Score metrics, and transit accessibility for spatial analysis.
Extract zoning codes, lease rates, net operating income estimates, and total square footage for commercial assets.
Aggregate upcoming open house dates, times, and viewing instructions for targeted marketing.
Query listings by custom geographic polygons rather than standard city or postal code boundaries.
Extract high-resolution image URLs, virtual tour links, and floor plan documents associated with listings.
Run continuous pipelines that detect new listings, sold properties, and status changes in real time.
Extract listing descriptions and metadata in both English and French directly from the source.
Brief in. Clean data out.
Provide target provinces, cities, property types, or specific MLS numbers. We map the extraction schema.
We configure Scrapy spiders, proxy rotation, and session management to handle Realtor.ca API endpoints.
Schema validation, null-rate checks, and geographic boundary verification before full launch.
Structured data pushed to your warehouse or S3 bucket via JSON, CSV, or Parquet on your required cadence.
Realtor.ca uses strict spatial limits and API tokens to prevent automated extraction. Here is how we maintain reliable data flow.
Realtor.ca relies on complex internal APIs for map-based searches. We reverse-engineer these endpoints to extract structured JSON payloads directly, bypassing brittle DOM parsing.
The platform caps search results at a few hundred listings per query. We programmatically divide large geographic regions into smaller bounding boxes to ensure 100% listing coverage.
Aggressive rate limits block single-IP scraping. We distribute requests across a pool of Canadian residential proxies, maintaining low request volumes per IP to avoid detection.
Realtor.ca requires valid session tokens and CSRF headers for API access. Our Playwright orchestrator generates and rotates these tokens to keep the pipeline authenticated.
Listing agents input data inconsistently. We normalise property types, format addresses, and standardise currency and metric/imperial measurements before delivery.
Real estate analysts track inventory levels, median days on market, and pricing trends across Canadian provinces.
Startups use historical and active listing data to train automated valuation models and recommendation engines.
Institutional investors identify undervalued commercial and residential properties using custom yield criteria.
B2B service providers extract agent contact details and listing volumes to build targeted sales outreach lists.
Municipalities and researchers analyse demographic shifts, housing density, and transit accessibility metrics.
Financial institutions monitor property valuations and listing statuses to assess portfolio risk and originate loans.
"Realtor.ca holds the definitive dataset for Canadian real estate, but accessing it systematically requires overcoming strict spatial pagination and API rate limits."
Building a reliable pipeline for Realtor.ca means managing complex bounding box queries, intercepting undocumented API responses, and rotating Canadian residential proxies. DataFlirt handles the infrastructure layer so your team receives clean, normalised property records ready for analysis.
Everything supported by our realtor.ca scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Our orchestration layer automatically sub-divides large geographic areas into micro-grids, ensuring deep pagination without hitting the 500-listing API limit.
Instead of parsing HTML, we intercept the underlying JSON payloads powering the Realtor.ca frontend, guaranteeing exact field extraction.
We route requests through geographically accurate Canadian residential IPs, preventing region-based blocking and rate limiting.
Data delivered to where your team already works — no new tooling required.
About realtor.ca scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available listing data is generally permissible for analysis and internal use. DataFlirt extracts only public information and does not bypass authentication to access private board data.
We use a proprietary spatial engine that recursively divides target regions into smaller bounding boxes until the result count falls below the API limit, ensuring 100% coverage.
No. In Canada, final sold prices are typically gated by regional real estate boards and require authenticated access via a VOW. We only extract public listing prices and price drops.
We configure pipelines to run daily, hourly, or near real-time depending on your requirements, capturing new listings and status changes as they appear on the platform.
Yes. We extract agent profiles, brokerage affiliations, public phone numbers, and active listing counts from the professional directory.
Yes. We can apply filters for property type, price range, bedrooms, bathrooms, and specific keywords within the listing description.
Data is delivered via JSON, CSV, or Parquet directly to your S3 bucket, BigQuery dataset, or via Webhook for real-time integration.
20-minute scoping call. Pilot dataset within the week. Production within two. From targeted neighbourhood scans to nationwide daily listing updates, we build and manage the pipeline. Specify your requirements and get structured data.
