We extract residential listings, commercial properties, agent profiles, and neighbourhood demographics from Point2Homes. 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 point2homes.com. All fields typed and schema-versioned.
"listing_id": "P2H-894312", "price": 450000.0, "bedrooms": 3, "bathrooms": 2.5, "sqft": 2100, "city": "Toronto", "state": "ON", "property_type": "Single Family"
| # | listing_id | url | property_type | price | currency | bedrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent Profiles objects from point2homes.com. All fields typed and schema-versioned.
"agent_name": "Sarah Jenkins", "brokerage_name": "Century 21", "phone_number": "416-555-0198", "active_listings_count": 14, "service_areas": "['Toronto', 'Mississauga']", "languages_spoken": "['English', 'French']"
| # | agent_id | agent_name | brokerage_name | phone_number | languages_spoken | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Demographics objects from point2homes.com. All fields typed and schema-versioned.
"neighbourhood_name": "Liberty Village", "population": 11200, "median_age": 32, "median_income": 85000, "walk_score": 92, "transit_score": 100
| # | location_id | neighbourhood_name | population | median_age | median_income | households |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Property History objects from point2homes.com. All fields typed and schema-versioned.
"tax_year": 2023, "tax_amount": 4250.0, "assessment_value": 410000.0, "last_sold_price": 380000.0, "last_sold_date": "2018-05-14", "days_on_market": 24
| # | listing_id | parcel_number | tax_year | tax_amount | assessment_value | last_sold_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Commercial Data objects from point2homes.com. All fields typed and schema-versioned.
"property_subtype": "Retail", "building_size": 5400, "cap_rate": 6.5, "lease_rate": 24.0, "lease_type": "NNN", "parking_spaces": 12
| # | listing_id | property_subtype | building_size | zoning | cap_rate | lease_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Point2Homes scraper handles map-based pagination, dynamic loading, and aggressive bot mitigation to extract accurate real estate data across North America.
Extract beds, baths, sqft, lot size, HOA fees, and high-resolution image URLs for every residential listing.
Map agent names, contact details, brokerages, and active listing counts across target regions.
Execute JavaScript to extract coordinates and listings hidden behind map-based search interfaces.
Capture specific commercial data points including cap rates, zoning, and lease terms.
Extract Point2Homes neighbourhood statistics, median incomes, and population metrics.
Monitor daily price adjustments, delistings, and status changes across target zip codes.
Scrape listings across the US, Canada, and international markets from a unified pipeline.
Bypass Cloudflare and perimeter defenses using residential proxies and browser fingerprinting.
Diff-based delivery ensures you only process new listings or changed properties, reducing compute costs.
Brief in. Clean data out.
Provide target cities, zip codes, or agent directories. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and CAPTCHA handling for point2homes.com.
Schema validation, null-rate checks, coordinate verification, and sample datasets before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate portals actively block automated access. Here is how we maintain reliable extraction.
Point2Homes relies on map bounds for search results. We simulate viewport panning and zooming, splitting large areas into smaller coordinate grids to extract all listings without hitting 500-result limits.
Cloudflare challenges block basic HTTP clients. We use Playwright with stealth plugins and residential IPs to mimic human browsing patterns and bypass perimeter defenses.
Real estate portals frequently update DOM structures. We use multiple fallback chains for critical fields like price and square footage to ensure pipeline stability.
Phone numbers and emails are often masked or require interaction. We automate the necessary clicks to reveal contact data securely and efficiently.
We maintain a hash index of active listings. Subsequent runs only push new properties, price drops, or sold status changes, optimising downstream processing.
Investors track median listing prices, days on market, and cap rates to identify undervalued properties.
Startups build valuation models and automated valuation machines (AVMs) using historical listing data.
Real estate agencies monitor competitor listings, agent performance, and market share across specific postal codes.
B2B service providers extract agent directories to build targeted outreach lists for software and marketing services.
Analysts correlate Point2Homes demographic data with property prices to forecast neighbourhood gentrification.
Appraisers use recent comparable sales and active listings to generate accurate property valuation reports.
"Point2Homes holds critical inventory data for the North American market, but extracting it requires navigating aggressive bot mitigation and map-based pagination."
Most teams underestimate the investment required: reliable real estate scraping demands residential proxies, full JavaScript rendering, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our point2homes.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. Playwright handles JavaScript rendering, map interactions, and cookie sessions.
We maintain pools of residential ISP proxies across North America. Rotation happens per-request to avoid IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About point2homes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property and agent data. We do not extract VOW/IDX restricted fields that require credentials. Clients should review Point2Homes' ToS and consult legal counsel for specific use cases.
We use Playwright with stealth plugins, residential ISP proxies, and request timing modelled on human behaviour to bypass perimeter defenses.
Yes. We use grid-based coordinate iteration to simulate map panning and zooming, ensuring we capture all listings within a target area without hitting pagination limits.
Pipelines can be configured for daily or hourly cadences based on your requirements. Change-detection diffing ensures rapid updates for price drops and status changes.
Yes. We automate the necessary clicks to reveal masked phone numbers and email addresses on agent profile pages.
Our smallest packages start at defined zip codes or cities with weekly delivery. For larger regional coverage, we price based on volume and frequency.
Yes. We provide a sample run of up to 500 listings as part of the pre-engagement scoping process to 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 North America, we scope, build, and operate the pipeline.
