We extract MLS listings, pricing signals, property histories, and agent directories from Homefinder. 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 homefinder.com. All fields typed and schema-versioned.
"mls_id": "TX-192834", "address": "123 Maple Street", "city": "Austin", "state": "TX", "zip_code": "78704", "price": 850000, "beds": 4, "baths": 3.5, "sqft": 2800, "status": "Active"
| # | mls_id | address | city | state | zip_code | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Tax History objects from homefinder.com. All fields typed and schema-versioned.
"mls_id": "TX-192834", "current_price": 850000, "price_per_sqft": 303.57, "tax_assessed_value": 790000, "tax_year": 2025, "annual_tax_amount": 14200, "last_sold_date": "2018-06-15", "last_sold_price": 620000
| # | mls_id | current_price | price_per_sqft | tax_assessed_value | tax_year | annual_tax_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Building & Lot Details objects from homefinder.com. All fields typed and schema-versioned.
"mls_id": "TX-192834", "property_style": "Single Family", "roof_type": "Composition", "foundation_type": "Slab", "heating_system": "Central", "cooling_system": "Central Air", "parking_spaces": 2, "hoa_fees": 150
| # | mls_id | property_style | construction_materials | roof_type | foundation_type | heating_system |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent & Broker Data objects from homefinder.com. All fields typed and schema-versioned.
"agent_name": "Sarah Jenkins", "brokerage_name": "Austin Premier Realty", "phone_number": "512-555-0198", "active_listings_count": 14, "license_number": "TREC-982374", "office_city": "Austin", "office_state": "TX"
| # | agent_name | agent_id | brokerage_name | phone_number | email_address | active_listings_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Neighbourhood & Schools objects from homefinder.com. All fields typed and schema-versioned.
"mls_id": "TX-192834", "neighbourhood_name": "Zilker", "walk_score": 82, "transit_score": 54, "elementary_school": "Zilker Elementary", "high_school": "Austin High", "school_district": "Austin ISD"
| # | mls_id | neighbourhood_name | walk_score | transit_score | elementary_school | middle_school |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Homefinder scraper handles every layer of the platform: active listings, dynamic pricing, tax histories, and agent directories, with coordinate-based pagination and anti-bot circumvention built in.
Capture beds, baths, square footage, lot size, construction details, and HOA fees for every active property.
Monitor list price changes, price drops, and delistings with timestamped records per crawl.
Extract past sales records, tax assessments, and historical price adjustments attached to the listing.
Pull contact information, brokerage affiliations, and active listing portfolios for real estate professionals.
Bypass standard pagination limits using coordinate-based bounding box extraction across city grids.
Identify distressed properties, pre-foreclosures, and auction schedules listed on the platform.
Standardise schema across different MLS regions, normalising property types and status codes.
Extract high-resolution image URLs, virtual tour links, and floor plan documents.
Configure continuous extraction pipelines at daily or weekly cadences with strict change-detection diffing.
Brief in. Clean data out.
Provide zip codes, cities, or specific MLS regions. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, bounding box pagination, and CAPTCHA handling for homefinder.com.
Schema validation, null-rate checks, price-outlier detection, and geographic coverage checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate portals invest heavily in scraping detection and data obfuscation. Here is how we stay resilient.
Homefinder caps standard search results at a few hundred listings. We use automated bounding box subdivision, splitting geographic grids until every listing is captured.
Real estate portals aggressively block datacenter IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing.
Different regional MLS feeds cause DOM structure variations. Our selector strategy uses fallback chains and normalisation rules to output a consistent schema.
For large geographic areas, we maintain a hash index of last-seen values per property. Subsequent runs only push diffs, reducing compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or coverage drops, ensuring SLA uptime.
Institutional buyers track price drops, days on market, and neighbourhood trends to identify undervalued assets.
Aggregators and analytics platforms enrich their internal databases with active listing data and tax histories.
Brokerages monitor competitor listing volume, agent performance, and regional market penetration.
Data science teams feed structural details, lot sizes, and historical sales into ML models to predict property values.
Commercial analysts overlay residential density and housing price trends to optimise retail footprint expansion.
Mortgage brokers and contractors track newly listed or recently sold properties to target high-intent homeowners.
"Homefinder holds millions of active MLS listings and historical property records, but aggregating this fragmentation into a unified warehouse requires dedicated pipeline infrastructure."
Most teams underestimate the investment required: reliable real estate scraping requires residential proxies, map-based bounding box pagination, anti-bot circumvention, and daily schema maintenance. DataFlirt absorbs that complexity so your engineers can focus on property analysis, not infrastructure.
Everything supported by our homefinder.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 retry logic. Playwright handles JavaScript rendering and interactive map loading.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required.
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 homefinder.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property and agent data. We do not extract personal user data or bypass authentication walls.
Homefinder restricts search results to a maximum number of properties per view. We programmatically subdivide geographic areas into smaller coordinate bounding boxes until every listing falls under the display threshold.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per MLS ID for price changes and status updates from the date your pipeline starts.
Yes. We parse the historical data tables present on property detail pages, delivering them as nested JSON arrays or separate relational CSV tables.
We configure pipeline cadences based on your requirements. Active market monitoring typically runs on a daily schedule, while full historical backfills are executed as one-off batches.
Our smallest packages start at a defined geographic scope, such as specific states or major metropolitan areas, with weekly delivery. We price based on listing volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous price-monitoring feed across US markets: we scope, build, and operate the pipeline. Tell us what you need.
