We extract off-market properties, FSBO listings, pricing signals, and neighbourhood data from Fizber. 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 fizber.com. All fields typed and schema-versioned.
"listing_id": "FZ-892341", "address": "1428 Elm Street", "city": "Austin", "state": "TX", "zip_code": "78704", "price": 850000, "bedrooms": 4, "bathrooms": 3.5, "square_feet": 2850, "property_type": "Single Family", "listing_status": "Active"
| # | listing_id | address | city | state | zip_code | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Taxes objects from fizber.com. All fields typed and schema-versioned.
"listing_id": "FZ-892341", "current_price": 850000, "original_price": 875000, "days_on_market": 42, "price_per_sqft": 298.24, "tax_year": 2025, "tax_amount": 14250, "tax_assessment": 795000, "hoa_fees": 150
| # | listing_id | current_price | original_price | days_on_market | price_per_sqft | estimated_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Features & Amenities objects from fizber.com. All fields typed and schema-versioned.
"listing_id": "FZ-892341", "cooling": "Central Air", "heating": "Forced Air, Gas", "parking_spaces": 2, "garage_type": "Attached", "pool": false, "basement": "Finished", "flooring": "['Hardwood', 'Tile']", "roof_type": "Composition Shingle"
| # | listing_id | cooling | heating | parking_spaces | garage_type | pool |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Neighbourhood & Schools objects from fizber.com. All fields typed and schema-versioned.
"zip_code": "78704", "neighbourhood_name": "Zilker", "walk_score": 82, "transit_score": 55, "elementary_school": "Zilker Elementary", "high_school": "Austin High", "school_district": "Austin ISD", "crime_rating": "Low"
| # | zip_code | neighbourhood_name | walk_score | transit_score | elementary_school | middle_school |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Media & Open Houses objects from fizber.com. All fields typed and schema-versioned.
"listing_id": "FZ-892341", "primary_image_url": "https://media.fizber.com/img/892341-main.jpg", "open_house_date": "2026-05-16", "open_house_start": "13:00", "open_house_end": "16:00", "contact_name": "Sarah Jenkins", "contact_phone": "512-555-0192", "listing_url": "https://www.fizber.com/fsbo/tx/austin/1428-elm-st"
| # | listing_id | primary_image_url | all_image_urls | virtual_tour_url | open_house_date | open_house_start |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Fizber scraper handles every layer of the platform: property coordinates, dynamic pricing, tax histories, and open house schedules with JavaScript rendering and session management built in.
Address, beds, baths, square footage, lot size, and every metadata field Fizber surfaces scraped at the individual property level.
Capture current price, original listing price, days on market, and price reductions timestamped per crawl.
Isolate For Sale By Owner listings from flat fee MLS entries. Extract direct owner contact details where publicly available.
Extract upcoming open house dates and time windows to feed direct marketing or local agent schedules.
Capture high-resolution image URLs, virtual tour links, and floor plan documents associated with each listing.
Extract local school ratings, walkability scores, and community data appended to the property records.
Crawl listings across all 50 US states, filtering by zip code, city, county, or specific neighbourhood boundaries.
Identify status changes from Active to Pending or Sold, reducing downstream processing load.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences.
Brief in. Clean data out.
Provide zip codes, cities, or state-level targets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for fizber.com.
Schema validation, null-rate checks, and address normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate platforms invest heavily in scraping detection to protect their inventory. Here is how we stay resilient.
Fizber monitors IP traffic to block automated extraction. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and full cookie session management.
Property discovery relies heavily on interactive map interfaces. We run full Playwright browser sessions to execute JavaScript, pan maps, and trigger lazy-loaded listing data.
A condo listing has different DOM structures than a rural estate. Our selector strategy uses multiple fallback chains to ensure consistent data extraction across all property types.
We maintain a hash index of last-seen values per property. Subsequent runs only push diffs, such as price drops or status changes, reducing storage bloat.
Every run emits structured logs. We alert on null-rate spikes in critical fields like price or address, and respond before you notice missing data.
Real estate investors target FSBO listings to negotiate directly with sellers before properties hit the broader MLS.
AVM (Automated Valuation Model) providers ingest FSBO pricing data to refine their machine learning algorithms for off-market properties.
Agencies monitor FSBO listings that have been on the market for 30+ days to pitch professional representation to frustrated sellers.
Analysts track the ratio of FSBO listings to traditional MLS listings to gauge seller sentiment and market heat.
Lenders identify properties with upcoming open houses to target potential buyers with pre-approval marketing.
Appraisers use historical FSBO sale prices as comparable properties when evaluating non-traditional local real estate.
"Fizber aggregates the fragmented For Sale By Owner market into a single view, but extracting this off-market inventory at scale requires dedicated infrastructure."
Most teams underestimate the investment required: reliable real estate scraping requires US residential proxies, full JavaScript rendering for map interfaces, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our fizber.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 handles JavaScript rendering, cookie sessions, and map interaction flows.
We maintain pools of US residential ISP proxies. Rotation happens per-request with sticky sessions where required to maintain search context.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About fizber.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 data. We do not extract private user data or circumvent authentication walls. Clients should review Fizber's ToS and consult legal counsel for specific use cases.
We use US residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate-limit spikes in real time and trigger pool rotation automatically.
Yes. We configure pipelines to iterate through state, county, and zip code directories to ensure comprehensive coverage of the available inventory.
Pipelines can be configured for daily refreshes to capture new listings, status changes, and price reductions within 24 hours of them appearing on the platform.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per property to track price drops and days on market from the date your pipeline starts.
Our smallest packages start at defined zip code or county lists with weekly delivery. For national coverage or daily cadences, we price based on compute volume.
Yes. We extract the direct URLs for all property images and virtual tours. We deliver the URLs rather than binary files to keep your warehouse lean, allowing you to download assets as needed.
Absolutely. We provide a sample run of up to 500 listings in a specific zip code 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 specific county export or continuous national FSBO monitoring, we scope, build, and operate the pipeline. Tell us what you need.
