We extract residential listings, historical price changes, Zestimate signals, tax records, and agent intelligence from Zillow. 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 zillow.com. All fields typed and schema-versioned.
"zpid": "20847321", "address": "123 Maple Ave, Austin, TX 78701", "price": 745000, "zestimate": 752400, "beds": 3, "baths": 2.5, "sqft": 2100, "property_type": "SINGLE_FAMILY", "listing_status": "FOR_SALE", "days_on_zillow": 12, "in_stock": true
| # | zpid | address | city | state | zip_code | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Historical & Tax objects from zillow.com. All fields typed and schema-versioned.
"zpid": "20847321", "price_history": [ {"date": "2023-05-12", "event": "Listed", "price": 745000}, {"date": "2019-11-01", "event": "Sold", "price": 520000} ], "tax_history": [ {"year": 2023, "tax_paid": 12450, "value": 680000} ]
| # | zpid | price_history | tax_history | last_sold_date | last_sold_price | assessment_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Agent & Broker objects from zillow.com. All fields typed and schema-versioned.
"zpid": "20847321", "listing_agent_name": "Sarah Johnson", "brokerage_name": "Austin Elite Realty", "agent_rating": 4.9, "verified_agent": true
| # | zpid | listing_agent_name | listing_agent_phone | agent_license_num | brokerage_name | brokerage_phone |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Zillow scraper handles every layer of the platform: property metadata, Zestimate trends, price history, agent contact data, and high-res imagery — with bypass for PerimeterX and complex geolocation rendering built in.
Full address, bed/bath count, square footage, lot details, home facts, and every amenity listed on the property page.
Capture current Zestimates, rental estimates, price per square foot, and historical price changes for market trend analysis.
Automated extraction of historical property taxes, assessed values, and previous sales events directly from Zillow’s public record integration.
Scrape by ZIP code, neighborhood boundaries, or map coordinates to ensure 100% coverage of specific local markets.
Extract listing agent names, contact numbers, brokerage details, and professional ratings for lead generation or competitive mapping.
Gather high-resolution property image URLs, floor plans, and 3D tour links delivered in a structured media manifest.
Capture school ratings (GreatSchools), walk scores, transit scores, and nearby property comparisons for valuation modeling.
Monitor search result positions for specific filters, tracking new arrivals and 'Coming Soon' listings as they hit the market.
Continuous monitoring for price drops, status changes (Pending/Sold), or new photo uploads with automated diff delivery.
Brief in. Clean data out.
Specify ZIP codes, cities, or specific ZPIDs. We define the search parameters and the data schema required for your model.
We deploy high-reputation residential proxies and Playwright browsers to navigate Zillow’s advanced PerimeterX/Datadome protection.
We clean unstructured text descriptions, normalize address formats, and validate numeric values like price and square footage.
The property records are pushed to your S3, BigQuery, or Snowflake instance as Parquet or JSON on your schedule.
Zillow employs some of the web's most sophisticated anti-scraping technology. Here's how we ensure your data flow never stops.
Zillow uses advanced behavioral analysis to block bots. Our pipeline uses hardened Playwright instances, human-like mouse movements, and TLS fingerprint spoofing to bypass these blocks reliably.
Zillow often serves different content based on IP location. We use a massive pool of US-based residential proxies, allowing us to 'appear' in specific ZIP codes to extract local pricing and agent data without triggering alerts.
Modern Zillow pages are highly dynamic. We don't just scrape the HTML; we intercept the background API responses and parse hidden JSON-LD blocks to ensure 100% accuracy of property facts.
With millions of listings, we use a recursive grid-search algorithm. We split the US into small geographical tiles to bypass Zillow's 20-page/500-result search limit, ensuring we see every listing.
If Zillow changes a property attribute name (e.g., 'Baths' to 'Bathrooms'), our schema monitoring alerts us within minutes. We maintain 99.9% field coverage via constant automated QA.
Institutional buyers track Zestimate-to-price ratios and days-on-market to identify undervalued properties across thousands of ZIP codes simultaneously.
Prop-tech companies feed our historical sales and tax data into ML models to automate property valuations and risk assessment for lending.
Mortgage brokers and listing agents monitor 'For Sale By Owner' (FSBO) or new listings to reach out to potential clients the moment a property goes live.
Investors compare Zillow sale prices with Airbnb revenue data to calculate potential ROI and Cap Rates for vacation rental acquisitions.
Academic and government researchers use our historical price data to study urban migration patterns and housing affordability trends.
Brokerages track market share by monitoring which firms are winning the most listings and closing sales fastest in specific regions.
"Zillow is the heartbeat of US real estate — but its data is locked behind layers of anti-bot defenses and dynamic React components."
Building a Zillow scraper that lasts more than a week requires institutional-grade proxy management and resilient selector chains. DataFlirt handles the technical debt of real estate extraction so you can focus on finding the next great deal.
Everything supported by our zillow.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 Scrapy for high-speed API interception where possible, and Playwright for 'tough' property pages that require full browser execution.
Our requests are routed through the specific US state or city being scraped to ensure the data matches what a local buyer would see.
Zillow listings are messy. Our pipeline includes automated normalization for addresses (USPS standard) and unit conversions.
Data delivered to where your team already works — no new tooling required.
About zillow.com scraping, legality, and pipeline operations.
Ask us directly →We use a combination of residential proxy rotation, browser fingerprint randomization, and human-behavioral emulation. We treat every request as a unique session, making it nearly impossible for Zillow to flag our traffic as bot-driven.
Yes. Because Zillow limits search results to 500 per query, we use a 'Map Tiling' approach—splitting the city into hundreds of smaller sub-grids until each grid contains fewer than 500 results, ensuring 100% coverage.
Yes, we extract listing agent names, phone numbers, and brokerage details wherever they are publicly displayed on the listing page.
We can run price-monitoring pipelines at any cadence. Most clients choose daily updates, but for high-velocity markets, we can provide updates every 4–6 hours.
Yes. We can extract the entire 'Price History' and 'Public Tax History' table for any property, dating back as far as Zillow has records (often 10+ years).
Absolutely. We offer a trial run of up to 500 property records for your target area so you can verify the data quality and schema fit before starting a full pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. From daily price-drop alerts to massive nationwide property snapshots — we operate the tech so you can focus on the real estate. Tell us your target market.
