We extract active foreclosures, short sales, bid histories, and property metadata from Hubzu. 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 hubzu.com. All fields typed and schema-versioned.
"property_id": "HZ192847", "address": "123 Maple St", "city": "Atlanta", "state": "GA", "zip_code": "30303", "beds": 3, "baths": 2, "sqft": 1850, "estimated_value": 245000.0
| # | property_id | address | city | state | zip_code | property_type |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Auction Details objects from hubzu.com. All fields typed and schema-versioned.
"auction_id": "AUC-9921", "starting_bid": 150000.0, "current_bid": 175000.0, "bid_increment": 1000.0, "reserve_met": false, "buyers_premium": 5.0, "time_remaining": "2d 14h 30m"
| # | property_id | auction_id | auction_start | auction_end | starting_bid | current_bid |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Bid History objects from hubzu.com. All fields typed and schema-versioned.
"auction_id": "AUC-9921", "bid_id": "B-48192", "bid_amount": 175000.0, "bid_timestamp": "2023-10-24T14:32:01Z", "bidder_alias": "User***49", "is_winning_bid": true
| # | auction_id | bid_id | bid_amount | bid_timestamp | bidder_alias | is_winning_bid |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Foreclosure Data objects from hubzu.com. All fields typed and schema-versioned.
"property_id": "HZ192847", "foreclosure_status": "REO", "title_status": "Clear", "occupancy_status": "Vacant", "financing_available": false, "seller_type": "Bank Owned", "case_number": "FC-2023-091"
| # | property_id | foreclosure_status | title_status | occupancy_status | financing_available | seller_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Market Data objects from hubzu.com. All fields typed and schema-versioned.
"search_query": "Atlanta, GA", "zip_code": "30303", "total_results": 42, "position": 1, "property_id": "HZ192847", "days_on_market": 14
| # | search_query | zip_code | total_results | page_number | position | property_id |
|---|---|---|---|---|---|---|
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Our Hubzu scraper handles every layer of the platform: REO listings, dynamic bid histories, auction timers, and property metadata with JavaScript rendering and session management built in.
Extract bank-owned, short sale, and foreclosure auction properties across all US states.
Monitor starting bids, current bids, and countdown timers with high-frequency polling.
Capture beds, baths, sqft, lot size, year built, and structural details.
Extract timestamped bid logs and bidder aliases to model auction velocity.
Track whether properties are vacant, occupied, and if title clearance is guaranteed.
Calculate bid-to-value ratios using Hubzu's proprietary estimated value figures.
Iterate through zip codes, counties, and MSAs to map total available inventory.
Capture buyer premium percentages, technology fees, and earnest money requirements.
Archive completed auctions to build pricing models for specific neighbourhoods.
Brief in. Clean data out.
Provide target states, zip codes, or auction types. We design the extraction schema together.
We configure Scrapy crawlers, residential proxy rotation, and session management for hubzu.com.
Schema validation, null-rate checks, and auction timer sync verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Auction platforms employ strict rate limits and dynamic data loading. Here is how we ensure reliable delivery.
Hubzu employs aggressive IP blocking. We route requests through US-based residential proxies to maintain persistent sessions without triggering rate limits.
Auction countdowns and live bids are hydrated via WebSockets and XHR. We intercept these API calls to capture millisecond-accurate bid updates.
Full bid logs often require authenticated session emulation or specific DOM interactions. We script these flows using Playwright.
Access from non-US IP addresses is frequently blocked. Our infrastructure guarantees requests originate from compliant US residential nodes.
We use redundant XPath and CSS selectors for critical fields like property status and current bid to prevent pipeline failure during site updates.
Identify distressed properties, calculate repair margins, and automate bidding strategies for REO portfolios.
Ingest Hubzu inventory into unified real estate portals and investment analysis platforms.
Train pricing algorithms using Hubzu's estimated values, final auction prices, and bid velocity.
Filter for vacant, bank-owned properties in specific zip codes to source high-margin flip opportunities.
Monitor bid histories to identify institutional purchasing patterns and geographical focus areas.
Track days on market and auction failure rates to gauge real estate liquidity at the county level.
"Hubzu holds critical inventory for distressed real estate, but tracking volatile auction states requires infrastructure most teams do not have."
Building a reliable Hubzu scraper means managing US residential proxies, handling WebSocket bid streams, and parsing complex property metadata. DataFlirt manages this pipeline end-to-end so your acquisitions team can focus on modeling deals, not maintaining crawlers.
Everything supported by our hubzu.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 XHR interception for auction timers and dynamic bid histories.
We maintain dedicated pools of US residential IPs to bypass Hubzu geo-fencing and rate-limiting infrastructure.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About hubzu.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings and auction data is generally permissible. DataFlirt extracts only public property data and bid histories. We do not bypass authentication walls for confidential documents.
We intercept the backend XHR requests and WebSocket feeds that Hubzu uses to update the frontend, ensuring we capture the exact server-side bid state and time remaining.
Yes. We can scrape completed auctions to capture the final sale price, winning bid, and total bid count for comparative market analysis.
For targeted high-priority auctions, we can configure high-frequency polling to deliver bid updates via Webhook with sub-second latency.
Yes. We extract all available metadata, including occupancy status, property condition, and financing eligibility.
Hubzu often restricts access from non-US IPs. We route all extraction traffic through a distributed network of US-based residential proxies to ensure uninterrupted access.
No. Accessing specific legal documents often requires a registered, authenticated account and agreeing to specific terms, which falls outside our public data extraction mandate.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous bid-monitoring feed across active auctions, we scope, build, and operate the pipeline. Tell us what you need.
