Hibid Auction Listings Scraper extracts structured auction listing data from HiBid, enabling detailed analysis of bids, lots, sellers, and auction timelines. It solves the challenge of manually tracking large auction inventories by delivering clean, analysis-ready data. Ideal for market research, pricing intelligence, and auction trend monitoring.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project collects comprehensive auction listing data from HiBid category pages and search results. It removes the need for manual browsing and data compilation across thousands of auction lots. It is built for analysts, resellers, researchers, and businesses that rely on auction market insights.
- Extracts live and historical auction lot information in a structured format
- Supports category-based and search-based discovery workflows
- Captures bidding behavior, pricing signals, and seller metadata
- Enables scalable analysis across multiple auction segments
| Feature | Description |
|---|---|
| Multi-source scraping | Collects data from category URLs or dynamic search filters |
| Detailed bid tracking | Captures current bids, bid history, and bidding status |
| Seller & auction metadata | Extracts auction house, event details, and location info |
| Proxy-ready execution | Designed for stable large-scale data collection |
| Structured output | Delivers consistent JSON suitable for analytics pipelines |
| Field Name | Field Description |
|---|---|
| auction | Core auction event details and seller information |
| id | Unique auction or lot identifier |
| item_id | Internal item reference ID |
| lot_number | Sequential auction lot number |
| bid_amount | Current highest bid value |
| bid_list | Historical list of bid increments |
| lot_state | Status and timing details of the auction lot |
| description | Detailed product or lot description |
| lead | Short headline or summary of the item |
| featured_picture | Primary image and thumbnail URLs |
| picture_count | Total number of available images |
| shipping_offered | Indicates if shipping is available |
| distance_miles | Geographic distance for location-based analysis |
| site | Auction site or venue metadata |
[
{
"id": 261252067,
"item_id": 73938,
"lot_number": "163",
"lead": "speakers",
"bid_amount": 123.45,
"bid_list": [2,3,4,5,6,7,8,9],
"auction": {
"event_name": "Flitz trail #24 Litchfield Il",
"event_city": "Litchfield",
"event_state": "IL",
"currency_abbreviation": "USD",
"bid_open_date_time": "2025-08-24T00:55:00",
"bid_close_date_time": "2025-08-29T07:30:00"
},
"shipping_offered": true,
"picture_count": 1
}
]
Hibid Auction Listings Scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── listings_parser.py
│ │ └── auction_state.py
│ ├── outputs/
│ │ └── formatter.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Market analysts use it to track bid trends so they can forecast auction pricing behavior.
- Resellers use it to research sold and active lots so they can identify profitable inventory.
- Auction professionals use it to benchmark performance so they can refine listing strategies.
- Data teams use it to build pricing models so they can automate valuation workflows.
Does this scraper support both category URLs and keyword searches? Yes, it can extract data either from predefined auction category URLs or from dynamic search parameters such as keywords, location, and auction status.
Can it handle large volumes of auction listings? It is designed for scalable execution with configurable item limits and retry logic to ensure stability across large datasets.
What types of auctions are supported? The scraper supports online-only, webcast, absentee, and mixed auction formats as exposed in listing data.
Is the output suitable for analytics tools? Yes, the structured JSON output is optimized for direct use in databases, dashboards, and data science workflows.
Primary Metric: Processes approximately 20–30 auction lots per page with consistent extraction accuracy.
Reliability Metric: Maintains stable execution with high completion rates across multi-page runs.
Efficiency Metric: Optimized batching minimizes redundant requests while maximizing data throughput.
Quality Metric: Captures complete bid, lot, and auction metadata suitable for downstream analysis.
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