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AUCTION_DATA_SOURCES.md

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# Auction House Data Sources for Luxury Handbags

**Date**: November 16, 2025
**Research**: Christie's, Sotheby's, Heritage Auctions, LiveAuctioneers

---

## 🎯 GitHub Projects Found

### 1. **Christie's Auction Scrapers**

#### A. **marcusrprojects/What-Makes-Art-Valuable**
- **URL**: https://github.com/marcusrprojects/What-Makes-Art-Valuable
- **Description**: Web scrapers using Selenium for Sotheby's and Christie's
- **Technology**: Selenium, Python
- **Data Format**: Likely CSV/database
- **Status**: Multi-stage scraper (Stage1, Stage2)
- **Use Case**: Art auction data collection

#### B. **gabrielAHN/Christie-Data-Scrape**
- **URL**: https://github.com/gh15hidalgo/Christie-Data-Scrape
- **Description**: BeautifulSoup4 + Selenium scraper for Christie's
- **Data Collected**:
  - Auction events
  - Items sold per event
  - Auction results
- **Technology**: BS4, Selenium, Python
- **Use Case**: Event-level Christie's data

#### C. **GloriaiXIII/christies_final_project**
- **URL**: https://github.com/GloriaiXIII/christies_final_project
- **Description**: Data analytics project on Christie's British Auction House
- **Method**: Web scraping directly from Christie's official website
- **Analysis**: Sales data analysis
- **Technology**: Python, data analytics tools

### 2. **Sotheby's Auction Data**

#### A. **jasminefederer/sothebys_auction_price_classifier**
- **URL**: https://github.com/jasminefederer/sothebys_auction_price_classifier
- **Description**: ML classifier for Sotheby's auction prices
- **Data Fields**:
  - Art movement
  - Artist gender
  - Sale value
- **Technology**: Naive Bayes Classifier
- **Use Case**: Price prediction

#### B. **Idanlau/Art_Price_Prediction_Model**
- **URL**: https://github.com/Idanlau/Art_Price_Prediction_Model
- **Achievement**: 2nd place at Cloudera/AMD Applied ML 2023 Hackathon
- **Description**: Scrapes Sotheby's website for image data
- **Output**: CSV file with image download links
- **Technology**: Web scraping, ML
- **Use Case**: Art price prediction (methodology applicable to handbags)

#### C. **Sotheby's Official GitHub**
- **URL**: https://github.com/sothebys
- **Repos**: 17 repositories
- **Type**: Development tools (not data extraction)
- **Access**: Public but not data-focused

### 3. **Multi-Platform Auction Scrapers**

#### A. **dreamingspires/auction-scraper** ⭐ HIGHLY RECOMMENDED
- **URL**: https://github.com/dreamingspires/auction-scraper
- **Platforms**:
  - ✅ eBay
  - ✅ LiveAuctioneers
  - ✅ Catawiki
- **Features**:
  - CLI tool
  - Direct Python interface
  - SQLite3 database output
  - Optional image downloads
  - Backup web pages
- **Modes**:
  - Auction scraping
  - Profile scraping
  - Search scraping
- **Installation**: `pip install auction-scraper`
- **Usage**:
  ```bash
  auction-scraper [OPTIONS] DB_PATH BACKEND COMMAND [ARGS]
  ```
- **Data Format**: SQLite3 + images
- **License**: Not specified
- **Status**: Active, extensible

### 4. **Heritage Auctions**

#### A. **guanquann/HeritageAuctions_Currency_Scraper**
- **URL**: https://github.com/guanquann/HeritageAuctions_Currency_Scraper
- **Current Focus**: Currency/coins
- **Adaptability**: HIGH - Can be modified for luxury goods category
- **Technology**: Python, HTTP requests
- **Authentication**: Requires login cookies
- **Output**: CSV format
- **Method**:
  1. Login to Heritage Auctions
  2. Capture request cookies
  3. Convert to Python using curlconverter.com
  4. Configure category parameter
  5. Run scraper

**Adaptation for Handbags**:
```python
# Change category from currency to luxury goods
# Heritage Auctions has "Luxury Accessories" category
# URL pattern: https://fineart.ha.com/c/search-results.zx?N=...
```

### 5. **Artsy API** (Art Auction Aggregator)

#### A. **indexzero/node-artsy**
- **URL**: https://github.com/indexzero/node-artsy
- **Type**: Node.js API client for Artsy
- **Endpoints**:
  ```
  GET /api/v1/artist/{artist_id}/auction_lots.json
  ```
- **Coverage**: Aggregates data from Christie's, Sotheby's, Phillips, Bonhams
- **Public API**: https://developers.artsy.net
- **Status**: Check current API availability

#### B. **artsy/artsy-ruby-client**
- **URL**: https://github.com/artsy/artsy-ruby-client
- **Type**: Ruby API client
- **Status**: Obsolete - redirects to developers.artsy.net
- **Note**: Public API available at developers.artsy.net

---

## 📊 Data Availability Assessment

### Christie's
- ✅ **Web Scraping**: Possible (3 GitHub projects)
- ❌ **Public API**: None
- ⚠️ **Terms of Service**: Likely prohibits scraping
- **Data Format**: HTML → Python scraping → CSV/DB
- **Coverage**: General art auctions (handbags included in "luxury goods")

### Sotheby's
- ✅ **Web Scraping**: Possible (2+ GitHub projects)
- ❌ **Public API**: None (only internal tools)
- ⚠️ **Terms of Service**: Likely prohibits scraping
- **Data Format**: HTML → Python scraping → CSV
- **Official GitHub**: 17 repos but not data-focused

### Heritage Auctions
- ✅ **Web Scraping**: Adaptable scraper available
- ❌ **Public API**: None
- ✅ **Category Support**: "Luxury Accessories" category exists
- **Authentication**: Required
- **Data Format**: CSV

### LiveAuctioneers
- ✅ **Working Scraper**: dreamingspires/auction-scraper
- ✅ **SQLite Output**: Structured database
- ✅ **Image Downloads**: Optional
- **Installation**: `pip install auction-scraper`
- **Coverage**: All luxury goods auctions on platform

### Artsy (Aggregator)
- ⚠️ **Public API**: developers.artsy.net (check availability)
- ✅ **Data Coverage**: Christie's, Sotheby's, Phillips, Bonhams
- ✅ **Auction Lots**: API endpoint available
- **Status**: Check API documentation for current access

---

## 🛠️ Implementation Recommendations

### Option 1: LiveAuctioneers (EASIEST)
```bash
# Install scraper
pip install auction-scraper

# Scrape luxury handbag auctions
auction-scraper ./handbag_auctions.db liveauctioneers search \
  "Hermes Birkin" "Chanel Classic" "Louis Vuitton"

# Export to CSV for analysis
sqlite3 handbag_auctions.db ".mode csv" ".output auctions.csv" \
  "SELECT * FROM auctions"
```

**Advantages**:
- Ready-to-use tool
- SQLite database
- Active maintenance
- Legal gray area but tool exists

### Option 2: Heritage Auctions (MEDIUM)
```bash
# Clone currency scraper
git clone https://github.com/guanquann/HeritageAuctions_Currency_Scraper

# Adapt for luxury goods category
# 1. Login to Heritage Auctions
# 2. Navigate to Luxury Accessories category
# 3. Capture network request with browser DevTools
# 4. Convert curl to Python using curlconverter.com
# 5. Update scraper.py with new category parameters
```

**Advantages**:
- Adaptable existing code
- CSV output
- Heritage Auctions has luxury handbag category
- Sample code available

### Option 3: Christie's Web Scraping (ADVANCED)
```bash
# Clone working scraper
git clone https://github.com/gabrielAHN/Christie-Data-Scrape

# Modify for handbag category
# Update selectors for handbag-specific pages
# Run scraper targeting luxury goods auctions
```

**Advantages**:
- Multiple working examples
- Selenium + BeautifulSoup stack
- Proven methodology

**Disadvantages**:
- May violate ToS
- Requires maintenance (selectors change)
- Authentication challenges

### Option 4: Artsy API (BEST IF AVAILABLE)
```bash
# Check public API
curl https://developers.artsy.net

# If API available, register for key
# Use API endpoints for auction data
GET /api/v1/artist/{artist_id}/auction_lots.json
```

**Advantages**:
- Legal API access
- Aggregates multiple auction houses
- Structured data
- No scraping required

**Disadvantages**:
- API may be limited or restricted
- Requires API key
- Coverage may not include all handbags

---

## 🎯 Recommended Approach for LUXVAULT

### Phase 1: Quick Win (LiveAuctioneers)
1. Install `auction-scraper` package
2. Search for luxury handbag auctions
3. Export SQLite to CSV
4. Import into handbags.db
5. Analyze price trends

**Timeline**: 1-2 days
**Cost**: Free
**Risk**: Low (tool already exists)

### Phase 2: Heritage Auctions Integration
1. Adapt currency scraper for luxury goods
2. Schedule weekly scraping
3. Build historical database over time
4. Cross-reference with Yahoo Japan data

**Timeline**: 1 week
**Cost**: Free
**Risk**: Medium (requires adaptation)

### Phase 3: Multi-Source Aggregation (Long-term)
1. Check Artsy API availability
2. If available, integrate as primary source
3. If not, build multi-site scraper using existing code
4. Combine LiveAuctioneers + Heritage + Artsy

**Timeline**: 2-4 weeks
**Cost**: Time investment
**Risk**: Medium-High (ToS violations possible)

---

## ⚖️ Legal Considerations

### Terms of Service
- Most auction houses **prohibit web scraping**
- Commercial use may require licensing
- Data may be copyrighted

### Best Practices
1. **Check robots.txt** before scraping
2. **Rate limit** requests (1-2 seconds between calls)
3. **Respect ToS** - use for research/personal use only
4. **Consider API access** when available
5. **Cache results** to minimize repeated requests

### Alternative: Manual Data Collection
- Subscribe to auction house email alerts
- Manually record sold prices for key models
- Build dataset over time
- 100% legal, no ToS violations

---

## 📈 Expected Data Structure

### Auction Record Fields
```sql
CREATE TABLE auction_results (
  id INTEGER PRIMARY KEY,
  auction_house TEXT,      -- Christie's, Sotheby's, Heritage, etc.
  sale_date DATE,
  lot_number TEXT,
  brand TEXT,              -- Hermes, Chanel, Louis Vuitton
  model TEXT,              -- Birkin, Kelly, Classic Flap
  material TEXT,           -- Crocodile, Lizard, Calf leather
  hardware TEXT,           -- Gold, Palladium, Silver
  color TEXT,
  size TEXT,               -- 25cm, 30cm, 35cm
  condition TEXT,          -- Excellent, Very Good, Good
  estimate_low INTEGER,    -- Pre-sale estimate low
  estimate_high INTEGER,   -- Pre-sale estimate high
  hammer_price INTEGER,    -- Final sale price
  buyer_premium INTEGER,   -- Auction house fee
  total_price INTEGER,     -- Hammer + premium
  currency TEXT,           -- USD, EUR, GBP
  provenance TEXT,         -- Original owner, history
  image_url TEXT,
  lot_url TEXT,
  notes TEXT
)
```

### Sample Record
```json
{
  "auction_house": "Christie's Hong Kong",
  "sale_date": "2024-05-28",
  "lot_number": "2156",
  "brand": "Hermès",
  "model": "Birkin 30",
  "material": "Nilo Crocodile",
  "hardware": "18K Gold",
  "color": "Himalaya",
  "size": "30cm",
  "condition": "Excellent",
  "estimate_low": 200000,
  "estimate_high": 300000,
  "hammer_price": 280000,
  "buyer_premium": 56000,
  "total_price": 336000,
  "currency": "USD",
  "provenance": "Private Collection",
  "image_url": "https://...",
  "lot_url": "https://..."
}
```

---

## 🚀 Next Steps

1. **Install auction-scraper**: Start with LiveAuctioneers
2. **Test scraping**: Search for "Hermes Birkin" auctions
3. **Analyze results**: Compare auction prices vs. retail
4. **Adapt Heritage scraper**: For additional data source
5. **Check Artsy API**: If available, integrate for multi-house data
6. **Build historical DB**: Combine with existing Yahoo Japan data

---

## 📚 References

- dreamingspires/auction-scraper: https://github.com/dreamingspires/auction-scraper
- Heritage Auctions Scraper: https://github.com/guanquann/HeritageAuctions_Currency_Scraper
- Christie's Scraper: https://github.com/gabrielAHN/Christie-Data-Scrape
- Artsy API: https://developers.artsy.net
- Sotheby's Art Price Model: https://github.com/Idanlau/Art_Price_Prediction_Model

---

**Generated**: November 16, 2025
**Updated**: November 17, 2025
**Research**: Claude Code
**Status**: Automated System Deployed

---

## ✅ DEPLOYMENT STATUS (Nov 17, 2025)

### Implemented
- ✅ **Cron Job**: Daily scraping at 6:00 AM (`0 6 * * *`)
- ✅ **Database**: SQLite (`/data/auction-history/auctions.db`)
- ✅ **CSV Export**: Historical tracking (`/data/auction-history/auction-history.csv`)
- ✅ **22 Luxury Brands**: Hermès, Chanel, LV, Dior, Gucci, Prada, Bottega Veneta, Celine, Saint Laurent, Balenciaga, Fendi, Goyard
- ✅ **Logging**: Comprehensive logs (`/data/auction-history/scraper.log`)

### Current Status
- **Script**: `/root/Projects/handbag-auth-nextjs/scripts/scrape-auction-data.py`
- **Dependencies**: BeautifulSoup4, requests, lxml (removed broken auction-scraper)
- **Issue**: LiveAuctioneers requires JavaScript rendering (Playwright/Selenium needed)
- **Workaround**: Script runs successfully, creates database/CSV structure

### Next Steps for Production Data
1. **Option 1**: Implement Playwright scraper for LiveAuctioneers
2. **Option 2**: Use Heritage Auctions scraper (no JS required)
3. **Option 3**: Manual CSV population until API access is obtained

### Files
- `scripts/scrape-auction-data.py` - Main scraper (310 lines)
- `scripts/setup-auction-cron.sh` - Cron installer
- `data/auction-history/auctions.db` - SQLite database
- `data/auction-history/auction-history.csv` - CSV export
- `data/auction-history/scraper.log` - Execution logs