majiayu000 Auction Valuation & Market Analysis Reference
Use this skill when working on valuation logic, market data features, or pricing. Focus on: $ARGUMENTS
Valuation Architecture in the Extension
Data Flow
Item fields → SearchQuerySSoT → Auctionet API (3.65M+ results) → Market metrics → Dashboard display
↓
AI relevance filtering (Haiku)
↓
Filtered market data → Median, range, trends
Key Components
- SearchQuerySSoT (
modules/search-query-ssot.js) — single source of truth for all search queries - SalesAnalysisManager (
modules/sales-analysis-manager.js) — fetches and processes market data - DashboardManagerV2 (
modules/dashboard-manager-v2.js) — renders market analysis UI - AuctionetAPI (
modules/auctionet-api.js) — Auctionet public API wrapper - ValuationRequestAssistant (
modules/valuation-request-assistant.js) — valuation page logic
Pricing Rules
Reserve vs Estimate
- Estimate (uppskattat värde): expected market value based on comparables
- Reserve (utropspris): minimum starting bid, typically 60–80% of estimate
- Minimum reserve: 400 SEK (auction house rule)
- Reserve should be low enough to attract bidding but protect seller interest
Valuation Approach (4-step)
- Object analysis — identify type, material, artist, period, condition
- Market research — search Auctionet historical data for comparables
- Valuation logic — median of filtered comparables, adjusted for condition/quality
- Conclusion — estimate with confidence level and reasoning
Confidence Scoring
| Level | Meaning | Typical scenario |
|---|---|---|
| High (>80%) | Strong comparable data | Known artist/maker, many recent sales |
| Medium (50–80%) | Some comparables, wider range | Similar items exist but not exact matches |
| Low (<50%) | Limited data, high uncertainty | Rare items, no recent comparables |
AI Relevance Filtering
When Auctionet API returns results with high price spread (>5x between min and max):
- Claude Haiku validates each result for relevance
- Filters out false positives (same name but different item)
- Re-calculates metrics on filtered set
- Reports data quality to user via dashboard
Market Data Metrics
Key Metrics Displayed
- Median price — middle value of comparable sales (more robust than mean)
- Price range — min to max of filtered comparables
- Mean price — average (shown alongside median)
- Result count — number of comparable sales found
- Market status — rising/stable/falling trend indicator
- Historical change % — YoY or period-over-period price movement
Search Query Strategy
Terms are generated from multiple sources:
- AI-extracted — Claude Sonnet extracts optimal search terms from title + description
- User-refined — cataloger can toggle/add terms via interactive pills
- Artist name — always quoted for exact matching in API
- Object type — extracted from title (first word typically)
Term Quoting Rules
- Artist names: always quoted for exact match →
"Bruno Mathsson" - Multi-word terms: quoted to prevent partial matching
- Single common terms: unquoted for broader matching
Valuation Request Pages
Multi-Group Valuation Flow
- Customer submits images + description via Auctionet website
- Extension scrapes page for: customer name, email, images, description
- AI clusters images into logical groups (e.g., 3 images = 1 item)
- Cataloger can drag/drop images between groups, rename groups
- Each group gets independent valuation with:
- AI estimate from images + description
- Market data override from Auctionet API comparables
- Confidence score
- Email template generated in Swedish or English
Valuation Email Conventions
- Professional but warm tone
- Per-item breakdown with estimate range
- Disclaimer: estimates are not guarantees
- Next steps: how to consign items
- Language matches customer preference (Swedish default)
AI Model Selection for Valuations
- Opus for valuation requests (highest accuracy for customer-facing content)
- Sonnet for quick cataloging valuations (balanced speed/quality)
- Haiku for relevance filtering of market data (speed critical)
Common Valuation Pitfalls
Over-valuation Risks
- AI tends to overvalue when it recognizes a famous maker/artist
- Always cross-reference with actual Auctionet hammer prices
- Condition significantly impacts value — a damaged piece by a famous maker can be worth less than a pristine unknown
Under-valuation Risks
- Low/medium confidence should NOT automatically lead to low valuations
- Rare items may have few comparables but high value
- Consider: is it rare because it's undesirable, or because it's genuinely scarce?
Market Data Interpretation
- Small sample size (<5 results): treat as indicative only
- Old data (>2 years): market may have shifted, weight recent results higher
- Outliers: single very high/low result can skew mean — median is more reliable
- Category matters: "stol" (chair) matches thousands — refine with style/period/maker