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)

1. Object analysis — identify type, material, artist, period, condition
2. Market research — search Auctionet historical data for comparables
3. Valuation logic — median of filtered comparables, adjusted for condition/quality
4. Conclusion — estimate with confidence level and reasoning

Confidence Scoring

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:

1. AI-extracted — Claude Sonnet extracts optimal search terms from title + description
2. User-refined — cataloger can toggle/add terms via interactive pills
3. Artist name — always quoted for exact matching in API
4. 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

1. Customer submits images + description via Auctionet website
2. Extension scrapes page for: customer name, email, images, description
3. AI clusters images into logical groups (e.g., 3 images = 1 item)
4. Cataloger can drag/drop images between groups, rename groups
5. Each group gets independent valuation with:
   • AI estimate from images + description
   • Market data override from Auctionet API comparables
   • Confidence score
6. 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