valuation

name	valuation
description	Reference for auction item valuation logic, market data analysis, pricing strategies, and confidence scoring. Use when working on valuation features, market analysis dashboard, sales analysis, or search query logic.
argument-hint	["topic or item type"]

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

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:

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