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 → 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 (auctionet-api.js calculateConfidence())

Cumulative scoring algorithm, base 0.5, floor 0.1, capped at 0.95:

• Total matches: +0.1 (20+), +0.2 (50+), +0.3 (100+), +0.4 (500+) — market coverage
• Analyzed sales: +0.05 (3+), +0.1 (5+), +0.15 (10+), +0.2 (20+) — sample quality
• Recency: +0.1 if >50% within 2 years, +0.15 if >70%
• Artist match: +0.1 if >50% match, +0.15 if >80%
• Object type match: +0.1 if >80% match object type

See calculateConfidence() in modules/auctionet-api.js for current thresholds.

Data Filtering Pipeline

After fetching market data, results pass through two filters (see auctionet-api skill for details):

1. AI relevance filtering — Haiku validates each result when price spread >5x or sample >15 items
2. IQR outlier removal — standard 1.5x IQR fences, minimum 4 data points

Valuation Suggestions (sales-analysis-manager.js analyzeValuationSuggestions())

• Compares cataloger's estimate/upper estimate/reserve against market data
• 30% tolerance band — warns if cataloger's values fall outside
• 3x/0.3x extreme thresholds for clear over/under-valuation
• Positive feedback when valuation aligns with market

Model Fallback

• Opus falls back to Sonnet when overloaded (429/overloaded errors)
• Applies to valuation requests and clustering

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 (SearchQuerySSoT)

The SearchQuerySSoT singleton enforces strict query control on the edit page:

• User-selected queries: ONLY that query is used — NO fallbacks, even if zero results
• AI-generated queries: can use fallbacks only if <3 results
• Terms generated from: AI extraction, user refinement, artist name (always quoted), object type

Two-stage generation: AI Rules first (fast, rule-based) → Claude API fallback if rules insufficient.

Term Quoting

All search terms are force-quoted (Auctionet treats unquoted terms as optional). formatArtistForSearch() wraps multi-word names in quotes. See auctionet-api skill for query syntax details.

Live Auction Analysis

analyzeLiveAuctions() (sales-analysis-manager.js)

Fetches currently active auctions to show real-time market activity:

• Uses SearchQuerySSoT exclusively — zero fallbacks (strict SSoT enforcement)
• Returns: currentEstimates, currentBids, marketActivity, marketSentiment
• Market sentiment based on reserve met %: strong (>70%), moderate (40–70%), weak (<20%)

searchLiveAuctions() (auctionet-api.js)

• Searches WITHOUT is=ended for active/published items
• Filters: state === 'published', ends_at > now, !hammered
• 5-minute cache (shorter than 30-min historical cache)
• Category relevance filtering for broad searches

analyzeLiveMarketData() (sales-analysis-manager.js)

• Calculates estimate ranges, bid ranges, and market sentiment
• Returns top 5 live items sorted by bidding activity

Exceptional Sales Detection

identifyExceptionalSales() finds high-value outliers above market median:

• Threshold: 3x median OR 2x Q3
• Highlights premium sales for category awareness
• Tracks price vs. median, estimate, and cataloger's valuation ratios

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