B

Art Auction Hammer Price Tracker

3.30

Derivation Chain

Step 1 Growth in art auction market activity
→
Step 2 Price analysis tools for auction participants
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Step 3 Hammer price history tracking and fair value estimation service

Problem

Small-to-mid-size galleries and art dealers with annual revenue of $750K-$7.5M spend 2-4 hours per piece manually collecting past auction data to determine fair pricing for consignment or purchase. Hammer price results from major auction houses (Seoul Auction, K Auction, etc.) are scattered across platforms, making it impossible to grasp price trends by artist, genre, or at a glance. This leads to opportunity costs of thousands of dollars per artwork from incorrectly set reserve prices causing failed auctions or below-market sales.

Solution

Automatically aggregates publicly available hammer price results from major domestic auction houses (Seoul Auction, K Auction, MyArt Auction) and provides a dashboard with price trend analysis by artist, genre, and size. Includes AI-based fair reserve price recommendations and comparable artwork analysis to support consignment and purchase decision-making.

Target: Small-to-mid-size gallery operators with $750K-$7.5M annual revenue, art dealers, art investment collectors (ages 30-50)
Revenue Model: SaaS Monthly Subscription at $37/account, Premium Plan (with AI price recommendations) at $74/month, 20% discount for Annual Subscription
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (71%)

Tech Complexity
28.0/40
Data Availability
23.1/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (53/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
7.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] Data Pipeline [medium] Frontend [low] AI/ML [medium]
Dashboard