B

Apartment Maintenance Fee Anomaly Detector

3.30

Derivation Chain

Step 1 Migration of public data to private cloud
→
Step 2 Fixed household cost management for people in their 50s
→
Step 3 Overpayment due to lack of understanding of apartment maintenance fee line items
→
Step 4 Absence of same-complex comparison and historical trend analysis for maintenance fee items

Problem

Apartment residents in their 50s receive monthly maintenance fee statements but cannot assess whether the 15+ line items — common area electricity, repair & maintenance, long-term repair reserve fund, etc. — are at appropriate levels. They have no way to compare whether their fees are higher than similar-sized units in the same complex or which items spiked compared to the same month last year, making it impossible to detect improper charges by building management. This can result in annual overpayment of 100,000-300,000 KRW (~$75-$225).

Solution

By uploading a photo of the maintenance fee statement, OCR extracts amounts by line item and automatically performs: (1) comparison against same-complex, same-size unit averages, (2) monthly trend graphs for the user's own fees, (3) alerts for anomalous spikes in specific items, and (4) monitoring of long-term repair reserve fund accumulation trends.

Target: Apartment residents aged 50-63 (100-165 m² units), paying 200,000+ KRW (~$150+) monthly in maintenance fees, interested in building resident council activities
Revenue Model: Once-monthly analysis Free. Auto-monthly analysis + anomaly alerts + annual Report at Monthly Subscription of 1,900 KRW (~$1.40). Apartment management service partnerships.
Ecosystem Role: Consumer
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 (70%)

Tech Complexity
29.3/40
Data Availability
20.6/25
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
7.5/15
Pick-Axe Fit
10.5/15
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

Frontend [low] Backend [medium] Data Pipeline [medium]
Dashboard