B

Apartment Energy Performance Comparison Report

3.05

Derivation Chain

Step 1 Expansion of environmental and weather data utilization
→
Step 2 Growing interest in apartment energy efficiency
→
Step 3 Integration of apartment energy performance assessment infrastructure with real estate transactions

Problem

Prospective apartment buyers find it difficult to assess maintenance fee levels in advance. The Ministry of Land, Infrastructure and Transport's public housing management information system (K-apt) discloses energy usage data, but it is impossible to compare across complexes after adjusting for differences in area, number of households, and heating method. A monthly difference of 100,000 KRW (approx. $75) in maintenance fees amounts to 24,000,000 KRW (approx. $18,000) over 20 years, yet there is no way to know this before purchase.

Solution

Collects K-apt data to calculate energy efficiency grades (A-F) adjusted for area, number of households, heating method, and year of completion, and provides comparison reports with similar complexes in the same region. Differentiation: integrates with real estate listings to support purchase decisions, and predicts seasonal maintenance fees.

Target: Prospective apartment buyers (30s-50s), real estate agents (as a listing differentiation tool)
Revenue Model: Report per case 9,900 KRW (approx. $7.43), real estate agent monthly subscription 49,000 KRW per month (approx. $36.75) for unlimited reports.
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
2.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
2.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (67%)

Tech Complexity
29.3/40
Data Availability
17.9/25
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 (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
Solo Buildability
8.0/10
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

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