Jeonse Registry Risk Self-Check

4.05

Derivation Chain

Step 1 Self-checking risks in secondhand Real Estate and Jeonse contracts

→

Step 2 Automating property registry interpretation

Problem

Even after reviewing the property registry before signing a Jeonse (lease deposit) contract, tenants don't understand what '120% mortgage ratio' means, what the total senior lien amount is relative to the property value, or what additional checks are needed when the landlord is a corporation. Every year, people in their 50s lose their entire Jeonse deposit after trusting their real estate agent's reassurance that 'it's fine.' There is no self-service tool for interpreting property registries.

Solution

Upload a property registry PDF and the system automatically extracts key items from Section A (ownership) and Section B (mortgages, Jeonse rights). It calculates the senior lien total / estimated market value ratio (negative equity risk), identifies landlord type (individual/corporation), and flags provisional seizures or injunctions using a traffic light system (green/yellow/red). Each risk item includes an explanation of why it's dangerous and recommended next steps.

Target: Ages 40–60 preparing to sign a Jeonse contract, especially couples in their 50s relocating after children's independence, anxious about Jeonse fraud news but unfamiliar with registry interpretation

Revenue Model: 1 registry analysis free. 3+ analyses per month: $3.70/month (4,900 KRW/month). Affiliate commission for Jeonse deposit insurance enrollment guidance.

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

5.0/5

M Market

5.0/5

R Realizability

4.0/5

V Validation

4.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (75%)

Tech Complexity

34.7/40

Data Availability

20.6/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (62/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

18.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Frontend [low] Backend [medium] AI/ML [low]

Dashboard