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During major concerts and events (BTS, World Expo, etc.), nearby accommodations charge 3-10x normal rates, causing consumer harm. Local government tourism department staff (2-3 people) spend 40+ hours per week manually monitoring hundreds of listings, struggling to compile enforcement evidence.
A SaaS that auto-crawls public pricing from major accommodation OTAs (Yanolja, Yeogi Eottae, etc.) and visualizes price change rates versus baseline on a real-time dashboard. Automatically flags abnormal surges (300%+) and generates one-click enforcement evidence PDFs (screenshots + price history). Core features: (1) Real-time price deviation dashboard vs. baseline, (2) Automated anomaly flagging for surge pricing, (3) One-click enforcement evidence PDF generation.
| 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. |
| 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. |
| 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. |