Data Methodology & Evidence Standards
Check the evidence label before using a number. Calculator inputs and illustrative scenarios are different from measured property or market data. Source-checking cards describe research steps; they do not report collected destination observations.
1. Five Tiers of Evidence Classification
Every quantitative data point rendered across our platform must belong to one and only one classification. We do not blend hypothetical estimates with verified observations.
[Source to check] identifies a research direction with no collected observation. It is outside the five measurement tiers below. A publisher name or link alone does not substantiate a local trend, supply count or dated snapshot.
Exact Approved Source
Drawn from an authoritative government census, verified official register, or validated first-party cohort with demonstrated provenance, known geography, explicit time frame, and permitted commercial reuse rights.
Direct Observation Sample
Empirical, dated observations recorded directly from public storefronts or anonymized client ledgers (e.g. manual storefront audits or 90-day retainer cohorts). Never extrapolated as a universal market census.
Correlated Directional Signal
A measurable indicator that correlates with hospitality demand without measuring the KPI directly (e.g., Google Trends search interest index, state-level tourism arrivals, OpenStreetMap mapped accommodation density).
Deterministic Mathematical Derivation
Produced deterministically from user-supplied operational variables or explicitly defined constants using verified formulas (e.g., RevPAR = ADR × Occupancy Rate).
Hypothetical Scenario Benchmark
Scenario defaults provided solely to initialize interactive diagnostic tools. Never displayed as measured market data; clearly separated so operators can enter their own operating figures.
2. Strict Proxy Isolation Rules
To prevent synthetic or misleading assertions, we prohibit deriving one KPI from an unrelated proxy:
- ✕ No Converting State Tourism Visits into City Occupancy: State-level aggregate tourist counts cannot be mathematically converted into citywide or property-level hotel occupancy rates.
- ✕ No Converting Google Trends into Booking Counts: Relative search interest reflects consumer discovery queries and seasonal intent, not confirmed booking volume or revenue.
- ✕ No Converting OpenStreetMap Counts into Commercial Census: OpenStreetMap provides an observable mapped supply density proxy; it must never be called "Total hotels in the city."
- ✕ No Secret OTA Algorithm Claims: MakeMyTrip, Booking.com, and Airbnb do not publish complete organic ranking algorithms. We refuse to publish fabricated rank thresholds or guaranteed page-one criteria.
3. Pure Mathematical Calculation Engine
All financial projections and diagnostic models across our calculators and spoke pages use standardized formulas implemented in our core engine:
Where Occupancy Rate is expressed as a decimal between 0.0 and 1.0. Measures room revenue yield across all sellable inventory.
Models gross baseline room revenue for an operating year.
Quantifies net channel leakage to third-party aggregators before direct booking transition.
Evaluates the true cash yield delivered to the hotel ledger after accounting for all direct and intermediary acquisition costs.
4. Approved Source Hierarchy & Licensing
External feeds and indicators must originate from registered sources evaluated for commercial licensing, attribution compliance, and stability:
| Source Name | Publisher | Licence | Status |
|---|---|---|---|
| Open Government Data Platform — Tourism Statistics & Accommodation | Government of India (data.gov.in) | Government Open Data License - India (GODL) | APPROVED_OPEN |
| India Tourism Statistics Reports | Ministry of Tourism, Government of India | Public Government Publication (GODL-India aligned) | APPROVED_OPEN |
| Mapped Accommodation and Tourism Supply Signals | OpenStreetMap Contributors | Open Database License (ODbL) 1.0 | APPROVED_OPEN |
| Civic Infrastructure, Demand Anchors & Geography Entities | Wikidata / Wikimedia Foundation | Creative Commons CC0 1.0 Universal | APPROVED_OPEN |
| Destination Accommodation Relative Search Interest Index | Google Trends | Google Terms of Service (Permitted public citations & exports) | PERMITTED_WITH_TERMS |
| Hotel Establishment Performance Dataset | Dubai Digital Authority / Dubai Pulse | Licence Review Required (historical metadata: notspecified) | LICENCE_REVIEW_REQUIRED |
| Short-term Rental Listing Scrapings & Summaries | Inside Airbnb | CC BY 4.0 with restrictive website data reuse policies | CONDITIONAL |
| Anonymized Client Engagement Cohorts & Diagnostic Observations | Scale My Hotel Intelligence | Proprietary Scale My Hotel Commercial Intelligence | APPROVED_FIRST_PARTY |
5. Dual-Mode Benchmarking Architecture
Unlike platforms that synthesize fake competitor averages, Scale My Hotel maintains a strict bifurcated model for destination benchmarking:
Verified Performance Benchmark
Activated only when authoritative public datasets publish verified citywide hotel performance metrics (e.g. verified open-data municipal registers with clear commercial reuse rights). Displays exact ADR, occupancy, and RevPAR with full provenance citation.
Market Demand Signals + Revenue Benchmarking
Applied to destinations where reusable municipal performance registries do not exist. We display verified demand signals (search interest trends, official tourism arrivals, mapped supply density) and provide an interactive property calculator so operators can benchmark their own real numbers.
6. Core Metric Definitions & Industry Benchmarks
To ensure semantic clarity and unbiased citation for search engines and AI assistants, Scale My Hotel defines all operational KPIs in self-contained passages aligned with international lodging accounting standards (USALI) and global research benchmarks (STR, HVS, Kalibri Labs):
What is RevPAR (Revenue Per Available Room) and how is it calculated?
RevPAR is the fundamental hospitality performance metric calculated by multiplying Average Daily Rate (ADR) by Occupancy Rate, or dividing total room revenue by total available rooms over a specific period. Unlike ADR, RevPAR accounts for unsold inventory, measuring overall room revenue yield across a property's total capacity.
What is the difference between ADR and RevPAR?
ADR measures the average rental revenue earned per paid occupied room, excluding unsold rooms and ancillary services. RevPAR factors in both pricing power (ADR) and volume (occupancy), providing a complete picture of room revenue performance across all sellable capacity.
What is GOPPAR (Gross Operating Profit Per Available Room)?
GOPPAR reflects true operational profitability by dividing gross operating profit (gross revenue minus departmental and operational expenses) by total available rooms. Defined under the Uniform System of Accounts for the Lodging Industry (USALI), it measures bottom-line management efficiency across all revenue streams.
What is the OTA Dependency Ratio and why does it matter?
The OTA Dependency Ratio represents the percentage of total booked room nights or gross revenue sourced through third-party Online Travel Agencies relative to direct channels. Ratios exceeding 70% expose independent properties to severe commission drag (15%–25% per booking) and volatile platform algorithm shifts.
What is Rate Parity in hotel distribution?
Rate parity is the pricing consistency requirement where a hotel maintains identical room rates and conditions across all public distribution channels, including OTAs and its own website. Modern direct booking strategies counter strict parity through value-add packages, member rates, and mobile perks.
Corrections & Feedback Policy
We welcome review from hoteliers, researchers, and government data custodians. If you identify an outdated figure, unverified signal, or mapping discrepancy, please notify our research unit at hi@scalemyhotel.com. Verified corrections are reviewed and deployed within 48 hours.