The Same City, Two Categories, Opposite Answers
Run 208 cities through the 5-star hotel lens and the boutique lens and you get 82 No Go against 123 Conditional Go. Same markets, same engine. Why a single 'hospitality score' would be worthless.
Here is a result worth sitting with. We ran the same 208 cities through two hotel lenses — full-service 5-star, and boutique — using the same engine, the same framework and the same underlying market data. The verdicts diverge almost completely:
| Verdict | Hotel 5-star | Hotel boutique |
|---|---|---|
| Strong Go | 5 | 13 |
| Conditional Go | 86 | 123 |
| Hold | 35 | 32 |
| No Go | 82 | 40 |
Only 5 of 208 cities clear Strong Go for a 5-star property. Boutique returns three times as many, and half the outright rejections.
Nothing about the cities changed. What changed was the cost base, the required room-night volume, the brand-tier competition and the seasonality tolerance — and those differences are large enough to flip the answer in roughly a third of the world's markets.
Why an averaged "hospitality score" is worse than no score
If you collapsed those two columns into one hospitality number, you would produce a figure that is wrong for both formats simultaneously. It would overstate the opportunity for anyone underwriting a 5-star cost base and understate it for anyone building boutique. The averaged number wouldn't be a compromise — it would be a fiction describing a business nobody is actually in.
This is the practical case for calibrating metrics per category rather than stretching one template across all of them. The framework stays constant — demand, competition, location, financials, verdict — because that's what makes cities comparable. The signals inside each dimension change, because that's what makes the verdict true:
| Category | What actually drives the score |
|---|---|
| Venues & flex | Day-delegate rate · five fixed delegate-capacity bands · corporate-event market size |
| Hotels & resorts | ADR / RevPAR · room-night demand by segment · brand-tier gap · seasonality |
| Restaurants & F&B | Catchment footfall · daypart demand · average check · cuisine saturation |
| Retail & flagship (scoping) | Catchment spend · trade-area density · footfall · co-tenancy mix |
A venue analysis that never mentions delegate bands isn't a venue analysis. A hotel analysis that never mentions the brand-tier gap is describing a different asset class. The vocabulary isn't decoration — it's the evidence that the model is reasoning about your economics rather than about "hospitality" in the abstract.
What this means when you already operate in one category
Two things follow, and the second is the one people miss.
Your rejected list is category-specific, not permanent. If your expansion team screened forty markets against a full-service cost base and rejected thirty of them, that rejection was about the format, not the city. A meaningful share of those thirty may support a smaller-footprint concept. Most operators never revisit them, because re-screening was historically as expensive as screening.
Your competitive set is category-specific too. The 5-star operator and the boutique operator in the same city are not really competing for the same room-night demand, which means a market that looks crowded through one lens can be genuinely under-served through the other. City-level "saturation" is nearly always the wrong unit of analysis.
What this means if you're allocating capital
For a PE fund or REIT underwriting an operator, this is the whole diligence question in miniature. A platform's expansion pipeline is only as good as the format-market fit behind it. Being able to run the sponsor's target list through the lens of their actual format — rather than a generic hospitality view — is the difference between validating a growth story and taking it on trust.
It also travels across categories, which matters for anyone holding a mixed portfolio. The same framework produces a comparable demand-durability read whether the asset is a venue business, a hotel platform or an F&B group, so the IC is comparing like with like rather than three consultants' incompatible house styles.
The honest caveat
Category calibration isn't magic, and it doesn't rescue a market whose fundamentals fail. Where a city's demand is genuinely thin, every lens says so — the correlation between the columns above is real, just far from perfect. The claim here is narrower and more useful: for roughly a third of markets, the format decides the verdict, and any process that scores cities without specifying the format has thrown that information away before the analysis starts.
The takeaway
Before your next screening round, ask which format the scores were built against. If the answer is "hospitality" or "F&B" rather than a specific cost base and revenue model, the shortlist is describing a business nobody runs.
Book a demo and we'll run your markets through the lens of the format you're actually building.
See this run on the markets you’re actually weighing.
A short live session on your shortlist — we run the engine on the call and you keep the output.
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