A hotel market fills on what the city has coming. Living Lab shows what drove last year’s rate and occupancy, and what the calendar, the routes, and the origin markets will bring next, on the same numbers.
One model of everything that fills the rooms: every event on the city calendar, every route in and its seat capacity by week, the paydays and holidays in each origin market, and the search interest and sentiment there. Signal137 shows what moved rate and occupancy last year and what caused it. Demand137 shows room-night demand months ahead, by origin and by event, and what the market can absorb. The revenue manager, the owner, and the market authority read the same forecast.
What moved rate and occupancy, and why: which events, routes, and origin markets drove each compression night, attributed rather than assumed. Where the rate premium held and where it was given away. What new supply did to the market after it opened. Visitor spend beyond the room, so the hotel’s contribution to the city is stated in GVA, tax, and jobs. Season over season, on one method.
Room-night demand by origin market, event, and week, months ahead. The access ceiling for the market: the seats on the routes in and the last mile to the door. Compression nights flagged before they arrive, so rate windows are set on evidence. New supply and licensing tested against the calendar before approval. Every number marked observed, derived, or inferred, with the interval stated.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether and when to add room supply, new build or conversion | Demand trajectory by source market and event calendar, not trailing occupancy | Supply timed against a dated forecast, not a seasonal average |
| Demand137 | How to price and staff around event-driven compression nights | Which nights carry event-driven compression demand, and which are seasonal peaks | Rate and labour plans set against a specific, dated demand pattern |
| Demand137 | How to respond to short-term rental competition in a submarket | Where share is moving, by segment and price point | Pricing and product response aimed at the submarket under real pressure |
| Demand137 | How to defend a development or investment case to lenders | Room-night demand under base, downside, and upside scenarios | A scenario-tested case an investment committee can stand behind |
| Demand137 | What does a new route, or a lost one, do to our demand? | Route demand linked to hotel nights by origin and stay length | The aviation change priced into the rate plan before it lands |
| Signal137 | Did that event create the room nights it appeared to? | Gross room nights through counterfactual and displacement to attributable revenue, per hotel | Attendance and attributable room nights treated as two different numbers |
| Signal137 | Which hotels saw the rate premium during the tournament? | Per-hotel measurement with fixed effects, not a city average | The premium located hotel by hotel, including where there was none |
| Signal137 | Did the rate strategy work, or did the market move anyway? | The strategy's result set against the counterfactual period | Next season's pricing built on what the strategy actually did |
| Signal137 | What did the calendar deliver to the portfolio, season by season? | Monthly, seasonal, and annual roll-ups across every property, on one method | One account of the year for the board and the asset manager |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of revenue director or owner |
Delegate origin, stay pattern, and price sensitivity modelled against current availability. The operator sets the rate on the demand shape, not on last year’s event.
Each event ranked by room-night contribution, average rate, and length of stay. That identifies the events worth holding inventory for and separates out the ones that fill rooms at compressed rates.
Property performance set against destination-level demand signals: event calendar, route capacity, and search trends. The owner sees whether the gap is management or market.
New supply modelled against the demand forecast. Where the new rooms compete directly with your positioning and where they serve a segment you don’t. Living Lab states the impact on rate, occupancy and yield before the rooms open.
Demand by origin mapped against the route network. A new direct route from a high-yield origin market changes the demand profile, and a cancellation changes it the other way. Both show before the effect reaches the booking curve.
A hotel pipeline decision is usually made on an occupancy trend, extrapolated forward. That answers a different question from the one a planner, a developer, or an investor faces: is the market short of rooms overall, or short of rooms on the 18 nights a year when demand peaks?
A new route photographs well on launch day. Whether it is still flying in year three, without deep discounting, and still worth the airport incentive that started it, is a quieter question, and it is the one that comes up at renewal.
International visitors to the United States fell 4.2% in 2025, the first annual decline since the pandemic, while worldwide travel grew. The spending did not disappear. It moved. Five economies are absorbing most of it, and the shift now looks structural.
During the 2026 World Cup, host-city hotel rates rose 13% on average. Miami rose 30.3%. New York fell 4.5%. The average was true and useless: it would have told all 16 cities the same thing, and been wrong for most of them.
We turn messy event and tourism data into decisions that help communities grow. Bring curiosity and rigour. We will hand you questions nobody has answered yet.
