Living Lab’s first economic model was for the Singapore Grand Prix in 2012. In 2025 we became a software platform: government-grade intelligence for the visitor economy
Hotel demand forecasting. Living Lab connects the signals a single property rarely sees together: event calendars, route and aviation capacity, comparable pricing, short-term rental listings, and visitor movement into the destination, so a spike in one is traced to its effect on room-night demand. Reference data is published by UN Tourism.
Attributes after the fact whether a rate strategy or a specific event created the room nights it appeared to: gross room nights through counterfactual and displacement to attributable revenue. Per hotel, not a city average, because the average told every hotel in a host city the same thing and was wrong for most of them.
Forecasts room-night demand by origin, timing, stay length, and price sensitivity, and tests supply, pricing, and investment decisions before they are committed. Which nights carry genuine compression and which are ordinary peaks mistaken for it. Where short-term rentals are absorbing overflow. Base, downside, and upside cases a lender or investment committee can interrogate. Demand137 is the primary product for this audience.
| 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 genuine compression demand, and which are seasonal peaks mistaken for it | Rate and labour plans set against a specific, dated demand pattern |
| Demand137 | How to respond to short-term rental competition in a submarket | Which segments and price points are losing share, and where | 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 found where it was, and its absence found where it was not |
| 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 |
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.
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