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
Tourism demand forecasting. Living Lab connects search, booking, pricing, aviation schedule, accommodation, and event signals to a shared geography and calendar, so a new route, a currency shift, or a rival city announcing an event is traced through to the others rather than sitting in a separate report. Reference data is published by UN Tourism.
Checks the forecast against what happened once the window has passed, and reports the discrepancy rather than backfilling to look accurate. A forecasting method never allowed to be wrong in public is not being tested. Signal137 accounts for the demand that landed, vector by vector, so the next forecast starts from evidence.
Forecasts what is forming: which streams are growing, which are shrinking, and which are about to be disrupted by a nameable factor. Scenario-tested and stress-tested against plausible shocks, with a confidence range on every vector. Demand137 sequences capacity, capital, and campaign spend against where demand is actually forming.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Where to add capacity, a route, or room supply first | Demand broken down by origin, timing, and purpose, not a single total | Investment sequenced against where demand is forming |
| Demand137 | When to move on price, promotion, or scheduling | A forecast that updates as source-market and booking-window signals shift | The adjustment made inside the window that still matters |
| Demand137 | How to defend a demand assumption inside a business case | A vectorised forecast with a stated confidence range, not a single-line projection | Demand assumptions a finance committee can stress-test |
| Demand137 | What would change this forecast? | The specific factor behind each vector named: a capacity cut, a rival host, a competing venue | A forecast that can say what would move it is a working model, not a guess with a date on it |
| Demand137 | How far ahead is this reliable? | The horizon stated explicitly for the market and signals available | The decision is scoped to the horizon the evidence supports |
| Demand137 | What happens when a shock hits? | The affected vectors flagged and re-run, not the whole forecast quietly abandoned | The forecast survives the shock because it names which part was hit |
| Signal137 | Was last year's forecast right, and where was it wrong? | The forecast checked against what landed, vector by vector | The discrepancy reported, not backfilled |
| Signal137 | Which of the streams we forecast actually arrived? | Origin, stay, and purpose attributed against the forecast | The next forecast starts from what the last one got right and wrong |
| Both | What data do you need from us? | Much of it built from public, licensed, and third-party signals; yours sharpens specific vectors | Nothing moves until it is scoped |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of official or consultant |
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.
Oman has announced plans for a high-altitude destination at Jabal al Akhdar, combining sports training, wellness, and tourism at 2,400 metres. It is the kind of project a Gulf government can commit to at once, and the kind that lives or dies on a demand case nobody has yet published.
International arrivals to the United States fell 4.2% in 2025 while worldwide travel grew about 4%. Living Lab’s displacement model puts the forgone spending at roughly US$50bn, and about US$10.6bn of it within reach of five economies. Each is limited by a different constraint.
Most destinations run near capacity for part of the year and well below it for the rest. A visitor added in the peak displaces one already there. A visitor added in the shoulder fills an empty room and is served by staff who would otherwise be idle. Almost no destination runs the calculation before fixing a date.
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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