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 tool. Demand137 is Living Lab’s forward demand and scenario model. It answers the narrower, harder question a single forecast total hides: not whether demand will grow, but whether this route, this site, or this programme is the right place to spend against that growth. Reference data is published by UN Tourism.
Demand137 tests what happens next. When the question is retrospective, what an event, policy change, or shock delivered, Signal137 is the product. Most engagements use both across a cycle, on a shared evidence base, so the ex-ante case and the ex-post result are built on comparable assumptions.
Uses machine learning to forecast and optimise your visitor economy against your objectives: jobs, tax, sector growth, risk, infrastructure, routes, the event portfolio, or regional growth. Returns the options and reacts as you make each choice. Base, downside, and supported cases with the counterfactual stated and an uncertainty range on every figure.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Where is demand forming, where will it land, and what could change it? | Demand vectorised by origin, route, timing, stay, price, and purpose, with a confidence range | Capacity, capital, and campaign spend sequenced against where demand is forming |
| Demand137 | Which route strengthens the network and the place? | Route viability, frequency, and incentive case tested for the specific city pair | The route sized to the demand that is there, not the comparator's |
| Demand137 | How to sequence and mix events into a calendar that holds together | Net incremental demand each event adds once the rest of the calendar is counted | The mix that maximises calendar-wide value, not the sum of bids |
| Demand137 | Does the market need more rooms, or a better pattern of demand? | The peak-night gap beside the average, against total effective supply | Keys approved in the order demand will fill them |
| Demand137 | Which site can the demand reach and the operator sustain? | Catchment, connectivity, and modelled utilisation for each shortlisted site | The site chosen on reachable demand, not land cost |
| Demand137 | Is the destination promise an investable demand case? | The case broken into the parts that could fail independently, each tested against a downside | The prospectus survives due diligence |
| Demand137 | Where will a capacity constraint bind before the market prices it? | A pressure calendar by date and zone, with the intervention window flagged | The public body acts before the market does |
| Demand137 | What happens if we do nothing? | The base case as the reference, with the counterfactual stated | The cost of waiting priced alongside the cost of acting |
| Demand137 | Can the scenarios show an intervention is not worth it? | Yes, and they should | A tool that only justifies the intervention on the table is not one a committee should trust |
| Demand137 | 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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