Every destination has a ceiling: the rooms, the routes, the transport, and the tolerance of the people who live there. Living Lab shows where the ceiling is and when demand will meet it, and what to do about it, on the same numbers.
One model of the destination’s capacity and the demand pressing on it: every route and its seat capacity, every hotel market and its rate, the event calendar, the last mile, and the search interest and sentiment in each origin market. Signal137 shows what the last peak revealed about where capacity bound. Demand137 shows when and where the next peak will bind, with the intervention window flagged. The public body acts before the market and the residents do.
What the last peak revealed: where rate compression appeared, which districts and weeks bound first, where visitors were displaced to the neighbouring city, and what residents said about it. Which events and markets drove the pressure. Attributed, so the constraint is named. Season over season, on one method.
When and where the next peak will bind, by district and by week. The access ceiling stated: seats, rooms, transport, and the last mile. Whether held-back capacity should be released now or kept for the peak. What a levy, a licence cap, or a date move would do to the pressure calendar. Every number marked observed, derived, or inferred.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether to release additional capacity, and when | The gap between forecast demand and available capacity, by date and zone, not an annual average | The release timed to the specific pressure window, not the planning calendar |
| Demand137 | Whether to intervene on price or access | Modelled price sensitivity and displacement under the forecast scenario, with a stated range | Intervention aimed at the dates and locations where it changes the outcome |
| Demand137 | Can current capacity absorb a peak without lasting local damage? | Demand tested against an explicit operational and social threshold, not a headline visitor total | Mitigation triggers set before the peak |
| Demand137 | Does this tell us the exact price to set? | A price and displacement range under each scenario, not a single tariff | The price remains the operator's, city's, or venue's decision, made on evidence |
| Demand137 | How far ahead does this need to run? | Long enough to act before the market reacts: weeks for a hotel or event, longer for a transport or access change | The horizon set to the decision, not a default |
| Demand137 | Can it factor in a specific event or seasonal calendar? | Event dates, school holidays, and known peaks as standard inputs beside the demand vector | The calendar is in the model, not beside it |
| Signal137 | What did the last peak do to rate, resale, and rents? | Rate compression, resale activity, and rent movement attributed to the peak | The next peak is planned on what the last one did |
| Signal137 | Did the intervention we made change the outcome? | The result set against the counterfactual of no intervention | The next intervention is sized on evidence |
| Both | Does this account for residents, not only commercial pressure? | The same calendar built against a social or congestion threshold, not only a commercial ceiling | The community's question answered on the same evidence as the operator's |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of official or consultant |
Living Lab models demand by date and zone against the destination's capacity profile. The destination tests event scheduling, pricing interventions, and supply release before committing to them.
Each constraint is mapped separately. One zone may run out of hotel rooms while another has availability. That needs a different intervention from a citywide transport limit.
Attribution compares what happened with and without the intervention, on the same method used to plan it. The committee sees whether the action changed the outcome.
Demand137 sets thresholds per zone and date. When a trigger is crossed, the pre-agreed response activates.
Resident displacement is modelled alongside visitor spend. The destination sees where growth and liveability support each other, and where one comes at the cost of the other.
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.
