Living Lab’s first economic model was for the Singapore Grand Prix in 2012. In 2025 we built a government-grade intelligence platform for the visitor economy
Airlines are excellent at predicting route profitability, and Living Lab does not try to. What sits on top of that answer is the destination’s question: what the route delivers to the economy it serves. A route brings visitors, spend, hotel nights, tax, jobs, and inward investment. Living Lab measures those on the same geo-temporal spine as every event, hotel, and destination it records, so an airport, a government, or an airline can see the economic fit of a route as clearly as its load factor.
For airports and governments: which routes in the network deliver the most to the destination, and which are running behind the incentive that funded them. Visitor spend, hotel nights, GVA, tax, jobs, and FDI fit, traced per route and per season, so the board sees like-for-like what was promised against what arrived.
For the next network review: the economic-fit ranking across every candidate route, so sequencing follows destination priorities as well as airline yield. And for any route that needs public co-funding, the case for state support built on the same evidence: what the route brings, what it costs the public purse, and where the two cross.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether to launch, grow, hold, or withdraw a route | Demand vectorised by origin, timing, purpose, and price sensitivity, not a single total | You back the routes the underlying demand can support |
| Demand137 | How to price or target an airport incentive | The destination value the route creates: visitor spend, hotel demand, investment signal | Incentive budget goes to routes that pay back to the destination, not only to the airline |
| Demand137 | How exposed is the network to one hub, one source market, or one shock? | A concentration and resilience view across the route network | You diversify before a disruption forces the point |
| Demand137 | How do sustainable aviation fuel cost and transition risk change the case? | Forward transition scenarios layered onto route viability | Transition risk is priced now, not at the next renewal |
| Demand137 | What happens if we do nothing? | Base, downside, and supported scenarios with a stated counterfactual | The cost of waiting is priced, so acting and not acting compare on the same terms |
| Signal137 | What did the route we supported actually deliver? | Attributed destination value: room nights, GVA, tax, and jobs, per route | You report to the board against the case that funded the route |
| Signal137 | Which routes create destination value, and which only fill seats? | Every route ranked on destination return, on one method | You renew the routes that pay back and let the others lapse |
| Signal137 | Can I have the answer before the slot decision, not after it? | Reports on demand: any route, season, or market, in minutes | Evidence arrives while the decision is still open |
| Both | Can this work when the airline's booking data is commercially sensitive? | Route demand vectorised from destination-side and market signals | The airline's own data is not a precondition |
| 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 traces each route through the destination economy: visitor spend, hotel nights, GVA, tax, jobs, and FDI fit. The busiest route is not always the most valuable one, and the spine shows the difference.
Profitability is the airline’s call. Living Lab adds the economic-fit layer: which origin markets bring the highest-value visitors, which connectivity gaps are holding back investment, and what sequence builds destination value fastest. The four routes come back ranked on what each one does for the economy.
Living Lab runs the route through the destination’s economic engine: what it returns in GVA, tax, jobs, and FDI against what public co-funding would cost. The case for support comes out stated in terms a treasury and an airline’s network team can both read, with the counterfactual shown.
Attribution runs on the same spine as the original case. The airport board gets a like-for-like comparison between what was committed and what arrived, per route, per season, with new demand separated from demand that moved from a competing hub.
Transition cost is carried in every case. The routes most exposed are identified before the cost lands, so the network review can sequence around them.
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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