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
Event impact attribution. Living Lab connects ticketing, accommodation, transport, spend, and sentiment data to the event’s actual footprint and a defined control period, and separates what changed from what merely coincided. Reference data is published by the OECD.
Runs the attribution for a bounded past event: what would have happened anyway, what moved from elsewhere in the city, what left the local economy the moment it was spent. States the counterfactual before the result is produced, not after. Every event in the calendar on one method, so a festival and a tournament can be compared on a shared basis.
The forward version of the same evidence: what the next edition, or the next bid, would deliver under base, downside, and legacy-backed cases, with displacement stated. Demand137 decides what to host next; Signal137 accounts for what was hosted.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Signal137 | What did the event actually change, net of what would have happened anyway? | Gross activity through counterfactual, displacement, and leakage to attributable retained value | The legacy figure is built to survive the first hard question |
| Signal137 | How much of the spend left the local economy? | Leakage through non-local suppliers and contractors, traced and stated | The share the local-benefit test cares about is known, not assumed |
| Signal137 | Did the event grow the economy or rearrange it? | Displacement against known local spending patterns | Growth and reshuffling are two numbers, not one |
| Signal137 | How do we report the legacy to funders, council, or the public? | A result stated as attributable, with the counterfactual made explicit | A claim an auditor or journalist cannot unpick in one question |
| Signal137 | Can this be done for a first edition with no baseline? | Comparable events, markets, and destinations, with a wider uncertainty range stated | The result says so rather than hiding behind false precision |
| Signal137 | How does this event compare with others in the calendar? | Attribution on one consistent method across every event | Events compared on a shared basis, feeding the portfolio decision |
| Signal137 | What if the result shows less than was claimed? | The result reported as found | A bounded, defensible result is worth more to a funding decision than an inflated one |
| Demand137 | Whether to host again, or renew the agreement | The next edition modelled on the last one's attributed result, with the counterfactual stated | The renewal negotiated from evidence, not precedent |
| Demand137 | What would the next bid deliver? | Base, downside, and legacy-backed cases, net of displacement | The funding position set before the announcement |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of official or consultant |
None of the three states what it includes, what period it covers, or whether the money stayed. A footprint figure is only useful when it carries its own definitions, and it is only defensible when it is kept separate from impact.
A major event is a rallying point for civic pride, and just as easily a rallying point for protest. The difference is usually whether residents can see what the event does for their street, in terms they recognise, before the road closures start.
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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