A policy change in the visitor economy moves demand, revenue, and housing at once. Living Lab tests each option on one model before it is signed and measures what it did afterwards.
One model of the visitor economy the policy acts on: every route and its seat capacity, every hotel market and short-term rental, the event calendar, the demand from each origin market with its price sensitivity, search interest, and sentiment. Demand137 runs the proposed levy, visa reform, licensing rule, or incentive against it, option by option, on one number. Signal137 measures what the policy actually did once in force, against the counterfactual.
What the policy did once implemented: demand by origin market, revenue raised, housing stock and rents, hotel occupancy and retained spend, compared with the counterfactual of no policy. Which visitor segments responded and which did not. What the neighbouring destinations gained. Every figure marked observed, derived, or inferred, so the post-implementation review survives the audit office.
What the proposed policy does before it is signed: demand by origin market and price sensitivity, revenue after the demand response, and the effect on housing, hotel performance and every other domain. No rule then helps one sector by quietly taxing another. Alternatives compared on one model. The tax burden by segment benchmarked against competitor destinations on the same method. The cost of doing nothing priced alongside the cost of acting.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether to introduce or adjust a levy, cap, or licensing rule | The modelled effect on demand and behaviour, not only projected revenue at current volume | The instrument and its level set with the displacement effect already priced in |
| Demand137 | Where to phase a reform against commitments already in motion | The reform tested against the hotel pipeline and hosting bids approved under the old rules | The change sequenced so it does not undercut its own prior commitments |
| Demand137 | What does the rule do to the neighbouring district? | Displacement to adjacent streets, unlicensed listings, and neighbouring authorities, modelled | Each neighbouring area's cost visible before the rule is set |
| Demand137 | Which of three levels of cap actually works? | Licensed supply and displacement under each, side by side | A cap's gain on one measure and its cost on another, seen together |
| Demand137 | What happens if we do nothing? | The no-change case as the reference, with the counterfactual stated | Doing nothing priced next to acting |
| Signal137 | What did the comparable reform elsewhere actually change? | Attribution for a past reform, in this or another jurisdiction | The precedent measured, not cited |
| Signal137 | Did our last reform do what it said? | The result set against the counterfactual, with displacement named | The next reform is designed on what the last one did |
| Both | How do we defend the choice once the affected parties respond? | A traceable chain from source and assumption to result, with uncertainty stated | One evidence base policy, finance, and communications can each stand behind |
| Both | Does this replace consultation or legal review? | No. It produces the counterfactual, assumption set, and range those processes ask for | The consultation response cites the counterfactual, assumptions and range |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of minister or official |
Gross levy revenue modelled against the demand drop by origin market and price sensitivity. The net fiscal position after visitors who would have come choose a competitor destination instead.
Each option modelled against demand by origin, purpose, and length of stay. The option that opens the highest-value segments without creating the risks the security team flagged.
Housing stock, rental price, hotel occupancy, and visitor numbers compared to the counterfactual. Whether the restriction achieved its housing objective and what it cost in visitor revenue.
Tax burden by visitor segment benchmarked against competitors on the same demand model. Where the destination leads on tax and where it has room to win back price-sensitive segments from lower-tax alternatives.
Employment impact modelled through the economic engine, net of displacement. The number the industry claims tested against what the demand data supports. The minister sees the evidence before the lobby meeting.
A hotel, a resort, or a mixed-use scheme fails underwriting when a single route, a single annual event, or a single source market turns out to be carrying more of the demand case than the committee realised. The dependency is the risk, and it can be measured before signature.
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.
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.
At the Italian Grand Prix, the fleet of guest vehicles arrives on German plates with German drivers. The hospitality guests do not sleep in Monza, which hosts the race. They sleep in Milan, which does not. The town that pays captures a fraction of what the event generates.
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.
