A hotel market fills on what the city has coming. Living Lab shows what the last approval cycle did to rate and occupancy, and how much supply the calendar and the routes can absorb next, on the same numbers.
One model of the room market and everything that fills it. It holds every hotel and short-term rental with its supply and rate, every event on the calendar, and every route in and its seat capacity. It adds the paydays, holidays and search interest in each origin market. Signal137 shows what new supply and new events did to the market. Demand137 shows what the market can absorb next, and in what order the keys should land. The planning authority and the operators read the same forecast.
What the last approval cycle did to the market: whether new supply was absorbed or competed for the same nights, and where rates compressed or were given away. Which events and routes drove the peak nights. Short-term rental growth read against hotel performance and housing. Season over season, on one method, ready for the next approval.
What happens to rate, occupancy, and yield under each scenario: a new hotel, a rental licence change, a new route, an added event. The access ceiling for the market: seats on the routes and demand in the peak week. Keys sequenced so each opening is absorbed by demand that exists. Compression nights flagged ahead, so rate windows are set before the comp set moves. Every number marked observed, derived, or inferred.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether to approve new room supply, and at what scale and class | Demand broken out by origin, purpose, timing and price sensitivity, not an occupancy trend alone | Supply sized and phased against the demand pattern, not the headline trend |
| Demand137 | Where and when will compression nights concentrate? | Event calendar, route capacity, and comparable-market seasonality, linked | Pricing, inventory, and staffing matched to the modelled compression window |
| Demand137 | Can existing stock, short-term rentals included, absorb demand without new build? | Total effective supply, not licensed hotel stock alone | Approvals sized against total capacity |
| Demand137 | How many short-term rental licences to release, or withdraw? | The yield, displacement, and fiscal effect of each licensing level | Licences move with hotel supply, not against it |
| Demand137 | In what order should the keys open? | Absorption by location and by year, so each opening is filled before the next | Rooms approved only where demand will fill them |
| Demand137 | Can this tell us not to build? | Yes. If the gap is covered by existing and informal supply, or the peak is too short, that is the result | The model does not favour the proposal |
| Signal137 | Did the last approval fill with demand or with discounting? | Occupancy and rate for the new stock set against the counterfactual | The next approval sized on what the last one did |
| Signal137 | Did the peak we built for recur? | The strong season separated from the underlying trend | The pipeline sized to the underlying trend |
| Both | What is the difference between this and capacity, pricing, and market pressure? | This decides whether and where new supply is warranted; that page covers the operational response once supply is fixed | Buyers use this first, at the planning or investment stage |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of official or consultant |
Supply modelled against the demand forecast by week. The model finds, before approval, the point where new rooms start pulling occupancy from existing stock instead of capturing new demand.
Demand by event week, shoulder period, and off-season modelled separately. A market that sizes to the annual average is undersupplied in peak weeks and oversupplied the rest of the year.
Constrained weeks where demand exceeds supply identified alongside oversupplied weeks where rate is under pressure. Each calls for a different intervention.
Release timing modelled against the booking curve and the event calendar. Releasing too early compresses rate and releasing too late loses the booking, so the model identifies the window between them.
The pipeline tested against the demand forecast by location and timing. Where the new rooms sit on the demand curve determines whether they grow the market or dilute it.
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.
A race track, an arena, a stadium: each is built for a peak it reaches a few dozen times a year. The gap between peak and off-peak operational need is the commercial challenge behind every sports-anchored development, and the reason mixed use is the answer rather than the fashion.
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.
