Knowwhatanassetcanearn—beforeyoucommitcapital.

Regulatory risk meets volatile demand. Municipalities are tightening short-term rental licences, and investors often overpay based on peak-season projections that ignore shoulder-month vacancy. We model against actual booking patterns and regulatory constraints.

RevPAR is the number that matters, but it's the number sellers most often inflate. Small hospitality assets are highly sensitive to competitive set changes, seasonal demand shifts, and OTA commission structures. We benchmark against actual comparable performance, not aspirational rate cards.

Occupancy looks high until you account for lease cycle gaps, summer void periods, and the risk of a single university policy change wiping out demand. We model against enrolment trends, competitive supply pipeline, and actual rent achievability by room type.

High turnover rates and flexible leases make revenue forecasting unreliable. Operators often quote headline rents that ignore vacancy drag from frequent move-outs and the operational cost of maintaining furnished, serviced units. We model net effective income after churn.

Appears simple until you realise that occupancy and rate optimisation are deeply interlinked. A facility at 90% occupancy with below-market rates is leaving money on the table; one at 70% with premium rates may be overpriced. We model the rate-occupancy curve for each micro-market.

Revenue depends on a complex mix of monthly subscribers, transient hourly users, and event-driven spikes. EV charging and mobility shifts add further uncertainty. We model utilisation patterns across user types and time-of-day demand curves.

Extreme seasonality makes annual revenue projections unreliable when based on peak-season performance. Weather dependency, planning restrictions on unit counts, and the capital cost of maintaining experiential accommodations are routinely underestimated. We model against full-year demand curves.

Berth pricing varies dramatically by vessel size, and occupancy is constrained by physical capacity that's expensive to expand. Waiting list dynamics, seasonal versus annual contracts, and ancillary revenue from fuel, maintenance, and chandlery are often mispriced. We model the full revenue stack.

Razor-thin operator margins mean small misjudgements in footfall or average ticket become existential. Landlords pricing rent against optimistic turnover projections create a mismatch that collapses when the operator fails. We model sustainable rent against evidence-based turnover estimates.