Evidence for the decision.

Calibrated cancellation models; revenue management; asymmetric loss

Define a booking-time prediction point and audit whether each field was available then. Exclude final reservation status/date, later booking changes and other post-outcome fields. Compare calibrated logistic/additive models with boosting; aggregate predicted cancellations across arrival dates with dependence sensitivity.

Evaluation: Use time-based arrival holdouts and a hotel-transfer stress test. Report cancellation calibration, log loss and arrival-date prediction error. Compare fixed-rule and model-based overbooking in a simulator with explicit room capacity, room-night logic, rates and displacement costs.

Boundary: Two hotels do not establish universal performance. Original booking-time versions of some fields may be unavailable; label that limitation. Capacity and counterfactual overbooking revenue are simulated, not measured business improvement.

Hotel Booking Demand datasets ↗

Hedonic models; spatial dependence; quantile regression

Choose a city and reproducible snapshot set; normalize currency, minimum-stay rules and listing types. Fit a spatial hedonic model and a quantile-boosting challenger to advertised nightly prices. Group repeated listings across splits and use coarse location features with spatial-block validation.

Evaluation: Compare neighborhood/type medians with model predictions on later snapshots and held-out spatial blocks. Report absolute/log-price error, interval coverage and stability after outlier filtering. Audit calendar availability as a separate recorded field rather than converting it into observed occupancy.

Boundary: Unavailable calendar nights may be booked or blocked. Do not claim measured occupancy, annual rental yield or causal revenue gains from amenities; location and host details should remain appropriately aggregated.

Inside Airbnb ↗

Seasonal/state-space models; hierarchical forecasting; pooling

Select comparable Eurostat monthly nights/arrivals series at a supported geographic level, separating resident and nonresident demand where available. Harmonize coverage and reporting breaks. Compare seasonal baselines, dynamic regression and globally pooled probabilistic forecasts with consistent totals.

Evaluation: Use rolling forecast origins across normal and disrupted periods. Report scaled error, quantile loss, interval coverage and peak-season bias by region. Compare simple seasonal recovery assumptions with fitted models; include missing-series and definition-change sensitivity.

Boundary: Aggregate nights are not individual bookings or a hotel's revenue. Match the geography and frequency actually available; do not infer property-level occupancy or staffing needs from incompatible regional totals.

Eurostat tourism accommodation statistics ↗