Understanding the Backtest Methodology
The metrics on this page are derived from backtests on aggregated public market data for North Italy hotel markets. The dataset combines monthly occupancy rates, average daily rates, and tourism arrival figures from ISTAT, Banca d'Italia, and ENIT. The referenced data file (nexorev_north_italy_hotel_market_data.json) is available for download so that anyone can verify the inputs and reproduce the analysis.
Walk-forward validation is used to prevent overfitting. The model is trained on a rolling window of historical months and tested on the immediately following period. At no point does the model see future data during training. This protocol mimics how the model would perform in real-time deployment and produces accuracy estimates that are more conservative than in-sample testing.
What 9.8% MAPE Means in Practice
A Mean Absolute Percentage Error of 9.8% on monthly occupancy forecasts means the model's predictions are, on average, within about 10 percentage points of the actual occupancy rate. For a hotel with 70% actual occupancy, the forecast would typically fall between 63% and 77%. This level of accuracy is useful for pricing and staffing decisions but is not precise enough for same-day operational planning.
The model's performance should be compared against your current forecasting approach. If your hotel does not formally track forecast accuracy today, any systematic forecasting methodology is likely an improvement over ad-hoc estimation. If you already use a structured forecasting process, ask for a side-by-side comparison during the pilot evaluation.
The 6.4 percentage point RMSE captures the typical magnitude of forecast errors in absolute terms. Combined with the MAPE, these metrics indicate that the model performs reasonably well on aggregate market data but will need to be re-evaluated once property-specific PMS data is available. Individual hotel demand patterns can diverge significantly from market averages, particularly for properties with unique positioning, niche segments, or irregular event-driven demand.
RevPAR Lift Simulation: What It Is and What It Is Not
The +7.6% simulated RevPAR lift is not measured revenue improvement from a deployed hotel. It is calculated by applying Nexorev's dynamic pricing rules to historical occupancy and ADR data, then comparing the simulated outcome to a baseline of static market-average pricing. The simulation assumes that rate changes would not significantly alter demand volume, an assumption that may not hold in practice, particularly for large rate increases.
This simulation provides a directional estimate of potential impact, not a guaranteed outcome. Actual RevPAR changes in production will depend on the hotel's competitive positioning, demand elasticity, channel mix, and operational execution of rate recommendations. The first pilot hotels will be used to measure real RevPAR impact and replace these simulation-based estimates with verified production data.
Known Limitations and Applicability to US Markets
The current backtest has several known limitations. First, the data is market-level aggregates, not individual hotel reservation records. Market averages smooth out the variability that individual properties experience. Second, the backtest does not account for competitive response: in practice, competitors adjust their rates in response to your changes, creating feedback loops that the simulation ignores.
Third, the North Italy dataset may not generalize directly to other markets. Demand patterns in US resort destinations, urban business-travel markets, or tropical leisure destinations follow different seasonal and cyclical patterns. The model architecture is designed to adapt to new markets, but accuracy metrics should be verified on each market's data before production deployment. Pilot hotels in new geographies will generate market-specific accuracy benchmarks that will be published alongside the North Italy results.
How to Evaluate These Results for Your Hotel
When evaluating Nexorev's model performance data, consider three factors. First, compare the metrics to your current forecasting accuracy. If you are not tracking forecast accuracy today, any systematic forecasting is likely an improvement over ad-hoc methods. Second, assess whether the data sources and methodology are relevant to your market and property type. Third, ask for a pilot-specific backtest using your historical PMS data, which the founder can produce during the evaluation process.
Hotel owners evaluating revenue management software should request transparency from every vendor they consider. Ask for published MAPE and RMSE figures, the dataset used for validation, whether the results are from backtests or production deployments, and whether the methodology has been independently reviewed. Nexorev publishes all of this information because the founder believes it should be standard practice in the industry.