HOSPITALITY REVENUE MANAGEMENT
STUDY GUIDE 2026 ACCURATE SOLUTIONS
GRADED A+
⩥ Types of Forecasting Data. Answer: Historical data (past events),
current data (events happening now or in the very near term), and future
data (events expected to occur in the future).
⩥ Trailing Period Data. Answer: A data set that discards the oldest data
when the newest data is added, keeping the set size constant; commonly
used to calculate rolling averages.
⩥ Fixed Average. Answer: An average calculated using historical data
from a specific and unchanging time period.
⩥ Rolling Average. Answer: An average calculated using historical data
from a changing time period as new data replaces old data.
⩥ Historical Data Tracked by RMs. Answer: Typical data include
reservations booked per day, reservations denied per day, cancellations,
room nights canceled, check-ins, check-outs, no-shows, walk-ins, ADR
achieved, occupancy % achieved, average number of guests per room,
and average length of stay.
,⩥ RevPAR Forecast Formula. Answer: Forecasted RevPAR =
Forecasted ADR × Forecasted Occupancy %. Used to compare revenue
performance with and without new business.
⩥ Current Data - What RMs Monitor. Answer: Number of rooms
available to sell, number of rooms reserved, number of rooms
blocked/held, and estimated ADR based on current bookings.
⩥ Sellable Rooms Formula. Answer: Sellable rooms = Total rooms -
Unsellable rooms - Pre-sold/blocked rooms.
⩥ Minimum Length of Stay (MLOS). Answer: A revenue management
restriction instructing agents to decline reservation requests that do not
meet a predetermined minimum number of nights.
⩥ Group Rooms Pace Report. Answer: A summary report describing
future demand for group rooms and the rate at which that demand is
being booked.
⩥ Pick-Up (Group Rooms). Answer: The proportion of previously
reserved rooms that are ultimately occupied; Pick-up = Rooms occupied
÷ Rooms originally reserved.
, ⩥ Non-Rooms Revenue Pace Reporting. Answer: A pace report for
significant non-room revenue sources such as restaurants, bars,
banquets, spas, and other departments.
⩥ Demand Generator. Answer: An entity or event that produces a
significant increase in business, such as a convention, festival, or large
local event.
⩥ Demand Drain. Answer: A circumstance that produces a significant
decrease in business, such as road construction, area closures, or
competing events.
⩥ Demand Forecasting - RM Tasks. Answer: RMs must understand
special events, competitor demand, hotel openings/closings, pricing
strategies, weather, unusual events, and adjust forecasts quickly when
demand changes.
⩥ Impact of Demand on Price. Answer: Allowing demand alone to
dictate selling price is poor revenue management; in periods of high
demand, RMs should seek to eliminate discounts rather than simply raise
rack rates.
⩥ Impact of Price on Demand. Answer: Changes in price affect
willingness to buy; lowering prices in low demand periods does not
always result in sufficient additional demand to increase total revenue.
STUDY GUIDE 2026 ACCURATE SOLUTIONS
GRADED A+
⩥ Types of Forecasting Data. Answer: Historical data (past events),
current data (events happening now or in the very near term), and future
data (events expected to occur in the future).
⩥ Trailing Period Data. Answer: A data set that discards the oldest data
when the newest data is added, keeping the set size constant; commonly
used to calculate rolling averages.
⩥ Fixed Average. Answer: An average calculated using historical data
from a specific and unchanging time period.
⩥ Rolling Average. Answer: An average calculated using historical data
from a changing time period as new data replaces old data.
⩥ Historical Data Tracked by RMs. Answer: Typical data include
reservations booked per day, reservations denied per day, cancellations,
room nights canceled, check-ins, check-outs, no-shows, walk-ins, ADR
achieved, occupancy % achieved, average number of guests per room,
and average length of stay.
,⩥ RevPAR Forecast Formula. Answer: Forecasted RevPAR =
Forecasted ADR × Forecasted Occupancy %. Used to compare revenue
performance with and without new business.
⩥ Current Data - What RMs Monitor. Answer: Number of rooms
available to sell, number of rooms reserved, number of rooms
blocked/held, and estimated ADR based on current bookings.
⩥ Sellable Rooms Formula. Answer: Sellable rooms = Total rooms -
Unsellable rooms - Pre-sold/blocked rooms.
⩥ Minimum Length of Stay (MLOS). Answer: A revenue management
restriction instructing agents to decline reservation requests that do not
meet a predetermined minimum number of nights.
⩥ Group Rooms Pace Report. Answer: A summary report describing
future demand for group rooms and the rate at which that demand is
being booked.
⩥ Pick-Up (Group Rooms). Answer: The proportion of previously
reserved rooms that are ultimately occupied; Pick-up = Rooms occupied
÷ Rooms originally reserved.
, ⩥ Non-Rooms Revenue Pace Reporting. Answer: A pace report for
significant non-room revenue sources such as restaurants, bars,
banquets, spas, and other departments.
⩥ Demand Generator. Answer: An entity or event that produces a
significant increase in business, such as a convention, festival, or large
local event.
⩥ Demand Drain. Answer: A circumstance that produces a significant
decrease in business, such as road construction, area closures, or
competing events.
⩥ Demand Forecasting - RM Tasks. Answer: RMs must understand
special events, competitor demand, hotel openings/closings, pricing
strategies, weather, unusual events, and adjust forecasts quickly when
demand changes.
⩥ Impact of Demand on Price. Answer: Allowing demand alone to
dictate selling price is poor revenue management; in periods of high
demand, RMs should seek to eliminate discounts rather than simply raise
rack rates.
⩥ Impact of Price on Demand. Answer: Changes in price affect
willingness to buy; lowering prices in low demand periods does not
always result in sufficient additional demand to increase total revenue.
