Forecasting, Inventory and Rooms Availability Control

Introduction

Front office managers make daily decisions under uncertainty. They must estimate future demand, decide how many rooms to sell, protect space for priority segments, manage cancellations and no-shows, and determine whether a new reservation should be accepted when the hotel appears nearly full. These decisions affect occupancy, average daily rate (ADR), revenue per available room (RevPAR), guest satisfaction and operational feasibility.

Forecasting and rooms availability control are therefore not merely clerical activities. They are an integrated managerial process linking marketing, reservations, front office, housekeeping, maintenance, revenue management and finance. The central challenge is to balance two risks:

• Underbooking: rooms remain unsold because inventory was protected too conservatively.
• Overbooking or inappropriate acceptance: the hotel accepts more demand than it can serve, causing relocation, compensation costs and reputational damage.

A sound control system combines historical data, current booking behaviour, room-status information, market intelligence and explicit decision rules. It also recognises that a room is a time-sensitive product: an unsold room tonight cannot be stored and sold tomorrow.

Learning Objectives

By the end of this chapter, the learner should be able to:

1. Explain the role and limitations of occupancy forecasting.
2. Construct and interpret booking curves, pickup and pace measures.
3. Calculate basic occupancy, ADR, RevPAR, wash and overbooking indicators.
4. Distinguish physical room inventory from sellable inventory.
5. Apply room-type controls, stay restrictions and availability rules.
6. Evaluate reservations using displacement logic and opportunity cost.
7. Integrate housekeeping, maintenance and front-office information into capacity decisions.
8. Assess the ethical, technological and governance implications of overbooking and automated controls.

1. The Nature of Rooms Inventory

A hotel room is a perishable, capacity-constrained service. The hotel has a finite number of rooms, but each room can be sold repeatedly on different dates. Inventory must therefore be understood by room-night, not simply by room.

If a hotel has 200 rooms and operates for 365 days, its annual theoretical room-night capacity is 73,000 room-nights, assuming all rooms are available every day. In practice, rooms may be unavailable because of maintenance, refurbishment, safety restrictions, out-of-order conditions or operational decisions.

1.1 Physical, operational and sellable inventory

These terms should not be treated as interchangeable.

A room may be physically present but not sellable. For example, a room under repair is part of physical inventory but not operational inventory. A clean, vacant room may still be closed to a particular distribution channel because the hotel wishes to protect it for a higher-value segment.

1.2 Room-night capacity

For a date, theoretical room-night capacity is:

where:

• C_t = theoretical capacity on date t, measured in room-nights;
• R = number of installed rooms;
• O_t = operating factor on date t, between 0 and 1.

If all rooms operate, O_t = 1. If 8 of 200 rooms are unavailable, O_t = 0.96 and capacity is 192 room-nights. This is a capacity measure, not a forecast of demand.

2. Forecasting Hotel Demand

A forecast is a structured estimate of future demand based on information available at a particular time. It is not a guarantee. The quality of a forecast depends on data accuracy, model suitability, managerial interpretation and the stability of the market.

2.1 Forecast horizons

Hotels normally use several horizons:

• Strategic forecast: months to several years; supports expansion, staffing models and capital planning.
• Tactical forecast: several weeks to several months; supports pricing, group evaluation and labour planning.
• Operational forecast: today through approximately 30 days; supports room allocation, housekeeping and arrival management.
• Intraday forecast: same-day updates based on arrivals, departures, extensions, cancellations and no-shows.

A forecast should be date-specific. “The hotel will achieve 80% occupancy next month” is less useful than “The hotel is currently expected to achieve 92% occupancy on the citywide-convention Saturday, with 14 rooms likely to wash.”

2.2 Demand segmentation

Forecasting is more accurate when demand is divided into meaningful segments, such as:

• transient business;
• transient leisure;
• corporate negotiated accounts;
• groups and conferences;
• wholesale and tour series;
• government or institutional business;
• loyalty or direct-booking demand;
• complimentary, house-use and staff rooms.

Each segment has different booking windows, cancellation behaviour, price sensitivity, length of stay and no-show probability. A single occupancy forecast can hide these differences and lead to poor allocation decisions.

2.3 Core performance measures

Let:

• A_t = occupied rooms on date t;
• S_t = rooms sold on date t;
• C_t = available rooms on date t;
• RM_t = room revenue on date t.

In a simple hotel operation, occupied rooms and rooms sold may be equivalent. However, house-use rooms, complimentary rooms and multiple rooms attached to a reservation can create reporting differences. Definitions must therefore be fixed before analysis.

Occupancy percentage is:

Average daily rate is:

Revenue per available room is:

Under consistent definitions, the relationship is:

These equations assume that room revenue and room counts refer to the same date, room scope and accounting basis. RevPAR is not a measure of total hotel profitability; it excludes many non-room revenues and operating costs.

Worked Example 1: Basic operating measures

A hotel has 240 available rooms on a Saturday. It sells 204 rooms and earns room revenue of £30,600.

Occupancy:

(204 ÷ 240) × 100 = 85%

ADR:

£30,600 ÷ 204 = £150

RevPAR:

£30,600 ÷ 240 = £127.50

The identity also holds: £150 × 0.85 = £127.50.

3. Booking Curves, Pickup and Pace

3.1 Booking position

A booking position is the number of reservations currently held for a future arrival date. It may be reported by room type, segment, rate category or channel.

For date t, the booking position at observation time d can be represented as:

The observation date must be specified because the same arrival date has a different booking position 60 days, 14 days and 1 day before arrival.

3.2 Pickup

Pickup is the additional demand accumulated between two observations:

where d₂ is later than d₁. Pickup can be measured in rooms, room-nights or revenue. It should not be confused with total bookings.

Example: if a Saturday stood at 118 rooms booked seven days ago and 142 rooms booked today, seven-day pickup is 24 rooms.

3.3 Pace

Pace compares current booking position with a historical benchmark at the same lead time. If the current position 21 days before arrival is 96 rooms and the comparable historical average is 120 rooms, the pace index is:

Thus, 96 ÷ 120 = 0.80, or 80% of benchmark pace. A pace index below 1 does not automatically indicate weak final demand: a major event may produce late bookings, or current prices may be deliberately higher.

3.4 Booking curves and final occupancy forecasts

A booking curve plots cumulative bookings against days before arrival. Historical curves can be used as reference patterns. A basic forecast can combine current bookings with expected future pickup:

The formula is useful only when the terms are defined consistently. Expected future pickup should reflect comparable dates, segment mix, price, event conditions and channel behaviour. Expected wash represents cancellations, no-shows and other expected reductions.

A more formal segment-based forecast is:

where:

• F_t = forecast rooms sold for date t;
• B_t = current confirmed rooms for date t;
• p_s,t = estimated probability that an unbooked opportunity in segment s will convert before arrival;
• U_s,t = estimated eligible remaining demand in segment s;
• W_t = expected wash.

This is a planning model, not a universal law. It requires reliable estimates and should be recalibrated as market conditions change.

3.5 Wash and net demand

Wash is the difference between reservations on the books and rooms that ultimately materialise. It may include cancellations, no-shows, early departures and reservation reductions.

A simple historical wash rate is:

If 160 rooms were booked at the reference point and 152 were consumed, wash is 8 rooms and the wash rate is 5%. The rate is meaningful only if the reference point, segment and booking conditions are comparable.

4. Rooms Availability Control

Availability control converts forecasts into operating rules. It answers questions such as:

• Should a room type remain open for sale?
• Should a low-rate category be closed?
• Should a one-night stay be restricted on a peak date?
• Should group inventory be returned to general sale?
• Should the hotel accept a reservation that displaces another possible booking?

4.1 Room-type controls

Room-type control involves monitoring capacity by category, including standard rooms, deluxe rooms, suites and accessible rooms. Controls may include:

• opening or closing a room type;
• applying a room-type upgrade or downgrade strategy;
• limiting the number of rooms available through a channel;
• protecting accessible rooms for guests who require them;
• controlling oversell by category rather than only at total-hotel level.

A hotel should not oversell a room type simply because total rooms remain available. A guest who has booked a specific accessible or family configuration may not be satisfactorily accommodated by an arbitrary substitute.

4.2 Rate and channel controls

Availability may be controlled by rate plan and channel. A hotel can close a discounted rate while continuing to sell a flexible or premium rate. Similarly, inventory supplied to an online travel agency may be limited while direct channels remain open.

These controls should be coordinated across the property-management system (PMS), central reservation system (CRS), channel manager and distribution partners. Unsynchronised systems create phantom availability, duplicate sales or preventable overselling.

4.3 Stay restrictions

Common restrictions include:

• Closed to arrival (CTA): guests cannot begin a stay on the restricted date, but may continue an existing stay.
• Closed to departure (CTD): guests cannot depart on the restricted date.
• Minimum length of stay (MLOS): a reservation must cover at least a specified number of nights.
• Maximum length of stay (MaxLOS): a reservation may not exceed a specified duration.
• Stop-sell: no further reservations are accepted through a particular rate or channel.

A two-night MLOS on a Saturday may protect the Friday and Saturday combination from being fragmented by one-night bookings. However, restrictions can also reduce conversion, inconvenience guests and shift demand to competitors. They should be applied selectively, reviewed frequently and removed when the forecast weakens.

4.4 The displacement effect of length of stay

A reservation consumes capacity across every night of its stay. A one-night booking may be attractive on a soft night but harmful if it prevents a higher-value multi-night booking over a compressed peak period. Therefore, the decision variable is often room-night contribution, not arrival-night occupancy alone.

5. Displacement Analysis and Capacity Decisions

Displacement analysis evaluates whether accepting a booking prevents a more valuable use of the same capacity. It is especially important for groups, contracted business and long-stay reservations.

5.1 Net contribution

A basic net contribution estimate is:

Variable service costs may include housekeeping, linen, amenities, breakfast inclusions and commissions that vary with the booking. Fixed costs should not be subtracted as if they changed immediately with one additional room-night.

5.2 Opportunity cost

The opportunity cost of accepting a booking is the contribution from the best realistic alternative displaced by that booking. A booking should normally be accepted when its expected net contribution exceeds the expected contribution of the displaced alternative, subject to strategic and service constraints.

For a candidate booking with room-nights n, a simplified decision rule is:

This rule requires realistic alternatives. It is not sound to compare a guaranteed group with an entirely speculative retail booking at the highest published rate.

Worked Example 2: A group displacement decision

A 40-room group requests two nights during a high-demand period at £130 per room-night. Variable room-related cost is £25 per occupied room-night, and the group commission is £5 per room-night.

Group net contribution per room-night:

£130 − £25 − £5 = £100

Total group contribution:

40 × 2 × £100 = £8,000

Revenue management estimates that, without the group, 30 of those room-nights could be sold to transient guests at £175, with variable cost of £25 and channel cost of £15 per room-night. Expected transient contribution for those 30 room-nights is:

30 × (£175 − £25 − £15) = £4,050

The remaining 50 room-nights are expected to be unsold or sold only at a lower contribution. On these assumptions, the group creates £8,000 and displaces £4,050 of expected contribution, before considering food-and-beverage spend, operational complexity, brand strategy or service risk. The group is economically attractive. The conclusion would change if the transient demand estimate were higher or if the group created substantial service costs.

5.3 Capacity protection and bid-price logic

Advanced revenue-management systems assign a notional minimum acceptable contribution, sometimes called a bid price, to a room-night. A booking is accepted when its expected contribution exceeds the sum of the bid prices for the capacity it consumes. Such systems can account for room type, date, length of stay and segment.

Bid prices are model outputs, not objective truths. Managers must review data quality, forecast error, group value, guest commitments and legal or accessibility obligations before applying automated recommendations.

6. Overbooking: Controlled Risk, Not a Sales Target

Overbooking occurs when confirmed reservations exceed physical or sellable capacity in anticipation of cancellations, no-shows or early departures. Because realised demand is uncertain, a carefully calibrated overbooking level can protect against empty rooms. Poorly governed overbooking can produce relocation, financial compensation, distress and reputational damage.

6.1 A simple expected-value model

Suppose a hotel considers accepting one additional reservation. Let:

• p = probability that the extra booking ultimately requires a room;
• G = contribution if the room is sold and accommodated;
• L = loss if the guest must be relocated or otherwise compensated;
• C = contribution that would be earned if the room remained available for a later booking.

A simplified incremental expected value is:

This expression is not correctly structured if L applies only when capacity is insufficient. A more appropriate formulation separates the probability of the relevant states. If [[MATH;q]] is the probability that the hotel lacks a room when the extra guest arrives, and G is contribution when accommodated, then:

where C_alt is the expected contribution forgone by accepting the booking instead of preserving capacity for an alternative. The model is intentionally simplified: q depends on all bookings, wash, room status and room-type compatibility, not only on the extra reservation.

Overbooking limits should be based on historical error distributions, not merely average cancellation rates. Averages conceal volatile dates and segment-specific behaviour.

6.2 Protection of guest welfare

When relocation is unavoidable, the hotel should follow a documented walk policy. It should prioritise fairness, communicate promptly, arrange equivalent or superior accommodation, provide transportation where appropriate, protect the guest’s rate and record the incident. Policies must comply with applicable consumer-protection, contract, accessibility and anti-discrimination requirements.

Hotels should never treat accessible rooms, family configurations or safety-critical requirements as interchangeable inventory without a suitable alternative. Revenue optimisation does not override the duty of care.

7. Integrating Front Office, Housekeeping and Maintenance

Availability control fails when commercial systems and physical reality diverge. The front office must reconcile:

• arrivals, departures and stayovers;
• room status and inspection status;
• rooms out of order and out of service;
• early departures and extensions;
• room moves and unresolved defects;
• VIP, group and accessibility requirements;
• late cancellations and no-shows.

A room marked “vacant” in the PMS may not be sellable if it is dirty, under inspection, blocked for maintenance or reserved for a specific operational purpose. Conversely, a room left blocked after repair completion reduces sellable capacity unnecessarily.

A daily rooms-control meeting should compare the forecast with the physical room plan. On high-demand dates, managers should establish a cut-off time for release of unrepaired rooms, confirm room-type protection and identify likely early check-ins and late departures.

8. Technology, Data and Artificial Intelligence

Modern hotels use PMS, CRS, revenue-management systems, channel managers, housekeeping applications and business-intelligence platforms. These systems can provide:

• real-time booking position;
• pickup by segment and channel;
• automated rate and restriction recommendations;
• cancellation and no-show forecasts;
• room-type availability;
• exception alerts;
• scenario analysis for groups and stay patterns.

Machine-learning systems may detect nonlinear relationships involving events, weather, flight patterns, search behaviour and competitor pricing. However, predictive performance does not remove the need for managerial governance. Risks include biased historical data, feedback loops, unexplained recommendations, privacy breaches, cybersecurity threats and automated discrimination against particular markets.

Best practice includes role-based access, audit trails, data-minimisation, validation of model outputs, clear override authority and post-event accuracy review. Managers should distinguish a forecast from a decision: a model may estimate high demand, but a human decision-maker remains responsible for whether a restriction is commercially and ethically appropriate.

9. Illustrative Case: The Meridian Riverside Hotel

Background

The 220-room Meridian Riverside Hotel is preparing for a three-day medical congress. The peak night is Thursday. At 21 days before arrival, the hotel has 176 rooms booked for Thursday, including 60 rooms contracted to the congress organiser. Historical data show that transient bookings accelerate during the final 14 days, but the current pace is 90% of comparable congress dates. The hotel has eight rooms blocked for refurbishment, reducing operational capacity to 212 rooms.

Problem

The organiser requests 20 additional rooms for Thursday at a discounted group rate. A corporate account also requests a five-night stay covering Wednesday through Sunday, requiring 12 rooms per night. The revenue manager is concerned that accepting both requests could displace higher-rated transient demand on Thursday and Friday.

Analysis

The current Thursday occupancy position is:

176 ÷ 212 = 83.0% approximately.

The group’s additional 20 rooms would raise confirmed rooms to 196. The corporate request would add 12 rooms on Thursday, producing 208 confirmed rooms before expected wash. This leaves only four rooms of operational capacity on the peak night. If the forecast predicts 10 additional transient rooms and wash of only six rooms, the hotel would face a potential shortage.

The manager evaluates:

1. expected net contribution by night, not only the Thursday rate;
2. the probability and cost of wash in each segment;
3. whether the group can accept a different room type or shoulder-night pattern;
4. the strategic value of the corporate account;
5. the likelihood that transient demand will materialise at the proposed rate;
6. whether refurbishment rooms can safely return to service;
7. the consequences for accessibility and room-type commitments.

Approach and solution

The hotel accepts the organiser’s additional rooms only if the group releases unused rooms by an agreed deadline and accepts a modestly higher rate. It accepts the corporate account subject to a room-type substitution agreed in advance and protects the required accessible inventory. The hotel closes the lowest discounted public rate for Thursday, retains flexible premium rates, and removes the eight-room refurbishment block only after engineering inspection and housekeeping release.

On Wednesday and Sunday, where capacity is less constrained, the hotel keeps more public rates open. A daily pickup review is scheduled, with special attention to cancellations and transient demand during the final seven days.

Outcome

The hotel sells 207 rooms on Thursday, achieves high occupancy without a forced relocation, and preserves the corporate relationship. The decision succeeds because it combines displacement analysis, operational verification, segment-specific controls and explicit release deadlines. It would have been weaker to accept every request solely because the PMS initially showed rooms available.

Learning points

• Available inventory is operational, not merely numerical.
• A multi-night request must be evaluated across all nights.
• Group and corporate value includes strategic and ancillary effects, but these must be estimated rather than assumed.
• Restrictions should be dynamic and removed when conditions soften.
• Room-type and accessibility commitments constrain apparent capacity.

10. Limitations and Professional Judgement

Forecasts are vulnerable to structural breaks such as pandemics, airline disruption, new competitors, political events and sudden changes in consumer behaviour. Historical averages may be misleading when the future is unlike the past. Booking curves can also create self-fulfilling outcomes: closing low rates may slow bookings, while opening them may stimulate demand.

Managers should therefore use scenario ranges rather than a single point estimate. For example:

• conservative case: weak late pickup and high cancellations;
• base case: most likely segment behaviour;
• optimistic case: strong event-related pickup and low wash.

Decisions should be stress-tested against room outages, group cancellation, unexpected extensions and channel failure. Forecast accuracy should be reviewed after arrival dates pass, using measures such as mean absolute error:

where n is the number of evaluated dates. Both actual and forecast values must use the same unit, such as rooms sold. MAE describes average absolute error but does not show whether forecasts systematically overestimate or underestimate demand; bias should therefore also be monitored.

Best-Practice Control Framework

A professional rooms-control process should:

1. define room, revenue and occupancy measures consistently;
2. separate physical, operational and sellable inventory;
3. forecast by date, segment, room type and lead time;
4. track booking position, pickup, pace and wash;
5. use comparable historical dates while adjusting for events and market change;
6. evaluate groups and long stays using displacement and opportunity cost;
7. apply CTA, CTD, MLOS and stop-sell restrictions selectively;
8. reconcile PMS inventory with housekeeping and engineering status;
9. protect accessibility, safety and contractual commitments;
10. govern automated recommendations through audit, human oversight and post-event review;
11. document overbooking limits and relocation procedures;
12. measure forecast accuracy and revise assumptions continuously.

Chapter Summary

Forecasting, inventory and rooms availability control form an integrated decision system for managing a perishable hotel resource. Forecasting estimates future room demand using booking position, booking curves, pickup, pace, segment behaviour and expected wash. Occupancy, ADR and RevPAR provide complementary measures of operating performance, but each depends on consistent definitions.

Inventory control requires managers to distinguish physical rooms from operational and sellable rooms. Room-type controls, rate closures, channel limits and stay restrictions can protect capacity, but they must be dynamic and commercially justified. Displacement analysis compares the expected contribution of a proposed booking with the realistic contribution that its capacity may displace. Overbooking can reduce empty-room risk, yet it must be governed through evidence, ethical safeguards and documented guest-relocation procedures.

Effective control depends on coordination among reservations, front office, housekeeping, engineering, revenue management and technology platforms. Forecasts remain uncertain, so scenario analysis, accuracy measurement and managerial judgement are essential. The professional objective is not maximum occupancy at any cost, but the balanced achievement of revenue, service quality, operational feasibility and fair treatment of guests.

Key Terms

Available-to-sell inventory: Rooms currently eligible for sale for a specified date, type and channel.

Booking curve: Cumulative pattern of reservations as the arrival date approaches.

Booking position: Reservations held for a future arrival date at a specified observation time.

Closed to arrival (CTA): Restriction preventing a guest from beginning a stay on a specified date.

Closed to departure (CTD): Restriction preventing departure on a specified date.

Displacement analysis: Evaluation of the demand and contribution displaced by accepting a booking.

Forecast: Structured estimate of future demand or operating results.

Length-of-stay restriction: Rule controlling the minimum or maximum number of nights.

MAE: Mean absolute error, an average measure of forecast deviation.

MLOS: Minimum length of stay.

Occupancy: Occupied rooms expressed as a percentage of available rooms.

Opportunity cost: Contribution forgone by choosing one use of capacity over the best realistic alternative.

Overbooking: Accepting reservations beyond a defined capacity in anticipation of wash or other non-arrivals.

Pace: Current booking performance compared with a historical or planned benchmark at the same lead time.

Pickup: Additional reservations accumulated between two observation points.

RevPAR: Room revenue divided by available rooms.

Sellable inventory: Capacity the hotel is willing and able to sell under current conditions.

Stop-sell: Closure of further reservations for a rate, room type or channel.

Wash: Reservations that fail to materialise through cancellation, no-show, reduction or related effects.

Review Questions

Short Answer Questions

1. Distinguish physical, operational and sellable room inventory.
2. What is the difference between pickup and pace?
3. Define a minimum length-of-stay restriction and give one reason for using it.
4. Why is displacement analysis important for group reservations?
5. State two operational causes of a room being unavailable despite appearing vacant in the PMS.

Long Answer Questions

1. Explain how a hotel can construct a date-specific occupancy forecast using booking position, pickup, pace and wash.
2. Discuss the commercial and service implications of CTA, CTD, MLOS and stop-sell controls.
3. Explain the relationship between occupancy, ADR and RevPAR, including the assumptions required for valid comparison.
4. Analyse the risks and governance requirements associated with hotel overbooking.
5. Discuss how revenue management, front office, housekeeping and engineering should coordinate rooms availability control.

Analytical / Application Questions

1. A 180-room hotel has 12 rooms out of order and 140 rooms booked for a future date. Historical evidence suggests 18 additional rooms will book and 6% of current bookings will wash. Estimate expected rooms sold and occupancy, clearly stating your assumptions.
2. A 50-room, two-night group at £115 per room-night is being considered. Its variable cost is £22 and commission is £3 per room-night. It is expected to displace 70 transient room-nights contributing £82 each. Should the group be accepted on contribution grounds? Identify non-financial factors that could change the decision.
3. Design a control plan for a hotel facing a citywide event, including forecast updates, room-type protection, stay restrictions, overbooking governance and communication with guests.

Multiple Choice Questions with Answers

Question 1

A hotel has 240 available rooms and sells 204 rooms. What is its occupancy percentage?

A. 75%

B. 80%

C. 85%

D. 90%

Correct Answer: C. 85%

Question 2

A future Saturday has 118 rooms booked seven days ago and 142 rooms booked today. What is the seven-day pickup?

A. 24 rooms

B. 118 rooms

C. 142 rooms

D. 260 rooms

Correct Answer: A. 24 rooms

Question 3

Which restriction prevents a guest from beginning a stay on a specified date?

A. CTD

B. CTA

C. Stop-sell

D. MaxLOS

Correct Answer: B. CTA

Question 4

Which measure equals room revenue divided by available rooms?

A. ADR

B. Occupancy

C. RevPAR

D. Wash rate

Correct Answer: C. RevPAR

Question 5

A hotel has 200 physical rooms, but 8 are unavailable for maintenance. What is its operating capacity?

A. 192 rooms

B. 200 rooms

C. 208 rooms

D. 25 rooms

Correct Answer: A. 192 rooms

Question 6

If current booking position is 96 rooms and the historical benchmark at the same lead time is 120 rooms, what is the pace index?

A. 0.20

B. 0.80

C. 1.20

D. 2.00

Correct Answer: B. 0.80

Question 7

What is the central purpose of displacement analysis?

A. To calculate housekeeping labour hours

B. To compare a booking with the realistic contribution it may displace

C. To determine the legal room count of a hotel

D. To eliminate all group reservations

Correct Answer: B. To compare a booking with the realistic contribution it may displace

Question 8

Which statement about overbooking is most accurate?

A. It is always illegal and never used professionally

B. It guarantees full occupancy without service risk

C. It is a controlled risk decision requiring evidence and guest-protection procedures

D. It applies only to complimentary rooms

Correct Answer: C. It is a controlled risk decision requiring evidence and guest-protection procedures

Question 9

A group produces £8,000 contribution and is expected to displace £4,050 of transient contribution. Ignoring other factors, what is the contribution advantage of accepting the group?

A. £3,950

B. £4,050

C. £8,000

D. £12,050

Correct Answer: A. £3,950

Question 10

Which operational condition most directly makes a vacant room temporarily unsellable?

A. The hotel has a high ADR

B. The room is awaiting maintenance release or inspection

C. The guest has a loyalty membership

D. The booking curve is above benchmark

Correct Answer: B. The room is awaiting maintenance release or inspection