Forecasting Room Availability

 

1.Forecasting Room Availability:

Forecasting room availability involves predicting the number of rooms that will be available for sale on specific future dates. This is a critical process for managing reservations, optimizing staff scheduling, and making informed pricing decisions. Room availability forecasts are based on historical data, booking patterns, cancellations, no-shows, walk-ins, and other factors that influence room occupancy.

 

Forecasting is a difficult skill to develop. The skill is acquired through experience, effective  recordkeeping, and accurate counting methods. Experienced front office managers have found that several types of information can be helpful in room availability forecasting:

o   A thorough knowledge of the hotel and its surrounding area

o   Market profiles of the constituencies the hotel serves

o   Occupancy data for the past several months and for the same period of the previous year

o   Reservation trends and a history of reservation lead times (how far in advance reservations are made)

o   A listing of special events scheduled in the surrounding geographic area

o   Business and historical profiles of specific groups booked for the forecast dates

o   The number of non-guaranteed and guaranteed reservations and an estimate of the number of

expected no-shows

o   The percentage of rooms already reserved and the cut-off date for group room blocks held for the forecast dates

o   The room availability of the most important competing hotels for the forecast dates (as discovered through blind calls)

o   The impact of citywide or multi-hotel groups and their potential influence on the forecast dates

o   Plans for remodeling or renovating the hotel that would change the number of available rooms

o   Construction or renovating plans for competitive hotels in the area

 

Ø In order to forecast room availability, the following data are needed

 

·        Number of expected room arrivals

·        Number of expected room walk-ins

·        Number of expected room stayovers

·        Number of expected room no-shows

·        Number of expected room understays

·        Number of expected room check-outs

·        Number of expected room overstays

 

Example: Let's consider a hotel with 150 guest rooms. On a specific date, 30 rooms are reserved, and there are 5 cancellations, 2 no-shows, and 3 walk-ins. The hotel has 8 stayovers, 10 expected check-outs, and 1 overstay.

The formula for calculating forecasted availability would be:

Forecasted Availability = Total Rooms - Reserved Rooms + Cancellations - No-Shows + Walk-ins - Stayovers + Expected Check-outs – Overstays

 

Forecasted Availability = 150 - 30 + 5 - 2 + 3 - 8 + 10 - 1 = 127 rooms available for sale.

 

 

Sample question

Using the following data, calculate the rooms availability for Hotel Sharma for 1st December. Mention the rooms’ availability forecast formula:

Total number of guest rooms : 500

Number of reservations generated : 275

Estimated percentage of no-show : 8%

Number of estimated overstay : 4

Number of estimated Understay : 3

Number of out of order rooms : 18

Total number of stays overs : 100

Total number of expected departures : 10

Calculation for Room Availability on 1st December

Using the above formula and the given data, we can calculate the room availability for Hotel Sharma on 1st December as follows:

 

Room Availability = 500 – (275 – (8% of 275)) - 4 + 3 – 18 – 100 + 10 = 500 – (275 – 22) - 4 + 3 – 18 – 100 + 10 = 500 – 238 = 138

 

2.Forecasting Data

The process of forecasting room availability generally relies on historical occupancy data as well as business already committed. Historical data is used to take some of the guesswork out of forecasting. To facilitate forecasting, the following daily occupancy data should be collected:

o   Number of expected room arrivals: based on existing reservations and historical trends for new reservations and on cancellations prior to the arrival date.

o   Number of expected room walk-ins: based on historical records.

o   Number of expected room stayovers (rooms occupied on previous nights that will continue to be occupied for the night in question): based on existing reservations.

o   Number of expected room no-shows: based on historical records.

o   Number of expected room understays (check-outs occurring before expected departure date): based on historical data.

o   Number of expected room check-outs: based on existing reservations.

o   Number of expected room overstays (check-outs occurring after the originally reserved departure date)

 

 1. Percentage of No-Shows:

The percentage of no-shows indicates the proportion of reserved rooms that guests did not arrive to occupy on the expected arrival date. This metric helps hotel managers make decisions about selling unoccupied rooms to walk-in guests. The calculation is as follows:

 

Percentage of No-Shows = (Number of Room No-Shows) / (Number of Room Reservations) * 100

 

# Example:

Let's say a hotel had 300 reservations for a specific date, but only 250 guests actually checked in. The number of no-shows would be 300 - 250 = 50.

 

Percentage of No-Shows = (50 / 300) * 100 = 16.67%

 

This means that 16.67% of the reserved rooms remained unoccupied due to no-shows.

 

 2. Percentage of Walk-Ins:

The percentage of walk-ins indicates the proportion of rooms occupied by guests who arrive at the hotel without prior reservations. It's calculated by dividing the number of rooms occupied by walk-ins by the total number of room arrivals (including reservations and walk-ins). The formula is:

 

Percentage of Walk-Ins = (Number of Walk-In Rooms) / (Total Number of Room Arrivals) * 100

 

# Example:

Suppose a hotel had a total of 200 room arrivals, out of which 40 were occupied by walk-in guests.

Percentage of Walk-Ins = (40 / 200) * 100 = 20%

This means that 20% of the occupied rooms on that specific day were taken by walk-in guests.

 

 3. Percentage of Overstays:

The percentage of overstays represents rooms occupied by guests who stay beyond their originally scheduled departure dates. It's calculated by dividing the number of overstay rooms by the total number of expected room check-outs. The formula is:

 

Percentage of Overstays = (Number of Overstay Rooms) / (Total Number of Expected Check-Outs) * 100

 

# Example:

Consider a scenario where there were 15 overstay rooms, and the total number of expected check-outs was 100.

Percentage of Overstays = (15 / 100) * 100 = 15%

This means that 15% of the guests extended their stays beyond their scheduled departure dates.

 

 4. Percentage of Understays:

The percentage of understays represents rooms occupied by guests who check out before their originally scheduled departure dates. It's calculated by dividing the number of understay rooms by the total number of expected room check-outs. The formula is the same as for the percentage of overstays:

 

Percentage of Understays = (Number of Understay Rooms) / (Total Number of Expected Check-Outs) * 100

 

# Example:

Suppose there were 10 understay rooms, and the total number of expected check-outs was 150.

Percentage of Understays = (10 / 150) * 100 = 6.67%

This means that 6.67% of the guests checked out earlier than their scheduled departure dates.

forecasting data is used to calculate various important occupancy-related metrics that help hotel managers make informed decisions about room availability, pricing, staffing, and operational strategies.

 

1. Percentage of No-Shows: Forecasting data, including historical no-show rates, is used to calculate the percentage of reservations that result in no-shows. This helps managers anticipate how many reserved rooms might go unoccupied and make decisions about selling those rooms to walk-in guests. The percentage of no-shows also influences revenue and helps in managing room inventory efficiently.

 

2. Percentage of Walk-Ins: Historical data on walk-in guests is used to calculate the percentage of rooms occupied by guests who arrive without prior reservations. This metric helps managers understand the volume of last-minute arrivals, which can impact staffing, pricing decisions, and the overall occupancy rate.

 

3. Percentage of Overstays: By analyzing historical data on guests who extend their stays beyond their original departure dates, the percentage of overstays is calculated. This information helps managers anticipate the number of rooms that might be occupied by guests who choose to extend their stays, impacting room turnover and availability.

 

4. Percentage of Understays: Forecasting data is used to calculate the percentage of guests who check out before their originally scheduled departure dates. This metric helps managers understand early departure patterns and the impact on room availability.

 

Overall, forecasting data enables hotels to:

 

- Optimize Revenue: By anticipating no-shows and adjusting walk-in rates, hotels can maximize revenue by filling as many rooms as possible.

- Improve Room Availability: Understanding overstays and understays allows hotels to better plan room turnover and availability.

- Enhance Guest Satisfaction: By accurately predicting room availability, hotels can manage reservations effectively, ensuring guests have the accommodations they expect.

- Staffing: Anticipating guest arrivals and departures helps in managing staff levels, ensuring there are enough resources to handle check-ins, check-outs, and guest needs.

- Operational Efficiency: With accurate forecasting, hotels can allocate resources efficiently, reduce operational inefficiencies, and improve overall performance.

 

Front office may have several types of forecasting formats.

 

Occupancy forecast are developed typically on monthly basis and are reviewed by food and beverage department and room division management to forecast

·        Revenue

·        Project expenses

·        Labour schedule

Example: A Ten Day Forecast can be used to update labour scheduling and cost projection and may be later supplemented by a more current Three Day Forecast.

 

Conclusion: forecasts help all departments to maintain appropriate staff levels for expected business volume.

 

A.    Ten-Day Forecast.

 At most lodging properties, the ten-day forecast is developed jointly by the front office manager and the reservations manager, possibly in conjunction with a forecast committee. Many properties develop their ten-day forecast from their yearly forecast. A ten-day forecast usually consists of:

·        Daily forecasted occupancy figures, including room arrivals, room departures, rooms sold, and number of guests

·        The number of group commitments, with a listing of each group's name, arrival and departure dates, number of rooms reserved, number of guests, and perhaps quoted room rates

·        A comparison of the previous period's forecasted and actual room counts and occupancy percentages

A special ten-day forecast may also be prepared for food and beverage, banquet, and catering operations. This forecast usually includes the expected number of guests, which is often referred to as the house count. Sometimes the house count is divided into group and non-group categories so that the hotel's dining room managers can better understand the nature of their business and their staffing needs.

B.     Three-Day Forecast.

 A three-day forecast is an updated report that reflects a more current estimate of room availability. It details any significant changes from the ten-day forecast. The three-day forecast is intended to guide management in fine-tuning labor schedules and adjusting room availability information. Exhibit 10 shows a sample three-day forecast form. In some hotels, a brief daily revenue meeting is held to focus on occupancy and rate changes for the next few days. The results of this meeting are often included in the three-day forecast.