Bt Marvel: The Owner Of The 100 Room Finney Motel
Bt Marvel The Ownermanager Of The 100 Room Finney Motel Forecasts
B.T. Marvel, the owner/manager of the 100 room Finney Motel, forecasts room revenues based on the number of room sales and expected average daily rate (ADR). The monthly room sales in units for July-September 20X3 were as follows: July: 2,480, August: 2,542, September: 2,325. B.T. believes the room sales for each month will increase by two occupancy points. In addition, B.T. expects the average daily rate for July-September will be $75 (July), $78 (August), and $72 (September).
Answer this two-part question:
1. Forecast the expected number of rooms to be sold for each month - that is July, August, and September 20X4.
2. Forecast the room revenue for each of the three months.
Paper For Above instruction
Forecasting Room Sales and Revenue for Finney Motel
The task involves forecasting the number of rooms sold and the corresponding revenue for the months of July, August, and September 20X4, based on historical data and expected increases. The problem requires understanding of basic forecasting techniques, specifically percentage increases in occupancy and their impact on room sales, as well as calculating revenues by multiplying the number of rooms sold by the Average Daily Rate (ADR).
Step 1: Analyze Past Data
Initial room sales data for July to September 20X3 are as follows:
- July 20X3: 2,480 rooms sold
- August 20X3: 2,542 rooms sold
- September 20X3: 2,325 rooms sold
The ADR for these months are:
- July: $75
- August: $78
- September: $72
Step 2: Forecast the Future Room Sales
B.T. believes room sales will increase by two occupancy points each month. Occupancy points represent the percentage of rooms sold relative to the total available rooms (which is 100). Currently, it’s implied that the sales figures correspond to occupancy rates, since the motel has 100 rooms.
To forecast the August and September 20X4 sales, we first need to calculate the estimated occupancy rate for July 20X3 and then apply the same increase of two occupancy points for the forecast months.
Calculating the occupancy rate for July 20X3:
Rooms sold in July / Total rooms = 2,480 / 100 = 24.8%
Forecast for August 20X4:
- Occupancy rate increases by 2 points from July 20X3: 24.8% + 2% = 26.8%
- Expected rooms sold: 26.8% of 100 rooms = 26.8 rooms (rounded to nearest whole number, 27 rooms)
Forecast for September 20X4:
- Occupancy rate increases by 2 points from August 20X3: 26.8% + 2% = 28.8%
- Expected rooms sold: 28.8 rooms (rounded to 29 rooms)
Forecast for July 20X4:
- Similarly, increase occupancy by 2 points from the previous month (September 20X3: 2,325 rooms sold / 100 = 23.25%)
- Occupancy in September 20X3: 23.25% + 2% = 25.25%
- Expected rooms sold: approximately 25 rooms
Step 3: Forecast the Room Revenue
The revenue is calculated by multiplying the forecasted rooms sold by the ADR for each month.
July 20X4:
- Rooms sold: 25 (from previous step)
- ADR: $75
- Forecasted revenue: 25 rooms × $75 = $1,875
August 20X4:
- Rooms sold: 27
- ADR: $78
- Forecasted revenue: 27 × $78 = $2,106
September 20X4:
- Rooms sold: 29
- ADR: $72
- Forecasted revenue: 29 × $72 = $2,088
Summary of Forecasted Data
| Month | Expected Rooms Sold | Average Daily Rate (ADR) | Forecasted Room Revenue |
|---|---|---|---|
| July 20X4 | 25 | $75 | $1,875 |
| August 20X4 | 27 | $78 | $2,106 |
| September 20X4 | 29 | $72 | $2,088 |
Conclusion
This forecast model assumes a steady increase in occupancy points by two each month, reflecting increased demand or improved marketing strategies. The revenue projections follow directly from these sales forecasts, providing B.T. Marvel with a clear view of expected monthly income. By maintaining these assumptions, the forecast offers a logical estimate based on recent trends and expected growth in occupancy.
References
- Ching, L. (2019). Hospitality Revenue Management. Journal of Hospitality & Tourism Research, 43(3), 351-367.
- Kimes, S. E. (2011). The Future of Revenue Management. Journal of Revenue and Pricing Management, 10(1), 45-50.
- Lumsdon, L., & Caldwell, N. (2011). Tourism and Sustainable Development: The Challenges of Change. Routledge.
- Morgan, N., Pritchard, A., & Pride, R. (2011). Destination Branding and Brand Equity. Journal of Vacation Marketing, 17(4), 273-278.
- O'Neill, J., & Mattila, A. S. (2010). Hotel Brand Strategies: The Role of Service Quality and Customer Perceptions. International Journal of Hospitality Management, 29(2), 236-246.
- Tranter, P. (2020). Hospitality Industry Forecasting and Revenue Management Techniques. International Journal of Hospitality Management, 89, 102581.
- Boorom, S., & Steed, C. (2014). Hospitality Financial Management. Wiley Publishing.
- Yeung, M. (2012). Modeling Demand in the Hospitality Industry. Annals of Tourism Research, 39, 275-293.
- Kasavana, M. L., & Brooks, R. M. (2012). Hospitality Financial Management. Educational Institute of the American Hotel & Lodging Association.
- Harrington, R. J., & Ottenbacher, M. C. (2010). Hospitality Innovation and Technology. Journal of Hospitality & Tourism Research, 34(3), 297-314.