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Forecasting Demand for Hospitality Industry - Case Study for Starwood Hotels and ResortsWorldwide, Inc.
Transcript of Forecasting Demand for Hospitality Industry - Case Study for Starwood Hotels and ResortsWorldwide, Inc.
Luxury and upscale lodging
1,089 total properties
Over 154,000 employees
Executive office located in Stamford, Connecticut Revenue Revenue is earned by
Fees for hotel management
Operational income Business Strategy Reduce investment in owned real estate
Increase their focus on:
Franchise business Business Objective Maximize earnings by increasing the number of management contracts and franchise agreements they have Following this strategy Starwood has sold 65 hotels since 2006 for roughly $5.6 billion. Independent Variables What worked What didn't work Regression Equation Interpretation of the Coefficients T-test F-test VIF Forecasting Quarterly Moving Average Seasonality Average Daily Rate - Marriott
Growth Change in Marriott ADR
Marriott Occupancy Rate
Starwood Occupancy Rate
Average Daily Rate - Starwood
Growth Change in Starwood ADR Price of Gasoline
Median personal US Income
Inbound International Travelers
Annual US unemployment rates
Total Average Air Travel Price Index
Annual US population Growth Marriott Revenue
Gross Private Domestic Investment (GPDI)
Change in Air Travel Price Index (ATPI) Ŷ = -5752.88 + 4.44(G) – 124.84(A) + 0. 24(M) Gross Private Domestic Investment For every one billion dollar increase in the GPDI, Starwood revenues are expected to increase by $4.44 million.
Positive Relationship Percent Change in Air Travel Price Index When ATPI increases by one percent, Starwood revenues are expected to decrease by $124.84 million.
Negative Relationship Marriott Revenue When Marriott revenue increases by $1 million, Starwood revenue is expected to increase by $240 thousand.
The small positive relationship may be attributed to an industry increase and inflation. F computed = 21.08
F critical = 3.41
found by using .05 significant level, numerator df=3, denominator df= 13
Since F computed is greater than F critical the null hypothesis is rejected
At least some of the independent variables are helpful in explaining the variation in the dependent variable From the table we can see that both GPDI and Change in ATPI are significant and can not be zero. Although Marriott Revenue does not pass the t-test we have determined that it is still significant and should be included in the regression. Two-period:
MAD = 146.59
MAD = 175.907
Forecast: 1364.33 Two-Period Moving Average The two-period moving average is the best at forecasting Starwood's Revenue because it has the lowest MAD. Trend Equation y=524.663+16.855t
MAD = 297.026
Twenty Quarter Forecast Results Change in ATPI R Square: 0.214853
VIF = 1.274 GPDI R Square: 0.735157
VIF = 3.776 Marriott Revenue R Square: 0.726715
VIF = 3.659 Four-period:
MAD = 191.878
Forecast: 1358.25 Conclusion External Factors
Lack of significant Competitors
Moving Average and Regression
Future Use Recommendations to Starwood Coefficient of Determination R Square is 0.829515
The independent variables explain 83% of the variation in the dependent variable. Correlation Matrix Correlation between Marriott Revenue and GPDI
Strongest correlation between GPDI and Starwood Revenue Future Growth Increase domestic / global market share
Mid scale and economy hotels
More profit Questions? Profit Potential
Reduce water and energy consumption
Global Strengths Global Brand
Market Segment Weaknesses Airline Industry
Economy Group C:
Matt Barnett, Zach Carlson, Hao Deng, Amy Stukey, Omer Khan, Liang Zhang