Running head: FINAL PROJECT – REVENUE FORECAST FOR NYSE:AHT 1
FINAL PROJECT Sullivan University, Lexington, KY QNT 550: Advanced Quantitative Methods Instructor: Dr. Ahmadin June 7, 2015
TEAM: Koti Cherukuri Srinath Meduri Harith Rojanala Bharathi Rajana Vasudha Nukala
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Introduction Ashford Hospitality Trust (NYSE:AHT) is a subsidiary of Ashford (NYSE MKT:AINC). Ashford is a leading provider of asset management and other services to companies within the hospitality industry. Currently, Ashford serves as the advisors to two NYSE listed real estate investment trusts, namely Ashford Hospitality Trust, our Subject Company and Ashford Hospitality Prime. Combined, Ashford Hospitality Trust and Ashford Hospitality Prime have 126 hotels with more than 28,000 rooms and approximately $6 billion in assets. In particular, Ashford Hospitality Trust has 135 properties. Ashford being a billion dollar company, there needs to be a system in place to recognize revenue and forecast future business in order to make key strategic decisions. This particular project talks about the statistical application to forecast revenue based on key parameters such as rooms available, rooms sold, revenue per each room, total revenue generated, gross operational income, etc. Background Application of Statistical analysis for revenue projection: Advantages and similar practices
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Selection of appropriate quantitative technique We found regression analysis and Crystal Ball predictor as the appropriate technique for our analysis. We have analyzed the data and found that the Gross Operating Profit to be the independent variable. Total Rooms Available, Total Rooms Occupied, Actual Revenue and Room Revenue as the dependent variables. So we ran a regression analysis to find the co-relation between these variables. The reason we felt multiple regression might be a good quantitative technique as there seemed to be a relationship between these variables and we felt we could use these variables to predict the profit forecast for the year 2015. Later on to come up with the Total Revenue Forecast for the year 2015 we ran a multiple regression using Rooms occupied and Rooms available. We found these three variables having a relationship and we felt that Rooms occupied could be used to predict the Total Revenue.
We decided to use Crystal Ball to predict the right forecasting technique for predicting the Rooms occupied. As we felt the Rooms occupied was a dependent variable for forecasting the Total Revenue and Room Revenue. Hence we ran Crystal Ball Predictor for each of the 130 odd properties of Ashford Hospitality for all the 12 months of the year 2014. Based on the right forecasting method given by crystal ball for each property, we decided to forecast Rooms occupied for the next 12 months of the year 2015. Based on the forecast of rooms occupied we could do a regression forecast to determine the Total revenue, using which we decided to arrive at the Gross Operating Profit. True to our expectations we could use the above forecasting techniques to arrive at an optimal forecast for Gross Operating Profit, Total revenue and Rooms Occupied for the Year 2015.
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Identification of data When we received the data we were given monthly data for the year 2014 for five parameters Rooms Available, Rooms Occupied, Actual Revenue, Room Revenue, and Gross Operating Profit for each of the 130 odd Ashford properties. We had to make sense of this data to arrive at a dependable forecast for the Gross Operating Profit and Rooms occupied for the year 2015.
To forecast the Gross Operating Profit for 2015 we had to run a regression using these five parameters. We had to select relevant data and analyze it to arrive at relevant results. So in effect we had to sum up the monthly values of all the five individual parameters to come up with the total Rooms Available, Total Rooms Occupied, Total Actual Revenue, Total Room Revenue, and Total Gross Operating Profit for the 130 odd properties for the year 2014. After having arrived at the summed values for each of the five parameters for the 130 odd properties, we decided to use this data for multiple regression analysis.
To arrive at the forecast for the monthly rooms occupied for the year 2015, we decide to use the monthly data to run it through Crystal Ball Predictor to arrive at the forecast for the next 12 months of the year for individual property. We are enclosing sample snapshot of the sorted and cleaned data in the Appendix.
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Analysis of the data and discussion of results We initially ran multiple regression on the five variables for each of the 130 odd properties using Gross Operating profit as the independent variable and the other four parameters as dependent variables. We found a relationship between Actual revenue, room revenue and Gross Operating profit as the p value was less than 0.05. We found the regression model to be significant with R square value of 0.96.
The relationship is Gross operating Profit = -313736 + 0.225 * Actual Revenue + 0.336 * Room Revenue To forecast Gross Operating Profit we had to forecast Actual Revenue. Having found a relationship between Gross Operating profit and Actual revenue, room revenue, we decided to find the relationship between Actual revenue and the Rooms Occupied and Rooms Available. We ran multiple regression between Actual revenue as Independent variable and Rooms Occupied
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and Rooms Available as dependent. We found the model to be significant with R square of 0.86. We found that the actual revenue was dependent on only total Rooms occupied with p less than 0.05.
The regression equation is Actual Revenue = 4688930 + (-348.156) *Rooms Occupied After having arrived at the relationships we had to find out the Room Revenue linkage. We ran a third regression on Room Revenue, Rooms available and Rooms Occupied. We found a relationship. The model was significant with R square of 0.84 Room Revenue = 1711349+ 122.74 * Rooms available + (-337.31) * Rooms Occupied
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Lastly we had to forecast the rooms occupied using Crystal Ball predictor. We ran crystal Ball Predictor for each of the property for each month to arrive at the optimal forecasting method and Forecast for each property.
Ethical implications Results obtained vs. Research Conclusion REFERENCES
