Define supply in the context of tourism
A. Access the “Hotel Industry Foundations” STR MS Power Point presentation in the NRRT 602 folder under the “Classes” directory on the HDNR N: Drive. The entire presentation is there for your reference, but for this exercise, begin with Part 4 “Benchmarking,” (beginning on Slide #161). After reviewing the power point slides, briefly answer the Discussion questions for Part 4 listed on slide # 200.
B. Next, access the STR “Hotel Foundation Application Exercises” spreadsheet under the NRRT 602 folder on the N: Drive. This spreadsheet contains tabbed workbooks that include the entire Nashville hotel market (where STR is headquartered), a series of example or “subject” hotels, and the Nashville Hotel “Census.” Follow the steps in the “Application Exercise,” beginning on Slide #201 in the Power Point, to identify a “Subject” hotel and establish a competitive set for that property. For your assignment, identify and describe the key attributes of your subject hotel, as well as the key attributes of at least five competitive hotels in the Nashville market. Describe according to the process outlined how you selected these specific hotels.
Part II Subject Hotel Performance Metrics and Competitive Set Comparisons (20 Points). The second part of the assignment works through “subject” and “competitive set” (comp set) data to calculate and compare basic performance analytic metrics or Key Performance Indicators (KPI’s), such as average daily rate (ADR), occupancy rate (OCC), and revenue per available room (REVPAR). This application of competitive analytics is the process of comparing your subject property performance, period over period (monthly in this case), against the aggregated performance data of your competitive set. To begin, access the “Hotel Math Fundamentals” MS Power Point slides in the NRRT 602 Class Folder on the N: Drive and save a copy of the spreadsheet to your computer, saving as yourname_NRRT602_IWA1.
A. After reviewing the Hotel Math Fundamentals MS Power Point for Part 1: Property Data, briefly answer the Discussion Questions listed on Slide #45.
B. Next, access the “Prop Raw Data” (Monthly data) tab in the “Math Fundamentals Application Exercises” spreadsheet, on the NRRT 602 Folder on the N: Drive. Using the operations outlined in the Power Point regarding an individual “subject” hotel property calculations, complete Application Exercise #1 (Slide #46) to calculate the Average Daily Rate (ADR), Occupancy (OCC), and Revenue per Available Room (REVPAR) for a sample “subject” property. The data provided is an example set and not necessarily from the hotel that you selected in Part I.
C. Next, after reviewing the Hotel Math Fundamentals MS Power Point for Part 2: “Competitive Set Data,” briefly answer the Discussion Questions (Slide #94).
D. Finally, access the “Subject & Comp set Raw Data” tab in the “Math Fundamentals Application Exercises” spreadsheet as above. Using the operations outlined in the Power Point regarding Comp Set calculations, complete Application Exercise #1 (Slides 95-97) to calculate the KPI’s for your competitive set and then estimating your subject property’s position in comparison to the comp set by calculating the competitive index for each key performance indicator (KPI).
Part III Demand Forecasting. (50 Points). The third part of the assignment asks you to perform a series of forecast exercises using STR performance data for a subject hotel that illustrates different approaches to forecasting. During your NRRT 650 course, Dr. Bright went through these various techniques for forecasting performance. I have provided a PDF of the Entrepreneurial Finance, Adelman (2009), Chapter 6 “Forecasting and Proforma Financial Statements,” from Dr. Bright’s course, available in the NRRT 602 Folder under the Classes directory on the HDNR N: Drive. In this exercise, you will use STR data from a particular property to determine the best method for your forecast.
After reviewing Adelman, Chapter 6, begin by once again accessing your saved copy of “Math Fundamentals Exercises” spreadsheet in the NRRT 602 folder under the “Classes” directory on the HDNR N: Drive. Then open the “Prop Raw Data” tabbed workbook. This workbook provides you with four years of monthly “Supply,” “Demand,” and “Revenue” performance data for an individual subject hotel property ranging from January 2008 through December 2012. You will use this monthly performance data to perform three different types of forecasting calculations in order to determine which forecast model results in the best fit, or yields the lowest “Mean Absolute Value Deviation” (MAD) from the actual performance over these four years. Each model will project a forecast and you will examine each in terms of the Mean Absolute Value Deviation (MAD) for the forecast from actual to determine which forecast model provides the best fit, or most accurate approximation of performance in future years.
Perform the following analyses. Reference the Adelman chapter and the Regression power point provided for help.
A. Perform a three month moving average forecast calculation. Using the monthly data provided for an individual subject property, month by month from January 2008 to December 2012, follow these steps:
1. Insert a column next to the Revenue column and name the new column Dem_3MMA (Demand_three month moving average). Calculate this by accessing the third row of data (row four with labels) and adding the first, second and third rows of demand data and dividing by 3 (200801+200802+200803/3). Copy the Excel formula for this equation into the cells in this column through 201212 and then one row beyond. In the YYYYMM column, add additional dates for 2013: 201301 through and 201312. Once you have determined which method of forecast is the best, you can calculate a forecast for 2013 by extending the selected forecast of demand through 2013, month by month.
2. Next, insert a column next to Dem_3MMA and label it Dem_Deviation; and a column next to that labeled ABS_DemDev. In Dem_Deviation column, row #3, next to the first figure for Dem_3MMA, calculate the difference (deviation) of your forecasted amount and the actual demand (Dem_3MMA (-) Demand). Copy and paste this formula in the Dem_Deviation column through the row for 201212. Then calculate the absolute value deviation by using the ABS function in Excel placed before the formula from the Dem_Deviation column. Copy and paste this formula into the ABS_DemDev column in rows through the row for the 201212 period. Now calculate the mean for the entire ABS_Dem column in the cell just below the last row of data. This gives you the mean of the absolute value of the deviation of the 3 month moving average forecast, and the actual demand numbers in the original dataset.
This is the indicator used in the Adelman chapter to compare forecast methods. I like to use a percentage difference. You can calculate this on a monthly basis just like the percentage change in the earlier exercises by subtracting the Demand column (last year) numbers from the Regression estimate (this year) numbers each month and dividing by the Demand number.
Calculating Percentage Deviation This year – Last year
Calculate the percentage deviation for the MAD for easier comparison. Apply the formula above to the calculated mean of the Demand column (last year) and the calculated mean of the Forecast estimate numbers Dem_3MMA (this year). This gives you the MAD % Deviation. An easier number to compare the percentage difference between the deviation between actual demand and forecast demand for each model.
B. Perform a “weighted” three month moving average forecast calculation. Follow the steps below to calculate the weighted three month moving average forecast for demand and revenue. The only difference in calculating this forecast method over the 3 month moving average is to include the weighting coefficient for each month used in the calculation and then dividing by the sum of those coefficients (see the equation below).
1. Insert a new column next to your current work and label it Dem_W3MMA. In the third row of data (row 4), calculate the weighted moving average of the first three months using the Excel formula for the equation shown below. Effectively multiplying the actual demand number in January 2008 by the first weighted coefficient 3906(.1); then adding that to the February of 2008 demand figure times the second weighted coefficient 5242(.3); then adding that to the March 2008 demand number times the third weighted coefficient 8168(.6); and then dividing this sum by the sum of the weights .1+.3+.6.
Calculating a weighted 3 month moving average 390.60+ 1572.60+4900.8 = 6864
Copy and paste this formula into the remaining cells in the column through the row for January 2013.
2. Calculate the deviation from actual, the absolute value deviation from actual, and the Mean Absolute Deviation for this forecast. Then, also calculate, as in subpart A, the MAD % Deviation.
3. Compare the Mean Absolute Deviation and MAD % Deviation for the three month moving (Dem_3MMA) and the weighted three month moving average (Dem_W3MMA). Which is less? What does this mean?
Note: You could continue to calculate other the weighted forecasts using alternative weighting coefficients, as described in the Adelman Chapter, to see which weighting yields the least deviation from actual and therefore is the best forecast. I haven’t asked you to do that here.
C. Perform an example regression forecast. Review the Power Point slides for Regression Forecasting available under the NRRT 602 Folder in the Classes directory on the HDNR N: Drive. As this brief presentation points out, there are many approaches to creating a predictive model through regression. The first step in the process would be to determine which “explanatory” variables would be the most useful in generating a predictive module using regression. The other thought, as pointed out in the Goeldner chapter on Demand Measurement, is that there may be multiple factors that influence demand and the predictive models can become quite complex, as in the case of the Hong Kong airport model described in the Week Two video.
After reviewing this brief Power Point, follow the steps below to create a forecast using regression analysis. Remember that the dependent variable (y) for this exercise is the demand variable in the STR “Prop Raw Data” spreadsheet tab. For the purposes of this exercise, I have selected the Revenue figure provided by STR as the independent, or “explanatory” variable (x) for this example, assuming that there exists a highly elastic relationship between price and demand, when supply is held relatively constant. This is the role of experience, or the employment of best practices, by the forecaster. What Goeldner calls the “Delphi Method.” This expertise may be industry or sector specific, or even applicable only for the individual business environment where the forecast is being performed.
For this model, you will be regressing the STR Revenue monthly actual figures on the STR monthly actual Demand figures to determine a regression line, Beta Coefficients that will complete the regression formula and allow you to calculate the forecast demand figures for each period. When running the regression analysis in SPSS, you will generate an output that provides you with these coefficients, as well as an unstandardized R2 value that estimates the amount of variation in the dependent variable (demand) attributable to the influence of the independent variable (revenue).
1. To begin this exercise, access an original copy of the “Hotel Math Fundamentals” data file in Excel and access the first tab, once again, “prop raw data.” Copy all of the data in this spreadsheet into the second tab of your saved Excel data file from Part II.
2. Since regression is easier to perform in SPSS, start by copying the original data provided by STR in the “prop raw data” tabbed spreadsheet (4 columns, including the YYYYMM, supply, demand and revenue variables). Once you have highlighted and copied these data, you can simply paste them into a blank data window in SPSS by clicking in the top left cell, right clicking and selecting paste. You will need to label each variable in the variable view.
3. Once the data are pasted into SPSS, run a regression analysis by selecting the Analyze function tab->Regression->Linear Regression. In the operation window, you want to select the “Demand” variable as the dependent variable and the “Revenue” variable as the independent variable. Also make sure to click on the statistics that you want displayed—I just pick them all.
4. Paste the operation into Syntax, then run the regression. Reviewing the Output, look first for the unstandardized R2 value. Why do we use unstandardized? (I’m channeling Jerry here). What does the R2 value tell you about the fit of this model? Next, look for the unstandardized Beta Coefficients described in the power point and in Jerry’s 601 lectures. These coefficients (bo), the constant for revenue, and the slope intercept (b1), are then used in the regression formula to calculate a forecast demand value (ŷ). See the regression formula below.
Regression formula: Ŷ=bo + b1 (xi)
5. Next, you will calculate a new variable for the forecast demand (ŷ). Compute a new variable in SPSS by selecting the Transform tab and the COMPUTE function. In the dialogue window, name this new output variable Dem_regfrcst. Then, use the equation above to create a formula using the Coefficients from your regression analysis, and the Demand variable as the independent variable value (x). Now you have a computed variable for estimated demand in each monthly period based on your regression analysis.
6. Next, you will paste this variable back into your saved Excel spreadsheet tab for the forecast analysis (Part III) by coping the column of data in SPSS and pasting the data into the next open column in your Excel spreadsheet. You will again need to name the column Dem_regfrcst. Working in Excel now, replicate your calculations from the earlier forecasts to generate the Deviation, Absolute Value Deviation, Mean Absolute Value Deviation (MAD), and the percentage MAD for this regression-based forecast. Once again, examine the percentage deviation from the actual demand and compare with the other two models. Which model seems to produce the best forecast?
7. Creating a forecast. Once you have determined which method is the best to use for your forecast, you can extend the forecast beyond current time periods (2012 in this case) through the next year or beyond. Without actual data available for 2013, what information would you need to use to predict demand in 2013?
The Adelman text used a different method for creating a regression using the time period 1-36, regressed on the dependent variable “Sales.” Because the time period is predictable (adding 1 to the previous time period), Adelman was able to use the original regression formula to predict values for sales in year three. Because of the equal interval of increase and the lack of variability in sales, the predictive model was pretty simple, basically adding one to each succeeding month’s forecasted sales.
In contrast, the exercise that we undertook used a theorized relationship between revenue and demand that turned-out to be highly predictive. However, the model was dependent on actual revenue numbers 2008-2012. In order to use the regression formula to predict future revenue, we would need to have predicted revenue numbers. The information needed to extend the forecast through 2013 and beyond is either a rolling forecast as each new month’s revenue figures are realized, or an extrapolation of revenue numbers for the future periods. One way to accomplish this is to create a new monthly revenue forecast for the future based on an average of the same month in the previous five years.
To do this, use your completed regression forecast in Excel, use a formula to add the actual revenue numbers for January 2008 + Jan 2009 + Jan 2010 + Jan 2011 + Jan 2012 and then divide by 5.
Jan 2008 + Jan 2009 + Jan 2010 + Jan 2011 + Jan 2012
Paste this formula in the revenue column in January 2013 cell and copy forward through December 2013, Excel will calculate each cell with the average of revenue for that month over the last five years. You could also using the same weighting technique used in the weighted three month moving average since the more recent years are likely to have more bearing on the forecast.
Copy these new revenue numbers into the SPSS data file below the last revenue figure provided and then COMPUTE a new variable using the same regression Beta coefficients and the revenue variable.
This new variable will show a forecast demand through 2013. This provides a good base line forecast, but in practice, many other variables can be introduced to create a more realistic picture of your monthly numbers, and updating the forecast is critical as new information becomes available. One such factor would be a significant change in supply—if you built a new wing on your hotel, or took a floor out of service for a period. The predictive model that we used assumed a relatively static supply. Another factor might be a large group in the hotel in 2013, which was not there in 2012, or a city-wide event that is taking place. In addition, as we say in Goeldner, many exogenous factors can affect demand, and some of these, including seasonality and cyclicality can be incorporated into a more complex multiple regression model. This is where forecasting crosses over from science to art and the skill and experience, or well established best practices become crucial in providing a truly actionable forecast.
Part IV Analysis and Conclusions. (20 Points)
Briefly answer the following questions:
1. How might the Demand propensity and resistance factors outlined in the Goeldner Chapter 13 reading relate to creating a forecast model for a destination? How might you use these factors to establish some measures that could be incorporated into a predictive model?
2. In looking at the three techniques for forecasting, which of these methods might be the most beneficial in predicting the demand for a destination, rather than a hotel? What sources of data can you think of that would provide a basis for predicting demand a destination?
3. Briefly define supply in the context of tourism. Be specific and make sure to include attributes of tourism supply and some examples, as well as a general definition.
4. Using Goeldner’s formula on page 353, this report summary from Visit Nashville, this report from the Nashville airport. Complete the following:
a. Define the variables R, T, P, L, S and give what you believe to be the figures provided these two sources. Is there enough information to calculate the estimated room demand per night in Nashville on a given day?
b. If so, calculate the estimated demand for rooms per night for a given year.
c. Now, open the STR “Hotel Foundations” spreadsheet and access the tab at the far right (Nashville Census). By looking at the total rooms inventory for the Nashville market, does your calculation demonstrate that Nashville is oversupplied, or undersupplied with sleeping room?
What affect would you expect an oversupply of rooms have on room rates in Nashville? Does the performance data provided year over year for your subject hotels and the compset hotels you reviewed seem in line with your conclusions?
Below are some screenshots from the Smith Travel Research CHIA training MS Power Points that highlight specific KPI, percent change and competitive index calculations. The full slides are available on the N: drive. For help with the forecasting exercises, refer to the Adelman Chapter, the short Regression MS Power Point, or Jerry’s lectures and text regarding regression analysis.
1. Calculate Average Daily Occupancy (OCC). In both the subject dataset and in the competitive data set, Insert a column next to the “Revenue” column and label that column OCC. Then calculate the occupancy rate using the following formula: OCC=Demand/Supply. Multiply this fractional number by 100 to create a percentage rate.
2. Calculate Average Daily Rate (ADR). In both the subject dataset and the competitive set dataset, Label the column next to the Occupancy (OCC) column as ADR ($). Then Calculate ADR for the first row of data using this formula: ADR=Revenue/Demand. Then copy the formula, highlight all of the remaining cells in the ADR column and paste the formula in those cells. This provides you with an average revenue per roomnight for each month based on rooms sold (Demand) and Revenue generated.
3. Calculate Revenue per Available Room (REVPAR). In both the subject dataset and the competitive dataset, Label the column next to the ADR column as REVPAR ($). Then calculate REVPAR ($) for each month using this formula:
REVPAR=Revenue/Supply. Then copy the formula into each succeeding month as above.
4. Calculate Competitive Index. Next you will calculate the index comparison between subject property performance and the performance of the competitive set in aggregate for each KPI. Following the RevPAR column for the competitive set, insert three columns a column labeled as shown below.
To calculate the index comparison of each metric, use the formulas described below:
a. Index Occupancy=SubjectOcc/CompsetOcc (multiplied by 100 to create a percentage).
b. Index ADR=SubjectADR/CompsetADR (multiplied by 100 to create a percentage).
c. Index RevPAR=SubjectRevPAR/CompsetRevPAR (multiplied by 100 to create a percentage).
5. Create subtotal and grand totals using the Excel subtotal function for each data column (supply, demand, and revenue). Then apply the same calculations for each metric and the indexes to create year by year totals or “year to date” (YTD) metrics for comparison’.
6. Calculate the percentage change from period to period, creating points of comparison for subject property level and competitive set longitudinal performance. Begin calculations by creating another column and calculating the change from year one to year two, etc.
7. Regression Analysis. Regressing the Revenue variable over time on the Demand variable over time for the subject property.
a. Open the dataset in SPSS and select Regression on the Analysis tab. Then select the Demand Variable as the dependent variable (y) and the Revenue Variable as the independent or “explanatory” variable (X). Select the appropriate statistics and paste into Syntax. Run the syntax for this analysis.
b. Test the goodness of fit by examining the R2 squared value to determine the amount of change in the dependent variable that is explained by the independent variable (Revenue). What percentage of change is explained by your model?
Note: Remember that there is a theoretical relationship between a demand variable and price. We are assuming that there does exist price elasticity in relation to demand. The regression model also takes into account the variation in occupancy and revenue over a several year period in order to create Beta coefficients that will enable the calculation of daily predicted demand and revenue numbers.
Identify the intercept or “constant” Beta coefficients and the Beta coefficient for the independent “explanatory” variable, Demand to satisfy the following regression equation:
Where ŷ represents the estimated value for y (Passengers in the example below, Demand in our example) for any given value of x (Fare in the example below, Revenue in our example).
2. Use the estimated value for y (dependent variable Pass) for each daily x (Fare) value.
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