Marriott Case

1310 WordsOct 26, 20086 Pages
Executive Summary We found the weighted average cost of capital for Marriott as a whole to be 9.68%. The divisions of Lodging, Contract Services and Restaurants had WACCs of 8.14%, 13.33%, and 9.63% respectively. The only variable between these divisions that remains consistent is the tax rate. Marriott has a target rate for each of the divisions’ capital structures, which affects their debt and equity betas. Also, there are stark differences between the betas in the segments, as well as the different assumptions a financial analyst must use when calculating risk-free and market rates for fixed and floating debt issuances. In order to calculate the WACC, we first estimated the cost of debt using the specific guidelines and…show more content…
If a firm operates in more than one industry, we used a simple average of the relevant costs of debt in order to calculate the total cost of debt for the specific firm. The grouping can be seen in table 3. The unlevered beta calculation takes out the tax-shield effect to achieve the beta of a firm, if it had no debt. The unlevered return is the return that a firm's shareholders would expect should it be free from debt. This holds true for Marriott and all of its peers. Estimating Divisional Market Betas Having the unlevered betas for Marriott and all of its competitors is however not enough, as we need to estimate the relevant beta for each of the three divisions and Marriott as a whole. This is to be done by utilizing the unlevered betas just found. One of the first problems we encountered is whether to use the simple average or weighted average unlevered betas for each industry. For example, in our calculations, we found that McDonalds beta greatly influences the market beta because of the company magnitude in our weighted average calculation. McDonalds has a different market segment than Marriott’s Restaurant division, so we felt that the level of influence was inappropriate for our beta calculation. In our simple average calculation we found that Frisch’s restaurants also influenced the data in that their beta with the market is significantly lower than most of the other restaurants in the data table. However, the
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