Compass Maritime Case Study Essay Example

as confirmed time charter contracts, location of the ship, loading equipment, and other specifics which could affect the prices of these ships has been left out. Methodology When determining a price for a ship such as the Bet Performer, it is important to utilize various types of analysis, including comparative analysis and regression analysis. While comparative analysis would usually be useful, because of the recent surge in the Baltic Dry Index, and the multitude of variables available for analysis, it was deemed inappropriate for this situation.

Therefore a series of regression analyses were used, ending in a multiple regression accounting for all variables provided for by the data given. The Analysis portion will go into further detail. Data Requirement In order to determine an optimum price and negotiation strategy for the purchase of the Bet Performer, historical data regarding recent sales transactions of comparable ships was necessary. The most pertinent data that was collected from the recent sales transactions include the age of the ship sold, its sales price, and its deadweight ton capacity. This information is contained in Appendix A.

The other required data was the information about our target ship, the Bet Performer. Its information is detailed in Appendix B. Key Assumptions In order to determine the fair market value of the Bet Performer, and more importantly a value that would yield a price acceptable to the sellers, a few key assumptions were made. First, it is assumed that the market for charter rates for 170K DWT capesize ships will remain steady at almost $250,000 per day. That is important because the investor will need to perform its own financial analysis to determine whether or

not to go ahead with the purchase at the recommended price.

Second, it is assumed that the global economy will remain growing at a steady pace, pushing the demand for shipping vessels higher. This is important because if there were to be a sudden slowdown in the global economy, shipping rates could possibly decline, leaving the buyer with a vessel worth less than the purchase price. A third assumption is that the client will be able to obtain financing for the vessel. Although credit markets have been relatively open recently, it is still something to consider.

A fourth assumption is that the Bet Performer will be useable for a ‘typical’ lifespan of 25 years. This promises our buyer at least 14 years of useful life left. Analysis Initial analysis focused on single-regression analysis, utilizing only one variable at a time. The first analysis involved the price of the ship sold as a function of its age at the time of sale. This analysis returned an Adjusted R-squared score of 0. 61184. Although this is a relatively strong score, it was determined to not be strong enough, and it also leaves out an important variable, the size of the ship.

The next round of analysis tried to determine an optimum price by finding the price as a function of the vessels size (DWT. ) This analysis returned an Adjusted R-squared score of 0. 249047. Again, while this is a low R-squared score, it was noted that it left out other important variables, such as the age of the ships and how recent the transaction was. Next, a multiple regression analysis was performed, using the ships age at time of

sale and DWT capacity to determine a price. This multiple regression returned a stronger Adjusted R-squared correlation of 0. 42638. Although this method did incorporate two of the most important factors in the pricing of capesize ships, it leaves out how the increase in the Baltic Dry Index has impacted the price of the ships. The next round of analysis attempted to include all of the variables associated with the purchase of the Bet Performer, including the age (11 years old,) its Dead-Weight Tons capacity (172,000,) as well as factoring in the recent surge in Baltic Dry Index for Bulk Carriers. This was done utilizing a dummy variable.

The surge in the index occurred after June, 2007, so for all sales starting in July 2007 a dummy variable of 1 was introduced, and all sales prior to that received a dummy variable of 0. After running the regression analysis with this new variable introduced to account for the change in the market landscape, an Adjusted R-squared coefficient of 0. 901814 was returned. This demonstrates that utilizing the dummy variable has returned a much stronger correlation. It was this analysis that delivered our equation of Price=(-4. 62538*age)+(. 271862*DWT)+(35. 08358*DV)+78. 1402, where DV is the Dummy Variable, either a 0 or 1. Since the purchase of the Bet Performer would occur after June 2007, we use a 1 in the equation to account for the rise in recent sales prices. Appendix C, Exhibits 1-4 outline each regression analysis, and Exhibit 5 is a breakdown of each suggested price derived from the analyses. Conclusion and Concerns Some concerns that will surface during this negotiation and pricing include the condition

of the Bet Performer, any pre-purchase inspections that need to be completed, and how many other potential buyers there are.

Although it will be impossible to know how many other buyers are out there, if we bid too low, there is a chance we may not receive a counteroffer, and therefore lose the opportunity altogether. It is thought that through this analysis we will provide our buyer the best chance to purchase the Bet Performer, balancing the current market conditions with the specifics of the vessel. It is my conclusion that a suggested bid price of $109. 2 million will give our client its best opportunity to purchase the Bet Performer. Compass Maritime Case Study MBA 603 2/21/2012 Andrew Schneider