Systematic approach to Modelling

The approach that I thought would be best would be to take a weighted average of Price/DWT for 4 closely comparable ships. Upon taking an average of these Price/DWT factors, I multiplied this value by the 172,000 weight of the Bet Performer to get its worth. I also considered the Capsize index in pricing the Bet Performer off of its comparable. The max Index value was now 12,479 in May 2008, so solving for an index factor to adjust my 76.65 million result, yields a selling price of $135.36 million. Given all the information from the case, Fertilia seems to be the closest comparable to the Bet Performers statistics (11 years old at sale, 172k tons, and built in 1997) The interpretation of the above regression is intuitive in nature and makes sense. As the age of a ship increases, the ship value will decrease. This is shown to us with the negative coefficient on X1 (age). The interpretation of Weight and Capsize index are similar. Both having positive coefficients, we would expect a larger capacity ship to be priced higher, as well a ship with a higher Capsize Index rating to be priced at a higher level b. Some other factors that could have an impact on the price of a ship

1. Engine Size – This variable, per the case, explains that the size of the engines has a significant impact on the sale price of ships
2. Threat of Substitute Shipping Methods – The presence of air shipping methods could have a strong impact on the demand for ships, thus driving down the sale price.
3. Macro-economic conditions – The primary goods that are being shipped as well as the material that these ships are made out of are very sensitive to economic fluctuations. This could then have a direct impact on ship prices if demand for the goods being shipped is reduced.

c. Based on the regression output above, I would say that these three variables jointly explain ship prices very well. Looking at R2 first, this regression yielded a very high result of 92%. The interpretation here is that