Ship valuation model proposal: An application for dry bulk ships

The values of ships, which are the most important capital assets of maritime transportation, are of utmost importance in decision-making for many relevant units, from ship owners to insurers and finance providers. In order to make the right decisions, it is equally important to have accurate ship values that reflect the market. The essence of accurate ship valuation is to choose an appropriate valuation methodology for accurate ship valuation. At this point, Machine Learning (ML) algorithms, which are widely used today, come to the forefront compared to traditional methods. The aim of this study is to propose a highly accurate ship valuation model for the Supramax/Ultramax segment to interested parties using ML methods, with models established on the basis of linear regression. For this purpose, a four-stage path was followed. (i) The first data set, in which the significance of independent variables for Supramax/Ultramax ships was tested and linear regression models were created with statistically significant variables, covers the period from August 2005 to December 2022. At this stage, a model was first created that takes into account the values of the Baltic Exchange indices in the month of sale as an independent variable, and then another model that takes into account the values in the month of sale and the values in the months before the month of sale as independent variables. (ii) For the two linear regression models created; Price predictions were made with Linear Regression, Decision Tree, Random Forest and XGBoost ML algorithms. (iii) In the next stage; the two models created were simplified to include independent variables that can be easily obtained from the market in the model; and the obtained simplified models were re-predicted with ML algorithms. The fact that the first model and the simplified model are close in terms of prediction performance shows that the simplified model can be used in prediction. (iv) In order to show that the simplified model can produce reliable results when the data set is expanded, 2023 data was added and price predictions were made again with ML algorithms. As a result, the simplified model's predicting performance was further improved with the addition of new data; the model established with the Baltic Exchange indices in the months before the sales month provided a significant superiority over the other model. XGBoost stands out as the best method among the methods used according to performance criteria.