This paper presents mathematical relationships that allow forecast of the estimated sale price of new container ships, based on data concerning vessels built in 2005–2015. The presented approximations allow estimation of the price based on deadweight capacity (DWT) or the number of containers the ship will carry (TEU). The approximations were developed using linear regression and the theory of artificial neural networks. The presented relations have practical relevance in the estimation of container ship sale price needed in transport studies or preliminary parametric design of the ship. It follows from the above that the use of artificial neural networks to predict the price of a container ship brings more accurate solutions than linear regressions.
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