The Maritime
Dry Bulk Freight Index3,057 -0.2%Capesize5,052 -0.8%Dirty Tanker Index2,631 +2.2%Panamax2,275 +1.7%Supramax1,608 -0.2%Clean Tanker Index1,417 -0.6%Handysize876 -0.3%Dry Bulk Freight Index3,057 -0.2%Capesize5,052 -0.8%Dirty Tanker Index2,631 +2.2%Panamax2,275 +1.7%Supramax1,608 -0.2%Clean Tanker Index1,417 -0.6%Handysize876 -0.3%Dry Bulk Freight Index3,057 -0.2%Capesize5,052 -0.8%Dirty Tanker Index2,631 +2.2%Panamax2,275 +1.7%Supramax1,608 -0.2%Clean Tanker Index1,417 -0.6%Handysize876 -0.3%Dry Bulk Freight Index3,057 -0.2%Capesize5,052 -0.8%Dirty Tanker Index2,631 +2.2%Panamax2,275 +1.7%Supramax1,608 -0.2%Clean Tanker Index1,417 -0.6%Handysize876 -0.3%Dry Bulk Freight Index3,057 -0.2%Capesize5,052 -0.8%Dirty Tanker Index2,631 +2.2%Panamax2,275 +1.7%Supramax1,608 -0.2%Clean Tanker Index1,417 -0.6%Handysize876 -0.3%Dry Bulk Freight Index3,057 -0.2%Capesize5,052 -0.8%Dirty Tanker Index2,631 +2.2%Panamax2,275 +1.7%Supramax1,608 -0.2%Clean Tanker Index1,417 -0.6%Handysize876 -0.3%
Vessel valuation

Valuation accuracy report

Our valuations, scored against reality. Every model is backtested walk-forward on real sales it never trained on, so you can see how close the values land before you rely on one.

Accuracy-weighted error
±20.0%
mean absolute % error, out-of-sample
Within ±20% of price
68%
share of held-out sales
Real sales tested
4,266
transactions the models never saw
Segments covered
4
bulker · tanker · container · gas

Bulkers

fit on 6,561 sales · as of 9 Aug 2026

ModelMethodMAPEWithin 20%Tested
PLIMSOLLStatistical±17.3%72%2,731
HALCYONMachine learning±17.5%70%2,731
SEXTANTStatistical±17.6%69%2,730
WAYFINDERMachine learning±24.2%60%2,731
DRYDOCKFundamental±34.6%34%2,731
Best model lands within ±17.3%, beating a naive size-bucketed $/dwt baseline (±43.3%) by 26.0 pts.

Tankers

fit on 3,893 sales · as of 9 Aug 2026

ModelMethodMAPEWithin 20%Tested
HALCYONMachine learning±21.5%66%1,305
SEXTANTStatistical±22.8%66%1,304
PLIMSOLLStatistical±31.6%49%1,305
DRYDOCKFundamental±34.2%38%1,305
WAYFINDERMachine learning±36.6%51%1,305
Best model lands within ±21.5%, beating a naive size-bucketed $/dwt baseline (±44.0%) by 22.5 pts.

Container ships

fit on 296 sales · as of 9 Aug 2026

ModelMethodMAPEWithin 20%Tested
HALCYONMachine learning±47.1%31%189
SEXTANTStatistical±55.4%25%189
PLIMSOLLStatistical±67.5%30%189
WAYFINDERMachine learning±77.7%27%189
DRYDOCKFundamental±152.5%5%189
Best model lands within ±47.1%, beating a naive size-bucketed $/dwt baseline (±460.5%) by 413.4 pts.

Gas carriers

fit on 126 sales · as of 9 Aug 2026

ModelMethodMAPEWithin 20%Tested
WAYFINDERMachine learning±27.0%49%41
SEXTANTStatistical±27.2%55%40
PLIMSOLLStatistical±30.6%51%41
DRYDOCKFundamental±34.8%51%41
HALCYONMachine learning±51.6%29%41
Best model lands within ±27.0%, beating a naive size-bucketed $/dwt baseline (±50.4%) by 23.4 pts.

How this is measured

Walk-forward. Each model is refit on older sales and scored only on more recent sales it never trained on. No sale is ever tested against itself.

MAPE. The mean absolute percentage error between the model's value and the real price achieved. Lower is better; ±20% is the industry-relevant band.

Baseline lift. We compare against a naive size-bucketed $/dwt rule. The gap is the value the models add over a back-of-envelope estimate.

Figures recompute as new transactions settle, so accuracy tracks the current market rather than a frozen snapshot. Accuracy is a track record, not a guarantee: any single valuation carries the error range shown on its report.

Value a specific vessel

Run the full multi-model report on a named ship or an arbitrary spec.

Open the valuation calculator