Platts Market Data - Shipping

Platts global freight assessments have provided a basis for trade across commodities for decades. As the market evolves, our scientific and transparent methodology is updated to reflect market changes more accurately. We bring consistency to our price assessments through principles and parameters that guide our market specialists at every stage of the process.

With Platts Market Data – Shipping you will receive:

- Global coverage
- Platts shipping assessments deeply embedded and underpinning our commodity price assessments
- Cross commodity, bulk market coverage
- Market relevance - 0.5% sulfur compliant, new routes are added, old routes removed

Coverage includes:

- Dirty Tankers - Freight rates by vessel/cargo size for 85+ key crude and fuel oil routes; dual rates - Worldscale/Lumpsum vs $/MT pricing
- Clean Tankers - Freight rates by vessel/cargo size for 90+ key refined oil and LPG routes and 25+ key petrochemical routes; dual rates - Worldscale/Lumpsum vs $/MT pricing
- LNG Carriers - Freight rates by vessel/cargo size for 99 LNG key routes
- Dry Bulk Carrier – 30+ Timecharter equivalent rate ($/day) for capesize, panamax and supramax size vessels and 120+ Voyage charter rates for capesize to handymax size vessels to transport alumina, bauxite, coking and thermal coal, ferrous scrap, grains, iron ore, limestone, petcoke, polymers and sugar
- Container box rates and bunker surcharges – Box and bunker surcharges rates ($/FEU) for 20 routes transporting manufactured goods

<p><br/></p>

Sample Tables:<br/>A ) PRICEDATA<br/>B ) REF_DATA_MARKETDATA

Sample Fields:<br/>A ) PRICEDATA

- SYMBOL
- MDC
- DESCRIPTION
- BATE
- ASSESSDATE
- UOM
- CURRENCY

B ) REF_DATA_MARKETDATA

- COMMODITY
- DELIVERY_REGION
- DESCRIPTION

View Descriptions:

- PRICEDATA - Get the latest and historical assessments by Symbol
- CORRECTIONS - Get all corrections to the assessments by Symbol
- REF_DATA_MARKETDATA - Get additional reference data for Symbols
- MARKETDATA_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset