Valuation : Forecasting of Revenues

Forecasting of Revenues

Revenue from a mine is determined from 2 essential factors (or for that matter in any business). The first is the quantity of Saleable product produced and sold. The Quantity produced will be determined as per the mine plan for the particular mine. For the purpose of valuation the full mine plan till the end of mine life must be determined. It is essential to model the year wise (or quarter wise or month wise as may be possible) production quantities as any variations in the level of production is likely to have an impact on the cash flows of the project and the NPV.

A detailed long-term production plan for the mine is essential for the purpose of determining the cash flow forecasts for any mine or a project. Few key points that are critical to determination of the cash flows is to account for

• the appropriate losses (handling and processing losses).
• Variation in grades year on year – which will result in variation in the final production levels of saleable product
• Variation in strip ratios over the mine life – this will result in substantial variations in the cost of mining over the life of mine.

The Second factor in the determination of Revenues is the Selling Price. As a thumb rule, producers/miners remain one of the worst forecasters of the future market price of metals. Though neither have the analysts done any better in doing the same. There are certain alternatives available for forecasting the selling prices for the metals/intermediates/ore.

Consensus forecast - One of the safest and easily defendable assumptions is to take a consensus of a number of analyst’s forecasts. Either mean or mode can be taken or mean after removing the outliers can also be taken as a safe assumption. I personally do not recommend too much of “cleaning” the data to remove outliers as it tends to be get biased in either direction depending on the person doing the cleaning.

The consensus forecast for exchange traded metals are available on Bloomberg and Reuters. Reuters also runs a series of half yearly surveys of forecasts for traded metals. Other alternatives to get consensus forecasts are to service providers such as consensus economics or to collate the forecasts individually from the research reports/data sites of each analyst/forecaster though its an extremely tedious exercise and prone to selection bias.

Economic Forecast – A detailed economic forecast for the price of the metal/ore can be drawn based on

1. Supply demand situation
2. Proxy pricing with alternatives
3. Cost curve analysis – marginal cost of production

While these methods constitute entire subjects unto themselves, we will look at these in brief. Metals are linked to the economy. Demand for all metals is driven primarily arising out of consumption patterns and growth. Consumption is metal specific (due specific usage of each metal) but is driven by consumerism and infrastructure spends in different proportions. The economic forecasts will begin with a forecast of the economic growth of the world economy and in particular the major regions of consumption, followed by sectoral forecasts for consumption of the finished product and backward calculation being down to the level of consumption of primary metal. This alongwith the forecast of secondary metal consumption (scrap etc) and supply will provide a full forecast of the demand situation for primary metal.

On the supply side of the equation, a forecast of mine wise production (this data is generally collated by data suppliers like Brookhunt( for base metals) etc ) from existing as well as planned projects needs to be drawn. There are generally variations in the year wise production levels from existing mines, as well as delays in projects, project cancellations and extensions of mine life which all need to be factored in. Many analysts such as Brookhunt tend to classify projects as probable, possible etc to draw a probability based expected production level and supply for the future.

Proxy pricing – while in itself it is not a complete forecast, it also forms one of the inputs a jigsaw puzzle (which hitherto no one has solved entirely) of forecasting the pricing. Proxy pricing forms one of the reference points to ensure we are not straying too far from what might be the reality. It is also a powerful tool for derivatives of primary metals such as deriving a petrol price forecast on basis of Crude oil forecast.

Cost curve analysis is also one of the major inputs on the supply side. Cost curve is a chart of cumulative production vs. the cost of production. It is essentially a sorted list of all producers from low cost to high cost. The supply from mines is highly dependent on the current cost of production and the price of the finished product. There is substantial variation in the cost of production from various mines on account of intrinsic factors – technology, strip ratios, logistics costs etc which cannot be in most cases reduced with reduction in the selling price. As mines begin to run cash losses, they tend to shut down the operations resulting in drop in supply. Also with change in prices, the investments into capex and new projects tend to dry out resulting in reduced supply in the years to come. Generally, a baseline price for all commodities in the long term is drawn at 75-80 percentile position of the cost curve. This baseline price can be viewed as a long-term forecast for the price of the particular metal. Any drop in price below this level will generally tend to start bleeding even the efficient suppliers and will result in noticeable drop in production levels balancing the supply demand situation. Suppliers such as Brookhunt tend to provide cost curves (with forecasts/sensitivity) at a fee of course.

Flat forecast – One alternative for assumption for sale price is to assume a flat single price, usually the current price. While this may seem unscientific, it is a reasonable assumption to make if the cost of inputs and the output have a strong correlation over a period of time. It also makes sense to have a sensitivity built into the financial model for running a flat price forecast at long-term price levels.