[Editor's note: A version of this story appears in the November 2019 edition of Oil and Gas Investor. Subscribe to the magazine here.]

Mineral and royalty buyers are often required to assign value to miscella­neous, lower dollar-value, nonpro­ducing minerals. Buyers know how they want to price producing royalties and properties in attractive areas likely to get developed in the near term, but they rarely have confidence assigning value to mineral tracts that may be unleased and located in a county where there is little ongoing oil and gas development.

Historically, the market has used an ap­proach referred to as the multiple of lease bo­nus method (MLBM). The MLBM suggests that the value of nonproducing minerals may be equal to a multiple of 2.5x to 3x a represen­tative lease bonus. For example, if minerals in an area are being leased for $200 per acre, the MLBM suggests the minerals are worth $500 to $600 per net mineral acre. The MLBM is based on the logic that a mineral owner may lease and re-lease (upon expiration of the earlier lease) the subject minerals multiple times over the course of an assumed hold­ing period, earning a lease bonus payment upon each new lease.

However, as is wide­ly known, there are many shortcomings to the MLBM approach. The 2.5x to 3x range is somewhat arbitrary, and lease bonus income is not the sole source of mineral income. Also, the amount of lease bonus paid by a lessee is often contin­gent on or interrelated with the royalty rate so the MLBM would theoretically overvalue minerals where the lessor nego­tiated a high lease bonus rate at the expense of a lower royalty rate. Moreover, it can be difficult to define a representative lease bonus rate, espe­cially during periods of rapidly changing lease bonus rates.

We considered market evidence to evaluate the accuracy of MLBM. We analyzed transac­tion data for 87 nonproducing mineral prop­erties (lots) sold by EnergyNet from January 2013 to October 2018. EnergyNet is a major player in this marketplace. In 2018, Energy­Net sold over 2,300 separate lots (of all prop­erty types, including nonproducing minerals) with an aggregate sales value of approximate­ly $2.2 billion.

EnergyNet provided us with confidential and anonymized data on more than 500 nonproduc­ing mineral transactions during this period. We added other data points to this data set, includ­ing drilling permit counts, rig counts by county and lease bonus information, to create the mas­ter database described in this article.

Nonproducing mineral transaction database

The 87 transactions were sorted by selling price per net mineral acre (price/NMA) from highest to lowest and categorized the data into deciles. For each decile, we calculated a median, as shown in Figure 1. The data points include sales price, net mineral acres sold, price/NMA, baseline lease bonus and the oth­er items shown.

The main takeaway from the market study is that the actual price/NMA was widely diver­gent from the values predicted by the MLBM. Less than 5% of the 87 transactions had an MLB between 2.5x and 3x. About 23% of the transactions had an MLB of less than 1x.

A market-based valuation framework

So, if the MLBM is not a reliable nonpro­ducing mineral valuation model, can the market data be used to develop an alternative valuation approach? Using multiple variable regression, we attempted to develop an equa­tion that could be used to predict nonproducing mineral values.

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We recognized going into the analysis that there are multiple factors that impact mineral valuation, and our independent variables were based on more general, countywide data rather than data on a more specific area surrounding the subject minerals (such as a 5-mile radius).

After running the regression analysis, we found that the baseline lease bonus and drill­ing rig count (independent variables) were significant drivers of mineral value but other factors should be considered in developing value. A sample output from the model is shown in Figure 2.

The predicted values from the model could be high or low relative to true market value, depending on other factors not captured in the regression model. The most important other factor is the specific location of the minerals within the subject county. For example, map­ping may show that the subject minerals lie far from or just outside a clearly defined per­mitting and development area, in which case the model’s value likely would need to be ad­justed downward.

mineral

Other factors that influence nonproducing mineral value and should be considered in the valuation include oil and gas lease terms (royalty rate, gross or net royalty provisions, acreage retention language and continuous drilling clauses), the technical capability and drilling budget of the lessee or operator, the status of the minerals (for example, whether the subject minerals are HBP or recent per­mits have been filed directly on or close to the subject tract), size of the mineral tract, outlook for commodity prices, takeaway in­frastructure in the area, and political or envi­ronmental issues.

Mineral valuation in high-value, rapidly developing areas

The framework discussed in this article is not highly useful for high dollar-per-acre minerals. High-value areas include promising geological areas where lease bonus rates, drilling permits and drilling rig counts are high.

A current example would be the Midland and Delaware sub-basins of the Permian Basin where nonproducing minerals are sell­ing for $10,000 to $20,000 per NRA (net one-eighth royalty acre). For high-value, rapidly developing areas, the valuation pro­cess typically involves an Income Approach based on an engineering drill-out analysis as well as a market approach based on price-per-acre data.

In these areas, transaction data are more read­ily available (but still very difficult to obtain) because of buying by publicly traded mineral buyers as well as other funds which may be required to report the data. In these areas, the market approach is based on a price/NRA basis rather than a price/NMA basis.

A defensible approach

Mineral valuation cannot be mechanized or boiled down to a formulistic approach. Many variables are involved in the process, and professional judgment is required. The regression model discussed herein is based on market transaction data and should be a more defensible valuation approach for mis­cellaneous, nonproducing minerals if used as a starting point as compared to the commonly used MLBM.

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Alan Harp Jr. is a managing director in the valuation advisory group of Stout Risius Ross LLC’s Houston office. The author wishes to thank EnergyNet for its assistance and for providing the market transaction data used to develop this article, and Stout interns Phillip Schwartz and Joel Ompendoguelet for their research and other assistance.