The Debt-Equity Distinction and Tribune Media

I. Introduction

Common law debt-equity characterization depends on the synthesis of more than a dozen factual and circumstantial elements. In real-world situations, with so many considerations in play, ambiguity is endemic. The threshold challenge for taxpayers, the IRS, and, ultimately, the courts is to determine the most appropriate characterization for a given financing, all things considered. This is particularly difficult to do in cases that are close calls, in which there are balanced sets of factors alternately favoring debt and equity. Those cases often lead to judicial squinting to identify distinctions. This can magnify slight differences that, in the ordinary course, would unlikely be influential, let alone dispositive.

Indeed, when there is a balanced set of factors in play, it can be difficult to reach reliable conclusions and produce compelling reasons. Historically, judges have occasionally confessed their uneasiness in these kinds of close situations. As Sir Wilfred Greene, Master of the Rolls, remarked in 1937, “There have been many cases which fall upon the borderline: indeed, in many cases it is almost true to say that the spin of a coin would decide the matter almost as satisfactorily as an attempt to find reasons.”¹

In cases in which a difficult judgment must be made with reasonably balanced factors, it can be worthwhile to garner the “wisdom of crowds”² to base one’s analysis on the entirety of the case law. Fastidiously collecting training data, and identifying the facts and circumstances of past cases along with the resulting debt or equity characterization, provides a data-rich foundation to train a machine-learning model to reliably and accurately assess the likelihood that a decision-maker would reach a characterization of debt or equity.

Blue J has done just that by assembling a detailed data set of debt-equity decisions from 1956 on. The Blue J debt-equity model yields 95.6 percent agreement with the decisions of the courts. It has been thoroughly back-tested against historical case law and, for the past few years, has been making accurate predictions of new debt-equity cases as they are decided. It is being used as a teaching tool to inform debt-equity analyses in leading university tax law courses and programs. Practitioners increasingly leverage Blue J’s debt-equity predictor to produce evaluations of the strength of novel situations involving new variations of facts and circumstances, many of which have never been directly judicially tested.

To illustrate the insights possible with machine learning, we use the Blue J debt-equity predictor to analyze part of the decision recently rendered in Tribune Media.³ Tax Court Judge Ronald L. Buch discussed and assessed the 13 Dixie Dairies⁴ factors in Tribune Media, analyzing whether the obligation ought to be properly characterized as debt or equity. Tribune Media was not a particularly close call, as the court (and Blue J’s model, with 86 percent certainty) concluded that the obligation in question was an advance on account of equity, with seven factors pointing in the direction of equity, three factors weighing toward debt, and three neutral factors. But as any tax lawyer with experience in analyzing the debt-equity distinction will remind us, an accurate assessment on the merits is not a simple matter of counting factors on each side of the ledger.

Indeed, analysis leveraging the Blue J debt-equity model shows that a different position on only two of the Dixie Dairies factors (one of which was characterized by the court in Tribune Media as neutral) would have flipped the outcome of the debt-equity characterization (with 77 percent confidence). This would not have been a long shot; on these two factors, the taxpayer had a prima facie reasonable prospect of success. However, the text of the decision does not disclose that these two factors could have made such a difference. In fact, as we will explore, there are curious differences between the decision’s determination of the factors’ significance and the importance suggested by Blue J’s model based on the accumulated wisdom from the decisions of judges in past debt-equity cases. We attribute this gap, at least in part, to the rhetorical challenge of producing compelling reasons in close cases.

II. Background

A. The Facts in Tribune Media

In 2008 Tribune Media Co. found itself in financial difficulties. One of its assets, the Chicago Cubs baseball team, was not an integral part of its principal business. Tribune decided to sell the team to mitigate its financial problems. It ran a bidding process to find a suitable purchaser. The Ricketts family prevailed in its bid to buy the Cubs, eventually agreeing to terms with Tribune. One of these terms was particularly important to Tribune: that the purchase be made through a partnership structure as a disguised sale. This structure was chosen to allow Tribune to optimize the change in ownership from a tax perspective. The stakes for Tribune were significant. As compared with an outright sale, Tribune avoided approximately $181 million in taxes linked to a capital gain that otherwise would have been triggered as a result of the appreciation of the value of the team.⁵

B. Disguised Sales

The court in Tribune Media explained how disguised sales work conceptually.⁶ Generally, when the owner of property with an accrued gain disposes of its ownership interest by way of sale, a capital gain subject to tax is realized upon the disposition. If instead, the owner uses the property as security on a loan, the loan proceeds that the owner obtains are not taxable, and the loan does not trigger the realization of accrued gains. Similarly, contributing property to a partnership and receiving an interest in the partnership as consideration will also not necessarily constitute a taxable event. However, if the partner receives a distribution of cash from the partnership in excess of the basis in the property at the time of contribution, the excess amount would generally be considered a taxable gain under section 731(a)(1). Indeed, since what is happening economically is the transfer of property and the receipt of cash, the IRS considers this a disguised sale under section 707.⁷ The IRC and the regulations determine how the gain that arises from this disguised sale is to be calculated and taxed.

An exception to the treatment of this structure as a disguised sale with capital gains consequences applies to a debt-financed distribution. That exception applies when an owner contributes property to a partnership and the partnership later borrows against the property and then makes a cash distribution to the previous owner when the previous owner is personally liable for the loan. To invoke the debt-financed distribution rule, the partner must “retain substantive liability for repayment” of the debt, meaning that the partner must bear responsibility for the liability.⁸ In these circumstances, the cash distribution would be tax free up to the combined amount of the tax basis in the property and the loan liability.

For the debt-financed distribution exception to apply, the liability must be bona fide debt, and the previous owner of the property that is transferred to the partnership must have assumed or guaranteed the debt. In light of this, how does the court’s debt versus equity analysis fit? If it turns out that the obligation the partnership entered into was not bona fide debt, but instead that the cash the partnership received was for equity (or the previous owner did not guarantee the debt), the debt-financed distribution exception would not apply. This would render the change in ownership a disguised sale, giving rise to capital gains tax, provided the property had an accrued gain in the hands of the taxpayer.

Since Tribune had acquired the Cubs in 1981 for $21.1 million and the team was valued at almost $770 million at the time of the sale in 2009,⁹ one can understand the motivation behind Tribune’s insistence on a tax-friendly structure for the change of ownership. The transaction is depicted in Figure 1 and is described in more detail in the following section.

C. The ‘Debt’-Financed Transaction

To fit the transaction into the debt-financed distribution exception to the disguised sale rules, Tribune contributed the Cubs to Chicago Baseball Holdings LLC (CBH), a newly formed limited liability company (treated as a partnership for tax purposes). Ricketts Acquisition Company (RAC) was a 95 percent owner of CBH, and Tribune owned 5 percent of CBH. CBH in turn borrowed $249 million from an entity closely related to the Ricketts family, RAC Education Trust Finance LLC (RAC Finance), as well as $425 million from unrelated banks. These loans were secured by Tribune in the form of guarantees (see Figure 1). The loan proceeds were then used for a special distribution from CBH to Tribune.

So far, the parties seem to qualify for the debt-financed distribution exception, but as is often the case, the devil lies in the details. Citing the antiabuse rules under section 701 and the substance-over-form doctrine, the IRS determined that Tribune’s guarantees were not valid and that the disguised sale of the Cubs ought to be taxable. Once the details were subject to the IRS’s and the court’s scrutiny, it was revealed that the debt, as far as the loan from RAC Finance was concerned, was not bona fide debt. The court found that the $249 million in subordinated debt (shown in red in Figure 1) that CBH had taken on from RAC Finance was appropriately characterized as equity. Accordingly, the terms of the guarantee provided by Tribune for that part of the debt could not satisfy the requirement that Tribune be liable for the repayment of the debt. Further, Tribune was not liable for the loan under the terms of the guarantee, so the debt-financed distribution exception did not apply. Tribune was subject to capital gains tax under the disguised sale rules insofar as the Cubs had appreciated in value since 1981.

We now focus our attention on the court’s debt-equity analysis. Although it is important to the court’s resolution of the case, we will not address the court’s determination of whether the terms of the guarantee gave rise to Tribune’s liability for CBH’s obligations toward RAC Finance and the unrelated banks.

D. Debt vs. Equity

The traditional corporate debt-equity test has often been applied to partnership cases. However, in some partnership cases in which the courts have taken issue with the characterization of an advance, the courts have turned to additional factors not present in corporate cases. Foremost among these are those factors that determine whether a partnership relationship existed between the parties. This question has been raised by courts predominantly in cases that revolved around debt-like equity, as opposed to equity-like debt, as only partners can claim to make a contribution to the partnership in exchange for equity.¹⁰ Leaving aside those complications, Tribune Media, an equity-like debt case, applies the usual 13 factors set out in Dixie Dairies¹¹ without modification for the partnership context.

In Dixie Dairies, the traditional 13 factors considered in a debt versus equity analysis are:

[1] the names given to the certificates evidencing the indebtedness; [2] presence or absence of a fixed maturity date; [3] source of payments; [4] right to enforce payments; [5] participation in management as a result of the advances; [6] status of the advances in relation to regular corporate creditors; [7] intent of the parties; [8] identity of interest between creditor and stockholder; [9] “thinness” of capital structure in relation to debt; [10] ability of corporation to obtain credit from outside sources; [11] use to which advances were put; [12] failure of debtor to repay; and [13] risk involved in making advances.¹²

Following Dixie Dairies, the court in Tribune Media noted that these factors are not to be equally weighted, that none is determinative on its own, and that not all the factors are necessarily relevant in all cases. Properly understood, these factors are “only aids in answering the ultimate question whether the investment, analyzed in terms of its economic reality, constitutes risk capital entirely subject to the fortunes of the corporate venture or represents a strict debtor-creditor relationship.”¹³

We now look at how the court weighed the debt-equity factors in Tribune Media.

E. The Importance of the Factors

The court in Tribune Media commented on the relative importance of nine of the 13 factors. Sometimes the court’s comment related to the importance of the factor for a debt-equity analysis in general; sometimes it related to the importance of the analysis in this case. The court also referenced one factor in which its importance in general differs from the importance it had in this case.

For example, the first factor, concerning the nomenclature on the certificate of indebtedness, is generally of low probative value because related parties can simply agree to label the obligation as desired. This was the case in Tribune Media, as both parties were controlled by the same players. The sixth factor, on the other hand, which relates to the status of the impugned obligation as compared with other creditors, is of “some import” in general, yet neutral in this case. The court stated that the 11th factor, concerning the use of the advance, had a strong influence on the outcome of the analysis in Tribune Media; it did not explain whether this is usually the case.

To support its assessment of the sixth factor’s general influence (the status of the debt compared with other creditors is “of some import”), the court cited Estate of Mixon.¹⁴ Just as in Tribune Media, in Estate of Mixon the court said in general that the status of the advance is always of some import in similar cases, citing six cases as examples. In Estate of Mixon, however, the court found that the subordinate status of the advance argued for by the government was “at least not free from legal doubt,” so the sixth factor could not support the government’s argument that the advance was equity. In cases such as this in which it is “not at all clear where the advance stood on the preference scale,” the weight of the sixth factor is minimal.

In Tribune Media, the court could not decide whether the subordinate debt was closer in character to that of the other debtors or to the interests of the equity holders. Accordingly, the court declared the factor to be neutral. This is consistent with the idea from Dixie Dairies that the influence of a factor is fact-dependent and cannot be determined generally for all cases. It is, however, understandable why the court, despite its initial recognition of the relative weight of the 13 factors, had an opinion on the factor’s relevance generally. A factor, such as the first factor, that can be deliberately manipulated by the parties with little economic effect should be less influential than other factors that are less malleable and are potentially associated with materially greater economic substance. Yet, when other factors are concerned, there may be less obvious reasons to weigh one factor more heavily than another. Recourse is then taken to legal intuition. But there is legal intuition and then there is data analysis driven by artificial intelligence.

We now turn to the Blue J machine-learning model of the debt-equity case law to assess the legal intuition that guides the weighing of the factors in a debt-equity analysis.

III. Machine-Learning Analysis

A. Blue J’s Analysis

Blue J’s debt-equity predictor can evaluate the likely outcome of a debt versus equity analysis based on case law going back to 1956. It takes into consideration the 13 factors the courts have established as the drivers of the decision and some additional ones that our research has verified as being important in some circumstances. Mirroring the body of available debt-equity decisions, the Blue J debt-equity database predominantly includes cases involving the conventional corporate context, with a significant minority of cases involving individual taxpayers. The database also includes decisions in which the analysis relates to debt-equity characterization outside the typical context, as is the case in Tribune Media, a case decided in the partnership context.

In the case of Tribune Media, the Blue J predictor indicates with 86 percent confidence that the obligation CBH took on in relation to RAC Finance would be characterized as equity, based on the court’s manner of characterizing the facts and circumstances.

To determine the relative importance of each of the 13 factors based on the wisdom of crowds from the hundreds of previously decided debt-equity cases, we change every factor individually, and the Blue J predictor indicates the magnitude of the change in the confidence level for the outcome. So, for example, if we tell the module that there was a fixed maturity date (in contrast to the court’s finding), the confidence with which the module predicts the outcome to still be equity is 3 percent lower (83 percent instead of 86 percent). We indicate that in Table 1 with “-3%.”

Table 2. The Weight of the Debt vs. Equity Factors

Factor	Influence	Finding	Δ in Blue J’s Confidence in Equity Characterization
2. Fixed maturity date	“Weighs heavily in our analysis”	Equity	-3%
4. Right to enforce payment	Critical	Equity	-4%
6. Status compared with other creditors	Some import	Neutral	- 28% (with equal status)
8. Identity of interest between creditor and stockholder	Significant here	Equity	-1%
10. Ability to obtain outside credit	NA	Equity	-14%
11. Use of the advance	Strong here	Equity	-12%
13. Risk	Significant here	Equity	-1%

As Table 1 shows, factors 2, 4, 8, and 13, which were identified as “significant,” “weighing heavily,” or “critical,” actually affect the outcome only modestly, according to the Blue J predictor. These factors moved the needle between 1 and 4 percent. On the other hand, the sixth factor, the status of the obligation compared with other creditors, changes the confidence level of an equity outcome by 28 percent, making it by far the strongest single factor in this scenario. The court in Tribune Media tells us that this sixth factor is of “some import,” citing case law from 1972. The second strongest factor is the ability to obtain outside credit (factor 10), with a change of 14 percent. The third strongest factor is the use of the advance (factor 11), with a 12 percent impact. Factor 11 had a strong influence in this case, the court said, but it did not comment on the factor’s general significance. The influence of factor 10 is not addressed at all.

We can see from this analysis that the court’s indication of the weight of the factors is not consistent with what can be gleaned from careful scrutiny and synthesis of hundreds of debt-equity decisions since 1956. The data show a significantly different weighing of the factors, while the court and the machine-learning module both come to the same conclusion.

B. Relevance of the Factors’ Weights

If the court and Blue J reach the same outcome — as they do in 95.6 percent of the cases, why should we be concerned about the court’s ostensible ranking of the debt-equity factors in terms of their influence on the final characterization? Why should tax practitioners care about the weight of the individual factors as determined by the data when they have to convince the court, which has expressed a differing opinion on the weighing, and the overall outcome is the same?

Let’s first address the usefulness of gaining insights into the weight of each factor. The practical value of going through the exercise of changing individual factors and taking note of the change in the outcome is that this allows a tax professional to scenario-test. Let’s assume for a moment that Tribune had been able to sell the subordinate debt to external investors. Indeed, it had set out to do so by developing marketing materials and soliciting potential investors. Some of these potential investors had expressed interest in buying the debt, the court noted.¹⁵ Let’s also assume that this success had convinced the court to decide differently on the 10th factor concerning the ability to obtain outside credit. In this instance, the likelihood of the outcome to be equity changes from 86 percent to 72 percent (-14 percent). Knowing that the 10th factor is the second strongest factor could significantly shape the litigation strategy. Perhaps even better, it might be used to inform the strategy embraced at the planning stage of the transaction. For instance, it might have convinced the Ricketts family to simply sell the debt after all, had they been aware of the influence this decision could have had on the overall assessment of the transaction.

Second, why should one care about or trust the weight the data reveals the individual factors to have in a particular fact pattern when the court believes differently? After all, it is the court’s decision to make. Well, for one thing, foresight is better than hindsight. The court’s overall weighing of the factors in a case can only be known after a decision is rendered. This will generally be too little, too late, as this information is more valuable when planning the transaction or a litigation strategy. Further, the importance of individual factors in general gathered from previous case law — that is, outside of a particular fact pattern — is not as helpful to tax professionals because it does not take into consideration the context of the factors in combination with the other facts and circumstances of a case.

The direction of one factor toward equity can amplify the influence of another factor, even if the relation between the two factors may not be immediately obvious. While it intuitively makes sense that factors 2, 4, 6, and 13 are mutually dependent, as they all get at the question of the likelihood of repayment, a machine-learning model can discover interdependencies among factors that are hidden through obscurity, simply because the effect is too weak to be picked up through careful reading with comparable precision in a practical length of time.

To illustrate this point, let’s tweak the facts of Tribune Media a bit further. Let’s assume that in addition to being able to access outside credit (factor 10), Tribune had been successful with its argument that the subordinate debt ranked equal to trade creditors (factor 6). As we observed earlier, the court reached a neutral finding on this question, not being convinced by either party’s argument. This suggests that it is not unrealistic to stipulate that Tribune might have been able to win this point with a stronger focus on it. Assuming that the court had thus decided differently on factor 6 concerning the status of the debt compared with other creditors, as well as on factor 10 concerning the ability to obtain outside debt, the outcome would flip, according to Blue J’s analysis. With just these two changes, Blue J indicates that a debt characterization would be the more likely outcome with 77 percent confidence.

IV. Conclusion

We can conclude from the findings we have set out that a court’s explicit guidance about the relevance of a legal test’s factors should be received with caution. We are more inclined to confidently follow what judges do in terms of placing weight on specific factors, rather than what judges say they might do in terms of assigning weight to factors. Machine-learning tools can leverage an extensive database of previously decided cases and produce accurate and reliable predictions of how courts will decide new debt-equity cases. The main advantage of using machine learning is that it can overcome the rhetorical challenge that courts face in producing compelling reasons in borderline cases. Human intuition and experience alone are not able to reliably reveal the precise role factors in a legal analysis have played in hundreds of previously decided cases. Machine learning can. Human experts then serve an indispensable role in leveraging machine-learning insights for even more powerful tax advice. These ongoing machine-learning developments promise to continue to materially improve analysis, planning, and dispute resolution in the debt-equity context.