People often mention risk-adjusted returns when describing the return profile or business plan of an investment. The concept of risk-adjusted returns is fairly simple; the projected returns of an investment should more than compensate for the perceived risk to be taken by the investment. For example, you might say a 20% IRR is great but what if that is the projected return for a luxury hotel development in Zimbabwe underwritten to an assumed 95% occupancy rate? In other words, the risk level of an investment should dictate your minimum return hurdle. Typically, we look for a 15%+ IRR net of fees for stabilized multifamily deals which can be financed with long term agency debt. For heavier value-add projects requiring a bridge loan, we look for an 18%+ minimum IRR. These hurdles vary by location, vintage, and size/scope of project and renovations. Shrewd investors price projects differently not because they are greedy and simply want a higher return, but because the higher projected return provides a larger cushion of safety for an investment that carries higher risk.

It is my opinion that if a multifamily investment is being purchased in a strong market/sub-market and is being financed with Fannie Mae or Freddie Mac debt, then it would appear the deal carries very little risk. This is in part due to the fact Fannie and Freddie underwrite conservatively and simply will not over lever a deal. Even if you overpay, the agencies will constrain their proceeds based on a minimum DSCR requirement, thus eliminating the risk of being overlevered due to overpaying. Furthermore, agency debt typically has a 10-year term and carries a fixed interest rate which essentially eliminates interest rate risk as well as refinance risk. Logically, an investor would be willing to accept a lower rate of return in exchange for this lower level of risk.

Now that we have discussed why investments should be valued on a risk-adjusted basis and not on absolute returns, we can take it a step further and ask, how can we quantify risk? If you know me, you know I follow Howard Marks and subscribe to his school of thought. One of his ideas is that risk cannot be quantified or teased out from historical data. Many professionals in finance look at standard deviation as a quantification of risk as well as use a Sharpe ratio to calculate a risk-adjusted return. However, I don’t believe these two methodologies are a very good means of assessing investment risk as risk is far more nebulous than it seems due to randomness. Here is a quick example supporting this claim: if the risk of getting hit by a car when running out into the street is 20% and you run out and make it across safely, does that mean the 20% risk was right? Wrong? Neither? There is too much randomness involved in this test and the only way to account for randomness is to perform a statistically significant number of trials. In the world of real estate investing this is extremely difficult to do since these transactions typically play out over several years and it would take an operator decades to do a enough deals to prove out their risk hypothesis.

So how can we get just a little bit smarter about how we view risk-adjusted returns? One way I really like in particular is to evaluate the cost of capital associated with each position in the capital stack. This concept relates specifically to leverage and how to properly calculate the appropriate structure and amount of leverage an investment should take on. When evaluating leverage, there are a few metrics to pay attention to: LTV, LTC, DSCR as well as the spread between the cap rate and the cost of debt. The cap/debt spread can also be applied to any other cash flow position which is being levered. For example, you could look at the projected yield and IRR for the common equity of an investment and compare it to the cost of preferred equity to determine whether it may be a good idea to take on additional leverage through a mezzanine debt or preferred equity slug.

The cost of each capital stack position can shed some light on the way the market is pricing the risk (and the return) of senior debt, subordinate debt, preferred equity, common equity, and the last dollar of each.

It can be difficult to evaluate two different debt term sheets which have different leverage points and different interest rates. Obviously if both lenders are offering the same amount of proceeds, then it is likely the better option is the one with a lower interest rate. However, when you increase the number of variables the question becomes increasingly difficult to answer. Below is an example comparison of two senior debt structures which illuminates the risk-adjusted nature of one versus the other.

**Example: Higher leverage, higher cost Vs. lower leverage, lower cost**

The Scenario

- $10MM project cost
- Borrower seeking bridge loan to finance project
- Borrower is underwriting to a 11.3% unlevered IRR and typically has a 20% cost of equity for this type of deal.
- Our example deal underwrites to a higher IRR than usual so the borrower is contemplating a higher leverage loan.

Loan #1

- Proceeds: $8MM (80% LTC)
- Cost: 6%
- Resulting projected return: 26.5% IRR

Loan #2

- Proceeds: $8.5MM (85% LTC)
- Cost: 7%
- Resulting projected return: 27.3% IRR

This is a simplified scenario as lenders typically charge points up front and upon exit and may have other levers to pull as they tweak the terms of the loan. In a real-life scenario, points and other fees, must be factored into a sophisticated analysis.

On the surface, it may seem that loan #2 is the better option since it offers higher leverage, which means the borrower only has to bring in $1.5MM of equity instead of $2MM (a 25% decrease!) and the projected IRR is higher by 3%. However, we can dissect this analysis further and uncover which loan would result in the better risk-adjusted return. The additional leverage offered in loan #2 only costs an additional 100 basis points of interest. Conversely, if you were to break down loan #2 into an A and B piece and assume the A has the same terms as loan #1, then the additional 5% of leverage provided by loan #2 can be viewed as a mezzanine loan carrying a 23% rate of interest. This can be calculated by finding the difference in total annual interest payments between the two loans and dividing the difference by the difference between the two loan proceeds and more specifically as follows [(7%*$8.5MM – 6%*$8MM)/($8.5MM-$8MM)]. After understanding the true cost of the additional leverage, this is not a loan I would like to take on. This means loan #2 is decidedly less attractive even though it offers higher leverage and projects a higher IRR for the equity. This is because the cost of the additional financing is nearly the same as the return on the common equity and only increases the projected return by 3% (26.5% to 27.3%), not to mention it increases the refinance risk and coverage risk of the deal.

The next question is what is a fair price for that “synthetic” mezzanine piece. Certainly not 23%, as that is nearly the projected return for the equity. Additionally, a 23% mezz piece carries a much higher risk than taking on common equity. This means the equity investment in this capital structure does not have a favorable risk-adjusted return since a similar return can be achieved at a much lower leverage point and a more protected attachment point (debt rather than equity). Furthermore, after the GP’s fees and promotes, the equity is likely to earn a return lower than 23% which makes it an easy decision to not take on the additional debt at 23%.

This methodology is one of the more interesting ways of examining risk/return throughout a capital stack. Modeling subordinate debt and/or preferred equity structures can help reveal the optimal capital structure for a given deal and also shed light on the appropriate return for a particular position in the capital stack. This is why I like to reach out to many different capital providers in an effort to understand how and why they price their different capital programs. This is why I believe a thorough understanding of various debt and equity markets/providers is essential in helping to determine the proper pricing for different investments and capital structures.