How To Set a Loan's Loan-To-Value

When it comes to underwriting commercial property for a bank loan how do you determine the right loan-to-value (LTV) ratio? Many readers will look at that question an answer – “We set our LTV at the maximum of our policy,” or “We set LTV where the borrower wants to the maximum of our policy.” While not bad answers, that methodology does not optimize the risk/reward profile of a commercial real estate (CRE) loan for a bank. Quantitatively inclined banks can do a better job both in term of risk management and return by expanding their analysis. In this article, we look at the current state of LTVs and discuss a methodology to better underwriting collateral value.

The LTV Equation

We have discussed the relationship between pricing and LTV in the past (HERE) and the interrelation between LTV and the probability of default (HERE). We have also looked at loan loss severity based on LTV (HERE) where we amazed many bankers by showing them the data on why 100% LTV loans often have had lower losses than 75% LTV loans. Our past writings lay a masters-level foundation for bankers looking to more accurately set LTV at the time of origination. Now, let’s take it a step further.

While underwriting a business or project determines the probability of default (POD), understanding the value of the collateral determines the loss given default (LGD). While the two efforts are often related, they are distinct endeavors. A property’s value while largely influenced by cash flow also has other supply/demand considerations. An indoor cannabis grow house in an industrial building may produce tremendous cash flow but if they get regulated out of business that warehouse needs to find its way to the next highest and best use which is likely at a much lower valuation.

Bankers must take into account a property’s value volatility when setting, and pricing, LTV. Below we have two underwritten properties from CenterState’s portfolio. 

Judging by a time series of property values, either calculated on a discounted cash flow methodology or appraised value, Property A (above) is more volatile with a standard deviation of 17.3% when compared to Property B that has a standard deviation of 7.8%. For Property A, that means, within a 68% probability, that property’s value, based on history, can move plus or minus 17.3%.

Let’s assume that both properties were underwritten to a 75% LTV level per policy. Should a one standard deviation credit shock occur (a mild assumption), and the borrower not be able to pay, then assuming the current 22% LGD for both properties (another mild assumption), then we would have a loss of 14.3% of our principal on Property A compared to a loss of 4.8% on Property B. That is the difference to being a capital altering event versus just a mild hit to earnings.

To place this in context, we point out that the last downturn was a three standard deviation event in many areas and the loss given default ranged from 10% to 80% with about 33% being the most common.

Sector Context

To further place the above in historical context or when property level discounted cash flow or appraised value history is not available, consider what one standard deviation of property value looks like for a nationwide set of properties gathered over a 20 year period from different sectors:

Keep in mind that what you are looking at above are averages, and the difference between regions can vary greatly. Multifamily in many parts of the Midwest has volatilities around 3% while the same sized apartment building in GA, FL or CA has a value volatility of 24%.

Current Data

Of course, while history gives you context, what you really want when determining what LTV to underwrite a loan at is a forward looking measure. For that we look at net cash flow trends by property trends by CRE loan type and see while most sectors are doing well, some are slowing down. Hospitality, for instance, went from producing a strong 6.5% net cash flow average growth rate to a trend where we now predict that they will shrink as more product comes on line and the net average room rate starts to decline in many markets. 

You would want to adjust the above data for your particular area to account for geographical strengths and weaknesses. Metro areas like D.C., Dallas, Charlotte, Los Angeles, Chicago, Cincinnati, Columbus, Las Vegas and Pittsburg are all gaining strength producing positive growth in property values while areas like San Antonio, Houston, Denver, Miami, Phoenix, and Austin are starting to turn and contract. Markets like Tampa, Orlando, Boston, Atlanta, Nashville, and San Francisco remain strong but are markedly slowing. Finally, on average major metro markets are still expected to do better than secondary and tertiary markets, both of which are slowing.

Banks can take more risk and underwrite to a higher LTV when property value volatility is low, and both the sector and area are improving in net cash flow. Conversely, banks need to become more conservative when volatility is high and/or net cash flow is slowing.

Putting This Into Action

When it comes to CRE underwriting, a significant amount of resources should be spent understanding the quantity and quality of cash flow since that is the primary driver of a loan’s profitability. However, in the 1% to 5% probability that you have to look towards the collateral for repayment, banks that make sure their LTV is set correctly will come out ahead. As can be seen in our first example, even with a property that has a valuation history with a low volatility, at 75% LTV banks have little room for safety given today’s inflated prices. A small shock can produce losses for a bank in excess of reserves.

Factoring in the volatility of a property’s value to at least the one standard deviation methodology will start to approximate an average credit shock and will give banks a rule of thumb on where to set the LTV. In cases where the borrower requires a higher LTV than a bank feels comfortable with, lenders need to take steps to price and structure accordingly. Increasing the strength of covenants, requiring additional properties that have low correlations to the primary collateral and additional guarantees are all ways that banks can mitigate their risk and hold their expected risk-adjusted return on equity in place. 

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