In addition to traditional underwriting, some banks utilize a scorecard to rank their commercial properties. Projects are run through a scorecard and then rated on a numerical value. For banks without a credit or pricing model that provides a probability of default and expected loss, the scorecard allows an intermediate way to compare loan quality. In this article, we take a look at a sample scorecard and give banks some examples of how to use the methodology for better commercial real estate (CRE) underwriting and pricing.
The Retail Example
By way of example, we will use a retail center scorecard that we have put together from several banks, including JP Morgan Chase. While retail focus, the purpose here is to showcase the methodology as banks could easily adapt the scorecard for a variety of asset classes including multifamily, office and specialty real estate such as hospitality, convenience stores, and self-storage.
Debt Service Coverage Ratio (DSCR): No factor is more important than current debt service coverage and so a bank awards no points for DSCR between 1.2 and 1.3, a point for 1.30 and above and then an additional point for every 0.20x over 1.3x. Thus, a project with a DSCR of 1.5x to 1.7x would earn two points.
DSCR Trend: If the year-over-year change is positive, the project earns a point.
DSCR – Breakeven: A bank calculated the minimum vacancy rate to produce a debt service coverage of 1.0x. If that breakeven is 75% or below, the project earns a point.
Maturity LTV Level: The project gains one point if the projected LTV at the time of maturity using current valuation is less than or equal to the median of the portfolio.
Debt Yield Level: If the current year debt yield is greater than or less than the median of the portfolio, one point is earned.
Debt Yield Trend: Year-over-year debt yield change earns one point.
Occupancy Level: Occupancy level of 85% earns one point.
Occupancy Trend: A positive improvement since underwriting of occupancy rate earns one point.
Surrounding Occupancy: An occupancy rate greater than the median of surrounding like properties earns one point.
Dark Anchor: Properties with fully leased anchor tenants earn one point.
NOI Trend: Net operating income rate of change greater than the area average earns one point.
Sales Productivity: Properties that produce sales greater than $350 per square foot earns one point.
Positioning: Alignment of sales positioning relative to the area earns one point. That is, the price point and brand position of stores should reflect the local area.
Household Level: If household density in a ten-mile radius is greater than the surrounding area, the loan earns one point.
Household Formation: The number of new households is increasing year-over-year, the property earns a point.
Captive Clientele: The presence of a major work facility (military base, manufacturing plant, corporate campus, etc.) within five miles earns one point.
Competition: If the number of similar retail centers in a five-mile radius is less than that of a “donut” that extends from five to ten miles, then a point. If there are no retail outlet centers within a ten-mile radius, a second point is earned.
Sponsors: One point is awarded for a strong sponsor/owner of the property that has substantial financial wherewithal. An additional point is also earned if there are additional commitments (personal/corporate guarantees, make whole provisions, etc.) or the sponsor has a history of curing defaults.
Using A Scorecard
There are a number of ways that utilizing a commercial scorecard adds depth to your bank underwriting. JP Morgan Chase, for example, ranks each underlying loan property and then groups them into “tiers.” Each tier is a group of a third. Thus the top tier (Tier 1) is the top 33%, while the bottom 33% is Tier 3.
As validation, we can look at publically traded loans to retail centers. Utilizing data from JP Morgan, Bloomberg, and Trepp, properties that are currently ranked in Tier 1, grew NOI at a rate of 15% since 2012 while projects in the bottom tier, saw NOI decrease by 28%.
Extending this tool out one more level, we can now correlate tier ranking with future performance as well. Projecting towards maturity, the analysis indicates that those loans with Tier 1 properties will increase NOI an average of 14% at the time of maturity versus those ranked as Tier 3 which will suffer a 57% drop in NOI since underwriting. Looked at another way, this equates to a 46% increase in loan value (price) and 80% decrease in loan price, respectively.
Still another way to use this scorecard is to project termination events for those loans that have a balloon structure. Those loans in Tier 1 and a portion of Tier 2 get refinanced. Other loans in Tier 2 and all the loans in Tier 3 get worked out and/or liquidated. The correlation between ranking and outcome tends to be a strong 82% correlation which is highly predictive.
Having this type of ability to see three to five years in the future gives banks the ability to better manage the credit, capital, market price and liquidity risk of commercial loans that are destined to run into trouble. In the same vein, banks can use the scorecard to fine tune their CECL and allowance for loan loss analysis thereby not only improving underwriting and loan management but also improving the credit process itself.
Putting This Into Action
Banks that already use a scorecard credit methodology may want to review the above and adapt it for their purposes. Banks that don’t utilize a scorecard can use the above as a template and modify it as they see fit for a variety of property types.
Utilizing a scorecard can provide a way to more holistically quantify a commercial loan and its underlying project. While not a perfect solution, it is a step in the right direction of better quantifying the risk that we all take.
Submitted by Chris Nichols on March 13, 2019