Commercial mortgage brokers can be forgiven for viewing mortgage default-risk analysis as the lender’s job. Nevertheless, it makes good business sense for brokers to understand the basis for such risk assessments. After all, these calculations affect credit availability, loan terms, approval timing and, indirectly, the pricing of the real estate assets themselves.
Traditional risk-assessment models, however, have been out of sync with market realities for some time. During a strong commercial real estate market over the past decade, such shortcomings have been overlooked with minimal impact on the majority of loans. The onset of the COVID-19 global pandemic, however, clearly raises the prospect of more defaults, creating heightened urgency for lenders to retool their models and better understand the risks they are taking on.
Fortunately, the necessary data for accurate modeling is readily available in the loan application itself or through other information in a lender’s possession. Mortgage brokers are well-advised to understand looming changes in how lenders will evaluate risk. This knowledge will help them anticipate potential financing bottlenecks and streamline the closing process in what is shaping up to be a challenging market.
Understanding the expected loss for a commercial mortgage is essential to any credit decision. The formula for calculating default losses has remained static for years: The expected loss is simply the probability that the loan will default multiplied by the projected magnitude of the loss.
This equation is deceptively simple. If these variables were known with a high degree of confidence, an underwriter’s job wouldn’t be nearly as difficult. But it is especially difficult to estimate the loss severity as the projections are often based on subpar data.
Smaller commercial loans tend to have a higher loss severity than larger ones.
Predicting the future
Several key pieces of data needed to project losses are often incomplete or missing in today’s modeling. These factors include the amount of equity, market variations, property types and the borrower-lender relationship. In 2015, the Federal Deposit Insurance Corp. (FDIC) published a research paper that examined commercial mortgage losses and bank failures after the Great Recession, pointing out several factors as important yet problematic influences on the loss calculation.
The FDIC reported that smaller commercial loans tend to have a higher loss severity than larger ones. Another factor is that the loan’s seasoning makes a difference in predicting loss. Loans that are more seasoned and remain in good standing before experiencing problems stand a much better chance of recovering and tend to have lower losses. Cash is another important factor in controlling losses. Surprisingly, the borrower’s cash on hand is seldom taken into consideration as a potential lever to reduce risk.
Another factor in controlling loss, according to the FDIC, involves the loan’s servicing history. Many commercial mortgages change servicers over the life of the loan and that transition creates added risk for loss. During a transition, for example, emerging problems with the loan may not be immediately identified by the new servicer. Other external factors also can affect losses. Defaults can become a longer and more costly process in states with judicial foreclosures. Generally speaking, the longer the workout period, the higher the loss.
One key measurement that banks use to determine loan quality and default risk is the interest rate offered to borrowers, the FDIC reports. Banks also sometimes require a personal guarantee or other concessions to minimize their risk. These concessions, however, can throw off the calculation for the expected loan loss. A banker, for example, can find a personal guarantee hard to enforce if their broader goal is to maintain a customer relationship, so that loan may have a higher actual loss probability than was reflected in the terms. The FDIC didn’t discuss loan guarantees and covenants as part of its study, but such intangibles play a critical role in the borrower experience and debt collection.
Building a model
Many of the aforementioned factors can be difficult to quantify when the loan is made. Even when these variables are taken into consideration, however, the current approach to calculating expected loss overlooks arguably the two most important factors: the borrower’s equity cushion, and the potential devaluation for properties of that particular type and location. It is apparent that a better approach to the loss estimate is needed that incorporates these key variables, both of which are readily available.
The equity cushion has been aptly described by federal regulators as a “shock absorber.” It is the banker’s first line of defense against asset devaluation. A loss will only occur if a property’s value falls below its equity level, minus the transaction costs. Thus, these two key variables — equity and potential devaluation — can be combined to better estimate the expected loss due to default.
In the FDIC study, the troubled loan portfolio had an average asset-price reduction of 20% and a median reduction of 25%. This reinforces the notion that for higher-leverage loans, a borrower’s equity is quickly erased by market declines, pushing the loans into a loss position and making them riskier.
Fortunately, it is not difficult to obtain accurate information on property values through large commercial real estate brokerages and analysts. Many lenders also do an independent analysis of values by modeling their own estimates of rents, net operating incomes, discounts and capitalization rates. By adding the simple yet powerful data points of equity and devaluation — and ideally reflecting some of the other key factors identified by the FDIC — lenders can generate more accurate loss estimates when the loans are made.
Another nuanced consideration in evaluating default risk and potential loss involves changes in the market. Factors such as e-commerce and coworking have upended certain segments of commercial real estate. COVID-19 has only accelerated these trends, particularly for traditional brick-and-mortar retail. According to CBRE, e-commerce already represents about 15% of U.S. retail sales and is forecast to reach 39% by the end of this decade, even before the effects of the pandemic took hold.
Social distancing could push more restaurants to adopt takeout models, altering the nature of the space required. We also can expect the office market to change as more people work from home. The trends toward tighter office space and short-term leasing driven by WeWork and other shared-workspace providers may reverse. The average square footage for an office could increase as executive suites and individual offices return to vogue over dense open-floor plans. This rethinking of space strategies will spur activity by renters and property owners. Lenders will be more diligent than ever in evaluating the risks they’re assuming in this new environment.
As with all forecasting, the results are only as good as the data that goes into the model. This suggested approach to projecting expected losses uses two easily attainable and controllable data variables. This modeling can deliver the sharpened insights lenders need to make better informed decisions for the future. Better yet, the new model can be implemented on an individual loan basis, and later aggregated as needed for overall portfolio analysis with each loan still standing on its own merits.
For devaluation estimates, loan equity amounts can be adjusted to arrive at an acceptable loss projection and priced according to the risk. This approach can be applied to credit analysis in any industry, from banking to insurance, allowing mortgage brokers to focus on value-added factors such as the borrower experience and cross-selling opportunities. If this new approach provides today’s commercial real estate lenders a more accurate and actionable way to assess risk and price credit — giving them a better view of the future — it will benefit all players in the market.
As lenders become more cautious, it behooves commercial mortgage brokers to understand this mindset. The broker needs to understand what is motivating the decisions of a banker in order to facilitate a successful transaction. Both buyer and seller benefit from an efficient process with minimal hiccups. They want to be informed of the potential roadblocks before they become major issues. This is a great way for brokers to differentiate themselves and deliver an outstanding customer experience.
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Finally, successful mortgage brokers want to be a borrower’s go-to resource, the first person a prospective client thinks of to call for expert advice. Your specialized understanding of the mechanics of loan risk can become a calling card. ●