May CMBS remittance data showed a decrease in loans 60+ days delinquent, down from 2.03% to 1.88%.  This was driven by a large decline in retail (29 bps) and lodging loans (55 bps). At the same time, $22 million of retail loans and $635 million of lodging loans transferred to forbearance, which left the troubled loan percentage at 7.82% at month’s end.  Exhibit 1 shows the decline in delinquencies and how the increase in forborne loans left troubled loans pretty much unchanged during the month.

Exhibit 1:  CMBS Universe: Delinquency 60 Day+ and Troubled Loans (60 Plus Day Delinquency and Forborne Loans) (Click Image to View Large Version)

Source: Moody’s Analytics CMBS Loan Data, as of May 27, 2022
Source: Moody’s Analytics CMBS Loan Data, as of May 27, 2022

Tracking forborne loans provides credit insight, as many loans that cure still appear financially challenged to perform or refinance. Amongst this month’s curing loans was the $325 million Hyatt Regency New Orleans, which cured by repaying 11 months of missed debt service. Further, it has until April 2024 to repay another 12 months of previously missed debt service to avoid a default.  This modification extended the maturity to April 2026, but with 2021 fiscal year DSCR of -0.14 times and previously missed payments still outstanding, it may experience further setbacks. Many recently reported cured CMBS loans are in similar circumstances and will need to be actively followed to see how the post-COVID-19 credit story unfolds.

Loss Severities Provide Insight into How Servicers Are Reacting in an Uncertain Environment

In addition, during May the collateral for 13 loans, representing $214.7 million in outstanding balance, liquidated with a resulting 51.1% loan loss severity.  Exhibit 2 shows the historical monthly liquidations since 2014.  Until 2019, most liquidations involved CMBS 1.0 issuance, but from then on, we do see months with significant CMBS 2.0 liquidations. After COVID-19 hit, servicers continued with CMBS 1.0 liquidations, but by October and November of 2020, they seemed prepared to liquidate CMBS 2.0 loan assets in order to take advantage of the surprising recovery for real assets. In reviewing servicer commentary, there are frequent references to a national auction in the month prior to any large liquidation volume increase. The spike in August 2021 is a typical example. The residuals from two large CMBS 1.0 retail portfolios were liquidated that month. The prices realized on these assets appear reasonable versus the most recent appraisals, but CMBS 1.0 have accrued significant liquidation expenses, which created loan losses in the 70% to 100% range for some loans.

Exhibit 2: Monthly Loan Liquidations and Loss Severity ($Billions, January 2014 to May 26, 2022) (Click Image to View Large Version)

Source: Moody’s Analytics CMBS Loan Data, as of May 27, 2022

With the liquidation history of CMBS 1.0 loans drawing to a close and the liquidation history of CMBS 2.0 becoming more significant, studying the differences between the two loan generations warrants separate investigations. Exhibit 3 provides a summary line for pre-COVID CMBS 1.0 liquidations and then has a property type summary for CMBS 1.0 liquidations after March 2020. These CMBS 1.0 post-COVID liquidations created a 60.3% average loss severity, likely due to the long 64-month average resolution time.  Given that length of time, the liquidation expenses averaged 31.8% of the allocated loss. These liquidation expenses accrued with time at a rate of 0.5% per month, so time literally translated into severity loss.  If the appraised value had been realized with no expenses, then the loss severity would have been cut roughly in half to 32.7%.  The far right column nets out the liquidation expenses to show that the post COVID-19 realizations have been 4.25% greater than the values anticipated in the appraisals.  Looking through the property type results, this excess over the appraisal mostly came from the industrial and multifamily property dispositions, as those sectors have experienced heavy tenant demand in recent years and seen sales with lower cap rates.

Exhibit 3: CMBS 1.0 Liquidations – Pre and Post March 2020 (Click Image to View Large Version)

Source: Moody’s Analytics CMBS Loan Data

The amount of post-2020 CMBS 2.0 liquidated is roughly double the CMBS 2.0 dispositions that had taken place before March 2020. Exhibit 4 summarizes the CMBS 2.0 liquidations after March 2020, showing that the loss severity on CMBS 2.0 loans improved from 30.6% before COVID-19 to 27.3%. The realized sale price was 6.9% better than the appraisal on average, and for CMBS 2.0, all property types realized values better than their appraisals.  Interestingly, lodging and retail dispositions dominated the dispositions, with more than $1.7 billion of each type sold.  With disposition periods for these loans averaging only 17.7 months, the liquidation expenses are higher at 0.9% per month, as not all workout costs accrue with time.  The proceeds realization process usually took 17 or 18 months, except for the 4 industrial properties that were resolved in 13.1 months.  Again, multifamily and industrial resolutions showed the largest positive variance from appraisals, at 9.8% and 11.8% respectively.

Exhibit 4: CMBS 2.0 Liquidations – Pre and Post March 2020 (Click Image to View Large Version)

Source: Moody’s Analytics CMBS Loan Data

The increased CMBS 2.0 disposition pace from only $2.913 billion before March 2020 to ~$6 billion after March 2020 caused us to wonder if there were servicing differences among the special servicers.  Exhibit 5 groups CMBS 2.0 dispositions by special servicer.  Investors should note that special servicing is not a science and that different servicers frequently take different approaches based upon loan size, property type, and their economic outlook.  Reviewing the underlying loans showed that servicers that had less than 10 liquidations frequently had numbers dominated by just a couple of those liquidations. To qualify for Exhibit 5, the servicer must have undertaken 10 or more CMBS 2.0 liquidations after March 2020.

Exhibit 5: CMBS 2.0 Liquidations After March 2020 – Grouped by Special Servicer (Click Image to View Large Version)

Source: Moody’s Analytics CMBS Loan Data

There are considerable differences in average special servicer disposition timing.  Wells Fargo (with only 10 loans) was the fastest servicer, having resolved those loans in 3.4 months on average, which resulted in a small average severity of only 7.6%. LNR and CW Capital had more typical experiences, as they have broader servicing obligations and averaged approximately 20 months each to realize loss severities of 31.9% and 32.7%, respectively. Greystone and KeyBank have had quicker resolutions so far, with resolution times of 10.8 and 15.5 months, respectively. In reviewing Greystone’s resolutions, there were many smaller loans, which can be expensive to service and which may have justified quick loan resolutions via note sales and auctions.  KeyBank also had many small balance commercial loans, which likely contributed to their high monthly liquidation costs. KeyBank’s liquidations also included two large malls, which is a property type that historically has not seen higher recoveries from extended workout periods. In that context, KeyBank’s quick disposition time should be commended.  The sale price to appraisal comparison suggests that most servicers exceeded the value anticipated in a recent appraisal, but then the negative result for Greystone and Midland may just indicate that the timing of appraisals or the appraisals accepted may be more conservative (a practice that should not necessarily be penalized).

Overall, we did not see comments that raised any flags, and most of the 679 loan resolutions seemed to reflect property and market conditions that can vary among the loans each servicer resolved.  But there are clearly some differences among the special servicer styles when timing is considered. Some servicers are moving very quickly to resolve loans while others are taking just a little more time for the property or market to improve. In previous recessions, there are examples that highlight how taking time has improved outcomes, as demonstrated back in Exhibit 3 with many loans beating their appraised values.  But some of the CMBS 1.0 loss severities approached 100% due to liquidation expenses, highlighting the risk of taking too long to resolve loans. In our appendix, we provide a liquidation summary for all the CMBS loans ever resolved. This demonstrates that resolving liquidations at the wrong point in the cycle can lead to higher loss severities. That resolution history also shows that it has not always paid to be quick to resolve loans. But at this point, the average resolution times in Exhibit 5 are relatively short and vary by just a few months, suggesting that most servicers saw the post-COVID period as an opportunity to resolve troubled situations and reduce the CMBS pools’ future uncertainty. These CMBS 2.0 resolutions could prove prudent as the Federal Reserve raises rates and potentially causes a recession.  So at this point, all we can do is note that special servicers seem to be moving quickly on CMBS 2.0 loan resolutions and achieving reasonably low loss severities as they take advantage of currently strong market conditions.

Appendix: Historical Summary by Liquidation Date and Vintage

Source: Moody's Analytics CMBS Data


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Darrell Wheeler is a CMBS researcher for Moody's Analytics Structured Solutions group. He creates insights from their commercial real estate data and extensive loan performance database. Darrell has more than 20 years’ experience generating CMBS research, and has held positions across various financial institutions.

David Salz leads the Moody’s Analytics CMBS desk within the Structured Content Solutions group, providing timely and insightful data analytics to CMBS and CRE professionals. Prior to his current role, he managed the ABS desk and worked on various CLO related projects.

Brian Schoenfeld is an analyst on the Moody’s Analytics CMBS desk within the Structured Solutions group. Prior to this role, he attended Dartmouth College, where he majored in Mathematics.

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