January 2019

CRE Research

Not All is Created Equal: Identifying Property Characteristics That Impact Loan Performance

Over the last few years, the state of the retail sector has the property was built or renovated; exposure to the been frequently categorized as distressed. Many market largest tenant (a proxy for single-tenant concentra- observers noted how many brick-and-mortar chains have tion), and geographic region. struggled to adapt to technological disruptors (such as and Alibaba) and seasoned players () alike. The list of retailers which have declared major Identifying the Best and Worst Performing Types of Retail Properties store closures, restructuring, and/or defaults has only grown in recent months. That rundown now includes A total of 16,678 retail loans which were outstanding at such stalwarts as , National Stores, some point within the past five years were observed in , and Mattress Firm. our study. Once all of the retail loans were ranked, the 16,678 loans were segmented into ten buckets based Still, it would be irresponsible for us to press the panic on their ranking position. Bucket numbers indicate loan button and conclude that the US retail market is a failing performance in descending order: bucket 1 represents industry. Retail continues to be a powerhouse in CRE the top 10% of retail loans in terms of loan perfor- finance, as outstanding retail loans currently make up mance, while bucket 10 represents the bottom 10% of approximately 27% of all outstanding CMBS notes. retail loans in terms of performance. Additionally, 25% of the total net operating income (NOI) for CMBS is tied to retail properties. Given that there OUTPERFORMERS are still strong performers in this sector, we analyzed Property Characteristic Outperformers our database to find which loans are outperforming or Property Type Drug Stores and Urban/Street Retail underperforming the average. Tenant Mix Single Tenant Region Middle Atlantic (NY, NJ, PA) Measuring the Performance of the Retail CMBS Year Built Very Old (1940s or Earlier) and Sector Very Young (2000s or Later) Source: Trepp We tapped into our database of retail CMBS loans to rank relative loan and property performance of all out- UNDERPERFORMERS standing retail notes. We analyzed this data to identify Property Characteristic Underperformers common characteristics of the loans and properties that Property Type Community Shopping Centers performed similarly. Tenant Mix Diverse (Largest tenant occupies less than 35% of space) Loan performance was measured using five met- Region Mountain (MT, ID, WY, NV, UT, CO, rics: occupancy rate at securitization; net operating AZ, NM) income (NOI) per rentable square foot (PSF) at secu- Year Built 1980s ritization; minimum occupancy during the loan’s life; Source: Trepp minimum NOI PSF over the loan’s life, and the loan’s worst delinquency status. Descriptive property char- We have compiled a list of property characteristics that acteristics include property subtype; the year that were found amongst the strongest and weakest per-

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forming retail loans. The tables below contain a list of Drug store and urban/street retail properties were the property characteristics associated with the strongest strongest performing property subtypes. Slightly more performing retail loans and a list of property character- than 70% of drug store loans were found in the top istics exhibited by underperforming loans. 20% of our retail CMBS ranking. Drug store notes per- formed well due to their collateral’s consistently high Property Subtype occupancy rates, high NOI PSF, and low delinquency rates. On average, drug stores carried occupancy rates The property subtype is a characterization assessed by of 99.87% at securitization with an average minimum using a combination of the property type provided by occupancy of 99.83% across 1,892 loans. These prop- the servicer and features of the underlying collateral. erties also exhibited one of the highest average NOI Please refer to Appendix A for definitions of each prop- PSF figures at securitization with $23.34/PSF. erty subtype.

PROPERTY SUBTYPE RANKING

Community Convenience Center Drug Store 20 14 50 12 40 15 10 8 30 10 6 20 4 5 10 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Neighborhood Center Outlet Regional Mall 16 25 20 14 20 12 15 10 15 8 10 6 10 4 5 5 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Single Tenant Retail Superregional Mall Urban/Street Retail Percent of Loans by Property Subtype 30 20 70 25 60 15 20 50 40 15 10 30 10 5 20 5 10 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Source: Trepp

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Our second-strongest performing property subtype Year Built was urban/street retail. Approximately 60% of these It’s difficult to pinpoint the exact influence a prop- loans were in the top 10% of the retail loan pool. The erty’s age will have on the corresponding loan’s property subtype’s strong performance can be credited performance. Intuition would probably lead us to to its massive NOI PSF at securitization of $85.17. assume that newer properties should do better Contrary to the strong performance of drug stores than older properties, but that may not be the case and urban/street retail properties, we found that com- for retail where there was significant overbuilding munity shopping centers, regional malls, and neigh- leading up to a major recession. On the other hand, borhood shopping centers skewed toward the lower older properties may require greater maintenance end of our ranking. The three subtypes share a theme and other operating costs due to depreciation, of mediocre occupancy rates and relatively low NOI which may lower NOI and prompt tenants to seek PSF figures. On average, community shopping cen- more updated properties. ters were the worst performing properties in terms The year built or year renovated for each property of both NOI PSF at securitization and the minimum was broken down by decades between the 1940s NOI PSF throughout the loan’s life ($11.02 and $9.85, and the . All properties built before the 1940s respectively). Regional malls represented the greatest were given the designation of “Old.” Figure 2 be- number of delinquent loans and neighborhood shop- low contains the ranked performance of each prop- ping centers trailed closely behind it. erty decade. Occupancy Levels - Subtype 105 We found that old properties, properties built in

100 the 1940s, and properties built after the 2000s per- formed the strongest. Properties in the 2010 vin- 95 tages generally outperformed properties from the 90 2000 vintages. 85 Occupancy % 80 Old properties were the clear breakout stars in our

75 retail ranking. Nearly 50% of the properties built Community Convenience Drug Store Neighborhood Outlet Regional Mall Single Tenant Superregional Urban / Street Shopping Center Center Retail Mall Retail Center prior to the 1940s were found within the top 10% Occupancy at SecuriDzaDon Minimum Occupancy of our retail ranking. Old properties outperformed the other decades due to their high NOI PSF at NOI Levels - Subtype securitization and the average minimum value ex- 90

80 hibited by the property. Properties constructed

70 during the 1940s behaved similarly, with extremely

60 high NOI PSF statistics paired with impressive oc- 50 cupancy rates. It is likely that many of these older 40

NOI PSF properties are in densely populated, high perform- 30 ing geographies. 20 10 While not as impressive as properties built prior to 0 Community Convenience Drug Store Neighborhood Outlet Regional Mall Single Tenant Superregional Urban / Street Shopping Center Center Retail Mall Retail the 1950s, properties built in the 21st century per- Center NOI at SecuriIzaIon Minimum NOI formed well due to their strong NOI PSF and rela-

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YEAR PROPERTY BUILT RANKING "Old" 1940s 1950s 35 16 60 30 14 50 12 25 40 10 20 30 8 15 6 20 10 4 10 5 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

1960s 1980s 16 1970s 14 16 20 14 12 12 15 10 10 8 8 10 6 6 4 4 5 2 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Percent of Loans by Year Built 1990s 2000s 2010s 16 20 30 14 25 12 15 10 20 8 10 15 6 10 4 5 2 5 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Source: Trepp

tively low delinquency rates. Properties built in the It seems that while stark differences can be found 2010s had the lowest number of properties with between older and younger properties, they will recorded delinquencies, the third-highest NOI PSF continue to outperform middle-age properties due at securitization ($24.60), and the third-highest av- to unique features exhibited by each vintage clas- erage minimum NOI PSF ($21.54). Stronger per- sification. Any concern that older properties incur formance by these properties may be attributed to higher upkeep expenses was put to rest by the their construction following the recession. The re- high average NOI income exhibited. Additionally, cession may have weeded out weaker performing it appears that newer properties haven’t failed to properties between 2007 and 2010, which would attract tenants since properties built in the 2010s make properties constructed during the beginning exhibited the highest occupancy rates among the of the 2010s the strongest of the population. decades observed.

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Occupancy Levels - Year Built Top Tenant Exposure 100 We classified any properties where the top tenant 95 occupies 95% or more of the property’s space as 90 single-tenant properties. Our full list of property cat-

85 egories for the percentage of space leased by the top tenant are as follows: single-tenant (occupying 80 95% of space or more); 75%-95% of space occu- Occupancy % 75 pied, 55%-75%; 35%-55%, and less than or equal to Old 1940s 1950s 1960s 1970s 1980s 1990s 2000s 2010s 35% of space. Occupancy at Securi:za:on Minimum Occupancy Single-tenant properties were the standout category NOI Levels - Year Built exhibiting the highest average NOI PSF, highest av- 70 erage occupancy rate, and the lowest average delin- 60 quency rate. Nearly 50% of single-tenant loans were 50 ranked in the top 20% of our retail CMBS ranking. 40 Properties where the largest tenant occupies less 30

NOI PSF than 35% of the space were the weakest perform- 20

10 ing, as they carried the lowest NOI PSF and the low-

0 est minimum occupancy rates. Old 1940s 1950s 1960s 1970s 1980s 1990s 2000s 2010s NOI at Securi:za:on Minimum NOI

LESSEE RANKING

<35 35-55 55-95 16 14 14 14 12 12 12 10 10 10 8 8 8 6 6 6 4 4 4 2 2 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Source: Trepp 75-95 >95 18 30 16 25 14 12 20 10 15 8

Percent of Loans by Lessee Occupancy 6 10 4 5 2 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 www.trepp.com 5 CRE Research January 2019

Occupancy Levels - Top Lessee % credited to the large number of single-tenant proper- 105 ties, which is a common trend for large cities like New 100 York and Philadelphia. In terms of the numbers, the 95 Middle Atlantic’s strong performance came from the

90 high average NOI PSF at securitization ($28.89) and

85 the minimum NOI PSF ($25.38). Occupancy % 80 The Pacific and New England regions were the next 75 strongest areas, while there was pretty even distri- <35 >95 35-55 55-95 75-95 bution among the remaining regions. This could indi- Occupancy at Securi8za8on Minimum Occupancy cate that regional-centric features play a smaller role in the performance of a loan, except when located NOI Levels - Top Lessee % in the Middle Atlantic, Pacific, or New England. 25

20 Looking Ahead: Retail Performance in 2019 and Beyond 15

10 At this point, the future of the retail market re- NOI PSF mains unclear. While the demise of major retailers 5 such as Sears, Toys “R” Us, National Stores, and 0 Mattress Firm sparked some panic in the industry, <35 >95 35-55 55-95 75-95 many critics argue that the retail apocalypse has NOI at Securi7za7on Minimum NOI been vastly overstated. Some have argued that the fall of major retail giants was inevitable; a sign of a growing technological world weeding out players Region slow to adapt to the online marketplace. It is too soon to make any definitive predictions, but there The final descriptive categorization we used was is enough data to analyze property performance the property’s corresponding US Census region. and determine which kind of properties may do There are a total of nine regions across the US: well in this rapidly evolving market. Northeast Central; Southeast Central; Middle At- lantic; Mountain; New England; Pacific; South At- lantic; Northwest Central, and Southwest Central. Please refer to Appendix B for the states in each of these regions.

Middle Atlantic properties were the clear standout performers in our regional classification. The strong performance of the region is helped along by the mas- sive volume of retail CMBS outstanding in New York, which has a tendency to perform above average. Ad- ditionally, the region’s exemplary performance can be

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REGION RANKING

Northeast Central Southeast Central Middle Atlan3c 14 14 25 12 12 20 10 10 15 8 8 6 6 10 4 4 5 2 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Mountain New England Pacific 14 20 16 12 14 10 15 12 10 8 10 8 6 6 4 5 4 2 2 0 Percent of Loans by Region 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

South Atlan3c Northwest Central Southwest Central 12 16 14 10 14 12 12 8 10 10 8 6 8 6 4 6 4 4 2 2 2 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Source: Trepp

Occupancy Levels - Region NOI Levels - Region 98 35

96 30 94 25 92

90 20

88 15

86 NOI PSF 10 Occupancy % 84 5 82

80 0 Northeast Southeast Middle Mountain New Pacific South Northwest Southwest Northeast Southeast Middle Mountain New Pacific South Northwest Southwest Central Central Atlan9c England Atlan9c Central Central Central Central Atlan8c England Atlan8c Central Central Occupancy at Securi9za9on Minimum Occupancy NOI at Securi8za8on Minimum NOI

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Appendix A Property Subtype Definitions

Property Subtype Concept

General-Purpose Centers General merchandise or convenience- oriented offerings. Wider range of apparel and other soft goods offerings Community Shopping Center ("Large Neighborhood than neighborhood centers. The center is usually configured in a straight line as a strip, or may be laid out in an L Center") or U shape, depending on the site and design. Attached row of stores or service outlets managed as a coherent retail entity, with on-site parking usually located in front of the stores. Open canopies may connect the store fronts, but a strip center does not have enclosed Convenience Center walkways linking the stores. A strip center may be configured in a straight line, or have an "L" or "U" shape. A convenience center is among the smallest of the centers, whose tenants provide a narrow mix of goods and personal services to a very limited trade area. Neighborhood Center Convenience oriented.

General merchandise or fashion-oriented offerings. Typically, enclosed with inward-facing stores connected by a Regional Mall common walkway. Parking surrounds the outside perimeter.

Superregional Mall Similar in concept to regional malls, but offering more variety and assortment.

Specialized-Purpose Centers Drug Store Retail property with the purpose of distributing medicines and micellaneous articles.

Outlet Manufacturers' and retailers' outlet stores selling -name goods at a discount.

Single Tenant Retail Retail property with a single-tenant.

Different types of service stores that are typically found in high-density walking neighborhoods with not store Urban / Street Retail parking

Source: ICSC

Appendix B US States in Regions Region US States Northeast Central WI, MI, IL, IN, OH Southeast Central KY, TN, MS, AL Middle Atlantic NY, NJ, PA Mountain MT, ID, WY, NV, UT, CO, AZ, NM New England ME, VT, NH, MA, CT, RI Pacific WA, OR, CA South Atlantic DE, DC, WV, VA, NC, SC, GA, FL Northwest Central ND, SD, MN, IA, NE, KS, MO Southwest Central OK, AR, TX, LA

US Census

For more information about Trepp’s commercial real estate data, contact [email protected]. For inquiries about the data analysis conducted in this research, contact [email protected] or 212-754-1010.

About Trepp

Trepp, LLC, founded in 1979, is the leading provider of information, analytics and technology to the CMBS, commercial real estate and banking markets. Trepp provides primary and secondary market participants with the web-based tools and insight they need to increase their operational efficiencies, information transparency and investment performance. From its offices in New York, San Francisco and London, Trepp serves its clients with products and services to support trading, research, risk management, surveillance and portfolio management. Trepp is wholly-owned by Daily Mail and General Trust (DMGT).

The information provided is based on information generally available to the public from sources believed to be reliable. 8