High End or Low End Multifamily - Which Does Better in A Recession?

A few days ago a loan officer trying to persuade me we should do an maximum loan on a 1960's motel style apartment project in a not so great submarket assured me that, even though we are entering a slowdown, the project would be fine because the rents were affordable and in a recession tenants fall back on affordable housing. I had to laugh, because a few days before another loan officer was assuring me her high end project would be fine because high end tenants were immune from economic downturns. So, projects that are high end or low end both do fine in a recession. The logical extension is, projects in the middle do fine too, because they have elements of both high end and low end. Don't worry about that recession, all projects will be just fine.

The difficulty is that, depending on your experience with multifamily properties during recessions, both stories could ring true. You might know of low end projects that held up well in hard times and high end properties that struggled, supporting the first story. Or, you might have had the opposite experience, leading you to believe the second. All it takes for the human mind to latch onto "truth" is experience, no matter how limited, supported by a plausible narrative (Robert Burton has an excellent book, On Being Certain: Believing You Are Right Even When You're Not", which discusses this phenomenon in depth).

Another difficulty is that to conclusively answer the question you need a large data set which has captured the right attributes. The institutions and organizations with the data (the GSEs, large servicers, CMBS data aggregators, rating agencies) don't share the data and/or the analysis they do because they consider it proprietary, and in any case, they haven't captured the proper attributes (a problem discussed in my post Why What you Know About Income Property Performance is Probably Wrong).

I have worked for two large multifamily lenders and have done the analysis, and I can tell you with confidence that lower end properties get hurt worse in a recession than upper end properties. Here are the reasons why:

1) Recessions equal job losses (see this post for evidence of the correlation). Job losses result in reduced housing demand across the spectrum. However, there is pretty good evidence job losses in recessions hit minorities, the less educated, and part time workers disproportionately (see, for example, The State of Working America 2004/2005). Consequently, low end rental housing is hit disproportionately.
2) The devastating impact even short term unemployment has on the working poor hits low end projects hard. Even during a recession, most of the unemployed find new jobs (see What Do We Know About Job Loss in the United States? Evidence from the Displaced Worker Survey,1984-2004). Middle and upper income renters cover their housing expenses in the interim through a combination of unemployment benefits, savings, and borrowing. Low income tenants are much less likely to have such resources, and the only alternatives are often moving in with another household, living in a vehicle, or homelessness (Barbara Ehrenreich eloquently chronicles this cycle and how difficult it is to climb back up once the downward spiral begins in Nickled and Dimed: On (Not) Getting By In America). Again, low end properties are disproportionately hit.
3) What about the idea that demand for affordable housing increases in a recession? It doesn't seem to work that way. Even though logically it may make sense to scale back on housing expenses when times are tough, psychologically it's a difficult thing to do. There is a well documented endowment effect; once you have something it's worth more to you and you're reluctant to trade. So, tenants at relatively expensive apartments hang on even though it may make economic sense to trade down. On the flip side, lower income tenants who keep their jobs have an opportunity to take advantage of lower rents and/or concessions at nicer projects. Again, the net impact is to drain tenants from the bottom price tier of projects.
4) Low end projects get hammered on the expense side of the equation too. Although some expense components are higher for high end projects (e.g., real estate taxes, management fees, on-site management salaries to a limited extent), many expense components are the same or higher for low end projects when looked at on a per unit basis (e.g. maintenance, insurance, utilities). As a result, operating expenses as a percentage of effective gross income for low end projects tend to by substantially higher (50-60%) than high end projects (30-40%). When effective gross income declines in a recession as a result of higher vacancies and concessions and/or lower rents, the operating margin of a low end project disappears faster than a high end project's margin.

Of course, there have been some conspicuous failures of high end projects during recessions. However, in my experience these failures usually occur during the construction and/or leaseup phase, and are attributable to cost overruns or inability to attract tenants at pro forma rent. Once stabilized, high end projects withstand recessionary pressures well.

Does this mean lenders should avoid financing low end projects? That's one approach to risk management, but the same portfolio analysis that led me to this conclusion points to a better way. Despite the fact that low end projects underperform in a recession, when it comes to actual loan defaults by far the most important factors are the financial strength and experience of the sponsor. Experienced sponsors familiar with low end projects in their market place make the right management decisions and chose an approrpriate level of debt. Smart lenders will back such sponsors over inexperienced and/or financially weak sponsors of high end projects and come out ahead.

Why What You Know About Income Property Performance is Probably Wrong

Get a bunch of real estate people together, and inevitably the conversation will eventually turn to deals that went bad. The war story usually ends with a guiding principle the person took away from the experience, and if the story is a good one everyone who hears it adopts that principle too. However, way too often these principles are wrong. A few examples:

1) The manager of a group I used to work with (a very bright, experienced guy) was absolutely, positively convinced that Low Income Housing Tax Credit (LIHTC) projects which underwent only moderate rehabilitation were higher risk than gut rehab or new construction projects. The argument was that future capital needs were almost always underestimated for moderate rehab projects, while gut rehab and new construction projects started out in new condition. This story made sense, and when you looked at the problem properties in the portfolio it was true that 75% of them were moderate rehab projects. The only problem was, 75% of the entire portfolio were moderate rehab projects, which meant the moderate rehab projects were not underperforming after all.

2) The manager at another bank I worked at was convinced that mobile home parks were relatively high risk. That was contrary to my experience, so I did some digging into the past negative experiences and discovered it was true that there were previously underperforming mobile home park loans in the bank's portfolio. But, those loans underperformed during the most recent recession, during a period when many loans of all property types underperformed. In fact, although they were on the bank's watch list the mobile home park properties performed better than many of the other property types.

Why did these mistaken conclusions happen? Several biases are at work:

Availability Bias. People sometimes erroneously draw conclusions based on how easily an example comes to mind. The manager in the first example could easily call up many examples of underperforming moderate rehab projects, which led him to the erroneous conclusion that these type of projects underperformed. Recent events and vivid events also are overweighted. For example, after September 11, 2001, terrorism insurance on even moderate size real estate projects in moderate size metropolitan areas was seen as essential. This is no longer true, although there is no reason to think the risk has abated.

Attentional Bias . People sometimes focus on a limited number of possible explanations when examining correlations, and exclude the correct explanation. The manager in the second example focused on property type as the explanation for poor performance, and ignored the time element (recession conditions) which was a better explanation for the underperformance.

To understand why a property underperforms, you need to have a large, diverse sample and look at the correlation between a characteristic and underperformance. This is not something that is commonly or easily done. Most lenders do not have portfolios that are large and diverse enough to draw good conclusions from. I have worked for two organizations that did have such portfolios and did the work, but the conclusions were always viewed as proprietary. Fannie Mae, Freddie Mac, some of the large servicers, and CMBS data aggregators have the data and are probably using it internally, but it's unlikely to become public.

Even if the data were publicly available, I doubt it would be very useful because there is a huge omission in the data being collected. When I did my previous work, I initially was very disappointed to find only a few attributes that correlated with performance, and those correlations were weak. Property age, initial LTV and/or DSC, refinance vs. purchase, initial property condition, size of project - nothing I looked at correlated with property performance. It was only when I went back to original loan files and started looking at sponsor characteristics that I found some meaningful correlations. Specifically, sponsor net worth, liquidity, and experience in the property's market all had strong correlations with property performance. Why this is so is a topic for another post, but without controlling for these attributes any conclusions about other factors are going to be faulty.

Unfortunately, no one captures this information in a data tape form - it has to be dug out of the original loan files. I've asked people I know at rating agencies and Fannie Mae why they don't capture these attributes and the answers are (a) these are non-recourse loans so borrower financial attributes are not relevant, (b) the loans are generally assumable so the sponsor may change, and (c) the sponsor's financial situation will change over time, and there is generally no requirement for updated financial statements. These answers all miss the point; the attributes are highly correlated with performance, and should be captured.

In an ideal world the ratings agencies and regulators (FDIC, OFHEO) would require lenders to report standardized data at loan origination (including sponsor experience and financial information) and again at loan default (something similar to a death certificate). This data (redacted to remove confidential information) should be available for analysis to academia and the public. Until then, lending decisions will continue to be based on the limited experiences and biases of the lending officers making the decisions.