This is part four of a four-part series on multifamily bank lending
In the first of our four-part series on bank multifamily lending, we highlighted the remarkable growth in multifamily loans issued by commercial banks. Among all bank lending categories, none has grown faster than multifamily over the last six years. Multifamily as a share of overall lending is now at a 20-year high. In Part 2, we outlined how most of the megabanks have sat on the sidelines of this wave. Instead, it’s the second-tier banks (by asset size) driving the trend. In Part 3, we examined evidence showing that the apartment industry isn’t exhibiting any clear signs of reaching “bubble” status.
Today, we pick up where we left off. While “bubble” seems to be too strong a word, today’s climate is very different from what was typical in the apartment sector’s recovery stage. NOI growth, loan yields and cap rates all appear past peak levels, while apartment construction has surged to 25-year highs. At the same time, investor appetite for apartments hasn’t abated. Competition among lenders has picked up, returning some bargaining power back to borrowers.
The world has changed. How can lenders adapt? Before adjusting standards on credit or terms, it’d be prudent to begin thinking more narrowly about the apartment sector. What are the characteristics of apartment properties most likely to be successful in the next cycle?
Let’s start with a lesson learned from the single-family bust: Not all markets are the same. A lender overexposed to single-family loans in Florida fared much worse than one heavy on Texas. Even within Texas, some metros did better than others. And even within strong metros, there are always weak or speculative submarkets with heavier risk. And while banks undoubtedly consider geographic weighting in their overall loan portfolio, how many strategically weight their multifamily portfolios down to the submarket or neighborhood level? Real estate, the old adage tells us, is local, local, local. Real estate lending should be, too.
Now let’s take a look at how banks underwrite multifamily loans. Most banks know how to evaluate a borrower. Good tools to do so are plentiful. But evaluating an apartment property is a different beast, and even best practices are rife with holes. Consider:
- Traditionally, many banks may favor office or retail deals because they offer a more granular view of the tenant base and its ability to pay the bills – plus they’re typically committed to longer-term leases, providing stability. Many apartments have 300+ individual leases, most with terms around 12 months or less, and therefore have constant churn. That’s added risk.
- Lenders often get a property’s rent roll, but churn allows apartment owners to perfume their rent rolls in advance of a deal. For example: A property that has trouble staying full may offer concessions or even non-financial, unreported incentives to improve their occupancy rate. But such tactics aren’t sustainable. And in buying occupancy, they may actually be reducing revenue.
- Lenders may also look at third-party market research for performance data and forecasts for a particular area. But submarkets are subjectively defined and most offer a wide spread of opportunity within them. For example, say there’s a submarket centered around a major college campus. A property located 10 miles away, with no nearby anchors, gets lumped into the same submarket. But every other apartment may be within walking distance of the campus, making the subject property an outlier that might not follow the same trajectory.
- Most lenders like to see specific comps. But comps are also subjective and they’re often provided by the borrower, who is incentivized to pick comps that tell the right story. Furthermore, to get comps, you trust that whoever answers the phone provides honest answers for a snapshot of one day in time that may not be representative of a broader pattern. And it’s such a small dataset that even one foul number can throw the comp set out of balance. In a world where risk measurement is paramount, do you want the integrity of a deal to be dependent on a cold call placed to someone who has no incentive to help you? Even if the data is correct for that point in time, should the integrity of a deal be dependent on one day’s snapshot?
What can we conclude from this? A few ideas:
- Certain apartment segments are more likely than others to slow down. Reduce risk by refining acceptable characteristics for a multifamily loan – looking much more narrowly at geographies and classes most likely to grow NOI. For example, in this next cycle, some of the best opportunities could be in submarkets with upper-income renter bases but manageable supply volumes.
- Seek more sophisticated benchmarking and forecasting data that allow you to see how an asset is positioned relative to its market or submarket. Is this property an outlier based on its rents or address? How does the renter base (incomes, credit scores, etc.) compare to peers? Does the borrower want funds for a renovation, even though the property already commands high rents relative to the area? Does the rent roll’s expiration schedule allow the property to capitalize on forecasted upticks in the market cycle?
- Push for more exposure into rent rolls. Look for ways to bring best practices from commercial real estate underwriting into apartment deals. Some of the same data you’re accustomed to getting for commercial deals exists for apartments, but it’s often unused due to complexity or borrower preference.
- Identify risk-optimizing loan monitoring techniques that aren’t limited to macroeconomic trends, but also track performance and refresh forecast expectations regularly for a surrogate group of properties representative of each asset.
- On construction deals, get ahead of the trends. Real estate is a follow-the-leader industry that has a habit of punishing late adopters. Right now, the trend is to build high-end apartments in urban core submarkets. But at this point in the cycle, there may be less-saturated spots that offer a healthy revenue growth outlook with less risk.
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