In our last blog post we mentioned that we would continue our dialogue with our readers.  Our post this time will discuss another core element of our underwriting process, bad debt.  

In the real estate universe, bad debt is the amount of unpaid rental income that is determined to be uncollectible.  The term bad debt is often referred to or used interchangeably with “credit loss” or “collection loss.”  

Bad debt is an adjustment line item to net operating income (“NOI”), and as it relates to our projections, this is an adjustment to potential operating income.  In other words, the bad debt line item is a forward-looking provision, which takes into account probabilities and other statistical factors.  

The primary thing that you should takeaway is that bad debt has an inverse relationship with NOI.  An increase in bad debt causes a decrease in NOI and asset value.  To determine more accurate outcomes of bad debt in our projections, let’s walk through our underwriting process. 

Because bad debt is an adjustment to NOI just like vacancy, you may notice similarities to our last post (The Leased We Could Do: Let’s Talk About Vacancy Underwriting).  

We begin our process by gathering national data from financial institutions for current and historic credit loss levels.  By gathering data from ten years back or sometimes even further, we can formulate some sort of credit loss baseline average for the multifamily market.  It also helps us to understand how the US multifamily market performed across market cycles, while taking special notes on the worst bad debt levels throughout history.  

Applying our experience and knowledge of the multifamily market, we would immediately bring our attention to post-GFC (global financial crisis) because of its extreme impact on all real estate market forces.  

At this point in time, our research has led us to assume that the ten-year national average for credit loss for multifamily real estate is roughly 0.7%.  Due to the economic impacts of the COVID-19 pandemic, bad debt has doubled and continues to trend upward toward the highest historical levels (2.7% annually over a two year-period).  This is important for our next step when we investigate and analyze credit losses in more specific areas.

After reviewing the regional and state data, we narrow our focus to an individual market.  

Just as we did at the national, regional, and state levels, we collect current and historical data on our target market.  The historical average establishes a baseline, the current levels help to gauge where we are in the market cycle, and the highest historical bad debt levels provide guidance in a worst-case scenario.  

From our research, we gathered that the ten-year historical average of our target market was 0.6%.  For Class B and C Garden Style properties the average was 0.5%.  We also gathered that current bad debt levels have risen to 1.2% due to the pandemic, but have not yet reached their historic high point (2.6% annually over a two-year period).  However, prior to COVID, our target market was trending downward, hitting a low point of 0.4%.  

From this current information, we can see that our target market reveals signs of better health and overall resiliency relative to the rest of the nation.  This is a good sign!  It tells us that, aside from other market forces and economic health indicators, it would be wise to continue our underwriting process by moving to the next step in which we probe into individual multi-family properties.  

Before we start our projections, we gather current and historical data on bad debt of our prospective properties.  After analyzing these numbers and other relevant quantitative measures, we proceed by focusing on a property that provides initial evidence of a promising opportunity.  

Our target is a Class B property and fits in the broad Garden Style category.  It’s historical average levels for bad debt have been 0.4%, and before the pandemic, levels have been trending downward to a low point of 0.2%.  We also see that current levels are 0.8% and that the historical high point was 2.9% during the global financial crisis.  

Focusing exclusively on the bad debt metric, we examine the property for any anomalies.  In this case, the one anomaly we notice immediately is the historical high level; at its worst, the property had higher bad debt levels than both the nation and its respective market.  

Just as we would with any core element of our underwriting process, we seek to uncover the true reasons for an anomaly.  What were the root causes for this anomaly?  What were the market forces at play? Or was it something specific to the property and its management?  In an attempt to address these questions, we research other factors such as location, vacancy rates, unemployment, and supply and demand levels of the respective market.  

With regard to our target property, we have discovered that a lack of diversification in tenants and poor collection policies led to this anomaly.  Given that our research has not identified any major red flags at this point, we believe we are ready to build out our models. 

We begin by entering all of the data into our proprietary model.  To the same degree as vacancy, our approach to bad debt is conservative.  In our projections, we like to “think like a lender,” which means that we factor in current circumstances and the probability for extraordinary negative outcomes in the economy. 

Due to the current pandemic, we believe there is a realistic scenario for an abnormally weak economy.  We expect that one of the potential outcomes is higher-than-average levels for credit loss.  

Our conservative philosophy causes us to handle our credit loss projections a little different; we add a margin of safety in our scenario analysis.  

The projections of our current acquisition prospects consider historical highs and a buffer for any deviation from previous market scenarios. To rephrase, our projections involve a worst-case scenario that factors in an even higher estimate for bad debt. Typically we add a 1.0 to 2.0% margin.  

As we make any other adjustments to a worst case scenario, we can begin to build a case as to why this deal would work or not. Part of that equation is seeing if a change to the financing structure, capital improvement, or insurance costs could push the deal forward.  

It is worth noting that any discovery or projection is one of the deciding factors in trying to establish the type of loan we might obtain should we choose to invest in the property. Also, beyond financing structure, we take into account any data related to bad debt and formulate a strategy in our business plan that seeks to minimize credit loss.  For the sake of maintaining our focus on bad debt, we will save a detailed discussion on those topics for another time.  

Currently, our projections for this target property fit within our comfort zone. As a result, we have decided to move beyond the underwriting stage to our next step of the investment process.

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