By Narendra Srivatsa Director of Product Development, Verisk Commercial Real Estate

 

“Every person who invests in well-selected real estate in a growing section of a prosperous community adopts the surest and safest method of becoming independent, for real estate is the basis of wealth.”

— Theodore Roosevelt

 

Known for fiery replies and sometimes startling insights into fellow politicians and policies, Teddy Roosevelt was never revered for his advice on real estate. And while his statement is bullish for investors, what does it reveal about finding ways to fill investment properties with reliable tenants?

Read closely, the statement makes a series of conditions: Real estate must be “well-selected” and located in a “growing” neighborhood of an area that is already “prosperous.” Only then can the investment become the “surest and safest” possible. In the era of Roosevelt’s bully pulpit, definitions of those categories would have been derived primarily by well-honed market instincts. Whatever the end goal in real estate, the advent of data and analytics has changed what was formerly informed guesswork into reasoned decision making, although specific instruments are needed to make the most accurate assessments. What tools are now available to help find the “surest and safest” clues to a profitable investment? What are, for example, the most reliable criteria for telling a likely defaulter from a promising tenant? Given the increasing complexity of real estate transactions, a prospective investor should probably search for better ways to assess rental-income risks if only to flag potential delinquencies. Rental income can be considered a key to commercial real estate (CRE) investments in determining acceptable returns over the long term. While many recognize rental income’s role in CRE investment management, tools to assess risk of rental delinquencies have often not been applied to their fullest advantage. The current criteria using lease data makes the assumption that tenants can and will pay rent through their lease period — that’s a hypothesis that has been proven wrong time and again. Assessing risk from tenant delinquencies can be vital to improving the return on investment (ROI) of your CRE investment and avoiding loan default. The paradigm frequently used in investment decisions is that CRE follows the macroeconomy. That has often led to a reliance on econometric forecast models as formulated by Torto Wheaton, Moody’s, and other leading research organizations. Those macroeconomic factors generally assess supply-and-demand effects on rental income in a potential metropolitan statistical area (MSA) or submarket based on a number of data elements, including available inventory of space, absorption levels, rent per square foot, employment, demographics, and median income.

Tried but Not True

The rental income assessment from this econometric research for an MSA or a submarket is then set beside the lease income of a particular asset to make comparisons and determine the attractiveness of the investment. And yet there’s often a huge gap, because this does not assess a major risk to specific rental incomes — the tenant’s inability or reluctance to pay rent isn’t part of the analysis. This has sometimes resulted in unexplained variances showing up in the investment manager’s quarterly reports and, in many cases, can lead to default. The challenge facing the industry is that macroeconomic factors don’t provide the granular level of assessment typically required to determine risk in rental income. More than 20 million businesses in the United States occupy some form of commercial real estate space in office, retail, industrial, or mixed-use property types. Of those businesses, only about 15,000 are public companies. Little information is tracked about other private businesses and their ability to pay their rent consistently. That has resulted in rental delinquencies, increasing losses, and larger investment risks. Having risk assessment tools at hand to forecast rental income risk can provide CRE investors with the opportunity to:

• underwrite to higher standards and not just depend on debt-service coverage ratio (DSCR), loan-to-value (LTV) ratio, leasing, and macroeconomic data ‚Äî all of which are often insufficient
• manage portfolios to timely and context-driven risk measurements that assess tenant risk concentrations and market conditions to rebalance portfolios
• aggregate the renter‚Äôs risk data at ZIP-code level and develop a rental income risk score to compare different macros and property types, just as can be done with unemployment, median income, and so forth ‚Äî which can help assess market areas and property types for investment potential

True and Worth a Try

Generally, there is very little financial information from private businesses available to the public. But new tools can be used to address this specific gap to develop a competitive advantage and improve certainty of rental income. Suppose we were able to obtain time-series information on the businesses for several characteristics, such as spending in major areas of business growth and payment history (including past-due payments, liens, growth clues, and payment risk score indexed against similar businesses). We would have the tools to better assess rental income risk based on viability of the business tenant at that location. For example, if a business is showing significantly increased past-due payments over time, it can be a reliable red flag that the company is in some sort of serious financial trouble. Just as past-due payment information may help predict business failures, it can also flag rental delinquencies, sometimes a year in advance — a handy piece of information when renewing leases or preparing to find alternative tenants. Let’s say a tenant, Wallingford LLC, is an efficient business that shows increased spending in five major areas required to operate its business successfully. Also suppose that, in the past, Wallingford ran without liens or falling behind on payments. Its payment risk score and payment history over time have shown a stable history with no past-due payments and now continues to maintain the same positive pattern. The reasonable conclusion would be that this is a well-run business with good growth and an ability to pay the rent and remain a desirable tenant. Many companies fall into this exemplary class and would be good tenants. Yet further assessment of this company’s growth and its comparison to similar companies and their developing scores should provide deeper insight. If a company is buying more, hiring more, and paying bills on time, then it’s probably on a growth curve — that might be a good tenant to have discussions with concerning additional space for office, storage, or other needs. Knowing more about a company’s stage of growth may allow exploration for increased rental area requirements or additional space. If another company, Eggleson Operating LLC, is growing at a rate slower than its competitors and has several key payments past due, then rent payments are certainly at risk. For a landlord, knowing the situation ahead of a business default provides the opportunity to line up another tenant before worse news arrives. Prominent examples of this would be the many social media start-ups once optimistically reported in the news as the “next big thing.” Although press and the prevailing industry trend may be favorable, if a company is burning through cash faster than it collects revenues, it may ultimately be destined for bankruptcy. Again, for the landlord, wouldn’t having insight into a company’s payment history and spiraling risk scores suggest putting a plan in place?

Tools of the Rental Trade

Fortunately, there are several new analytics that provide insight into commercial lenders and investors. A Risk Behavior Score (RBS), based on past-due payments and indexed against similar firms across the United States, helps shed light on payment delinquencies — information that translates into predictions of rent delinquencies. Similarly, a Payment Score (PS) provides an index showing how fast this business has paid its bills in the past three months. And a Payment Risk Score Segment (PRSS) provides a comparison of how well the payment risk was in the past three to six months compared with other businesses. Having that level of granularity can result in a better assessment of the business tenant’s ability to meet basic obligations and brings an investor closer to assessing true levels of risk to rental income. In addition, the detail of this information could be applied on a macro basis for portfolio assessments and to derive a risk score for an entire MSA. Risk assessment often involves finding and plugging the right data into new analytics formulated for a specific use — a method Teddy Roosevelt might have endorsed to make the “well-selected” investments he spoke of. With tools available for calculating the risks from potentially crippling delinquencies of business tenants, investors may be facing a brighter, less dependent, and more certain future. Narendra Srivatsa, Ph.D., is director of product development, Verisk Commercial Real Estate, a Verisk Analytics (Nasdaq:VRSK) business. He has an extensive background in operations, business development, product development, workflow management, and marketing at Fortune 500 and midsized companies in a variety of industries. For more information about Verisk Commercial Real Estate, go to or e-mail us at .