Can the Marriage of Big Data and Sustainability Help Bankers Avoid Another CRE Debacle?

Banks are using big data to stress test and manage credit risk associated with commercial mortgages. A recent article in American Banker highlighted that even community banks, with under $5 billion in assets, are using market and tenant data software to inform commercial real estate (CRE) lending decisions and to monitor exposures. In light of recent warnings from regulators about growing concentration risk in CRE lending and the importance of prudent risk management practices, leveraging big data appears to be a sensible practice.
After all, the overall health of a bank— indeed, the entire financial system—could be in jeopardy if loan book concentration risk aligns with real economic weakness in a particular industry or sector. We saw this during the 2007-2008 financial crisis when the housing bubble burst and residential property values, followed by commercial property values, plummeted. In 2006, 31% of all U.S. banks exceeded at least one (of two) CRE concentration levels outlined by supervisory criteria. Among banks that exceeded both CRE supervisory criteria, 23% failed during the 2006-2009 economic downturn.
In today’s prolonged, low interest rate environment, banks are chasing higher yields, including those available in CRE lending opportunities. In fact, many investors are. In Europe, where banks accounted for 90-95% of all CRE lending sources, pre-crisis, non-bank lenders such as private equity/debt funds, insurers and pension funds comprised 40% of the market in 2014. In 2015, U.S. life insurance companies achieved their highest CRE origination volumes on record.  With CRE investment sales back to near peak (2007) levels and low interest rates driving refinancing, demand for loans and competition among lending sources, has resulted in relaxed underwriting standards.
Why is this a concern for regulators? What goes up must come down. As interest rates—linked to cap rates, which are used to value commercial property—tick upward, property values, which move inversely, will decline. In other words, property values have likely topped out. This is a normal part of the economic and real estate cycle. If, however, banks have disproportionately high exposure to CRE, the adverse knock on effects—collateral value deterioration, impairment, limited exit strategies, higher default rates, and more non-performing loans—could translate into systemic problems like insufficient liquidity and poor access to credit.
What does any of this have to do with sustainability? The business case for sustainability is well established in the real estate sector, where cost savings (e.g., the utilities line item) directly impact the bottom line: net operating income and property value. Numerous market-based studies, including these by CBRE, CoStar, Journal of Portfolio Management, and Royal Institute of Chartered Surveyors (RICS), have found that high performance green buildings outperform their merely code-compliant peers, not only in environmental impact—water consumption, energy efficiency, carbon emissions, etc.—but also in economic payoffs related to lower vacancy rates, higher rental rates and quality tenant retention. Research from Harvard Business School and the University of Oxford has demonstrated that companies engaged in ESG-based practices deliver superior economic outcomes. And this 2015 study from the University of Arizona, which examines 80,000+ property loans pooled for securitization in the CMBS market, indicates lower default rates associated with loans secured by certified green buildings.
Relying on big data to better manage credit risk is a good idea. But without incorporating the granularity provided by collateral and sponsor-level sustainability performance, big data may paint every CRE mortgage with the same brush and mislead lenders in identifying risks vs. opportunities. Big data that incorporates environmental, social and governance (ESG) data points and uses it to stratify CRE loan portfolios could result in more refined mortgage risk profiles. In turn, this could result in more accurate stress testing and bank capital levels. This type of smart big data can also inform transaction decision-making and contribute to the construction of more resilient CRE loan portfolios.
The rapidly growing green bond market and increasing number of financial institutions issuing green bonds is another driver for the marriage of big data and sustainability. As more financial institutions issue green bonds, internal tracking of “green” CRE projects will allow banks to more seamlessly report to bondholders on use of proceeds.  In 2015, ABN AMRO became the first bank to integrate GRESB data on property companies and funds into its real estate lending process.  Perhaps not surprisingly, they issued a green bond to fund highly sustainable property the same year.
As big data goes mainstream and property values peak, it may be the perfect time for banks to experiment with ways to integrate ESG data into their CRE risk management practices.
This article is written by Sara Anzinger.