Where Automation Falls Short in Credit Analysis
External and internal pressures require commercial real estate professionals to streamline costs and improve efficiency across their leasing, asset management, and acquisitions responsibilities. The siren song of automation and technology is compelling. They allow for self-service capabilities and real-time decision making, but their effectiveness is currently limited to analysis of simple and homogeneous entities and tenants. Complex credit analysis in tenant diligence remains beyond the capabilities of most software and low cost credit platforms. While there are many tasks that can effectively leverage software, these platforms often fall short due to their limited understanding and interpretation of accounting standards and non-financial factors.
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Changes to Accounting Rules
Accounting principals are often a moving target. Take for example the recent changes around revenue recognition. Prior to the rule change, there were industry specific standards for revenue recognition. Following the changes, which were enacted in January 2018 for public companies (December 2018 for private companies), all firms will need to comply with the same revenue recognition standards regardless of industry. There will likely be far reaching changes to media companies and their treatment of licensing revenue, financial service companies and their recognition of certain underwriting and asset management fees, software companies and the timing of their revenue recognition, and so on. Proper analysis requires to not just be up to date on accounting rules, but also maintain a bit of judgement to interpret financial statements prepared under the new guidelines, which is something that current financial analysis platforms are woeful to address.
Knowledge of Non-GAAP Adjustments
As old accounting rules sunset and new ones emerge to govern how companies report results, generally accepted accounting principals (GAAP) old and new can create a lot of noise in those results. Part of an analyst's role is to cut through that noise and distill the numbers to arrive at a conclusion of a company's financial health. Doing so requires an in-depth review of the financial statements and accompanying notes so that the analyst can make certain financial adjustments that strip out the excess noise created by any number of accounting principals. Again, the judgement required to do so exceeds the grasp of most automated credit review platforms.
Understanding of Non-Financial Metrics
A thorough credit analysis of any company also requires an interpretation of certain non-financial metrics. Some metrics are relatively straight forward and mundane, like customer concentration and revenue models. But others are considerably more nuanced and complex, like the impact of legal and regulatory changes on an industry or business. Take health care as an example. Changes to Medicaid reimbursement rules have an out sized impact on healthcare providers in rural areas, and could strain operations. Banking is another example, where regulatory roll-back is likely to produce a more favorable environment for lending that is less constrained by the added costs around capital adequacy and oversight. Automated platforms still require significant human input to both include and comprehend the impact of many of these non-financial metrics.
The digital transformation of existing commercial tenant credit analysis tools remains in its infancy and lacks the sophistication to adequately meet customer needs. Automated solutions may be the norm for simple or homogeneous tenants, but for more complex entities, professional analysis will likely remain the norm for some time.
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