In prehistoric times before smart phones, all of ten years ago, lenders could (and did) produce underwriting packets twelve inches tall. The file wasn’t complete until measured in terms of height. The taller the better. You could tell how good an analyst was based on the thickness of the files on their desk.
Today’s CRE analyst is more interested in quality over quantity, in learning insights that impact yield and risk versus straight-up CYA box checking, granted, the boxes must still be checked. It’s a win-win every time an analyst can replace a manual task with an automated data system concurrent with improving information accuracy. The real value of the CRE analyst today is in their technical intuition about the asset class and trust; trust in their findings and recommendations.
In the early 2000’s CMBS loans exceeded $200B annually. In 2017 there were approximately $90B in CMBS loans. Still- no small number. Thus, while lending volume is smaller, the level of intensity in underwriting is “white hot” as lenders are intent on meeting their lending goals while compartmentalizing risk.
As technology enables real estate to become a more liquid investment, investors in this competitive marketplace rely on their analyst to quickly and reliably underwrite more opportunities to find the potential winners before the competition. This requires the analyst eliminate non-competitive deals from their desk as soon as possible to focus their time on deeply understanding the risk and reward of promising opportunities.
Tools of the trade that are changing this paradigm include automated rent roll and T12 underwriting using machine learning, statistical comp methodology, and programatically delineated market boundaries. These are the tools of the forward-thinking CRE pro that does not sacrifice quality for speed of analysis. Both Lenders and Borrowers are seeking solutions that reduce the workload of manual underwriting and condense the review period to accelerate closing timeframes.
Automation - Upload Rent Roll & T12 Versus Manual Entry
For any organization, people are the most important resource, and processes that allow a company to leverage this resource are extremely valuable to the overall organization. Automated tools that enable the analyst to focus on decision-making over data entry can increase productivity exponentially. For example, technology exists today for automating rent roll and T12 review to instantly parse and cross-reference pay records with average term of tenancy and rent collection stability going forward.
In underwriting, the two most important documents are an accurate rent roll and T12. For a sizeable property, there is no reason to manually input the rent roll today when there are tools readily available to transport rent and expense data sets into usable information for the analyst to make more informed decisions.
Comp Methodology & Smart Cluster Versus Mile Radius
Finding comps begins with a review of comparable assets in the same submarket as the asset under consideration. Comparable property identification follows a simple ideology; find property of similar location, age and amenities. That’s the first step. Mile radius data is great for retail sites looking at drive-times, but in multifamily analysis, you need to have a handle on much more.
Use smart clusters to pinpoint comps with high subject property correlations. With smart clustering, underwriting takes on a whole new meaning that is beyond the radial circle. Smart clustering blends economic, demographic and real estate market data that directly impacts the asset in review. Enodo creates a Smart Cluster around the subject property to define the market boundary based on census tracts with similar supply and demand characteristics. This market boundary is visualized in the platform to gain a better understanding of the specific market area that will affect a property's performance.
The competition never slows down, and real estate moves quicker than ever before. Commercial real estate is nearing a tipping point. As assets become more liquid, it is imperative that the time spent underwriting is greatly reduced in order to keep up with the demands of buyers and sellers. Take these points as building blocks to gaining trust in the underwriting narrative for both go and no-go investment decisions.