The commercial real estate finance industry has traditionally been a sluggish innovator — a sector that has been slow to pioneer cutting-edge improvements or advancements. That is no longer the case, however.
Commercial mortgage brokers seeking financing options for smaller loan deals should be happy to learn where that change is taking root. One of the leading edges in technology innovation in the commercial real estate sector can be found in the small-balance lending niche.
Small-balance mortgage debt is currently filling a need for the financing and refinancing of smaller multifamily properties, for example. Many of these properties are designated as workforce housing, which is a high-demand product — particularly in major U.S. metropolitan areas where employment is strong, populations are high and housing has become too expensive.
Typically categorized as mortgage loans ranging from $1 million to $7.5 million in size, small-balance loans don’t often get the notice or publicity that large-balance transactions do. They fill an essential role in the commercial lending landscape, however.
The lenders filling small-balance lending needs in the marketplace, by their sheer nature, operate a business whose profitability is driven in large part through volume, as compared to their larger-balance peers. They must identify and employ efficiency measures if they are to succeed at increasing their volume and profitability. It is precisely this point that positions them to be the ones driving innovation in commercial lending.
Notable innovation has been brewing with this group over the past few years, with technology playing a pivotal, if not starring, role in the advancements. Lenders began in recent years to implement the move away from cumbersome, outdated paper processes toward increased automation and digitization in small-balance loan processing.
By implementing internet-driven processes, lenders provide borrowers with 24/7 access to complete loan applications, as well as other key documents and milestones in the loan process. They allow loan steps to continue during nontraditional business hours. They eliminate the time that was traditionally lost searching for and filing paper documentation.
The digital-automation models being employed have vastly improved communications between the lender and borrower, and they also have reduced the length of time for touch points between the two. As a result, in the process of adding undeniable value for both parties, the move toward automation has dramatically reduced the start-to-finish loan-processing timeline.
These digitization improvements are, however, merely the beginning. There are additional areas of small-balance lending that are ripe for enhancement. While current automation models have improved the storage, access and movement of debt documentation, as well as communications between the lender and customer, there is another obvious area of commercial lending that is well-positioned for improved efficiency: the analysis of large sets of data in underwriting.
“ Even with the advent of artificial intelligence in the small-balance lending sector, don’t assume all human touch will be eliminated from the equation. ”
It’s important to note that in commercial loan underwriting, it is a much harder job to assess risk than in the underwriting that precedes, for example, a consumer’s new-home loan. Whereas the latter analyzes and assesses the risk associated with the one buyer in question, the former must analyze numerous sets of data pertaining to the regional market, rental rates, leasing trends, incidents of crime, property expenses and other operational information to ascertain the projected profitability and risk associated with the property. The magnitude of data that must be reviewed for a commercial loan poses issues for the lender in relation to the amount of time and labor it requires to complete the process.
Currently, a lot of this data review is handled by staff underwriters. They look closely at regular patterns within the data sets and search for variations, or deviations, among those data patterns. These deviations represent concerns or risks associated with the property or the borrower. Identifying and addressing these concerns is obviously key for commercial mortgage brokers hoping to find financing for the borrowers, although it is the responsibility of the lender, which utilizes the information to decide if a loan will be granted. Again, the time and resources needed to thoroughly review all of this information can be problematic.
What has been discovered by many mortgage professionals in the small-balance lending arena is that artificial intelligence (i.e., computer intelligence) can be leveraged to analyze these mountains of data. Not only has advancing computer intelligence been proven to work in this particular lending application and other related fields, but to work well. When reviewing and analyzing big data specifically to seek out data-point abnormalities (such as lending concerns and risks), machine intelligence can often discover the deviations quicker and with greater accuracy than humans can.
The argument can easily be made that machines, specifically computers and the software they use, don’t suffer from fatigue or other human-related conditions (such as illness, distractedness, emotions, etc.). The results that the computer gets from reviewing and analyzing large quantities of data will thus be more consistent on a day-to-day basis, whereas a human’s results will likely vary. When you consider this point, along with the speed at which a machine can review data, as well as the improved accuracy it brings to the task, it’s clear that the use of artificial intelligence can be a huge benefit to the underwriting process.
Small-balance lenders can only increase profits by increasing transaction volume. Without the efficiency created through technological advancements, such as automation and artificial intelligence, small-balance lenders striving to increase transaction volume are normally forced to increase staffing levels. Such an increase in the workforce, however, absent any additional efficiency improvements, won’t likely lead to increased profits. It’s easy to predict why artificial intelligence will be the next-step solution the industry increasingly moves toward in the drive toward efficiency, speed and, ultimately, increased profitability.
Even with the advent of artificial intelligence in the small-balance lending sector, however, don’t assume all human touch will be eliminated from the equation. In the immediate future, it is expected that innovation-minded lenders will leverage the technology primarily to identify data-pattern abnormalities more quickly, accurately and efficiently.
Human experts will still be needed to review those deviations and then make all of the key underwriting decisions. In essence, human decisionmaking will not be replaced. Instead, the artificial-intelligence technology will ensure the credit decisions made by experts are better informed.
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Ultimately, the power of technological advancements in the commercial real estate field will be a source of monumental change and progress. Still, the industry remains a people business. Consequently, humans will continue to play an integral role in commercial lending for a long time to come.