How AI and ML are changing the CRE Underwriting Process?
Commercial real estate thrives on data. To analyze a deal properly, commercial real estate brokers, investors, and lenders need to know a myriad of details about the subject property.
The current reality of the CRE underwriting process.
This data helps to determine a property's value. But investors and brokers are not the only players in analyzing a CRE deal. Investors need to also provide data to their lender for the funding to purchase the property. The lender will take the property data through their underwriting process to determine the risk of taking on the new loan.
The Commercial Real Estate underwriting process itself has many moving parts and people involved. The process equates to a lot of time spent looking over paperwork and transferring data from one form to another. Manual data transfer is tedious work that can often result in errors, unnecessary costs, and inefficiencies.
How are AI and ML changing the underwriting process?
To speed up the process, many have turned to the use of Artificial Intelligence (AI) and its subset Machine Learning (ML). AI and ML can help create a faster, more accurate, automated underwriting process. By being able to quickly scan documents in multiple formats and extract the necessary information for underwriting, automated underwriting with AI takes minutes rather than hours and days.
Machine learning is taking AI a step further with algorithms that can learn from previous data. ML makes the correlated analysis of seemingly unrelated assets to make predictions quickly to determine the creditworthiness of a borrower. This data gives lenders a fast, error-free, and secure way to underwrite a loan with less risk.
How will lenders benefit from utilizing AI and ML in commercial real estate underwriting?
Utilizing AI and ML in the Commercial Real Estate underwriting process will allow lenders to become more efficient. Automated underwriting will cut expenses, and the human resources it will take to approve a loan.
Another "human" factor taken out of the equation is the possible bias a lender may have toward a borrower. With AI and ML algorithms, loans are based strictly on data and numbers with no feelings involved.
• ML algorithms also gather large data sets across every asset class in the database and apply all the knowledge toward every new loan opportunity. Which means over time and as more data is collected, the CRE automated underwriting process will continually get faster. Lenders will be able to make decisions more quickly with more confidence, and borrowers will be able to close more deals.
• Another benefit of ML is the ability to identify patterns in the lender's and borrower's portfolios. Identifying these patterns will proactively alert the lender to credit risks and poor decision making by both parties. This process could warn lenders of deals that could have a higher probability of default based on the data from other loans.
The only setback is the amount of data. While ML is continually working to collect new data, not every brokerage or lender has data on every possible deal available. By working with an automated underwriting company who has worked with other investors, and lenders in other areas, diverse data sets are being built into the algorithm.
Larger data sets give investors and lenders more information to work with and open up the landscape on possible deals, and better decision making in the loan approval process.
The future of commercial real estate underwriting as AI and ML are used by more lenders.
AI and ML are changing the world of CRE automated underwriting to be more efficient, secure, and profitable for both lenders and borrowers. The combination of AI and ML may not completely replace the decision making process by Commercial Real Estate brokers, investors, or lenders. But it will augment the capacity at which borrowers can borrow, lenders can lend, and deals get done.
The Clik Servicing Hub utilizes AI and ML in their automated underwriting process.By working with the Servicing Hub, Commercial Real Estate investors and lenders can reduce operating expenses, stay competitive, free up time to focus on customer relationships, and spend time on deal analysis rather than data input.