AI Automated Commercial Real Estate Loan Underwriting vs Traditional Underwriting


Artificial intelligence is automating routine tasks and providing data-driven insights to streamline the underwriting process for commercial property loans.

Easy-to-use AI tools give lenders quick access to the information they need to decide whether a certain CRE investment is worth the risk. AI also integrates several features that speed up the approval process so lenders can move faster and close more deals.

For example, lenders can assess risks and analyse market data faster to shorten the application review process. These tools also proactively spot potential issues early in the loan application process to prevent delays and boost success rates.

And they’re not just beneficial for CRE lenders—AI tools ultimately help borrowers secure commercial property loans via a fairer and more transparent process.

In this blog, we talk about how AI is changing how commercial property loan applications are evaluated. We take a look at:

  • traditional CRE loan underwriting,
  • the problems of conventional underwriting and
  • how AI addresses these issues.

CRE loan underwriting the traditional way

In a traditional (largely manual) commercial loan underwriting process, the underwriter evaluates the borrower’s creditworthiness and overall risk to see if they can repay the loan.

The process for commercial real estate loans goes even more profound—here, underwriters don’t just rely on the applicant’s credit scores.

They need to conduct comprehensive reviews to evaluate factors like the property’s actual value and financial performance and verify the property’s income streams.

In essence, traditional CRE underwriting manually analyzes a property’s potential for cash flow and its ability to generate income stably and predictably.

Underwriters consider factors such as rental rates, tenant creditworthiness, current market conditions, and local trends to minimize loan risk.

All this is done using a combination of several manual tools:

  • Spreadsheets – For inputting and manipulating data on income, expenses, loan terms, and potential returns
  • Financial calculators – For calculating debt service coverage ratio, capitalization rate, internal rate of return, and other factors that can help evaluate a property’s financial viability
  • Leasing documents and market research are used to analyze existing leases, tenant creditworthiness, and market research reports on rental rates and vacancy trends to assess the CRE asset’s future income potential.
  • Public records and regulatory filings – Underwriters must manually research property records for zoning restrictions, environmental issues, or potential liens.
  • Property appraisal reports – These physical inspections and reports (done by licensed appraisers) help determine the property’s fair market value

The role of a CRE loan underwriter

A commercial real estate loan underwriter’s main job is determining the risk of approving or denying an application.

Aside from reviewing all the financial documents the borrower provides, they also order property appraisals and credit reports to measure the accurate asset’s value and verify the borrower’s credit status. First, CRE underwriters look at several factors:

  • Property type and value
  • Vacancy rates
  • The borrower’s credit score and history, net worth, and bank and cash flow statements

After this, they evaluate the viability of a commercial real estate investment based on several rates and ratios:

  • The Net Operating Income represents a property’s annual income after deducting operating expenses such as maintenance fees, property tax, utilities, and CAM fees. Adjustments for vacancy allowances or lousy debt expenses are often included to provide a more accurate estimate.
  • The Capitalization Rate (represented as a percentage) helps underwriters determine the property’s return on investment. It’s calculated by dividing the annual Net Operating Income by the property’s purchase price. Lower cap rates (anywhere from 3% to 6%) indicate a higher purchase cost for the borrower, while higher cap rates (ranging from 7% to 10%) suggest more affordability.
  • The Debt yield Ratio measures the cash flow available to cover all loan payments. Underwriters calculate the proposed mortgage amount using the net operating income. Lenders use this ratio to assess the borrower’s ability to repay their loan and adhere to loan covenants over time. Banks typically require debt yields of at least 1.15 times, though some may require higher ratios.
  • The Debt Service Coverage Ratio measures borrowers’ ability to repay debts from their cash flows. It compares Net Operating Income to total debt service and should generally exceed 1.2 times, although some lenders might demand higher ratios depending on the loan’s terms and conditions.
  • The Loan-to-Value Ratio measures the leverage taken out against the value of an asset and helps lenders assess their risk exposure. This ratio generally ranges between 70% and 80%, but some lenders might allow up to 90%, depending on factors like collateral type and location.


Aside from these, lenders and underwriters also examine the market conditions to decide if a property investment is viable given the current trends and expected future shifts.

They consider factors such as the local economy, job growth, population trends, and the balance of supply and demand in the CRE segment for which the loan is secured.

Underwriters also scrutinize the financials of the property or project, including cash flow projections, sale price trends, and proforma data.

They must also assess environmental factors such as zoning regulations, potential natural disasters, and other environmental risks. If some information conflicts with expectations, they may do additional research before investing. Based on this comprehensive evaluation, they recommend approving or denying the loan.

Additional factors assessed in underwriting existing buildings versus new developments

Do note that the underwriting process can vary depending on whether the property is an existing building or a new development.

For existing buildings, underwriters can use historical data to analyze current lease expirations, the likelihood of renewal, market rent rates, tenant improvements required, leasing commissions, and downtime associated with tenant turnover.

This information lets them generate a more predictable forecast of the asset’s performance under new ownership.

The process is often more complex for new developments because underwriters need to account for potential construction delays that could affect occupancy timelines.

The upfront capital required in new-development CRE loans is often higher because they must cover land acquisition and construction costs before any rental income starts flowing.

Underwriters, therefore, need to factor in the uncertainty of how long it takes to fully lease the space (even with pre-leasing) into their evaluations.

The many problems in traditional loan underwriting

Traditional underwriting is notoriously slow, with approvals taking days to weeks, depending on the loan’s complexity, which could be faster.

This is because it involves a lot of paperwork. This problem makes the process longer and deters potential borrowers who need funding quickly for time-sensitive CRE deals.

In addition, inefficient manual processes are prone to errors that may compromise risk assessment and pricing, affecting the financial viability of loans.

This means lenders might miss out on good opportunities and lose deals to competitors who can offer faster approvals, while borrowers might need more funding for perfectly viable projects.

AI in CRE loan underwriting

Banks have long used AI to underwrite personal and small business loans. Still, even CRE lenders are increasingly adopting AI-based platforms to increase the efficiency of their underwriting processes.

AI is turning the traditionally slow and manual CRE loan underwriting process into a smoother digital workflow. It uses algorithms to rapidly analyze and assess a borrower’s creditworthiness to make the approval process quicker, more accurate, and fairer.

Note that AI is not meant to replace human underwriters—it simply improves the entire process and makes things faster.

The benefits of automated underwriting

1. Making underwriting more accurate

AI algorithms can parse large datasets in various formats to identify patterns and generate insights based on this data.

In theory, this enhanced accuracy may reduce the likelihood of loan defaults. It may also enable non-conventional borrowers—otherwise categorized as ‘high-risk’ by traditional underwriting—to qualify for loans based on limited data points.

2. Processing loans faster

Automation can accelerate the loan approval process. Functions that once took several days with manual processing (such as data verification and document reviews) can now be accomplished nearly instantly with automated systems.

3. Making terms and pricing fairer

Automated AI-powered CRE loan underwriting also improves risk visibility, allowing underwriters to offer the best pricing options and terms that are more closely aligned with the actual risk.

The road ahead

The increasing use of AI in CRE loan underwriting is a welcome change. Tech experts expect more sophisticated predictive models to be released in the next few years.

However, the shift to AI-driven commercial property loan underwriting also presents challenges such as data privacy and potential algorithmic bias—when AI learns and replicates patterns from biased data sets that can unfairly disadvantage certain property types or locations.

Lenders who want to use this technology must have strong data security measures and transparent data governance practices in place.

They must also establish human oversight mechanisms at critical stages of the underwriting process to mitigate bias and ensure fairness.

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