How AI is Transforming CRE Underwriting

A Practical Guide for Community Banks

Commercial real estate lending stands at an inflection point. The same community banks that have built their reputations on relationship-driven lending and local market expertise now face a critical question: how do you compete in a world where larger institutions are deploying artificial intelligence to process loans faster, assess risk more accurately, and serve borrowers more efficiently?

The answer isn't to abandon what makes community banks special. It's to augment those strengths with technology that handles the manual, time-intensive aspects of underwriting—freeing your team to focus on the judgment calls and relationship management that no algorithm can replicate.

This guide explores how AI is reshaping CRE underwriting, what the data tells us about adoption and results, and how community banks can implement these tools practically and responsibly.

The Adoption Gap: Where Community Banks Stand Today

Community banks have historically punched above their weight in commercial real estate lending, servicing approximately 30% of all CRE loans despite their smaller balance sheets. Yet when it comes to AI adoption, a significant gap has emerged between smaller institutions and their larger competitors.

According to Fannie Mae's Q3 2023 Mortgage Lender Sentiment Survey, 50% of smaller institutions have not used or explored AI in any capacity, compared to just 18% of top-tier lenders. The Cornerstone Advisors 2024 study paints a similar picture: only 13% of community banks have deployed AI in credit and lending functions, while 29% aren't even considering AI tools.

This gap matters because the broader market is moving decisively toward AI-powered lending. Market.us projects the AI in lending market will grow from $7 billion in 2023 to $58.1 billion by 2033—a compound annual growth rate of 23.5%. McKinsey estimates generative AI could deliver $200-340 billion annually in additional value to banking overall.

Within commercial real estate specifically, adoption is accelerating. Deloitte's 2024 Commercial Real Estate Outlook found that 72% of CRE owners and investors are either piloting, in early-stage implementation, or in full production with AI-enabled solutions. JLL's 2025 Global Real Estate Technology Survey reveals that 88% of investors and landlords have initiated AI pilot projects, averaging five use cases simultaneously.

But here's the opportunity: only 5% of those organizations have achieved all their AI goals, and over 60% remain strategically unprepared. The market is early. Community banks that move thoughtfully now can establish competitive advantages before the technology becomes table stakes.

What the Data Shows: Efficiency Gains from AI Underwriting

The efficiency claims around AI in lending aren't just vendor marketing. Multiple independent sources document substantial improvements in underwriting speed and accuracy.

Overall Time and Cost Reduction

The most authoritative validation comes from Freddie Mac's May 2025 analysis. Lenders maximizing AI-enabled underwriting achieve up to 40% cost savings in loan processing, $1,500 savings per loan (a 14% reduction), and a five-day shorter production cycle. Lenders using Freddie Mac's Loan Product Advisor at full capacity generate 10% higher net margins.

Industry case studies consistently show time-to-decision improvements in the 50-75% range, with commercial loan approval cycles compressing from weeks to days when AI handles document extraction and initial analysis.

Task-Specific Improvements

Financial spreading represents one of the most time-consuming manual tasks in CRE underwriting. Moody's QUIQspread delivers 95%+ accuracy on completed spreads. Nedbank now processes over 90% of borrower financials through automated spreading. Acuity Knowledge Partners documents 40-50% cost reduction in spreading operations.

Rent roll analysis shows even more dramatic improvements. Industry analysis demonstrates 70-99% reduction in manual effort per lease, with case studies showing an 85-page rent roll processed in under 5 minutes versus 10 hours manually. PRODA—validated by JLL Spark and ING Ventures—delivers data ready for analysis in minutes versus hours or days.

Appraisal review benefits significantly as well. Aivre and Restb.ai document 180+ minutes saved per appraisal. CAPE Analytics research found AI systems flag 70% of property issues that human inspections miss. Freddie Mac's ACE appraisal waiver program has saved borrowers over $2 billion since 2017.

Document extraction accuracy consistently reaches 99%+ across AI-powered OCR solutions, compared to a 1-4% error rate for manual human processing depending on verification protocols.

Volume Throughput

MBA Newslink reported that Candor Technology implementations increased underwriter productivity from 2.5-3 files per day to 8 files per day—a 3x improvement. Freddie Mac's LPA Choice qualified 18,000 additional borrowers through enhanced automation.

Why Community Banks Are Well-Positioned

Despite the adoption gap, community banks bring distinct competitive strengths to CRE lending that technology can amplify rather than replace.

FDIC data shows CRE loans comprise nearly half of all loans at community banks—a significantly higher concentration than at larger institutions. More significantly, Bernstein's 2024 analysis found community bank CRE loans outperform those of large regional banks and CMBS markets by more than 5x on credit quality measures. This outperformance stems from smaller loan sizes, full recourse lending, local relationship expertise, and different property type concentrations.

Meanwhile, large banks are actively retreating from CRE. CBRE's Q3 2024 data shows bank market share in non-agency CRE closings dropped from 38% to 18% year-over-year, while alternative lenders increased from approximately 27% to 34%. Industry analysts describe community banks as stepping in where larger lenders are pulling back due to regulatory scrutiny and market volatility.

This creates a strategic window. Community banks can capture market share vacated by larger competitors—if they can process loans efficiently enough to meet borrower expectations.

Practical Applications: Where AI Delivers Value Today

Understanding where AI adds value in the CRE underwriting workflow helps prioritize implementation efforts.

Document Intelligence

AI excels at extracting structured data from unstructured documents—appraisals, rent rolls, financial statements, leases, and environmental reports. The technology reads documents in seconds, identifies key fields, and populates underwriting models automatically. This eliminates the manual data entry that traditionally consumed hours per deal.

Financial Analysis Automation

Automated financial spreading transforms borrower tax returns, P&L statements, and balance sheets into standardized formats for analysis. The technology handles the mechanical work of categorizing line items and calculating ratios, allowing analysts to focus on interpreting the results and identifying concerns.

Risk Assessment Support

AI can cross-reference deal data against market comparables, flag anomalies for human review, and provide consistent application of underwriting criteria. The technology doesn't make credit decisions—but it ensures analysts have complete, accurate information to inform those decisions.

Portfolio Monitoring

Beyond origination, AI enables continuous monitoring of existing loans. The technology can track covenant compliance, flag early warning indicators, and surface properties requiring attention—turning reactive portfolio management into proactive risk mitigation.

Addressing the Real Barriers

Community banks cite legitimate concerns about AI adoption. Understanding and addressing these barriers is essential for successful implementation.

Regulatory Compliance

The Cornerstone Advisors survey found 70% of community bank credit officers cite regulatory scrutiny as their top AI adoption concern. This caution is warranted—the CFPB has been explicit that "there is no advanced technology exception to federal consumer protection laws."

However, AI tools can actually strengthen compliance when implemented properly. The key requirements center on explainability (understanding AI recommendations well enough to explain decisions), documentation (maintaining clear audit trails), testing (regular disparate impact analysis), and governance (appropriate board oversight and model inventory).

The Federal Reserve's SR 11-7 guidance on model risk management provides a framework that applies to AI systems. Institutions that follow these principles—disciplined development, independent validation, ongoing monitoring—can deploy AI responsibly.

Integration Complexity

The ABA's 2024 Core Platforms Survey found only 53% of bankers are satisfied with their core platform provider, yet 69% remain likely to stay despite dissatisfaction due to switching costs. Many institutions operate on legacy cores up to 40 years old.

Modern AI solutions increasingly address this through API-based architectures that work alongside existing systems rather than requiring wholesale replacement. The goal is augmentation, not disruption.

Cost Considerations

Implementation costs are real, but the ROI data is compelling. An SMA Technologies 2024 survey found 52% of banks saved $100,000+ annually from automation, with 19% saving $250,000+. Beyond direct cost savings, consider the opportunity cost of slow underwriting: deals lost to faster competitors, borrowers who take their relationships elsewhere.

The positive trend: technology investment is accelerating across community banking. 75% of banks increased technology budgets in fiscal 2024. According to the Conference of State Bank Supervisors (CSBS) Annual Survey, fintech partnership adoption has risen dramatically—in 2023, 59% of community banks had no fintech relationship; by 2024, only 32% had none.

The Market Context: Why Now Matters

The CRE market is creating urgency for operational improvements.

The loan maturity wall demands attention. MBA confirmed $929 billion in CRE loans matured in 2024—41% higher than original estimates due to extensions from 2023—and $957 billion will mature in 2025. Principal Asset Management estimates approximately $2 trillion in maturities over the 2024-2026 period.

This creates a wave of refinancing activity that will stress underwriting capacity. Institutions that can process loans efficiently will capture disproportionate share. Those constrained by manual processes will watch deals go elsewhere.

Lending volumes are already recovering. MBA data shows $498 billion in total CRE lending in 2024, up 16% year-over-year. Q4 2024 saw exceptional activity with originations surging 84% year-over-year. MBA forecasts $583 billion in 2025 lending.

Spread compression adds competitive pressure. CBRE reports average commercial mortgage spreads tightened 49 basis points year-over-year in Q4 2024 to 184 bps. Tighter margins make operational efficiency more important than ever.

Moving Forward Thoughtfully

AI in CRE underwriting isn't about replacing relationship lending with algorithms. It's about eliminating the mechanical work that prevents your team from spending time on what matters: understanding borrowers, evaluating deals, and making sound credit decisions.

The community banks succeeding with AI adoption share common approaches. They start with specific, high-impact use cases rather than attempting wholesale transformation. They maintain human judgment at the center of credit decisions while letting technology handle data extraction and analysis. They address compliance requirements proactively, building explainability and documentation into their processes from the start.

The data is clear: AI can reduce underwriting time significantly, cut costs by 40% or more, and improve accuracy while freeing your team for higher-value work. The institutions moving first are establishing advantages that will compound over time.

The question isn't whether AI will transform CRE underwriting. It's whether your institution will be leading that transformation—or responding to competitors who did.

Sources

This article draws on research and data from: Fannie Mae Mortgage Lender Sentiment Survey (Q3 2023), Cornerstone Advisors (2024), Market.us AI in Lending Market Report (2024), McKinsey & Company, Deloitte Commercial Real Estate Outlook (2024), JLL Global Real Estate Technology Survey (2025), Freddie Mac (May 2025), Moody's Analytics, MBA Newslink, FDIC, Bernstein Research (2024), CBRE Q3/Q4 2024, ABA Core Platforms Survey (2024), SMA Technologies (2024), Conference of State Bank Supervisors (CSBS) Annual Survey, Principal Asset Management, CAPE Analytics, CFPB guidance, and Federal Reserve SR 11-7.