How AI and Data Analytics Can Reduce CRE Lending Risk

Fintech tools that leverage artificial intelligence, natural language processing, data analytics and geospatial technology can book better commercial real estate loans — and help with workouts too.

By Carey Ransom Managing Director, BankTech Ventures

Published on January 23rd, 2025 in Banking Technology

The last few decades have seen record demand for commercial real estate credit. Community banks have become leaders in CRE lending, an asset class that has enjoyed a usually, relatively attractive risk-reward profile, regulatory support and a growing securitization market. In fact, community and regional banks tend to have higher concentrations of CRE than larger banks, with about one-third of FDIC-insured institutions reporting a prevalence of CRE loans in their portfolios.

For this reason, smaller banks are particularly sensitive to changes in the CRE sector, which has undergone quite a shift since the pandemic and the rise of remote work. Moody’s reported last year that U.S. office real estate value is likely to decrease by over 25% through 2025. Compounding these predictions, some banks have reported that office loans account for up to 30% of their total CRE loan portfolio. Adding to this, a sizable amount of office debt is set to mature in the near future, introducing further refinance and maturity risks.

Despite challenges in the sector, the CRE market has potential for well-positioned community banks with strategic foresight. Specifically, there continue to be emerging opportunities in multifamily, senior living, warehouses, data centers and other categories. Institutions that proactively invest in risk management, data, technology and supportive partnerships will be best positioned to adapt and thrive.

Potential Game-Changer: Streamlining CRE valuations

Historically, assessing commercial real estate property values has been a highly manual process for banks. Among the reasons:

  • Wider range of property types: From offices to warehouses to multifamily buildings, each CRE type has its own unique market dynamic and valuation methods. Office values may differ based on a variety of factors even within the same city.
  • Less frequent transactions: CRE sales are less frequent than residential sales, limiting data for comparables.
  • Private deals: Detailed CRE data is often available only through specialized, pricey subscription-only platforms.
  • Income-driven valuations: Valuations in this sector tend to require deep financial analysis of net operating income, cap rates, futures cash flow projects and more.
  • Customized financing structures: CRE leases tend to have longer, more customized terms than residential ones.

Thanks to advancements in data and technology, the once difficult and time-consuming commercial real estate valuation process grows easier.

By analyzing factors such as location, property characteristics and recent sales, automated valuation models (AVMs), utilizing algorithms and large data sets, are rapidly providing more accurate valuations, enhancing efficiency in the lending process. Similarly, AI-driven tools can now process vast amounts of data to identify patterns and trends in the CRE market, delivering accurate property assessments in seconds.

Geospatial information systems can study geographic areas to better identify location-based factors that may affect property values. Among them: proximity to amenities that may strengthen investment potential or, on the flipside, infrastructure issues that may be costly.

Read more: Maximizing AI Payoff in Banking Will Demand Enterprise-Level Rewiring

AI and Automation Opens the Door for Smarter Underwriting

Beyond valuation, advancements in data and technology are also making it easier for banks to underwrite CRE loans. Fintech tools that leverage artificial intelligence and natural language processing, data analytics and geospatial technology enable banks to better understand comparables in underwriting by analyzing vast amounts of market data, borrower profiles and property performance metrics.

AI agents can now predict borrower risk more accurately by identifying patterns that traditional underwriting might miss, enabling more precise loan terms and interest rates.

For example, when considering the purchase of a retail property in a residential area, an underwriter can use an AI-powered tool to analyze such risk factors as declining foot traffic or a decrease in tenant diversification within the property. AI and machine learning models also compare the individual risk of a given loan against the entirety of the bank’s CRE portfolio. This allows more dynamic decision-making.

Read more: AI and Automation Are Different Tools. Misunderstanding the Difference Will Hit Your Wallet

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Rethinking the Commercial Real Estate Landscape

As many urban office spaces continue to struggle with occupancy and cash flow in the wake of the pandemic, some banks are finding themselves taking back the loans into their real-estate-owned groups, partnering with developers or investors to turn around distressed properties.

The market is still in the early stages of what this trend will ultimately bring. However, we can expect to see many long-term changes. As the pandemic has forced developers into using real estate in unique ways, reimagining CRE properties is again where banks and their partners should take advantage of today’s technology.

For example, geospatial tools allow REO teams to analyze location-based factors that may affect the success of a redevelopment project, such as population density, proximity to amenities, and infrastructure availability. These insights are crucial for assessing whether a former office site will be suitable for turning into a multifamily project or other uses.

Reimagining CRE goes beyond usage changes.

Some of our community banking partners are adding value by integrating solar panels or cellular network towers into existing buildings, maximizing tax credits. This is a key opportunity for banks, where they can be facilitators to finance the additions, working with real estate owners as a conduit. Data mapping tools have the ability to assess whether a commercial solar project would work and be profitable in a specific area at a much faster rate than humans, better monitoring risk and assessing comparables.

Read more: How Associated Bank Uses Data Analytics to Speed New Products and Drive Growth

The Inside Story: A Building’s Hidden Layers Matter

It’s not just what’s on the outside of commercial buildings that can make them more valuable — or less valuable. What’s on the inside counts, too.

In recent years, repair and maintenance costs for office properties has surged by 12.3%, according to Trepp’s Office Property Expense Series. Massive costs have been driven by aging infrastructure and maintenance that may have fallen to the wayside during the pandemic, when many offices sat empty.

In the uncertain commercial real estate market, it’s never been more critical to use data and technology to understand the structural backbone of any building.

Gaining a solid understanding of a building’s plumbing, wiring, roofing, HVAC (heating, ventilation and air conditioning), fire protection systems, drainage, wireless networks and soundproofing is critical. For example, a seemingly modern office building might conceal an aging plumbing system prone to leaks or wiring that fails to meet updated safety codes.

Such hidden problems quickly turn a prospective borrower’s attractive investment opportunity into a lender’s costly nightmare.

Though we’re still at the outset of moving from "dumb" buildings to "smart," self-optimizing buildings, the shift is already proving valuable. For instance, internet-of-things enabled sensors from companies like Siemens and Johnson Controls help owners and operators monitor HVAC performance and detect leaks in real-time. This can prevent costly damages that can exceed $50,000 per incident.

Similarly, platforms like Schneider EcoStruxure leverage real-time data to optimize energy use, potentially cutting utility costs by up to 30%. These technologies not only improve operational efficiency, but also enable more accurate and dynamic risk assessments.

Read more: Why Your AI Strategy Should Align With DC’s Policy Priorities in 2025

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Elevating the Role of the Back Office CRE Professional

The integration of advanced data and technology tools in CRE lending has not only streamlined risk analysis but is also shaping the role of the next-gen back office professional.

AI platforms can now handle repetitive tasks such as data entry and document processing. This in turn enables employees to focus on higher-value responsibilities, like structuring complex deals.

For example, thermal imaging can detect structural vulnerabilities in assets, while geospatial technology pinpoints location-specific risks like location in flood zones, allowing CRE professionals to make data-backed decisions rather than handling the grunt work to find that data. This shift is slowly redefining the back office role in institutions that invest in these tools, making the work more dynamic, particularly for digital-native workers eager to engage in impactful, tech-enabled work.

However, technology isn’t magic. Effective CRE management still requires human judgment to interpret data, understand borrower intent, and navigate market nuances.

To bridge this gap, banks should invest in robust employee training programs, pairing AI-driven risk models with workshops on interpreting outputs and identifying use cases. This hybrid approach ensures CRE portfolios are managed with precision and a deep understanding of the market, striking the ideal balance between technological innovation and human insight.

Read more: Chase’s AI Chief Explains Why the Biggest Banks Will Win the AI Race (Probably)

Prioritizing CRE Resilience in a Shifting Market

Black swan events — like the dot-com bust, the 2008 financial crisis, and the pandemic — are unpredictable but can be planned for.

As banks assess their CRE portfolios and prepare for the changing landscape, those who confront reality and take proactive steps will succeed. Stress testing is now critical, with regulatory agencies recommending sensitivity analysis, loss rate testing, and advanced scenario analysis.

The most astute banks also manage risk by reserving cash and diversifying portfolios across asset classes and geographies. They focus on identifying the highest and best use for troubled properties, transforming them for optimal value. Banks that combine these strategies and personalize them for their portfolios will navigate uncertainty with resilience and foresight.

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