Across commercial lending, banks are investing in AI to modernize operations, strengthen credit decisions, and improve review efficiency. Yet one area continues to challenge even the most innovative institutions: structuring the data inside commercial real estate appraisals. What seems like a straightforward use case quickly reveals its complexity once banks begin exploring build-your-own approaches.
Appraisals sit at the center of valuation and credit risk, and lenders increasingly recognize the need for reliable access to the assumptions shaping those reports. These documents are long, inconsistent, and heavily narrative-driven, with key financial logic dispersed across multiple sections. Extracting decision-ready insight requires a level of domain understanding and contextual modeling that general-purpose AI tools weren’t designed to handle.
Recent conversations across the industry echo themes highlighted in our research on how 30 banks are approaching appraisal data and AI, underscoring a common pattern. Many institutions begin internal AI projects with optimism, only to find that appraisal data demands far more domain training, governance oversight, and ongoing maintenance than expected. “We talk to banks of all sizes, and a pattern has emerged,” said Manus Clancy, head of Data Strategy at LightBox. “Institutions often discover that the level of domain expertise and oversight required is far greater than they anticipated. Appraisal data is a specialized challenge, not a general automation task.”
Why Appraisal Reports Resist Standardization
Commercial appraisals contain some of the most complex financial reasoning lenders encounter. Each firm structures its reports differently. Tables, valuation scenarios, rent rolls, and commentary may appear in different sections or in different formats. Terminology shifts by market and property type. Even within a single firm, writing styles vary widely.
A model must interpret:
- Cap rate derivations
- Discounted cash flow logic
- Operating assumptions embedded across multiple sections
- Comps data presented in inconsistent tables or embedded narrative
General-purpose AI tools like Copilot and ChatGPT can read the words in a commercial appraisal, but they often miss the logic that ties those words together. Appraisal review is a regulated function, and data accuracy directly affects credit outcomes. When context is missing or misinterpreted, reliability breaks down.
“One bank we spoke with tested several appraisals through ChatGPT to see whether they could replicate the output of Fundamentals,” Clancy said. He noted that the experiment surfaced immediate issues: the volume of data overwhelmed the model, accuracy was inconsistent, and the output lacked standardized structure from report to report.
Commercial appraisals are highly nuanced documents. A data point on page 30 may directly relate to a supporting input on page 55, and without industry expertise the model has no way of understanding that connectivity. The underlying logic of the report gets lost, and the extraction fails.
Clancy said the test was enough to shift the bank away from building in-house. “AI without appraisal expertise cannot produce reliable extraction, and lenders cannot afford that level of uncertainty.”
What Banks Discover When They Try to Build Internally
Many institutions begin with the assumption that an internal extraction model can be developed quickly. The early steps seem promising, but momentum slows as teams confront the size of the training effort required. Building a dependable model means assembling thousands of real appraisals, annotating them consistently, and refining the model through extensive ground-truthing.
The work does not end once the model is deployed. Report formats evolve. New fields are requested. Terminology shifts. Each change requires ongoing adjustments to keep accuracy stable. Internal teams often underestimate the operational load needed to maintain long-term performance.
Several banks have shared that the most significant cost is not the initial build but the continuous upkeep. Without CRE domain expertise, engineering support, and regular retraining cycles, accuracy erodes quickly.
Extraction Is Only the First Step
Even when a bank succeeds in structuring appraisal data, the next challenge emerges: how to put that data to work.
Appraisal insights only create value when they flow into the systems where decisions are made. Review teams need data inside Excel. Underwriters need structured fields that support cash flow and collateral analysis. Credit and portfolio teams need standardized inputs that can be compared across markets and vendors. Risk groups need auditability and transparency.
Purpose-built solutions solve this by connecting structured data directly into these workflows. Many internal efforts stop at producing a structured output, leaving teams with data but no operational lift.
Oversight, Governance, and Regulatory Expectations
AI in commercial lending carries governance responsibilities. Banks must demonstrate control over sensitive information, validate model behavior, and maintain clear audit trails. Examiners have raised concerns about tools that do not provide visibility into where data is stored, how it is processed, or how models learn.
Any solution used in appraisal review—internal or external—must support model risk management frameworks, data retention policies, and system-level controls. These requirements influence not only how a model is built but how it is deployed and monitored.
Why the Industry Is Moving Toward Purpose-Built Solutions
Banks that explore build-your-own approaches often reach the same conclusion: the scale of the challenge extends far beyond model development. Success requires CRE-specific training data, domain expertise, workflow integration, governance, security, and continuous maintenance. These components are difficult and costly to replicate internally.
LightBox built Fundamentals with these needs in mind. The platform reflects years of domain modeling, lender collaboration, and exposure to thousands of real appraisals. Its structured output—more than 300 standardized fields per report—addresses the practical requirements of review, credit, portfolio analysis, and reporting. The goal was not simply to extract data but to support the entire lifecycle of appraisal-driven decisions.
Looking Ahead: How Banks Should Approach Buy vs. Build
As lenders consider how AI fits into their appraisal workflows, the decision is not only about capability but sustainability. Any approach must balance accuracy, governance, integration, and long-term maintenance. The industry is moving toward solutions that combine CRE domain expertise with technical depth, because appraisal data affects nearly every downstream credit decision.
Fundamentals was created to meet that need. Banks that adopt purpose-built solutions benefit from a model that is maintained continuously, governed appropriately, and designed for the real workflows reviewers and credit teams rely on.
In an environment where precision and defensibility matter, reliable appraisal intelligence is becoming essential.
