Banks are navigating one of the most demanding credit environments in over a decade, where examiners expect faster turnaround times and more defensible appraisal data. That pressure is accelerating a shift toward automated extraction models built around accuracy, transparency, and repeatability. In our recent blog (AI is Only Half the Battle for Data Extraction), we explored why AI alone isn’t enough to meet these expectations. Over the summer, LightBox began offering its appraisal data extraction workflow to banks, and many institutions have already integrated the Fundamentals Platform to move an appraisal from submission, through structured extraction and quality guardrails, to an analyst-ready database.
This follow-up looks at what comes next: how banks are expanding the field counts they extract, why comps-level detail is becoming strategically important, and how these capabilities will shape credit, risk, and regulatory readiness in 2026.
Building V1: Starting Narrow to Get the Workflow Right
Creating an end-to-end extraction workflow required months of prompt development, guardrail design, and accuracy testing. For that reason, LightBox limited the scope of Version 1 to a curated set of highly repeatable fields.
The initial commercial rollout focused on nine core data categories — roughly 80 fields per appraisal. Because many reports include multiple premises (“as is,” “as completed,” “as stabilized”), the actual number of extracted data points typically exceeded 150 fields per appraisal.
The V1 dataset included:
- General report information (date, appraiser, client)
- Location and parcel details
- Site information including zoning and flood
- Improvements
- Value conclusions across scenarios
- Approaches to value (income, cost, sales)
- Key inputs supporting direct capitalization and DCF models (NOI, cap rate, occupancy, EGI, expenses, discount rate, terminal cap rate, etc.)
Even at launch, it was clear this represented only a fraction of what banks ultimately need for trend analysis, model calibration, and reviewer consistency.
Expanding the Dataset: Why Comps Are the Next Frontier
Immediately after V1 launched, LightBox began expanding its “ground truth” and prompt library to support a more complete view of appraisal data. After months of testing and validation, V2 introduces full extraction of improved sales comps and land comps, a critical component in how banks understand the rationale behind an appraiser’s opinion of value.
On average, 40 data points per comp will now be captured and fed directly into the database, using the same structure and guardrail process as V1 — bringing the total dataset for a single appraisal to well over 300 fields in many cases.
This includes core details such as sale date, cap rate, NOI, building size, unit count, building class, and other characteristics that appraisers use to select and adjust comparable transactions.
Why Comps-Level Extraction Changes Appraisal Review
Comps-level data changes the way banks evaluate appraisal quality, giving reviewers clearer insight into how and why appraisers select certain transactions. Instead of flipping through PDFs or relying on inconsistent summary tables, analysts can instantly view:
- All comps used in a report
- How those comps compare to similar recent appraisals
- Where an appraiser may have overlooked relevant transactions
- Whether comp selection varies across vendors or markets
Imagine reviewing a 400-unit garden apartment appraisal outside Charlotte. With centralized comps extraction, reviewers can pull the last three comparable appraisals and immediately see whether appraisers relied on the same transactions, or if potential outliers signal a need for more scrutiny.
This moves appraisal review from file-by-file analysis to pattern recognition, improving both efficiency and defensibility.
Hydrating the Rest of the Bank: The Bigger Strategic Shift
The long-term value goes far beyond review efficiency, giving banks a consistent appraisal dataset that can be applied across credit, risk, and regulatory teams. As comps and core appraisal fields flow into a shared database, they begin to “hydrate” other parts of the bank:
- Originations: better market context for new loans
- Portfolio management: clearer trendlines in value, income, and expense assumptions
- Special assets: quicker visibility into property performance during workouts
- Scorecards and models: more consistent inputs across the credit lifecycle
- Regulatory readiness: defensible, repeatable data with full auditability
For banks already on V1, this additional comps dataset will integrate automatically, without any added effort and no added cost.
If you’d like to see how appraisal extraction and comps-level data flow into a structured, audit-ready database, explore a live demo of the Fundamentals Platform.
What’s Next: 2026 and the Expansion of Appraisal Intelligence
V2 is a major milestone, but it is not the finish line. A fast-follow release in early 2026 will add:
- Rent comps extraction
- Line-item operating expenses (including high-impact categories like insurance)
These additional layers will give banks unprecedented visibility into the assumptions driving value, revenue projections, and operating fundamentals.
2026 is shaping up to be a pivotal year in appraisal data, and the institutions that can operationalize this data early will have a structural advantage.
Buckle up!
