Cracking the Code on AI Lease Abstracts: Production Data CRE Teams Can Trust
Author:
Managing Director
CREModels has delivered lease abstracts since 2010. Now its AI lease abstraction turns dense lease files into reliable, production-grade data faster, with expert analysts signing off on every field.
Madeline Miller, head of CREModels‘ lease abstraction practice, recently kicked off a project covering about 200 retail tenants for a major shopping center landlord. She loaded a single ZIP file into Mercury, CREModels’ proprietary AI-assisted abstraction system, and then stepped away.
By the time Miller came back, Mercury had pulled hundreds of unsorted documents straight from the due diligence vault’s export, populated the tenant folders, and sorted financial statements and offering memoranda into their own folders as well.
The first-pass lease abstracts were already running.
“Three or four years ago I was doing all of this by hand,” explained Miller, who has touched thousands of leases over the past five years. “Now my time goes into the actual analysis instead.”
That project finished in under two weeks; a pre-AI team might have needed up to six weeks for a portfolio of that size.
Since late 2024, CREModels has used this AI-assisted workflow across multiple production client projects, including closed transactions and completed portfolio onboardings.
Lease abstraction has always been a bottleneck in commercial real estate. Before underwriting a property, closing an acquisition, onboarding a portfolio or reporting to investors, it is critical to know what the leases actually say. That is the job of a lease abstract. The high-stakes work can be tedious: a single tenant file can run 200 pages across an original lease and ten or more amendments, assignments, estoppels, and option notices. Across a portfolio, that complexity compounds quickly.
The one click that starts Mercury is the easy part. What makes the output trustworthy is everything CREModels built behind it, and the expert review that still comes after.
It’s Still the AI Wild West
When real estate professionals hear AI claims these days, they should bring some skepticism, said CREModels Cofounder and COO Mike Jaworski, a 20-year commercial real estate veteran who has worked on more than $20 billion in deals.
“It’s the AI Wild West right now in commercial real estate,” he said. “People are using general, off-the-shelf models for lease abstraction and getting inconsistent results. They get miscalculated financial data that can lead to bad decisions, and they’ll often get a different answer every time they ask the same question.”
Generic AI tools can be useful for quick summaries, but production-grade CRE lease abstraction requires more than document extraction. A multi-lease, amendment-heavy project that supports real financial decisions requires document organization, amendment chronology, structured outputs, real estate judgment, and quality control against the source documents.
“In our testing, general-purpose tools were more likely to miss items like common area maintenance, rent abatement, base years, timelines and financial data,” Miller said. “Once you feed them several documents for one tenant, the risk of mixing up the material terms gets much higher.”
“Our clients tell us they’ve been getting 75% or 80% accuracy with some of the AI tools and companies they’ve tried,” Jaworski said. “But in the world of lease abstracts, 80% accuracy is no better than 0% accuracy.”
Delivering Real Results, Not Pitching Product
The apps flooding today’s market tend to be about selling a tool and leaving the client to run it.
CREModels delivers clean, structured, carefully reviewed lease data that clients can base real business decisions on, with Mercury compressing the heavy, repetitive first pass and experienced analysts supplying the judgment.
“Clients come to us because they need reliable lease data,” Jaworski said. “AI lets us deliver that work faster, but the responsibility for the final output is still ours.”
How AI Lease Abstraction Works
Unlike a general chatbot, Mercury was built to read a lease the way a CRE analyst does. Once it has sorted a portfolio, it groups files by tenant, works through the amendment history in order, and abstracts the leases and amendments together. This allows the system to know when a later amendment supersedes the original and which terms are actually material. This is especially valuable on the retail side, where tenants often go through years of expansions, contractions, renewals, and assignments.
“By the time you reach the tenth amendment it can get really confusing,” Miller said. “Mercury can work through that chronology quickly.”
Abstracts can be grouped by portfolio, exported to Excel, or converted to rent rolls. And because the data is standardized, it drops cleanly into the underwriting and cash flow models that the client’s teams already use. The structured output also serves as useful context for downstream AI workflows.
“Say the deal is in due diligence,” Jaworski said. “Instead of reading every lease, summarizing it and keying it into a model, Mercury gives CREModels the ability to just drag and drop the documents and have 50 leases abstracted and ready for QC in about an hour. This then forms the foundation for a larger cash flow model that reflects what’s actually in those leases.”
Human Review Is the Final Word
After Mercury’s first pass, a CREModels analyst on Miller’s team reviews the output against the source documents, corrects errors, and resolves the questions that take actual real estate judgment. Do the recoveries account for the right exclusions and base years? Does a co-tenancy or termination right matter to the underwriting?
“We go through each document to confirm everything is there and accurate,” Miller said. “Is it still a manual step? Yes. But it’s far faster than before, with no sacrifice in accuracy, and accuracy is the entire point of lease abstraction.”
Building Trust in AI Lease Abstraction
A May 2026 study from First American Data & Analytics and DealGround found that 66% of CRE professionals now use AI weekly or daily, yet only 5% trust it enough to inform real deal decisions. More than half use it strictly for support.
“Our approach gives clients the confidence to actually reap the benefits of AI,” Jaworski said. “When a document abstraction project is materially faster and you can still trust the information, that’s a real change. A lot of the tools out there are just faster with an asterisk on quality.”
Once a firm’s leases are organized this way, that data becomes a durable foundation for asset management, reporting, portfolio analytics, and future AI work. Raw lease PDFs are a poor starting point for many AI initiatives. Reviewed abstracts stored as structured data are much better.
Today, CREModels uses Mercury internally on client projects. Over time, the company expects to bring AI-assisted abstraction deeper into CRE Suite and offer clients more flexible delivery options, including AI-generated first-pass abstracts, analyst-reviewed deliverables, and structured lease data for underwriting, reporting, asset management, portfolio onboarding, and AI-enabled workflows.
“Depending on the project, people can have us review the output,” Jaworski said, “or they might use the AI versions for smaller or inline tenants and have us do the full QC on anchors and big boxes, so they can close confidently and get moving on their next deal.”
The real shift in AI lease abstraction is a service that CRE firms have always needed, now delivered faster but still backed by a team of experienced real estate analysts.