By: Joe Leach, Founder & CEO, Elysium
A lease abstraction workload has outgrown its process when the volume of documents, amendments and deal deadlines is more than the team has hours to cover file by file. The usual signs are amendments arriving faster than abstracts can be updated, rent rolls that take extra work to tie out, complex clauses that only get a headline field, abstracts without source links, CAM reconciliations adjusted after the fact, field definitions that drift as the team grows, diligence that relies on sampling, and messy document folders. If two or three of these sound familiar, the team probably needs more capacity.
We work with in-house abstraction teams and abstraction service providers, and the people doing this work are usually careful and very experienced, so what we look at is the volume and the timelines around them. Below is where the pressure comes from, the signs we'd look for, and what we'd do about it.
Most of the pressure comes from the gap between an abstract done at a point in time and a lease file that keeps changing. After the abstract is done, amendments, side letters, renewals and expansions keep arriving, and each one has to be read against everything before it. When those updates land in the middle of other deadlines, there's rarely time to replay the whole file.
The terms that move money also rarely sit in one clause. A recovery cap might be set in the lease, redefined in an exhibit and changed again by an amendment, and reading that in order for every tenant takes more hours than most deal timelines allow.
This was the pattern that stood out most in our lease accuracy benchmark. In one example, a 2018 lease set a 5% non-cumulative cap on operating expenses and strictly excluded snow removal. A 2022 amendment replaced "non-cumulative" with "cumulative and compounded" and revoked the snow removal exclusion. An abstract built from the original lease would still show the old cap and the old exclusion until someone had time to work the amendment through.
A quick check: pick ten leases with at least two amendments and see whether each abstract reflects the latest controlling language.
The rent roll is often built from the abstracts, so when abstracts fall behind, the rent roll does too. It's worth checking base rent against the lease and latest amendment, any free rent or abatement still running, and the next escalation.
Here's an illustrative example (not customer data). A rent roll shows a tenant at $30,000 a month. The lease has a 3% annual bump that took effect two months ago, so the rent in force is $30,900. That's $10,800 a year on one tenant, before anyone checks whether a renewal reset the schedule.
When the queue is long, the practical move is to record that a clause exists and come back to the detail later, so a field reads "Co-Tenancy: Yes" or "60 Month Option."
In the benchmark, a termination option required nine months' notice and a clawback fee of 3 months of base rent plus the unamortized tenant improvements and leasing commissions, paid at the same time as the notice. If the fee is short or late, the notice is void. A co-tenancy clause in the same set let inline tenants switch to substitute rent of 2% of gross sales if the anchor went dark for more than 90 consecutive days. Both affect cash flow and take real time to model across a portfolio.
When every value links to the document, page and section it came from, nobody has to go back to the lease to answer an investment committee, lender or tenant's auditor. The abstractor's work can be checked in a click, and the record holds up after the file has changed hands. Adding those links by hand for every field takes real time, so many teams only do it for the most important terms.
When the annual true-up gets challenged or adjusted after it goes out, the cause is often a recovery term that changed somewhere in the lease file: a cap method changed by an amendment, tenant-specific exclusions, a base year reset on renewal, or a pro-rata share changed by a remeasurement. More detail in our CAM reconciliation software buyer's guide.
Field definitions that worked for a small team and one asset class tend to stretch as more people, properties and lease types get added. One property's abstracts might record the cap percentage and another's the cap method. Each abstract can be complete on its own terms and still be hard to compare across the portfolio, which is a standards question more than anything.
On a tight acquisition timeline, teams often review the largest tenants closely and sample the rest, because there aren't enough hours to read every file in full. The risk then sits in the leases that didn't get a full read, and our OpEx pass-through leakage post covers where under-recoveries tend to hide in a lease stack.
For a sense of scale, Saber-Hightower reviewed 150+ leases and amendments on a portfolio acquisition with Elysium in under 4 hours, compared with 40 to 50 hours by hand, and got its LOI out in a day (case study). ABJNY reconciled about 200 leases, amendments and service contracts on a medical office acquisition in under 12 hours, compared with 30 to 40 hours by hand (case study).
Duplicate amendments, scanned PDFs, files named "Lease_final_v3" and multi-suite leases in one folder all add time before abstraction starts. A duplicated amendment can get counted twice, a multi-suite lease can get flattened into one line, and time spent sorting folders comes out of the review.
We'd start small and use your own documents.
That last option is what we built Elysium for, as leverage for abstraction teams. Elysium takes the first read of every document, so your team can cover every file and spend its time validating the terms that matter. It reads leases, amendments, exhibits, rent rolls and vendor contracts as one set, applies later amendments in order, and links every value to its document, page and section. Each field has a confidence score so reviewers know where to look first, and they can validate it with a timestamp. It connects to Google Drive, Dropbox, SharePoint, Box and Egnyte and reads scanned PDFs. If you'd like to run one property through it, reach out here.
What causes errors in lease abstraction for investment portfolios?
Mostly volume and timing: amendments that change terms after the original abstract, terms that depend on several documents read together, and field definitions that drift as a portfolio grows. Tight deal timelines add to all three.
How often should lease abstracts be updated?
Every time a new amendment, side letter, renewal or expansion is signed, read against the full file. It's also worth a full check before a sale or refinancing.
Does AI replace lease abstraction teams?
No. People still need to validate the material terms, and experienced abstractors are the ones who know which terms those are. What software can take on is the reading volume: the first pass through every lease, amendment and exhibit, with source links attached. Look for tools that record who validated each field and when.
Can I use ChatGPT for lease abstraction?
For reading one clause in one lease, often yes. The trouble is scale. Across 200 leases, each with its own amendments, plus a reconciliation against the rent roll or CAM billing, every added layer is another chance for a chatbot to get something wrong, and a chat window doesn't link each value back to the source clause so you can check it. More in why AI for CRE documents needs a paper trail.
Do we need to clean up our document folders before bringing in software?
Not with Elysium. Connect folders as they are, and Elysium sorts documents by tenant or property inside Elysium while your originals stay put.
Joe Leach
Founder & CEO
Elysium
www.elysium-cre.com