How AI Built for Real Estate Solves Lease Abstraction (And Why Generic AI Can't!)

Miss a break clause or overlook an expiry date on a lease, and you're looking at some serious losses. But even with these high stakes, companies still rely on using manual extraction that delivers subpar accuracy – at best. For an industry where a single overlooked clause can be the difference between making profit and everything going wrong, it’s not really good enough.

What you’re probably getting wrong about lease abstraction 

It’s fair to say that there’s something broken about how real estate handles lease data, and it's costing the industry more than anyone would like to admit.

The typical story goes something like this … someone misses a rent review clause, or a break option goes unexercised. You get the picture. The financial impact is often hundreds of thousands, sometimes millions, depending on the portfolio size and the asset in question. And now you’ve got an owner  or occupier finding out the hard way. 

The problem isn't that people aren't trying. After all, analysts might spend 14 days extracting hundreds of data points from thousands of leases, only to achieve 80% accuracy. The number might sound respectable until you realise that a 20% error rate impacts the entire portfolio. When you're managing 500 properties, or 2,000, even, those errors multiply. It’s not a good look. 

Inconsistent data makes your portfolio illegible. You literally can’t answer "what's the average lease term" or "which properties have termination clauses exercisable in Q2" because the underlying data is corrupted. You're making decisions based on information you can't trust, and the gap between what you think you know and reality grows with each quarter.

The document system no one understands

Most people think of a lease as a single document you can read and extract data from. That's not how leases work.

• The original lease says the start date is February  5th. 
• Then you get the first amendment on December 12th. 
• The second amendment references the first but uses different language for the rent review clause. 
• A commencement letter from the tenant's solicitor contradicts both dates. 
• And there's a side letter from three years ago that nobody filed in properly, which apparently overrides everything.

So which date is correct? How do you even begin to reconcile all of this systematically when you're dealing with hundreds of these?

One thing is for sure, and it’s that manual extraction can't solve the problem. An analyst sits down with the documents and starts making calls, whether it’s about which document supersedes which or how to interpret "material alterations" in the context of this specific lease. 

The error rate lives in these judgment calls, because once you make one wrong call, the rest of your data follows it. You miss a termination clause due to misreading the amendment hierarchy. Your rent roll calculation is off because you chose the wrong escalation clause. The cash flow model you showed the board last month is wrong. 

At thousands of leases with hundreds of data points each, you're extracting hundreds and thousands of pieces of information. If your accuracy is 85% (pretty good for manual work), you've got about five-figure errors sitting in your system. Most won't matter. A few could well be catastrophic. The problem is you won't know which ones until a tenant exercises a break option you didn't know existed.

AI is the answer, but not in the way you might think

Surely the obvious response to a data extraction problem is to throw AI at it, right? You wouldn’t be the only company doing it, launching ChatGPT, Claude, Gemini or Copilot to process your leases. The problem is these models don't really know what they're looking at.

General models can't handle the nuance of real estate

If you’ve ever heard the term “jack of all trades and master of none”, welcome to the world of general LLMs, which definitely can extract text from a document. They can also identify patterns and pull out dates, names, figures – any general task, they are there to help.

What it can't do, however, is understand that a "termination right" is different depending on jurisdiction and lease structure. It doesn't know that a rent review calculated on "fair market value" is fundamentally different from one based on "CPI-linked increases." It can't reconcile conflicting clauses across multiple amendments because it doesn't understand document hierarchy in real estate. To a general model, each file is just another document. It’s certainly not part of a legal chain that evolves over time.

The models are primarily trained on the internet. They've seen millions of documents, sure, but they haven't been taught the specific nuances of commercial real estate law across different jurisdictions. When they encounter ambiguity, which is constant in lease documents, they’re mostly doing guesswork. And they guess with confidence, which is worse than admitting uncertainty.

You’ve still got an accuracy problem

The result is extraction that looks like it stacks up until you cross reference it against the source documents. There are still accuracy issues in the same range as manual work. Except now the errors come wrapped in algorithmic certainty that makes them harder to spot.

Most companies coming to Fifth Dimension have already tried the LLM approach after spending months feeding leases into ChatGPT or Copilot and getting back clean-looking data. It’s only later on they realise the output was wrong. The model extracted a date from the wrong amendment, or it missed the break clause because it was phrased unusually in amendment four. Maybe it flagged a critical provision as boilerplate because it didn't understand the context. At that point, you’ve got a trust problem, which is even worse than speed or automation being the primary bottleneck. 

You need AI built for real estate. But you definitely don’t need AI that's only seen a handful of real estate documents in its training data.

Lease abstraction that works

Lease abstraction is a specialised problem that requires niche tools. General AI scaled up doesn't cut it. At Fifth Dimension, we've pushed extraction accuracy to 98.49% through purpose-built AI and real estate domain expertise. 

Our approach is built around a three-stage process that general models don’t have. 

• The system extracts the relevant data from every document in the lease pack. 
• Then it references each data point back to the clause it came from.
• It applies a confidence score that reflects how consistent that information is across the full document set.

The best part isn't even the percentage but what comes next.  Every extracted data point is referenced back to its source document. If the start date appears as February 5th in the original lease but December 12th in four subsequent amendments, the system flags the discrepancy and shows you where each date appears with a confidence score. You can see how it made the decision and audit it against the original documents.

It’s the combination of extraction, traceability and reasoning that makes the output usable at an institutional level. Instead of guessing which value is correct, the system shows you the evidence it used and how much weight it gave to each source.

When we tested our system against a human benchmark (1,729 leases across the US and UK, 15 years of history), we hit 98.49% accuracy. The discrepancies we found had been embedded in their system for a long time and were concentrated in areas where the meaning of a lease depends on how clauses interact across multiple documents.

What accurate lease data unlocks for occupiers 

Once the base layer is accurate, what you can do with it changes. For large occupiers, lease data shows up as a planning problem. When you're managing thousands of locations and break options are exercisable across multiple regions, you need to know what's coming before it becomes urgent. That depends on whether expiry dates and break options have been reconciled across amendments. 

When they are, patterns start to show up. You can see 10,000 square feet coming vacant in APAC with enough notice to find replacement tenants or offload the commitment instead of discovering it three weeks too late when someone finally updates the spreadsheet. At that point, abstraction feeds directly into how an estate is managed.

Portfolio-level risk and reconciliation for owners

For portfolio managers s, accurate lease data underpins tracking risk as it develops. The contracts don’t stay the same from one year to the next, and if those changes aren’t reflected in the way the portfolio is tracked, the picture people rely on slowly moves away from what the leases say in reality.

That’s why some organisations pull the information into central data layers using systems like Snowflake or Microsoft Fabric. The same approach works for loan documentation and valuation reports, any document set where conflicting terms need to be reconciled across multiple versions. The point is to keep the legal position and the portfolio view in step as things change. When something is agreed at the end of the month, it needs to show up in the same place people use to understand exposure, rather than waiting for the next round of manual updates.

Handled this way, risk becomes something that can be watched as it develops. Teams don’t have to discover problems through a report or a valuation after the fact. They can see pressure building and deal with it while there is still time to act.

Getting lease data right

Getting lease data right changes what becomes possible. Saving one major lease from an unplanned expiry justifies the investment, but that's only the starting point.

You can stop portfolio risk management from becoming a quarterly panic because you can model scenarios in minutes instead of weeks. When market conditions change, you move with them and know which leases have rent reviews coming up so you can adjust accordingly.

Everything in real estate flows from the terms you've agreed with your tenants. Get that foundation wrong and every decision built on top of it is compromised. Most companies realise this after the fact, like missing a break clause on a high-performing asset and suddenly the cost of bad data becomes clear.

The gap between where most portfolios are on lease data and where they need to be won't close through incremental improvements. Manual processes delivering 85% accuracy won't suddenly hit 98%. Companies need to treat lease abstraction as the specialised problem it is.

Stop making guesses about lease data

The losses from bad lease data aren't going away with incremental fixes. If you're managing a portfolio of manual processes or generic AI that delivers 85% accuracy, you're sitting on a ticking problem.

Book a chat to see how Fifth Dimension handles lease abstraction differently and provides up to 98% accuracy rates.