Top real estate firms are losing deals to faster competitors using AI. Here’s how to cut deal screening from 3 hours to 15 mins.

You've just raised your sixth fund, with two billion in fresh capital ready to go. Your investment committee is hungry for deals, and your LPs are expecting returns. They're not known for patience. Your deployment clock, meanwhile, is ticking louder every quarter.

There’s just one little problem, and that’s capacity.

Whether it’s a six-person acquisition team only being able to screen 20 to 30 deals a week, or a global investment platform with dozens of professionals reviewing thousands of opportunities a year, the constraint is the same. There is always more deal flow than there is time to evaluate it properly.

What you’re looking at here is a structural constraint in modern deal screening.. And it’s costing millions in missed opportunities.

The capacity trap

Most institutional investors won’t publicly admit the problem isn’t capital constraints as much as it’s an operational problem.

The pattern tends to be consistent amongst investment teams. They control the dry powder and the market relationships without  the bandwidth to evaluate every opportunity that crosses their desk.

For instance, one international PE house we work with at 5D reviews around 2,000 deals per year. They close 40 to 80 of them, a 2% to 4% close rate. Nothing unusual there. But their investment committee believes they could scale to 6,000 deals per year with automation.

They're currently doing what they call "superficial reviews" on most opportunities simply because they have no other choice. There aren't enough hours in the day to give every deal the attention it deserves.

What you’re looking at is 4,000 potential deals per year that don’t receive the proper scrutiny. How many of those contained a hidden gem or two? How many would have cleared your hurdle rates if someone had enough time to take a deep dive into the numbers?

You'll never know. And that's the hidden cost.

Making speed your strategy

Real estate investment has always been a relationship business, but there’s also an argument that it’s also becoming a speed business. Agents and brokers remember who responds quickly with good feedback. They remember who can turn around a preliminary view in 48 hours versus two weeks. And when the next off-market deal lands on their desk, they're calling the fast movers first.

One investment manager we spoke with put it like this: whereas you’d assume that the biggest competitive advantage is the cost of capital or the operational expertise, in reality, it’s the ability to move quickly on opportunities while competitors are stuck in preliminary analysis.

But speed without quality often turns out to be reckless, so the challenge goes beyond moving faster and focuses on maintaining rigour while accelerating the process. 

You might manually

• Extract data from offering memorandums
• Build comparables from multiple data sources
• Synthesize market intelligence from 18 different feeds

And when you do, speed and quality feel mutually exclusive. But they don't have to be.

The 10x investment professional

There's a fork in the road taking place right now in institutional real estate. On one side, you have companies going down the traditional route of raising more capital and hiring extra analysts with the idea of scaling linearly. On the other side, companies are turning to AI to create what we call “10x professionals”.

A 10x professional isn't someone who works ten times harder, though that might be the obvious conclusion. Think of them more as a liberated employee from the mind-numbing tasks that no longer depend on human judgment. Data extraction from PDFs. Reconciling rent rolls against lease documents. Building the exhibits that make up 80% of an IC memo – these are all important tasks in their own right, but they're not strategic work, if we’re being honest.

A traditional fund raising two billion in new capital might hire three or four additional analysts to handle the increased deal flow. An AI-enabled fund raising the same amount, however, might hire one analyst and use an AI operating system to handle the preliminary screening and data synthesis. And this is all while it automates the documentation grunt work that typically consumes hours of analyst time each week.

It’s the same capital under management, but a fraction of the headcount. Their senior investment professionals are spending time on analysis and deal sourcing as opposed to wrestling with Excel and endless documentation for days.

The real deal (screening process)

A deal pack lands in your inbox. What happens next in practice? Someone (usually a junior analyst) opens the offering memorandum. They start pulling key metrics, such as:

• Property type
• Location
• Square footage
• Occupancy
• Tenant mix
• Rent roll
• Lease terms
• Capital expenditure requirements

Working through all of that can take a good 45 minutes to an hour, depending on how well the offering memorandum is put together and the complexity of the deal

The market data marathon

Then comes the data gathering. The information you need is scattered across different systems, some your firm has relied on for years and others that promise better insight but add yet another place to look. Pulling it together means stitching fragments from across the stack into something that resembles a coherent view of the market.

Each system comes with its own login and interface, along with behaviours you learn through habit rather than design. You move between tabs, pulling data into spreadsheets and trying to reconcile numbers that don’t quite line up because they’re defined or calculated in slightly different ways.

The irony is that your company already has plenty of historical deal data. Years of past transactions, analysis and outcomes exist inside the business, but they’re locked away in file servers and legacy models. While that knowledge sits untouched, you’re still pulling fresh data from external platforms and trying to square it with what your own experience already tells you.

Another hour passes. Maybe 90 minutes if the market is complex or the datasets contradict each other in ways that need investigating.

Building the financial picture

Next comes the preliminary financial analysis, which might sound straightforward until you're in the weeds of it. You're building out a rough pro forma from scratch while stress testing the assumptions buried in the broker's model, and brokers are always optimistic about their assumptions. 

You need to run sensitivity analyses on the key drivers to understand what happens if rents don't grow as projected or if exit cap rates come in lower than expected. If you're being thorough rather than rushing through it, this is another two to three hours of focused work.

Pulling it all together

The last part involves someone writing the screening memo. The executive summary needs to capture the essence of the opportunity without regurgitating what's in the OM. The property overview has to go deeper, and the market assessment needs to synthesise all that data you just pulled from six different sources into something that tells a story. 

Your financial highlights need to explain why this deal does or doesn't make sense, backed by a preliminary recommendation that's based on reasoning rather than gut feeling. There are red flags that need addressing before anyone gets too excited. Making all of this coherent and comprehensive rather than just a disconnected pile of observations takes another hour, sometimes more if you’re writing for an investment committee and the output needs to be presentation-ready.

Getting lost in the pipeline 

Investment managers often think about their deal funnel in terms of conversion rates, like how many deals screened versus deals closed? But there's another metric that is rarely discussed, and it’s how many deals never make it into the funnel at all because you don't have capacity to give them the attention required. 

One multifamily-focused institutional investor told us they "see every single development deal" in their market from brokers and off-market sources. That's a massive volume of opportunities. But seeing and evaluating them are two different things.

When you're capacity constrained, you start making quick judgment calls. This market's too competitive, pass. The basis seems too high, pass. The sponsor is unknown to us, pass. Some of those snap decisions are correct. But some of them may be wrong, and you're eliminating opportunities before anyone's had time to understand them.

The hidden cost is the market intelligence you're not capturing because you don't have time to step back and see patterns. You're not tracking pricing trends in any systematic way or noticing shifts in what's coming to market because you're too busy trying to get through the week's deal flow.

Why the old playbook doesn't work anymore

The traditional solution to this problem has always been to hire more people. You need more capacity, you add more analysts. Simple, right?

Except it's not so simple anymore. Good real estate analysts are expensive and increasingly difficult to find. Training them takes months; managing a larger team creates its own overhead. Perhaps most importantly, the marginal cost per deal screened stays relatively constant as you add headcount.

What if you could increase your deal screening capacity by three to four times without proportionally increasing headcount? How much better would the process be if you could reduce screening time from two to three hours to 10 to 15 minutes? What if every deal got the same comprehensive evaluation regardless of your team's current workload?

This isn't theoretical. We're seeing it happen right now with institutional investors who've used AI specifically designed for real estate deal screening.

Deal screening with AI

AI in deal screening automates the grunt work, the 40% to 60% of the process that doesn't require human judgment. That frees up your team to focus on the strategic decisions separating good investments from bad ones. Nobody's handing over investment decisions to an algorithm or replacing experienced professionals. Instead, the goal is to get your people out of the Excel and heavy document minefields and into work that requires their expertise.

When a deal pack comes in, the extraction of key metrics from the offering memorandum happens in minutes rather than hours. Market data from multiple sources is pulled and synthesised into something coherent. The preliminary financial analysis is built using your company’s specific underwriting standards, and a standardised screening memo comes out the other end covering the dimensions your investment committee cares about.

What it can't do – and shouldn't do –  is make the  investment decision. That requires intuition and market knowledge, the kind of relationship context and judgment provided by experienced professionals. 

But there are changes. Instead of your senior investment professionals spending three hours taking data and building exhibits, they're taking 20 minutes to review an AI-generated analysis and applying their expertise to determine whether the opportunity warrants a closer look.

What you get is faster work, of course. Its genuine strength, however, lies in being able to work on different things entirely.

The memory factor

One of the best capabilities in modern AI for deal screening is what we call institutional memory.

Every company has specific investment criteria. You might avoid anything with exposure to a certain asset class , or it might be minimum clear height requirements for industrial assets. 

Whatever it is, in a manual process, these criteria live in someone's head or are buried in an investment policy document that no one reads. Every analyst applies them slightly differently, meaning consistency is a challenge.

With AI, you can encode these criteria once, so every deal that comes through is evaluated against the same standards. If an office building has Japanese knotweed, and that is detailed in a report for example , it’s flagged immediately. If an industrial property doesn't meet your clear height requirements, you know before anyone wastes time on detailed analysis.

It extends beyond simple screening criteria too, as AI can learn from your historical decisions. If you've consistently passed on deals in certain submarkets, it learns why. If you've found success with particular asset classes or investment structures, it recognizes those patterns.

It's scaling judgment across every opportunity that comes through your door.

Your team

So what does it all look like in practice for your team? After all, the human element is still what matters most.

Your analysts stop spending their days extracting data from PDFs and focus on analysis. They're evaluating the market dynamics that make deals attractive or risky rather than building analysis for the 20th deal this week. They're building relationships with brokers and sourcing off-market opportunities instead of manually reconciling rent rolls. 

Senior investment professionals stop reviewing preliminary analyses that tell them what they already know (this is an office building in Manchester with 85% occupancy) and start focusing on the questions that matter (is the tenant mix sustainable in a hybrid work environment, what's the repositioning potential, how does this compare to everything else we're seeing in the market).

Investment committees become more consistent, comprehensive screening memos on every deal instead of varying quality depending on who did the analysis and how busy they were that week.

Nobody's talking about cutting headcount anymore. Analysts do analysis instead of data extraction, while senior professionals make judgment calls instead of wading through preliminary work. Every deal is screened, not just the ones that land during quieter weeks. Most importantly, those promising opportunities stop slipping through the net because someone lacks bandwidth.

The competitive advantage is already being built

Right now, while you're reading this article, there’s every chance that someone at a fund with the same team size is screening 60 to 100 deals per week and getting back to brokers with feedback in as little as 48 hours. 

These companies are capturing market intelligence on thousands of opportunities and building proprietary databases that inform their investment strategy. Capital is used more efficiently because more deals can be evaluated, with faster decisions on the ones that matter.

The gap is only getting bigger. Yes, it’s true that AI doesn't make better investment decisions than experienced professionals, but that's not the point. AI-enabled teams can evaluate more opportunities and move faster on the good ones. Better broker relationships are built through responsiveness, even on passed deals.

When the best opportunities move fast, speed becomes strategy.

The cost of standing still

The burning question isn't whether to use AI for deal screening, as the market has already decided that. It’s how to use  it in a way that works for your team's specific process and integrates with your existing systems. The companies figuring these nuts and bolts out now will have a significant advantage. Whereas the ones waiting will likely struggle to keep up.

We work with some of the world's largest institutional investors to automate document-heavy workflows while maintaining the rigour and customisation their investment processes require. 

Book a conversation with our team to discuss your requirements and see how Fifth Dimension can improve your deal screening process.