EP 395 How We Use AI to Analyze Real Estate Deals Faster

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What if the secret to growing your real estate portfolio isn’t finding more deals or hiring more analysts—but building a smarter investment process?

Can I tell you something I wish someone had told us when we first started investing in real estate?

You don’t have to do everything yourself to build something extraordinary.

And yet, when you’re building an investment business, it can certainly feel that way.

One minute, you’re researching markets and reviewing apartment buildings. The next, you’re buried in spreadsheets, calling lenders, answering investor questions, or trying to figure out why the numbers in a rent roll don’t match the offering memorandum.

And somehow, every important decision still ends up on your desk.

Sound familiar?

My sister Nan and I know that feeling very well.

When we started investing in multifamily real estate, we didn’t come from Wall Street. We came from fashion.

We didn’t know how to underwrite an apartment building, structure financing, evaluate a market, or even read a rent roll.

We learned by doing. By asking questions. By making mistakes. And by building relationships with people who knew more than we did.

Fast-forward more than seven years, and we’ve built a real estate portfolio approaching half a billion dollars in assets, returned over $45 million to investors, and helped investors realize more than $110 million in tax savings.

But here’s something that hasn’t changed.

The more your portfolio grows, the more decisions your investment business needs to make.

And that’s exactly why we started building our own AI-powered investment operating system, AIREI.

Not because we wanted technology to make our decisions.

Because we wanted to build the investment capacity to grow without having to grow our headcount at the same rate.

Today, we’re sharing what that actually looks like, what we’ve learned along the way, and how you can begin applying AI to your own real estate investment process.

The Real Estate Investing Bottleneck Nobody Talks About

Let’s imagine you cut your underwriting time in half.

Amazing, right?

Now you can review twice as many deals.

But what happens when every acquisition decision, financing question, and asset management issue still needs your approval?

You’re still the bottleneck.

That’s something we experienced firsthand.

Early in our investing journey, we were doing everything we could to find our next acquisition.

We researched markets, built relationships with brokers, reviewed financials, spoke with lenders, and tried to understand which opportunities actually made sense.

Then we acquired a property.

Suddenly, our attention shifted to executing the business plan, working with property management, monitoring financial performance, and taking care of the investment.

And finding the next deal?

That became harder.

Because acquisitions and asset management are two completely different responsibilities.

One helps you build your future pipeline.

The other makes sure you’re executing on the investments you already own.

Both are important. Both require attention.

And when you’re operating with a small team, they compete for the same limited hours.

This is why we believe the biggest opportunity with AI isn’t simply doing your existing work faster.

It’s creating a repeatable investment process that doesn’t depend on one person doing everything.

What AI Can Do for a Small Real Estate Investment Team

Here’s where things get exciting.

For years, sophisticated investment firms have had access to analysts, market researchers, underwriting teams, risk officers, and investment committees.

Smaller operators haven’t always had the resources to build that same infrastructure.

But AI is changing what smaller teams can accomplish.

Today, we can use AI to help organize market research, review offering memorandums, analyze rent rolls, compare financial statements, and prepare questions for investment committee review.

In one recent exercise, we used AI to conduct initial analysis on 15 real estate deals.

Work that could take more than ten hours per deal was reduced to minutes for parts of the initial review.

And then we verified the findings.

That last part matters.

Because faster analysis is only valuable if the information is reliable.

On one $40 million multifamily deal, AIREI flagged a $135,000 error.

On another $60 million opportunity, our AI analysis identified an income discrepancy of approximately $670,000.

We didn’t simply accept the findings.

We went back to the source documents, investigated the differences, and evaluated what they meant for the investment.

After reviewing the $60 million opportunity, we decided to pass.

AI surfaced the issue. We investigated it. We made the decision.

That’s the distinction we want every investor to understand.

Before You Ask AI to Analyze a Deal, Tell It What You’re Looking For

One of the most common questions we hear is:

“I’m looking to buy my first apartment building. What should I give AI first?”

Our answer might surprise you.

Don’t start with the property.

Start with your investment criteria.

Because how can AI tell you whether a deal is right for you if it doesn’t know what you’re trying to accomplish?

We call this your investment mandate.

Think of it as your personal investment rulebook.

It defines the markets you want to invest in, the types of properties you’re interested in, your preferred business plans, financing requirements, target returns, and risk tolerance.

Maybe you’re looking for stabilized apartment communities that generate predictable income.

Or perhaps you’re comfortable with a more involved renovation strategy.

Those are different investments, and they should be evaluated differently.

Once you’ve defined your criteria, you can begin analyzing opportunities consistently.

In our investment business, we evaluate deals through five important areas.

1. Market: Where are you buying, and why?

Is the population growing? What supports rental demand? How much competing apartment supply is coming online?

AI can help organize market research, but you still need to verify the information and understand what it means for your investment.

2. Underwriting: Do the numbers actually work?

What is the property earning today?

How much does it cost to operate?

What assumptions are driving the projected returns?

This is where you want to distinguish historical performance from the seller’s projections.

3. Capital Stack: How is the deal being financed?

How much debt are you using? What are the interest rate, loan terms, and reserve requirements?

And how will cash flow and profits be distributed among investors?

A property can look attractive operationally and still become a poor investment if the financing doesn’t work.

4. Business Plan: What needs to happen for the investment to succeed?

Are you renovating apartments? Increasing rents? Improving operations?

What will those improvements cost, and what evidence supports the expected results?

5. Operators and Partners: Who is responsible for making it happen?

A great spreadsheet doesn’t execute a business plan.

People do.

Understanding the experience, responsibilities, and capacity of the team matters just as much as the projected returns.

These five areas give you a framework for asking better questions before you commit capital.

Why We Built an AI Investment Committee Instead of Just Using a Chatbot

Now, you might be wondering:

“Can’t I just upload a deal into ChatGPT and ask whether it’s a good investment?”

You certainly can ask.

But here’s the problem.

What does good mean?

Good for someone who wants immediate cash flow?

Good for someone pursuing appreciation?

Good for someone who can tolerate significant risk?

A general question often produces a general answer.

That’s why we built an AI Investment Committee into AIREI.

Instead of relying on one broad response, we assign specific responsibilities to different AI roles.

Our Risk Officer examines potential downside exposure.

Our Growth Advocate looks at the upside case.

Our Debt and Structure role challenges the financing.

Our Market Economist evaluates market assumptions.

And our Chair brings the findings and unresolved questions together.

The purpose isn’t to create five AI personalities that agree with one another.

It’s to create a structured process that challenges assumptions from different perspectives.

For example, imagine the upside case depends on raising rents by 15%.

The Growth Advocate might explain why the opportunity looks attractive.

But the Risk Officer could question whether the local market can support those increases.

And the Debt and Structure role might identify what happens to loan coverage if those rent increases take longer than expected.

Now you have a much more useful conversation.

Not simply, “Is this a good deal?”

But, “What assumptions have to be true for this investment to succeed, and what happens if they’re wrong?”

One important distinction: different AI roles are not the same as independent human experts. They can share errors and blind spots.

That’s why the evidence still needs to be checked.

The goal isn’t to make AI sound more confident. It’s to make the investment review more disciplined.

How to Catch an AI Answer That Sounds Right but Isn’t

Let’s talk about something that makes us a little nervous.

AI can sound incredibly convincing.

It can produce beautiful tables, detailed explanations, and professional-looking investment summaries.

And sometimes, it can be completely wrong.

That’s why we always want the analysis to be traceable.

When AIREI identifies an important number, we want to know where it came from.

Which document?

Which page or spreadsheet row?

What time period?

What formula?

What assumptions?

Then we verify it.

Let’s say the offering memorandum shows one income figure, the T-12 shows another, and the rent roll suggests something different.

That doesn’t automatically mean anyone made a mistake.

The documents may be measuring different things.

The offering memorandum might include future projections.

The T-12 reflects historical operating results.

The rent roll captures leases and occupancy at a particular point in time.

So instead of immediately concluding that something is wrong, we ask AI to help reconcile the differences.

Are we comparing scheduled rent with actual collections?

Are rental concessions included?

Do the numbers cover the same dates?

And if something still doesn’t make sense, we ask for supporting information.

This is a habit worth developing early.

Never confuse an organized answer with a verified answer.

Already Have an Analyst? Here’s Where AI Fits In

Another question we hear is whether AI should replace existing analysts or underwriting teams.

That’s not how we approach it.

If you already have a team, start by identifying one recurring task that consumes too much time.

Maybe it’s organizing a new deal package.

Maybe it’s comparing updated financial statements.

Maybe it’s preparing an initial underwriting review or drafting questions for a broker.

Use AI to help prepare that work.

Then have your analyst review the output, investigate discrepancies, and incorporate the verified findings into your existing process.

And measure whether it actually helped.

Did it save time?

Were the results accurate?

Did it make the review more consistent?

If the answer is yes, you can begin expanding the workflow.

But if your process is unclear, adding AI won’t magically fix it.

And if your team genuinely needs specialized expertise or more capacity, hiring may still be the right decision.

The goal is to improve how your team works—not eliminate the people responsible for the investment.

What If You’re a Passive Real Estate Investor?

Here’s something we want passive investors to understand.

You don’t have to be the person managing an apartment community to benefit from learning how to analyze one.

Even if you’re investing alongside an experienced operator, it’s important to understand where your money is going.

AI can help you review investment materials, organize questions, and understand financial concepts.

For example, you might ask:

• What assumptions are driving the projected returns?
• What evidence supports the expected rental growth?
• How much debt is being used, and what happens if interest rates or operating performance change?
• How is the debt-service coverage ratio calculated?
• What are the biggest risks to the business plan?

You can also ask AI to explain unfamiliar terms in plain English.

But remember, the goal isn’t to use AI to approve an investment.

It’s to become a more informed investor who knows what questions to ask.

Always confirm material information with the operator and appropriate qualified professionals.

The Biggest Opportunity With AI Isn’t Speed

Here’s what we’ve come to believe after building our investment business.

Speed is wonderful.

But speed without judgment can become expensive.

Imagine being able to review ten times as many deals.

Sounds incredible.

But if you’re relying on incorrect assumptions or skipping verification, you might simply be making bad decisions ten times faster.

That’s not the kind of leverage we’re interested in.

We want AI to help us build a system that consistently reviews opportunities, challenges assumptions, identifies missing information, and makes it easier for our team to focus on the decisions that matter.

Because the real bottleneck isn’t always finding another deal.

Sometimes it’s having the capacity to understand the opportunities already in front of you.

And that’s why we believe real estate investing × AI = wealth that lasts is such an important idea.

Real estate creates the opportunity to build wealth through ownership.

AI can expand the capacity to evaluate and manage those investments.

But neither guarantees financial success.

Your strategy, your people, your processes, and your judgment still matter.

Want to See What an AI Investment Committee Actually Looks Like?

If you’ve been wondering how to use AI in your real estate investment business but aren’t sure where to begin, we’d love to show you what we’re building.

My sister Nancy and I are hosting a free AI × Real Estate Masterclass, where we’ll walk through how we’re using AI to analyze real estate deals and build the investment capacity to grow AUM without growing headcount at the same rate.

We’ll share how we put AI to work in our own investment business, what the analytical process looks like, and why verifying the findings matters just as much as generating them.

Whether you’re evaluating your first multifamily investment or managing an existing portfolio, our goal is to help you see how AI can support a more repeatable investment process.

Save Your Free Seat at the AI × Real Estate Masterclass

Because the future of real estate investing isn’t about handing your decisions over to technology.

It’s about building a better system around the decisions you’re already responsible for.

AI does the work. You make the judgment.

Frequently Asked Questions

Can AI analyze multifamily real estate deals?

Yes. AI can help review offering memorandums, T-12 operating statements, rent rolls, market data, and investment assumptions. However, important calculations and conclusions should be verified against original documents before making investment decisions.

How can AI help real estate investors save time?

AI can accelerate repetitive work such as organizing documents, extracting financial information, preparing initial reviews, comparing assumptions, and identifying questions for further investigation. Actual time savings depend on the workflow, data quality, and verification required.

What is an AI Investment Committee?

An AI Investment Committee is a structured analytical workflow that assigns different review responsibilities to AI roles, such as risk assessment, market analysis, financing review, and investment synthesis. Its purpose is to help challenge assumptions and organize findings, not replace human investment decisions.

Can AI replace a real estate investment analyst?

AI can assist analysts with repetitive analytical tasks, but experienced professionals remain important for verification, market interpretation, due diligence, and investment judgment.

How should a beginner start using AI for real estate investing?

Start by defining your investment criteria. Then use AI to organize deal documents, explain financial concepts, review assumptions, and identify questions that require further investigation. Verify important findings before relying on them.