This is One Question - where I set the stage and let my guest take it from there.

Introduction

I am honored and excited to kick off the One Question series with Aleksey Chernobelskiy. I can’t recall when or how I became acquainted with Aleksey’s excellent Substack: LP Investing Lessons. But I do know that I immediately consumed everything he had produced up to that moment, and have read everything since.

In a space dominated by GP promises and investor dreams, Aleksey delivers a rare voice of truth that every LP desperately needs to hear - with the math to back it up.

Aleksey is a busy guy. He:

• Writes a weekly newsletter at LP Investing Lessons;

• Created LP/GP Match, an online deal sourcing tool;

• Consults with LPs and GPs on current and future deals; and is

• Hosting a small-format Real Estate LP/GP Summit in September.

Prior to advising LPs, Aleksey ran STORE Capital’s $10 billion commercial real estate portfolio and oversaw the firm’s underwriting team. He is a graduate of the University of Arizona with a quadruple major in Finance, Mathematics, Economics, and Accounting.

Connect with Aleksey on: LinkedIn | X | Substack | LP/GP Match.

My One Question to Aleksey

In response, Aleksey suggested revisiting a topic he covered just over two years ago that remains just as relevant today: Why LP investments won’t let you quit your 9-5.

Apparently we LPs can be slow learners…

To see the original post, click here. An updated version with my comments follows.

Why LP investments won’t let you quit your 9-5

As I peruse websites and LinkedIn pages of syndications, I continue to see the “quit your 9 to 5” narrative. I recently even saw it written as “grind to five”… which honestly just reminded me to write this article - welcome back! 😊

Today I’m going to try to explain, using a model, why these marketing messages are -at best - misleading.

Side note from me: unlike public equities, private equity does not have ethical and regulatory standards controlling marketing claims. Buyer beware.

I’ve been outspoken about this in the past (screenshot below), and I know many don’t like it.. but I’m here to show my work and will obviously take any and all criticism as always - in fact here’s a link to post your refutation! :)

Leave a comment

Today’s agenda:

1. What is a monte carlo simulation?

2. Why is it relevant to LP investing?

3. Assumptions

4. Discussing the results

5. Takeaways

Let’s dive in.

1) What is a monte carlo simulation?

I mean, first of all, what in the world is a monte carlo simulation?! Good question.

Simply said, a monte carlo simulation is where you start at a given point, assume some things about what happens along the way, and see where you end up! The result looks something like this (a random picture I found on Google).

You’ll notice that everything starts in the same spot… and then the results vary quite a bit depending on what happens probabilistically at each step of the model.

One easy way to think about this is coin flips:

• Take 50 people, and give them 100 coins each.

• Heads means they win a dollar, tails means they don’t make anything.

• You can imagine a chart of outcomes in your head:

  • Each person would have somewhere between $0 and $100 dollars.

  • All 50 people started at $0 and the outcomes will vary.

Ok, cool… but why is this relevant to us? 😊

2) Why is it relevant to LP investing?

Monte carlo simulations are relevant to LP investing because outcomes (even with the best due diligence) will vary … and to add to that, they’re generally outside of your control after you invest. This is why I always say that the best passive investments take active work in the beginning.

Most (all?) of the websites and ads that preach this “passive income to quit your 9-5” story want you to think that investing in syndications will lead you to retirement .. and the question I have is whether that’s actually true!

Side note from me: I can’t wait to hear this. I have searched in vain for data to support the cause for PE. If you know of data that supports the argument for or against PE, please email me: hello@netzeroisawin.com.

3) Assumptions

Recall that our goal is to figure out whether “investing as an LP will help me quit my 9-5.”

Let’s subdivide the “population” into 3 categories:

1: “Very wealthy”

• If someone already has the cash saved up to retire through dividends in the stock market (or investing in bonds, treasuries, etc), investing in LP investments doesn’t change the fact that they can retire.

• Therefore, this can’t be the case these websites are speaking of, since the individual is already ready to retire!

• QED (that’s short for a proof being over, sorry to bring you into my math major nightmares haha)

Side note from me: I believe there are a number of people who are already in this category and may not know it. There is no other plausible reason to take on more work and risk, unless the person enjoys it and/or has higher goals than funding one’s own retirement.

2: “Accredited”

• This is the section we’ll be dealing with in the analysis below

• Note that the accredited world is large - roughly 18.5% of US households (which includes the “very wealthy” group above as well)

• Nobody, in my opinion, should put all of their investments into one strategy (with the only exception being a business they’re running perhaps, but that’s not our topic).

• Therefore, it’s important to allocate only a certain percentage to LP investments - which is the case we’ll deal with in this article! We will assume $100k below.

Side note from me: today the number of accredited households is pushing 20%, and has lost nearly all of the intended restrictive nature from when it was crafted in 1982. To understand the history and modern implications of “accredited” status, check out “HEURISTICS: Accredited = Astute”.

3: “Non-accredited”

• Both 506(b) and Reg CF (regulation crowdfunding) investments allow investors that aren’t accredited

• There’s a lot to say on this, but for the sake of simplicity let’s remember that this is ~81.5% of the US population and I would argue one of these will always be true:

  • Investor doesn’t know how to vet LP investments (i.e. loss probability is high).

    • I would also argue that for many people that fall into this bucket investing the capital into themselves (e.g. education) or using the time they would spend on vetting these deals on advancing their career would probabilistically yield higher returns in dollar terms over a long horizon.

  • Investor doesn’t have enough saved up to make a material dent in saving towards retirement (e.g. if you want to invest 10% of your net worth into syndications a $100k check would already put you into the 1MM net worth bucket that we talked about in point 2 above).

    • There are some edge cases here, but I think you’ll agree after reading this that we’ll deal with them via investor #3’s case presented below.

• To be absolutely clear, where I stand here is that for a non-accredited investor it’s very unlikely that these outcomes will lead to helping you retire:

  • QED again :)

  • One path is over allocating and “risking it all,” but I hope we can all agree that’s called luck rather than investing.

Alright, so now let’s get to the fun parts - the modeling!

PS if you’re still reading… you’re in the 1% of nerdy LPs, perhaps an “accredited LP nerd” would be proper!? 🤓

4) Discussing the results

The Methodology

First, let me introduce you to the uniform distribution- this is basically where you pick any number at random between two numbers.

In our case, I chose 0 to 1. The number on one draw could be 0.9999, 1, or perhaps 0.11111. I hope that makes sense.

We will use this variable to determine what happens to someone’s money after a five-year period (which is a typical hold period in a syndication). Then we’ll use it again to see what happens in the next five-year period…for a total of 40 years.

Now, one clear problem is that a multiple between 0x and 1x is clearly unfair - nobody invests in those odds, right?! You’re right, so we’re going to adjust it 3x whatever the number generator gives out.. in other words, pick a number between 0 and 1, and then multiply by 3. This accounts for the fact that some syndications will have outlier results, while others might lead you to lose your money (but recall that our original draw of 0.5x, a loss, will now be 1.5x .. so the odds have moved!).

The Results

Here are the results:

• We start with $100 (for the sake of our example, let’s assume that’s thousands).

• Two examples based on the table below:

  • Investor 3 did well on their first deal - a 2.35x multiple in 5 years, and the second deal was alright, but then the third deal (a random number generator again) didn’t do so well… the experiment continues on until the investor ends up with 150 (vs original 100) at the end of 40 (!) years

  • Investor 18 absolutely crushes it - $56 million from a $100k check!

• Over a 40 year period, the average IRR with this simulation is 8.6%, where you’ll end up with $2.7 million (and you started at $100k, pretty cool!).

  • Let’s remember that there’s inflation…we’re ignoring that

• But remember that averages are skewed by outliers.. and $56 million from above is quite the outlier… so now look at the median… roughly a 2x multiple over a 40-year period, or a 1.9% IRR - ouch!

Here’s a graphical representation of the first 25 (50 gets a bit busy):

A fair question on the above methodology is “but outcomes shouldn’t be equally spread between 0 and 3x. For example, it’s more likely (across a large enough sample size) to get all your money back in a real estate syndication vs losing it all.”

You might even imagine a normal distribution (a bell curve), but the problem is that this curve has no bounds (while with an LP investment, the “worst” outcome is losing it all as opposed to losing more than what you had). I have a few ideas for the right distributions for this and will do part 2 soon for the ultimate accredited LP nerds - stay tuned!

5) Takeaways

I think there are a few lessons we can take away from our work today:

• Accuracy matters - you can see how expensive it is to lose money, in some cases our investors couldn’t recover from the loss in a very long time horizon. More on that here.

• Secondly, and not including inflation, your $100k check will get you to $2.7MM on average… but only $212k using a median (here the median matters more, since it’s an actual outcome, as opposed to the average).

  • Even the average IRR of 8.6% isn’t so incredible… and the entire time you didn’t have liquidity like you would in the stock market (which also averages 8% over a long hold period).

• Having said all that, $2.5MM*~8% is a nice $200k check every year while still having your equity invested … and this doesn’t account for additional investments that you might have invested in along the way!

• I hope this helps you conclude initially (more to come with another probability distribution) that investing as an LP will only help one (of our three populations of people described in the assumptions section) get to retirement (1) AFTER 40 years (!) and (2) with an investment return that doesn’t differ greatly from a stock market index.

  • But to be clear this subset (accredited investors) of the population accounts for roughly 18.5% - while the websites target much more than this.

    • Perhaps one could even argue that within the subset of 18.5% of accredited investors, there are people who are so inexperienced (recall that accuracy matters) at investing that the entire argument falls apart altogether.

  • Again, more coming on this with a new distribution, where I hope to also show how much accuracy of your investments matter.

My Thoughts

My takeaways based on the results of this methodology:

• In the absence of actual results, the modeled data is relevant and worth consideration.

• PE returns are not very compelling:

  • the median PE return is 1.9%, which barely tracks inflation.

  • The (inherently skewed) average return of 8% mirrors a public market index over the long term - and requires far more initial and ongoing work.

• Exceeding the long-term average return of 8% provided by a public market index fund via PE may be possible, but requires the knowledge and dedication to consistently outperform the market - a skill and practice few people may be capable of achieving.

• Approximately 65% of the results fell below an IRR of 8% (the index fund comparable return), and 6% of results fell below a 4.5% return (the treasuries equivalent). Would you risk your retirement on a 65% chance of not achieving an equivalent result?

• Marketing claims are just that: marketing. Making informed decisions requires using unbiased, data-driven information.

Thank you

Thank you to Aleksey for answering my one question so thoroughly and with the math to back it up.

I encourage you to engage with Aleksey on: LinkedIn | X | Substack | LP/GP Match.

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There are so many smart, insightful, and generous people in this world whose words merit sharing. That’s why I am creating a new series, One Question, that is dedicated to providing a platform for those words.

The premise is simple: I pose one question - crafted just for that person - and let them do the rest. I can’t wait to see what they have to share with us.

My guests will be covering a variety of topics like: the lessons we just can’t seem to grasp; the handful of conclusions that only millions of private data points can provide; how to capitalize on losses; the evolution of transparency in private investments; and more.

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Have a question you’d like answered?

A person whose insights you’d like to hear more about?

Interested in being a guest?

Email me at hello@netzeroisawin.com or use the message button below.

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I’m shifting to summer vacation mode, so the twice-weekly delivery of new content will be temporarily paused through August.

But I have a summer school challenge for you:

Test the Risk Radar and/or submit a deal for The Debrief, and you’ll be entered to win a chocolate bar exclusive to Switzerland - your choice of more than ten varieties, hand-picked and mailed by me.

Get your homework in on time: I’ll be selecting ten winners at random the last week of August.

Summer School Challenge #1

Test the Risk Radar

The Risk Radar is unproven - let’s try to break it!

How to do it:

1. GO to the Risk Radar resources page.

2. DOWNLOAD the Risk Radar and the AI-powered how-to guide.

3. PLOT the Radar against the offering materials of a deal. Ideally an exited deal, though one at least two years in could work as well.

4. ANALYZE the results. Did the Risk Radar foreshadow breaking points that stressed the deal? psst: you can use AI for that too… here’s the guide.

5. EMAIL a summary of your findings to hello@netzeroisawin.com. Please include:

   1. Did the guide and AI prompts work?

   2. How did the Risk Radar projections compare to the actual results?

   3. What was missed?

   4. Comments or other suggestions that would make the Risk Radar more effective?

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Summer School Challenge #2

Submit a Deal for The Debrief

I started The Debrief to help myself and others sharpen their analysis skills via retrospective analysis. Each deal is unique, but every review provides valuable lessons and surprising insights.

How to do it:

1. READ the Submissions page.

2. USE the AI-powered anonymization guide.

3. GENERATE the anonymized summary of your deal.

4. EMAIL the anonymized summary to hello@netzeroisawin.com. Please include:

   1. The anonymized summary as an attachment, or a link to a place I can download it.

   2. Any topics or questions you’d like addressed and/or avoided.

Extra Credit

Check out the six-part pro forma series that demonstrates how flawed assumptions and seemingly inconsequential changes can dramatically impact investor returns.

• No. 1: Pro Forma Data Is Correct

• No. 2: Rent Always Goes Up

• No. 3: Costs Are Predictable

• No. 4: Real Estate Always Appreciates

• No. 5: Market Corrections Are Passé

• No. 6: Stress Testing the Pro Forma With AI

Happy Summer Everyone!

I look forward to hearing from you.

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This is Decoded: AI tools for getting straight answers from complex investment documents.

If you’re stuck in the tension between sharing information with others in order to become a better investor while not completely dismissing confidentiality obligations, this guide can help.

First, consider what confidentiality means, your obligations, and how YOU feel about confidentiality. Every deal’s verbiage and every investor’s motivations are different. The choice is personal and highly dependent on context. I covered the topic recently in the post Heuristics: Confidentiality, No One Cares.

After you have:

• Reviewed the confidentiality clauses of your subscription documents;

• Determined your personal comfort zone between serving conflicting LP and GP interests; and

• Taken a stance on adhering to the letter versus intent of confidentiality,

you may use the following guide to bridge the gap between no sharing and prudent sharing.

How to Use NotebookLM to Anonymize Deal Details

I created the following guide to using NotebookLM to generate deal summarization reports that keep the narrative but remove identifying information.

Step 1

Understand the Limitations

Keep in mind that:

• AI makes mistakes and is not a replacement for human review. NotebookLM can do the heavy lifting, but only you can judge the quality of the results. Always review the outputs.

• NotebookLM will share information if you use the feedback buttons. To learn more about data sharing and AI tools, see DECODED: Confidentiality, AI and You.

Step 2

Create a New Notebook

Go to NotebookLM and create a new notebook.

Step 3

Upload the Offering Materials

Add every file provided during the capitalization period to the sources. DO NOT upload anything provided after the capitalization period.

• Pitch Deck: yes

• Quarterly Reports: no

Step 4

Configure the Notebook

Click on the “configure notebook” button - the bars in the top right corner of the chat box.

Populate the “Configure Chat” pop-up window as follows:

1. Select the “Custom” button.

2. Populate the instructions box with the prompt provided below.

3. Select the “Longer” button.

4. Click “Save”.

Prompt for the Configure Chat box:

Step 5

Run Prompt #1: the Pre-Investment Narrative

• Verify that the source files include only offering materials (nothing provided after the capitalization period).

• Verify that the checkbox for every source is selected.

• Run the following command.

• Note that if any source files have been included that are dated after the capitalization period, the notebook will ask you to remove them. Delete or deselect those sources, and then instruct the notebook to proceed.

Prompt for Creating the Pre-Investment Narrative:

Step 6

Save the Results

• Click the “Save to Note” button at the bottom of the completed response. The response will be saved to the “Studio” section at the right of the chat screen.

• Click the drop-down menu (the three dots) to the right of the generated note. Select “Export to Docs”. This will convert the note to a Google Doc, which will open automatically.

• Print or save the document to a PDF. Name the document “Pre-Investment Narrative”. Do not name the GP or deal in the file name.

Step 7

Run Prompt #2: Generate the Risk Radar Data

• Do not change anything.

• Run the following command.

• Note that this generates only the data points for plotting on the Risk Radar. If you want to source the data for the inner and outer rings of the Risk Radar, you will need to use the Risk Radar Guide.

• Save the response as detailed in Step 6. Name the document “Risk Radar Data”.

Prompt for Creating the Risk Radar Data:

Step 8

Change the Sources from Pre- to Post-Capitalization

• Delete or uncheck all of the previously-used, pre-investment sources.

• Load all of the files provided after the capitalization period through the disposition and full deal closure into the notebook sources. DO NOT upload anything provided during the capitalization period.

  • Pitch Deck: no

  • Quarterly Reports: yes

Step 9

Run Prompt #3: Generate the Post-Investment Narrative

• Verify that only post-investment sources are checked before running the following prompt.

• Note that if any source files have been included that are dated before the closing of the capitalization period, the notebook will ask you to remove them. Delete or deselect those sources, and then instruct the notebook to proceed.

• Save the Results

  • Save the response as detailed in Step 6.

  • Name the document “Post-Investment Narrative”.

Prompt for Creating the Post-Investment Narrative:

Step 10

Check the Results

Read and/or use a search function to ensure that identifying information has been removed sufficiently, and that the narrative is accurate. Edit manually as needed.

Process Complete

You now have three documents that carry the full story: the pitch, the risk profile, and the outcome - with the identifying details removed. This is what The Debrief requires from a contributor: the substance but not the identity.

If you’ve got an exited deal worth dissecting, or questions or comments about this guide, you can contact me at hello@netzeroisawin.com.

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This is Heuristics - where assumptions are called out and broken down so that you can invest with greater clarity.

Confidential is used a lot in syndicated investments, but what does it mean for LPs?

What is “Confidential”?

It’s complicated: subscription agreements are unique, and rights and restrictions can vary widely regarding confidentiality obligations for LPs and GPs. However, the terms of confidentiality generally apply to four areas: 1) Pre-Investment; 2) Active Investment; 3) GP Control; and 4) Disputes.

Before we cover each area in detail, keep in mind that:

subscription documents are written by the GP’s legal team to protect the GP’s interests - which are not necessarily the same as the LPs’ interests;

and generally speaking,

confidentiality covenants persist indefinitely. Even after the deal exits, confidentiality endures.

1. Pre-Investment

Before investing, LPs typically sign (or more often check a box) agreeing to basic confidentiality terms restricting the use of the offering materials, such as:

• Exclusive Use: materials are provided for the sole purpose of evaluating the deal.

• No Sharing: Copying, reproducing, or forwarding documents is prohibited.

• Return or Destroy: If you do not invest, you must return or destroy all materials.

So, oops. Sharing a new offering with your friend is not allowed. And you’d better track down and delete all those old pitch decks…

2. Active Investment

As an LP in a deal, you have an obligation to exercise discretion such as:

• Non-Disclosure: You cannot share non-public fund details with outsiders, except for professional advisors with a “need to know” such as your CPA or attorney.

• No Market Trading of Material Non-Public Information (MNPI): Investor updates often contain MNPI; using this data to trade securities or tip off others is prohibited.

• No Publicity: Issuing press releases or speaking to reporting publications about deal terms is not permitted.

OK, so…

Sharing with your attorney and CPA: OK. Everyone else: not OK.

Insider trading: not OK.

Sharing with a publication: not OK.

3. GP Control

The GP also has confidentiality rights and responsibilities such as:

• Information Withholding: The Manager can choose to withhold data to protect business interests.

• Reduced Reporting: Management may restrict disclosures during sensitive periods (e.g., distressed-asset workouts or to mitigate insider leaks).

• Privacy: Contact lists are generally kept confidential to protect investor privacy.

Basically, the GP can choose what is and is not shared “to protect business interests” - which could become a very big umbrella that leaves LPs in the dark.

4. Disputes

These terms outline what happens during a dispute, and may include:

• Secrecy in Disputes: The existence, proceedings, and outcomes of any internal arbitration or litigation must remain entirely confidential.

• Subpoena Mandate: If subpoenaed, you must immediately notify the Manager before releasing data.

If you get into any sort of dispute, the details of that mess stay private.

In Practice

So, what does all this confidentiality legalese mean in practical terms? Again, I am NOT a lawyer and this is my interpretation and opinion.

• No sharing with anyone outside of the deal with a few exceptions for legal or business purposes.

• The term never expires. Even if the deal or the GP no longer exists, confidentiality obligations never sunset - they can live on in perpetuity.

But what information is considered confidential?

Are LPs expected to behave like CIA special agents, never uttering a word about anything to anyone?

Clearly, that is not realistic.

Under normal circumstances, an LP has a few practical ways to determine what can never be shared, and what is acceptable to share.

Never Share

Outside of your “need to know” contacts, you should never share:

• Raw documents.

• Granular-level investment details.

Acceptable to Share

Again, review your documents for specifics but the following generally apply:

Anything already in the public domain (that you didn’t place there).

• Acceptable: if Google can find it (such as court filings or information in a publication), you can share it.

• Not Acceptable: Documents, quotes, or communications sent to LPs by the GP or other LPs.

Generic opinions about GP performance without deal-level specifics.

• Acceptable: “The GP was timely and transparent with communications” (a personal opinion that does not reveal deal specifics).

• Not Acceptable: “The GP’s negotiated debt with XYZ Bank was a terrible decision” (an opinion coupled with granular-leve deal specifics).

• Not Acceptable: “The GP is a fraudulent crook” (unless the GP actually committed fraud, this is slander and is not acceptable).

• Acceptable: “I believe the GP is (un)trustworthy” (an opinion).

Generic non-specific and unidentifiable deal performance.

• Acceptable: “my best investment to date was a deal that performed according to plan despite requiring a capital call” (generalized performance that does not identify the deal specifics).

• Not Acceptable: “MakeMeRich.com’s Fantasy Lane project located on Sunshire Road in Miami promised to deliver a 29.5% IRR using preferred financing from MoneyBags Bank, but the principal, Mr. BigShot, had a heart attack and the deal went into foreclosure and all capital was lost - here’s the link to the last investor meeting notes” (divulgence of granular-level deal specifics).

• Not Acceptable: “GP based in Arizona (whose name rhymes with Fast) did a deal in Dallas for a 368-unit ground-up development that started in April 2022” (divulgence of information that could be triangulated to identify the deal and/or GP mentioned).

Points to Consider

When thinking about confidentiality, an LP should consider:

1. What are my obligations? Review your documents to fully understand what you have agreed to.

2. What do I want to share? If you’re discussing general terms with trusted peers, that’s probably OK. If you want to comb over a pitch deck, that could be out of bounds.

3. Why am I sharing? If you’re looking to slander or otherwise lay waste to a GP as an emotional outlet, it’s probably best to find another way to vent. If you’re looking to share meaningful, objective information that could be OK but is certainly treading the line if it extends beyond opinion.

4. What will the receiver do with the information? This is potentially the most important point since the intent of confidentiality is to keep important information from falling into the wrong hands. While sharing with one’s investment professionals is permitted, sharing with trusted contacts (while legally not permissible) can be a benign act.

5. Does anyone care? The prevailing norm is for GPs to ignore and/or generally accept when information is shared for legitimate and benign purposes.

Option C: All of the Above

What if you want Option A: to adhere to your confidentiality agreements AND Option B: to have detailed conversations about a deal?

AI can help provide an Option C: anonymization.

Stay tuned for my AI guide to providing anonymized deal information.

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Your Call

We all knowingly or unknowingly violate rules. Like driving 60 in a 55 zone, for example. You may or may not realize you are exceeding the speed limit. You may or may not choose to continue to do so. Maybe you didn’t see the sign. Maybe you’re late for an appointment. However, should you be caught, the law is not on your side. Ignorance or justification does not negate one’s obligations.

• Will the law be enforced? Maybe.

• Is it enforceable? Yes.

• Are you being sensible or reckless? Depends.

Given the long and complex nature of subscription documents, it is unlikely that many LPs are aware of confidentiality other than the prevailing social norm of their environment. And GPs don’t seem to care about enforcing violations.

But choosing to not enforce does not mean unenforceable.

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This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of actual real estate syndication investments to better understand risk and invest accordingly.

It’s summer vacation season and there are many new faces around here (welcome new subscribers!), so I’m rerunning the first Debrief I posted back in March of this year.

Which seems especially timely as more distressed deals move to lost deals.

Keep reading to see how this apparent oxymoron is a reality that may be waiting for you in your next quarterly report.

Plus: I have updated the post to include the Risk Radar to better illustrate the risk points presented in the offering materials.

To respect the confidentiality of private investment vehicles, all names, specific locations, and exact proprietary figures have been anonymized or rounded.

The analysis provided is for educational purposes to illustrate market mechanics and risk management.

The Plan

A standard value-add play driven by renovating and adding amenities to a dated multi-family community. A time-tested formula to lift rent and grow equity. Acquired in 2022 with a three- to five-year hold period.

I plotted the offering material metrics on the Risk Radar and found that nine of the 17 points are in the highest-risk/most aggressive position for pro forma assumptions. This means that a majority of the business plan metrics assume the market will continue at peak conditions through the three- to five-year hold period. Let’s see how reality played out against the plan…

The Reality

Where things deviated from the plan:

Debt burden increased 60%.

Approximately six months after closing, the effective interest rate hit its cap of 6% - quickly exceeding the NOI break-even interest rate of 4% and resulting in a DSCR of less than 1x. The project was poised to bleed cash.

Gross income stalled.

Gross income initially exceeded expectations through 2023. However, by mid-2024, rents softened, increasing expenses necessitated revised and abandoned renovation plans, and gross rental income growth stalled by late 2024 and never recovered.

Operational expenses grew.

Insurance costs rose more than 20%, and an increase in crime required hiring private security.

Market value decreased.

Increasing interest rates tend to drive higher cap rates and an associated reduced market value. Despite achieving a 35% increase in NOI, the 6.2% market cap rate depressed the value of the property dramatically to $15M less than its initial capitalization.

If my work has been worth your time, please subscribe.

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The Result

NOI up 35% + Debt payments up 60% = 100% loss of equity

How it happened:

In late 2022 - just months after closing - the heavy debt burden was communicated by the GP.

Through 2023 and 2024, the GP successfully executed the value-add plan and took meaningful steps toward improving NOI. However this required several capital calls and taking on additional rescue capital. These capital infusions subordinated common equity under multiple layers of senior capital and put common equity recovery at greater risk.

By early 2025, the prolonged severity of the debt burden, depressed market valuation, increasing lender pressure, and no viable exit strategy left the deal underwater.

Out of options and under pressure from the lender, the GP attempted to sell the property in early 2025 for slightly more than the senior loan balance. This sale failed.

Failure was recognized toward the end of 2025, when the GP reported a negotiated transfer of the asset to the lender for a value below the total debt, thus resulting in a complete loss of equity.

Risks and Reality

The near-zero allowance for deviation from the plan made the project high-risk. The reserves and break-even points for nearly every metric were razor-thin. Although not explicitly stated in the pitch deck, sufficient material was provided for investors to assess that the proposed plan left little room for error.

Assumptions that Failed

The GP successfully implemented the plan but the market did not go according to plan. The rapid interest-rate increase created a downward spiral of suffocating debt, depressed valuation, and unachievable lender requirements. Assuming peak market conditions would persist across debt, demand, and exit valuation led to the project’s demise.

• The debt burden increased by 60%, which resulted in the asset violating DSCR requirements.

• Higher interest rates resulted in higher cap rates, which depressed the underlying asset value that should have been achieved from the impressive 35% increase in NOI.

• The final (and third) capital call (and fifth injection of capital) to fund a possible cost reduction strategy failed. After repeated capital calls, LPs declined to provide additional funds.

• Common equity was irretrievably lost regardless of whether the deal ultimately exited successfully. It had already fallen behind expensive layers of preferred equity and rescue capital — layers that would only have grown had the project needed further infusions to satisfy the lender.

Conclusion

If this project had been launched in 2018 when the market cycles were moving in a positive direction, there is a high likelihood that investors would have been rewarded. Unfortunately, this deal started at the absolute worst time: when investor and bank caution were low and accepting of thin margins, and the risk of a market correction was high. In 2022, the crescendo of syndicated real estate investing had reached its peak. The mob mentality was strong, track records were limited to market growth periods, and investor tools were few. But the signs were visible: the Risk Radar makes it plain that most of this deal’s business plan was based on market extremes not only at capitalization but persisting indefinitely.

Of course, all markets correct and every flawed, optimistic assumption erodes returns. In this case, the pressure of high debt combined with diminished renter demand and depressed asset values made it mathematically impossible for common equity to survive.

Whether you hold a similarly structured deal or are evaluating one, I hope this retrospective analysis helps you understand risk and the mathematical mechanics that can erase your capital.

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This is Decoded: AI tools for getting straight answers from complex investment documents.

Posts one through five of this series have illustrated how small changes can have large effects on cashflow and investor returns by asking “what happens if X variable moves by Y?”. The fictional Fantasy Acres project profiled in this series illustrated how the end point of one market cycle (the peak and collapse of 2020-2026) brought so many deals to the point of not only stress but - in many cases - complete failure.

Now we’re going to switch from backward to forward looking and test a deal’s durability: at what point does the deal go to zero, and how far is that point from the historical average and end points?

Zero applies to:

1. Free cashflow: the point at which operating income no longer covers debt service and operating expenses. The property is now consuming cash rather than producing it.

2. Investor return: the point at which the LP equity multiple falls to 1.0x: full return of (but no return on) capital.

3. DSCR covenant breach: the point at which the asset’s cash flow is in violation of the loan terms, which could require a debt buy-down and infusion of additional capital.

Finding the breaking point of just one variable (let alone a handful of the most critical variables), and then comparing those to the historical context is a daunting task if done manually. Here AI handles the math and market context - leaving the human brain more time and space for decision-making.

The following guide takes you through the process step by step.

Let’s go!

Part 1: Open Your AI Tools

We’re going to use two tools for this job:

1. NotebookLM for the deal documents.

2. Gemini/ChatGPT/Claude for market data.

Step 1

Open two tabs in your browser: one for NotebookLM and one for Gemini/ChatGPT/Claude so you can easily toggle between the two.

Step 2

Open a new notebook in NotebookLM and upload the source materials: the pro forma, PPM, OM, and any other offering materials provided by the GP.

Part 2: Confirm the Baseline

Ask NotebookLM to confirm that the baseline numbers are provided in the source materials. Use the following.

Prompt for confirming baseline numbers

Do not proceed until this comes back clean. If the model can’t cite a figure, it may not exist in the uploaded documents, or it may be buried in an exhibit you didn’t upload. If it’s not in the offering materials, ask the GP to provide it. It’s best to have all the figures provided before proceeding in order to receive accurate and complete responses.

Part 3: Solve for Zero

Here we will find how far each variable can go before it breaks. It includes the most relevant common stress points for a single-asset real estate deal:

• Interest rate

• Exit cap rate

• Rent growth

• Vacancy

• Operating expense growth

If you want to test other variables, use the prompt below as a template. Substitute the variable within the bracketed text and rerun.

Prompt for Solving for Zero

Part 4: Gather Historical Market Data

In order to understand the zero points identified in the previous step, we need to gather the historical high, low, and average values. This will require a bit of back and forth before getting the data we need.

Step 1

Go to your preferred AI assistant (Gemini/ChatGPT/Claude). Use the following prompt to gather the pertinent market data.

Prompt for Historical Market Data

Step 2

Here we toggle between the notebook and the AI assistant:

• AI Assistant → notebook: copy and paste the deal details requested by the AI assistant into the notebook.

• Notebook → AI assistant: copy and paste the deal details from the notebook back into the AI assistant.

• AI assistant: specify your preferred lookback period (I suggest using 30 or more years).

• AI assistant: send the request.

• AI assistant: save the results as a document that can be uploaded to the notebook.

• AI assistant: close the AI assistant; we will only use NotebookLM from here.

• Notebook: go back to the notebook and upload the market data document to the source documents.

Part 5: Market Cycle Breaking Points

Step 1

Ask NotebookLM to compare the market context against the breaking points identified previously. But, we are not going to do this individually since some variables (such as interest and cap rates) move in tandem. So, we must stress test the variables as they are most likely to be experienced, not just in isolation.

Prompt to run market downturn scenario analysis

Prompt for infographic

NotebookLM has now provided an effective market-correction sensitivity analysis: you can see exactly where each variable breaks and how investor returns are impacted based on an asset-specific, historically-accurate stress test.

Subscribe now

Step 2 (optional)

If you’d like to see the breaking point for each variable as a stand-alone item, those prompts are provided here.

Prompt to compare individual breaking points with historical data

Prompt to create an infographic

Part 6: Verify the Model’s Work

AI makes mistakes and treats all information with equal weight regardless of source quality. Before relying on any output:

• Ask for the underlying calculation on at least one threshold, not just the answer.

• If a number looks off, ask the model to show its work rather than accepting a revised number on faith.

• Verify investor returns against the waterfall mechanics.

Conclusion of the Pro Forma Series

The pitch deck and the pro forma are not predictions. They’re marketing materials designed to showcase a story that assumes nothing goes wrong. High returns are made bold while risks may be minimized or not disclosed at all.

When reviewing a pitch deck, ask:

• Where and how can this break?

• What assumptions are flawed, optimistic, or missing altogether?

• What conditions would turn this deal upside down, and how likely is that to happen?

Having completed this series, you now know that small changes to inputs can have outsized effects on the output - your investment. More importantly, you don't have to become an LP to test the GP’s projections against market realities; the tools provided here let you test the deal yourself - before you invest. So net zero is more likely to remain in the hypothetical than your portfolio.

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This is Heuristics - where assumptions are called out and broken down so that you can decide with greater clarity.

This post will stress test a market correction: when interest and cap rates increase, and when supply outpaces demand.

Before then, let’s revisit the rosy future predicted by the pro forma for the fictional project, Fantasy Acres - a value-add multifamily project with a five-year hold, and the historical market data shown below.

Projected Returns

Pro Forma

50-year Market Measures

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Testing the Assumptions

Interest and Cap Rates

In 2021, commercial real estate had been borrowing at less than 4% for the better part of 13 years - a period that was unusual not only for its length and stability, but for holding, without precedent, at the historic low.

In 2022, the run ended when the Fed raised rates steadily for 16 months from near zero percent to 5.5% - a pattern more aligned with norms than anomalies.

Back at Fantasy Acres, the GP and LPs were in shock - they had never seen anything like this - having built their business models on the previous ten years’ liberal lending terms with predictably low interest and cap rates - which produced high purchase values and all but guaranteed equivalently high sale prices. Anything more than a half a percentage point deviation was not considered possible. Reversion to the mean relegated to economic theory, not market reality. This was the 2020s, not the 1980s.

But, revert it did.

The effects of interest rates on Fantasy Acres are covered in no. 3 of this series, which demonstrated how an interest rate increase from 3% to 8% consumed all cash flow and reserves by the end of Y2 that necessitated a capital infusion in Y3, but exited in Y5 with a below-expected IRR of 17%.

In no. 4 of this series, the increase in the cap rate from the projected 5.1% to 6% realized at disposition, reduced investor returns by 30% to a 16% IRR.

But these are mathematical experiments; the returns did not account for the reality of cap rate changes that follow interest rate changes. Here is how rate changes affect cash flow and investor returns when accounted for more accurately: in tandem.

How Interest and Cap Rate Increases Destroy Value

Since we’re using actual (not hypothetical) conditions we will retain the previously-modeled interest rate change of 3% increasing to 8%, and assume that the prevailing exit cap rate in Y5 (2026) is 6.5% (not the previously modeled 6.0%). Under these conditions, much remains the same as before:

• Increasing debt load consumes cash flow and reserves by Y2.

• Rescue capital is provided in Y3 by the GP (structured as a non-interest-bearing, non-dilutive loan).

• Cash flow turns positive in Y3 as the value-add strategy performs to plan.

• Investors receive zero distributions from free cash flow Y1-Y5; all returns are provided via sale proceeds at the exit.

When adding the expanded cap rate at exit to the equation, investor returns are reduced by 54% to 85% from pro forma projections, with the actual return coming close to the risk-free rate on a very much not risk-free investment.

Effect on cash flow and return metrics

To understand how this happened, let’s look at the cash flow.

Note:

• The larger cap rate reduced the sale value (within the red circle) by 22% and $6.3M from the projected $29.1M to the realized $22.8M.

• The addition of the interest-free rescue capital (highlighted in yellow), saved the project from ruin in Y3, but further reduced the project’s profitability.

• The project’s profits (within the blue circle) are nearly completely erased from the forecasted $6.4M to little more than $240,000.

• The final distributions in Y5 (within the green circle) fulfill ~$1.2M of the $1.7M cumulative preferred equity due to LPs.

• Funds never reach the second tier of the waterfall agreement - the promote split. All parties were returned their investment, but LPs received a small return on their investment, and the GP received no return.

Supply and Demand

Supply in excess of demand drove strong rental increase rates across many regions - until the tables turned and rental increases turned into decreases and concessions.

The reasons for the dramatic rise and fall of rental rates are multifaceted but the following graphic helps to put the market drama into perspective.

Again, the GP at Fantasy Acres was facing a reality it had never seen before: rent decreases. The pro forma assumed a steady 5% YoY increase in rents with a 10% vacancy rate - safe assumptions that worked for the previous ten years.

2021 was a dream: 3% interest rates, zero vacancy, and rents increasing in excess of 10%. But 2023 was a nightmare: 8% interest and 10% vacancy rates, rent reductions, and concessions all coalesced to dramatically erode NOI. Multiply the reduced NOI by the expanded cap rate and the exit returns quickly go into the red.

Effect on cash flow and return metrics

Let’s look at the cash flow to see what went wrong.

Note:

• The reduced NOI (resulting from lower rents and higher vacancy) multiplied by the larger cap (driven by increased interest rates and market uncertainty), resulted in a much lower final sale price (shown in the red square).

• Despite the reduced NOI, Fantasy Acres was cash flow positive by Y3 and continued as such through Y5: the value-add lifted NOI to a level sufficient to service the debt (as shown by the green boxes).

• The DSCR never reached the required 1.2x. By Y5 the lender was no longer willing to extend and pretend. Like many other deals, the GP was unable to float additional rescue capital and the LPs weren’t interested in additional capital calls. As a result, the lender forced a sale in Y5 under terrible market conditions.

• Sale proceeds were nearly $3M less than the total capitalization - sufficient to pay off the debt principal, but LPs lost ~$2.1M of their $4.9M; the GP lost ~$0.4M of equity plus its entire $0.5M rescue loan (as shown in the red text).

• A historically accurate reenactment of the full array of market stressors experienced from 2023 through 2026 was not accounted for in this example. Increasing material and labor costs, insurance premium hikes, losses to extreme weather, the high interest rate of rescue capital, and a higher cap rate typical of a forced sale were all conveniently ignored. At this point, it should be clear that the addition of any one of these items could easily wipe out all equity and potentially part of the debt principal.

Conclusion for Today

The dual market stress of higher interest and cap rates coupled with reduced rental income has vaporized millions in equity capital across the US. Add in the plethora of other challenges to profitability, and asset values go underwater and the bank starts taking losses too.

Some say “interest rates rose at an unprecedented rate to a historic high”.

Others blame “unforeseeable rental rate softening”.

But is “unprecedented”, “unforeseeable”, and “historic” an accurate depiction of a normal market correction?

Whatever the reason(s) and no matter how surprising or predictable they may be, the flaw is in the assumption that the market will remain stable and favorable. This series shows how those flawed assumptions resulted not only in distressed assets but lost capital at every layer of the capital stack.

Next in the Series

Now that the mechanics of the pro forma, its assumptions, and market variation are clear, the last post in this series will show how AI can take a pitch deck and provide two crucial pieces of information:

1. the risk profile (as plotted on the Risk Radar), and

2. sensitivity analysis (to show where the pro forma breaks).

Don’t miss the next part of this series. Subscribe for delivery to your inbox.

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This is Decoded: AI tools for getting straight answers from complex investment documents.

The Risk Radar paired with your preferred AI bot and NotebookLM makes comprehensive deal analysis more attainable and easier to understand.

This guide walks through using AI to do two jobs: gather the historical high, low, and average benchmarks to populate the rings of the Radar, and pull a deal’s pro forma assumptions out of the offering documents to plot against the market data.

Let’s go!

Part 1: Prepare the Risk Radar

Step 1: Download the Risk Radar

Download the Risk Radar here.

Step 2: Prepare the Radar for Plotting

You can print the Radar and write the relevant data onto it, mark it up as a PDF, or insert into a Word document to do the same. The point is to put the Radar into a format that works for you.

Step 3: Understand the Spokes

For each spoke of the Radar you need two things:

1. The market’s high, low, and average (as indicated by the spokes bisected by the black dashed line) for the asset type and submarket you are reviewing, and

2. The deal’s pro forma numbers.

To understand the mechanics of the Radar, review the following:

• Read the Introduction

• Read the Radar in Practice

• Check out the podcast:

  • Watch on YouTube

  • Listen

Important: The Risk Radar is structured to review a typical equity position in a single-asset deal.

It is NOT structured for debt or other funds that hold multiple assets and use a different business model.

Part 2: Plot the Historical Data

Using AI to Gather Historical Data

This is what makes the Risk Radar powerful - a visual representation of where the deal metrics fall in relation to historical metrics. But we’re going to have to use both NotebookLM and your preferred AI bot to get it done right: open one tab for each in your browser so you can easily navigate between the two tabs.

A Note About NotebookLM

Before diving into the details, it’s important to understand how NotebookLM operates.

• Source data must be provided. NotebookLM does not source data independently; all information sources must be uploaded and marked as a source for it to be used to answer a prompt.

• All data is weighted equally. NotebookLM cannot differentiate between the validity of a Reddit comment and a CoStar report. To compensate for this, we will only provide data from the GP in this step to avoid results muddled by potentially conflicting narratives presented in the GP’s materials and industry data reports.

• Prompt length is limited. NotebookLM does not use project rules, only prompts - which cannot be too lengthy. To overcome this, the prompts provided here break the task down into sections.

• Confidentiality is only maintained if feedback is not provided. If you’re concerned about confidentiality, do NOT click the thumbs up/down button.

Step 1: Populate a new notebook in NotebookLM

Open NotebookLM and create a new notebook.

Pro Tip:

1. Name the notebook with the sponsor and deal so you can easily find it later.

2. This notebook can also be leveraged to understand deal performance, as outlined in Tackling the Paper Mountain.

Step 2: Gather and Upload the Documents

Gather everything the GP gave you and upload to the notebook. This may include:

• The pitch deck and any marketing materials.

• Complete package of subscription documents (OM, PPM, etc.).

• Every quarterly report, financial statement, and investor update.

• Any other relevant data - NotebookLM can read a wide variety of file types.

If you are evaluating a deal you have not subscribed to yet, the offering materials alone are enough to plot the Radar - that is the whole point of plotting it before investing, but the Risk Radar is also helpful to clarify the risk points of a current investment you may not have fully understood at the time you made the investment. Both uses are valid and offer learning opportunities.

Step 3: Check Legibility

It’s important to ensure NotebookLM can read what was provided. The following prompt provides a legibility check, but if a file is really corrupt it may come back as “unreadable” or may slip through without NotebookLM notifying you of its inability to read the information in the file.

AI is a tool - not a replacement for your own eyes and brain. Always verify.

You should get a response that looks like this:

LEGIBILITY PROMPT:

Step 4: Start a new chat in Gemini/Claude/ChatGPT

Use the AI bot of your choice, and provide the following prompt.

PROMPT FOR HISTORICAL DATA:

The bot will ask for the project details with a response that should look like this. Highlight the relevant text (as shown below in gray), then copy and paste into NotebookLM.

Step 5: Gathering the Details

Copy and paste the information requested from your AI bot into the notebook. NotebookLM should give a response that looks like this, which you can now copy/paste back into your bot:

The bot will then ask one last question: your preferred time horizon. I use 50 years; others prefer 20 or 30 years. It’s your choice.

Step 6: Plot the Radar with the Historical Data

The AI bot will provide all the historical data needed to plot the rings of the Radar in a table that should look like the image below. You can now follow the table and populate the inner and outer rings of the Radar in clockwise order. In doing so, you will also gain a mental understanding of the historic and current market conditions for the deal.

A note on data

The best sources of market data - CoStar CCRSI, Green Street CPPI, full Preqin and NCREIF data - sit behind paywalls. When precision matters for a decision, consider paying for the CoStar or Green Street data.

Part 3: Plot the Deal

Step 1: Extract the numerical data

We are finally at the point where you can start putting dots on the Radar!

Go back to the notebook you set up in Part 2.

We will prompt for each section of the Radar - one at a time. You should get a response that looks something like the image below, which will allow you to plot a point for each spoke onto the Radar against the market data completed in the first step.

A few notes on reading the result:

• “NOT PROVIDED” is a point. A deal that hides its basic financials is showing you a high-risk position. Plot a missing fundamental at the outer ring.

• Ask for clarification as needed. Sometimes the data provided is not exactly what is asked. Ask follow-up questions to get what you need. And if all else fails, ask for the pages containing the information you are seeking, and look at the source document itself.

  ---

The Prompts

FIXED AT CLOSING:

MARKET DRIVEN:

SPONSOR DRIVEN:

Step 2: GP Data Points 2.0

The three GP spokes (years as a team, expertise, roaches) call for deeper research than just what the GP wants you to see in the offering materials, so we will take some additional steps.

Public Records Search

Use AI and/or Google to complete a public records search of the principals and the involved entities.

Navigate back to the bot used in Step 2, and use the following prompt.

GP BACKGROUND PROMPT FOR CLAUDE/GEMINI/CHATGPT:

If you don’t have access to the chat in Part 2 or the bot asks for the project details, use the following to easily get all the deal specs: go back to your notebook and ask the following. Then copy the response and send it back to your other AI bot to complete the search.

DEAL SPECS PROMPT:

Online Libraries

Find online tools for screening GPs in the Resources Section.

Part 4: Radar Complete

You should now have a complete picture for each spoke on the Radar that looks something like the image below. I used two different colored stars: the blue is for information provided by the pitch deck; the yellow marks information I had to extrapolate because it wasn't directly provided, which I am noting as a warning sign about the quality and transparency of the offering materials.

Why not use AI to plot the Radar?

I could provide a prompt that would fully automate this entire process. However, by manually noting the most important data - the historical market points and the deal’s assumptions - the mind is forced to engage with and internalize it.

This is a tool to facilitate critical thinking and analysis - not a replacement for it.

Keep in Mind

The Risk Radar is just that - a measurement of relative risk that is directional. Risk is not necessarily good or bad. The quality of the deal and its suitability against your objectives are not captured here - those are personal decisions you must evaluate on your own.

Try it and Share!

Does it work? Tell me if the Risk Radar and the AI instructions work, if they break, and ideas for improvement.

Educational and informational only. Not investment, legal, tax, or financial advice. Always verify AI output against primary sources and consult your own licensed professionals before making any investment decision.

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This is Decoded: AI tools for getting straight answers from complex investment documents.

The Risk Radar paired with your preferred AI bot and NotebookLM makes comprehensive deal analysis more attainable and easier to understand.

This guide walks through using AI to do two jobs: gather the historical high, low, and average benchmarks to populate the rings of the Radar, and pull a deal’s pro forma assumptions out of the offering documents to plot against the market data.

Let’s go!

Part 1: Prepare the Risk Radar

Step 1: Download the Risk Radar

Download the Risk Radar here.

Step 2: Prepare the Radar for Plotting

You can print the Radar and write the relevant data onto it, mark it up as a PDF, or insert into a Word document to do the same. The point is to put the Radar into a format that works for you.

Step 3: Understand the Spokes

For each spoke of the Radar you need two things:

1. The market’s high, low, and average (as indicated by the spokes bisected by the black dashed line) for the asset type and submarket you are reviewing, and

2. The deal’s pro forma numbers.

To understand the mechanics of the Radar, review the following:

• Read the Introduction

• Read the Radar in Practice

• Check out the podcast:

  • Watch on YouTube

  • Listen

Important: The Risk Radar is structured to review a typical equity position in a single-asset deal.

It is NOT structured for debt or other funds that hold multiple assets and use a different business model.

Part 2: Plot the Historical Data

Using AI to Gather Historical Data

This is what makes the Risk Radar powerful - a visual representation of where the deal metrics fall in relation to historical metrics. But we’re going to have to use both NotebookLM and your preferred AI bot to get it done right: open one tab for each in your browser so you can easily navigate between the two tabs.

A Note About NotebookLM

Before diving into the details, it’s important to understand how NotebookLM operates.

• Source data must be provided. NotebookLM does not source data independently; all information sources must be uploaded and marked as a source for it to be used to answer a prompt.

• All data is weighted equally. NotebookLM cannot differentiate between the validity of a Reddit comment and a CoStar report. To compensate for this, we will only provide data from the GP in this step to avoid results muddled by potentially conflicting narratives presented in the GP’s materials and industry data reports.

• Prompt length is limited. NotebookLM does not use project rules, only prompts - which cannot be too lengthy. To overcome this, the prompts provided here break the task down into sections.

• Confidentiality is only maintained if feedback is not provided. If you’re concerned about confidentiality, do NOT click the thumbs up/down button.

Step 1: Populate a new notebook in NotebookLM

Open NotebookLM and create a new notebook.

Pro Tip:

1. Name the notebook with the sponsor and deal so you can easily find it later.

2. This notebook can also be leveraged to understand deal performance, as outlined in Tackling the Paper Mountain.

Step 2: Gather and Upload the Documents

Gather everything the GP gave you and upload to the notebook. This may include:

• The pitch deck and any marketing materials.

• Complete package of subscription documents (OM, PPM, etc.).

• Every quarterly report, financial statement, and investor update.

• Any other relevant data - NotebookLM can read a wide variety of file types.

If you are evaluating a deal you have not subscribed to yet, the offering materials alone are enough to plot the Radar - that is the whole point of plotting it before investing, but the Risk Radar is also helpful to clarify the risk points of a current investment you may not have fully understood at the time you made the investment. Both uses are valid and offer learning opportunities.

Step 3: Check Legibility

It’s important to ensure NotebookLM can read what was provided. The following prompt provides a legibility check, but if a file is really corrupt it may come back as “unreadable” or may slip through without NotebookLM notifying you of its inability to read the information in the file.

AI is a tool - not a replacement for your own eyes and brain. Always verify.

You should get a response that looks like this:

LEGIBILITY PROMPT:

Step 4: Start a new chat in Gemini/Claude/ChatGPT

Use the AI bot of your choice, and provide the following prompt.

PROMPT FOR HISTORICAL DATA:

The bot will ask for the project details with a response that should look like this. Highlight the relevant text (as shown below in gray), then copy and paste into NotebookLM.

Step 5: Gathering the Details

Copy and paste the information requested from your AI bot into the notebook. NotebookLM should give a response that looks like this, which you can now copy/paste back into your bot:

The bot will then ask one last question: your preferred time horizon. I use 50 years; others prefer 20 or 30 years. It’s your choice.

Step 6: Plot the Radar with the Historical Data

The AI bot will provide all the historical data needed to plot the rings of the Radar in a table that should look like the image below. You can now follow the table and populate the inner and outer rings of the Radar in clockwise order. In doing so, you will also gain a mental understanding of the historic and current market conditions for the deal.

A note on data

The best sources of market data - CoStar CCRSI, Green Street CPPI, full Preqin and NCREIF data - sit behind paywalls. When precision matters for a decision, consider paying for the CoStar or Green Street data.

Part 3: Plot the Deal

Step 1: Extract the numerical data

We are finally at the point where you can start putting dots on the Radar!

Go back to the notebook you set up in Part 2.

We will prompt for each section of the Radar - one at a time. You should get a response that looks something like the image below, which will allow you to plot a point for each spoke onto the Radar against the market data completed in the first step.

A few notes on reading the result:

• “NOT PROVIDED” is a point. A deal that hides its basic financials is showing you a high-risk position. Plot a missing fundamental at the outer ring.

• Ask for clarification as needed. Sometimes the data provided is not exactly what is asked. Ask follow-up questions to get what you need. And if all else fails, ask for the pages containing the information you are seeking, and look at the source document itself.

  ---

The Prompts

FIXED AT CLOSING:

MARKET DRIVEN:

SPONSOR DRIVEN:

Step 2: GP Data Points 2.0

The three GP spokes (years as a team, expertise, roaches) call for deeper research than just what the GP wants you to see in the offering materials, so we will take some additional steps.

Public Records Search

Use AI and/or Google to complete a public records search of the principals and the involved entities.

Navigate back to the bot used in Step 2, and use the following prompt.

NOTE: a Google search is the minimum GP screening. Always perform comprehensive GP screening yourself. The online resources page provides links to multiple platforms for doing so.

GP BACKGROUND PROMPT FOR CLAUDE/GEMINI/CHATGPT:

If you don’t have access to the chat in Part 2 or the bot asks for the project details, use the following to easily get all the deal specs: go back to your notebook and ask the following. Then copy the response and send it back to your other AI bot to complete the search.

DEAL SPECS PROMPT:

Online Libraries

Find online tools for screening GPs in the Resources Section.

Part 4: Radar Complete

You should now have a complete picture for each spoke on the Radar that looks something like the image below. I used two different colored stars: the blue is for information provided by the pitch deck; the yellow marks information I had to extrapolate because it wasn't directly provided, which I am noting as a warning sign about the quality and transparency of the offering materials.

Why not use AI to plot the Radar?

I could provide a prompt that would fully automate this entire process. However, by manually noting the most important data - the historical market points and the deal’s assumptions - the mind is forced to engage with and internalize it.

This is a tool to facilitate critical thinking and analysis - not a replacement for it.

Keep in Mind

The Risk Radar is just that - a measurement of relative risk that is directional. Risk is not necessarily good or bad. The quality of the deal and its suitability against your objectives are not captured here - those are personal decisions you must evaluate on your own.

Try it and Share!

Does it work? Tell me if the Risk Radar and the AI instructions work, if they break, and ideas for improvement.

Educational and informational only. Not investment, legal, tax, or financial advice. Always verify AI output against primary sources and consult your own licensed professionals before making any investment decision.

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This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of real deals to better understand risk and invest accordingly.

The Debrief is dedicated to learning how to assess risk. Syndicated real estate is data-heavy and complex. Accurately and comprehensively assessing risk in a deal takes time and skill.

In order to more easily visualize risk, I created the Risk Radar.

How it Works

I have identified 17 potential breaking points and plotted each as a spoke on the “radar” against historic market metrics and industry norms. The outer ring represents the least favorable/highest risk position for a deal. The innermost ring represents the most favorable/lowest risk position. The closer to the center any given variable lands, the lower the risk. Note that risk is relative: low versus high. There is no risk-free position. Any one of the spokes has the potential to single-handedly lead to a total loss.

For a thorough explanation of how the Risk Radar works, see the introductory Debrief.

In Practice

By plotting pro forma data against long-term high, low, and average values for key metrics and industry norms, a risk profile quickly emerges.

To use the Risk Radar, follow three steps:

1. Gather the pro forma data for each section shown on the Radar.

2. Gather contextual data for high, low, and average numbers for each section.

3. Plot the pro forma number on the radar against the high and low number.

Subscribe to get the upcoming Decoded guide to completing step #2 in the coming weeks.

Subscribe now

Does it Work?

I have plotted the data provided in the offering materials on the Risk Radar (indicated by the blue stars) for relevant deals previously covered by The Debrief to see how well the Radar revealed the risk profile and possible breaking points.

Multi-Family

How a Value-Add Became a Total Loss

A 35% increase in NOI wasn’t enough to avoid a 100% loss of investor capital.

The Risk Radar clearly shows that the asset was purchased at a very low cap rate and that every debt metric is in the red zone upon closing - the stress points which ultimately led to an unrecoverable death spiral of debt, no potential for a sale price in excess of the debt due, and total loss of this deal despite the GP exceeding expectations on every other front.

Crossing the Gap

A joint venture to deliver a ground-up multi-family asset from capitalization through construction, stabilization, and disposition within 36 months.

This deal is a good example of a shrewd business plan that was well-executed and captured a strong market environment: the project was sold well in advance of plan - thus outpacing the impacts of the high percentage of new inventory coming to market. Although the high risk of new deliveries was noted by the radar, the GP’s quick execution and sale avoided this possible point of disaster.

However, the floating rate debt quickly depleted reserves despite the otherwise well-crafted and executed business plan. A capital call was necessary to bridge the gap between depleted reserves and a pending sale. As a result, the project was able to survive a brief period of negative cashflow to close a profitable sale and provide returns in excess of the pro forma.

Ripley

Generic value-add business plan for a multi-family apartment complex.

This deal seemed very boring and simple - a generic value-add multi-family using average rent lift metrics. Unfortunately for investors, the inexperience of the GP was the weak point: the offering materials lacked meaningful information, LP interests were grossly mismanaged at multiple points, and capital was ultimately lost. This deal didn’t break on one point, but on multiple points. All of which were visible during capitalization.

Industrial

Note that all metrics are reflective of national long-term averages and industry norms for industrial real estate. The expenses as a percentage of gross income assumes a NNN structure.

Bond. James Bond-like Returns

Opportunistic value-add and expansion of a light industrial property using a well-known and routine value-add and expansion business plan.

There were several risk points that could have easily killed this deal - assuming strong demand and low debt rates would persist. Both assumptions were quickly proven to be incorrect and were threatening to potentially lead to a certain loss, but the experienced GP quickly swapped the original business plan for an alternative. By making a well-crafted capital call, the GP protected investor interests and delivered returns in excess of the pro forma.

A Real Ringer

Ground-up industrial development by a local GP with a track record that predates CDs, email, and the internet.

Similar to the previous deal, the assumption that strong demand and low debt would persist could have killed this deal. However, the GP demonstrated real skill in outpacing the softening market conditions to deliver a win while narrowly avoiding a major loss.

The Roaring 2020s…

The Sirens Song + Fantasy Acres

In The Sirens Song, 100 years of regulatory and financial changes that created a frenzy of investment in crowdfunded real estate illuminates how its ultimate collapse was unavoidable - and predictable.

To further illuminate risk, the Heuristics pro forma learning series created the Fantasy Acres project based on underwriting assumptions commonly used in 2021. The hypothetical offering information is plotted here.

Observe the number of spokes at the outer edge of the radar: cap rates, favorable market demand, and debt were all at the extreme edge of historical rates. This is what the end of a growth cycle looks like before collapsing: rampant investment coupled with metrics pushing the limits of market end points. It is not a matter of if but when the tide will turn. That turn was precipitated by the increase of interest rates in 2022, which directly impacted borrowers’ ability to make debt payments. But was compounded by softening demand that was the direct result of the flood of newly constructed supply. Both factors expanded cap rates, making exit via a sale and repayment of capital a mathematical impossibility.

Any one of these factors has the ability to compromise returns. None should have been surprising. Data clearly indicated assumptions at the extreme edge of the historical range. A reversion was imminent and could have been expected. By late 2021 the Fed was signaling rate increases; the hikes began in March 2022 and ran through mid-2023. By the end of H1 2022, institutional capital flows into real estate dropped from its 2021 peak. Experienced players were aware of and heeding the warning signs. Inexperienced retail investors, euphoric with newly granted access to the “investments of the 1%,” were blind to the risks and continued to pour funds into syndications through 2022 - well after interest rate risk was clear. And ultimately paid the price of ignorance.

Risk ≠ Doom

These examples demonstrate the risk profile of a deal as plotted against historical market data and industry norms, but also demonstrates that just because a deal has one or more high risk points does not mean it will fail. The story is more nuanced. GP skill and favorable market timing can offset a stress point the Radar flags; the deals above show both happening repeatedly. The Risk Radar is a tool to support risk analysis; not a replacement for your own consideration of a deal in whole.

Up Next

Next week I’ll outline how to leverage AI to source the industry data and pro forma assumptions needed to plot the Risk Radar.

Challenge the Radar

Download the Risk Radar and test it yourself: plot the data of the offering materials of an exited or mid-life deal. Does the Radar accurately depict stress points experienced by the deal? What was missed?

Share in the comments below.

Share

Introduction Materials

• Introduction to the Risk Radar: what it is and how it works.

• Risk Radar in Practice: case studies.

• Using AI to Plot the Risk Radar: a step-by-step guide to using AI to populate the Risk Radar.

• Check out the podcast:

  • Watch on YouTube

  • Listen

Graphics for Download

The following are broad data sets that provide 50-year averages for the entire U.S. For the most relevant data, populate the market data for the location and time period most pertinent to the deal under review.

Blank Base Radars:

• jpeg

• pdf

U.S. Multifamily 50-year averages

• jpeg

• pdf

U.S. Industrial 50-year averages

• jpeg

• pdf

U.S. Hospitality 50-year averages

• jpeg

• pdf

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AI Guide

• PDF guide with AI prompts and basic workflow.

• Detailed how-to: Using AI to Plot the Risk Radar.

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This is Heuristics - where assumptions are called out and broken down so that you can invest with greater clarity.

This post stress tests the disposition assumptions: cap rate and hold time. The pro forma and market data for the fictional project, Fantasy Acres, are shown below.

Projected Returns

Pro Forma

50-year Market Measures

Testing the Assumptions

Cap Rates

In the simplest of terms, the cap rate is the rate of return on an investment. But the cap rate incorporates many variables, is highly volatile, and is subject to many exceptions. Check out the Investopedia cap rates page to learn more.

For this discussion, we’ll focus on two of the mathematical mechanics that use cap rates:

• Cap Rate = NOI ÷ Price

  • typically provided for the acquisition/purchase price

• Market Value = NOI ÷ Cap Rate

  • typically used to calculate the disposition/sale price

Fantasy Acres Example

The Fantasy Acres pro forma is using cap rates at two important points:

Acquisition: 4.75% cap rate

• 4.75% cap rate = $780,000 (NOI) ÷ $16.4M (Price)

• These figures are known and fixed at the time of acquisition.

Disposition: 5.1% cap rate

• 5.1% cap rate = $1.5M (NOI) ÷ $29.1M (assumed sale price)

• These figures are estimates. The sale price is entirely dependent on the actual NOI and actual cap rate at the time of disposition.

To contextualize the previous numbers, consider the inputs and how accurately they may forecast returns:

The 50-year historical low cap rate is 4.7%; the average is 7.5%. The purchase price is at a 4.75% cap rate - a figure near the historical low edge. And, the sale price is anticipated at a 5.1% cap rate - an allowance of just 35 bps from the purchase cap rate and far closer to the historical low than the historical average.

The NOI is assumed to nearly double from $780,000 to $1.5M in five years. Is this estimate accurate?

The Effects of Cap Rate Expansion

Let’s assume that the projected NOI is achieved but true to the Fantasy Acres timeline, which planned a sale in 2026, cap rates expanded in tandem with interest rates and market uncertainty and are currently at about 6.0%.

By simply adjusting the assumed exit cap rate of 5.1% by 90 bps to the current market norm of 6.0%, the exit figures change as follows:

Effect on Investor Returns

A 0.9 pt expansion of the cap rate (an increase of about 18%) reduced investor returns by 23% to 40%. A small movement resulted in an outsized impact.

Effect on Return Metrics

Recall the math: Market Value = NOI ÷ Cap Rate. In this scenario, the assumed NOI at exit was realized but the cap rate expanded 0.9 pt / 18% to 6.0% - a variable that is subject to market forces and cannot be controlled. If NOI remains the same and is divided by a cap rate that is a larger number, the result (the sale price) will be smaller.

Note:

• The assumed sale price was $29.1 M = $1.5M (NOI) ÷ 5.1% (cap rate), but

• The realized sale price is $24.7M = $1.5M (NOI) ÷ 6.0% (cap rate).

• The 0.9pt / 18% cap rate expansion reduced the exit value by $4.4M or 15% - as shown by the red arrow.

• Y1-Y5 cashflow distributions were made according to plan, but the reduced exit value also reduced the profit on the sale by 61% from $6.4M to $2.5M, which then reduced:

  • the promote split to common equity investors by 53% from $5.9M to $2.8M, and

  • total return to investors by 23% to 40%- depending on the measurement method.

Hold Time

Let’s assume the GP decides to not sell in 2026 and wait for better market conditions - and all other variables stay the same: the debt terms, NOI, growth rates, etc. But the hoped-for reduced cap rate market conditions aren’t forthcoming. After 24 months of waiting, the GP decides to sell in 2028 at the same 6.0% cap rate.

Effect on Investor Returns

The returns have improved: the equity multiple increased from 2.0x to 2.8x (and outperformed the projected 2.6x), the IRR improved by 2 pt from 16% to 18% (but still fell well behind the projected 23%), and the ARR increased from 20% to 26% (but also fell behind the projected 33%). Maybe waiting was the right move?

Effect on Return Metrics

To understand how extending the timeline by 24 months actually improved investor results, let’s look at the numbers…

Note that:

• By holding all variables steady and only adjusting the timeline (and the expanded cap rate already accounted for), the NOI increases steadily year over year, which meaningfully improves the exit value (as shown by the red arrow) and free cashflow that boosted investor returns (as shown by the blue arrow). Here’s the math showing how that happened:

• The final exit value was lifted to $27.3M from $24.7M in the previous scenario since the cap rate remained the same but the NOI increased from $1.5M in Y5 to $1.65M in Y7 (a larger number divided by the same number = a larger result). The NOI increased by ~$150,000 and 10%, but provided $2.5M and an equivalent 10% increase in value.

  • $1.5M (NOI) ÷ 6.0% (cap rate) = $25M*

  • $1.65M (NOI) ÷ 6.0% (cap rate) = $27.5M*

  • * rounded figures; actual metrics are as previously stated: $24.7M vs the rounded $25M; $27.3M vs the rounded $27.5M.

• The free cashflow resulting from the value-add plan started yielding strong cashflow in Y3-Y7, which flowed through to investor distributions and ultimately buoyed the investor return metrics.

  • Remember: IRR is time sensitive. By returning cash to investors sooner via the strong cashflow in Y3-Y7, the IRR increases.

  • Equity multiple is NOT time sensitive. The actual amount of cash returned to investors did improve meaningfully via the strong cashflow of Y3-Y7, but a 2.8x multiple received in 7 years is not as valuable as a 2.6x multiple received in 5 years - which the IRR accounts for.

Conclusion for Today

Some metrics can be controlled, others not at all, and others are somewhere in between. NOI is one of the in between variables: subject to change based on market conditions and GP management decisions. Cap rates cannot be controlled; rates move freely with market conditions. Small changes in NOI and cap rates have the power to erode a property’s value - and investor returns. When considering this mathematical fact with the reality that future market conditions cannot be known in advance, the risk becomes apparent: no pro forma can predict investor returns and business plans that rely on favorable market conditions to persist into the future indefinitely are a high-risk gamble.

Real estate does NOT always appreciate. Riding out unfavorable market conditions can be the right choice. The numbers here verify both. However, surviving a market downturn can be difficult if not mathematically impossible.

The next post in this series, Market Corrections can be Avoided, will show what happens during a market correction when nearly every assumption is proven false and every number is off-target.

Anticipating the impacts of a market correction and showing how much margin for error the business plan permits is where sensitivity analysis comes in. That will be covered at the end of this series: How to use AI for Stress Testing and Sensitivity Analysis.

Don’t miss the next part of this series. Subscribe for delivery to your inbox.

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This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of real deals to better understand risk and invest accordingly.

The Debrief is dedicated to learning how to assess risk. Syndicated real estate is data-heavy and complex. Accurately and comprehensively assessing risk in a deal takes time and skill.

In order to more easily visualize risk, I created the Risk Radar.

How it Works

I have identified 17 potential breaking points and plotted each as a spoke on the “radar” against historic market metrics and industry norms. The outer ring represents the least favorable/highest risk position for a deal. The innermost ring represents the most favorable/lowest risk position. The closer to the center any given variable lands, the lower the risk. Note that risk is relative: low versus high. There is no risk-free position. Any one of the spokes has the potential to single-handedly lead to a total loss.

For a thorough explanation of how the Risk Radar works, see the introductory Debrief.

In Practice

By plotting pro forma data against long-term high, low, and average values for key metrics and industry norms, a risk profile quickly emerges.

To use the Risk Radar, follow three steps:

1. Gather the pro forma data for each section shown on the Radar.

2. Gather contextual data for high, low, and average numbers for each section.

3. Plot the pro forma number on the radar against the high and low number.

Subscribe to get the upcoming Decoded guide to completing step #2 in the coming weeks.

Subscribe now

Does it Work?

I have plotted the data provided in the offering materials on the Risk Radar (indicated by the blue stars) for relevant deals previously covered by The Debrief to see how well the Radar revealed the risk profile and possible breaking points.

Multi-Family

How a Value-Add Became a Total Loss

A 35% increase in NOI wasn’t enough to avoid a 100% loss of investor capital.

The Risk Radar clearly shows that the asset was purchased at a very low cap rate and that every debt metric is in the red zone upon closing - the stress points which ultimately led to an unrecoverable death spiral of debt, no potential for a sale price in excess of the debt due, and total loss of this deal despite the GP exceeding expectations on every other front.

Crossing the Gap

A joint venture to deliver a ground-up multi-family asset from capitalization through construction, stabilization, and disposition within 36 months.

This deal is a good example of a shrewd business plan that was well-executed and captured a strong market environment: the project was sold well in advance of plan - thus outpacing the impacts of the high percentage of new inventory coming to market. Although the high risk of new deliveries was noted by the radar, the GP’s quick execution and sale avoided this possible point of disaster.

However, the floating rate debt quickly depleted reserves despite the otherwise well-crafted and executed business plan. A capital call was necessary to bridge the gap between depleted reserves and a pending sale. As a result, the project was able to survive a brief period of negative cashflow to close a profitable sale and provide returns in excess of the pro forma.

Ripley

Generic value-add business plan for a multi-family apartment complex.

This deal seemed very boring and simple - a generic value-add multi-family using average rent lift metrics. Unfortunately for investors, the inexperience of the GP was the weak point: the offering materials lacked meaningful information, LP interests were grossly mismanaged at multiple points, and capital was ultimately lost. This deal didn’t break on one point, but on multiple points. All of which were visible during capitalization.

Industrial

Note that all metrics are reflective of national long-term averages and industry norms for industrial real estate. The expenses as a percentage of gross income assumes a NNN structure.

Bond. James Bond-like Returns

Opportunistic value-add and expansion of a light industrial property using a well-known and routine value-add and expansion business plan.

There were several risk points that could have easily killed this deal - assuming strong demand and low debt rates would persist. Both assumptions were quickly proven to be incorrect and were threatening to potentially lead to a certain loss, but the experienced GP quickly swapped the original business plan for an alternative. By making a well-crafted capital call, the GP protected investor interests and delivered returns in excess of the pro forma.

A Real Ringer

Ground-up industrial development by a local GP with a track record that predates CDs, email, and the internet.

Similar to the previous deal, the assumption that strong demand and low debt would persist could have killed this deal. However, the GP demonstrated real skill in outpacing the softening market conditions to deliver a win while narrowly avoiding a major loss.

The Roaring 2020s…

The Sirens Song + Fantasy Acres

In The Sirens Song, 100 years of regulatory and financial changes that created a frenzy of investment in crowdfunded real estate illuminates how its ultimate collapse was unavoidable - and predictable.

To further illuminate risk, the Heuristics pro forma learning series created the Fantasy Acres project based on underwriting assumptions commonly used in 2021. The hypothetical offering information is plotted here.

Observe the number of spokes at the outer edge of the radar: cap rates, favorable market demand, and debt were all at the extreme edge of historical rates. This is what the end of a growth cycle looks like before collapsing: rampant investment coupled with metrics pushing the limits of market end points. It is not a matter of if but when the tide will turn. That turn was precipitated by the increase of interest rates in 2022, which directly impacted borrowers’ ability to make debt payments. But was compounded by softening demand that was the direct result of the flood of newly constructed supply. Both factors expanded cap rates, making exit via a sale and repayment of capital a mathematical impossibility.

Any one of these factors has the ability to compromise returns. None should have been surprising. Data clearly indicated assumptions at the extreme edge of the historical range. A reversion was imminent and could have been expected. By late 2021 the Fed was signaling rate increases; the hikes began in March 2022 and ran through mid-2023. By the end of H1 2022, institutional capital flows into real estate dropped from its 2021 peak. Experienced players were aware of and heeding the warning signs. Inexperienced retail investors, euphoric with newly granted access to the “investments of the 1%,” were blind to the risks and continued to pour funds into syndications through 2022 - well after interest rate risk was clear. And ultimately paid the price of ignorance.

Risk ≠ Doom

These examples demonstrate the risk profile of a deal as plotted against historical market data and industry norms, but also demonstrates that just because a deal has one or more high risk points does not mean it will fail. The story is more nuanced. GP skill and favorable market timing can offset a stress point the Radar flags; the deals above show both happening repeatedly. The Risk Radar is a tool to support risk analysis; not a replacement for your own consideration of a deal in whole.

Up Next

Next week I’ll outline how to leverage AI to source the industry data and pro forma assumptions needed to plot the Risk Radar.

Challenge the Radar

Download the Risk Radar and test it yourself: plot the data of the offering materials of an exited or mid-life deal. Does the Radar accurately depict stress points experienced by the deal? What was missed?

Share in the comments below.

Share

This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of real deals to better understand risk and invest accordingly.

The Debrief is dedicated to learning how to assess risk. Syndicated real estate investments are data-heavy and complex. Accurately and comprehensively assessing risk in a deal takes time and skill.

In order to more easily visualize risk, I have created the Risk Radar.

After reviewing dozens of exited deals, patterns of success and failure emerge. When viewed through the lens of market cycles, it becomes clear that although there are infinite ways to structure and execute a deal, the breaking points are finite and can be seen as a risk before the breakage occurs.

How it Works

I have identified 17 potential breaking points and plotted each as a spoke on the “radar” against historical market metrics and industry norms. The outer ring represents the less favorable/higher risk position for a deal. The innermost ring represents the more favorable/lower risk position. Note that risk is relative: low versus high. There is no risk-free position. Any one of the spokes has the potential to single-handedly lead to a total loss.

Let’s look at each spoke in detail to understand how the Risk Radar works. For the sake of brevity, I am assuming the definition of each spoke is known. Discussion here is focused on risk positioning and use of the Risk Radar.

Locked at Closing

All metrics in this section are fixed at the time of acquisition, and do not change over time.

GP

Years as a Team

The ability of the GP to maximize returns depends on the skill and experience of the senior leadership and their partners. A team with a long working history has navigated an array of operating conditions and is better positioned to succeed.

Why not consider years of experience by individuals? Projects are completed by a team, not a single person. The more experience the team has working together, the less risk there is for team failure.

Expertise

A GP who has mastered their trade is much more likely to succeed. That includes asset type, business plan, and geography. Diversification is good for investments, but not when sourcing an expert.

Roaches

A spotless record is the best indicator of a GP’s ability to reliably serve investors. Even one credible negative mark is reason to avoid a GP since - like cockroaches - if you see one there are likely hundreds more you don’t see.

Multifamily example: A team with more than 20 years of experience that has navigated a variety of market conditions and emerged with a sterling reputation is the best indicator of a team’s ability to serve LPs - no matter how difficult the conditions may be.

Cap Rate

Purchase Cap Rate

Purchasing an asset at a premium or a discount will affect the profitability of the deal at disposition. A purchase made at a low cap rate is riskier since the market tends to revert to the mean (thus potentially losing value over time), and any profitability is solely dependent on increased NOI. Conversely, a purchase made at a high cap rate has market forces potentially lifting the underlying value regardless of NOI.

Multifamily example: NOI and cap rates rule the value of an asset. But cap rates are set by the market and cannot be controlled. No matter how much value is added to a property or how well NOI has been optimized, if a purchase is made at a low cap rate and a sale is forced at a larger cap rate, much if not all of that hard-earned equity can be lost.

Sponsor Driven Metrics

This section covers the variables that can be driven by the GP, and can change (sometimes dramatically) over time.

Debt

Term

Floating rate debt leaves one of the largest costs to chance, which is why it is noted on the outer/riskiest position. A fixed rate for at least the entire duration of the deal’s expected hold time is at the inner/least risky position.

DSCR (Debt Service Coverage Ratio)

A low DSCR leaves little margin for fluctuations in NOI while still covering debt in full. And, a lender can call a loan that is not meeting the DSCR threshold irrespective of the payments being current. Accordingly, the outer ring is for a lower/riskier DSCR and the inner ring is for a higher/less risky DSCR.

LTV (Loan-to-Value)

A high LTV can boost returns, but (like DSCR) can leave little margin for deviations in NOI. Plus, if market conditions depress an asset’s value (thus increasing the effective LTV), the loan can be called - whether or not it’s performing. It’s also important to understand what number is being used for “V”. A 70% LTV loan on the purchase price is not the same as 70% on the ARV (after-repair value), which is essentially financing a value that is not yet recognized - a very common scenario in value-add deals. The higher the LTV, the higher the risk.

Why the interest rate is NOT noted:

A deal can be profitable at any interest rate. It’s the increase in the rate that can kill a deal.

Multifamily example: If any single lesson can be learned from 2023 it is that debt cuts both ways. Leverage boosts return projections, but a floating rate that triples can kill an otherwise good deal - either by the debt payments ballooning to an unaffordable number, or by the debt burden exceeding the DSCR or LTV requirements - in which case the lender may require refinancing or reduction of the principal to bring the loan back within the lender requirements. This is the source of many a capital call and the loss of numerous multifamily deals.

Expenses

Expenses as a Percentage of Gross Income

No matter how efficiently managed, assuming expenses below industry averages can be achieved - and maintained - is unrealistic. Regulatory changes and natural disasters can increase expenses quickly. Changes to property tax incentives and tariffs are recent examples of unforeseeable changes that increased costs with no possibility of mitigation.

Reserves

The best defense against unexpected costs of any type is reserves. Deals with little margin for error and thin reserves have been forced to issue capital calls or source rescue capital to bridge cash flow gaps.

When a capital call is the bridge between success and failure:
The Debrief: Crossing the Gap

Multifamily example: Expenses, like rent lift, are frequently toyed with in pro formas to increase NOI. However, no matter how effective the operator, the reality of expenses will always come to light.

Capital

Senior (Sr) Layers

The further back in line a position sits to be paid, the greater the risk that returns never reach it. An investment with zero layers above it (such as senior debt) is the lowest risk, whereas common equity can stand behind many senior layers such as mezzanine debt, pref equity, and different classes of common equity.

Waterfall

The waterfall agreement is where LP and GP interest alignment is determined, and dictates how and when investors receive returns on and of their invested capital. The flow of cash will always influence behavior. The more a GP is rewarded in advance of the LPs, the greater the risk of undesirable behavior.

Multifamily example: Ideally, the waterfall agreement will incent the GP to act in the LPs’ best interest, and the GP will manage the capital stack with respect for the original investors’ interests. However, this isn’t always the case.

For example, a GP may choose to issue pref equity rather than make a capital call, leaving the original common equity investors last in an ever-lengthening queue of payment obligations. In this scenario, the deal may exit successfully but common equity returns may be severely depressed or lost.

When common equity is a total loss due to a poor waterfall agreement and a changed capital stack: The Debrief: Ripley

Market Driven Metrics

This section is dedicated to metrics that change with market forces and revert to the mean over time. The long-term average is indicated by the dotted black line.

Cap Rate

Assumed cap rate at disposition/sale

Cap rates are highly sensitive to market changes. Assuming a hot market that trades at low cap rates will persist is not realistic, and is the demise of many a deal.

Multifamily example: The image below shows the historical low cap rate of under 4% at the outer edge and the historical high of over 8% at the inner edge, with the average near 6%. A pro forma that assumes a low exit cap - plotting toward the outer edge - is risky: the market tends to revert to a higher cap rate, potentially cutting the asset's value below plan. A pro forma that assumes a high exit cap - plotting toward the center - is conservative: reversion would compress the cap and lift value.

Deliveries

Delivery of new inventory is one of the most easily sourced yet overlooked data points. Simple supply and demand economics have dramatic effects on NOI. If a pro forma is modeling a market with demand outpacing supply indefinitely, there is little evidence to believe this is possible. The following two metrics account for future supply and demand risk.

% New (New Deliveries as a Percentage of Existing Inventory)

The anticipated number of deliveries as a percentage of the existing inventory easily demonstrates how supply dynamics will shift over time.

Absorption Rate

Absorption rate measures demand. A low figure means demand is outrunning supply; a high figure means the reverse. Because the radar plots the assumed future absorption rate (not the rate at the time of acquisition), assuming a tight, fast-absorbing market will persist is the riskier position - the efficiency of markets dictates that supply will arrive to meet demand, and possibly exceed it.

Multifamily example: It may appear counterintuitive that low deliveries and quick absorption rates are the highest-risk position, but we are looking at assumptions for future (not current) market conditions. If the market currently has few deliveries (low supply) and a fast absorption rate (high demand), we must assume that future supply will arrive to meet demand and that the market will equalize. Assuming otherwise is typically unrealistic, and is thus the riskiest assumption.

Income

The income assumptions plotted here account for market risk - not cash flow risk. A value-add or development deal must pass through periods of investment before recognizing income. Scrutinizing the business plan for cash flow management in relation to market assumptions can illuminate gaps in the business plan.

Rate

The income rate is determined according to asset class and market norms such as price per square foot (residential and commercial) or RevPAR (hospitality).

Growth/Year

The assumed annualized growth rate.

Vacancy Rates

The rate of loss to vacancy.

Multifamily example: Above-average rental rates, high year-over-year rental rate growth, and low vacancy rates are favorable market conditions, but assuming these conditions will exist over long periods of time is not realistic and can present an assumption with a high potential for failure.

Next Time

The Debrief will plot every retrospective on the Risk Radar to test whether the stress points that surfaced in reality were visible in the offering materials.

I will also plot the Fantasy Acres project from the Heuristics pro forma series to show that 2021/22-vintage deals struggling today are no surprise, but a foreseeable market norm.

Concluding the series, I will provide a step-by-step guide to using AI to source and apply deal-specific data to the Risk Radar for tailored analysis.

Subscribe now

Try it Yourself

Plot a deal on the Risk Radar and judge the risk profile for yourself. And if there’s a breaking point that the radar doesn’t account for, tell me where it’s wrong.

Download the Risk Radar here.

Comment with suggestions and feedback!

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This is Heuristics - where assumptions are called out and broken down so that you can invest with greater clarity.

This post covers how costs affect investor returns - specifically debt and operating expenses. In 2022 interest rates nearly tripled, moving from a historical low of about 3% to a more historically typical 8%. At the same time, operating expenses increased by 20% to 30% as a result of rising insurance premiums, rapidly increasing material and labor costs, and - in some places such as Texas - substantial changes to property tax policy. The combination of these two factors had a huge and unavoidable impact on profitability and has wiped out not only investor capital but also driven many deals into foreclosure. Let’s break down how this happened.

Pro Forma Baseline

The projected returns, expense sections of the pro forma and market data for the fictional project, Fantasy Acres, are shown below.

Projected Returns

Pro Forma Expenses

Notice that the DSCR is below 1.0 and cashflow is negative in Y1. This is due to renovation expenditures and vacancy loss, so this dip in cashflow is a normal part of a value-add project.

50-year Market Measures

Testing the Assumptions

What happens if the pro forma assumptions aren’t realistic? To understand how cashflow and investor returns are impacted, we will adjust the numbers on the Fantasy Acres pro forma to see how the results change.

Interest Rate

Assumption:

Debt will remain at the assumed 3% interest-only rate throughout the life of the project.

Challenge Question #1:

What are the terms of the debt and how well can the project meet those terms throughout its lifetime?

The assumed debt terms are typical of a 2021-vintage deal:

• Floating rate; currently at 3%

• Interest-only for the initial term

• Five-year term with option for two one-year extensions

• Requires maintaining at least a 1.2x DSCR and no more than a 70% LTV

As projected, the project has a 70% LTC (not LTV on acquisition cost), and a skinny 0.98x DSCR in Y1 that is covered by the reserves. However, after Y2 and renovations are complete, the boosted NOI brings the DSCR to 3.57x by Y5. There is seemingly little threat from the floating rate debt.

However, recall that the floating rate debt is assumed to remain at 3% over the five-year hold time - although this rate is at the extreme low of the historic rate range. What if rates revert to the mean and increase?

In 2022 interest rates started to rise rapidly. By Y3 of our hypothetical deal, the floating rate interest had risen from the assumed 3% to 8%, which increased the debt burden by 2.7x. By Y3, reserves are tapped, and additional capital of $520,000 is added to the deal. Let’s assume the GP funds the cashflow gap themselves without altering the capital stack.

Note that the addition of capital can have dramatic effects on common equity investors. Depending on the subscription documents, the GP may have broad discretion to insert additional cash (and layers) into the deal.

Effect on Investor Returns

Although the debt burden nearly tripled, investor returns took a relatively modest hit of between 15% and 31%. But that’s just part of the story…

Effect on cashflow and return metrics

An abbreviated version of the pro forma is provided here to illuminate how the increased debt burden flows through all of the resulting calculations.

Note:

1. Debt payments rise far beyond the pro forma, ultimately growing by 2.7x.

2. Total free cashflow (shown in blue) goes negative quickly and consumes all reserves by the end of Y2.

3. An infusion of rescue capital in Y3 is required to restore reserves and ensure the project has sufficient cash to achieve stabilization - and positive cashflow - in Y4. But the rescue capital is not a donation - this is an additional layer of capital repayment obligations, as shown in yellow. However, due to the generous nature of the GP, this is added to their equity position with no preferred return accruing on it. A possible but unlikely scenario; rescue capital typically demands steep preferred rates of return.

4. Distributions (shown in green) were suspended until disposition.

5. The DSCR falls to half of the lender requirement in Y1 and Y2. A lender could call a loan at this point, but let’s assume the GP has a strong working relationship with the lender and convinces the bank to hold off based on the value-add performing to plan and a positive NOI being an imminent reality.

At this point, you may be wondering why rising interest rates are killing deals when this example shows only a small bump in the road. Well, debt has a shadow named cap rates who we’re ignoring for now, but the compounded effect of rising interest and cap rates will be illustrated in the market correction post towards the end of this series.

Challenge Question #2:

What if operating expenses increase?

The pro forma assumed average operating expenses of about 40% of revenues during the renovation period and a stabilized expense rate of 30% post-renovation. These are reflective of industry norms, but 2022 and 2023 saw material, labor, and insurance costs rise rapidly - effectively increasing operating expenses by about 30%.

If we factor in this 30% increase and raise the cost of operations to 52% during and 39% post-renovations, the cashflow is immediately impacted.

Effect on Investor Returns

The increased expenses coupled with the ballooning debt burden dropped investor returns by nearly 50-80% - nearly two-and-a-half to three times the impact of the increased debt burden alone.

Does it seem strange that a relatively small increase of 30% in expenses had a large effect on investor returns, yet the 2.7x increase in debt only decreased investor returns by 15% to 31%? Let’s look at the pro forma to see how this happened…

Effect on cashflow and return metrics

Here is the abbreviated pro forma with operating expenses modified.

Note:

1. Operating expenses (shown in purple) are 30% higher than the pro forma.

2. Free cashflow (shown in blue) goes negative in Y1 before recovering in Y4, reserves are depleted before the end of Y2, and an infusion of nearly $1M of rescue capital is required in Y3.

3. Distributions (shown in green) are not made as planned in Y1-Y4, but the sale still yields enough to fulfill the accrued preferred return due to equity investors plus a small promote. Let’s assume that again, the GP is very generous and self-funds the additional capital needed in Y3 as an addition to their equity position - a highly unlikely reality since the addition of $1M in capital placed as equity with the reward of a small promote is not a compelling financial arrangement for the GP. A more likely rescue capital scenario could be the addition of mezzanine debt or pref equity, which would have added an expensive layer to the capital stack that could erode some to all of the profits before reaching common equity.

4. The DSCR is below the required minimum of 1.2x for the life of the project. Again, we are assuming that the GP has a very generous banker that is willing to “extend and pretend” indefinitely. In a more likely scenario, the lender could require buying down the principal in order to meet loan performance standards. This would require yet more rescue capital, which - as illustrated above - could further compromise equity investors’ capital returns.

5. Sale price (shown in orange) is $4.5M (16%) less than projected. Sale price = NOI x cap rate; a reduced NOI automatically translates into a lower value. As the sole source of investor returns, the reduced sale value coupled with the absence of interim payments had severe impacts on the investment’s performance metrics.

Conclusion for Today

Not all metrics have the same effect on cashflow and investor returns. Although both operating income and debt burden reduced investor returns, the impact of operating income had the greater effect since depressed NOI flows directly into the exit valuation. Debt is not part of valuation, so the impact of debt is strictly on the cashflow without flowing through to the exit value.

So far, Fantasy Acres has been able to return all invested capital plus a modest return. However, this example has made some meaningful allowances in favor of equity investors: rescue capital was provided as GP equity without expensive interest or preferred returns, and debt buy-down to meet DSCR requirements was avoided. Both are possible, but unlikely outcomes. Had either or both of these scenarios occurred, common equity investors may have recognized capital losses.

Also, look at the risk premium: is a 7% IRR reflective of the risk position of common equity? It’s easy to look at a projected return of 23% IRR and accept that as fair compensation for risk. But that 23% is only attainable should everything go right. Understanding how and where risk occurs and erodes returns puts the 23% in the correct context for better understanding risk.

The next posts will continue to test variables in a vacuum until it all comes together as a market correction at the end. Once the mechanics are understood, I’ll show you how AI can expedite the process.

• Exit: cap rates and hold time

• Market Correction: when everything goes wrong

• AI Assist: how to use AI for stress testing

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This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of real deals to better understand risk and invest accordingly.

The Debrief is dedicated to learning how to assess risk. Syndicated real estate investments are data-heavy and complex. Accurately and comprehensively assessing risk in a deal takes time and skill.

In order to more easily visualize risk, I have created the Risk Radar.

After reviewing dozens of exited deals, patterns of success and failure emerge. When viewed through the lens of market cycles, it becomes clear that although there are infinite ways to structure and execute a deal, the breaking points are finite and can be seen as a risk before the breakage occurs.

How it Works

I have identified 17 potential breaking points and plotted each as a spoke on the “radar” against historical market metrics and industry norms. The outer ring represents the less favorable/higher risk position for a deal. The innermost ring represents the more favorable/lower risk position. Note that risk is relative: low versus high. There is no risk-free position. Any one of the spokes has the potential to single-handedly lead to a total loss.

Let’s look at each spoke in detail to understand how the Risk Radar works. For the sake of brevity, I am assuming the definition of each spoke is known. Discussion here is focused on risk positioning and use of the Risk Radar.

Locked at Closing

All metrics in this section are fixed at the time of acquisition, and do not change over time.

GP

Years as a Team

The ability of the GP to maximize returns depends on the skill and experience of the senior leadership and their partners. A team with a long working history has navigated an array of operating conditions and is better positioned to succeed.

Why not consider years of experience by individuals? Projects are completed by a team, not a single person. The more experience the team has working together, the less risk there is for team failure.

Expertise

A GP who has mastered their trade is much more likely to succeed. That includes asset type, business plan, and geography. Diversification is good for investments, but not when sourcing an expert.

Roaches

A spotless record is the best indicator of a GP’s ability to reliably serve investors. Even one credible negative mark is reason to avoid a GP since - like cockroaches - if you see one there are likely hundreds more you don’t see.

Multifamily example: A team with more than 20 years of experience that has navigated a variety of market conditions and emerged with a sterling reputation is the best indicator of a team’s ability to serve LPs - no matter how difficult the conditions may be.

Cap Rate

Purchase Cap Rate

Purchasing an asset at a premium or a discount will affect the profitability of the deal at disposition. A purchase made at a low cap rate is riskier since the market tends to revert to the mean (thus potentially losing value over time), and any profitability is solely dependent on increased NOI. Conversely, a purchase made at a high cap rate has market forces potentially lifting the underlying value regardless of NOI.

Multifamily example: NOI and cap rates rule the value of an asset. But cap rates are set by the market and cannot be controlled. No matter how much value is added to a property or how well NOI has been optimized, if a purchase is made at a low cap rate and a sale is forced at a larger cap rate, much if not all of that hard-earned equity can be lost.

Sponsor Driven Metrics

This section covers the variables that can be driven by the GP, and can change (sometimes dramatically) over time.

Debt

Term

Floating rate debt leaves one of the largest costs to chance, which is why it is noted on the outer/riskiest position. A fixed rate for at least the entire duration of the deal’s expected hold time is at the inner/least risky position.

DSCR (Debt Service Coverage Ratio)

A low DSCR leaves little margin for fluctuations in NOI while still covering debt in full. And, a lender can call a loan that is not meeting the DSCR threshold irrespective of the payments being current. Accordingly, the outer ring is for a lower/riskier DSCR and the inner ring is for a higher/less risky DSCR.

LTV (Loan-to-Value)

A high LTV can boost returns, but (like DSCR) can leave little margin for deviations in NOI. Plus, if market conditions depress an asset’s value (thus increasing the effective LTV), the loan can be called - whether or not it’s performing. It’s also important to understand what number is being used for “V”. A 70% LTV loan on the purchase price is not the same as 70% on the ARV (after-repair value), which is essentially financing a value that is not yet recognized - a very common scenario in value-add deals. The higher the LTV, the higher the risk.

Why the interest rate is NOT noted:

A deal can be profitable at any interest rate. It’s the increase in the rate that can kill a deal.

Multifamily example: If any single lesson can be learned from 2023 it is that debt cuts both ways. Leverage boosts return projections, but a floating rate that triples can kill an otherwise good deal - either by the debt payments ballooning to an unaffordable number, or by the debt burden exceeding the DSCR or LTV requirements - in which case the lender may require refinancing or reduction of the principal to bring the loan back within the lender requirements. This is the source of many a capital call and the loss of numerous multifamily deals.

Expenses

Expenses as a Percentage of Gross Income

No matter how efficiently managed, assuming expenses below industry averages can be achieved - and maintained - is unrealistic. Regulatory changes and natural disasters can increase expenses quickly. Changes to property tax incentives and tariffs are recent examples of unforeseeable changes that increased costs with no possibility of mitigation.

Reserves

The best defense against unexpected costs of any type is reserves. Deals with little margin for error and thin reserves have been forced to issue capital calls or source rescue capital to bridge cash flow gaps.

When a capital call is the bridge between success and failure:
The Debrief: Crossing the Gap

Multifamily example: Expenses, like rent lift, are frequently toyed with in pro formas to increase NOI. However, no matter how effective the operator, the reality of expenses will always come to light.

Capital

Senior (Sr) Layers

The further back in line a position sits to be paid, the greater the risk that returns never reach it. An investment with zero layers above it (such as senior debt) is the lowest risk, whereas common equity can stand behind many senior layers such as mezzanine debt, pref equity, and different classes of common equity.

Waterfall

The waterfall agreement is where LP and GP interest alignment is determined, and dictates how and when investors receive returns on and of their invested capital. The flow of cash will always influence behavior. The more a GP is rewarded in advance of the LPs, the greater the risk of undesirable behavior.

Multifamily example: Ideally, the waterfall agreement will incent the GP to act in the LPs’ best interest, and the GP will manage the capital stack with respect for the original investors’ interests. However, this isn’t always the case.

For example, a GP may choose to issue pref equity rather than make a capital call, leaving the original common equity investors last in an ever-lengthening queue of payment obligations. In this scenario, the deal may exit successfully but common equity returns may be severely depressed or lost.

When common equity is a total loss due to a poor waterfall agreement and a changed capital stack: The Debrief: Ripley

Market Driven Metrics

This section is dedicated to metrics that change with market forces and revert to the mean over time. The long-term average is indicated by the dotted black line.

Cap Rate

Assumed cap rate at disposition/sale

Cap rates are highly sensitive to market changes. Assuming a hot market that trades at low cap rates will persist is not realistic, and is the demise of many a deal.

Multifamily example: The image below shows the historical low cap rate of under 4% at the outer edge and the historical high of over 8% at the inner edge, with the average near 6%. A pro forma that assumes a low exit cap - plotting toward the outer edge - is risky: the market tends to revert to a higher cap rate, potentially cutting the asset's value below plan. A pro forma that assumes a high exit cap - plotting toward the center - is conservative: reversion would compress the cap and lift value.

Deliveries

Delivery of new inventory is one of the most easily sourced yet overlooked data points. Simple supply and demand economics have dramatic effects on NOI. If a pro forma is modeling a market with demand outpacing supply indefinitely, there is little evidence to believe this is possible. The following two metrics account for future supply and demand risk.

% New (New Deliveries as a Percentage of Existing Inventory)

The anticipated number of deliveries as a percentage of the existing inventory easily demonstrates how supply dynamics will shift over time.

Absorption Rate

Absorption rate measures demand. A low figure means demand is outrunning supply; a high figure means the reverse. Because the radar plots the assumed future absorption rate (not the rate at the time of acquisition), assuming a tight, fast-absorbing market will persist is the riskier position - the efficiency of markets dictates that supply will arrive to meet demand, and possibly exceed it.

Multifamily example: It may appear counterintuitive that low deliveries and quick absorption rates are the highest-risk position, but we are looking at assumptions for future (not current) market conditions. If the market currently has few deliveries (low supply) and a fast absorption rate (high demand), we must assume that future supply will arrive to meet demand and that the market will equalize. Assuming otherwise is typically unrealistic, and is thus the riskiest assumption.

Income

The income assumptions plotted here account for market risk - not cash flow risk. A value-add or development deal must pass through periods of investment before recognizing income. Scrutinizing the business plan for cash flow management in relation to market assumptions can illuminate gaps in the business plan.

Rate

The income rate is determined according to asset class and market norms such as price per square foot (residential and commercial) or RevPAR (hospitality).

Growth/Year

The assumed annualized growth rate.

Vacancy Rates

The rate of loss to vacancy.

Multifamily example: Above-average rental rates, high year-over-year rental rate growth, and low vacancy rates are favorable market conditions, but assuming these conditions will exist over long periods of time is not realistic and can present an assumption with a high potential for failure.

Next Time

The Debrief will plot every retrospective on the Risk Radar to test whether the stress points that surfaced in reality were visible in the offering materials.

I will also plot the Fantasy Acres project from the Heuristics pro forma series to show that 2021/22-vintage deals struggling today are no surprise, but a foreseeable market norm.

Concluding the series, I will provide a step-by-step guide to using AI to source and apply deal-specific data to the Risk Radar for tailored analysis.

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Try it Yourself

Plot a deal on the Risk Radar and judge the risk profile for yourself. And if there’s a breaking point that the radar doesn’t account for, tell me where it’s wrong.

Download the Risk Radar here.

Comment with suggestions and feedback!

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This is Heuristics - where assumptions are called out and broken down so that you can invest with greater clarity.

In 2020 and 2021, asking rents on Sunbelt apartments were rising at double-digit annual rates. Phoenix was up 24%. Tampa was up 22%. Charlotte, Austin, Nashville - the returns were undeniable. New units were being absorbed before they were finished. GPs were pitching value-add multifamily deals with five-year hold projections that did little more than extend the steep upward ramp of rent growth like a flight path.

Two years later, the same submarkets were offering two months free on twelve-month leases. Lease-up timelines had doubled. Rents in several MSAs were down year-over-year for the first time in a decade. Deals underwritten in 2021 with assumed rent growth of 5% per year were left with a cash flow gap between the pro forma and reality that was rapidly consuming reserves and triggering capital calls.

The wise could see the folly of these forever upward revenues. But it doesn’t take decades of experience to see the warning signs; anyone can do it.

Let’s analyze the Fantasy Acres pro forma to see the signs that were ignored.

Pro Forma Baseline

Projected Returns

Pro Forma Rents

50-year Market Measures

Compare the assumed rental and vacancy rates used by the pro forma to the historic data provided here. A pitch deck will rarely provide this context, but it’s vitally important to consider.

How realistic do you believe the projections are?

Testing the Assumptions

What happens if the pro forma assumptions aren’t realistic? To understand how cashflow and investor returns are impacted, we will adjust the numbers on the Fantasy Acres pro forma to see how the results change.

Rental Income

Assumption:

Renovation will provide a 60% “lift” in rental rates from $1.90 per square foot to $3.00 per square foot.

Challenge Question #1:

What is the anticipated lift based on, and how realistic is that expectation?

Future rental rates for a property that are unproven are, essentially, made-up numbers. A figment of the GP’s imagination. If a project has not already achieved at least a small number of post-renovation rents to test the hypothesized rental rates, the next best option is comps. By reviewing rents for similar projects, a reasonably accurate guess can be made for the market rental rate. The accuracy of these assumptions is contingent on:

• Geography: Are the comps in the same neighborhood?

• Time: Are the comps recent?

• Quality: Are the comps and planned renovations and community of similar quality?

• Renter economics: Are the projected rents affordable to the resident population? What demographic trends are at play and why?

• Supply economics: How much new inventory is coming available, and what is the projected absorption rate?

Hopefully solid comparables are provided in the pitch deck to clearly illustrate how the rental rates were calculated. However, it’s prudent to check the provided data against industry data yourself to ensure the comps are accurate and not aspirational.

Challenge Question #2:

What happens if the renovations don’t achieve the anticipated lift?

If any one of the above-listed factors is inaccurate or changes during the life of the project, the assumed rental rate may not be the market rental rate. Since this is a 2021 deal, let’s assume the number of deliveries of new apartments was not accurately accounted for and the resulting flood of supply cut the rent lift in half to 30% ($2.45 sq ft) from the planned 60% ($3.00 sq ft).

Effect on Investor Returns

The result of a 55-cent lower rental rate is effectively 35% to 55% lower investor returns.

How can a 20% reduction in gross cash flow result in a 50% lower return? Let’s look at how the rental revenue flows through the pro forma.

Pro Forma

Adjusted rents

NOI is the driving number of investor results. As as result:

1. Distributions (shown in green) from free cash flow (NOI - debt service [shown in blue]) are depressed but sufficient to fulfill the LP pref return and the promote.

2. The sale price (NOI ÷ cap rate = value) is $5M (18%) less (shown in orange).

3. The reduction of distributions and terminal returns to investors reduced the overall cash returned to investors and the associated metrics.

You may be thinking, the returns aren’t so bad. But, lower rental rates are rarely the only factor at play. Softening market demand also increases vacancy rates.

Challenge Question #3:

What happens if market demand softens and both rental and vacancy rates are impacted?

Softening demand can result in a longer lease-up time and/or concessions (such as one month free) over the projected five-year hold. If we increase vacancy by 5 pts to 35% during the renovation period, and post-renovation to 15% (in addition to the reduced rental rates) the impacts are compounded.

Effect on Investor Returns

The combined effect of a 55-cent lower rental rate plus a 5pt higher vacancy rate is roughly half to three-quarters of investor returns wiped out, depending on the metric.

Effect on cash flow and return metrics

Pro Forma

Adjusted rent and vacancy rates

As NOI is depressed, the compounding effects become apparent:

• Reduced NOI results in a much lower valuation at sale, which results in a net negative in proceeds. There are not sufficient funds to fully repay investor capital from the sale.

• Cash flow remains positive in Y3 to Y5, but distributions are a fraction of expectations.

Investors were effectively returned all of their investment plus a modest return, but a small change to net income variables has destroyed most of investor returns.

Conclusion for Today

The seemingly trivial change to rental and vacancy rates had exponential impacts on investor returns. And notice what we did not change: Annual rent growth stayed at a healthy 5%. Debt remained fixed. Renovation came in on plan. And the “stressed” rental rate of $2.45 still sits above the 50-year mean of $2.10.

Recall the market history:

What happens when two assumptions break at once? When three do? When the cycle turns and they all break together?

That is the rest of this series. Because rents don't always go up – no matter what the pitch deck says.

Next in the Series

The next posts will continue to test variables in a vacuum until it all comes together as a market correction at the end. Once the mechanics are understood, I’ll show you how AI can expedite the process.

• Expenses: debt and operating costs

• Exit: cap rates and hold time

• Market Correction: when everything goes wrong

• AI Assist: how to use AI for stress testing

  ---

Don’t miss the next part of this series. Subscribe for delivery to your inbox.

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This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of real deals to better understand risk and invest accordingly.

I started The Debrief to help myself and others sharpen their analysis skills.

Over the past three months, The Debrief has delivered one thing: a retrospective analysis. Each deal is unique, but every review provided valuable lessons and surprising insights.

The Debrief is the only source for comprehensive analysis from acquisition to disposition.

Keep the insights coming: submit a deal for consideration.

How it works:

• I only review exited, syndicated real estate deals.

• All identifying information is anonymized.

• I don’t accept raw documents; I only accept de-identified fact sheets. My AI-powered guide makes this manageable.

• All materials are kept confidential and deleted post-analysis.

• I don’t post unless the investor is 100% satisfied with the content.

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Coming Next

Heuristics is delivering a six-part series on pro forma projections and how seemingly inconsequential changes can dramatically impact investor returns.

• No. 1: Pro Forma Data Is Correct

• No. 2: Rents Always Go Up

• No. 3: Costs Are Predictable

• No. 4: Real Estate Always Appreciates

• No. 5: Stress Is Singular

• No. 6: Stress Testing With AI

Get the series in your inbox; click subscribe.

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This is The Debrief - a post-game analysis for LPs. Breaking down the plan, the reality, the result, and key takeaways of real deals to better understand risk and invest accordingly.

I started The Debrief to help myself and others sharpen their analysis skills.

To do that, The Debrief has delivered one thing: a retrospective analysis. Each deal is unique, but every review provided valuable lessons and surprising insights.

The Debrief is the only source for comprehensive analysis from acquisition to disposition.

Keep the insights coming: submit a deal for consideration.

How it works:

• I only review exited, syndicated real estate deals.

• All identifying information is anonymized.

• I don’t accept raw documents; I only accept de-identified fact sheets. My AI-powered guide makes this manageable.

• All materials are kept confidential and deleted post-analysis.

• I don’t post unless the investor is 100% satisfied with the content.

Share

Past Reviews

Check out all the past reviews in The Debrief section.

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