Net Zero is a Win: Resources

Using AI to Plot the Risk Radar

Sun, 28 Jun 2026 14:02:17 GMT

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

This is the last in the introductory series to the Risk Radar. To learn more about the Risk Radar:

Read the Introduction
Read the Radar in Practice
Check out the podcast:
- Watch on YouTube
- Listen

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:

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
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.

Concerned about AI and confidentiality?

See Confidentiality, AI, and You

Step 1: Populate a new notebook in NotebookLM

Open NotebookLM and create a new notebook.

Pro Tip:
Name the notebook with the sponsor and deal so you can easily find it later.
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:

^{Act as a skeptical LP analyst auditing the SOURCE QUALITY of the uploaded documents BEFORE any data is extracted. Work only from the uploaded files. Report in four parts.}

^{PART A - LEGIBILITY}

^{For EACH uploaded file, state: fully legible / partially legible / not legible. Flag specifically:}

^{- pages that are image-only or scanned with no readable text layer}

^{- blurred, cut-off, rotated, or low-resolution pages}

^{- tables, charts, or maps whose figures are too small or distorted to read with confidence}

^{- any file that is primarily photos with little or no extractable text}

^{Name the file and page for each issue. If a file is fully clean, say so.}

^{PART B - COMPLETENESS}

^{Note anything missing or truncated that would limit analysis:}

^{- documents referenced but not uploaded (exhibits, schedules, appendices, side letters, amendments)}

^{- pages that appear to be missing from a sequence}

^{- blank fields, “TBD,” or placeholder values where a figure is expected (e.g., DSCR “TBD by lender”)}

^{- tables that are cut off before they end}

^{PART C - CROSS-SOURCE CONSISTENCY}

^{Cross-check the SAME figure across every source that states it (PPM, OA, pitch deck, any text-only versions, exhibits). Report every disparity with the value AND location of each version. Check at minimum: unit/door counts, total project value, offering size, sponsor co-investment, submarket inventory, rent and rent-growth figures, vacancy, reserves, loan amount, interest-rate description, and all fees. Distinguish a genuine conflict from a normal drafting progression (e.g., documents dated weeks apart).}

^{PART D - INTEGRITY VERDICT}

^{Conclude with: (1) the figures or spokes most at risk of being tainted by the issues above, and (2) which files, if any, should be re-uploaded in a cleaner form before extraction. If the document set is sound enough to extract from as-is, say so plainly.}

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:

^{Act as a CRE market analyst assembling benchmark market data for a limited partner.}

^{DO NOT SEARCH YET. First ask me for the deal specs below, then STOP and wait. Do not assume or fill in any value I have not given you.}

^{Ask me for:}

^{Asset type (residential, commercial, hotel, etc.)}
^{Size (unit count, square footage, or keys)}
^{Submarket and metro}
^{Asset vintage (year built; note any major renovation year)}

^{After I answer, confirm the four back to me in one line. Then ask me one more question: the historical lookback period — 20, 30, or 50 years. Stop and wait. Once I answer, run the search below.}

^{--- SEARCH (run only after I confirm specs and lookback) ---}

^{Pull historical market data for the asset type, size, and submarket I gave you. For EACH metric report the observed range over my lookback period, plus the average and most recent reading. If a metric’s data does not reach that far back, use the longest available series and state its start year.}

^{Then map each metric to the Risk Radar’s bands. On the radar the OUTER ring = highest risk and the INNER ring = lowest risk.}

^{CRITICAL — RISK IS THE RISK OF THE PRO FORMA ASSUMPTION, NOT CURRENT MARKET CONDITIONS. A forward-looking assumption is risky when it bets on a favorable extreme holding (low supply, high demand, low vacancy, low expenses), because that leaves no cushion when the market reverts to the mean. The optimistic assumption is the OUTER (high-risk) band. Map the bands exactly as follows:}

^{Purchase cap rate: LOW cap = outer/high risk (overpaying, thin basis); HIGH cap = inner/low risk.}
^{Exit cap rate: same prevailing series as purchase — report ONE cap-rate series for both rows, no invented entry-to-exit spread. LOW = outer, HIGH = inner.}
^{New supply: assuming LOW new supply = outer/high risk; assuming HIGH supply = inner/low risk.}
^{Absorption (demand): assuming HIGH demand / fast absorption = outer/high risk; assuming LOW demand = inner/low risk.}
^{Rental rate: assuming rent far ABOVE the market mean = outer/high risk; at or below mean = inner/low risk. Normalize across three bases: (a) per square foot, (b) per unit for the equivalent unit type/size, (c) asset-class standard (RevPAR for hotel). State each.}
^{YoY rent growth: assuming HIGH growth = outer/high risk; flat or conservative = inner/low risk.}
^{Vacancy: assuming LOW vacancy = outer/high risk; assuming higher/normalized vacancy = inner/low risk.}
^{DSCR: LOW DSCR = outer/high risk; HIGH DSCR = inner/low risk.}
^{LTV: HIGH LTV = outer/high risk; LOW LTV = inner/low risk.}
^{Debt term: the interest RATE is not the risk — the TERM STRUCTURE is. Floating, or fixed for LESS than the planned hold = outer/high risk; fixed for at least the full anticipated hold = inner/low risk. Report the band in these terms, not as a rate range.}
^{OpEx ratio: assuming LOW expenses = outer/high risk (optimistic, hard to sustain); assuming higher/normalized expenses = inner/low risk.}

^{SUBJECTIVE SPOKES — no historical series exists. State the rule, not a data range:}

^{Senior layers: a count of capital layers senior to the LP position. Outer/high risk = MORE than one senior layer; inner/low risk = fewer than one (LP sits directly behind a single senior loan or better). Note this is subjective.}
^{Waterfall: subjective structural judgment. A CUMULATIVE preferred return is the lower-risk (inner) feature; a promote split with no preferred return (promote-only) is the higher-risk (outer) feature.}

^{GP spokes (years as team, expertise, roaches) are EXCLUDED entirely — they are not market data.}

^{OUTPUT — one table, this exact column order:}

^{| # | Spoke | Outer Band (High Risk) | Inner Band (Low Risk) | Average | Most Recent | Geography Used | Data Start Yr | Source URL | Reliability |}

^{Rows, clockwise:}

^{Purchase cap rate}
^{Exit cap rate (same series as #1)}
^{New supply}
^Absorption
^{Rental rate (all three bases in the cell)}
^{YoY rent growth}
^Vacancy
^DSCR
^LTV
^{Debt term}
^{OpEx ratio}
^Reserves
^{Senior layers [subjective — state the >1 / <1 rule, no percentage]}
^{Waterfall [subjective — cumulative pref = inner; promote-only = outer]}

^{For reserves (#12): LOW reserves assumed = outer/high risk; ample reserves = inner/low risk. Report in both $/unit/yr and months of operating expenses.}

^{After the table, add a short “Caveats” block: which spokes used metro/national proxies vs. true submarket data, and which rely on paywalled sources cited only secondhand.}

^RULES:

^{Cite a working source URL for every figure. Prioritize CoStar, Green Street, CBRE, JLL, Cushman & Wakefield, NCREIF, RCA/MSCI, and FRED. Exclude blogs, forums, and sponsor marketing.}
^{National sourcing is correct-by-nature for the financing spokes (DSCR, LTV, debt term); hold the market spokes (cap rate, supply, absorption, rent, growth, vacancy) to submarket data or label the substitution.}
^{If a gold-standard source is paywalled and only a secondhand citation is available, say so and rate reliability high/medium/low.}
^{Where sources disagree, show the range; do not average.}
^{If a metric cannot be sourced, say so plainly — do not estimate.}

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:

^{Act as a skeptical LP analyst using ONLY the uploaded documents.}

^{RULES (apply to this and my next two prompts):}

^{- Cite source doc + page + short quote for every figure.}

^{- Not stated → “NOT PROVIDED”; never estimate or infer.}

^{- If a figure only resembles the one asked, flag it and give both.}

^{- One figure per spoke; note conflicts.}

^{- Show arithmetic for any calculation.}

^{- “PF” = the pro forma’s own assumption at underwriting, NOT current/live market. Plot the PF value; note any current figure only as a flag.}

^{Return these 4 spokes in THIS order. Table: # | Subsection | Spoke | Value to Plot | Source (pg+quote) | Math | Flags.}

^GP

^{1. Years as team - years the named principals have worked together as a unit; give firm age AND shared tenure separately, flag if only firm age stated.}

^{2. Expertise - how broad/narrow across asset types, geographies, and business models. Return one value: FOCUSED (one of each), MODERATE (focus on one to two of the three), or DIVERSE (more than one in all three). State the count per dimension and the basis.}

^{3. Roaches - any credible negative information about the firm or principals.}

^CAP

^{4. Purchase cap rate.}

MARKET DRIVEN:

^{Same rules as the prior prompt. Return these 6 spokes in THIS order, same table format.}

^CAP

^{5. Sale/exit cap rate (PF).}

^Deliveries

^{6. % new - new inventory as % of existing submarket inventory (PF); show units / total. Not the current pipeline.}

^{7. Absorption - in months (PF). Not the current pace.}

^Income

^{8. Rental rate - the PF rate normalized to the asset-class standard unit (rent/sq ft for multifamily, RevPAR for hospitality, NNN rent/sq ft for industrial/retail, etc.). State the standard used and the normalized figure.}

^{9. YoY rent growth - each projected year (PF).}

^{10. Vacancy - the rate ASSUMED IN THE PF. If it varies across hold years, state the variation year by year, construction/renovation period called out. Not current submarket vacancy.}

SPONSOR DRIVEN:

^{Same rules as before. Return these 7 spokes in THIS order, same table format.}

^Debt

^{11. DSCR - with the NOI and annual debt service behind it.}

^{12. LTV - report BOTH LTV on acquisition cost AND LTV on total capitalization (as applicable). If only Loan-to-Cost is stated, say so and give both.}

^{13. Term - state the basic loan terms (e.g., “3-yr fixed + two 1-yr extensions”) AND the planned hold period, then the gap. Plot the EXPOSURE: a loan fixed 3 yrs on a 5-yr hold is fixed yrs 1-3, UNKNOWN yrs 4-5 (refinance/extension at then-current rates). Show: (a) maturity; (b) fixed duration, or if floating the index, spread, cap strike; (c) extension options + cost/conditions; (d) hold years EXPOSED after the fixed/cap period. If locked for full hold, say so.}

^Expenses

^{14. Expense as % of income - each projected year (PF), not just Yr 1.}

^{15. Reserves - state BOTH the initial reserve in absolute dollars AND the months of operating expenses it represents (PF); show math.}

^Capital

^{16. Senior layers - list EVERY layer senior to the offered LP position, each with $ and % of total capitalization, and the total count.}

^{17. Waterfall - preferred return (cumulative?) and the full promote split by tier.}

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:

^{Act as a due-diligence investigator vetting a real estate sponsor on behalf of a prospective limited partner. Search the open web and public records thoroughly. I need any credible negative information at three levels: the sponsor ENTITY (and affiliated/parent/management entities), the named PRINCIPALS, and other KEY STAFF (executives, fund managers, guarantors).}

^SUBJECTS:

^{- Entity/entities: [SPONSOR LEGAL NAME(S), DBA, AND AFFILIATES]}

^{- Principals: [NAMES + TITLES]}

^{- Key staff: [NAMES + TITLES]}

^{- Helpful context: [HEADQUARTERS CITY/STATE, ASSET TYPES, APPROX. AUM/UNITS, YEARS ACTIVE]}

^{SEARCH AND REPORT ON, AT MINIMUM:}

^{1. Litigation - lawsuits as plaintiff or defendant, judgments, liens, foreclosures, deed-in-lieu, receiverships. Check federal (PACER-indexed) and state court mentions, and county records where surfaced.}

^{2. Securities/regulatory - SEC actions, litigation releases, administrative proceedings; FINRA BrokerCheck and SEC IAPD records; state securities regulator actions; any Form D filings and whether offerings match claims. Check the SEC EDGAR full-text search.}

^{3. Bankruptcy/insolvency - entity or personal filings, defaulted loans, CMBS special-servicing or delinquency mentions tied to their deals.}

^{4. Investor complaints - LP grievances, capital-call disputes, alleged misrepresentation, on forums, news, BBB, Google/Yelp, and syndication communities.}

^{5. Reputation/press - negative news, investigative pieces, fraud or mismanagement allegations, principals’ involvement in prior failed or troubled deals (including under other entity names).}

^{6. Identity/background - prior business names, dissolved entities, name changes, undisclosed affiliations, or principals tied to unrelated controversies that bear on integrity.}

^RULES:

^{- Cite a working source URL for EVERY claim. No citation = do not include it.}

^{- Distinguish CONFIRMED (a primary or reputable source directly states it) from UNCONFIRMED/ALLEGED (rumor, single forum post, unclear identity). Label each.}

^{- Beware of NAME COLLISIONS - common names may return another person/company. Flag any item where you cannot confirm it is the same subject, and say what would disambiguate it.}

^{- “Nothing found” is a valid, useful result. If a category is clean, say so explicitly rather than padding.}

^{- Note the LIMITS of open-web search: paywalled dockets, sealed cases, and unindexed county records may not surface. Recommend where a manual PACER, county-recorder, or background-check pull is warranted.}

^OUTPUT:

^{- A findings table: Subject | Level (entity/principal/staff) | Category | Finding | Confirmed or Alleged | Source URL | Same-subject confidence.}

^{- Then a short verdict for the radar’s “roaches” spoke: any credible negative information found (yes/no), the most material item, and what still needs manual verification.}

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:

^{Tell me the following details:}

^{Sponsor Entity/Entities: [Insert legal name(s), DBAs, and any known parent/subsidiary companies]}

^{Principals: [Insert names and titles, e.g., Jane Doe (CEO)]}

^{Key Staff: [Insert names and titles of executives, fund managers, or key guarantors]}

^{Helpful Context: [Insert headquarters city/state, primary asset classes (e.g., multifamily, retail), approximate AUM/unit count, and years active]}

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.

The Risk Radar in Practice

Sun, 28 Jun 2026 11:26:23 GMT

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.

Risk Radar is an educational framework, not investment advice. It’s a visual aid for comparing a deal’s assumptions to historical norms - a prompt for your own analysis, not a rating, a recommendation, or a prediction of any outcome.1

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:

Gather the pro forma data for each section shown on the Radar.
Gather contextual data for high, low, and average numbers for each section.
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.

The market will always revert to the mean.

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.

The Risk Radar can’t tell you if an investment is right for you.

But it can tell you the relative risk of each breaking point.

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.

This post and the Risk Radar framework are for educational and informational purposes only. They are not investment, legal, tax, or financial advice, and nothing here is a recommendation to buy, sell, or hold any security or to invest in any particular deal or sponsor. I am an individual investor sharing my own framework and opinions - not your advisor or fiduciary - and reading this creates no such relationship.

Risk Radar is a visual heuristic, not a scoring system, a rating, or a forecast. It compares a deal’s stated assumptions against broad historical averages and industry norms; it does not measure or predict any deal’s actual result. A profile that plots toward the center is not “safe,” and one that plots toward the edge is not doomed - the tool is a starting point for analysis, not a substitute for it. Historical norms describe past tendencies, not guarantees, and markets can behave differently than they have before. Any deals referenced are illustrative and anonymized.

Always do your own due diligence and consult your own licensed legal, tax, and investment professionals before making any investment decision.

Risk Radar: Materials Library

Sun, 28 Jun 2026 10:10:24 GMT

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:

U.S. Multifamily 50-year averages

U.S. Industrial 50-year averages

U.S. Hospitality 50-year averages

Subscribe now

AI Guide

PDF guide with AI prompts and basic workflow.
Detailed how-to: Using AI to Plot the Risk Radar.

Introducing the Risk Radar

Sun, 21 Jun 2026 05:51:26 GMT

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.

Risk Radar is an educational framework, not investment advice. This is a visual aid for comparing a deal’s assumptions to historical norms - a prompt for your own analysis, not a rating, a recommendation, or a prediction of any outcome.1

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.

**For better or worse, the GP and the purchase cap rate cannot be changed*.**

*Yes, the GP can be removed for cause, but this is not a common or easy process, so I am ignoring that option here.

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.

All metrics in this section are subject to change due to market conditions, but how those conditions affect the deal is controlled by the GP.

The position at acquisition is not necessarily the position at disposition.

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.

The subscription documents detail the position in the capital stack of each investment class, and how it can be changed by the GP.

The capital stack can change over time, and dramatically alter the LPs’ risk position.

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.

No data point will remain at the outer limit indefinitely.

The mean is always the center of gravity.

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.

To understand how changes in income affect investor returns, see Heuristics: Rent Always Goes Up.

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.

Deals seldom fail for unforeseeable reasons - they fail at the spokes with metrics at the outer edge.

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.

The Risk Radar can’t tell you if an investment is right for you.

But it can tell you the relative risk of each breaking point.

Comment with suggestions and feedback!

Always do your own due diligence and consult your own licensed legal, tax, and investment professionals before making any investment decision.

THE DEBRIEF: How to Make a Submission

Fri, 29 May 2026 05:16:21 GMT

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.

Past Reviews

Check out all the past reviews in The Debrief section.

Subscribe now

And You?

What should be added or removed? What’s your experience? Share in the comments below.

Don't Waste Your Time with Podcasts

Christine Kwasny — Tue, 14 Apr 2026 08:07:03 GMT

Provided in no particular order.

Money Podcasts

The Rational Reminder
The Long View by Morningstar
BiggerPockets
PassivePockets
Freakonomics
DOAC (Diary of a CEO)
My First Million
Planet Money
Vyzer Weekly
Adam Bergman Talks

Know Thyself Podcasts

Mel Robbins
Hidden Brain
TED Radio Hour

Just for Fun

Dirtbag Diaries
Bundyville by OPB
Radiolab
This American Life
Mortified
Even the Royals
Civics 101
Serial

And You?

What do you recommend? Comment below.

Don't Waste Your Time Reading

Christine Kwasny — Sun, 12 Apr 2026 14:55:00 GMT

RECOMMENDED BOOKS

Scroll down to read my summary and top three takeaways.

A Random Walk Down Wall Street
Mastering the Market Cycle
The Psychology of Money
The Little Book of Common Sense Investing
The Bogleheads Guide to Investing
The Bogleheads Guide to Retirement Planning
The New Retirementality

A Random Walk Down Wall Street

Read it if:

You want a comprehensive, data-heavy history and analysis of investing vehicles, theory, and performance.

Tip:

Be sure to buy the latest edition. The original version is over 20 years old with many subsequent updates.

Top three takeaways:

There is no methodology that can predict the future of financial (or any) markets.
A portfolio using Modern Portfolio Theory (MPT) (particularly an 83%/17% US/International index) provides optimal risk-adjusted returns.
Judgment bias is strong: people attribute skill to luck and luck to misfortune. They own the good, but not the bad.

Mastering the Market Cycle

Read it if:

You want to understand the hows and whys of market cycles to avoid buying at the top and selling at the bottom.

Top three takeaways:

Read the entire book; it’s a treasure trove of wisdom.
Sign up for Howard Mark’s Oaktree Insights newsletter for periodic, pertinent market analysis.
Surface-level summary of the book’s overall ideas:

Think about your life-long vocation vs vacation.
Purpose is essential to satisfaction.
Use your own life expectancy number for planning.

DON’T WASTE YOUR TIME

Practicing Financial Planning

Practicing Financial Planning

Why I didn’t like it:

Very expensive and little in-depth or meaningful information - especially for such a large and costly book. Makes me wonder about the value of the CFP designation.

Books on the Shelf

Christine Kwasny — Sun, 12 Apr 2026 14:13:48 GMT

Currently Reading

The Intelligent Investor

by Benjamin Graham

This is a huge book but it’s worth bearing the weight of its 600+ pages. The commentary from Jason Zweig brings the lessons up to date.

Up Next

Thinking, Fast and Slow

by Daniel Kahneman

Because, you don’t get a Nobel Prize for nothing (unless sometimes you do but we won’t talk about that here)…

Private Debt (2nd edition)

by Stephen Nesbitt

The current belle of the ball that may not be what she seems when the clock strikes midnight…

Private Wealth Management (9th edition)

by Hallman & Rosenbloom

A huge book that looks like it came from the Wharton School. Targeted toward financial planners. Hopefully it’s better than the CFP training guide (that wasn’t worth the time or money).

Net Zero is a Win: Resources

Using AI to Plot the Risk Radar

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

This is the last in the introductory series to the Risk Radar. To learn more about the Risk Radar:

Part 1: Prepare the Risk Radar

Step 1: Download the Risk Radar

Step 2: Prepare the Radar for Plotting

Step 3: Understand the Spokes

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

A Note About NotebookLM

Concerned about AI and confidentiality?

Step 1: Populate a new notebook in NotebookLM

Step 2: Gather and Upload the Documents

Step 3: Check Legibility

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

LEGIBILITY PROMPT:

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

PROMPT FOR HISTORICAL DATA:

Step 5: Gathering the Details

Step 6: Plot the Radar with the Historical Data

A note on data

Part 3: Plot the Deal

Step 1: Extract the numerical data

The Prompts

FIXED AT CLOSING:

MARKET DRIVEN:

SPONSOR DRIVEN:

Step 2: GP Data Points 2.0

Public Records Search

GP BACKGROUND PROMPT FOR CLAUDE/GEMINI/CHATGPT:

DEAL SPECS PROMPT:

Online Libraries

Part 4: Radar Complete

Why not use AI to plot the Radar?

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

Keep in Mind

Try it and Share!

The Risk Radar in Practice

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.

How it Works

In Practice

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

Does it Work?

Multi-Family

Industrial

The Roaring 2020s…

The Sirens Song + Fantasy Acres

The market will always revert to the mean.

Risk ≠ Doom

The Risk Radar can’t tell you if an investment is right for you.

But it can tell you the relative risk of each breaking point.

Up Next

Challenge the Radar

Risk Radar: Materials Library

Introduction Materials

Graphics for Download

Blank Base Radars:

U.S. Multifamily 50-year averages

U.S. Industrial 50-year averages

U.S. Hospitality 50-year averages

AI Guide

Introducing the Risk Radar

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.

How it Works

Locked at Closing

For better or worse, the GP and the purchase cap rate cannot be changed*.

*Yes, the GP can be removed for cause, but this is not a common or easy process, so I am ignoring that option here.

GP

Years as a Team

Expertise

Roaches

Cap Rate

Purchase Cap Rate

Sponsor Driven Metrics

The position at acquisition is not necessarily the position at disposition.

Debt

Term

**For better or worse, the GP and the purchase cap rate cannot be changed*.**