In today's market, investors know that conducting your own due diligence before investing with a sponsor is an absolute must. Once you've decided on a sponsor, the next critical step is really learning how to evaluate the deals you're presented with. Every multifamily deal comes with its own set of spreadsheets, market reports, and “what-if” scenarios. This data overload can sometimes make your head spin, especially when you’re doing this on nights and weekends around your day job.
The good news? You don’t need an MBA to underwrite with confidence. In this article, I’m sharing two ways you can tackle this challenge yourself: an AI-powered shortcut using ChatGPT or Gemini, or a hands-on, five-step process to do a quick underwriting yourself. Whichever route you take, you’ll walk away with clear projections, an honest look at the risks, and the conviction to say “yes” or “no” on your next multifamily deal.
Technology has rapidly advanced, and AI offers a tremendous benefit to investors by helping to make your due diligence more thorough than you possibly have been able to do it before. Whether you’re using Chat GPT or Gemini or another AI / LLM, these tools are a great resource to help you get started in formulating your investment decision or verifying it. Here’s how to make the most of it:
Prompt: “Here’s the executive summary, rent roll and T-12 for a 50-unit garden-style asset. Summarize market rents, vacancy, NOI, and any red flags.”
Benefit: In seconds, you get a bulleted overview. AI even flags missing items like CapEx budgets or broker comps so you don’t miss important data points.
Prompt: “Create a one-tab cash-flow model with: $600k gross potential rent, 5% vacancy, 40% expense ratio, 4% interest on an 80% LTV 30-year loan, and $1M equity. Include yearly projections with 3% rent growth and 2% expense inflation.”
Benefit: AI outputs the column headings, row labels and formulas, plus a CSV you can paste directly into Excel or Sheets. Within minutes you’ll have a full five-year cash-flow pro-forma. Compare this to the materials the sponsor presented you, and ask questions if there are significant differences.
Prompt: “Give me year-over-year rent growth and vacancy trends in <City, State>, from 2022–24, and reference Census household formation data.”
Benefit: Finding clarity in multiple data sources. AI can summarize the stats, and even cite the Bureau of Labor Statistics or Census Bureau, so you know whether your rent projections are realistic.
Prompt: “Run downside, base, and upside scenarios for a rate hike of 100bps, vacancy at 7% and expense inflation of 10%. Show the impact on cash-on-cash returns.”
Benefit: In seconds, you receive a three-scenario table that pins down risk thresholds. This allows you to be fully aware of the range of outcomes of the investment. If the range sits well with you, it’s a good sign to move forward. If it does not, it’s a clear indication you might want to consider passing on the deal.
Prompt: “Draft a one-page memo explaining pro-forma returns, market fundamentals, and risk factors.”
Benefit: This memo helps make your own due diligence clearer, faster and more defensible. Use this to compare other opportunities you might be considering.
If you’re a real go-getter and you prefer the more traditional DIY route, here’s how you can break the information down to conduct your own quick underwriting of a deal:
Collect every document you need in one place so nothing slips through the cracks:
Missing docs? Flag them right away and reach out to your sponsor. You can’t underwrite what you haven’t seen.
A one-tab Excel or Google Sheet is all you need. Here’s a step-by-step:
|
Year 0 |
Year 1 |
Year 2 |
Year 3 |
… |
Year 5 |
Formulas:
Here’s an example from a recent deal we did in Dallas:
iii. Rows for Expenses starting:
List major line items (or use T-12 total):
iv. Net Operating Income (NOI):
v. Debt Service:
Use Excel’s PMT function to estimate annual mortgage payments:
=PMT(interest_rate/12, term_years*12, –loan_amount)
vi. Cash-On-Cash Return:
vii. Extend for Growth & Inflation:
viii. Add new rows for each year with those growth assumptions.
By the end, your sheet will show a clear snapshot of NOI, debt service, and cash-on-cash over your hold period. You can graph the annual returns to visualize performance.
Numbers are only as good as their context:
A deal that looks great on a spreadsheet can turn cold if the market is softening. Always marry your model to real-world signals.
Prepare for volatility by running downside scenarios:
Seeing where the model breaks helps you avoid unpleasant surprises, make informed decisions, and better interpret reports from your sponsor throughout the holding period.
At this point, you have:
If your base-case meets your target and even the worst-case stays acceptable, it’s worth a closer look. If not, simply share your feedback with your sponsor and pass on the deal to preserve your dry powder.
No matter which path you choose, letting AI do the heavy lifting or rolling up your sleeves with a hands-on model, you’ll finish the process with the same outcome: real clarity on what you’re buying, why it makes sense (or doesn’t), and what risks you need to monitor. Underwriting doesn’t have to be a black box or a weekend-long slog. With these tools in your toolkit, you can approach multifamily investment opportunities with confidence, ask smarter questions from your sponsor, and trust that your “yes” or “no” is grounded in solid analysis.
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