I analyzed 47 multifamily OMs — here are the 5 numbers that actually matter
I’ve been a rental property investor for several years and I review quite a few offering memorandums from brokers.
After running the numbers on dozens of them, I started noticing the same pattern: the deals that looked “too good to be true” almost always had problems in the same 5 areas.
I finally built a better system to analyze these OMs more quickly and honestly.
Now I can instantly see: • Realistic cap-rate valuation and suggested offer price • What-if scenario modeling (what if rents grow slower, expenses jump, etc.) • Clear flags for overly optimistic projections
Has anyone else had the same experience reviewing OMs? What’s the biggest red flag you look for when a broker sends you a deal?
Looking forward to hearing your thoughts.
#DealAnalysis #MultifamilyInvesting #RealEstateInvesting #CapRate
Most Popular Reply
Let's go through them one by one.
1. Unrealistically high rent growth assumptions
With a 2 minute search on apartments.com and any knowledge of the city, this one is easy to spot. My rent assumptions are always mine, not the broker's.
2. Expenses shown way lower than realistic post-purchase numbers (taxes, insurance, maintenance)
The T12 is probably the best starting place, not the broker's projections. On small properties you probably have to jack up those costs because the owner may be contributing a lot of free labor to get work done.
It is very rare that we take expenses down in our projections. I can only think of two times we did that. On a small property, we learned the property was paying for the sellers home insurance. We clearly were not going to continue paying for that. On the property we bought most recently, the trash costs were 2x our other two properties of similar size because they had configured their dumpster enclosures in a way that a truck could not use 6, 8, or 10 yard dumpsters. We took the trash costs down, but it required a $100,000 investment to completely redo the enclosures.
It is always useful to compare to the NAA averages, but even within a city or within a submarket expenses can vary widely based on how the property was constructed and managed. We own two apartments within 1/2 mile of each other. One was built in 1970 and the other in 1976. They are almost identical in square footage and number of units. They are nearly identical on paper, but they have very different operating expenses. This is one of those areas where experience makes a big difference and I do not trust averages or an AI model to give me the best answer.
3. Overly optimistic occupancy & stabilization timelines
Rookies will use the broker's timeline. My timeline assumptions are mine, not the broker's.
4. Aggressive exit cap rates that didn’t match current market conditions
Anyone can make a terrible deal look great, simply by using a lower reversion cap rate. If the person underwriting the property doesn't understand that, they should not be buying a commercial property. They are going to get destroyed. In my underwriting, the reversion cap rate is always higher than the purchase cap rate
5. Little or no sensitivity / what-if modeling
It is super easy with a spreadsheet to create what-ifs by modifying one or more inputs.
Most people really need a class called "Underwriting 101", which covers all of these. For anyone buying a multi-million dollar asset, you had best learn how to underwrite properly.
Following up on @Drew Sygit comment, if this is to sell an AI solution, I would caution all rookies to stay away. AI is useful in specific cases, but I wouldn't trust them to underwrite a deal.
I've delved heavily into AI. AI's outputs are about as reliable as many brokers. Why? Because it takes what it sees on the internet and regurgitates what it thinks is right based on probabilistic modeling. It can handle straight math, but qualitative outputs are probabilistic in nature. If all the public information on the internet is overstating one metric, when the AI underwrites a property, it will also overstate that metric. Garbage in, garbage out.
As an example, in early 2022, if an AI were underwriting a deal, it would know the prevailing low cap rate and use it. The Fed was talking about how inflation was transitory and that interest rates would remain low. Investors with more than 2 decades of experience were expressing caution. An AI model fed on current information would have gone headlong into that low cap rate, and the buyer would have overpaid for that asset.
In any event, that is my experience from having underwritten many hundreds of deals over the past 15 years.