by Brian DeChesare Comments (9)

How to Make Investment Recommendations in Private Equity Interviews and Case Studies in 4 Simple Steps

Private Equity Investment Recommendation

Is there a “deal-breaker” question?

A single question so important that an incorrect answer would sink your chances in interviews?

You bet.

One example is your “story” – what you say when someone asks you to walk through your resume/CV or to introduce yourself.

Make a poor first impression in the first 2-3 minutes, and you’ll never get the chance to make a second impression.

But in private equity and buy-side interviews, there’s another deal-breaker question that can just as easily ruin your chances:

Would you invest in or acquire this company?

This question is the goal of most case studies and modeling tests.

But I’ve never seen anything that generates so many poor responses.

It might be the #1 reason why so few candidates receive offers at private equity firms, even though every banker alive wants to work there.

WARNING: Red Alert

I have to warn you in advance: if you have no idea how to answer this question, chances are you do NOT belong in a buy-side role.

It’s similar to the “how to start a hedge fund” question: if you don’t already have preferred strategies and example investments to point to, you have no business starting a fund.

I’m pointing this out because I’ve seen a lot of rationale like the following:

“Well, I know how to do deals… and I’m good at running processes… I want something more interesting than pitch books! Private equity, here I come!”

Wrong, wrong, and wrong again.

Sure, there is some skill set overlap, but you spend most of your time in PE looking at deals and quickly deciding why you should not pursue them.

The mindset is completely different because you’re looking for red flags, not “top investment highlights.”

With that said, it is possible to “take the Red Pill” and improve your skills.

To kick off your training, I’ll introduce Morpheus explain what NOT to do:

The Top 3 Mistakes

I get to see a lot of last-minute presentations and the specific struggles that students had when making recommendations. Here are the top mistakes:

Mistake #1: No Clear Recommendation

I spoke with a Partner at a PE fund a few years ago who confirmed that this one was the most common mistake:

“We give people a company, ask them to write a page recommending for or against investing in it, and 90% of the time they can’t even give a clear ‘yes’ or ‘no’ response.”

If the first line of your presentation or written document does NOT contain an unqualified “Yes” or “No,” then you are getting it all wrong.

If you’re uncertain, or you think it’s a “maybe,” translate that to “No.”

Mistake #2: Left Brain Over Right Brain

The second-most common mistake is focusing too much on the model and not enough on the reasoning behind it.

I’ve seen some candidates spend days on linking dozens of small and insignificant items on the Balance Sheet and Cash Flow Statement, or figuring out exactly how to project CapEx in the most precise way possible.

Even if you have a week to complete the case study, this is 100% the wrong approach.

You’d be much better off with a simpler model and more time spent on the reasoning behind each part of it.

Mistake #3: No Consideration of Different Scenarios

This one is the #1 mistake made by engineers and people from math/science backgrounds: they always believe there is a “correct” answer.

But investment analysis is fundamentally different from solving a math or physics problem (well, except for quantum physics, maybe…).

Since you are predicting the future, it’s not about getting “the right answer,” but instead coming up with a plausible decision that you can justify.

Think of investment recommendations like courtroom trials rather than math or science problems: the goal is to assemble the evidence, make a compelling case, and then defend your views as well as you can.

There may not be a clear answer one way or the other, but you have to consider a range of different outcomes to argue for your point of view.

To do this, you must build in support for different scenarios. That could mean Base, Upside, and Downside Cases, but you could set up your analysis in many different ways.

But if you consider only one set of numbers, you are wrong.

These “scenarios” do not have to be complicated; you can 80/20 the process and come up with simple numbers that get the job done.

The 4-Step Investment Recommendation Process

To make investment recommendations successfully, here’s the 4-step process I use and recommend:

  1. Determine Your Investment Criteria – For example, many private equity firms target a 20% IRR and plan to hold companies for 3-7 years; some growth equity firms target a money-on-money multiple and might aim for a 3x multiple in the Base Case and a 1.5x multiple in Downside cases.
    The specific numbers don’t matter as long as they’re in-line with these ranges. For example, a 20% or 25% IRR would be fine, but you shouldn’t target a 50% IRR in a traditional buyout.If the firm gives you its targeted IRR or multiple, go with their numbers; if not, just make an assumption and state your targets upfront.
  2. Create and Examine the Numbers in the Base Case – If the company or asset continues to operate “as is,” what are the IRRs and multiples?If they suggest numbers or give you “management’s projections,” you could use those (though “management’s projections” should probably be the Upside case).

    But if not, you could assume that the company continues to grow revenue at a steady but declining rate, and keeps its margins, CapEx, and Change in Working Capital in the same range as percentages of revenue or the change in revenue.

    If you have access to industry data or other companies’ numbers, sure, use them to foot your assumptions.

    If there is no way to achieve your targeted IRR and/or multiple in this Base Case, then your work is done: it’s an easy “no” recommendation.

    On the other hand, if you get an IRR or multiple that’s in or close to the targeted range, you keep going and see how well it holds up in more pessimistic cases.

  3. Test the Downside Cases – You don’t need a complex model for this part – you just need some way to assess the IRR and multiples when the company or asset underperforms.PE firms know that not every investment will be a home run, but they also cannot afford to lose all their money in an individual deal.

    So unless you’re given specific targets, a 1.0x multiple in the Downside case might be appropriate.

    Some firms will target a 1.5x or 1.2x multiple, but in reality, they want to avoid losing money when a deal goes south.

    It’s harder to explain what to use for assumptions like revenue growth and margins in these downside cases, but a few ideas include:

    • Look at companies that are performing worse in this industry and use their figures. For example, if one company is losing market share and its revenue is declining by 5% per year, maybe the Downside case for your company can assume that its growth falls from a positive percentage to negative 2-3%.
    • Assume a reversion to the mean. For example, if the industry-wide average for hotel occupancy rates is 65% but your hotel company has a 75% rate, see what happens to its revenue and profits and the IRR when the occupancy rate declines to 65%.
    • Go back to the last recession. In real estate, you might go back to the last market downturn and use occupancy rates and rental income growth rates from back then in your Downside case(s). This approach also works for industries like commercial banking that are heavily linked to GDP growth.

    Once you have the numbers, see if you can still achieve that lower targeted multiple. This part is the most important step because it tells you whether or not you should pursue the deal.

    For example, if the multiple drops from 2.5x to 0.5x when the company’s growth reverts to the industry mean, and there’s a decent chance of that happening, it’s an easy “no”: the risk of losing money is too high.

    But if the multiple only drops to 1.2x when that happens, you could more easily justify a “yes” decision because there is a lower risk of losing money.

  4. Make a Decision and Back It Up with Qualitative Criteria – You can and should use market data and qualitative factors to support your scenarios, but once you’ve made a decision, you need to bring them back in and use them to justify that decision.

    For example, many PE firms are looking for companies with high switching costs, a high percentage of recurring revenue, a strong “moat,” and relatively low CapEx requirements.

    Whether or not a company meets all these requirements is subjective, so you could complete your analysis first and then spin the qualitative facts to support your decision.

    In real life, you don’t necessarily think about investment decisions like this; the qualitative criteria might be the most important part.

    However, you are under extreme time pressure in most case studies, which means that you have to focus on the tangible part – the numbers – first.

Your Final Recommendation

The result should be a recommendation that reads like the following:

“Based on the fact that we could achieve an IRR of 20% and a MoM multiple of 2.5x in our Base Case and a 1.2x multiple even in more pessimistic scenarios, we recommend doing the deal and acquiring Company X for an EV / EBITDA of 10.0x.

The company has been spending progressively less on CapEx as a % of revenue over time, even as it has grown its revenue, and we expect its capital efficiency to improve even more.

Even if its growth or margins decline back to the levels seen in the last recession, the math still works; for the deal not to work, its revenue growth would have to decline to (10%), which is well below even the worst-performing company in the industry.”

To show you this approach with real companies and assets, I’ll go through two examples, both taken from our courses (Financial Modeling Mastery course and Real Estate Financial Modeling):

Example #1: 7 Days Inn Investment Recommendation – “Normal” Private Equity

This case study was about a “budget” hotel chain based in mainland China that Carlyle acquired for $687 million USD; the company was growing rapidly, at 30% per year, and was shifting from Leased & Operated (L&O) hotels to Managed Hotels, i.e. the franchise business model.

A franchise business can be attractive because the company can spend less on CapEx, employees, and buildings, and can collect royalties from franchisees that license its system.

But there’s also a downside: it’s much tougher to ensure consistency and individual operators may not run their hotels or restaurants as well as the parent company can.

The key metrics here are the average occupancy rate and the average daily rate (ADR), along with the total number of hotels and available rooms.

The central question is simple: Will the company’s shift to a franchise business model and the resulting higher margins offset the possible decline in occupancy rates?

You can see the full exercise in the case study document.

Here’s how we applied the 4-step process above to this case study:

  1. Determine Your Investment Criteria
    The instructions give no investment criteria, so we create our own: a 20% 5-year IRR in the Base Case and at least a 10% 5-year IRR in Downside Cases (and in the absolute worst case scenario, we want to avoid losing money).The usual qualitative criteria also apply here: a fast-growing and highly fragmented market, strong positioning vs. competitors, a strong management team, and so on.
  2. Create and Examine the Numbers in the Base Case Here, we extended historical trends and used the company’s internal estimates for the # of owned/operated and managed hotels over the next few years, which were in its investor presentations.

    We assume increasing ADRs, a bit lower than the inflation rate to be conservative, and slightly higher margins along with significantly lower CapEx because of the business model switch.


    The deal seems like a clear winner in this scenario, with a 20% IRR assuming the same exit and purchase multiple, and an IRR above 10% even if the multiple contracts by almost 50%:


    So we have no reason to rule it out at this stage.

    (NOTE: Normally, it might be too aggressive to assume the same purchase and exit multiple, but the complete business model switch might justify it here.)

  3. Test the Downside CasesNext, we test the more pessimistic cases and assess what happens if the company opens fewer hotels than expected, incurs higher-than-expected operating costs, or achieves a lower-than-expected occupancy rate.

    Neither of the first two makes a huge difference; IRRs stay in the 15-20% range.

    The biggest risk, by far, is a falling occupancy rate:


    In the Base Case, we assumed a decline from 80% to 76% over 5 years; at a (5%) differential it would decline to 71% by the end, and at a (10%) differential it would decline to 67%.

    At first glance, this seems to kill the deal: how could we recommend a transaction where the IRR turns negative in downside cases?

    But we recommend the deal anyway because these occupancy rates do not seem plausible – data from the Asia-Pacific region shows figures close to 70%. And that data include luxury hotels, where the occupancy raters are almost always lower.

    The closest industry comparable, Home Inns, also maintained an 80-90% occupancy rate over the past 5 years.

    So based on the data, we don’t think this outcome is plausible.

  4. Make a Decision and Back It Up with Qualitative CriteriaAs a result, we recommend doing the deal and acquiring 7 Days Inn for $687 million (7.4x EBITDA) because:
    • We can get to a 20% IRR in the Base Case scenario, and even in more pessimistic cases it falls to only ~10%.
    • It’s in a fast-growing, highly fragmented market, the business model shift is very positive, and over 50% of the equity in the deal comes from a management rollover, indicating high conviction and commitment from the team.
    • The biggest risk – average occupancy rates declining from 80% to 65-70% – would kill us, but we think it’s exceptionally unlikely. And even if that happens, there are ways to mitigate the risk (e.g., we could recover about 75% of our equity if we liquidated the company at the end of the period).

You can see the full recommendation here.

Example #2: The Lyric Investment Recommendation – Real Estate Private Equity

You can get the case study document here.

The basic idea is that it’s a fairly nice multifamily property (i.e., apartment building) in Seattle with an asking price of $120 million.

If we’re targeting a 10% leveraged IRR, should we acquire the property?

Since properties are simpler than companies, the central question here is also simpler: with reasonable rental growth and Cap Rate assumptions, can we achieve that IRR?

Or will a possible downturn in the property market ruin our chances?

In real estate models, people often ignore the cyclical nature of the market.

You’ll see 10-year models where people assume that rent grows at 3-4% each year, which is obviously not true.

So we want to see how plausible that 10% IRR target is under various outcomes: stable growth, a decline followed by a recovery, and a high-growth phase followed by a decline and then a recovery.
Determine Your Investment Criteria
This one’s easy because they give it to us: a 10% leveraged IRR over 10 years.
Create and Examine the Numbers in the Base Case

In this case, rent and other income grow at 3% per year, the vacancy/collection loss rate stays steady at 5%, and expenses and the replacement reserve also grow at 3% per year.

CapEx, Tenant Improvements, and Leasing Commissions grow at 2% per year.

The going-in Cap Rate is 4.6%, so we assume a slightly higher 5.5% Exit Cap Rate because buildings become older and less appealing after 10 years.

With the same 4.6% Exit Cap Rate, the deal works; with a 5.5% Cap Rate, though, it does not:


At this point, we could stop and immediately recommend against the deal – but let’s go through the other steps to see what happens.
Test the Downside Cases

In the other cases, as expected, it looks even worse: the IRR drops to 2-3%.

And we think both these cases – declines followed by recoveries – are far more likely than steady-state growth, given the state of the Seattle property market:


Property prices and other metrics had already begun declining at the time of this case study, further suggesting a slight downturn (at least).

Make a Decision and Back It Up with Qualitative Criteria

So this one’s an easy “no”: we can’t even achieve the 10% leveraged IRR in the Base Case unless we assume the same Exit Cap Rate or much higher rental growth of 6-7%.

It looks even worse in other cases, and we have strong reason to believe that the market is near its peak, meaning it’s not a great time to be a buyer.

If this deal had come along 3-4 years ago, it might have made more sense.

To Invest or Not to Invest?

There’s no question more important in private equity and other buy-side interviews.

But if you’re coming from an investment banking background, you are not trained to answer this one at all.

Sure, you can build financial models, but you’re not used to thinking critically about companies and deals.

You have to re-train yourself not to focus on “Investment Highlights,” but on all the ways a deal could turn into a disaster – and then assess how likely each one is.

Do that, and this question will go from a deal-breaker into a slam-dunk success.

For Further Reading and Watching:

M&I - Brian

About the Author

Brian DeChesare is the Founder of Mergers & Inquisitions and Breaking Into Wall Street. In his spare time, he enjoys memorizing obscure Excel functions, editing resumes, obsessing over TV shows, traveling like a drug dealer, and defeating Sauron.

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Read below or Add a comment

  1. Would appreciate more new case studies on BIWS for preparing PE/HF and VC interviews!

    1. Thanks for your feedback. By my count, there are 6 full case studies on PE and LBO models, and several shorter ones:

      Shorter Exercises:

      And then there are a bunch of examples for hedge funds as well if you do a search for “stock pitch”

      And there are more coming later this year.

      I’m pointing this out because usually when we get these types of questions/requests, it’s because you haven’t seen everything that’s already available.

      While we do want to add more on PE in the future, we feel that there’s already quite a lot there and so it is probably not the highest priority right now.

  2. This is well written and an interesting read Brian, even for myself working within PE.

  3. When is the best time to start networking for IB jobs at top M&A shops/BB firms/ Large PE funds (lateral moves)? I’ve been networking for a couple of months but most places seem to have already gone through their cycle/aren’t hiring and the only options left are small boutiques. Is there a better time to be reaching out to colleagues and headhunters? Granted I’m assuming some of this is coming from the abismal financial markets.

    1. Most firms have started hiring earlier, so you’re better off starting earlier… but if you miss the start date, I don’t think there really is a “best time.” It’s more of a year-round process at smaller firms, so you’re definitely better off going for boutiques if you got a late start. I would continue with the small boutiques for now, and then reach out more aggressively to the big firms toward the end of the year, right before they begin to recruit new analysts. It’s kind of depressing that big firms attempt to recruit analysts just out of university who don’t know anything yet, but that’s just how it is.

      1. Thanks!

  4. This is great to read. Certainly will be useful as I look to transition to PE within the next year or so. Also thankful for the other PE focused articles you have produced in the past as they basically form the basis of my preparation in the process.

    1. Thanks! Glad to hear that these articles have been helpful for you.

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