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The Impending Demise of the American Analyst

Sooner or later our deals are headed to Bangalore.

Once upon a time, there was a young man who dreamed of a successful career in real estate.

This bright young chap, diploma in hand, stormed the commercial property landscape at the ripe age of 22. He knew that his ivy league education and exceptional internships and extra-curricular experience would put him on the fast track to success.

But our young hero faced a problem – paying dues. He was told to pay his dues, sit at a desk, and run numbers.

“Be and ARGUS-jockey,” they said.

“You have to know the numbers,” they said.

“You have to know how to model a deal to have any success,” they said.

Our hero was placed in front of a computer and told to model deals, stack leases, and build proformas. He didn’t leave his desk. He saw Excel more than he saw his family. He knew more about ARGUS than about how to save money. And every day, a little bit of that youthful exuberance, unbridled energy, and zeal for a career in commercial real estate died, crushed under the shear weight of computational monotony.

He was forced to do this for 8 years, until he was thirty and everyone agreed he had now “paid his dues” and could actually have some responsibility and become the deal-maker. Unfortunately, most of his ambition and zeal had been beaten out of him one model at a time. He had been obtaining and rearranging data for so long that he forgot about creativity, innovation, pushing-the-envelope, and the joy of waking up every day to new and exciting challenges. The very skills he would need to succeed as a creative deal-maker were the ones he had no time to develop while he spent every day reading and stacking leases.

He had gotten comfortable (and arrogant) in his ability to assess a deal and now passed the model-making to the hot-shot 22 year old new hire from Princeton, perpetuating the “pay your dues” commercial real estate career path. And another brilliant young mind is beaten into submission one lease abstract at a time . . .

 

I know I’m exaggerating a little in the parable above, but, really, how far off am I? How many people enter our industry as deal-makers or with any real responsibility? If it is more than 1% I will be shocked.

Now to be fair, I think it is TOTALLY reasonable to assert than anyone in our industry should know how to analyze a deal. If you don’t get the interplay of modelling and financing in commercial real estate, you will have a very difficult time finding success in our business. Every deal is based on numerical and economic assumptions and the most effective way to understand those assumptions is with a well-built model that you can manipulate and tweak as the market dictates. So understanding models is crucial, in my opinion.

But here is where I break from the classic model and our sad story above. I don’t think you have to build models to understand them. And I don’t think Americans will be building models much longer.

Here’s why –

I didn’t build my car. I had absolutely nothing to do with my car’s construction. But . . . I know it pretty well and I know how to use it. I have taken the time to get to know it and what it can and can’t do.

I know that is a bit of a silly example, but it rings true. Just because I didn’t physically input a rent roll into my model, doesn’t mean I don’t understand the occupancy rate, discounts, bad debt, etc. It is very easy for me to understand the output of a model without having done any of the input.

If you sent me a completed discounted cash flow model tomorrow, I would check out the property type, address, units/sf, profit & loss, rent roll summary, and any projections. I would look at your rent/expense growth assumptions, occupancy/rollover, discount rate, hold period, cap rate, and projected returns and decide which of those I agree with. Maybe I even take 4 minutes on CoStar to see if I agree with your cap rate (if I don’t know the submarket). The point is, after minimal training, it’s not hard to figure out 1) the assumptions that went into the model and therefore 2) if I agree with the model’s output.

So again I say that the only really tricky part of financial modeling is interpreting the output. The input is just shuffling numbers.

And that’s the problem.

For simplicity’s sake, let’s say that a 22-year-old analyst just out of college will be paid $50,000 per year or $25 per hour. Neat! Way to go, sport! Now sit at a desk and shuffle numbers all day, young fella!

Or . . .

Since it’s just number shuffling, I could pay someone in Pakistan $6 per hour to do it.

Seriously. I’m not kidding.

Anyone who has paid attention knows that off-shoring is spreading like wildfire in every industry in the country. Friedman’s The World is Flat, Pink’s A Whole New Mind, and Ferris’ 4-Hour Work Week all extensively profile the affects of off-shoring and the benefits it brings.  Off-shoring is real and here to stay. $6 per hour is not only a decent salary in developing countries, it will also get me an MBA-educated 30-something who specializes in analysis.

Yeah, his English may not be great. Yeah, it may take me a while to work out some of the kinks. But, frankly, every analyst needs training and correction and I expect there to be kinks. If I am saving $19 per hour every hour, then I am totally fine with working out some kinks.

The inherent beauty of the setup is the time difference. I can send 5 deals to my team of analysts on Bangalore at 7PM EST and having them ready and waiting for me in my inbox by 6AM the next morning. They work while I sleep.

Let me say that another way:

I am being productive while I sleep.

Cool, right?

So, you tell me. Would you rather pay an American financial analyst $25 per hour to stack leases and project cash flows during the day, or would you prefer to pay an offshore analyst $6 per hour to complete the model for you while you sleep?

Seems pretty obvious to me.

So, you heard it here from your buddy at the APJ. The days of the American analyst are numbered. In twenty years, we may have no more analyst positions in our industry based in the US. That doesn’t mean we stop needing to understand and interpret financial models. It just means we stop paying 4x as much for creating them.

– Duke

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