DCF Analysis applied to everyday decisions

Often you are faced with decisions about whether to pay a certain sum up front, or 6 or 12 easy installments – for Auto Insurance, Website hosting, and a myriad of other services.  Often you make the decision based on how “flush” you feel with cash.  When you are a boot-strapping entrepreneur, that means you almost always take the easy monthly payments.  But what is it really costing you?  Should you make the effort to pay up front – perhaps borrowing from a credit card or tapping a line of credit?

Of course, the answer is, “It Depends.”

Discounted Cash Flow (DCF) Analysis

DCF Analysis is a technique that allows you to compute the implied Annual Percentage Rate (APR) of taking the easy payment plan vs. up front lump sum.  With DCF Analysis, you apply a discount factor to future cash flows to bring them back to the present so that you can compare them to an amount of money today.   Take a simple example of a credit card with an monthly interest rate of 1.5%.  If you owe $1,000 today, you can pay it off for $1,000.  Or let it ride, and pay $1,015 in a month.  DCF analysis is a way of saying that the $1,000 today is the same as $1,015 one month from now – assuming a monthly discount (interest) rate of 1.5%.  

The formula for discounting a future payment back to the present is simple:

Present Value = Future Payment * 1 / (1 + i) ^N  where “i” is the interest rate per period, “^” means “raised to the power of”, and “N” is the number of time periods in the future that the Future Payment occurs.

Internal Rate of Return (IRR)

Then Internal Rate of Return is that discount rate that which causes the future “easy payments” to be equivalent to the lump sum payment today.  When you model the payment alternatives as a stream of cash flows, the IRR is that rate at which the Discounted Cash Flow is zero.

Practical Example of DCF/IRR Analysis

I recently joined an entrepereneur peer group and will use their membership fee structure to illustrate.  You can either pay $895 up front for annual membership, or $95 per month for 12 months – at the beginning of each month.

Here are the two alternatives:

Pay $895 today

Pay $95 today, and then $95 at one month intervals for the next 11 months

So think of the second alternative as the equivalent of borrowing $800 (the reduction in what you have to pay today: $895 less $95), and paying it back over 11 months.  You can state that as a stream of cash flows in Excel:

You get $800 today (cash inflow),  resulting in negative $95 per month (cash outflow) for the next 11 months.  You can use the IRR function built into Excel to determine the monthly discount rate that would cause the discounted cash flow stream to equal zero, and then multiply by 12 to get an annual rate.

C4=IRR(C2:N2)

C5=C4*12

 

 

 

 

So in this case, making the 12 easy payments is the equivalent of borrowing at an annual percentage rate of 56.88%.

Why would you ever do such a thing?  There are at least two or three reasons:

1. There may be some uncertainty about using the service for a full year, so you want to contain your risk by committing to just a month at a time.

2. You could be close to tapping out your credit cards and / or lines of credit, and you don’t want to use up any spare credit capacity.

3. Your cost of capital is above 56.88%, and this is a cheaper form of borrowing.

Over the hump, and gathering speed

I think that was the punchline for a birthday card my older brother got a few years back.  But I think it says a lot about our generation (I’m 58, he recently crossed a scary threshold).  We’re not slowing down.  I’m working harder now than I did in my 20’s, and 30’s, and 40’s – and continue to learn new things at a breakneck pace.

So as I was catching up on my Economist reading earlier this year (February 25th issue), I was nodding my head vigorously at the Schumpeter column on page 81, “Enterprising Oldies – Founding new businesses is not a monopoly of the young, even if it seems so nowadays”.

Some quotes:

The rise of the infant entrepreneur is producing a rash of ageism, particularly among venture capitalists. Why finance a 40-year-old (with a family and mortgage) when you can back a 20-year-old who will work around the clock for peanuts and might be the next Mr Zuckerberg? But it is not hard to think of counter-examples: Mark Pincus was 41 when he founded Zynga and Arianna Huffington was 54 when she created the Huffington Post.

It’s not just anecdotal evidence, but research backs up the idea that enrerpreneurship is alive and kicking among us greybeards:

Research suggests that age may in fact be an advantage for entrepreneurs. Vivek Wadhwa of Singularity University in California studied more than 500 American high-tech and engineering companies with more than $1m in sales. He discovered that the average age of the founders of successful American technology businesses (ie, ones with real revenues) is 39. There were twice as many successful founders over 50 as under 25, and twice as many over 60 as under 20. Dane Stangler of the Kauffman Foundation studied American firms founded in 1996-2007. He found the highest rate of entrepreneurial activity among people aged between 55 and 64—and the lowest rate among the Google generation of 20- to 34-year-olds. The Kauffman Foundation’s most recent study of start-ups discovered that people aged 55 to 64 accounted for nearly 23% of new entrepreneurs in 2010, compared with under 15% in 1996.

Here is a link to the full article:  Schumpeter Feb 25, 2012 Economist

The column concludes on a positive note about the implication of this phenomenon on our economic future:

The evidence that older people are if anything becoming more enterprising should help to calm two of the biggest worries that hang over the West (and indeed over an ageing China). One is that the greying of the population will inevitably produce economic sluggishness. The second is that older people will face hard times as companies shed older workers in the name of efficiency and welfare states cut back on their pensions.

Transaction Models

Transaction Modeling

For some people, a transaction model is a financial model of a complicated transaction, for example a real estate deal or an acquisition.  Transaction in this sense really means “deal.”

However, the type of Transaction Models I’m going to write about in this article refer to transactions as accounting type transactions – e.g. a sale that generates an invoice;  or a payment to a vendor.

Your accountant will tell you that each transaction consists of a debit and a credit.  But don’t let that scare you away!  You don’t have to be an accountant to do transaction modeling.  Rather you have to roll your sleeves up, and pay attention to the details that affect cash.  What you do is forecast all the cash-affecting transactions you expect to occur over some planning horizon (usually 3 or 4 months), and then tabulate them in such a way that reveals your cash balance week by week (or day by day) within that planning horizon.

Here are the most common types of transactions that impact cash:

  1. Transactions with Customers
  2. Transactions with Vendors
  3. Transactions with Employees
  4. Transactions with credit card companies
  5. Transactions with banks (loans, interest, repayment)
  6. Transactions with governments (withholding taxes, sales taxes, property taxes, etc.)

Transaction modeling is used for doing detailed cash planning.   If you can determine all the transactions that will occur for your company for a specific time period  in the future, you can determine the net change in cash for that time period.  So, if you know what your cash balance is to start with (I hope you do!),  you can estimate for each time period in the future how much cash you’ll have on hand.

Because for any time period:

Ending Cash = Beginning Cash + Net Change in Cash

Net Change in Cash = Cash InCash Out

Cash In = The sum of all transactions bringing in cash

Cash Out = The sum of all transactions sending out cash

Here’s how each of these looks:

Transactions with customers

Your Company  ==> Products or Services ==> Customers

Customers ==> Cash  [Delay based on terms] ==> Your company

Customers ==> Credit Cards [Delay based on processing] ==> Cash ==> Your Company

Transactions with Vendors

Vendors ==> Products and Services ==> Your Company

Your Company ==> Cash [Delay based on terms granted] ==> Vendors

Transactions with Employees

Employees ==> Work ==> Your Company

Your Company ==> Cash [Delay based on company policy] ==> Employees

Transactions with Credit Card Companies

Your company ==> Uses credit card for products or services ==> Increases balance owed to Credit Card company

Your company ==> Cash (for interest, fees, and principal) ==> Decreases balance owed to Credit Card Company

Transactions with Banks

Bank ==> Agrees to  loan Cash ==> Your Company

Your company ==> Cash (for principal and interest) ==> Bank

Transactions with Governments

Your Company ==> Cash  (for sales tax, withholding tax, income tax) ==> Government

Back to Transaction Modeling

So the idea is to forecast all the transactions for some period of time, and see how the cash shakes out week  by week.  Many of the transactions are easy to forecast – such as the rent payment, payroll, etc.  These are under your cotrol.   Others are not so easy to forecast:  what will sales be next week?  When will company ABC pay their invoice?  Will the bank agree to my loan request?

In upcoming articles we’ll examine different approaches to developing a Transaction Model and using it to do detailed cash projections for your business.  The same approach can be used for your personal finances as well.