Owners often establish valuation expectations by reasoning backwards from their perceived post-closing financial needs. For example, an owner might hold the following basic assumptions:
- Annual post-closing income requirement equivalent to the ownership period = $400,000
- Expected annual investment returns on after-tax closing proceeds = 5%
- Total investable assets necessary to produce $400,000 = $8 million (i.e., 5% X $8m = $400,000)
- Investable assets apart from business = $1 million
- After-tax net proceeds required from a business sale = $7 million
- Debt in the company to be paid at closing = $4 million
- Combined tax rate for local, state, and federal = 35%
- Tax basis in the business = 0
- Seller’s transaction costs = 4%
- Business EBITDA = $1.5 million
With the above assumptions, a reverse engineered value is a simple algebra problem. How much must the business be sold for to realize $7 million after taxes, transaction costs, and debt payoff? Let X be the total sale price:
$7 million after tax proceeds = (X – $4 million debt) – (X times (1 – (35% tax rate + 4% transaction costs))
$7 million + 4 million/.61 = X
X = $18,032,787 rounded to $18 million
According to the reverse engineered method, the sale price of the business would need to be $18 million to deliver $400,000 post-closing annual income.
As a sobriety check, one might compare the EBITDA multiple to other industry transactions. The current calculation would be $18 million required sale price divided by $1.5 million EBITDA = 12X.
The above seems entirely reasonable until one realizes that other transactions in the same industry were trading at a 6X multiple or EBITDA or below not 12X. In other words, the reverse engineering method produced a valuation about twice as high as other transactions from the same industry.