While our President was once best known for contending that there is art in making the deal, at the end of the day, the deal is actually based on cold, hard numbers. In 2018 some of these numbers became easier to find as one of the major databases containing information on sales of privately held companies was revamped and rebranded. The new, improved version is called DealStats (replacing the old name of Pratt’s Stats) and its website now allows for much easier searching of the 35,000 transactions dating back to 1990 that are included in the database.
Based on this information, I thought it was interesting to see some of the trends shown in the data for the multiples applied to the earnings of a company that are often used to determine a price. One of the key measures of a company’s performance is its earnings before interest, taxes, depreciation and amortization (EBITDA) – in effect the cash flow generated by its operations.
The database shows that since 1990 the median multiple of EBITDA was 4.2 for companies with revenues of less than $50 million, 3.9 for companies with revenues of less than $20 million, 3.3 for companies with revenues less than $5 million and 3 for companies with revenues less than $1 million. This reflects the generally accepted wisdom that small companies are less valuable than larger companies, mainly because they are riskier.
Looking over the recent past, the median multiple of EBITDA for companies with revenues of less than $20 million has trended as follows, presumably reflecting recent economic conditions:
| Year |
Median EBITDA Multiple |
| 2013 |
3.1 |
| 2014 |
3.4 |
| 2015 |
3.4 |
| 2016 |
3.6 |
| 2017 |
3.7 |
| 2018 |
3.4 |
The information in DealStats has to be taken with a pinch of salt. Many details of the individual transactions are not included in the database and the reported multiples are not adjusted to reflect payment terms. The effective multiple may be much lower than the reported multiple once payment over five years at a lowish rate of interest is factored into the calculation. Also, the multiples applicable to different sectors of the economy differ significantly.
However, the data does help when coming up with ballpark estimates of value.