Idea in short

Generally, estimating the value of a businesses is based on revenue and profit forecasts. For start-ups, these founders usually forecast these figures in a business plan. However, during the early stages of a start-up, such forecasts usually don’t exist. Founders only have a business idea and no business plan. The Berkus model circumvents this problem of quantifying the value of a start-up, which cannot be estimated using the traditional approaches.

Young start-ups often fail within 2 years of their incorporation. According to Investopedia, in 2019, approximately 90% of the the start-ups failed.

Lack of funding, corporate governance issues, incorrect product-market fit, ineffective marketing, and poor partnerships were the common reasons for start-ups’ failures.

The model

The American venture capitalist and angel investor Dave Berkus created the Berkus model as a reaction against the poor track record of start-up companies to meet their financial targets. Dave felt that he could not rely on the financial projections for startup valuation. Often, a start-up’s management forecasts aggressive revenue growth and unrealistic profit margins. Investment banks perform sensitivity / risk analysis on a company to assess its ability to repay loans before lending capital.

Likewise, the Berkus model approaches startup valuation from a risk perspective. This model assumes that a start-up’s management often forecast a rosy picture that does not reflect the real market situation.

According to Dave Berkus himself, the Berkus Method:

assigns a number, a financial valuation, to each major element of risk faced by all young companies — after crediting the entrepreneur some basic value for the quality and potential of the idea itself

This start-up valuation model assigns a value to the business idea and the start-up’s key success factors, or risk factors. Each factor counts for a maximum of US $500,000. In its original form, the Berkus model allows for a maximum valuation of US $2.5 million, including revenues, or US $2 million, excluding revenues. As Dave Berkus states:

The original matrix is too restrictive and should be a suggestion rather than a rigid form

Risk factors

The key value drivers represent risk areas that can make or break the start-up. A start-up should carefully manage these risks to be successful. The value of the start-up increases when it de-risks the key risk areas. Hence, risk reduction and company valuation go hand-in-hand. Therefore, the Berkus model uses both qualitative and quantitative factors to calculate valuation based on five factors, namely:

  1. Sound idea (represents the basic value)
  2. Prototype (reduces technology risks)
  3. Quality Management (reduces execution risks)
  4. Strategic relationships in its core market (reduces market risk), and
  5. Product roll-out or sales (reduces production risks)

The Berkus model assigns a maximum added value of US$ 500,000 for each of these value drivers. This explicitly indicates that these are maximum values that can also be decreased.

Driver Risk factors Added value (in US $)
Viable business model Base value 500,000
Available prototype Reducing technology risks 500,000
Management team capabilities Reducing implementation risks 500,000
Strategic relationships Reducing market risks 500,000
Existing customers / sales Reducing production risks 500,000
Maximum company value: Reducing investment risks US$ 2,500,000


The Berkus model sets the hurdle number at $20M (in the 5th year in business) to:

provide some opportunity for the investment to achieve a ten-times increase in value over its life

Below is an assessment of a fictitious pre-revenue startup. This example illustrates the general rules of the Berkus model:

Value driver Added Value (in US $)
Viable business model US $ 250,000
Available prototype US $ 350,000
Management team capabilities US $ 450,000
Strategic relationships US $ 200,000
Existing customers / sales US $ 50,000
Maximum company value: US $ 1,300,000

In the above example, with US $500K as the maximum value per category, I assigned the greatest value to the capabilities of the management team (US $450K) for the founders‘deep domain expertise. Logically, as the investor undertakes a lot of risks, the startup’s management team must be fully capable of achieving long term success. The startup’s prototype (US $350K) is sound. This minimizes technology risks. Ultimately, this startup achieved a pre-money valuation of approximately US $1.3 million.


Recently, Berkus stated that:

The original matrix is too restrictive, and should be a suggestion rather than a rigid form. The model should allow for higher maximum value on elements not listed in the matrix.

For example, the pre-money valuations might be higher in Silicon Valley than in New York. According to AngelList Valuation Data, the average pre-money valuation in Silicon Valley is $5.1M compared to New York City’s $4.6M. Correspondingly, this model can be easily accommodate the altered scenarios.

In Berkus’ own words:

Pre-revenue, I do not trust projections, even discounted projections

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