The hidden drivers behind a “high” or “low” biotech valuation

By Dr Patrik Frei, expert trainer of Pharma-Biotech Product & Company Valuation

Valuation is often presented as a number. But in reality, it is a reflection of assumptions.

Two companies can have similar assets, target the same indication, and sit at the same development stage, yet end up with different valuations. To an outside observer, that difference can seem arbitrary. It rarely is.

Behind every valuation lies a set of explicit and implicit assumptions that shape the outcome. Understanding these drivers is what separates a rough estimate from a meaningful valuation.
 

⚖️ Valuation starts where certainty ends

Unlike many other industries, biotech valuation is built on future potential rather than current performance.

Before market entry, there are no product revenues. Instead, there is a sequence of development steps, each requiring time, capital, and carrying a risk of failure. This means that value is not just about future cash flows, but about the likelihood of ever reaching them.

That is why structured approaches such as risk-adjusted NPV are widely used in life sciences. They force a clear separation between:

• expected future economics, and 
• the probability of achieving them 

But even with a robust framework, the outcome depends heavily on the inputs.
 

🔑 The five drivers that shape valuation outcomes

Across both product and company valuation, a consistent set of drivers explains most differences in outcomes.
 

1. Probability of success

Perhaps the most decisive factor is the assumed probability that a product reaches the market.

Small differences here have a disproportionate impact. Moving from a 10% to a 20% probability does not just double perceived likelihood, it can significantly shift valuation.

Importantly, probability of success is not purely statistical. It reflects:

• strength of preclinical and clinical data 
• mechanism of action 
• regulatory precedent 
• indication

This is why two similar assets can be valued very differently. The science may look comparable, but confidence in that science is not.
 

2. Development timelines and costs

Time is a critical but often underestimated variable.

Longer development timelines:

• delay value realisation 
• increase cumulative costs 
• increase exposure to risk

Similarly, assumptions on trial size, complexity, and cost can materially affect valuation. A more conservative development plan will typically reduce valuation, even if the end market opportunity is unchanged.

In practice, these assumptions are closely linked to regulatory strategy and indication choice, making them both scientific and strategic decisions.
 

3. Market assumptions

Once a product reaches the market, valuation becomes highly sensitive to commercial assumptions.

Key drivers include:

• target patient population 
• pricing and reimbursement 
• market penetration 
• competitive dynamics

Peak sales estimates often receive the most attention, but the shape of the commercial curve matters just as much. How quickly a product reaches peak sales, how long it sustains them, and how it declines toward patent expiry all influence overall value.

Two assets with identical peak sales can have very different valuations depending on these dynamics.
 

4. Discount rate and non-development risk

While development risk is typically handled through probability of success, broader uncertainties are reflected in the discount rate.

This includes:

• market risk 
• financing risk 
• execution risk 
• macroeconomic factors 

The discount rate is often treated as a technical parameter, but in reality it is a reflection of perceived risk at the company level. Strong management, clear strategy, and a credible development plan can justify lower perceived risk and therefore higher valuation.
 

5. Deal and financing context

Valuation does not exist in isolation. It is always tied to a context.

The same asset may be valued differently depending on:

• whether it is being licensed, sold, or financed 
• the stage of negotiation 
• the availability of alternative options 
• the strategic priorities of the parties involved

For example, a pharma company may value an asset higher if it fills a strategic gap in its pipeline. An investor may apply more conservative assumptions to reflect portfolio risk.

This is why valuation is often better understood as a range rather than a single number.
 

📊 Why assumptions matter as much as models

A common misconception is that valuation is primarily about choosing the right model.

In reality, many models in biotech valuation are structurally similar. What differentiates outcomes is not only the formula, but also the assumptions that feed into it.

This has two important implications.

First, transparency matters. A valuation is only as credible as the assumptions behind it. Being explicit about these assumptions allows for better discussion, alignment, and decision-making.

Second, sensitivity matters. Small changes in key inputs can lead to large changes in value. Understanding which assumptions drive the result is essential for interpreting any valuation.
 

🎯 From number to decision-making tool

A well-constructed valuation does more than produce a number. It provides insight into:

• where the key risks lie 
• which milestones are value inflection points 
• how much value is created by progressing through development stages 
• how different scenarios impact outcomes

For biotech companies, this informs fundraising and licensing strategy and positioning.
For pharma, it supports portfolio prioritisation and deal-making.
For investors, it helps distinguish between scientific promise and economic value.