The Art, Science, and Future of Private Asset Valuation

Private assets have typically been valued on a quarterly or even less frequent basis. In this conversation, we unpack the complexities and challenges of valuing private assets and look at the potential impact of AI on accelerating the frequency of valuations.

Robust demand for these investments due to the promise of outsized and diversified returns meant that investors were willing to overlook infrequent valuations, unlike publicly traded assets that are valued on a real time basis. Several factors are placing private asset valuation under scrutiny.

• Stale valuations: Private credit valuations often adjust slowly compared to public markets, raising concerns that portfolios are overvalued, particularly when interest rates rise.
• AI disruption in SaaS-heavy portfolios: A high concentration of private loans (roughly 20-40%) in software as a service (SaaS) companies has sparked fears that AI-driven disruption could trigger defaults.
• Retail exposure and liquidity needs: The growth of business development companies (BDCs) and semi-liquid funds has exposed individual investors to this market, demanding more frequent, accurate valuations to facilitate liquidity and manage redemption risk.
• Defaults due to rising interest rates: Rising interest rates are hindering borrower ability to service debt, while increased use of “payment-in-kind” (PIK) provisions suggests mounting distress.
• Valuation discrepancies: Research indicates that private funds may sometimes value loans higher than public markets, leading to questions about the accuracy of reported NAV.

Recent market turmoil —particularly on the private credit side—is sparking an industry conversation around the frequency of portfolio valuations. What are some of the challenges involved in accelerating valuations?

If we think more broadly about valuations in private assets, within the four major asset classes (private equity, real estate, private credit, infrastructure), and even within a single asset class depending on sector, region, or jurisdiction, you’ll see different valuation approaches, models, frequencies, requirements, data, and technology.
I started my career with a private equity software vendor, and even though it’s considered one of the more straightforward asset classes, in practice firms use multiple methodologies—sometimes five to seven. They often talk about a “football field” of valuations to strike what they believe is fair market value.

Unlike publicly traded assets, valuing private assets is challenging because we’re dealing with investments that are illiquid and opaque. This means valuation is both an art and a science. The art component is fascinating—two people valuing the same asset can arrive at different but entirely defensible values.

If we look at private credit, instruments often don’t trade; they’re mostly held to maturity. The core driver behind more frequent valuations is visibility. Valuing every six months versus every three months or monthly—or even weekly—is about understanding what’s happening within your portfolio. You can’t wait six months when macro or geopolitical events are unfolding daily.

How does liquidity impact valuation?

From a liquidity perspective, retail versus institutional investors behave very differently. Institutional investors understand cycles, but retail investors may panic when conditions change and they want to redeem their investments. That creates real liquidity pressure.

We’ve recently seen redemption gate announcements from several major firms and growing industry discussion around liquidity and credit risk. From a fund manager’s perspective, it’s tricky, because these vehicles offer some liquidity, but they hold illiquid assets. That makes it critical to understand underlying valuations in near real time.

What methodologies are typically used to value the four major private asset classes?

Starting with private equity, let’s look at leveraged buyouts. You acquire a company using debt, and value creation comes from paying down debt and improving operations. A key metric is EBITDA, and a common approach is an EBITDA multiple—say 10x or 12x on LTM (last twelve months or NTM (next twelve months) —to derive enterprise value, subtract net debt, and arrive at equity value.

Another widely used methodology across all four asset classes is discounted cash flow (DCF), where analysts forecast cash flows and discount them back. In real estate, you look at rental income, expenses, and net operating income. In infrastructure, DCF models may span 10, 20, or even 30 years. In private credit, you model interest payments, principal repayments, and fees.

DCF is common across asset classes, but the data, assumptions, and complexity vary significantly. In private assets, it’s not unusual to see spreadsheet models that are hundreds of megabytes in size. Public markets are very different—you start with market price. Private markets start with fundamentals that are not publicly available and most of the time, restated by the General Partner.

How long does it typically take to value a portfolio?

Historically, valuations were done quarterly because managers needed fundamental data from portfolio companies. Teams are often small—one person may cover five to ten investments.

From a timing perspective, it typically takes weeks, sometimes a month or more. You need to collect data, validate assumptions, restate forecasts, apply judgement in calculations, and go through committees and approvals.
The bottom line is that deriving accurate valuations is a complex, manual, and time consuming process.

How often does it make sense to revalue a portfolio?

In Europe, quarterly is still standard. Monthly is emerging. Daily or weekly valuations are mostly being pushed in the U.S., especially for private credit, where borrower default risk matters more.

For assets like real estate or infrastructure, daily valuation doesn’t always make sense. Instead, scenario analysis becomes critical—what happens if interest rates rise or inflation changes? Private market clients increasingly run daily scenario analysis even if formal valuations are less frequent. That’s where better decision making happens.

How might AI accelerate or improve valuation frequency?

Our view is that AI will make valuations more frequent and significantly reduce manual effort. If AI can extract, aggregate and normalize raw data faster that’s a great start. Secondly, enabling analysts and investors to answer questions through natural language prompts, that’s a huge value add.

Some market players claim they can support weekly or daily valuations. I can’t verify that, but I do believe AI can streamline the process—especially data aggregation from systems like Bloomberg and unstructured sources—so professionals can focus on decision making rather than manual modeling. Efficiency plus faster time to information—that’s the future.