5 Steps to Build a Robust Private Funds Liquidity and Pacing Model

“We need a pacing and liquidity model that actually works.”

This is the cry of many Limited Partners (LPs) trying to navigate the complexities of private markets such as Private Equity, Venture Capital, Private Debt, Real Estate, and Infrastructure. Without a well-structured model for private funds cash flows and NAVs, your entire private markets program is at risk.

A poorly designed model can lead to:

• Missed NAV and liquidity targets
• Underestimating or overestimating risks
• Turning pacing into pure guesswork

But here’s the truth: building a reliable model doesn’t happen overnight. It requires a methodical approach. This blog will guide you through a 5-step framework to create a liquidity and pacing model that works.

The foundation of any robust model lies in defining clear objectives. Start by answering the key questions the forecasting exercise should answer:

• Do you aim to hit a specific NAV target?
• How much liquidity will you need?
• How can you best mix private strategies to hit your target return and risk profile?

These questions help you align your model with the broader goals of your private markets program, ensuring that every decision is driven by purpose rather than guesswork.

A model is only as good as the data it relies on. To set up your private funds liquidity and pacing model, collect a comprehensive and up-to-date snapshot of your portfolio. 

For each fund, ensure to collect:

• Fund characteristics: Fund attributes such as GP, stage and substage, vintage, age, and termination date are critical to understanding fund behavior. These characteristics often account for much of the variation in performance, making it essential to track them closely for effective modelling
• Capital accounts: Forecasts should align with the fund's initial conditions, including the total commitment, amounts drawn and distributed to date, as well as the latest NAV and unfunded commitments.

Having a clear understanding of your portfolio’s current state is critical before building projections.

To build a strong foundation for your pacing model, consider leveraging established frameworks like the Yale Model. Applying the model to your portfolio requires a system that can:

• Import the latest portfolio data, ensuring all funds are included and their characteristics and capital account statements are up-to-date.
• Allow assumptions about future fund investments, which are critical for pacing and liquidity assessments.
• Enable calibration of forecasts either top-down (based on fund strategies) or bottom-up (on a fund-by-fund basis).
• Generate cash flow and NAV forecasts at the desired frequency (e.g., quarterly) and for the chosen horizon, ensuring consistency across outputs.

A well-structured setup helps you model cash flow patterns and NAV evolution efficiently, eliminating the need to reinvent the wheel each time. For simpler portfolios or basic modeling needs, a spreadsheet setup may suffice. However, for larger portfolios or when running multiple scenarios, a dedicated software solution might be essential.

Scenario analysis is crucial for evaluating the resilience of your model. Your scenarios should align with the objectives outlined in Step 1 and address the following:

• Scenario Variety: Ensure your scenarios reflect your strategic goals. Are you focused on a single “baseline” scenario, or do you need to explore “upside” and “downside” scenarios? 
• Historical vs. Forward-Looking Data: Strike a balance between past performance trends and expectations for future market conditions - or consider them as separate scenarios
• Top-Down vs. Bottom-Up Overrides: For younger funds or future commitments, top-down assumptions based on asset class may suffice. However, for older funds or those deviating from broader trends, it’s essential to incorporate specific fund-level insights.

Testing a variety of scenarios ensures your model remains flexible and robust, even in changing market conditions.

A good model is never static. Regularly revisit and update it to reflect:

• New portfolio data as it becomes available
• Deviations from initial assumptions
• Evolving market conditions and objectives

This iterative process will keep your liquidity and pacing model relevant and effective over time.

Building a liquidity and pacing model takes effort, but it’s a crucial part of optimizing your private markets strategy.

Here’s how we can help:

• Explore a practical example of this framework in action by checking out our blog on using the TA model and 2025 data to forecast key asset classes. It’s a hands-on guide that puts these concepts into practice.
• Reach out to Tamarix to discover how we can help you build smarter, more effective forecasting models for private markets.