“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:
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 through 5 steps:
A private markets liquidity and pacing model is a forecasting tool that helps Limited Partners project future cash flows and NAV trajectories across their portfolio of private funds, and use those projections to size and time new commitments. Without one, investors risk either over-committing and facing liquidity shortfalls, or under-committing and failing to reach their target allocation. A well-built model combines fund-level initial conditions with calibrated assumptions about call rates, growth rates, and distribution timing to produce a coherent forward view of the portfolio.
The foundation of any robust model lies in defining clear objectives. Start by answering the key questions the forecasting exercise should answer:
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:
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:
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:
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:
This iterative process will keep your liquidity and pacing model relevant and effective over time.
Building a robust private funds liquidity and pacing model requires five steps: defining clear objectives around NAV targets and liquidity needs; gathering comprehensive fund-level data including characteristics and capital accounts; setting up a forecasting model calibrated either top-down by asset class or bottom-up by fund; running scenario analysis across baseline, upside, and downside assumptions; and iterating the model quarterly as new data becomes available. The quality of the output is directly dependent on the quality and currency of the input data. A model that is not regularly updated becomes unreliable quickly, particularly for funds in active investment or harvesting phases.
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:
What is a private funds liquidity and pacing model?
A liquidity and pacing model is a forecasting tool used by Limited Partners (LPs) to project future cash flows and NAV trajectories across a portfolio of private market funds. It helps investors anticipate when capital will be called, when distributions will be received, and how NAV exposure will evolve over time. The model is used to manage liquidity reserves, plan new commitments, and ensure the portfolio stays aligned with long-term allocation targets.
Why do LPs need a commitment pacing model?
Private market funds call and return capital over multi-year periods, creating significant uncertainty around timing and size of cash flows. Without a pacing model, investors risk over-committing and facing liquidity shortfalls, or under-committing and failing to reach their target allocation. A pacing model allows investors to size and time new commitments deliberately, taking into account the expected behaviour of existing funds and the portfolio's overall cash position.
What data is needed to build a private funds pacing model?
The minimum data requirement is a current snapshot of each fund in the portfolio, including fund characteristics (GP, strategy, vintage year, fund life, termination date) and capital account information (total commitment, cumulative capital called, cumulative distributions, current NAV, and remaining unfunded commitment). This data provides the initial conditions from which cash flow and NAV projections are generated. The accuracy of the model is directly dependent on how current and complete this data is.
What is the difference between top-down and bottom-up calibration?
Top-down calibration sets forecast assumptions at the asset class or strategy level — for example, applying a single set of call rate, growth rate, and bow factor assumptions to all buyout funds in the portfolio. Bottom-up calibration sets assumptions at the individual fund level, drawing on fund-specific information such as GP guidance, historical pacing, or recent portfolio developments. Top-down approaches are faster and work well for younger funds or future commitments where fund-specific data is limited. Bottom-up approaches are more accurate for mature funds or those deviating significantly from asset class norms, but require more data and judgment.
How often should a liquidity and pacing model be updated?
Best practice is to update the model quarterly, aligned with the cadence at which GPs report NAV and capital account data. Each update should refresh the initial conditions for all existing funds (NAV, unfunded, cumulative cash flows) and review whether forecast assumptions remain appropriate given any new information about fund performance or market conditions. Models that are not updated regularly become unreliable quickly, particularly for funds in active investment or harvesting phases where cash flows are largest.
What is scenario analysis and why does it matter in a pacing model?
Scenario analysis involves running the model under multiple sets of assumptions — for example, a base case, an upside, and a downside — to understand how sensitive the portfolio's cash flow and NAV projections are to changes in key inputs. It is particularly important in private markets because the model inputs (growth rates, call rates, bow factors) are uncertain and can vary significantly across market environments. Running scenarios helps investors identify stress points — such as periods where liquidity needs could exceed available cash — and build appropriate buffers into their commitment pacing strategy.