Because real estate tenors, ticket sizes, and lifecycle events can make cash more variable, the differences feed directly into accounting and data.

Where the complexity hits: accounting and P&L

At the end of the day, cash flow is the name of the game in an accounting system. Cash flows decide how amortization and P&L will look. Managers map out expected cash flows to come up with amortization. But with real estate, there’s a lot that goes into mapping out expected cashflows which ultimately is used to come up with the yield that’s required to compute that amortization. If the cash flow changes, the yield changes. The yield changes, the amortization changes. The amortization changes, the P&L changes.

That’s where lifecycle events on real estate loans can bite. Paydowns, delinquencies/defaults, charge-offs, curtailments, advance payments/prepayments, payments in kind (PIKs)/interest capitalizations, and payoffs all keep changing the cash flows. A plain-vanilla loan engine that doesn’t cater to these events pushes a lot of work into manual processes. Operations teams end up manually trying to make the effect of these lifecycle events show up in the engine, which increases operational overhead, delays P&L, and holds up daily and monthly reporting. Waiting longer for core performance metrics, misstating performance, or not having a timely and accurate view of their books puts funds at risk.

The growth of private credit makes this pain point more noticeable. Insurance firms are taking up a lot of real estate loans. Accounting regimes such as IFRS 9 (Financial Instruments) and IFRS 17 (Insurance Contracts) for insurers, and CECL (Current Expected Credit Losses) under U.S. GAAP, require precise cash-flow, loss, and valuation modeling for insurers’ loan and private-credit holdings. Similar IFRS and U.S. GAAP requirements apply to hedge funds and traditional asset managers with some nuances.

As investors move into private credit and hold loans directly instead of only asset-backed securities, they need an investment accounting system that respects the intricacies between these loans while supporting the wide range asset classes that asset managers are already trading. It must include more nuance that can distinguish loan types and process them as they need to be processed, by modeling restructurings, resets, and step-ups as explicit events rather than manual overrides. When asset managers use point systems specifically for loans or real estate loans, it only adds complexity to their daily workflows.

Data and operating models for real estate

When firms hold real estate loans on their own books, they usually rely on servicers to run the day-to-day. A scalable platform needs to connect to the various servicers of loans in a portfolio. For each, it must bring the data in, normalize it, and convert it into lifecycle events the system can understand and process.

However, because each servicer is its own beast, there isn’t a standard template. For every onboarding, the first job is to figure out whether the files look more like position files, activity files, or something in between, and then deal with different formats and conventions coming into your accounting system.

The hard work sits in normalizing all that inbound data: transforming heterogeneous servicer feeds into a consistent lifecycle event schema that can feed the accounting engine. Only then, after the data is normalized, can systems apply the right logic to each event for processing. And once you have normalized lifecycle events and accurate cash flows, you can run stress scenarios that reflect how these loans behave.

In many private funds, there’s one group that deals with direct lending to companies, a group dedicated to real estate loans, and a group focused on other consumer and BNPL loans. Larger players do everything under the credit umbrella, but they still treat these as distinct businesses. The operating models are distinct for a reason, and the systems must also respect this. While this situation is improving, more work remains for data and systems to scale to support real estate loan exposure without drowning in operational noise.

Real estate loans as a core, differentiated asset class

Real estate loans are moving from a niche allocation to a core part of how private capital gets deployed. Recent fundraising data show that real estate debt strategies accounted for roughly a quarter of all capital raised for private real estate vehicles that closed in 2024.ⁱⁱⁱ The market is becoming more democratized as nonbank players step into real estate lending. At the same time, large managers are launching private-credit-specific funds and vehicles, many of which lean heavily on real estate allocations.

Once that avenue of capital exists, the natural question is: Why wouldn’t you use it? It gives you good yields. If you can manage the risk appropriately, why not? The “if” is where technology, data, and accounting come in. Otherwise, these growing allocations to unique snowflakes can turn into a blizzard very quickly.

Firms that adopt multi-strat operational platforms, supported by systems that normalize lifecycle data from servicers, and honor the nuances of each loan type, will be significantly better positioned to compete in the market. They can embrace real estate loans as a strategic asset class, with the confidence that their infrastructure can keep up with the weather.