It has never been more important for private real estate managers to understand and analyze the underlying assets in their portfolios, but it is harder than ever to create that data-driven view.

Private equity real estate (PERE) is unique among private asset classes. One of the oldest investment types and one of the most common direct investments, it hasn’t benefited to the same extent as newer asset classes from the wave of modernization and digitization that has swept through the private markets.

The industry’s delayed adoption of these technologies—especially those that support data collection, consolidation, normalization and visualization— have created a burden for managers at a time when LPs are demanding more transparency. Most managers work with a wide variety of financial statements, bespoke templates and other reports that are held in multiple data management systems as well as with external reporting intermediaries. Consolidating and mastering that data is challenging, but without that single source of truth, it's impossible to accurately visualize and understand the context around asset and portfolio performance.

The iLEVEL private markets operating system is tackling that challenge and helping managers create a single, replete data source capable of enhancing the quality of both decision-making and internal and external reporting.

Data-agnostic solutions

Because the data requirements of every asset class, every portfolio, and every management firm are so different, iLEVEL was designed to prioritize customization and flexibility. This agnostic approach to data makes the platform an ideal solution to the specific hurdles faced by PERE.

The solution enables managers to distribute bespoke data collection templates and collect comprehensive data sets that flow into a single source of quantitative and qualitative data. That data can then be used for a range of internal and external reporting, including dashboards that show total portfolio health and geographic exposure. It also powers views by asset and security type to inform quarterly portfolio reviews and conversations about project status, individual asset performance, and the portfolio's cumulative net operating income and debt. For LP reporting, the data can be broken down and visualized by fund. 

Real estate metrics

iLEVEL's flexible data collection process accommodates every data source and type, allowing managers to meet the unique requirements of real estate as an asset class. Requests for financial metrics such as NOI (net operating income) along with geographic and property-specific data, such as key counts for hotel properties, can all be captured and templatized to guarantee managers collect the information they need.

iLEVEL users have a range of options for analyzing data on and off platform. For example, the Excel add-in supports the needs of PERE by enabling data to flow from the iLEVEL platform into spreadsheets that allow users to slice and dice the data finely. This supports specialized reporting requirements while enabling users to trace each data point back to the source with a single click for verification.

Debt and equity support

For real estate portfolios that comprise both equity and debt, iLEVEL's built-in credit capabilities help managers collect and analyze loan data with the same level of detail and context as equity investments. It also enables them to see all their sub-strategies in one place and examine performance or exposure collectively or by geography, asset type or any other segmentation that is insightful.

Preparing for the future of PERE

High-friction data collection and data lockups are prevalent in PERE, but there is a growing appetite for change. Managers recognize that macro trends, including socioeconomic shifts and the rising cost of capital, are making it more important than ever to create visibility into total performance and capital requirements.

S&P Global Market Intelligence is committed to ensuring that iLEVEL gives GPs the tools they need to navigate this reality. Our product roadmap is focused on reducing friction even further by deploying more data collection models and automating more of the data ingestion process to enable GPs to extract and utilize more types of data from a wider array of sources.