As costs of financing and managing private assets rise, investment in private real estate is taking a hit. To help keep returns north of inflation, without taking on additional risks, managers are looking at ways to reduce costs, as well as be more transparent. Bubbling to the top of everyone’s “let’s look at this” list is data management and analysis.
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The US commercial real estate market is valued at about $20.7 trillion. (REIT.com)
The big data analytics market size was $271.83 Billion in 2022. It is expected to reach $745.15 billion by 2030. (Fortune Business Insights)
The global real estate analytics market is predicted to grow from $5.5 billion in 2019 to $13.4 billion by 2024 at a compound annual growth rate (CAGR) of 19.8%. (Predik data)
Investors in private markets are being forced to adapt to new macroeconomic, financial market and geopolitical dynamics. In this challenging global environment, deep-rooted, strategic operational changes to the efficiency and effectiveness of investment operations, especially regarding data management and analysis, are more important than ever.
The appeal of any investment is its ability to offer above inflationary returns, and private-markets growth over the past decade or two has been able to do that quite easily. However, today’s higher inflation means each deal must produce a higher return. This narrows the opportunities available to investors, placing them in greater competition to get into higher-returning deals.
This competitive environment in private markets is increasing pressure on investors to seek an edge. According to a survey recently released by State Street, “No Going Back: New Realities in Private Markets,” nearly two-thirds of the respondents said improving their data management processes could confer a competitive advantage on them, but more than half also admitted to wasting considerable time and resources on manual data management and transfer processes. An over-reliance on multiple, fragmented data repositories (often spreadsheets), and a failure to integrate technology systems, often acquired over many years, were singled-out as the main culprits.
Institutions spend considerable amount of time and effort around managing risk, investment analytics and forecasting. Thus, one solution proposed by industry insiders to improve efficiency is creating a universal data model for each asset class with a governance mechanism that would allow users to validate against, use and harness the information across portfolios, including public assets. Optimally, investors and managers could access private market data through APIs or such mechanisms and retain the flexibility to run their own analysis with their own functions, such as risk management, compliance or investor relations.
In addition to looking at improving data management processes to make investing more efficient, managers are looking at improving transparency to encourage more retail-investor involvement. If the challenges involved in making retail investment in private markets comparable to that in public markets can be resolved, it would generate significant new capital flows for asset managers and enhance the investment opportunities.
Finding a solution to this conundrum, given the level of sophistication and liquidity inherent in private markets, is not easy. However, addressing liquidity and data transparency would seem to be key to making private markets suitable for retail investors.
Respondents to the State Street survey recognized the demand for better data and transparency was growing and saw potential for digital tokenization to play a role in bringing retail investors into private markets. However, they were skeptical that the transition could be made in the near future, predicting the period of relatively scarce opportunities and increased competition would continue for several years.