The future of smart decision-making for real estate institutional investors and managers

Several factors appear to be limiting growth and profitability for real estate institutional investors. Faced with higher risks and competition, commercial real estate (CRE) investors posted negative average annual returns in 2018, both globally (-5.6 percent) and in the United States (-4.1 percent).1 In comparison, average annual returns were 6.4 percent and 6.9 percent globally and in the United States, respectively, during the 2014 –2017 period. Deloitte’s 2018 Global Real Estate Institutional Investor survey of 500 global institutional investors seems to show respondents remain committed to CRE as an asset class: 97 percent of the respondents plan to increase their capital commitment through 2019. However, their investment decisions are likely to be challenged by geographic market, tenant, and financing/interest rate risks (see figure 1). There are also growing headwinds around the 2020 US presidential elections, the potential threat of trade tariffs, a flattening yield curve, and a global economic slowdown. To respond to these possible challenges, investors are seeking new ways to improve efficiency and effectiveness.

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Investors and investment managers can use data analytics and artificial intelligence (AI) in their existing acquisition, disposition, and portfolio management processes to manage rising risks and complexities more effectively and mitigate fees and margin pressure. There is much more data available today than there was even just a few years ago. Information such as net effective rents, leasing spreads, lease comps, market demand, and tenant information have now become much more accessible and granular. In addition, alternative datasets from IoT sensors, social media, geospatial information, and satellite imagery are increasingly being used. And new forms of analytics solutions, backed by AI, can help investment managers harness this broader range of data to make more informed decisions faster and more accurately.

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