The 14-year era of low interest rates and depressed bond yields is over. Cheap debt and the wide spread between property and government bond yields that was a boon for real estate has given way to significantly higher global government bond yields and volatility. Rates are unlikely to drop back to where they were any time soon, if ever. The situation is more pronounced in the United Kingdom. Since 2022, yields on UK government bonds (gilts) have risen more than those from other major economies, reflecting that some risks are gilt-market specific.

Net-zero pledges are now a fixture in global real estate. ESG metrics dominate annual reports, and sustainability-linked assets appear in almost every investment strategy. Yet behind these commitments, progress remains slow, particularly in North America and Asia Pacific, where the gap between ambition and delivery is becoming more difficult to ignore. Delays are no longer just missed targets, they are mounting liabilities.

In the world of institutional real estate investing, you might say there are two kinds of bears: wild bears and zoo bears. Wild bears roam freely and are always on the prowl for opportunities in the markets. They take carefully calculated risks and shape their own destiny. Zoo bears tend to stay within the confines of conventional strategies, waiting in their self-imposed cages for opportunities to come to them. Judging by some of the directions in which the real estate investment team of the $358.4 billion (€304.1 billion) California State Teachers’ Retirement System (CalSTRS) has been heading, I personally would place them in the “wild bear” category.

Institutional real estate investment management is adapting to a new reality. Powerful artificial intelligence (AI) tools are now accessible to everyone and are beginning to transform the way we work. However, adoption rates in real estate are highly uneven. In some areas, AI tools are being used to drive efficiencies, but broader, strategic deployment is limited. This is a missed opportunity, especially because AI is improving at such a rapid rate.

A rushed implementation by the European Union of the revised Energy Efficiency Directive (EED) for data centres could undermine efforts to make the sector more environmentally friendly. The EU is recasting the EED and wants to introduce specific provisions to collect information on data centre power use through mandatory reporting requirements. It also includes options to develop a rating scheme for data centre facilities, as well as minimum performance standards (MPS).

German investors are increasingly focusing on foreign residential markets as the number of suitable domestic assets continues to drop. A panel of speakers addressing the state of the German residential market, during an online press conference held in early July, said high construction costs, lengthy approval procedures, unstable funding frameworks, high construction standards and regulatory uncertainties have created a situation where elevated excess demand is meeting a structural supply shortage in many major cities. At the same time, current yields are often insufficient to compensate for the market’s many risks.

An AI communications tool used to interact with tenants in the student accommodation sector is close to achieving a 100 percent answer accuracy rate. VerbaFlo, which is used by student accommodation operators such as Vita Student and Downing Students, requires human intervention on some 10 percent of all enquiries when it does not have the ability to satisfactorily answer a question. However, this rate should drop to 1 percent by the time the new academic year gets under way.

The world is running out of power. Not in the apocalyptic sense, but for one of the most vital sectors underpinning the digital economy — data centres. As artificial intelligence (AI) adoption accelerates, data centres face unprecedented power constraints that threaten to impede growth. Data centre capacity in the United States doubled between 2020 and 2024, and the surge shows no sign of slowing down. AI models are driving a quantum leap in power consumption. Before generative AI, computational demands grew eightfold every two years. Now, they are projected to grow 256 times faster.