Award Finalist
Real Estate’s reckoning: operating in a rapidly repricing world
A decade of free money through QE exacerbated by Covid-driven financial stimulus is rapidly reversing across the west. At the same time, geopolitical risk dashboards are flashing red, with elevated levels of risk and uncertainty from multiple fronts (including the expansion potential of conflicts, the breakdown of historic alliances, trade tensions, commodity price shocks, cyber and terror attacks, the rapid advancement of AI) . At the same time, a small number of AI companies absorb a growing share of capital: AI related equipment and software was 4% of US GDP but was responsible for 92% of GDP growth in H1’25 . The top 5 cloud providers are expected to double their capex spend in 2026 vs last year .
This is leading to a reckoning in Real Estate. A decade-plus of cheap capital driven by low rates and reallocation of sovereign and pension funds into alternatives is over. Investment returns are having to compete with bonds again, with the average UK spread down to 70bps from 360bps post-GFC, and leverage is no longer a cheap accelerator to returns. The speed of this change has been as important as the extent: UK 10 year gilts went from sub 1% in December 2021 to over 4% 9 months later , and this wasn’t just driven by political turmoil: US 10 year bonds moved similarly , and both have continued to climb. Asset managers and investment committees have had to adapt overnight to a new reality, one which most have not seen before in their careers.
This new reality requires a new operating model: owning the right sector is becoming less of a critical driver of returns, and outperformance comes from what you do with that asset. ‘Riding beta’ needs to be replaced with ‘driving alpha’. McKinsey and MSCI both identify asset level performance as driving c.70% performance, with “returns increasingly driven less by allocation between sectors and more by performance within them”6. This is exacerbated by increased bifurcation across the sector, as a shrinking pool of assets perform well, and more and more become ‘secondary’ . However, that is a non-trivial change. A investment model set up to perform based on sector and geography allocation now needs not only to increase the level of ‘hyperlocal’ asset diligence in an investment, but also to change the operating model from one of outsourced management, to a significantly increased level of control through the hold period to ensure that those assets really perform. Investors already recognise that returns are harder to find, but they haven’t yet adapted fully, changing how they make decisions, allocate their time, or resource their teams. They haven’t yet worked out at asset level how to have the hand on the tiller day in day out, not just plot the ship on the map.
Making this change requires rethinking how to drive returns from Real Estate. We now need to treat a real asset as a product with a customer, not a bond with a yield. Asset managers need to become product managers, accountable for the performance of their ‘product’ and the experience of their customer (both the tenant and the user).
A specific example of this is the growing need to consider non-traditional factors in investment appraisals, for example resilience vs climate change, geopolitical risk, or (in an office example) the potential impact of AI on the tenant’s workforce over the hold period of the building. What sort of tenants are in the building today? What part of their workforce uses the space (the call centre team in a bank will have a very different prognosis from the private wealth management team within the same bank, for example)? What will AI likely do for that workforce, and for the type of tenant and workforce that might replace them if they downsize? How does this drive local rental tension, and how does that change the rental growth you are already modelling? How would this impact ancillary real estate (e.g. F&B, gym space) that is incorporated in the building or masterplan? Although at a macro level there is much research and debate on this topic , this hasn’t filtered structurally to deal level decision making at ICs.
AI is also disrupting and challenging at corporate functions more directly, and Real Estate investors and owners now have to rethink the skills, roles and structures in their organisations. In a largely low-headcount, high-expertise industry, this is more about enabling faster, more accurate, better-informed decisions from already experienced teams than it is about headcount cost reduction Like many industries, these are urgent and potentially existential problems to solve, and one point of tension is the choice between big bang transformational change and distributed enablement, putting in place the governance, training, and guidance to support teams and individuals in getting to the right outcome. This isn’t easy. Last year, JLL said that nearly 90% of real estate companies have started piloting AI, but only 5% have achieved their programme goals. Bain and Company research suggest why this is the case: technology is rarely the main challenge; rather, it’s people and change management .
All this operating model change needs to be considered while maintaining the historic day job of managing the investment portfolio and defending what you own while deciding what’s next, finding the right balance between investing in assets with potential, divesting from those which don’t, and reallocating capital to new areas. At an asset level this means defining the specific asset’s full potential and value proposition – then either divesting or delivering. What are the refurb, development and masterplan opportunities? What energy and operating efficiencies are viable? What might the tenant mix need to become? How could such changes drive tenant NPS? At the portfolio level the mission is to build a portfolio of assets that is not just resilient, a traditional hedge against uncertainty, but actually antifragile and designed to thrive not just survive in an unstable world.
Those who succeed over the rest of this decade will act now when the need is urgent but not yet an emergency. They will reconfigure their operating model now to drive asset level returns in a world of AI, uncertainty, and a new financial reality. The winners will be those who go beyond resilience to antifragility and create businesses and portfolios that will gain from uncertainty by design.
6 Global private markets in real estate | McKinsey
7 “Real-Estate Asset Selection Drove Performance”
8 The Flight to Quality Quantified | CBRE
9 What converging real estate asset classes means for investors | World Economic Forum
11 e.g. Newmark in April taking a different view on the impact of AI on Office occupational demand, compared to C&W a month later (Newmark, “AI and the Future of Office: Quantifying Workforce Change and Space Demand Through 2030” vs Cushman & Wakefield)
12 AI Transformation: The Fears Are New. The Answer Isn’t. | Bain & Company