How macro factors could shape real estate performance – and why investors should care
A new academic paper finds that five key macroeconomic variables explain much of the performance of US real estate strategies. Understanding how these relationships shift through the cycle can help position portfolios for long-term success.
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The return behaviour of real estate strategies, at least in relation to directional performance trends, can be largely explained by a small number of macroeconomic variables, with the relationships evolving over time. This is true of both private and blended strategies, which incorporate private and public components – for example to meet the liquidity needs of dedicated defined contribution or semi-liquid programmes.
That’s the conclusion of new research I co-authored with Alex Moss, Associate Professor & Director of the Real Estate Centre, Bayes Business School, recently published in the Journal of Portfolio Management.
Our analysis found a strong relationship between five readily available macro factors – GDP growth, real interest rates, expected inflation, term structure (yield curve) and credit spreads – and the performance of a range of US-focused real estate strategies.
Specifically, GDP growth, inflation and term structures were all found to be positively correlated, meaning that real estate portfolio performance directionally mirrors shifts these metrics. The opposite was found for credit spread and real interest rate movements.
Shifting sensitivities through the cycle
Importantly, we also found that while ‘linear’ models – the most commonly adopted, which assume a constant relationship between economic factors and portfolio performance – offer reasonable explanatory power for performance trends, a richer understanding can be found by applying ‘non-linear’ models. These assess how relationships with macro factors evolve over time.
In short, traditional linear models assume stable relationships between asset returns and macro drivers. But in reality, these change materially, particularly during periods of market disruption.
Our study found in particular that, during volatile periods characterised by widening credit spreads or falling equities markets, the sensitivity of real estate performance to GDP growth doubles compared to normal conditions. In other words, real estate becomes substantially more exposed to changes in broader growth.
This is shown in the chart below, which shows linear and non-linear sensitivities of returns from a Core+ real estate portfolio to GDP growth. ‘Linear modelling’ showed a relationship of 1.1x. That is to say, a 1% increase in real GDP growth would result in a 1.1% increase in portfolio performance.
However, when exploring the impact of shifting regimes through non-linear modelling, we found that when the broader environment turned negative – which we assessed based on periods when credit spreads were greater than 3.3% – the sensitivity of real estate performance to GDP increased to 2.3x.
Sensitivity of a Core+ portfolio to GDP growth in different market regimes
Source: Burgiss, Fred, NCREIF, MSCI, Schroders Capital, October 2025. Core+ performance represents a blended 75% Core and 25% Non-Core US private real estate fund performance series. Credit spreads reflect US BAA corporate bond yield minus US government ten-year treasury yields.
Implications for institutional investors
For allocators and risk managers, there are several key takeaways from these findings:
- A small set of macro factors goes a long way. Even with only five variables, significant explanatory power can be achieved.
- Factor exposures are cyclical. Relationships between real estate performance and macro drivers strengthen or weaken depending on where we are in the cycle.
- Model choice shapes insight. Non-linear frameworks capture these evolving relationships more accurately, providing a stronger analytical foundation for allocation and risk management decisions.
For allocators, integrating this type of dynamic factor modelling into portfolio construction – alongside other data-led analytical tools, such as our own proprietary valuation framework – can help refine strategic decisions. It allows for a better understanding of how real estate exposures might behave under varying macroeconomic conditions, which is especially relevant in the current uncertain market environment.
We further believe that these findings can be applied across other private market asset classes – either in isolation at an individual private asset class level, for a blended portfolio approach to meet the liquidity requirements of certain investor profiles, or in a multi-asset context across a range of private and public exposures.
The full study was published in The Journal of Portfolio Management, Special Real Estate Issue: The Dynamic Impact of Macro Factors on the Performance of Blended Real Estate Equity Strategies | Portfolio Management Research.
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