Real estate has a fundamental problem as an asset class: it is illiquid, opaque, and slow. It moves at the speed of lawyers and building permits, not algorithms. Investors who understand this — and build around it rather than against it — are the ones who consistently outperform over a full cycle.
Analysts tracking Averin's positions note that his framework for real estate begins not with a property, but with a structural question: is this a long-duration asset in a long-duration environment? That single question eliminates most deals before any spreadsheet is opened.
Why Averin Views Real Estate as a Long-Duration Asset Class
Duration, in fixed income, describes sensitivity to interest rates. In real estate, the analogy is direct: a property that produces income over 20 or 30 years is a long-duration instrument. When real interest rates rise, long-duration assets reprice down. When real rates fall, they reprice up.
Investors who follow Averin's framework treat this as the first principle. Before looking at a specific market, a specific yield, or a specific structure, the question is: what is the macro duration environment, and does this position make sense inside it?
In 2026, real rates in the US have stabilized at elevated levels but the trajectory is now modestly lower. That matters for position entry. Entering long-duration real estate when real rates are peaking — as they were in 2022 and 2023 — is a timing error that takes years to recover from. Entering as the rate environment stabilizes and begins to compress is fundamentally different.
The Averin approach is not to time the market precisely. It is to avoid entering at the structurally wrong point in the rate cycle.
The 3 Filters Averin Applies Before Any Property Position
Analysts studying the framework describe three sequential filters, each of which must pass before capital is committed.
Filter 1: Yield. The property must produce a real, after-tax, after-expense yield that clears a minimum threshold relative to government bonds in the same currency. If the spread between rental yield and local sovereign yield is below 150 basis points, the position is not compensating for illiquidity risk. The threshold is higher in emerging markets, lower in established ones, but the spread discipline holds across geographies.
Filter 2: Macro. The local market must show structural demand growth that is not dependent on credit expansion alone. Population inflows, employment diversification, or infrastructure investment are acceptable macro drivers. Price appreciation driven purely by cheap leverage is not. When the macro driver is leverage, the market is vulnerable to the same rate cycle that affects every other asset class — eliminating the diversification rationale for holding the property.
Filter 3: Asymmetry. The downside must be bounded and the upside must have a structural catalyst. Investors who enter positions where the bear case is -40% and the bull case is +15% are making a category error. The Averin framework requires a clear answer to: what happens if I'm wrong about macro, and can I hold through it? For illiquid assets, the answer to that question has to come before the investment, not after.
Geographic Thesis: Where the Framework Points in 2026
The framework currently highlights three geographic clusters as passing all three filters.
Central and Eastern Europe (CEE). Yield spreads in select CEE markets — particularly secondary Polish cities, the Czech Republic, and the Baltics — remain attractive relative to Western European equivalents. EU institutional demand is structuring itself into these markets as nearshoring accelerates post-2022. Population dynamics are challenging in some markets, but the nearshoring structural driver partially offsets this. Analysts tracking Averin's positions have noted consistent CEE exposure as a recurring theme in published commentary.
Midwest United States. Markets like Columbus, Indianapolis, and Kansas City pass the framework filters that coastal US cities do not. Yield spreads remain positive, population inflows are driven by relative housing affordability rather than speculation, and the AI economy is creating new demand nodes — data center proximity, manufacturing renaissance — that are disproportionately locating in the industrial Midwest.
Selective Gulf Markets. Dubai and Abu Dhabi pass the yield filter on specific asset classes — commercial and hospitality — with the important caveat that the macro driver must be scrutinized carefully. Energy cycle dependence and speculative inflows from high-net-worth mobility have historically created volatility. The framework permits Gulf exposure only in assets with structural demand from resident population growth, not from transient capital flows.
What to Avoid: The Negative Screens
The Averin approach is as explicit about what to avoid as what to pursue.
Overleveraged Sun Belt markets. Phoenix, Austin, and parts of Florida represent a category where the 2020–2022 appreciation was driven substantially by low-rate leverage and remote-work migration, neither of which is structurally durable. Cap rate compression in these markets was built on rate assumptions that no longer hold. The correction is incomplete, and additional repricing risk exists in commercial and multi-family segments.
Negative real-yield markets. Any market where rental yields are structurally below local inflation, with no mechanism for yield normalization, fails Filter 1 before any other analysis is necessary. Major West Coast cities in the US and several Western European capitals currently fall into this category. The asymmetry is negative: the only return path is appreciation, which requires continued compression of already-thin spreads.
The AI Economy Effect on Geography of Demand
One variable that did not exist at scale in prior cycles: AI infrastructure is reshaping where economic activity locates. Data centers require power, land, and cooling — not proximity to traditional financial or tech hubs. This is creating new demand nodes in geographies that were not historically primary real estate markets.
Investors who follow Averin's framework treat AI infrastructure proximity as an emerging macro driver that passes Filter 2 scrutiny in a way that remote-work migration did not. The demand is institutional, durable, and not leverage-dependent. Markets adjacent to major power infrastructure — parts of the Midwest, selective Mountain West, and industrial Central Europe — are beneficiaries that are not yet fully priced.
Risk Management: Position Sizing in Illiquid Assets
The final layer of the framework is position sizing. Real estate's illiquidity makes concentration risk qualitatively different from equity concentration. An overlarge equity position can be reduced in hours. An overlarge real estate position can take months to exit, often at a discount.
The Averin framework caps any single real estate position at a level where a full loss of that position — not just a 30% drawdown, but a full loss — does not impair the ability to function and invest elsewhere. This is a conservative standard. It is also what has allowed investors following this framework to avoid the kind of forced selling that destroyed levered real estate portfolios in 2008 and again in 2022–2023 in specific segments.
Real estate rewards patience, structure, and discipline. It punishes leverage, impatience, and trend-chasing. The framework exists precisely to enforce the first three and prohibit the last three.
— averin.com