When Algorithms Meet Geopolitics: How Residential Markets Really Price Risk
Geopolitical Perspectives Series | Edition 6 1
Why residential real estate matters so much Real estate is the most important global investment asset class with an estimated value of almost US$400 trillion – larger than equities and bonds combined – and around three quarters of the total is residential property.1 Real estate is also the single largest non-hydrocarbon contributor to the UAE economy, accounting together with construction for 19.5 percent of real non-oil GDP in 2024.2 Most real estate deals have always been residential properties, which carry similar economic weight as housing markets in other developed economies. So, understanding how these properties are priced matters enormously to investors and regulators alike. Many people wonder whether hidden algorithms influence prices, especially when geopolitical tensions rise. Is this a reasonable concern? Four types of algorithms that shape residential markets An algorithm is essentially a rule-based tool that looks at past data and uses pattern recognition to estimate what a house should cost. 1. Automated Valuation Models (AVMs) The first and most well-known are Automated Valuation Models (AVMs). These support bank mortgage valuations and low-risk lending in many OECD jurisdictions, where they have been found to correlate more closely with observed market transactions. They have also been found useful in generating benchmarks for developers and funds, in developing reinstatement cost estimates, and for some institutional underwriting. The underlying drivers of AVMs are – or ought to be – comparable sales trends, location as well as, increasingly, property-specific attributes.3 They are therefore especially suited to residential markets where transactions are far more frequent than in their commercial counterparts and property is mostly homogenous in type and quality. In most jurisdictions they do not however themselves determine prices, let alone replace the role of valuers: their role is rather to ascertain the plausibility of individual valuations against established benchmarks.4 Residential pricing in the USA is however especially susceptible to AVM influence, as AVMs are deeply embedded not only in portals such as Zillow, Opendoor and Redfin,5 but also in mortgage underwriting by institutions such as Fannie Mae and Freddie Mac,6 home equity lending, refinancing approvals, insurance risk, and for many years now also, property tax assessments.7 US AVMs can therefore move markets, as the way in which Zillow’s algorithm overestimated prices and created local price cascades almost a decade ago now clearly demonstrated.8 This explains the significant degree of regulatory attention they now attract.9 The UK residential market is almost as susceptible: AVMs also underpin price estimates from familiar portals such as Rightmove in the UK,10 whilst British lenders use AVMs for low-risk mortgages, automated approvals, and portfolio revaluations. Moreover, because the UK does have high quality data and highly homogenous housing stock in urban areas, AVMs have the capacity to influence valuations: a single distressed sale in a terraced street can negatively affect the AVM valuation for an entire postcode. And the UAE? There exists equally high-quality transaction data from the Dubai Land Department11 and the Abu Dhabi Real Estate Centre,12 rapidly improving digital infrastructure and sophisticated investors who understand both the benefits and
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When Algorithms Meet Geopolitics: How Residential Markets Really Price Risk
the limitations of the AVM models used by portals such as Bayut, Property Finder, and Zoopla. But the UAE also exhibits rapid development cycles along with very significant off-plan sales, a relatively heterogenous property stock, and a relatively diverse universe of buyers. Taken together, this means that although AVMs continue to be researched,13 new models now even including climate and geospatial variables,14 and at the practical level they are useful tools for valuers, they remain far from driving the UAE’s still rapidly growing residential market. 2. Behavioural Algorithms Second and quite different are behavioural algorithms. These cover an enormous range of potential indicators, including how users conduct Google searches, use portals, express themselves on social media, and transact for goods and services.15 High correlations with price behaviour have been identified for some of these indicators. But there are two problems. One is that they occur at the same time as price changes, or very shortly beforehand.16 The second is that behavioural algorithms move quickly because attention shifts rapidly. But rapid shifts in attention do not necessarily correlate with equally rapid changes in market prices. The data they provide may therefore be exceptionally ‘fuzzy’ and not always useful in predicting prices, let alone driving them. More complex models that integrate macroeconomic variables often perform better in explaining house price market dynamics.17 3. Capital-Flow Algorithms Large investors like sovereign wealth funds, REITs, and private equity firms use these risk models. They operate at a high level of aggregation and incorporate a wide range of quantitative indicators, including interest rates, commodity prices, bond yields and spreads and real estate indices.18 4. Micro-Algorithms Developers and brokers use simple, internal algorithms to forecast what matters to them: lead quality, conversion rates, optimal pricing, and development phases. Short-term and local factors such as enquiry numbers and types, sales conversions and payment plan outcomes drive these.19 These reflect geopolitical realities amongst others but again do not shape prices themselves.
How geopolitics actually influences these algorithms Here is the critical insight: AVMs look backward, not forward. They do not immediately react to geopolitical events, no matter how sudden or serious. Their response comes through the data they track: transaction volumes, neighbourhood patterns, and eventually transaction prices themselves. By then, market sentiment has often already moved. Geopolitical shocks shift investor psychology long before the AVM data catches up. This matters everywhere, even in OECD markets where AVMs have more influence than in the UAE. If AVMs reflect change rather than drive it, how do behavioural algorithms influence markets? Behavioural economics has taught us that investors often respond to sudden news and events – whether a natural disaster, geopolitical crisis, or confined to a single sector or firm – with responses that can lead to overreaction and consequently to whiplash price movements. Adverse outcomes become temporarily accentuated.20 At a global scale this was most evidently seen in the Global Financial Crisis and the pandemic, where market performance was shaped by what quickly became recognised as collective overreaction, which in turn led to many successful opportunistic interventions into temporarily misplaced markets – including real estate. These market dynamics underscore the impact of psychological dynamics in times of crisis.21 Real estate is no exception to these dynamics. For example, investment in Poland declined precipitately following the Russian invasion of Ukraine in 2022. Recovery, however, has been relatively brisk, with those investors who committed during the Spring of 2022 achieving the highest returns,22 with a similar outcome for the Ukraine market itself.23 Those who bought New York real estate during the recession of the early 1990s24 or in London at the lowest point of market confidence during the Global Financial Crisis25 would find the sentiments that underlay more recent events very familiar. On the other hand, behavioural algorithms themselves do not only reflect actual market participation: in an effort to catch more leading indicators, they attempt to spread a much wider net. In so doing they catch much more that is not always useful. They may therefore serve as useful early-warning indicators, monitors of sentiment,26 and tools to understand investor psychology.27 They must however always be treated with great caution during geopolitical crises as their rapid gyrations are far from reliable predictors of the fundamentals that underlie demand and supply which in turn drive residential markets. This applies equally whether they are signalling optimism or pessimism. Even their advocates emphasise the importance of hybrid models under such circumstances.28 Nor do banks rely on them in setting lending policy, which depends far more on fundamentals such as income, interest rates, Loan-to-Value ratios, valuation reports based on comparables or long-term income potential, and developer pricing – many driven by regulation far more than by sentiment.29 The other two algorithm types have limited impact during geopolitical crises. Capital-flow algorithms may include geopolitical forecasts, but they typically just move money to safe havens when crises emerge. Micro-algorithms reflect eventual market impacts rather than driving them proactively.
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When Algorithms Meet Geopolitics: How Residential Markets Really Price Risk
Conclusions Geopolitics affects each algorithm type differently. In some markets where AVMs actively drive property pricing, they might amplify geopolitical shifts. But in the UAE, where human behaviour, developer strategy, and structural demand determine residential prices rather than automated models, this effect is much weaker. This is the situation today. Over the next two decades as data improves and valuation practices evolve; the balance may shift. Behavioural algorithms show higher volatility during crises, but the wild price swings they create make them unreliable guides for actual valuations or investment decisions. Capital-flow and micro-algorithms don't drive residential pricing whether during crises or normal times. Smart investors succeed by studying the real opportunities that geopolitical crises create, using sophisticated data analysis, solid market intelligence, and expert advice. They do not rely on algorithms that reflect yesterday's reality rather than tomorrow's.
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When Algorithms Meet Geopolitics: How Residential Markets Really Price Risk
Authors Julian Roche
Ali Siddiqui
Chief Economist Cavendish Maxwell julian.roche@cavendishmaxwell.com
Research Manager ali.siddiqui@cavendishmaxwell.com +971 50 877 0190
Key Contacts Zacky Sajjad
Ronan Arthur
Director, Business Development and Client Relations zacky.sajjad@cavendishmaxwell.com +971 50 644 5089
Director, Head of Residential Valuation ronan.arthur@cavendishmaxwell.com +971 50 296 1411
Dubai +971 4 453 9525 dubai@cavendishmaxwell.com 2204 Marina Plaza, Dubai Marina, P.O. Box 118624, Dubai, UAE Dubai
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Muscat
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Riyadh
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Disclaimer: The information and analysis contained in this publication are derived from sources generally considered reliable and based on assumptions deemed reasonable and current at the time of undertaking market research. However, no representation or warranty, express or implied is made regarding their accuracy or completeness. We reserve the right to vary our methodology and to amend or discontinue the indices at any time for regulatory or other reasons. The information and its analysis in this publication do not constitute legal, financial, or any other form of advice. Furthermore, it does not constitute a formal valuation, feasibility study or analysis of any property interest and should not be construed as such. Such analysis, including forward-looking statements are opinions and estimates only, and are based on a wide range of variables which may not be capable of being determined with accuracy. Variation in any of these indicators can have a material impact on the analysis and we draw your attention to this. Cavendish Maxwell accepts no liability whatsoever for any loss or damage whether direct or indirect arising from reliance on this information.
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