Residential real estate is often described as a data-rich industry. The sector has access to extensive listing inventories, transaction histories, property records, and increasingly sophisticated valuation models. With the growing use of artificial intelligence, it can appear that the core analytical challenges have largely been addressed.
However, beneath this surface, there is a structural limitation that continues to affect how pricing, analytics, and decision-making operate: the way markets are defined in practice is not consistently represented in the underlying data.
This is not due to a lack of understanding within the industry. On the contrary, experienced agents and appraisers routinely define markets with a high degree of precision. Before assigning value to a property, they determine what that property actually competes with. This is a foundational step in valuation.
In most cases, the relevant competitive environment is not defined by a ZIP code, a radius, or a broad geographic boundary. Instead, it is defined at a more granular level—such as a subdivision, a condominium building, or a specific residential community. These environments represent the contexts in which buyers evaluate alternatives and make trade-offs. In other words, they are the markets in which pricing is formed.
While this market layer exists operationally, it is not consistently structured in most real estate datasets. As a result, professionals must reconstruct it manually each time a property is analyzed. They identify the appropriate competitive set, isolate relevant comparables, and exclude properties that, while geographically proximate, do not compete in the same market.
This process is effective, but it is inherently context-dependent and does not scale across systems. It also introduces variability, as different practitioners may define the market slightly differently based on their experience and interpretation.
By contrast, most real estate data systems are organized around geographic and descriptive constructs such as city boundaries, ZIP codes, proximity, listing attributes, and transaction history. These frameworks are well suited for search, aggregation, and broad market analysis. However, they do not explicitly define the competitive environments in which properties actually interact.
In the absence of a clearly defined market layer, automated systems must rely on approximation. Comparable properties are inferred through statistical methods, proximity-based selection, or similarity in basic attributes. While these approaches can produce reasonable estimates in homogeneous areas, they often introduce noise in more complex or segmented markets.
This distinction becomes particularly evident when different systems produce different valuations for the same property. The discrepancy is often attributed to differences in modeling techniques. In many cases, however, the underlying issue is that each system is operating with a different implicit definition of the market. The variation arises not from the mathematics itself, but from the selection of comparables that feed into the model.
This structural gap is becoming more consequential as the industry evolves. Consumers now have access to multiple valuation sources and frequently compare outputs across platforms. At the same time, artificial intelligence is being integrated into real estate workflows, increasing reliance on automated analysis. These developments raise expectations for consistency and explainability.
Yet both consumers and AI systems are constrained by the same limitation: they depend on data structures that do not explicitly encode how markets are defined in practice. Without a consistent market definition, models must approximate what professionals determine directly.
Addressing this issue requires reframing the problem at a more fundamental level. Rather than focusing solely on improving models or expanding datasets, it is necessary to consider how residential markets are structured in the first place. The key question is not only how to estimate value, but how to define the competitive environment within which that value is determined.
In most cases, that environment corresponds to a subdivision, a building, or a defined community. Once this layer is established, the rest of the analytical process—comparable selection, pricing interpretation, and market analysis—can be built on a more stable foundation.
If this market layer were consistently defined and encoded within datasets, several outcomes would follow. Comparable sets would become more coherent across systems. Pricing would be interpreted within clearly defined contexts. Analytical outputs would be easier to explain and validate. AI models, in particular, would benefit from more structured inputs, improving both consistency and interpretability.
Importantly, this is not about introducing a new concept to the industry. The notion of defining a market before analyzing it is already well understood and widely practiced by professionals. The challenge lies in translating that implicit knowledge into a consistent, machine-readable form.
For years, innovation in real estate technology has focused on increasing data availability and improving analytical models. The next phase is likely to be more foundational. It will involve aligning data structures with how markets actually function in practice.
Zestimate and similar tools have played a significant role in making pricing more transparent. However, transparency alone does not ensure clarity. The next step is not simply to produce more estimates, but to improve the underlying structure that those estimates depend on.
Residential real estate does not lack data, and it does not lack expertise. What it lacks is a standardized way to define markets at the level where properties actually compete.
Until that layer is consistently represented in the data, comparables will continue to be approximated, markets will continue to be reconstructed, and analytical outputs will continue to vary across systems.
Ultimately, the accuracy of any real estate analysis depends not only on the model being used, but on how the market itself is defined before the analysis begins.
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