Rewriting the Real Estate Graph: Smarter Recommendations with Counterfactual Learning

Author: Denis Avetisyan

A new framework, CFRecs, uses graph neural networks and counterfactual reasoning to deliver more effective and actionable recommendations for both buyers and sellers in the competitive real estate market.

This paper introduces CFRecs, a novel approach leveraging counterfactual graph learning and graph variational autoencoders on user-listing interaction graphs to strategically modify graph structure and node attributes for desirable outcomes.

While recommender systems increasingly rely on complex graph-structured data, understanding why a particular recommendation is made remains a significant challenge. This paper introduces ‘CFRecs: Counterfactual Recommendations on Real Estate User Listing Interaction Graphs’, a novel framework that transforms interpretable counterfactual explanations into actionable insights for both home buyers and sellers. CFRecs leverages graph neural networks and variational autoencoders to strategically modify user-listing interaction graphs, identifying minimal changes that drive desirable outcomes. Could this approach unlock more transparent and effective recommendation strategies beyond the competitive real estate market?

Beyond Preferences: Modeling the Real Estate Landscape

The efficacy of any real estate recommendation system fundamentally hinges on its ability to model the intricate connections between prospective buyers and available properties. This extends beyond simple matching of stated preferences – such as bedrooms or location – and necessitates an understanding of implicit signals. Factors like a user’s browsing history, time spent viewing particular features, and even the sequence of properties explored all contribute to a nuanced profile. Similarly, listings aren’t static entities; their desirability is shaped by market trends, neighborhood characteristics, and subtle attributes often overlooked by traditional algorithms. Consequently, a robust recommendation engine must move beyond basic filtering and embrace a holistic representation of these multifaceted relationships to deliver truly relevant and personalized suggestions.

Conventional approaches to real estate recommendation frequently fall short because they oversimplify the factors influencing a purchase. These systems often prioritize easily quantifiable data – price, location, square footage – while neglecting the subtle interplay of user preferences, listing characteristics, and market dynamics. A buyer might, for instance, prioritize a neighborhood’s walkability over a larger yard, or be particularly sensitive to certain architectural styles; these nuanced desires are difficult for algorithms built on broad generalizations to discern. Consequently, the suggestions generated can feel impersonal, irrelevant, or simply miss the mark, leading to decreased user engagement and ultimately, fewer successful transactions. The limitations of these traditional methods highlight the need for more sophisticated models capable of capturing the complex, often unspoken, drivers of real estate choices.

Predicting Transactions: A Graph-Based Approach

Transaction prediction utilizes graph classification by representing user-listing interactions as a graph where nodes are users and listings, and edges denote interactions between them. This allows the model to consider the relational structure of interactions, rather than treating each interaction in isolation. The probability of a successful transaction is then estimated based on the learned characteristics of these interaction graphs. This approach moves beyond feature-based prediction by analyzing the graph’s topology – for example, identifying users with a history of successful transactions with similar listings, or listings frequently interacted with by users known to complete purchases – to determine the likelihood of conversion.

A Graph Neural Network (GNN) is utilized to analyze the user-listing interaction graph, extracting features relevant to transaction prediction. The GNN operates by learning node embeddings that capture both individual node attributes and the relationships between nodes within the graph structure. This allows the model to identify complex patterns – such as user preferences, listing characteristics, and interaction pathways – that indicate a higher or lower probability of a successful transaction. The learned embeddings are then used as input to a classifier, enabling the prediction of conversion likelihood based on the graph’s inherent structure and features.

The Graph Neural Network (GNN) classifier is trained using Binary Cross Entropy Loss, a standard metric for binary classification tasks, to optimize its ability to predict transaction probabilities. This loss function quantifies the difference between the predicted probabilities and the actual transaction outcomes, guiding the GNN’s parameter adjustments during training. Evaluation of the trained GNN classifier using the Area Under the Receiver Operating Characteristic curve (ROC-AUC) yielded a score of 0.71, indicating a reasonable capacity to discriminate between successful and unsuccessful transactions based on the graph structure of user-listing interactions.

CFRecs: Shifting from Correlation to Causation

CFRecs introduces a new approach to recommendation systems by leveraging counterfactual graph learning to determine which modifications to the user-listing interaction graph will most effectively increase transaction probability. Unlike traditional methods that analyze existing data, CFRecs actively generates alternative graph structures – counterfactuals – representing slightly altered interaction scenarios. This allows the framework to move beyond correlation and identify causal relationships between graph features and outcomes, ultimately providing actionable insights for improving recommendation strategies and achieving a measured 7.2% average lift in transaction probability.

The CFRecs framework utilizes counterfactual graph generation to assess the impact of potential interventions on user-listing interactions. This process involves creating modified versions of the original interaction graph, representing alternative scenarios where specific edges or node attributes are altered. By analyzing the differences in predicted transaction probabilities between the original graph and these counterfactuals, the system identifies which changes – such as recommending different listings to a user, or highlighting specific listing features – are most likely to result in a successful transaction. This allows for the pinpointing of actionable insights, moving beyond simple recommendation to proactive intervention strategies designed to improve outcomes.

The CFRecs framework employs a Graph-Variational Autoencoder (Graph-VAE) to construct counterfactual graphs, which are modified representations of the user-listing interaction graph. This Graph-VAE is trained to generate plausible alterations to the original graph structure while maintaining realistic relationships between users and listings. The generated counterfactuals allow for the simulation of alternative interaction scenarios, and subsequent analysis demonstrates an average improvement of 7.2% in transaction probability when recommendations are adjusted based on insights derived from these counterfactual graphs. This lift is a direct result of the Graph-VAE’s ability to produce structurally sound and statistically probable graph variations.

The Devil’s in the Details: Refining Counterfactual Validity

The generation of effective counterfactual explanations is predicated on the principle of ‘Sparsity of Changes’, which prioritizes minimal perturbations to the original input graph. This approach seeks to identify the smallest set of modifications – whether to node features or edge connections – required to achieve a desired outcome or prediction. By limiting the extent of alterations, the resulting counterfactuals are more interpretable and directly attributable to specific factors influencing the model’s behavior. Excessive modification can introduce noise and obscure the true causal relationships, diminishing the utility of the counterfactual explanation for understanding and debugging the system.

Validity in counterfactual graph generation necessitates the production of plausible scenarios that accurately reflect anticipated user behavior. This is achieved by ensuring that alterations to the original graph – whether through node or edge modification – result in predictions consistent with observed patterns. Specifically, generated counterfactuals demonstrate an average increase of 18.63% in view edges, 17.29% in save edges, and 40.7% in submit edges, indicating a strong correlation between the modified graph and expected user actions. The focus on validity ensures the counterfactuals are not merely structurally minimal, but also functionally realistic representations of potential user interactions.

Counterfactual graph generation utilizes two primary modification techniques: Node Feature Modification and Edge Modification. Node Feature Modification adjusts attribute values associated with individual nodes, while Edge Modification alters the connections between nodes. Implementation of these techniques resulted in quantifiable improvements to specific edge types; view edges increased by an average of 18.63%, save edges by 17.29%, and submit edges by 40.7%. These increases demonstrate the effectiveness of targeted graph adjustments in influencing predicted user behavior.

Beyond Prediction: Towards a Proactive Real Estate Experience

CFRecs elevates real estate recommendations through a sophisticated application of counterfactual learning, moving beyond traditional methods that rely solely on historical data. This framework doesn’t just analyze what a user has shown interest in, but actively considers what might interest them, by integrating both user preferences and detailed listing features. The system constructs hypothetical scenarios – counterfactuals – to assess how changes to listing attributes, such as price, location, or amenities, would impact a user’s likelihood of engagement. By learning from these simulated scenarios, CFRecs refines its understanding of individual tastes and delivers remarkably personalized suggestions, effectively anticipating needs and connecting buyers with properties they might not have otherwise discovered. This nuanced approach promises a more efficient and satisfying property search experience, tailored to the unique priorities of each individual.

Rather than passively presenting properties aligned with stated preferences, this innovative framework actively influences the path to purchase. By subtly highlighting features likely to resonate, and perhaps even introducing previously unconsidered aspects, the system encourages a shift in user behavior, guiding individuals toward listings with a demonstrably higher probability of success. Studies utilizing counterfactual reasoning – essentially asking ‘what if’ scenarios – reveal a compelling trend: 83% of the generated graphs indicate a significant increase in the likelihood of a transaction being completed when utilizing these proactive recommendations, suggesting a powerful ability to not just match buyers and sellers, but to actively facilitate successful outcomes.

The current real estate landscape, often characterized by information asymmetry and protracted searches, stands to be fundamentally reshaped by this new framework. By leveraging counterfactual reasoning to proactively connect buyers with properties aligned with their evolving preferences – and simultaneously highlighting features sellers can emphasize – the system promises a more efficient allocation of resources. This isn’t merely about faster transactions; it’s about fostering a marketplace where both parties benefit from increased transparency and a clearer understanding of value. The potential impact extends beyond individual deals, promising a data-driven ecosystem that minimizes wasted time and effort, ultimately creating a more fluid and responsive real estate industry for all stakeholders.

The pursuit of ever-more-sophisticated recommendation systems, as demonstrated by CFRecs and its counterfactual graph learning, feels predictably Sisyphean. This framework attempts to nudge user-listing interactions towards ‘desirable outcomes’ – a phrase that already hints at the inevitable compromises. It’s a clever application of graph neural networks, certainly, but one can’t help but recall Ken Thompson’s observation: “Software is like entropy: It is difficult to stop it from becoming disordered.” The elegant architecture, the strategic modification of graph structure… all will eventually succumb to the messy reality of production data and unforeseen user behavior. The system might initially steer interactions, but the market, with its infinite capacity for irrationality, will always find a way to reassert itself. It’s not a failure of the model, merely a testament to the enduring truth that everything new is old again, just renamed and still broken.

What’s Next?

The pursuit of ‘actionable’ recommendations invariably leads back to the same problem: the world refuses to conform to the elegance of the model. CFRecs, with its manipulation of graph structure and attributes, is no exception. It’s a beautifully complex system built, no doubt, on what started as a simple bash script querying a database. The inevitable drift will begin the moment it encounters a truly motivated realtor or a buyer determined to overpay. They’ll call it ‘emergent behavior’ and raise funding.

Future work will undoubtedly focus on refining the counterfactual generation process, perhaps attempting to model the irrationality of human decision-making. Good luck with that. A more pressing concern, though, is scalability. Maintaining a coherent counterfactual graph for even a moderately sized real estate market will require resources bordering on the astronomical. The documentation will lie again about inference times.

Ultimately, this research, like so many others, is a step toward automating the human element out of a fundamentally human process. The technical debt is mounting, and it’s not measured in lines of code, but in emotional commitments to a system that will, eventually, fail to account for the sheer, stubborn unpredictability of people and property.

Original article: https://arxiv.org/pdf/2602.05861.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/