Methodological Foundations and Algorithmic Core of an Agent-Based Model for a Real Estate Platform

Traditional macroeconomic and agent-based models (ABMs) of the real estate market largely ignore the digital layer of modern PropTech platforms. By modeling matching processes through abstract macro-functions, these approaches fail to capture the cognitively constrained realities of online property searches. Digital interfaces, however, act as active "choice architects." Their underlying ranking, pagination, and filtering algorithms create new digital search frictions that exacerbate information asymmetry and severely limit users' visibility into available objects. To bridge this methodological gap, this study develops the conceptual foundations and algorithmic core of an agent-based model for a real estate platform, grounded in empirical clickstream data. To ensure architectural integrity and scientific reproducibility, the proposed approach integrates five leading global methodologies—FREIDA, MAIA, the Smajgl et al. approach, the ODD+D protocol, and the TRACE framework—into a unified five-step development and validation protocol. As a result, a novel algorithmic core is developed that completely replaces aggregated macro-formulas with a stochastic system of discrete decision trees. Agent behavior is dynamically formalized through rules of bounded attention, local micro-conversion, seller price adaptation, and the satisficing heuristic. Meanwhile, empirical initialization of agent attributes is achieved by clustering raw server logs, thereby providing a robust, data-driven micro-foundation. The integration of the digital platform as an independent institutional environment allows for the authentic "bottom-up" generation of complex emergent macro-phenomena, such as the filter bubble effect and the quantitative impact of algorithmic ranking on asset liquidity. Ultimately, applying this framework shifts the paradigm of real estate research from abstract macroeconomic determinism to applied digital behavioral economics, ensuring high predictive validity and providing a reliable tool for testing business hypotheses across various scenarios.