The valuation of real estate is a fundamental process for the proper functioning of the market and the formulation of public policies. The mother of the traditional methodologies is the comparative method, which is based on data that is often incomplete or unreliable, especially in real estate markets with limited transparency. In contrast, online advertisements offer a wealth of unstructured information, which requires advanced analysis techniques. Even if they reflect price demands rather than real estate’s final market values, they provide a wealth of data that is particularly useful for market transparency. Real estate advertisements are also enriched with photographs and videos of the property. This paper proposes a methodological framework that integrates artificial intelligence techniques (natural language processing and computer vision) to extract structured features from text and photographs of advertisements. The resulting dataset feeds the comparative method of property valuation, applied to thousands of properties. Geographic analysis and neighborhood characteristics of properties enrich the proposed algorithm, covering all the available geodata that affect real estate values. The empirical analysis of apartments in the city of Thessaloniki demonstrates significant improvements in valuation accuracy and the completeness of the characteristics. At the same time, the prediction of the energy class of properties, among other traits that the proposed methodology can accurately calculate, further enriches the valuation process. The work highlights the potential integration of artificial intelligence into modern valuation practice, offering a transparent, scalable, and auditable tool for professionals and policymakers.

Urban public spaces function as complex social and spatial systems, serving as fundamental elements in preserving cultural heritage and fostering democratic participation and urban stability. The dominant valuation methods depend on economic principles that prioritize financial returns and property values over social, ecological, and cultural aspects. This study examines the distinction between social value, which is based on personal experiences and social connections, and fair value, which relies on market operations and financial regulations. The research investigates how valuation practices affect spatial justice and urban governance through a conceptual framework and four empirical case studies: Syntagma Square in Athens, the metro station areas, the city of Barcelona, and waterfront redevelopment projects in Thessaloniki, London, and Mumbai. The official valuation systems we studied conceal the political elements of space while preserving social inequalities and forcing out the requirements of local communities. The proposed solution establishes an integrative, pluralist valuation system that incorporates qualitative, temporal, and ethical elements, supporting the Sustainable Development Goals (SDGs) and the New Urban Agenda. The framework transforms public space discussions from commercialized urban resources into communal civic systems, creating sustainable cities for all.

Automated Valuation Models (AVMs) have progressed from hedonic and spatial statistics to machine-learning architectures, yet most remain optimized for one target, market, and observation period. This review asks whether the foundation model paradigm defines a substantively different research object. A structured, concept-centric review combines direct evidence from valuation, AVMs, multimodal property analysis, and professional standards with primary research on tabular, temporal, geospatial, urban, and multimodal foundation models. This framework separates computational foundationality from real-estate domain validity. A Real Estate Foundation Model (REFM) is a domain-scale, pre-trained, and adaptable model that learns reusable representations from broad real estate evidence and demonstrates support for heterogeneous tasks across non-identical settings. Four criteria define the computational baseline: domain-broad pre-training, a shared reusable backbone, adaptability to heterogeneous tasks, and demonstrated non-trivial transfer. A fifth criterion concerns the semantic and institutional foundation of real estate and establishes “real estate domain validity”. The core model’s capability is distinct from its implementation. The study concludes that a core model applied to real estate data is not necessarily a core model of real estate markets: foundation-model status must be demonstrated through the reuse of the representation, transferability, and semantic validity when compared with strong, specialized alternatives.

Major infrastructure investments alter accessibility and urban development patterns, yet their impact on housing prices varies significantly across regions. The prevailing interpretation attributes this heterogeneity to supply differences or regulatory constraints, treating land use regulations as exogenous variables. Nevertheless, even two regions with a nominally similar regulatory framework may produce substantially different outcomes in the housing market, depending on the effectiveness of rule implementation. This paper argues that this approach overlooks a critical variable: the ability of regional authorities to coordinate, regulate, permit, and implement spatial development in a predictable and timely manner. In line with this, a conceptual framework is developed, grounded in the literature on spatial and multi-level governance, in which regional institutional capacity is proposed as a potential mediator of capitalization around project milestones (announcement, funding, construction, operation), rather than as a backdrop. This capacity shapes outcomes through three interrelated dimensions: the responsiveness of supply, which depends on administrative capacity and regulatory consistency; the coherence of governance across jurisdictions within functional urban areas; and the management of land value through land value capture instruments. From this framework, testable propositions are derived regarding the intensity, timing, and spatial distribution of price effects. The study does not empirically estimate changes in housing prices, nor does it test the propositions put forward. Instead, it develops the conceptual framework and organizes the spatial and institutional units of observation required for a subsequent empirical test. The framework is specified spatially through Section A, Line 4 of the Athens Metro to organize the project’s spatial units, administrative jurisdictions, land uses, and milestones for future analysis. The contribution is threefold: conceptual, as it elevates regional institutional capacity from a contextual to an explanatory variable; theoretical, in that it bridges urban economics with the governance literature; and policy-relevant, since it repositions the reform of regional governance as a constituent element of housing policy and as a factor that may shape sustainable spatial development outcomes.