As urban transformation processes grow more complex, traditional real estate valuation methods struggle in addressing rapid socio-economic, cultural, spatial, and environmental shifts. Although spatial data and analytics have advanced significantly, key challenges persist in terms of usability, transparency, and integration into practice. This study seeks to identify the most widely used and impactful spatial methods in real estate valuation, tracing their evolution over the past two decades. Employing a semi-systematic literature review grounded in the PRISMA protocol, the research analyzes peer-reviewed articles retrieved from Scopus to map the development of spatial valuation approaches. The findings highlight a growing reliance on Spatial Hedonic Approaches and Spatial Econometric techniques, which incorporate spatial dependencies and improve the accuracy of value estimates. Geographically Weighted Regression (GWR) emerges as the most commonly used GIS-based method for capturing geographic variations in property values. While traditional hedonic pricing models remain foundational, Automated Valuation Models (AVMs) are gaining momentum due to their scalability and ability to handle large datasets. The review also points to an increasing interest in spatial-temporal models, which support real-time monitoring and forecasting of property values. These trends suggest a shift toward more data-driven, spatially explicit valuation practices that bridge multiple disciplines. However, significant gaps remain, particularly in data accessibility, methodological clarity, and the incorporation of social and environmental values. Enhancing spatial intelligence in valuation frameworks could play a crucial role in shaping more sustainable urban development and informing evidence-based policy-making.

Monitoring SDG 11 targets is crucial for making informed decisions and supporting multidimensional transitions in European cities. Among all the goals, SDG 11 emerges as a cornerstone for cities, offering a comprehensive framework to tackle their multifaceted challenges. Composite indicators and indices, as suited evaluation tools to monitor city progress or decline, allow sustainability problems to be included in local agendas by aggregating multi-dimensional variables at different time spans through data-driven approaches. The primary concerns about using indicators as evaluation tools to compare performances are inherent to inconsistencies related to different assessment frameworks and methods, data downscaling from global to local levels, choice of aggregation rules to obtain synthetic results, and data gaps. This contribution, in particular, focuses on data gaps by elaborating on a testing case, while critically discussing related issues. The research was addressed to identify normative, assessment, and methodological gaps in monitoring progress towards SDG 11 at global, European, and Italian levels. Application of Machine Learning algorithms to predict null values within an SDG 11 regional dataset was implemented to compare three Italian regions according to 18 common indicators. The contribution is part of the Research Project of National Relevance “GLOSSA - GLOcal knowledge System for Sustainability Assessment of urban projects”, coordinated by Polytechnic of Turin (Italy), and it supports its first-step knowledge phase aimed at identifying gaps in SDG 11 indicators downscaling and monitoring.