Mapping Real Estate Values

Abstract

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.