Geodemographic Segmentation for Site Selection: A Bayesian Approach in Commercial Real Estate

Site selection studies in urban planning have traditionally relied on subjective judgments, often made by a limited team, leading to decisions that may need more objectivity. This research addresses the need for a more systematic and objective method for choosing locations, particularly in cases involving large geographies and numerous properties and/or branches.The study introduces a novel approach to site selection by proposing a geodemographic segmentation method. Leveraging Geographic Information Systems (GIS), this method divides wider geographies into segments, facilitating more informed decision-making in location selection. By reducing the reliance on subjective judgments, the proposed method aims to enhance the efficiency of decision support systems, allowing for the utilisation of accumulated experience in a more structured manner.Central to the research is the development of a generalisable socioeconomic segmentation method tailored for the location selection of commercial real estate with a significant number of branches. The study employs Bayesian approaches to analyse demographic data, searching for a robust foundation for decision-making processes.The anticipated outcomes of this research include the generation of geodemographic segments through GIS and establishing a generalisable socioeconomic segmentation method. These segments will serve as the study's results and be integrated into location selection models, contributing to more effective decision support systems.