https://doi.org/10.1140/epjds/s13688-025-00551-z
Research
Exploring the spatial segmentation of housing markets from online listings
1
Instituto de Física Interdisciplinar y Sistemas Complejos IFISC, CSIC-UIB, Campus UIB, 07122, Palma de Mallorca, Spain
2
Eurecat, Technology Centre of Catalonia, Barcelona, Spain
3
Complex Systems Group and G.I.S.C, Universidad Rey Juan Carlos, Móstoles, 28933, Madrid, Spain
4
ISI Foundation, via Chisola 5, 10126, Turin, Italy
5
School of Geography and the Environment, University of Oxford, Oxford, UK
6
Department of Sociology, University of California, Berkeley, Berkeley, USA
7
Département de géographie, Université de Montréal, Montréal, Canada
8
Departament d’Economia de l’Empresa, Universitat de les Illes Balears, Campus UIB, 07122, Palma de Mallorca, Spain
9
UMR 8504 Géographie-cités (CNRS - EHESS - Université Panthéon-Sorbonne, Université Paris Cité), Campus Condorcet, 93322, Aubervilliers, France
10
UMR 5194 PACTE (CNRS - Sciences Po Grenoble - Université Grenoble Alpes), 38000, Grenoble, France
Received:
7
May
2024
Accepted:
9
April
2025
Published online:
2
May
2025
The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking.
Key words: Real estate agencies / Housing / Segmentation / Spatial network / Geolocated data
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-025-00551-z.
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
