# RECORD e4122cbc852a2eb3164aee351201e6331c41cf75 # TITLE: Urban Housing Markets as Data-Driven Innovation Systems: Hedonic Price Modeling and Digital Transformation in the Mexican Real Estate Market # YEAR: 2026.0 VENUE: Revista de Gestão Social e Ambiental DOI: 10.24857/rgsa.v20n5-004 # AUTHORS: G. Uribe; P. López-de-Alba; Francisco J. Villareal-Segoviano; Omar Trejoluna Puente; R. Rodríguez-Carvajal; Juan Oswaldo Robledo López; L. Castellanos # PAGES: 24 CANDIDATE TABLE PAGES (1-based): [] # ABSTRACT: Objective: This study aims to estimate housing listing values in a residential micro-submarket in Hermosillo, Mexico, by applying a hedonic pricing model. It seeks to understand how digital real estate platforms and data-driven systems reshape property valuation and transaction dynamics.   Theoretical Framework: The research is grounded in the SPIRAL model of innovation systems. It integrates theories on urban innovation ecosystems, where interactions between academia, industry, government, and civil society are transformed by digitalization and technological change.   Method: The methodology utilizes a quantitative approach, applying multiple regression techniques to analyze housing listings obtained from digital real estate platforms. The model examines structural determinants such as built area, number of bedrooms, and parking spaces.   Results and Discussion: The final model demonstrates a high predictive capacity, explaining over 97% of the price variation. The discussion highlights that digital platforms are fundamentally transforming how housing values are generated and communicated within urban innovation systems.   Research Implications: These findings provide insights for innovation management and urban economics. They suggest that econometric models can serve as analytical tools for data-driven governance and sustainable urban planning in the context of digital transformation.   Originality/Value: The study contributes to the literature by interpreting urban housing markets as data-driven innovation ecosystems. Its value lies in linking econometric valuation with the SPIRAL innovation model to analyze the evolution of real estate markets in Latin American cities. ===== PAGE 1 (score 13, numeric-paren 0, table-words 0, var-words 13) ===== Rev. Gest. Soc. Ambient. | Miami | v.20.n.5 | p.1-24 | e014421 | 2026. 1 RGSA – Revista de Gestão Social e Ambiental ISSN: 1981-982X Submission date: 2/9/2026 Acceptance date: 4/27/2026 DOI: https://doi.org/10.24857/rgsa.v20n5-004 Organization: Interinstitutional Scientific Committee Chief Editor: Ana Carolina Messias de Souza Ferreira da Costa Assessment: Double Blind Review pelo SEER/OJS URBAN HOUSING MARKETS AS DATA-DRIVEN INNOVATION SYSTEMS: HEDONIC PRICE MODELING AND DIGITAL TRANSFORMATION IN THE MEXICAN REAL ESTATE MARKET Gerardo Ramirez Uribe1 Pedro L. López-de-Alba2 Francisco J. Villareal-Segoviano3 Omar Trejoluna Puente4 Ricardo Rodríguez-Carvajal5 Juan Oswaldo Robledo López6 Luis Manuel Orozco Castellanos7 ABSTRACT Objective: This study aims to estimate housing listing values in a residential micro-submarket in Hermosillo, Mexico, by applying a hedonic pricing model. It seeks to understand how digital real estate platforms and data- driven systems reshape property valuation and transaction dynamics. Theoretical Framework: The research is grounded in the SPIRAL model of innovation systems. It integrates theories on urban innovation ecosystems, where interactions between academia, industry, government, and civil society are transformed by digitalization and technological change. Method: The methodology utilizes a quantitative approach, applying multiple regression techniques to analyze housing listings obtained from digital real estate platforms. The model examines structural determinants such as built area, number of bedrooms, and parking spaces. Results and Discussion: The final model demonstrates a high predictive capacity, explaining over 97% of the price variation. The discussion highlights that digital platforms are fundamentally transforming how housing values are generated and communicated within urban innovation systems. Research Implications: These findings provide insights for innovation management and urban economics. They suggest that econometric models can serve as analytical tools for data-driven governance and sustainable urban planning in the context of digital transformation. Originality/Value: The study contributes to the literature by interpreting urban housing markets as data-driven innovation ecosystems. Its value lies in linking econometric valuation with the SPIRAL innovation model to analyze the evolution of real estate markets in Latin American cities. Keywords: Hedonic Pricing Model, Housing Market Analysis, Innovation Ecosystems, Digital Transformation, Proptech Platforms, Urban Innovation Systems. 1 Universidad de Sonora, Sonora, México. E-mail: gerardo.ramirez@unison.mx Orcid: https://orcid.org/0000-0001-7036-764X 2 Virtual University of the State of Guanajuato (UVEG), Universidad de Guanajuato (UG), Guanajuato, México. E-mail: pllopezdealba@gmail.com Orcid: https://orcid.org/0000-0001-5583-1942 3 Virtual University of the State of Guanajuato (UVEG), Universidad de Guanajuato (UG), Guanajuato, México. E-mail: frvillarreal@uveg.edu.mx Orcid: https://orcid.org/0000-0002-6331-2314 4 Universidad de Guanajuato (UG), Guanajuato, México. E-mail: omartp@uto.mx Orcid: https://orcid.org/0000-0002-7544-6088 5 Virtual University of the State of Guanajuato (UVEG), Universidad de Guanajuato (UG), Guanajuato, México. E-mail: rodriguez.ricardo@ugto.mx Orcid: https://orcid.org/0000-0003-3105-9809 6 Universidad de Guanajuato, Guanajuato, México. E-mail: oswaldo.robledo@ugto.mx 7 Universidad de Guanajuato, Guanajuato, México. E-mail: orozcoz@ugto.mx Orcid: https://orcid.org/0000-0002-1621-9539