Smart Housing: A Multi-scale Conceptual Framework

This study seeks to reconceptualize smart housing within a multi-scale analytical framework by examining the role of artificial intelligence (AI) and the Internet of Things (IoT) across micro-, meso-, and macro-level urban systems. Drawing on a systematic literature review and thematic analysis, the findings reveal that smart housing is not merely a collection of in-unit technologies but part of a broader data-driven, learning ecosystem operating simultaneously at multiple scales. At the micro scale, smart housing units act as primary sites of data generation, enhancing indoor environmental quality, energy efficiency, and security through IoT sensors and machine-learning algorithms. At the meso scale, household-level data integrate into smart energy grids, urban service networks, and digital participation platforms, positioning the neighborhood as a “learning intermediary node” within the smart-city structure. At the macro scale, aggregated data from homes and neighborhoods are transformed by AI-enabled analytical tools into actionable indicators that inform urban policy-making, spatial planning, energy management, and climate-responsive governance. Building on these insights, the study presents an integrated conceptual model that demonstrates how multi-level data–decision feedback cycles constitute the soft infrastructure of the smart city. The proposed framework offers a foundation for design, planning, and policy interventions aimed at promoting smart housing and advancing sustainable urban development.