Work

PARS: an urban context framework for residential adaptive reuse

Year: 2026
Type: article
Abstract: Housing shortages in rapidly growing metropolitan regions have increased interest in adaptive reuse strategies that enable the residential conversion of existing non-residential buildings. However, most adaptive reuse evaluation approaches primarily focus on building-level characteristics, while the surrounding urban context that supports everyday residential life remains less systematically operationalised. This study develops a computational framework for evaluating the potential adaptive reuse of office buildings and shopping malls by analysing the urban environments surrounding them. The framework is grounded in the spatial logic of the 15-min city, which emphasises access to daily services and amenities within walkable distances as a key condition for supporting residential life. The empirical analysis focuses on 1,454 office, shopping mall, and commercial-use buildings across Istanbul, derived from the 2024 building inventory of the Istanbul Metropolitan Municipality. For each building, four urban context indicators were calculated to represent complementary dimensions of residential suitability: Population–Amenity Balance (PAB), Amenity Diversity, metro-based Connectivity, and Amenity–Rent Alignment (ARA). These indicators are subsequently aggregated into the Potential Adaptive Reuse Score (PARS), a composite urban-context readiness metric for residential adaptive reuse. PARS is calculated using an equal-weight aggregation scheme and subsequently evaluated against a typology-differentiated weighting configuration derived from clustering and Random Forest feature-importance analysis. The resulting metric provides a building-level ranking of urban-context readiness for residential conversion and reveals spatial variations in adaptive reuse potential across Istanbul. The proposed framework demonstrates how urban data analytics can support early-stage adaptive reuse assessment and provides a transferable computational approach for identifying potential residential conversion candidates in metropolitan contexts.
Language: English
Open Access status: diamond
OpenAlex ID: W7203767020
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Cites: 31
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