PARS: an urban context framework for residential adaptive reuse

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.

Keywords

Adaptive Reuse / Housing shortage / 15-min City

References

[1] Abbiasov, T., Heine, C., Sabouri, S., Salazar-Miranda, A., Santi, P., Glaeser, E., & Ratti, C. (2024). The 15-minute city quantified using human mobility data. Nature Human Behaviour. https://doi.org/10.1038/s41562-023-01770-y

[2] Alonso W. Location and land use: Toward a general theory of land rent. 1964Harvard University Press

[3] Armstrong, G., Wilkinson, S., & Cilliers, E. J. (2023). A framework for sustainable adaptive reuse: understanding vacancy and underuse in existing urban buildings. Frontiers in Sustainable Cities, 5. https://doi.org/10.3389/frsc.2023.985656

[4] ARUP, & WBCSD. (2024). Net-zero buildings Where do we stand?

[5] AYD. (2024). AVM Endeksi 2024 Yıllık Bülten. A similar divergence is evident in the retail real estate sector, where post-pandemic improvements in revenue indicators on a per-square-metre basis have not been accompanied by a full recovery in visitor numbers. National shopping mall indices show that footfall levels remain below pre-pandemic reference benchmarks, pointing to a structural shift in space-use patterns rather than a temporary market fluctuation (AVM Ciro Endeksi Yıllık Endeks 2024, 2024).

[6] Ball RM. Re use potential and vacant industrial premises: Revisiting the regeneration issue in Stoke-on-Trent. Stoke-on-Trent, 2001, 19(2): 93-110

[7] Beeckman, P., Galle, W., De Temmerman, N., & Lespagnard, M. (2024). The repurposing of our built environment: Circular strategies in reconversion projects of office buildings to affordable housing. IOP Conference Series: Earth and Environmental Science, 1363(1). https://doi.org/10.1088/1755-1315/1363/1/012044

[8] Bloch F, Jackson MO, Tebaldi P. Centrality measures in networks. Social Choice and Welfare, 2023, 61(2): 413-453

[9] Bonacich P. (1972). Factoring and weighting approaches to status scores and clique identification.

[10] Bruno M, Monteiro Melo HP, Campanelli B. A universal framework for inclusive 15-minute cities. Nature Cities, 2024, 1: 633-641

[11] Bullen P, Love P. A new future for the past: A model for adaptive reuse decision-making. Built Environment Project and Asset Management, 2011, 1(1): 32-44

[12] CBRE. (2024). Istanbul Office MarketView Q1 2024.

[13] Cellucci C. Circular economy strategies for adaptive reuse of residential building. Vitruvio, 2021, 6(1): 110-121

[14] Conejos, S., & Langston, C. (2010). Mirvac School of Sustainable Development Institute of Sustainable Development and Architecture Designing for future building adaptive reuse using adaptSTAR DESIGNING FOR FUTURE BUILDING ADAPTIVE REUSE USING adaptSTAR. http://epublications.bond.edu.au/sustainable_development

[15] Cook, C., Benkard, L., Couture, V., Cuesta, I., Currier, L., Diamond, R., Einav, L., Gentzkow, M., Glaeser, E., Handbury, J., Li, P., Mahoney, N., Reiss, P., Ross, B., Somaini, P., Van Dijk, W., Vasserman, S., Kim, S., Lawlor, D., … Pozdnoukhov, A. (2024). Heterogeneous Preferences for Neighborhood Amenities: Evidence from GPS Data *.

[16] Dodd, N., Cordella, M., Traverso, M., & Donatello, S. (2017). Level(s)-A common EU framework of core sustainability indicators for office and residential buildings Parts 1 and 2: Introduction to Level(s) and how it works (Beta v1.0). https://doi.org/10.2760/827838

[17] Feagan, M., Muñoz-Erickson, T. A., Hobbins, R., Baja, K., Chester, M., Cook, E. M., Grimm, N., Grove, M., Iwaniec, D. M., Iyer, S., McPhearson, T., Méndez-Lázaro, P., Miller, C., Sauter, D., Solecki, W., Tomateo, C., Troxler, T., & Welty, C. (2025). Co-producing new knowledge systems for resilient and just coastal cities: A social-ecological-technological systems framework for data visualization. Cities, 156. https://doi.org/10.1016/j.cities.2024.105513

[18] Geraedts, RP., & van der Voordt, DJM. (2007). The New Transformation Meter: A new evaluation instrument for matching the market supply of vacant office buildings and the market demand for new homes. In W. Bakens, N. J. Habraken, K. Kamimura, & Y. Utida (Eds.), Building stock activation 2007 (pp. 33-40). THEI Printing Co.

[19] Graells-Garrido, E., Serra-Burriel, F., Rowe, F., Cucchietti, F. M., & Reyes, P. (2021). A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelona. PLoS ONE, 16(5 May). https://doi.org/10.1371/journal.pone.0250080

[20] Hastie, T., Tibshirani, R., & Friedman, J. (2017). Springer Series in Statistics The Elements of Statistical Learning Data Mining, Inference, and Prediction.

[21] Honic, M., Kovacic, I., Aschenbrenner, P., & Ragossnig, A. (2021). Material Passports for the end-of-life stage of buildings: Challenges and potentials. Journal of Cleaner Production, 319. https://doi.org/10.1016/j.jclepro.2021.128702

[22] IBB Imar Sehircilik. (2025). Kadıköy Central Strategy Document.

[23] Iliopoulou, P., Krassanakis, V., & Kappelos, K. (2025). Modeling the Spatial Impact of Short-Term Rentals on House Prices: The Case of Athens, Greece. Urban Science, 9(12). https://doi.org/10.3390/urbansci9120539

[24] Katz, L. (1953). UAaCH, 1953 A NEW STATUS INDEX DERIVED FROM SOCIOMETRIC ANALYSIS*. https://doi.org/10.1007/BF02289026

[25] Keena N, Friedman A, Parsaee M, Mussio M, Klein A, Pomasonco-Alvis M, Pinheiro P. Housing passport knowledge graph: Promoting a circular economy in urban residential buildings. Sustainable Cities and Society, 2025, 119: 106050

[26] Kismet, B., & Colakoglu, M. B. (2025). Developing a building-scale circularity indicator through fuzzy analytical hierarchy process and Gini index-based circularity calculation methods. International Journal of Construction Management. https://doi.org/10.1080/15623599.2025.2541716

[27] Langston C, Shen LY. Application of the adaptive reuse potential model in Hong Kong: A case study of Lui Seng Chun. International Journal of Strategic Property Management, 2007, 11(4): 193-207

[28] Langston C, Smith J. Modelling property management decisions using “iconCUR”. Automation in Construction, 2012, 22406-413

[29] Liu D, Kwan MP, Wang J. Developing the 15-minute city: A comprehensive assessment of the status in Hong Kong. Travel Behaviour and Society, 2024, 34: 100666

[30] Madastar Platform. (2026). https://madaster.de/en/platform/

[31] Mattingly K, Morrissey J. Housing and transport expenditure: Socio-spatial indicators of affordability in Auckland. Cities, 2014, 3869-83

[32] Moreno C, Allam Z, Chabaud D, Gall C, Pratlong F. Introducing the “15-minute city”: Sustainability, resilience and place identity in future post-pandemic cities. Smart Cities, 2021, 4(1): 93-111

[33] OECD. The circular economy in cities and regions. 2020OECD Publishing

[34] Olivari, B., Cipriano, P., Napolitano, M., & Giovannini, L. (2023). Are Italian cities already 15-minute? Presenting the Next Proximity Index: A novel and scalable way to measure it, based on open data. Journal of Urban Mobility, 4. https://doi.org/10.1016/j.urbmob.2023.100057

[35] Remøy H, Van Der Voordt T. Adaptive reuse of office buildings into housing: Opportunities and risks. Building Research & Information, 2014, 42(3): 381-390

[36] ULASAV. (2025). Ulusal Açık Veri Akıllı Şehir Platformu,

[37] Zachary D, Dobson S. Urban development and complexity: Shannon entropy as a measure of diversity. Planning Practice and Research, 2021, 36(2): 157-173

[38] Zhao, W., & Zou, Y. (2025). Smart governance for affordable housing in China: Preparation, practice, and paradoxes. Cities, 156. https://doi.org/10.1016/j.cities.2024.105500

[39] Maciejewska, M., Cubells, J., & Marquet, O. (2025). When proximity is not enough. A sociodemographic analysis of 15-minute city lifestyles. Journal of Urban Mobility, 7, Article 100119. https://doi.org/10.1016/j.urbmob.2025.100119

[40] Wu, H., Wang, L., Zhang, Z., & Gao, J. (2021). Analysis and optimization of 15-minute community life circle based on supply and demand matching: A case study of Shanghai. PLOS ONE, 16(8), e0256904. https://doi.org/10.1371/journal.pone.0256904

[41] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

[42] Berkmen, N. H. (2018). İstanbul Tarihi Yarımada Yönetim Planı. İstanbul Büyük Şehir Belediyesi.

[43] Metro İstanbul. (2024). 2024 faaliyet raporu. https://www.metro.istanbul/icerik/faaliyet_raporlari.

[44] İstanbul Planlama Ajansı. (2022). İstanbul’da boş ofislerin konuta dönüştürülmesine yönelik yönetmeliğin teknik değerlendirme. https://ipa.istanbul/yayinlarimiz/173/istanbulda-bos-ofislerin-konuta-donusturulmesine-yonelik-yonetmeligin-teknik-degerlendirme.

[45] İstanbul Planlama Ajansı. (2021). Konut sorunu araştırması: İstanbul’da mevcut durum ve öneriler. İstanbul Büyükşehir Belediyesi Kültür A.Ş. https://ipa.istanbul/wp-content/uploads/2021/09/IPA_KONUT_REHBERI-web.pdf.

[46] İstanbul Planlama Ajansı. (2024). “Airbnb Yasası”: Kısa süreli kiralamaların konut kriziyle ilişkisi. https://ipa.istanbul/wp-content/uploads/2024/03/KENT-GUNDEMI-AIRBNB.pdf.

RIGHTS & PERMISSIONS

The Author(s)