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A biomimetic, AI-driven property management model: connecting cellular analogies and intelligent systems for next-generation real estate

Ünsal Özdilek

Property Management2026https://doi.org/10.1108/pm-04-2025-0035article
AJG 1ABDC B
Weight
0.50

What the paper says

Purpose Post-pandemic vacancy, task digitisation and ESG rules weaken location-centric playbooks. We pilot an AI-enabled biomimetic framework – Real-Estate Cell Management (RECM) – to anticipate and reconfigure space use. Design/methodology/approach We integrate a 1900–2025 US vacancy series with a ten-dimensional Smartness Index and a Delphi-weighted Occupant-Synergy matrix; χ2 and correlation tests indicate where portable tasks migrate. Findings Office vacancies rise as tasks become portable; housing behaves as an AI-supported “home-cell”. Smart-building upgrades that overlook occupant engagement can raise vacancy. Practical implications Thresholds (Smartness ≥ 0.60; Synergy ≥ 0.60) trigger retrofit, re-programme or divest decisions and can be embedded in covenants, ESG dashboards and adaptive zoning. Social implications RECM links asset strategy to digital-equity infrastructure and privacy-aware data governance. Originality/value RECM fuses biomimicry with AI occupant analytics to extend PM metrics and provide a reproducible decision toolset for occupant-centric portfolios.

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https://doi.org/https://doi.org/10.1108/pm-04-2025-0035

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@article{ünsal2026,
  title        = {{A biomimetic, AI-driven property management model: connecting cellular analogies and intelligent systems for next-generation real estate}},
  author       = {Ünsal Özdilek},
  journal      = {Property Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/pm-04-2025-0035},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.