Development of a Smart AI-Enabled Digital Platform for End-to-End Affordable Housing Delivery

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@article{1705612,
      author = {Tosin Samuel Oyetunji, Fasasi Lanre Erinjogunola, Rasheed O. Ajirotutu, Abiodun Benedict Adeyemi, Tochi Chimaobi Ohakawa; Saliu Alani Adio},
      title = {Development of a Smart AI-Enabled Digital Platform for End-to-End Affordable Housing Delivery},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {494-510},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705612.pdf},
      abstract = {The global affordable housing crisis necessitates innovative, scalable, and efficient solutions to bridge the growing gap between housing supply and demand. This paper presents a comprehensive framework for the development of a smart artificial intelligence-enabled digital platform designed to revolutionize end-to-end affordable housing delivery. By integrating advanced decision-making models, geospatial analytics, blockchain-driven transactions, and predictive site selection algorithms, the proposed platform streamlines critical housing processes, enhances efficiency, and improves accessibility for underserved populations. The study begins by analyzing the challenges in traditional housing delivery models, including bureaucratic inefficiencies, financing barriers, and supply chain constraints. It then explores the role of artificial intelligence, digital twin technology, and blockchain in transforming urban planning, construction, and housing allocation. A detailed architectural model is presented, outlining key stakeholders, functionalities, and workflow integrations necessary for platform implementation. Furthermore, the research evaluates pilot case studies, measuring performance based on affordability, efficiency, accessibility, and sustainability. Comparative analysis with conventional housing approaches highlights the platform?s potential to reduce costs, expedite project completion, and improve housing equity. The paper also discusses policy implications, emphasizing the need for regulatory support, smart contract recognition, and alternative financing mechanisms to facilitate widespread adoption. Future research directions include enhancing predictive analytics for housing demand forecasting, integrating robotics for automated construction, and exploring decentralized finance solutions for more inclusive mortgage accessibility. Ethical considerations such as data privacy, algorithmic bias, and digital inclusion are also critically examined to ensure responsible deployment. This research contributes to the growing body of knowledge on digital transformation in the housing sector and provides actionable insights for policymakers, real estate developers, financial institutions, and technology innovators seeking to address the housing affordability crisis through artificial intelligence-driven solutions.},
      keywords = {AI-Enabled Housing, Digital Twin Technology, Blockchain in Real Estate, Affordable Housing Solutions, Smart Contracts in Housing},
      month = {March},
  }