Harnessing Big Data to Revolutionize Real Estate Financing for Low-Income Earners in the U.S.

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@article{1706970,
      author = {Adedotun Ademowo, Ifeanyi Osigwe, Kwame Joseph Nkrumah},
      title = {Harnessing Big Data to Revolutionize Real Estate Financing for Low-Income Earners in the U.S.},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {373-380},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706970.pdf},
      abstract = {The housing affordability crisis for low-income earners in the U.S. persists as a pressing societal challenge, exacerbated by systemic inefficiencies in traditional real estate financing models. This article explores the transformative potential of Big Data in addressing these inefficiencies. Through the integration of predictive analytics, machine learning, and advanced data visualization techniques, this study demonstrates how technology-driven approaches can enhance accessibility, streamline processes, and foster equity in real estate financing. This comprehensive investigation underscores the significance of data-driven methodologies in designing innovative, scalable solutions that address the housing needs of underserved populations. By focusing on actionable strategies and implementation frameworks, this article aims to bridge the gap between technological advancements and practical applications in real estate financing.},
      keywords = {Big Data, real estate financing, affordable housing, predictive analytics, machine learning, data visualization, equity.},
      month = {March},
  }