Data and Algorithms: Reviewing the Role of Machine Learning in the Real Estate Sector |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-11 |
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| Year of Publication : 2023 | ||
| Authors : Prashanth Kumar Mally | ||
| DOI : 10.14445/22312803/IJCTT-V71I11P108 | ||
How to Cite?
Prashanth Kumar Mally, "Data and Algorithms: Reviewing the Role of Machine Learning in the Real Estate Sector," International Journal of Computer Trends and Technology, vol. 71, no. 11, pp. 55-64, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I11P108
Abstract
This study investigates the impact of Machine Learning (ML) on the real estate sector, analyzing its role in enhancing market prediction accuracy, valuation precision, operational efficiency, and customer engagement. Employing a comparative analysis methodology, the research synthesizes findings from a range of scholarly works, focusing on integrating data analytics within real estate practices. Key findings reveal that ML significantly improves predictive capabilities in market trends and property values and streamlines operations to great potential. The study also highlights the dual-edged nature of algorithmic decision-making, which is beneficial but poses risks of bias and ethical dilemmas. Advancements in chatbots, virtual assistants, investment analysis tools, and smart property management are identified as the latest trends driving the sector's transformation. Practical implications suggest a future of ML-augmented real estate practices, including real-time property valuations and data-driven investment strategies. Challenges such as data quality, model interpretability, integration with existing workflows, privacy, and affordability are acknowledged, with proposed solutions including standardization of data and industry-wide collaboration. The study concludes that the strategic application of ML in real estate promises a more efficient, equitable, and data-empowered industry, calling for ethical stewardship and continuous innovation.
Keywords
Machine Learning, Predictive analytics, Real estate valuation, Algorithmic bias, Data standardization.
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