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Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Author

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  • Marek Walacik

    (Institute of Spatial Management and Geography, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, Poland)

  • Aneta Chmielewska

    (Institute of Spatial Management and Geography, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, Poland)

Abstract

As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

Suggested Citation

  • Marek Walacik & Aneta Chmielewska, 2024. "Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation," Sustainability, MDPI, vol. 16(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1079-:d:1327193
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    References listed on IDEAS

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    1. Pereira, Robert, 2000. "Genetic Algorithm Optimisation for Finance and Investments," MPRA Paper 8610, University Library of Munich, Germany.
    2. Renigier-Biłozor, Małgorzata & Janowski, Artur & Walacik, Marek & Chmielewska, Aneta, 2022. "Modern challenges of property market analysis- homogeneous areas determination," Land Use Policy, Elsevier, vol. 119(C).
    3. Zhou, Wei-Xing & Sornette, Didier, 2008. "Analysis of the real estate market in Las Vegas: Bubble, seasonal patterns, and prediction of the CSW indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 243-260.
    4. Mcgrath, Patrick & Desai, Kaushal & Junquera, Philip, 2019. "Resistance is futile : How corporate real estate companies can deploy artificial intelligence as a competitive advantage," Corporate Real Estate Journal, Henry Stewart Publications, vol. 9(2), pages 121-129, December.
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    1. Renigier–Biłozor, Małgorzata & Chmielewska, Aneta & Kamasz, Ewelina, 2024. "The soft computing based model of investors’ condition and cognition on a real estate market," Land Use Policy, Elsevier, vol. 141(C).

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