IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1079-d1327193.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1079/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/3/1079/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
    3. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).
    2. Chmielewska, Aneta & Walacik, Marek & Grover, Richard, 2025. "Property valuation principles – How policy changes can be detrimental to urban development," Land Use Policy, Elsevier, vol. 150(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Can log-periodic power law structures arise from random fluctuations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 228-250.
    2. Li Lin & Didier Sornette, 2015. ""Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets," Papers 1510.08162, arXiv.org.
    3. Slimane Sefiane & Mohamed Benbouziane, 2012. "Portfolio Selection Using Genetic Algorithm," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(4), pages 1-9.
    4. John Fry, 2014. "Bubbles, shocks and elementary technical trading strategies," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(1), pages 1-13, January.
    5. Yan, Wanfeng & Woodard, Ryan & Sornette, Didier, 2012. "Leverage bubble," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 180-186.
      • Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Leverage Bubble," Papers 1011.0458, arXiv.org, revised Nov 2010.
    6. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    7. Aneta Chmielewska & Małgorzata Renigier-Biłozor & Artur Janowski, 2022. "Representative Residential Property Model—Soft Computing Solution," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    8. Fantazzini, Dean, 2016. "The oil price crash in 2014/15: Was there a (negative) financial bubble?," Energy Policy, Elsevier, vol. 96(C), pages 383-396.
    9. Tie-Ying Liu & Hsu-Ling Chang & Chi-Wei Su & Xu-Zhao Jiang, 2016. "China's housing bubble burst?," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 24(2), pages 361-389, April.
    10. Varun Sarda & Yamini Karmarkar & Neha Lakhotia Sarda, 2019. "An Empirical Study Applying Log Periodic Structures for Prediction of Realty Market Crashes in India," Vision, , vol. 23(4), pages 357-363, December.
    11. Didier Sornette & Ryan Woodard, 2009. "Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis," Papers 0905.0220, arXiv.org.
    12. Dean Fantazzini & Erik Nigmatullin & Vera Sukhanovskaya & Sergey Ivliev, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    13. Yang Hu & Les Oxley, 2018. "Bubbles in US regional house prices: evidence from house price–income ratios at the State level," Applied Economics, Taylor & Francis Journals, vol. 50(29), pages 3196-3229, June.
    14. Małgorzata Renigier-Biłozor & Sabina Źróbek & Marek Walacik, 2022. "Modern Technologies in the Real Estate Market—Opponents vs. Proponents of Their Use: Does New Category of Value Solve the Problem?," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    15. Chmielewska Aneta & Adamiczka Jerzy & Romanowski Michał, 2020. "Genetic Algorithm as Automated Valuation Model Component in Real Estate Investment Decisions System," Real Estate Management and Valuation, Sciendo, vol. 28(4), pages 1-14, December.
    16. Wanfeng Yan & Edgar van Tuyll van Serooskerken, 2015. "Forecasting Financial Extremes: A Network Degree Measure of Super-Exponential Growth," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-15, September.
    17. Gladys Elizabeth Kenyon & Dani Arribas-Bel & Caitlin Robinson, 2024. "Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid," Land, MDPI, vol. 13(5), pages 1-23, April.
    18. Fry, John, 2012. "Exogenous and endogenous crashes as phase transitions in complex financial systems," MPRA Paper 36202, University Library of Munich, Germany.
    19. Sinha, Pankaj & Chandwani, Abhishek & Sinha, Tanmay, 2013. "Algorithm of construction of Optimum Portfolio of stocks using Genetic Algorithm," MPRA Paper 48204, University Library of Munich, Germany.
    20. Wosnitza, Jan Henrik & Denz, Cornelia, 2013. "Liquidity crisis detection: An application of log-periodic power law structures to default prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3666-3681.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1079-:d:1327193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.