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Improving realty management ability based on big data and artificial intelligence decision-making

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  • Aichun Wu

Abstract

Realty management relies on data from previous successful and failed purchase and utilization outcomes. The cumulative data at different stages are used to improve utilization efficacy. The vital problem is selecting data for analyzing the value incremental sequence and profitable utilization. This article proposes a knowledge-dependent data processing scheme (KDPS) to augment precise data analysis. This scheme operates on two levels. Data selection based on previous stagnant outcomes is performed in the first level. Different data processing is performed in the second level to mend the first level’s flaws. Data processing uses knowledge acquired from the sales process, amenities, and market value. Based on the knowledge determined from successful realty sales and incremental features, further processing for new improvements and existing stagnancy mitigation is recommended. The stagnancy and realty values are used as knowledge for training the data processing system. This ensures definite profitable features meeting the amenity requirements under reduced stagnancy time. The proposed scheme improves the processing rate, stagnancy detection, success rate, and training ratio by 8.2%, 10.25%, 10.28%, and 7%, respectively. It reduces the processing time by 8.56% compared to the existing methods.

Suggested Citation

  • Aichun Wu, 2024. "Improving realty management ability based on big data and artificial intelligence decision-making," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-27, August.
    • Handle: RePEc:plo:pone00:0307043
    DOI: 10.1371/journal.pone.0307043
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