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Fuzzy Model for Determining the Risk Premium to the Rental Rate When Renting Technological Equipment

Author

Listed:
  • Yuriy Ekhlakov

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Sergei Saprunov

    (Faculty of Innovation Technologies, National Research Tomsk State University, 634050 Tomsk, Russia)

  • Pavel Senchenko

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Anatoly Sidorov

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

Abstract

The article is devoted to the method of determining the risk surcharge in rental rates for special technological equipment. The relevance and features of the task, as well as existing approaches to solve it in other subject areas, are described. The risk of landlords is highlighted as “the inability to fully ensure the receipt of a stable income recorded in the lease agreement”. The three most significant risk-forming factors are highlighted: the early return of equipment, the emergence of debt on payments from the tenant, and the breakdown of equipment due to the fault of the tenant. A fuzzy model for estimating the likelihood of the manifestation of risk-forming factors is proposed depending on the following challenges of the rental pillar: the size of the enterprise, financial stability, the age of the enterprise, the number of current trials, and the reputation of the enterprise. Describes: universal linguistics for input and output values characterizing risky components, logical output rules, and the assessment of the likelihood of risk in general. Based on the SciKit-Fuzzy library for the Python language, the model studies all available values of input variables, and tenants are presented separately on the boundary values of the enterprise parameters. A methodology for determining the rental rate, taking into account the risk surcharge, is proposed.

Suggested Citation

  • Yuriy Ekhlakov & Sergei Saprunov & Pavel Senchenko & Anatoly Sidorov, 2023. "Fuzzy Model for Determining the Risk Premium to the Rental Rate When Renting Technological Equipment," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:541-:d:1041435
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    References listed on IDEAS

    as
    1. Xin Feng & Chengbin Chu, 2022. "Online leasing problem with price fluctuations and the second-hand transaction," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1280-1297, July.
    2. Yinfeng Xu & Rongteng Zhi & Feifeng Zheng & Ming Liu, 2022. "Competitive algorithm for scheduling of sharing machines with rental discount," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 414-434, August.
    3. Mi Hye Park & Haeng Seon Shim & Won Ho Kim & Hyo-Jin Kim & Dong Joon Kim & Seong-Ho Lee & Chung Su Kim & Mi Sook Gwak & Gaab Soo Kim, 2015. "Clinical Risk Scoring Models for Prediction of Acute Kidney Injury after Living Donor Liver Transplantation: A Retrospective Observational Study," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    Full references (including those not matched with items on IDEAS)

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