Lease Modifications and Rent Concessions under ASC 842: COVID-19’s Lasting Impact on Lease Accounting
Keywords:
COVID-19 lease adjustments, ASC 842 complianceAbstract
The COVID-19 pandemic triggered widespread economic disruptions, creating significant challenges in lease accounting for many businesses. As organizations faced closures, reduced revenue, and operational shifts, lease modifications and rent concessions became crucial components of financial strategy. The Financial Accounting Standards Board (FASB) addressed these unprecedented issues under ASC 842, introducing practical expedients to help lessees and lessors manage lease modifications more efficiently. ASC 842 provided specific guidance that allowed companies to simplify the accounting for rent concessions & lease adjustments, reducing the administrative & financial burden during uncertainty. This paper delves into how these practical expedients have supported companies in maintaining compliance with lease accounting standards while adapting to financial pressures imposed by the pandemic. By focusing on the unique challenges of COVID-19-related lease modifications, this paper examines the alternatives granted to lessees and lessors, their application in real-world scenarios, and the lasting effects on lease accounting practices. Additionally, it explores the implications of these adaptations, including the ease of maintaining accurate reporting amidst shifting economic conditions, the impact on future lease agreements, & how these temporary accommodations may shape the evolution of lease accounting standards beyond the pandemic. Ultimately, FASB’s response through ASC 842 offered a critical lifeline, allowing businesses to manage their lease obligations without compromising compliance or overwhelming resources. The discussion provides insights into how practical expedients served as immediate relief and a foundation for future resilience in lease accounting, underscoring the flexibility and adaptability of ASC 842 in addressing unanticipated financial challenges.
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Closing, E., Center, E. C., Room, L., MEN, I. M. E. N., Start, M., Safety, P., ... & Fees, N. I. (2017). I. Student Resources.
Weidner, D. J. (2017). New FASB rules on accounting for leases: A Sarbanes-Oxley promise delivered. The Business Lawyer, 72(2), 367-404.
Freeman, M. S. (2018). ASC 842: Implementing the New Leasing Standard. Management Accounting Quarterly, 20(1).
Stancheva-Todorova, E., & Velinova-Sokolova, N. (2019). IFRS 16 leases and its impact on company’s financial reporting, financial ratios and performance metrics. Economic Alternatives, 1(1), 44-62.
Paik, D. G. H., van der Laan Smith, J. A., Lee, B. B., & Yoon, S. W. (2015). The relation between accounting information in debt covenants and operating leases. Accounting Horizons, 29(4), 969-996.
Comiran, F. H. (2014). Lobbying behavior: Evidence from proposed changes in lease accounting (Doctoral dissertation, UC Berkeley).
Glasscock, R., Ainsworth, P., & Shimpa, L. (2016). THE ENERGY INDUSTRY'S RESPONSES TO THE PROMULGATION OF ASC 842: LEASES. Petroleum Accounting and Financial Management Journal, 35(3), 43.
Munter, P. (2018). Lessor accounting under ASC 842–Not necessarily business as usual. Journal of Accounting Education, 43, 57-60.
Singh, A. (2012). Proposed lease accounting changes: Implications for the restaurant and retail industries. Journal of Hospitality & Tourism Research, 36(3), 335-365.
Yamen, S., Silvia, H., & Christiansen, L. (2019). In Defense of the Landlord: A New Understanding of the Property Owner. Urb. Law., 50, 273.
McLaren, M. (2019). Covid-19: The Airlines' Battle for Survival. Issues Aviation L. & Pol'y, 19, 173.
Wójcik-Czerniawska, A. (2017). Effective Methods of Tax Reduction for Organizations in the Era of COVID-19. In Economic Resilience and Pandemic Response (pp. 211-221). Routledge.
Stergiopoulos, V., Mejia-Lancheros, C., Nisenbaum, R., Wang, R., Lachaud, J., O'Campo, P., & Hwang, S. W. (2019). Long-term effects of rent supplements and mental health support services on housing and health outcomes of homeless adults with mental illness: extension study of the At Home/Chez Soi randomised controlled trial. The Lancet Psychiatry, 6(11), 915-925.
Nyeko, K. E., Sing, N. K., & Ling, V. M. (2018). The Impact of the Covid-19 Pandemic (Coronavirus) on Small and Medium Enterprises (SMEs) in Kampala Central Division, Uganda.
STARNONI, M. (2019). The effects of COVID-19 on the labour market.
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
Naresh Dulam. Snowflake: A New Era of Cloud Data Warehousing. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Apr. 2015, pp. 49-72
Naresh Dulam. The Shift to Cloud-Native Data Analytics: AWS, Azure, and Google Cloud Discussing the Growing Trend of Cloud-Native Big Data Processing Solutions. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Feb. 2015, pp. 28-48
Naresh Dulam. DataOps: Streamlining Data Management for Big Data and Analytics . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Oct. 2016, pp. 28-50
Naresh Dulam. Machine Learning on Kubernetes: Scaling AI Workloads . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Sept. 2016, pp. 50-70
Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
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