Random Forest Models to Enhance Real Estate Tax Compliance

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Apr 4 2024

In a paper published in the journal PloS One, researchers presented advancements in the random forest (RF) model to address tax risks within the real estate industry. Their work outlined the model's application in identifying tax risks, meticulous selection of indicators for risk assessment, and validation using actual taxpayer data.

The findings revealed detailed insights into tax compliance risks, including significant disparities between model judgments and declared values, highlighting underreporting issues in corporate income tax. This research significantly enhanced tax risk identification and offered a valuable tool for accurately assessing tax compliance risks in real estate enterprises.

Related Work

Past research has underscored the importance of effective tax risk management in industries like real estate, where compliance and risk identification are paramount. Researchers have continuously sought innovative methods to assess tax-related risks and develop targeted management strategies accurately. They leverage advanced data mining tools such as the optimized RF algorithm and past studies to enhance tax risk identification processes by leveraging big data and self-learning mechanisms.

For example, some studies categorized tax risks and explored manifestation mechanisms following tax reform, while others proposed recommendations to improve tax compliance risk management. Additionally, researchers have developed models to predict market values and calculate tax bases, suggesting their potential for property tax assessment systems.

Advancing Tax Risk Management

Tax risk management, particularly concerning income tax within real estate enterprises, is pivotal in maintaining fiscal stability and fostering compliance. Tax risks, inherently probabilistic and uncertain, pose challenges for taxpayers and governmental entities tasked with tax collection.

Developing effective tax risk identification models is essential to address these challenges, especially in industries like real estate, which face unique operational and accounting complexities. This paper selectively examines indicators relevant to income tax risk in the real estate sector, encompassing both general and industry-specific considerations. These indicators, ranging from asset profit rates to land value-added tax, provide crucial insights into real estate firms' financial standing and operational risks.

Specialized indicators play a vital role in financial scrutiny within real estate enterprises, with balance sheet data serving as a primary data source for assessing solvency, profitability, and return on investment. Given the sector's complexities, particular attention focuses on indicators such as development expense rates and gross profit anomalies, which directly impact operational costs and tax liabilities. Through reasoned analysis and judgment, integrating these indicators into tax risk identification models enhances the accuracy and precision of risk assessment within the real estate industry.

The RF model emerges as a robust approach for tax risk identification, leveraging extensive taxpayer data and decision tree integration methods to discern potential tax risks accurately and efficiently. The model enhances prediction accuracy and generalization capacity by training on historical data and employing collective voting mechanisms, thereby aiding tax departments and taxpayers in making informed decisions. This model process streamlines tax risk management, providing decision support for tax authorities and facilitating tax compliance within real estate enterprises.

Moreover, developing tax risk identification models based on the RF approach significantly advances tax risk management. By integrating AI technology and advanced algorithms, such models offer proactive risk prevention and control measures, surpassing traditional early warning systems and enhancing tax fairness and economic development. Through comprehensive risk analysis reports and intuitive data visualization, these models empower tax authorities to identify and manage tax risks earlier, promoting improved compliance levels and sustainable economic growth.

Real Estate Taxation

Tax risk management in real estate enterprises, particularly concerning income tax, is crucial for maintaining fiscal stability and ensuring compliance. This paper delves into identifying tax risks using the random forest model, focusing on indicators relevant to the real estate sector's unique complexities. By analyzing specialized indicators like development expense rates and gross profit anomalies, the model enhances the accuracy of risk assessment, aiding in proactive tax risk management.

The results from the random forest model highlight significant risk probabilities within real estate enterprises, mainly regarding financial expenses, business income, and total profit. These findings provide valuable insights for tax authorities to categorize and oversee taxpayers, guiding them in improving tax declaration and accounting practices. Moreover, the model's effectiveness in pinpointing potential underpayment issues underscores its significance in contemporary tax risk management within the real estate industry.

The random forest model is a robust approach for identifying income tax risks in real estate enterprises. Its ability to autonomously learn and analyze extensive data sets ensures continual enhancement in accuracy and quality, empowering tax authorities to manage taxpayers with varying risk levels efficiently. Compared to traditional models, the random forest algorithm offers enhanced stability, accuracy, and capability in capturing key risk factors, making it an invaluable tool for modern tax risk management.

Conclusion

In conclusion, the paper optimized the RF model to enhance tax risk identification in the real estate sector. It began by exploring tax risk indicators and developed a robust risk identification model validated through simulation experiments using taxpayer data.

Results highlighted the model's effectiveness in identifying income tax risks specific to real estate enterprises, revealing high-risk areas such as business income and total profit. The paper underscored the random forest model's superiority in accuracy and handling complex data structures typical in the real estate industry. However, researchers faced challenges, including limited availability of third-party data and deficiencies in risk indicator features, prompting them to address these issues in subsequent research efforts.