Complete Accrual Taxation

This Article considers and analyzes complete accrual taxation, the inclusion in the tax base of annual increases and decreases in the value of property, regardless of disposition. This system does away with the rule that income must be realized in order to be taxed. The Article outlines the complete tax accrual system and engages in a rigorous examination of the benefits resulting from such a system. It compares the consequences of such a system to the consequences of adopting other alternatives to the realization rule. It explores a computerized valuation system for nonmarketed business interests, and deals with legitimate privacy concerns raised by such a system. The author concludes by noting that complete accrual taxation is beneficial and feasible, and that further research should be done to address these issues

\u27Complete\u27 Accrual Taxation

Under the realization rule, accrued gains and losses generally are not taken into account for income tax purposes until a disposition occurs. Thus, the realization rule is responsible for tax deferral, which in turn likely leads to economic inefficiencies and inequities. The realization rule also contributes greatly to the complexity of the federal income tax system by necessitating numerous Internal Revenue Code provisions that address the many consequences arising from the decision to postpone taxation until a disposition occurs. An alternative to the realization rule is accrual taxation - the inclusion in the tax base of annual increases and decreases in the value of property, regardless of disposition. Accrual taxation may improve economic efficiency and equity, and certainly would obviate a substantial portion of the Code. Yet, accrual taxation presents serious problems of its own - the difficulty of valuing assets and possible taxpayer illiquidity. For these reasons, few have supported such a system. And the rare proposals made are merely calls for partial accrual taxation. This article considers and analyzes complete accrual taxation. Actually, like prior proposals, the system considered here also excludes certain assets as well as the imputed income on consumer items. However, this accrual system is more complete in that it seeks to value and tax on an annual basis what are likely the most difficult-to-value assets - nonmarketed business interests and collectibles. To value these assets, this article suggests the use of computer software, in particular, artificial intelligence. The goal of this article is to demonstrate that complete accrual taxation is potentially beneficial and feasible, and that further research should be done to address these issues. Much of the remaining work should be left to those who are expert in the fields of economics, artificial intelligence, and valuation. While this research may be difficult and costly, the potential benefits of complete accrual taxation warrant the endeavor

\u27Complete\u27 Accrual Taxation

Under the realization rule, accrued gains and losses generally are not taken into account for income tax purposes until a disposition occurs. Thus, the realization rule is responsible for tax deferral, which in turn likely leads to economic inefficiencies and inequities. The realization rule also contributes greatly to the complexity of the federal income tax system by necessitating numerous Internal Revenue Code provisions that address the many consequences arising from the decision to postpone taxation until a disposition occurs. An alternative to the realization rule is accrual taxation - the inclusion in the tax base of annual increases and decreases in the value of property, regardless of disposition. Accrual taxation may improve economic efficiency and equity, and certainly would obviate a substantial portion of the Code. Yet, accrual taxation presents serious problems of its own - the difficulty of valuing assets and possible taxpayer illiquidity. For these reasons, few have supported such a system. And the rare proposals made are merely calls for partial accrual taxation. This article considers and analyzes complete accrual taxation. Actually, like prior proposals, the system considered here also excludes certain assets as well as the imputed income on consumer items. However, this accrual system is more complete in that it seeks to value and tax on an annual basis what are likely the most difficult-to-value assets - nonmarketed business interests and collectibles. To value these assets, this article suggests the use of computer software, in particular, artificial intelligence. The goal of this article is to demonstrate that complete accrual taxation is potentially beneficial and feasible, and that further research should be done to address these issues. Much of the remaining work should be left to those who are expert in the fields of economics, artificial intelligence, and valuation. While this research may be difficult and costly, the potential benefits of complete accrual taxation warrant the endeavor

Deep Learning in Predicting Real Estate Property Prices: A Comparative Study

The dominant methods for real estate property price prediction or valuation are multi-regression based. Regression-based methods are, however, imperfect because they suffer from issues such as multicollinearity and heteroscedasticity. Recent years have witnessed the use of machine learning methods but the results are mixed. This paper introduces the application of a new approach using deep learning models to real estate property price prediction. The paper uses a deep learning approach for modeling to improve the accuracy of real estate property price prediction with data representing sales transactions in a large metropolitan area. Three deep learning models, LSTM, GRU and Transformer, are created and compared with other machine learning and traditional models. The results obtained for the data set with all features clearly show that the RF and Transformer models outperformed the other models. LSTM and GRU models produced the worst results, suggesting that they are perhaps not suitable to predict the real estate price. Furthermore, the implementations of Transformer and RF on a data set with feature reduction produced even more accurate prediction results. In conclusion, our research shows that the performance of the Transformer model is close to the RF model. Both models produce significantly better prediction results than existing approaches in terms of accuracy

A rule-based decision support system for real estate brokerage service evaluation

Real estate brokers or realtors are expected to possess superior knowledge of their local markets, and typically require commissions in return for their services. The evaluation of their performance is an important issue in justifying their commissions. We develop a learning-oriented decision-making process for evaluating real estate brokerage services, which concentrates on understanding the nature, the role and the interaction of the evaluation aspects of real estate brokerage service quality by integrating cognitive maps and the Decision EXpert (DEX) approach. Results suggest that this framework permits new rudiments to be considered in the realtor decision-making and sales process, facilitating transparency and understanding of realtor functions that may lead to recommendations to improve the performance and quality of these functions. Avenues for future research are also presented.info:eu-repo/semantics/publishedVersio

Automated mass appraisal system with cross-city evaluation capability: a test development in China

The appraisal of property value is extremely important in a modern economy. For example, developers and end-consumers use appraisals for their investment decisions. Governments use it for taxation purposes, while banks rely on appraisals to update their risk profile when managing mortgage and credit application activities. With fast developing economies, quickly valuing new cities and suburbs as they get built becomes particularly difficult. Globalisation has also increased the need for common international valuation standards and automated methods. This research investigates the present mass appraisal systems and the role of automated valuation models. Financial institutions and institutional investors are increasingly more concerned about constantly updating their present portfolio value especially in a dynamic market. Trends of significant peaks and troughs need to be accounted in a faster cycle time with short bursts of pricing adjustments. The problem poses a challenge because property transactions are infrequently traded unlike other commodities such as securities. Hence, there are not many recent transactions for the same property to receive an updated value with a simple adjustment based on economic conditions. The study proposes a method that solves both large-scale mass appraisal with an ability to search across cities to discover properties with similar characteristics for its update and comparison scheme. This research advances the automated valuation model for the residential property market with a test development performed in China. In particular, the resulting model was tested with data from Chinese Tier 1, 2 and 3 Cities to evaluate property values. This research performs several major accomplishments. First, it demonstrates the efficacy of reducing human cognitive effort in the mass appraisal exercise. Second, by applying Artificial Neural Network capabilities in the automated valuation model, pricing of residential properties are able to draw upon knowledge from more mature cities with greater number of transactions and apply to newer developments in less developed cities. Third, the proposed mass appraisal system shows the reliability and robustness that matches the rapid development of Chinas real estate market that had been verified by a real application. Finally, the approach developed provides a valuable new method for property valuation that reduces the possible bias, increases consistency and lowers the effort required by current manual methods, with a lower data requirement

Residential real estate valuation framework based on life cycle cost by building information modeling

Real estate markets are ideal investment options that lead to the construction industry’s and the economy’s growth. Therefore, having appropriate investment and valuation strategies is a critical success factor. Most established valuation methods emphasize market value and economic factors and are ignorant about buildings’ technical and structural attributes. Therefore, due to the process ambiguity and lack of information access, the estimated price usually differs from the real property value. In this research, a revised valuation framework is proposed based on the life cycle cost (LCC) of residential properties, focusing on the operation phase. LCC consists of all costs related to an asset during different phases of its lifecycle, and it helps determine the net present value of the property. For systematically storing and analyzing technical and financial information, building information modeling (BIM) was proposed. Despite being widely used in the design and construction phases, its application and competitive advantage to real estate developers and managers during the operation phase are not transparent. This research benefitted from the 5D BIM model with a level of development (LOD) of 300 to increase the transparency and validity of valuation. An 18.25% difference between the calculated price of two case studies in Tehran and their inflated market prices proved this assertion

A Review of Machine Learning Approaches for Real Estate Valuation

Real estate managers must identify the value for properties in their current market. Traditionally, this involved simple data analysis with adjustments made based on manager’s experience. Given the amount of money currently involved in these decisions, and the complexity and speed at which valuation decisions must be made, machine learning technologies provide a newer alternative for property valuation that could improve upon traditional methods. This study utilizes a systematic literature review methodology to identify published studies from the past two decades where specific machine learning technologies have been applied to the property valuation task. We develop a data, reasoning, usefulness (DRU) framework that provides a set of theoretical and practice-based criteria for a multi-faceted performance assessment for each system. This assessment provides the basis for identifying the current state of research in this domain as well as theoretical and practical implications and directions for future research

Knowledge-based FIS and ANFIS models development and comparison for residential real estate valuation

There has been an increasing concern on the development of alternative approaches to overcome the problems and deficiencies that occur during the application of real-estate valuation methods. This study was established to investigate the usability of the expert knowledge based fuzzy logic methodology in determining real-estates values. In addition, valuation with the Adaptive Neuro-Fuzzy Inference System (ANFIS) method provided model comparison. Samples were administered a questionnaire for the parameters planned for these models regarding the parameters that affect real estate values. To make value estimations for the Fuzzy Inference System (FIS) model by using the parameters obtained from the questionnaire analyses, the criteria that produced the best results were acquired from the various criteria alternatives. An algorithm was created and the valuation process for real estate was performed using the FIS in Konya/Turkey. As a result of poll studies the area, age, floor conditions, physical properties and location of the real-estate property were considered as the input variables and the market value as the output variable. The memberships were established with poll analysis and were rule based on expert knowledge. The model structure was formed by using the Mamdani structure in the MATLAB fuzzy toolbox. Model prediction performance was evaluated statistically with the Mean Absolute Percentage Error (MAPE) and a high accuracy of the model results to the market values indicated the reliability of the established model for residential real-estate valuation