Mass Appraisal Research Articles

Customizing a mass appraisal model for residential property tax assessment: a hedonic regression approach in Bahir Dar city, Ethiopia

PurposeThe paper aims to estimate the house rental values for the purpose of customizing mass appraisals in Bahir Dar City, Ethiopia. It seeks to identify the critical factors affecting the rental values of residential properties and customize a mass appraisal model for such properties. The study focuses on identifying attributes that significantly affect house rental values.Design/methodology/approachThe paper adopted a survey research design, utilizing a survey questionnaire, expert group discussion and document analysis. The data were analyzed using thematic, descriptive and inferential statistical analysis, including correlation and hedonic regression analysis.FindingsAmong the variables included in the model, the number of rooms, availability of schools, land value grading, type of nearest road, housing typology, built-up area, plot area, walling material, traveling cost and fencing materials were the most significant factors for predicting the annual rental value of residential properties in the city.Research limitations/implicationsThe findings of this study will provide valuable insights to tax assessors, property owners and local government authorities, including municipalities, concerning the key determinants of the rental values of residential properties. Besides, these findings will serve as a useful tool for valuers and researchers in the field of property value modeling.Originality/valueThis study represents the first attempt to develop a framework for mass appraisal of residential properties using annual rental values in the Ethiopian context.

Machine learning models in mass appraisal for property tax purposes: a systematic mapping study

The use of machine learning models in mass appraisal of properties for tax purposes has been extensively investigated, generating a growing volume of primary research. This study aims to provide an overview of the machine learning techniques used in this context and analyze their accuracy. We conducted a systematic mapping study to collect studies published in the last seven years that address machine learning methods in the mass appraisal of properties. The search protocols returned 332 studies, of which 22 were selected, highlighting the frequent use of Random Forest and Gradient Boosting models in the last three years. These models, especially Random Forest, have shown predictive superiority over traditional appraisal methods. The measurement of model performance varied among the studies, making it difficult to compare results. However, it was observed that the use of machine learning techniques improves accuracy in mass property appraisals. This article advances the field by summarizing the state of the art in the use of machine learning models for mass appraisal of properties for tax purposes, describing the main models applied, providing a map that classifies, compares, and evaluates the research, and suggesting a research agenda that identifies gaps and directs future studies.

Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks

Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal.

WAYS OF IMPROVING THE METHODOLOGICAL FRAMEWORK OF MASSIVE EVALUATION OF REAL ESTATE IN THE REPUBLIC OF MOLDOVA

This article focuses on researching the methodological development of real estate assessment in the Republic of Moldova, addressing the significant financial losses incurred by local public administrations due to the absence of an updated, appropriate, and transparent assessment system for taxation purposes. The fundamental hypothesis posits that enhancing the methodological and institutional framework of mass appraisal will lead to a progressive evolution of the real estate market relations and the stable development of efficient and equitable tax systems in Moldova. The study aims to identify and propose solutions to current issues in the national mass appraisal system, primarily related to the unavailability of market data and the use of outdated methodologies. Employing scientific techniques such as analysis, deduction, induction, grouping, comparison, analogy, scientific abstraction, synthesis, and statistical methods, the author assess the effectiveness of proposed methodological developments by comparing quality indicators of old assessment models with those developed based on the proposed methodologies. The results obtained support the idea of creating, implementing, and sustaining a mass appraisal system as a foundational element for a fair real estate taxation system, fostering regional development, and supporting economic activities related to the real estate market in the Republic of Moldova.

Addressing Methodological Challenges in the Mass Real Estate Valuation Process, Specifically Within the Context of the Republic of Moldova

This research investigates the precision of mass real estate valuation models, particularly focusing on the methodology of mathematical value modeling. The discussed challenges are particularly relevant to nations employing an ad-valorem taxation system. The Republic of Moldova's mass appraisal system for real estate serves as an illustrative case. Through a comparative analysis of the performance metrics between existing models and those refined based on the proposed recommendations, the study illustrates the improved efficiency of the advocated methodology.

The Use of Rate Study in Real Estate Market Analysis within the Mass Appraisal System in the Republic of Moldova

This article investigates issues related to determining real estate market indicators in the framework of mass appraisal of immovable property in the Republic of Moldova, according to the recent methodological framework issued by the authorities, set to be implemented in practice. Simultaneously, this study proposes an applied methodology for real estate market analysis, aiming to identify specific indicators necessary for an adequate and equitable assessment of the real estate market at both regional and national levels. The article systematically synthesizes the limitations associated with using arithmetic mean to calculate market prices per unit as market indicators, and highlights the advantages of rate study as an alternative for monitoring and analysing the real estate market. The effectiveness of the proposed methodology is demonstrated through a case study, arguing for its necessity in practice.

Impact Analysis of the Law on Diminishing Returns on the Mass Appraisal of Real Estate in the Republic of Moldova

The present study investigates the shortcomings of the linear models of massive evaluation of real estate in the Republic of Moldova, currently used to determine the taxable value, the author noting, in particular, the influence of the distortion of the calculated values, within the mathematical modeling and the need to respect the law of diminishing returns. The challenges are relevant for countries with an ad-valorem taxation system, such as the Republic of Moldova, which needs to harmonize its national legislation with the criteria for joining the European Union. The study uses the system of mass evaluation of real estate properties in order to identify and develop optimization solutions for the massive evaluation system of real estate properties, in order to tax them, by implementing log-linear models, with effective results compared to existing linear regressions. By benchmarking performance metrics between linear regressions from existing and refined models, the research improves the taxation system through more accurate methodologies.

A Local Gaussian Process Regression Approach to Mass Appraisal of Residential Properties

A Local Gaussian Process Regression Approach to Mass Appraisal of Residential Properties

Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data

Abstract In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value.

Аналіз інформаційного забезпечення в умовах становлення системи масової оцінки земель в Україні

The aim of this work is to analyze the compliance of the information indicated in existing state registers and databases with the requirements set for automated mass appraisal systems of real estate, according to the standards adopted by the International Association of Assessing Officers (IAAO), and the possibility of applying various methodological approaches to appraisal. To achieve the research objective, the following methods of scientific inquiry were applied: monographic, analysis, and synthesis. The monographic method was used to study scientific works dedicated to the introduction of mass appraisal systems in Ukraine and worldwide. The analysis method was employed to study existing registries, databases, standards, methodologies, and recommendations for the construction of such systems, as well as limitations regarding their application in Ukraine. The synthesis method justified the need for the introduction of a modern cadastral system and highlighted shortcomings in the content and methodology of maintaining existing state registers containing information about real estate objects in Ukraine. As a result of the analysis, it was found that, overall, the aggregate information available in various sources, databases, and registers is sufficient for conducting mass appraisal of non-improved land parcels. At the same time, the authors highlight the issue of the lack of a unified information exchange system, the implementation of which would enable the use of existing information. Additionally, the limited ability to use existing registers for implementing income and cost-based methodological approaches applicable to improved land parcels is emphasized. The scientific novelty and practical significance lie in highlighting the possibilities and feasibility of using the existing State GeoCadastre database as a basis for a future mass appraisal system, as it indirectly affects the values specified in the state register of property rights to real estate.

Mass land valuation serves as a prerequisite for optimizing the taxation system in Ukraine

The article highlights the issue of multiple tax base systems. It analyzes that according to existing international mass appraisal standards, the market value should serve as the tax base. In turn, determining the market value should be based on analyzing the most efficient use. The authors emphasize that the available information in state registers is insufficient to meet all the requirements for the integrity and quality of input data for mass appraisal systems. Thus, attention is drawn to the necessity of a comprehensive approach to implementing a cadastre system, including the use of the LADM standard. This standard would, on the one hand, systematize the exchange between existing state registers, and on the other hand, serve as a basis for developing a computerized mass appraisal system taking into account the requirements for CAMA. Overall, the authors believe that implementing a land mass appraisal system in Ukraine is an important step towards creating a transparent and fair tax system as a key element of economic recovery and a source of revenue for local budgets in the context of developing market land relations.

The New Stage of State Real Estate Cadastre Valuation (in the Russian Federation)

Accurate real estate market data is crucial for decision-making by governments, businesses, and individuals. Without it, costs rise and development of investment and tax policies suffers. In Russia, the state real estate cadastre valuation is the main data source, but its credibility wanes due to errors and delayed updates. This article reviews the 20-year evolution of this system through four stages. The aim of this study is to analyse the problems and shortcomings in conducting cadastre valuation and property assessment in the Russian Federation, and to propose recommendations for improving this system. In the process of research, normative documents that served as an information base for achieving the set goal were analysed, and a comparative analysis of property tax assessment systems and tax policies of various countries was conducted. Based on the conducted analysis, the author proposes recommendations to enhance tax policy and real estate cadastre valuation in Russia. The recommendations involve adjustments, updated tax value criteria, considering diverse property value factors, separating cadastre valuation from mass appraisal, and accommodating variations in tax base calculation. Implementing these recommendations can contribute to addressing issues in cadastre valuation and create a fairer, more effective taxation system for land and property.

Mass appraisal of urban land with homogenization factors: a spatial models-based approach

Este estudo aborda a avaliação em massa de terrenos urbanos utilizando técnicas de econometria espacial no contexto do município de Fortaleza. A metodologia emprega matrizes de pesos espaciais, testes de multiplicadores de Lagrange e modelos de regressão espacial para entender e modelar a estrutura espacial dos preços dos terrenos. Os resultados indicam que a seleção cuidadosa das matrizes de pesos espaciais é fundamental para reduzir a autocorrelação residual e melhorar a precisão das estimativas. Além disso, a aplicação de fatores de homogeneização baseados em um lote paradigma demonstrou ser uma abordagem eficaz para ajustar os preços dos terrenos, contribuindo para uma tributação mais equitativa e refletindo variações de preços devido a fatores locacionais. Os objetivos específicos do estudo incluem avaliar a influência da escolha das matrizes de pesos espaciais, analisar a eficácia dos modelos espaciais na modelagem da dependência espacial e investigar o impacto da homogeneização dos preços. Este trabalho também discute as limitações da metodologia, como a dependência de dados georreferenciados e a premissa de que os atributos dos terrenos impactam uniformemente os preços em diferentes áreas da cidade.

GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal

The aim of this study is to develop a value-based GeoValueIndex with AHP weights and GIS for the criteria of the Mersin University (MEU) Çiftlikköy Campus real properties, and it is referred to as the “GeoValueIndex” in this study. GeoValueIndex is a symbolic value that combines geographic and non-geographic features of real properties. The data of the real properties on the campus were collected and arranged for mass appraisal. One of the Multi Criteria Decision Analysis (MCDA) methodologies, Analytic Hierarchy Process (AHP), was used to weight the criteria. GeoValueIndex was calculated by multiplying each parcel’s geographic and non-geographic data by their weights and adding them. GeoValueIndex Map is obtained by associating GeoValueIndex and parcel in GIS software. GeoValueIndex of real properties save time, effort, and cost in mass appraisal processes. There are many techniques for doing GeoValueIndex operations, and the ones presented in this study are only proposals.

Mass appraisal: a statistical approach to determining the impact of a property’s attributes on its value

One of the problems occurring when estimating the value of a property is determining how the individual attributes of a property affect its value. Until now, this was estimated either on the basis of analyses of prices and market features of similar properties, or by analogy to other properties in terms of the type and area of markets, by examining or observing the preferences of potential buyers, or in other ways (Polska Federacja Stowarzyszeń Rzeczoznawców Majątkowych, 2008). This article refers to the last option and presents an alternative way of dealing with the aforementioned issue. An approach based on statistical methods, using dependency coefficients is introduced as an objective proposal to determine the impact of individual attributes on the value of real estate. The idea of including statistical methods (dependency coefficients) in the procedure of mass real estate valuation is the basis of the statistical approach of the Szczecin Mass Real Estate Valuation Algorithm (Pol. Szczeciński Algorytm Masowej Wyceny Nieruchomości – SAMWN). Considering the specificity of the features (attributes) describing real estate, which in most cases may be presented on an ordinal scale, four coefficients measuring the strength of the relationship between the features and the value of the property were proposed. Since previous studies, e.g. Dmytrów et al. (2020), indicated that the results obtained for partial coefficients reflect the relationships found on the market more efficiently, the analysis was extended to include partial variants of selected coefficients. Then, six procedures for calculating weights in SAMWN were proposed. The obtained results were subsequently ordered on the basis of linear ordering procedures, as described in the work by Strahl (1978) and Pawlukowicz (2010). The aim of the article was twofold. Firstly, to identify the coefficients of dependencies that can be used in property valuation based on SAMWN, and also in the analysis of those properties whose attributes are often described on an ordinal scale. The second aim was to identify a combination of methods describing the influence and weights of the attributes which under SAMWN procedure produces results closest to the property values determined by real estate appraisers.

THE MASS APPRAISAL MODELS FOR RESIDENTIAL REAL ESTATE

Individual real estate appraisal is a traditional approach that involves carrying out certain appraisal procedures for each appraised object. It is this assessment that provides the most possible accuracy in determining the value. Unfortunately, this technique cannot be used to evaluate a large number of real estate objects at the same time, for example, for taxation purposes. In such cases, an alternative is a mass appraisal, the technique of which allows for the simultaneous appraisal of a large number of properties. It is based on the automated processing according to a certain algorithm of a large volume of data on real estate objects, on the real estate market, urban planning conditions, geographical characteristics and other factors affecting the value of real estate. Mass assessment is widely used in the world, defined by the International Valuation Standard, but unfortunately it is not accepted by specialists who are involved by the Ukrainian authorities in the development of assessment methods and procedures with the motivation of the lack of necessary data. The purpose of the work is to prove the possibility and expediency of applying a mass assessment of real estate, including land, for the purpose of taxation on the basis of its adaptation to the conditions of the national market. This includes determining the necessary evaluation criteria, developing models and algorithms for determining the market value of real estate. Methodology. The methodology of statistical analysis was used, namely, the method of correlation-regression analysis and the method of parallel intersections were applied for the mass appraisal of residential real estate in one of the districts of Dnipro city. Scientific novelty. The models of mass appraisal for taxation of the residential real estate was developed, based on the analysis of the influence of factors on its value, which was implemented in the form of the model and implemented in the Cathedral district of the city of Dnipro. Practical significance. Entering data on the value in the state register of real estate based on the results of a mass appraisal solves a number of important economic, managerial and legal problems, namely, it opens the way to the application of an advalorem taxation system for the fair assessment of taxes and payments to the budget, other mandatory payments, increases the stability of the banking system, which has repeatedly suffered from improper valuation of real estate collateral, provides financial accounting with data for determining the fair value during revaluation of assets, facilitates effective management decisions in business, ensures effective use of real estate in society, and can be used for another purpose in the event of an open access to this registry information in accordance with the experience of the leading countries of the world. Results. The models of mass appraisal of residential real estate for the purpose of taxation was developed and tested based on the use of the method of parallel intersections and the value criteria determined by the analysis. The obtained results of testing the methodology indicate that it allows to obtain logically understandable results, which can be obtained in a simpler way compared to the application of correlation-regression analysis, and can be introduced for practical use for taxation purposes as for various other purposes without any particular difficulties.

Investigation of Real Estate Tax Leakage Loss Rates with ANNs

In Türkiye, many changes have been made in the law within the past fifty years to determine the real estate tax value close to the real market value. However, the changes did not establish a fair valuation system for determining real estate tax. Despite the regulations and records of immovable properties with a geographic information system (GIS)-based inventory in recent years, the problem of leakage loss in real estate tax was still not resolved. Within the scope of this study, a mass appraisal model was created with a dataset of 499 independent sections including trading values from the last year in the district of Kayseri to determine the real estate tax leakage loss rates. Multiple regression analysis (MRA) and artificial neural network (ANN) methods, widely used in mass appraisal, were used in the analysis. Considering the analysis of the test data and the model performances, the ANN model was found to give better results than the MRA model. To conclude this study, the housing values obtained with the mass appraisal methods and the real estate tax values obtained with the existing system were compared, and a 3.7-fold difference was found between them.

Comparing modelling performance and evaluating differences of feature importance on defined geographical appraisal zones for mass real estate appraisal

ABSTRACT The features influencing real estate value in different residential areas and cities are important for spatial economic analysis besides high appraisal accuracy. In this study, a methodology was developed for computer-assisted mass real estate appraisal with a case study implemented through the use of big geographical datasets including 121 features and around 200,000 samples of real estate in Istanbul and Kocaeli (Turkey). Prediction models using the random forest technique were developed for five appraisal zones determined with spatially constrained multivariate clustering. With machine learning and mass appraisal metrics, modelling performance improves in appraisal zones with a lower standard deviation expressing real estate value in neighbourhoods. Since importance levels and ranks of features vary in zones, the mass appraisal should be done with a sufficient number of features.

Sustainable tax system design for use of mass real estate appraisal in land management

Sustainable land management aims at land use by protecting nature. Real estate value is one of the important parts of land management, especially at the decision stages. The value associated with a major part of domestic real estate is tax value. The purpose of this study is to develop a tax system that is easy to understand and use as well as compatible with innovations. The sustainability of mass real estate appraisal is provided with legal, physical, locational, neighborhood and economic criteria according to ordinary and extraordinary conditions. The plots reserved for housing in urban areas were selected as the real property type with updates regarding appraised value for taxation. Valuation stages were discussed separately from general to detail, and coefficients were used under the current tax law. Sustainable mass plot appraisal system was designed by providing sustainable cycle economic and extraordinary criteria. The application was carried out in Konya/Lalebahçe Neighborhood and the sustainability of the system was observed. A multiple regression model was created from the samples of 2015 and the real estate value estimation was made with the data of the criteria affecting the value. With the model obtained from the economic criteria of 2005–2015, the economic valuation rate of real estate was found for the years 2016–2021. The results were compared with 10 market sample data for the years 2020–2021. The values estimated by the economic valuation rate of the real estate in 2020 approached the market values with an average of ± 7 % standard deviation and in 2021 with a ± 14 % standard deviation. A sustainable mass appraisal system for taxation was successfully tested and its sustainability was observed.

Developing an LADM Valuation Information Model for Mongolia

Modern land/property valuation practice requires three-dimensional (3D) valuation, which is crucial to better valuate and assess values of property units. However, conventional land/property valuation systems primarily exist in 2D form, which hinders the accurate valuation of buildings, condominiums and land. The present study introduces the first step toward establishing an advanced land/property valuation system in Mongolia, where it is urgent to meet such requirements. We examined relevant Mongolian geospatial standards and documents, those related to land valuation (i.e., cadastral parcels and buildings), and a valuation information model, which is based on the ISO 19152:2012 Land Administration Domain Model (LADM), with the aim of developing an LADM Valuation Information Model country profile for Mongolia. After the in-depth analysis of the data model of both the LADM Valuation Information Model and the national geospatial relevant standards, we proposed the LADM Valuation Information Model country profile as a conceptual model. Our study results demonstrate how the LADM Valuation Information Model can be adapted to the Mongolian land administration system. Our findings can be used to serve a reference data model to construct 3D land/property valuation systems for efficient valuation of land (e.g., mass appraisal) and taxation purposes.