Mass Appraisal Research Articles

Barriers to effective property tax reform in Nigeria: Implementation of the land use charge in Enugu state

The principal objective of this research is to explore the barriers to the effective implementation of the centralised property tax reform programme in Enugu state, Nigeria. This study adopts a qualitative research approach using primary and secondary information. Key stakeholders comprising the estate surveyors and valuers/property managers, a senior staff member of the Enugu state Board of Internal Revenue and members of the land use charge tribunal were selected for in-depth semi-structured interview. Findings show that policy design, political/governance, and technical are the three major barriers to the implementation of the land use charge. Based on these findings, this study recommends the decentralisation of the land use charge administration system; adoption of capital basis for property assessment; massive public sensitisation; demonstration of political will by the property-owning elites; adoption of the collection-led approach in the implementation of the property tax reform policy; deploying ICT such as geo-referencing software in property information gathering and computer-aided mass appraisal method (CAMA) in the valuation of properties. This research highlights how not to design and implement property tax reform in a developing economic context for enhanced property tax revenue performance.

Mass appraisal for property tax purposes: approach using global and local spatial regression

O IPTU é uma fonte de receita essencial para os municípios brasileiros, mas o sistema atual, baseado em valores de avaliação desatualizados, resulta em um tratamento desigual dos contribuintes. Para promovera equidade, são necessárias avaliações regulares de imóveis, especialmente no mercado imobiliário brasileiro, que evolui rapidamente. Um desafio chave neste processo é desenvolver um modelo que trate eficazmente os efeitos espaciais. Este estudo explora aaplicação de modelos de regressão espacial global e local para enfrentar a dependência e heterogeneidade espaciais em avaliações em massa de imóveis, visando garantir uma tributação justado imóvel. Analisando imóveis urbanos, avaliou-seo desempenho do modelo autoregressivo condicional (CAR) como um modelo de regressão global e o modelo de regressãogeograficamente ponderada(GWR) como um modelo de regressão local. As previsões de ambos os modelos são combinadas com a interpolação por krigagem ordinária para gerar uma Planta de Valores Genéricos (PVG)abrangente, representando a área de estudo. Nossos achados demonstram que a modelagem GWR supera o modelo CAR em precisão e sua capacidade de contabilizar a dependência e heterogeneidade espacial. Isso destacao GWR como uma alternativa viável para estabelecer uma base justa e equitativa para o cálculo de impostos sobre imóveis. Esta pesquisa contribui significativamente para o avanço das técnicas de avaliação em massa de imóveis, assegurando que a coleta do IPTU esteja alinhada com a natureza dinâmica do mercado imobiliário brasileiro. As percepções do estudo fornecem orientações valiosas para formuladores de políticas e profissionais na melhoria dos sistemas de tributação de propriedades, beneficiando, em última análise, tanto municípios quanto contribuintes.

A House Price Modeling Based on Clustering and Kriging: The Medellín Case

In this study, house prices are modeled using a mixed two-stage model for mass appraisal employing valuations of second-hand housing units conducted in Medellín, Colombia. In the first stage, submarkets of houses that share non-spatial attributes are created using clustering; in the second stage, the spatial dependency is incorporated into the house price estimation using kriging. The best results were obtained when the sample was divided into three submarkets using property area and age as the classification criterion and later applying a Matérn kriging model to submarket 1, a spherical kriging model to submarket 2, and a circular kriging model to submarket 3. These results may provide further guidance to enhance mass appraisal practice in other Latin American cities as well as potentially other cities in developing countries.

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity.

Hedonic, residual, and matching methods for residential land valuation

Accurate estimates of land values on a property-by-property basis are an important requirement for the effective implementation of land-based property taxes. We compare hedonic, residual, and matching techniques for mass appraisal of residential land values, using data from Maricopa County, Arizona. The first method involves a hedonic valuation model estimated for transactions of vacant lots. The second approach subtracts the depreciated cost of improvements from the value of improved properties to obtain land value as a residual. The third approach matches the sales of vacant lots with subsequent sales of the same properties once they have been developed. For each pair, we use a land price index to inflate the land price to the time of the improved property transaction and then calculate land leverage (the ratio of land to total property value). A hedonic model is estimated and used to predict land leverage for all improved properties. We conclude that the matching approach is the most promising of the methods considered.

Toplu taşınmaz değerlemede makine öğrenme algoritmalarının kullanımı ve konumsal/konumsal olmayan özniteliklerin tahmin doğruluğuna etkilerinin karşılaştırılması

The objective determination of real estate values with current technological approaches has an important role in effective and sustainable real estate management plans. Mass appraisal is the process of valuing a large number of real estate simultaneously instead of evaluating the real estate individually for reducing the loss in terms of time and cost. Machine learning methods, known as advanced estimation approaches, are used to obtain more objective, accurate and fast results in mass valuation processes. In addition, besides sufficient objectivity and accuracy in value determination, these methods can evaluate the relations between the value and the criteria affecting the value holistically. In this context, model successes in mass valuation were examined using Multiple Linear Regression (MLR), Generalized Linear Model (GLM), Support Vector Machines (SVM), Decision Trees (DT) and Random Forest (RF) algorithms. The datasets were divided into 3 groups as Geographic (G), Non-Geographic (NG) and Geographic + Non-Geographic (GNG) and applied separately for modeling with different methods. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R2 were calculated for determining the model measures. Pendik district of Istanbul province was chosen as the application area. By applying different methods with 1475 sampling points representing the real estate sales values for the application area, the performances of the models established were examined with 3 different data sets (G, NG, GNG). Accordingly, RF is the method that gives the highest accuracy while DT and GLM were found as the methods with the lowest accuracy. When the effects of different datasets on the model accuracy were examined, big difference was not observed between the use of the G and the GNG datasets.

An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants

In the mass appraisal of properties, Machine Learning (ML) algorithms have produced effective and promising results. Analysts use various algorithms to train their models with limited data and make appraisals on large data sets. However, research into which value determinants the models take into account when appraising values is insufficient. This research looks at how eXplainable Artificial Intelligence (XAI) methods can be integrated with mass real estate appraisal studies. Experimental studies were carried out on a data set containing 1002 samples and 43 independent variables. Tree-based ML regressors, namely Random Forest, XGBoost, LightGBM, and Gradient Boosting, were used for training the predictive models. The performance of these regressors was compared with that of classical multiple regression analysis. The Permutation Feature Importance (PFI) technique was used for the selection of the variables that contributed the most to the training of the models. Models retrained with selected variables were locally interpreted using the SHapley Additive eXplanations (SHAP) method. In this way, it was possible to observe the value determinants that contribute to the price estimation of each real estate sample. This study demonstrates that XAI approaches should be integrated into mass real estate valuation systems specifically, and into urban and housing research more generally, helping analysts and scholars to explain their models more transparently. The outcomes of this study can be a harbinger for analysts and scholars who wish to explain their models more transparently. Last but not least, this study advocates the use of tree-based ML algorithms since they not only allow us to implement XAI approaches but also outperform the stand-alone ML regressors.

Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach

AbstractData-driven machine learning algorithms have initiated a paradigm shift in hedonic house price and rent modeling through their ability to capture highly complex and non-monotonic relationships. Their superior accuracy compared to parametric model alternatives has been demonstrated repeatedly in the literature. However, the statistical independence of the data implicitly assumed by resampling-based error estimates is unlikely to hold in a real estate context as price-formation processes in property markets are inherently spatial, which leads to spatial dependence structures in the data. When performing conventional cross-validation techniques for model selection and model assessment, spatial dependence between training and test data may lead to undetected overfitting and overoptimistic perception of predictive power. This study sheds light on the bias in cross-validation errors of tree-based algorithms induced by spatial autocorrelation and proposes a bias-reduced spatial cross-validation strategy. The findings confirm that error estimates from non-spatial resampling methods are overly optimistic, whereas spatially conscious techniques are more dependable and can increase generalizability. As accurate and unbiased error estimates are crucial to automated valuation methods, our results prove helpful for applications including, but not limited to, mass appraisal, credit risk management, portfolio allocation and investment decision making.

아파트 대량 평가를 위한 기계 학습 알고리즘과 스태킹 앙상블 기법의 응용

Purpose The goal is to find an optimal data-driven model for apartment price appraisal system. We compares the predictive power of several machine learning algorithms and the ensemble model by using 17,779 apartment transaction data in Gangnam from 2006 to 2017. Methods We use three tree-based machine learning techniques, random forest, Adaboost, and Catboost. Also, an ensemble model is developed through the stacking technique using the above algorithm. 5-fold cross validation is used as model validation techniques. Results By applying the algorithm, it was found that the ensemble model constructed from stacking method performs better than individual machine learning models. Conclusion The results illustrate that the quality of mass appraisals or apartment price indices can be improved by using the ensemble model.

Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values

Updated cadastral land values are a matter of critical importance for local governments: higher revenue of property taxes, more equitable treatment to taxpayers, a fundamental input in the design of public policies related to access to land and housing for the most vulnerable and a key feature in land value capture strategies to finance public infrastructure, to name just a few public policies that require correct valuations of land. However, in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in the complexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucratic resistance to its implementation.The objective of this paper is to present a mass appraisal methodology that uses only free and open data to achieve robust urban land valuations. Information from the OpenStreetMap Project is used to generate several land variables. In addition, the Global Human Settlement Layer of the European Commission is used to determine the level of consolidation of urban sprawl. Land value data were obtained from the Mapa de Valores de América Latina, a collaborative initiative that systemizes more than 68,000 data from more than 900 cities.This information is used to train three tree-based machine learning models: Random Forest, Quantile Random Forest and Gradient Boosting Model. The results support the viability of the proposed strategy, simplifying the mass appraisal process in terms of costs, time and complexity of the information used.

Методи і моделі масової оцінки нерухомості

This article examines the approaches, methods and models of mass appraisal. The article also considers features of application of valuation approaches and methods when conducting mass appraisal taking into account the type of real estate, the impact of price-forming factors of demand and supply and etc. Based on the analysis of scientific works of foreign and domestic scientists, the classification of mass appraisal models is given, which includes parametric multiple regression models, spatial and nonparametric models, as well as more modern methods, in particular, artificial neural networks, genetic algorithms, rough set theory and etc. Hedonistic models as the most common type of parametric multiple regression have been studied. In particular, the mathematical interpretation of this model is given, the key pricing factors that are used as explanatory variables in the construction of the model are analyzed, as well as different types of hedonistic models, their advantages and disadvantages are considered. As complementation of the traditional hedonistic model, spatial models are investigated , while nonparametric models are studied as an alternative. In particular, foreign scientists’ works are analyzed, which consider the comparative analysis of the effectiveness of use of the above-mentioned models. The article also considers modern methods, in particular artificial neural networks, genetic algorithms, rough set theory and expert models, the use of which in the context of mass appraisal is the subject of discussion among scientists. Based on the results of the analysis, criteria are formulated which impact the choice of application of specific mass appraisal methods and models. Further research will be directed towards a more detailed study of the hedonistic model in order to develop the latter as a basic model of mass appraisal on the example of the residential real estate market of one of the districts in Kyiv.

Econometric Models for the Estimation of the Commercial Value of Residential Real Estate Objects in Latvia

Real estate market formation in Latvia occurs in the conditions of practical absence of a single system of methodological approaches to real estate estimation adapted for the Latvian conditions. In the article it is proved that along with individual estimation it is possible to use methods of mass appraisal of the commercial value of the real estate, constructed on the basis of econometric models. Application of such models will allow us to receive rather precise, authentic estimation of the commercial value of residential real estate in the conditions of dynamics of the Latvian market of real estate.

Might expert knowledge improve econometric real estate mass appraisal?

The article examines whether expert knowledge improves the estimation results of real estate mass appraisal models. Six econometric models were compared: OLS, mixed, the Bayesian model, the Inequality Restricted Least Squares (IRLS) model, ridge and LASSO regression (with regularization). In three of the models (mixed, Bayesian, and IRLS) prior knowledge was applied. In mixed and Bayesian models priors took the form of intervals for model parameters. In IRLS, restrictions in the form of inequalities were applied. In the empirical example mass appraisal models were applied in the valuation of undeveloped land for residential purposes. Models with prior knowledge turned out to be the best with regard to the consistency of estimates with theory. Also, prediction accuracy was better for models with prior knowledge. In the case of low quality data expert knowledge might significantly improve estimation results of real estate mass appraisal econometric models.

Is There Discrimination in Property Taxation? Evidence from Atlanta, Georgia, 2010-2016

In the past, some localities have taxed blacks at higher real rates than whites by over-assessing property values in predominately blacks neighborhoods while taxing those properties at the same nominal rates. In 1974, the NAACP sued Fulton County, Georgia – the principal county of the Atlanta metro area – over this very issue. In 1991, a mass reappraisal intended to remedy this discrimination incited a tax revolt in Fulton. However, there are few recent studies of whether discrimination is still taking place. Using assessment data from Fulton County 2010-2016, I find little to no evidence of any racial or socioeconomic discrimination in ratios of property assessment to sale prices. This suggests that (1) the assessment process is uniform and non-discriminatory, and/or (2) the process and fee for appealing one's assessment is not inaccessible to a degree that would allow any disparities to persist, and/or (3) regression-based mass appraisal techniques are capable of eliminating racial discrimination from property assessment.

A mass appraisal assessment study of land values using spatial analysis and multiple regression analysis model (MRA)

Valuation of land is one of the vital roles in building construction. Generally, building cost is vastly affected by the land value, even the experienced estimator also not estimating it appropriately due to the hasty change of land value. There are many Corporeal and incorporeal factors are involved in casing the land value, by and large of nearly available facilities. In this work, nine various factors were involved to determine the land value, the distance to educational institution, distance to roads, distance to the police station, distance to the railway station, distance to entertainment places, distance to industries, land-use type, and distance to government buildings. The spatial map was created for 9 factors and it was categorized using Analytical Hierarchical Process (AHP) in a Geographical Information System (GIS) environment. Significant results were shown regarding land value classifications such as low, moderate, high, and very high. A well-equipped area with infrastructure facilities and other facilities identifies a higher land value through this process. The valuation map enables quick decision-making, as all the values of each parcel are displayed graphically. Collected data are feed in the analysis model of multiple regression analysis (MRA). The process of performing a regression analysis allows determining more influencing factors to which independent variables are influencing dependent variables. The logical straight-line equation is generated to attain the land value of the research area using Statistical Package of Social Science (SPSS) software. This technique uses for academicians and Industrial people to determine the land value with minimum error.

Моделирование рынков жилой недвижимости крупнейших городов России

The existing mass appraisal models and mathematical tools for predicting the market value of residential property have a number of disadvantages, as they are developed for individual regions. Without considering the constantly changing economic environment, these models quickly become outdated and require constant updating. Thus, they are not suitable for construction business optimisation. The study aims to create a universally applicable real estate appraisal system for Russian cities, regardless of the constantly changing economic situation. This goal was achieved through the creation of a neural network, whose input parameters include construction and operational data, geographical factors, time effect, as well as a number of indicators characterising the economic situation in specific regions, Russia and the world. In order to examine the dynamics of real estate markets in the Russian Federation, statistical data for neural network training were collected over a long period from 2006 to 2020. Virtual computer experiments were performed for testing the developed system. They showed that minimum size one-room apartments of 16 square meters have the highest unit cost per square meter in Moscow. Two-room apartments with an area of 90 square meters have the maximum price, as well as 100 sq. m. three-room, 110 sq. m. four-room and 120 sq. m. five-room apartments. In Ekaterinburg, two-room apartments with a total area of 30 square meters have the highest cost per square meter; the same applies for 110 sq. m. three-room, 130 sq. m. four-room and 150 sq. m. five-room apartment. Thus, the proposed system can be used to optimise the construction business. It can be also be useful for government institutions concerned with urban real estate market management, property taxation, and housing market improvement.

Calibration of impact of attributes in the real estate mass appraisal

PurposeOur research aims in designation of a hybrid approach in the calibration of an attribute impact vector in order to guarantee its completeness in case when other approaches cannot ensure this.Design/methodology/approachReal estate mass appraisal aims at valuating a large number of properties by means of a specialised algorithm. We can apply various methods for this purpose. We present the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA) and the four methods of calibration of an attribute impact vector. Eventually, we present its application on the example of 318 residential properties in Szczecin, Poland.FindingsWe compare the results of appraisals obtained with the application of the hybrid approach with the appraisals obtained for the three remaining ones. If the database is complete and reliable, the econometric and statistical approaches could be recommended because they are based on quantitative measures of relationships between the values of attributes and properties' unit values. However, when the database is incomplete, the expert and, subsequently, hybrid approaches are used as supplementary ones.Originality/valueThe application of the hybrid approach ensures that the calibration system of an attribute impact vector is always complete. This is because it incorporates the expert approach that can be used even if the database excludes application of approaches that are based on quantitative measures of relationship between the unit real estate value and the value of attributes.

The novelty hybrid model development proposal for mass appraisal of real estates in sustainable land management

In this study, a new methodology has been developed for a sustainable mass appraisal system. A mathematical model was created with the combination of the Cobb-Douglas and the linear regression model. With the Analytic Hierarchy Process (AHP) method, real estate value criteria were grouped and weighted in a hierarchical structure. The weights obtained with AHP were integrated into the coefficients regarding the criteria weights and densities in the Cobb-Douglas hybrid model. The new hybrid model was confirmed with the features and price equivalents of 435 parcels for sale from the market. Besides, the model analysis results were compared with the Multiple Regression Analysis (MRA) modelling using market prices. While creating the methodology, Geographic Information Systems (GIS) was used to organize the geographic and regional data of the region. After developing the new hybrid model, criteria groups that developed the model and relevant sub-criteria were evaluated using Pearson's correlation analysis.

Comparison of different machine learning models for mass appraisal of real estate

The present study aimed to compare five machine learning techniques, namely, artificial neural network (ANN), support vector machine (SVM), chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and random forest (RF) for mass appraisal of real estate. Firstly, 1982 precedent data was collected throughout the entire study area for train and test models. Secondly, a total of 68 variables were considered for the mass appraisal. Subsequently, the five machine learning techniques were applied. Finally, the receiver operating characteristic (ROC) and various statistical methods were applied to compare five machine learning techniques.

Territorial Extrapolation of Basic Data as a Solution of the Problem of Its Deficiency during Mass Appraisal

The article is devoted to the application of the territorial extrapolation of basic data method during a mass (cadastral) assessment of a territory that is characterized by an acute lack of market information. In the framework of the study, an acute lack is understood as the conditions when for the assessing territory there are less than five transaction (offer) prices suitable for regression models. The idea of the method is to use market information of territories that are comparable in a composition of pricing factors and the nature of their influence on the cost, as well as in terms of price levels. The developed method includes such stages as collection of basic data, creation of thematic maps, grouping of estimated territories by price level and composition of pricing factors and modeling. The method was applied to assess land plots that have the type of permitted use “for individual housing construction” and belong to the mass appraisal segment “gardening and horticulture, low-rise residential buildings” in the settlements of the Republic of Udmurtia. The results of approbation shown that the method of territorial extrapolation helps to overcome an acute shortage of market information and build statistically significant models of the cadastral values of land plots.