scholarly journals Adjustment of comparison objects by processing of large amounts of cadastral data

2020 ◽  
Vol 17 (3) ◽  
pp. 208-220
Author(s):  
M. B. Laskin ◽  
◽  
L. B. Dampilon ◽  
Keyword(s):  
Growth Rate ◽  
Real Estate ◽  
Rate Of Change ◽  
Small Samples ◽  
Comparative Approach ◽  
Two Dimensional ◽  
Residential Real Estate ◽  
Market Data ◽  
Adjustment Method ◽  
Real Estate Valuation

The article proposes an adjustment method of comparison objects in the comparative approach to real estate valuation, based on the comparison of clusters of different groups of real estate objects formed by price-forming factors and the rate of change between the dates of cadastral valuation. Price-forming factors are divided into qualitative and continuous ones. The division of objects into clusters is carried out according to the growth rate in the period between cadastral valuation and to the qualitative factors. Then, in each cluster, two-dimensional distributions of cadastral values and the resulting dependencies of the changed cadastral value on continuous price-forming factors are considered. The proposed method makes it possible to adjust the objects of comparison of market data (including for small samples), based on the data of cadastral accounting of two periods. These examples are based on a comparison of cadastral valuation results of residential real estate in St. Petersburg in 2015 and 2018.

scholarly journals Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study

2019 ◽  
Vol 8 (2) ◽  
pp. 99
Author(s):  
Shawn L. Robey ◽  
Mark A McKnight ◽  
Misty R. Price ◽  
Rachel N. Coleman
Keyword(s):  
Pilot Study ◽  
Real Estate ◽  
Market Value ◽  
Residential Real Estate ◽  
Independent Variables ◽  
Real Estate Valuation ◽  
Scientific Approach ◽  
Traditional Approaches ◽  
Different Characteristics ◽  
Total Market

This paper advocates a more scientific approach to residential real estate valuation as opposed to more traditional approaches, which are flawed for two main reasons: (1) appraiser judgements are almost exclusively used and (2) appraisers’ sample sizes are too small to provide adequate estimated values. By using a regression model, this paper explores the impacts of different characteristics on market value. Three hundred and fifteen properties in Evansville, Indiana, were analyzed testing twelve different variables. This model suggests that 91.8% of the total market value variation is explained by four independent variables. These findings provide evidence that multiple linear regression could be used to better predict a property’s value.

scholarly journals Market and cadastral data comparison for the real estate market value forecasting

2020 ◽  
Vol 17 (4) ◽  
pp. 44-54
Author(s):  
M. B. Laskin ◽  
P. A. Cherkesova
Keyword(s):  
Real Estate ◽  
Normal Distribution ◽  
Market Value ◽  
Real Estate Market ◽  
Residential Real Estate ◽  
Market Data ◽  
The Real ◽  
The Real Estate ◽  
Saint Petersburg ◽  
The Real Estate Market

The aim of the research is to develop theoretical and methodological approaches to market value forecasting in the real estate market. The relevance of the research is determined by the system-forming place that the real estate market occupies in the economy of the country and regions, affecting the interests of owners of various forms of ownership, construction and development companies, insurance companies, banks. Another aspect that determines the actuality of the study is the discrepancy between well-structured cadastral databases and market data dispersed between different owners of information resources, and the unstructured nature of market data, which in most cases is focused on advertising, rather than on analytical market research.Materials and methods. The study uses a model of a multidimensional logarithmically normal distribution law of the ensemble of prices for residential real estate at equidistant points of time and cadastral value, the ARIMA model for predicting market value, taking into account the features of the logarithmically normal distribution of prices, as a distribution with positive asymmetry. As a statistical material, we used market data on residential real estate published in the periodical press in the period from the end of 2012 to 2018. The volume of samples of weekly publications is 15000-20000 objects; data for 21 quarters (more than five years) was used. As a comparison base, we used data from cadastral registration of real estate objects in Saint Petersburg for 2018. The total volume of the cadastral database of residential real estate in Saint Petersburg (individual apartments) is 2 226734 objects with a fairly complete (and well-structured) set of price-forming factors. The authors propose a method for estimating the most likely movement of the market value for a pre-selected real estate object that has passed cadastral registration and has a cadastral value entered in the register and predicting the market value in the future period.Results. The theoretical significance of the work is the proposed algorithm for estimating the most probable trajectory of the market value of the investigated object, based on the conditional multivariate log-normal distribution for a given value of the cadastral value. A well-developed and studied ARIMA time series forecasting model is applied to the logarithms of the obtained time series, the return from logarithmic prices to real prices is carried out taking into account the peculiarities of the logarithmically normal distribution. Results are compared with median scores and estimates, obtained by average values.Conclusion. The paper shows that the introduction of cadastral value in the Russian Federation opens up new opportunities for analyzing and forecasting market prices, since cadastral databases contain the most complete lists of real estate objects, including the cadastral value, which now, in accordance with the law, must be updated at least once every three years and, as of 2015 and 2018, was determined as the market value, therefore, until the next cadastral assessment, can serve as a basis for constant comparison with market data, which are constantly changing, primarily in the composition of objects.

scholarly journals Application of AHP Method in Assessment of the Influence of Attributes on Value in the Process of Real Estate Valuation

2019 ◽  
Vol 27 (4) ◽  
pp. 15-26
Author(s):  
Krzysztof Dmytrów ◽  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Comparative Approach ◽  
Ahp Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Valuation ◽  
The Impact ◽  
Hierarchy Process

Abstract Property valuation in the comparative approach requires the determination of the impact of market characteristics on the formation of prices on the local real estate market. Valuers have a variety of methods for determining weights. Some of them require the collection of a sufficiently large database of information on transactions. However, this is not always possible. In the absence of sufficient data, alternative approaches, including an expert approach, may be used. The goal of the article is the proposal of an expert approach at the stage of assessing the influence of attributes on the value of the real estate. The AHP (Analytical Hierarchy Process) method will be used. On its basis, pairwise comparisons of the importance of attributes will be done by experts (valuers). By means of the AHP method, the weights of each attribute will be obtained and, subsequently, the influence of each attribute on the real estate value will be assessed. Research will be done on the basis of 318 real estates in Szczecin.

scholarly journals MASS EVALUATION OF RESIDENTIAL REAL ESTATE USING MACHINE LEARNING METHODS

2020 ◽  
Author(s):  
V. Chernushevych
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Residential Real Estate ◽  
Learning Methods ◽  
Mass Estimation ◽  
Machine Learning Methods ◽  
Real Estate Valuation

The essence of the concept of mass assessment is considered. The peculiarities of real estate valuation with the support of taxation in Ukraine are analyzed. The model of mass assessment of real estate with the use of machine learning methods is investigated. The results of modeling of mass estimation on the basis of gradient amplification are demonstrated.

scholarly journals The cadastral value as a tool for monitoring the real estate market value

2021 ◽  
Vol 37 (1) ◽  
pp. 84-108
Author(s):  
Lyudmila Gadasina ◽  
◽  
Mikhail Laskin ◽  
Ekaterina Zaytseva ◽  
◽  
...  
Keyword(s):  
Real Estate ◽  
Market Value ◽  
Real Estate Market ◽  
Theory And Practice ◽  
Two Dimensional ◽  
Market Prices ◽  
Market Data ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

In the theory and practice of real estate valuation, in analytical studies of the dynamics of real estate markets there is a problem of tracking changes in market prices. The apparent simplicity of this task leads to the fact that in everyday practice both market participants and professional analysts are satisfied with observations of average prices. The advantage of this traditional approach is computational simplicity. However, in the conditions of presence of a large number of special software and extensive statistical material can be used more complex research methods. The purpose of this article is to research big current market data of real estate objects and compare these data with the cadastral value determined in accordance with Russian legislation as the market value at the specified date. In this regard, there are problems associated with the multidimensional distribution of market prices and cadastral values. The article presents the method of calculation of changes of the real estate market prices on the basis of comparison of two-dimensional prices distributions of offers and cadastral prices for two periods. The main problem in studying the dynamics of real estate market prices is the inability to track the change in market prices for each property, as objects are constantly put up for sale and removed from it. The work carried out in the Russian Federation in 2014 to establish the cadastral value of real estate opens opportunity to analyze two-dimensional distributions of current market and cadastral prices and to assess the dynamic characteristics of the market for any real estate objects. The main result of article is the method which allows to apprise the market value of real estate in real time when new market data come by their comparison with the previously established cadastral value. Cadastral value is assumed to be defined as market value at the valuation date.

scholarly journals KNOWLEDGE-BASED FIS AND ANFIS MODELS DEVELOPMENT AND COMPARISON FOR RESIDENTIAL REAL ESTATE VALUATION

2018 ◽  
Vol 22 (2) ◽  
pp. 110-118 ◽  
Author(s):  
Sukran YALPIR ◽  
Gulgun OZKAN
Keyword(s):  
Real Estate ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Inference System ◽  
Market Values ◽  
Knowledge Based ◽  
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.

scholarly journals PROPERTY VALUATION BY MULTIPLE CRITERIA METHODS/NEKILNOJAMOJO TURTO VERTINIMAS DAUGIAKRITERINIU METODU

1999 ◽  
Vol 5 (4) ◽  
pp. 272-284
Author(s):  
Vida Malienė ◽  
Edmundas K. Zavadskas ◽  
Artūras Kaklauskas ◽  
Saulius Raslanas
Keyword(s):  
Real Estate ◽  
Multiple Criteria ◽  
Multiple Criteria Analysis ◽  
West Germany ◽  
Value Analysis ◽  
Market Data ◽  
Valuation Methods ◽  
Advantages And Disadvantages ◽  
Property Valuation ◽  
Real Estate Valuation

Multiple criteria valuation methods are widely used in real estate valuation all over the world. In USA, UK and other countries these methods are part of techniques based on comparative and reinstatement values as well as on income of use. They are used in valuating various property characteristics, ie its location or obsolescence degree. In Germany, however, multiple criteria valuation technique refers to a separate group of property valuation methods applied when market data are unavailable or insufficient (ie purchasing, selling or renting prices are not known). The above methods have been used for real estate valuation since 1976. Dr. H. E. Auerhammer [1] was the first to apply these approaches to solving real estate valuation problem caused by the scarcity of market data. These methods supplemented with systems of criteria developed by other authors were later applied to particular cases when three major commonly used property valuation methods could not be applied. Thus, K. Gablenz [2] suggests using the method described in assessing plots intended for agriculture, while B. Bischoff [3] offers to use it for determining the investments into plots. R. Vogel [4] thinks that the approach may be used for determining the approximate value of land, whereas G. Sommer and P. Zimmermann [5] and Piehler [6] developed a system of criteria to be used as a part of the method described in determining the differences between the value of quantitative and qualitative characteristics of an object and its market value. T. Gierardy and R. Moeckel [7] described the advantages and disadvantages of methods based on multiple criteria analysis. The above methods are widely used in Germany for property valuation, the peak of their application being associated with the unification of East and West Germany in 1990 [8]. Multiple criteria analysis presented in this paper for property valuation may be used to the advantage of various interested parties (see Fig 1). The representatives of various parties including appraisers, buyers, sellers and investors may use it for their particular purposes: appraiser may apply this method to real estate value analysis for determining the market, use and other values of real estate being mortgaged, ensured, privatised, divided or nationalised; investor may rely on it for more efficient use of this property; buyer may use it for choosing property which would satisfy his personal needs to the best advantage; seller has to determine the market price of his property that would ensure its competitive ability on the market. To satisfy all the needs described multiple criteria valuation method presented in the paper may be successfully used. To show its efficiency the solution of a sample problem, representing a real case is provided.

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