Application of Passive Methods of Robust Estimation: Baarda's and Pope's in Real Estate Market Analysis

Real Estate Management and Valuation

2021

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The subject of the thesis concerns the application of selected statistical methods searching for outliers in the process of determining the value of real estate, based on a functional model adjusted to market data. The collected research material consisted of data on land properties, which were the subject of transactions on local markets, for which there was no information regarding the specific conditions of concluding the sale agreement. After the initial selection of data regarding the purpose of the property in the local plan, the type of property rights being sold and the size of the shares sold - a functional model was adjusted to the obtained data, showing the relationship between the price being the dependent variable and the features of the property being the independent variables. Then, two statistical methods of searching for outliers which are significantly different in their algorithms, i.e. Cook’s distance and robust estimation method called Pope’s method, were applied to each model. The last stage was to determine the model values of selected properties and to compare the obtained results with the known transaction prices of the parcels being the subject of the valuation. The conducted research allowed for the verification of the influence of significantly different statistical methods searching for outliers on the property valuation result and its accuracy.

Section: Pope's Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The Influence of Chosen Statistical Methods of Detecting Outliers on Property Valuation Result

Barańska

Real Estate Management and Valuation

2021

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“…The basis of passive robust estimation methods are statistical tests that allow, after estimating the model parameters by the method of least squares, to determine which observations may be suspected to be gross errors. This group of methods includes Baarda's method (Kamiński and Nowel 1992, Proszyński and Kwaśniak 2002, Śpiewak 2018 where the zero hypothesis in form "there are no outliers in the examined dataset" is verified. To perform statistical test, the suitable test statistics which comes from chi-square distribution should be calculated.…”

Section: Baarda's Methodsmentioning

confidence: 99%

Chosen statistical methods for the detection of outliers in real estate market analysis

Barańska²

2020

aspal

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The paper contains the comparison of mechanism of two separately constructed statistical methods for the detection of outliers in real estate market analysis. For this purpose, databases with various types of real estate from local markets were created. Then the estimation of parameters of functional models describing dependencies prevailing on the examined markets was carried out. Subsequently, statistical tools called Baarda method and model residue analysis were used to detect outliers in the collected datasets. The last stage was a comparison of the obtained results of the parameters' estimation of the analyzed models and the measures of their quality, before and after the removal of outliers. The obtained results indicate that algorithms of chosen statistical methods, detecting outliers, allow to eliminate a smaller number of them, at the same time obtaining an improvement of the parameters of the functional model and its adjustment to the analyzed dataset. This gives the premise for the development of criteria for the selection of statistical methods that look for gross errors in the analyzed databases, among others, depending on functional model used, type of property and number of properties.

Application of Chosen Methods of Robust Estimation: Baarda’s and Huber’s in Search for Outliers in the Real Estate Market Modeling

Folia Oeconomica Stetinensia

2018

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In the article, an attempt was made to compile a dataset which was devoid of outliers, on the example of Cracovian apartment market. Robust estimation was the tool which was used, but only its two methods were considered: Baarda’s and Huber’s. Huber’s method belongs to the so-called active methods which means that it allows to eliminate gross errors during the estimation of the parameters of multiple linear regression where a unit price is called a dependent variable or forecasted one. Baarda’s method is a passive method which is based on statistical tests and allows, after determining the parameters of a multiple linear regression model, to indicate the observations which may be burdened with gross errors. Thus both mentioned algorithms differ from each other substantially. In this publication, Baarda’s and Huber’s methods were compared in the context of their effectiveness for the analyzed dataset, and as tools of preparing the data for further analysis. The results showed that Baarda’s method is more appropriate for the analyzed dataset than Huber’s algorithm, but it does not mean that the active method is worse.