@misc{Dmytrów_Krzysztof_Prediction_2019,
 author={Dmytrów, Krzysztof and Gnat, Sebastian},
 identifier={DOI: 10.15611/eada.2019.2.03},
 year={2019},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Econometrics = Ekonometria, 2019, Vol. 23, No. 2, s. 33-48},
 language={eng},
 abstract={It is believed that the ad valorem tax will increase fiscal burdens. In order to verify this statement, with the use of the Szczecin Algorithm of Real Estates Mass Appraisal, the land plots were appraised and the ad valorem tax was calculated. Next, a training set was sampled, for which the composite variable was calculated by means of three approaches: the TOPSIS method, the Generalised Distance Measure as the composite measure of development (GDM2), and the quasi-TOPSIS. They were the explanatory variables in the logistic regression model. Next, for the test set, changes of tax burden were forecasted. The aim of the research was to check the effectiveness of the presented approach for the estimation of the consequences of introducing the ad valorem tax. The results showed that all three approaches yielded similar results, but GDM2 was the best one. The main finding is that these approaches can be used in the prediction of changes in the tax burden of land plots},
 title={Prediction of changes in the tax burden of land plots with the use of multivariate statistical analysis methods},
 type={artykuł},
 keywords={logistic regression, classification, multivariate statistical analysis, real estate mass appraisal, regresja logistyczna, klasyfikacja, wielowymiarowa analiza statystyczna, masowa wycena nieruchomości},
}