The Diversification of Communes’ Revenue from Real Estate across Provinces

Self Cite

The main aim of this paper is to identify differences between Polish communes as regards their revenues from property taxes. To this end, we distinguished and described types of communes with similar configurations of features under analysis (incomes from real property tax, agricultural tax and forest tax). In the research procedure, we applied methods of multidimensional analysis, with particular emphasis on cluster analysis. The research was conducted on the basis of aggregated (to eliminate random fluctuations) values of income from properties in the years 2013-2015. On the basis of typological classification, we distinguished six clusters (groups) of communes of different quantitative characteristics of budget revenues from property taxes. We identified specific regularities in the distribution of the distinguished commune types.

Section: Discussionmentioning

confidence: 99%

Recurrent Property Taxes in Communal Budgets – Identification of Types of Communes and their Spatial Differentiation

Trojanek

Kisiała

2018

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“…The main obstacle here is the public perception that an ad valorem tax will result in a significant increase in the tax burden, even leading, in some cases, to the insolvency of taxpayers. Various types of pilot and simulation studies are being carried out, which are confronting a number of issues related to property taxation reform (Trojanek & Kisiała, 2016), including the scale of changes in the tax burden (Głuszak & Marona, 2015;Gnat, 2009Gnat, , 2018. The pilot studies can be carried out on samples and the results should be highly reliable in extrapolating the results to populations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Testing the Effectiveness of Outlier Detecting Methods in Property Classification

Gnat

2020

The introduction of the property value tax in Poland may lead to an increase in the tax burden on real estate. Pilot studies may be carried out on samples and the results should feature a high degree of certainty as to the extrapolation of the results on populations (e.g. entire municipalities). Each study may, for various reasons, include outliers in the analyzed data sets. If their presence results from measurement errors or other reasons that cause such observations not to be the result of naturally occurring processes, they should be omitted in the calculations, because they interfere with the study of the occurring regularities.The study presents the results of statistical modelling carried out to determine whether individual objects (land properties), due to their attributes, are at risk of increasing the tax burden as a result of the introduction of ad valorem tax. First, logistic regression model estimation was carried out for the entire set of analyzed properties. Next, several methods of outlier detection were applied, and model estimation was repeated without the observations, i.e. real estates, pointed out as abnormal.The objective of the study is to verify the usefulness of outlier detecting methods in the context of improving the classification results of the analyzed properties.

“…Polish fiscal solutions regarding real estate are based on property area as the tax basis. There are many studies regarding communes' area-based revenue from real estate (TROJANEK, KISIAŁA 2016). The areabased system is criticized solution as being ineffective (ETEL, DOWGIER 2013).…”

Section: Introductionmentioning

confidence: 99%

The Impact of the Training Set Size on the Classification of Real Estate with an Increased Fiscal Burden

Gnat

2019

The introduction of an ad valorem tax can lead to an increase in the tax burden on real estate. There are concerns that this increase will be large and widespread. Before undertaking any actual actions related to the real estate tax reform, pilot studies and statistical analyses need to be conducted in order to verify the validity of those concerns and other aspects regarding the replacement of a real estate tax, agricultural tax and forest tax with an ad valorem tax. The article presents results of research on the effectiveness of the classification of real estate into a group at risk of an increase of tax burden with the use of the k-nearest neighbors method. The main focus was to determine the size of a real estate set (training data set) on the basis of which classification is conducted, as well as on the efficiency of that classification, depending on the size of such data set.