Powierzchniowy a katastralny system opodatkowania nieruchomości – symulacja wybranych skutków fiskalnych

The article presents the results of a simulation of urban land tax reform. On the example of one municipality current tax burdens of individual plots of land have been determined. Moreover, the process of determining its cadastral value has been carried out and then the effects of replacing the property tax by the cadastral tax for an assumed rate of cadastral tax have been investigated.W artykule zaprezentowano wyniki badań dotyczące symulacji reformy opodatkowania gruntów zurbanizowanych. Na przykładzie jednej gminy dokonano określenia wysokości podatków od nieruchomości poszczególnych działek gruntu, przeprowadzono proces ustalenia ich wartości katastralnej, a następnie dla założonej stawki procentowej podatku katastralnego zbadano wybrane skutki zastąpienia podatku od nieruchomości podatkiem katastralnym

Parametric and non-parametric methods in mass appraisal on poorly developed real estate markets

Purpose: The objective of the article is to identify machine learning methods that provide the best real estate appraisals for small-sized samples, particularly on poorly developed markets. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, taking into account sample sizes. Design/Methodology/Approach: Four types of regression were employed in the study: a multiple regression model, a ridge regression model, random forest regression and k nearest neighbours regression. A sampling scheme was proposed which enables defining the impact of a sample size in training datasets on the accuracy of appraisals in test datasets. Findings: The research enabled drawing several conclusions. First of all, the greater the training set was, the more precise the appraisals in a test set were. The conclusion drawn is that a reduction of a training set causes the deterioration of modelling results, but such deterioration is not substantial. Secondly, ridge regression model appeared to be the best model, and thereby the one most resistant to a low number of data. This model, apart from demonstrating the greatest resistance, additionally has the advantage of being a parametric, hence allowing inference. Practical Implications: Presented considerations are important, for instance in the case of valuations conducted for fiscal purposes, when it becomes necessary to determine the value of every type of real properties, even the ones featuring sporadically occurring states of properties. Originality/Value: The study contains modelling of the values defined by property appraisers, and not prices, as in the majority of studies. This decision enabled increasing the diversity of states of real estate properties, thereby including in the modelling process not just those real properties which are most typically traded.peer-reviewe

Effective augmentation of complex networks

Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our "effective augmentation" algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. "Effective augmentation" is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value