Abstract
The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon.
Highlights
The system of land taxation has a great influence on the use of land and its reallocation.In countries with established market economies, land and other property is usually taxed on the basis of its value
Repeated sampling of training sets was meant to enable the averaging of results and eliminating the risk, of which the results will be characteristic for a single dataset; more general conclusions cannot be constructed on the basis of the results
The results of applying two non-parametric regression algorithms in property mass valuation on an underdeveloped market were presented in this paper and they were compared to a multiple regression model
Summary
In countries with established market economies, land and other property is usually taxed on the basis of its value. This system has been in place for hundreds of years. Land taxation was usually based on its area, which distorted the nature of relations between market participants. Some post-communist countries have already reformed land taxation, while some of them are just considering it; Poland belongs to the latter group. The introduction of property value taxation will significantly change the way real estate market players behave. In order to carry out a reform of property taxation, it is necessary to carry out a mass valuation of property, which is a general term signifying a set of methods used for valuation of a large number of real properties in a uniform manner, determined at the same moment and carried out in a short time period
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