Abstract
The Organization for Economic Co-operation and Development (OECD) in the last Going for Growth report (2021) urged the Italian government to redefine the tax on the first home ownership and to review the cadastral rates. This could represent an opportunity to re-discuss the objectives of the property taxation, as a part of the general tax reform announced by the Italian government. The aim of this paper is to perform some preliminary analyses on the fiscal inequality related to luxury properties in six different Italian cities at municipal level. The proposed methodological approach is based on three steps and can be also applied to other urban contexts. A stratified sampling of data from real estate advertisements provide the basis for the calculation of the cadastral values and a set of innovative fiscal inequality indicators. Descriptive statistics and regression analyses are performed to study the relations between property prices, cadastral values and the inequality level expressed by the proposed indicators. Findings show that cadastral values are not related to property prices and that the fiscal inequality level is significant in the analysed cities, even if with some differences that highlight a chaotic fiscal situation. The regression results also highlight the random relationship between tax rates and property prices and thus suggest that redistributive policies are necessary in the Italian context. The outputs of these first exploratory analyses represent a good starting point that deserves to be carried on and developed to test the proposed methodological approach. In particular, further researches could be focused on other housing segments, such as economic properties and tenements, and the analyses could be improved by considering the different urban areas and the related sub-markets.
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Curto, R., Barreca, A., Malavasi, G., Rolando, D. (2021). The Paradox of Fiscal Inequality in Italy: Exploratory Analyses on Property Tax Rates. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12954. Springer, Cham. https://doi.org/10.1007/978-3-030-86979-3_6
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