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Proceedings Paper

Introducing local property tax for fiscal decentralization and local authority autonomy
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Paper Abstract

Charles Tiebout (1956), in his work “A Pure Theory of Local Expenditures”, provides a vision of the workings of the local public sector, acknowledging many similarities to the features of a competitive market, however omitting any references to local taxation. Contrary to other researchers’ claim that the Tiebout model and the theory of fiscal decentralization are by no means synonymous, this paper aims to expand Tiebout’s theory, by adding the local property tax in the context, introducing a fair, ad valorem property taxation system based on the automated assessment of the value of real estate properties within the boundaries of local authorities. Computer Assisted Mass Appraisal methodology integrated with Remote Sensing technology and GIS analysis is applied to local authorities’ property registries and cadastral data, building a spatial relational database and providing data to be statistically processed through Multiple Regression Analysis modeling. The proposed scheme accomplishes economy of scale using CAMA procedures on one hand, but also succeeds in making local authorities self-sufficient through a decentralized, fair, locally calibrated property taxation model, providing rational income administration.

Paper Details

Date Published: 19 June 2015
PDF: 9 pages
Proc. SPIE 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), 953509 (19 June 2015); doi: 10.1117/12.2193545
Show Author Affiliations
Thomas Dimopoulos, Cyprus Univ. of Technology (Cyprus)
Neapolis Univ. Pafos (Cyprus)
Tassos Labropoulos, National Technical Univ. of Athens (Greece)
Diafantos G. Hadjimitsis, Cyprus Univ. of Technology (Cyprus)


Published in SPIE Proceedings Vol. 9535:
Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015)
Diofantos G. Hadjimitsis; Kyriacos Themistocleous; Silas Michaelides; Giorgos Papadavid, Editor(s)

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