Professor Maaja Vadi (PhD), University of Tartu
Professor Emeritus Ene Kolbre (PhD), Tallinn University of Technology
Professor Hans Lind (PhD), Royal Institute of Technology, Sweden
Professor Enn Listra (PhD), Tallinn University of Technology
Despite of the remarkable amount of real estate assets owned by state, then dealing with real estate - its management, maintenance and other similar kind of activities - does not belong to the core functions of a state as an institution. Therefore, the government has to stand for the situation, where real estate activities burden the state institutions as less as possible and generating at the same time as less fiscal impact on state budget and government sector account as possible. Consequently, the aim of the dissertation was to elaborate on public sector real estate asset management models and evaluate their fiscal impact, in order to help the government to find the best possible practical solution in the filed of state real estate assets management. The term "model" refers to a set of qualitative parameters, taking account the asset owner, the manager and the space user. Before the implementation of empirical analysis, a theoretical-methodological framework for public sector real estate asset management was elaborated, based on the literature of public sector real estate.
The result of the cash flow based multi-level quantitative fiscal impact analysis revealed that the evaluated fiscal impact on state budget and government sector account for all public sector real estate asset management models during the 30-year forecasting period was negative. Considering the result of fiscal impact analysis based on discounted cash flows on government sector account, from special-purpose asset models the least negative fiscal impact was generated by a model, where was assumed both centralized ownership and management of state real estate assets by a state-mediated agent. The result of the analysis of general-purpose asset models revealed the utmost importance of the size of market rents and their inner components together with their growth rates as an empirical input data used in the quantitative fiscal impact models.
The research elaborated on the dissertation showed for the first time that model-based asset management decision-making and also quantitative evaluation of fiscal impact on the level of public sector real estate is possible, but at the same time it is extremely complex activity.