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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Public Real Estate Management: Estonian Case Study with Monte Carlo SimulationAnalyses

Kaljula, Kert January 2014 (has links)
With accumulation of sovereign debt in many large OECD countries it seems that attention is heightened on how to manage public resources more effectively. High levels of sovereign debt are partly related to the aftermath of the latest financial crisis, where resolution for many big economies was to intervene and use public resources to put an end to the expansion of the crisis. Public real estate is one of those resources, which’s efficient management has high importance on general public sector efficacy. It seems that governments around the world have a way to go toward efficiency in public real estate management. There seem to be rather wide differences in management practices and quality. This thesis is an attempt to quantify some choices Estonian government could take in terms of its public real estate management. Four different scenarios are compared and Monte Carlo Simulation tool is used for that purpose. Two of the scenarios are related to private sector involvement and two are not. Privatization of public assets does not only mean cashing out for the government. It has wider consequences by introducing market forces where they weren’t before. One of the most important points of interest in this thesis is what effect can market forces and change in incentives have on public real estate management. There can be both, positive and negative effects, but which ones would prevail? The model built during the process of the thesis tries to measure those effects with aggregate net present value and its volatility by looking at 30 years ahead. Simulation analyses is used to vary input variables in the range that seems to be supported by the observations made in the literature and in some cases, where data is not available, also according to more subjective view that of the author’s. As input and their characteristics are different for scenarios, it is of interest to document how do the main outputs, mean NPV and its volatility, vary along with inputs.

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