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Presentation at ERES 2013 20 th Annual Conference 3 rd -6 th July 2013 Kaia Kask

PUBLIC SECTOR REAL ESTATE ASSET MANAGEMENT MODELS AND THEIR FINANCIAL EVALUATION: THE CASE OF ESTONIA. Presentation at ERES 2013 20 th Annual Conference 3 rd -6 th July 2013 Kaia Kask University of Tartu. The aim of the paper.

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Presentation at ERES 2013 20 th Annual Conference 3 rd -6 th July 2013 Kaia Kask

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  1. PUBLIC SECTOR REAL ESTATE ASSET MANAGEMENT MODELS AND THEIR FINANCIAL EVALUATION: THE CASE OF ESTONIA Presentation at ERES 2013 20th Annual Conference 3rd-6th July 2013 Kaia Kask University of Tartu

  2. The aim of the paper The aim of the paper was to evaluate the financial impact of public sector real estate asset mana­ge­­ment (PREAM) models on state budget (SB) and government sector account (GSA), using the set of Estonian central govern­ment buildings as an example.

  3. Management(PREAM) State policy (real estate) Environment(general economy, real estate market) Space users (state employee) Figure 1.The identification of the research objects.(Source: compiled by the author)

  4. Management strategy proactive increase of strategic aspect PREM AM CREM FM PM passive Real estate environment single object portfolio increase in number of objects Figure 2. Conceptual change from property management concept towards corporate and public sector real estate asset management. (Compiled by the author) passive

  5. Table 1. Similarities and differences between CREM and PREM.

  6. Table 2. General basis for the PREAM models, based on the concept of CREM.

  7. Research methodology • Benefit-cost analysis (BCA), based on • cluster analysis (i.e., classified by general-purpose property and special-purpose property); • pro forma free cash flow estimation, forecasted at least for 30 years; • appropriate discount rate. • Scenario analysis, based on • different views on real estate asset classification. • Fiscalimpact analysis (FIA), based on • state budget (SB) and government sector account (GSA).

  8. Table 3. General description of PREAM models.

  9. Table 4. An overview of the cost-based (CB), cost-based rental (CBR) and market-based rental (MBR) models. GPP – general-purpose property; SPP – special-purpose property

  10. Data collection methods • Indirect • economic macrodata forceasts • real estate market data from various databases • Ministry of Finance database (public sector buildings´ space data, based on asset inventory during Autumn 2009) • RKAS database (microdata) • benchmarks • expert opinion • Direct • semi-structured interviews with real estate managers of Estonian ministries and other specialists

  11. The main indipendent input variables in PREAM models • Income data: • sales revenue from asset disposition in result of • space optimization, i.e. disposal of surplus property (in Model 2, 3 and 4) • asset privatization (in Model 4) • Cost data: • maintenance costs • periodical repair costs • capital expenditures • rental costs (both cost- and market-based rent structure) • costs of sales • cost of capital

  12. Figure 3. The real estate related cost structure in GSA in Estonia (EUR) (Source: Estonian Ministry of Finance) Light green – market rent; Dark green – side costs of the market rent; Light blue – maintenance costs; Dark blue – periodic capital expenditures; Red – one-time capital expenditures.

  13. Problems with market-based models Lack of information to get the exact numerical measurements for the input data to market-based models (market rentals, etc.). Adequate basis for the forecasting of market rent up to 30 years. The amount of the cash-flow to the government sector account (GSA) is heavily dependent on the size of the components of the market rent, which are not exactly measurable. It concerns basically only model 3, but because of that the two market-based models are not comparable with each other.

  14. Conclusions and discussions The concept of corporate real estate management (CREM) used in private sector is well-applicable also in public sector. The result of the benefit-cost analysis is heavily dependent on the quality of the input data to the PREAM models. The quality of data is dependant on the governmental information system in general, which is qradually improving. Currently, based on available input data, only cost-based models are comparable; it is not possible to compare cost-based and market-based models with each other. From cost-based models, model 3 is the most preferrable to implement, based on the analysis of Estonian public sector buildings. Final decision about the model preference for implementation should be made, based on single cases, not in aggregated form.

  15. Thank You for Your Attention!

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