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 KaskUniversity of Tartu

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The aim of the paper

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

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Figure 1. The identification of the research objects. (Source: compiled by the author)

State policy(real estate)

Environment (general economy, real estate market)

Space users (state employee)

Management (PREAM)

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Figure 2. Conceptual change from property management concept towards corporate and public sector real estate asset

management. (Compiled by the author)

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PMFM

CREM

PREM

AM

Real estate environmentsingle object portfolio

increase in number of objects

passive

proa

ctive

incr

ease

of s

trat

egic

asp

ect

Management strategy

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pass

ive

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Table 1. Similarities and differences between CREM and PREM.

Differences Similarities

CREM

• The main focus is on return on investment.

• Juristictional difference on the operational level.

• The same conceptual aims, considering dilemmas between “owning-leasing” and “inhouse-outsource” management.

• Use the same levels of handling (mission, strategic, tactic and operational).

• Handling of different interests.

• An overlap on the operational level.

PREM

• The main focus is on public and political goals.

- e.g. the goal is to achieve both economic and social return on investment, which complicates the comparison of alternative investments.

• Problems with measuring the performance of real estate because of it´s unique character.

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

Management (centralized,

decentralized)

Owning(centralized,

decentralized)

Leasing (cost-based, market-based)

Inhouse 1. owned, managed itself

3. leased, managed itself

Outsourced2. owned,

management outsourced

4. leased, management outsourced

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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.

• Fiscal impact analysis (FIA), based on – state budget (SB) and government sector account (GSA).

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Table 3. General description of PREAM models.

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Descriptive factors Model 1

(non-rental) Model 2

(non-rental) Model 3 (rental)

Model 4 (rental)

Ownership State ownership State ownership State-owned

enterprise (RKAS) Private ownership

Management Decentralized

(by state institutions)

Centralized (by state-owned

enterprise (RKAS))

Centralized (by state-owned

enterprise (RKAS)) Private owner

Financing State budget State budget

State budget through state-

owned enterprise (RKAS)

Private capital

Costing Cost-based Cost-based Cost-based,

market-based Market-based

Space optimization minimal moderate maximized/highest maximized/highest Returns to scale none moderate moderate high Management strategy passive reactive

proactive, value-based

Applied to... ...both general purpose and special purpose property ...general purpose

property only

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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

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Model 1 Model 2 Model 3 Model 4

GPP CB CB MBR MBR

SPP CB CB CBR –

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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 specialists04.07.2013

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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 capital04.07.2013

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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.

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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.04.07.2013

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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.

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Thank You for Your Attention!

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