A COMPUTER-ASSISTED MASS APPRAIS

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					            A COMPUTER-ASSISTED MASS APPRAISAL SYSTEM FOR
                 BRCKO DISTRICT REAL ESTATE TAXATION




November 2005

This publication was produced for review by the United States Agency for International Development.
It was prepared by Development Alternatives, Inc.
      A COMPUTER-ASSISTED MASS APPRAISAL SYSTEM
       FOR BRCKO DISTRICT REAL ESTATE TAXATION




The authors’ views expressed in this publication do not necessarily reflect the views of
the United States Agency for International Development or the United States
Government.


                                                                                      i
    COMPUTER-ASSISTED MASS APPRAISAL SYSTEM FOR BRCKO DISTRICT REAL ESTATE TAXATION
ACKNOWLEDGEMENTS
This report was authored by Mark Gallagher, Rajko Tomas, and Sandra Marjanovic as
part of the work program of the USAID Tax Modernization Project (USAID-TAMP).
We appreciate the comments and assistance of Dzelila Sahinagic, Pero Bosnic, and Steve
Rozner. We also very much appreciate the discussions we had with the Brcko District
Property Tax Working Group, led by Osman Osmanovic.

Others who have been very helpful in preparing this report or discussing it with the
authors include:

      Petar Đurić, BD Tax Revenue Agency
      Hajrudin Hamidović, Public Records Department - Cadastre
      Nevresa Hasandžiković, BD Tax Administration
      Milenko Zečević, BD Government Legal Advisor
      Josipa Lucić, Appellate Court
      Pejo Mendeš, Department of Agriculture, Forestry and Water supply
      Mehmed Raščić, Department of Agriculture, Forestry and Water supply
      Ferhat Ćejvanović, Department of Agriculture, Forestry and Water supply
      Hajrudin Hamidović, Public Records Department - Cadastre
      Azrudin Preljević, City Planning Department
      Amra Abadžić, City Planning Department
      Branka Đurić – Žilić, Department of Displaced Persons, Refugees and Housing
      Dragan Tomaš, Property Registry, Municipal Court
      Zoran Simeunović, BD Tax Administration Agency
      Ivo Marić, Public Affaires Department – Housing.

Although these people have all been helpful in the preparation of the report, any
remaining errors are solely those of the authors.




COMPUTER-ASSISTED MASS APPRAISAL SYSTEM FOR BRCKO DISTRICT REAL ESTATE TAXATION
EXECUTIVE SUMMARY
This study presents a methodology for carrying out a computer assisted mass appraisal
(CAMA) system for the purpose of property valuation for Brcko District. The CAMA
system is required for the initial implementation of the Real Estate Tax, while mimicking
market valuation and setting the stage for movement toward a full market- based system.
TAMP collected and analyzed data from 750 property transactions (transfers, sales,
inheritances, gifts) in Brcko District for which transfer taxes were paid. Based on these
data we applied regression analysis and generated the parameters for the CAMA system.
A precondition for applying the CAMA system is a reliable real estate database. Data on
real estate is available in Brcko, but it is not in a single database and the data are not
consistent across databases. With some work, these data, however, can be consolidated
and improved and as the information source for the CAMA system. Combining these
data with the CAMA model will ensure a fair and broad-based real estate tax in Brcko in
the first year of operation.




                                                                                        ii
       Computer-Assisted Mass Appraisal System for Brcko District Real Estate Taxation


ACKNOWLEDGEMENTS                                                                          I

EXECUTIVE SUMMARY                                                                        II

TABLES                                                                                   V

FIGURES                                                                                  V

1.      INTRODUCTION                                                                     1
1.1.      Background                                                                      1

1.2.      Objective                                                                       1

1.3.      Organization of this report                                                     2


2.      ANALYSIS OF CURRENT METHODOLOGIES                                                 2
2.1.      Current taxes on real estate in Brcko District                                  2

2.2.      Formal approach                                                                 3

2.3.      Valuation in practice                                                           6

2.4.      Data description                                                                6


3.      OVERALL APPROACH TO CAMA                                                         11

4.      THE STATISTICAL VALUE MODEL                                                      13
4.1.      Statistical value model for buildings                                          13

4.2.      Statistical value model for land                                               14

4.3.      Adjusting the statistical value model for location                             16

4.4.      Adjusting the statistical value model for apartment story                      18


5.      CAPITAL VALUE OF RENTAL PROPERTY                                                 18
5.1.      Capital value for rental apartments and garages                                19

5.2.      Capital value for rental houses                                                20

5.3.      Capital value for rented land                                                  20



                                                                                         iii
5.4.    Discount rate                                                    20

5.5.     Multipliers for rental real estate in Brcko District            22
   5.5.1. Apartments and garages                                         22
   5.5.2. Houses                                                         22


6.     DATA AVAILABILITY IN BRCKO DISTRICT                               23
6.1.    Introduction                                                     23

6.2.     The Status of Property Records                                  23
   6.2.1. Public Records Department – Cadastre                           23
   6.2.2. City Planning Department                                       25
   6.2.3. Department of Agriculture, Forestry and Water Supply           26
   6.2.4. Department of Displaced Persons, Refugees and Housing          26
   6.2.5. Municipal Court – Property Registry                            27
   6.2.6. Public Affairs Department – Housing                            27
   6.2.7. Tax Administration Agency                                      28

6.3.    Summary of findings regarding data and feasibility of the CAMA   29




                                                                         iv
Tables


Table 1: Elements related to building quality ..................................................................... 4
Table 2: Transfer tax database – average values by property use....................................... 7
Table 3: Transfer tax database descriptive statistics........................................................... 7
Table 4: Correlations among variables ............................................................................... 8
Table 5: Statistical value model for buildings .................................................................. 14
Table 6: Statistical value model for land, unadjusted for location ................................... 15
Table 7: Statistical value model for land, results adjusted for location ............................ 15
Table 8: Determinants of land prices ................................................................................ 16
Table 9: Location-adjusted land value coefficients .......................................................... 18
Table 10: Valuation guidelines for Brcko District apartments ......................................... 28



Figures

Figure 1: Log-linear relationship between building value and land value.......................... 9
Figure 2: Log-linear relationship between building and land size.................................... 10
Figure 3: Log-linear relationship between land area and land value ................................ 11
Figure 4: CAMA process .................................................................................................. 12
Figure 5: Interest rate spread and remuneration rates in BiH, 2002 – 2005 ..................... 21




                                                                                                                          v
    Computer-Assisted Mass Appraisal System for Brcko District Real Estate Taxation


1. Introduction

    1.1. Background

In April 2005, the Director of Brcko District’s Tax Administration Agency, Mr.
Miodrag Trifkovic; the Head of the Brcko District Revenue Agency, Mr. Osman
Osmanovic; and the Chief of Party of USAID-TAMP, Mr. Mark Gallagher; signed a
Memorandum of Understanding (MOU) to take the necessary steps toward establishing
the legal framework for a market-based real estate tax in Brcko District. To lend higher-
level support, Mr. Mirsad Djapo, Mayor of Brcko District, and Mr. Howard Sumka,
USAID/Bosnia and Herzegovina Mission Director witnessed the MOU signing.

Among other aspects of the MOU, USAID-TAMP agreed to help develop the basic law,
of which Mr. Osmanovic, coordinator of the Real Estate Tax Working Group approved
the pre-draft on Friday, June 10, 2005. The MOU also provided for the development of
a computer-assisted mass appraisal (CAMA) system that the Brcko authorities would
implement in early 2006. This CAMA system is required for the initial implementation
of the real estate tax in Brcko District and will be applied during the initial real estate
tax registration process in early 2006.


    1.2. Objective

This study presents a methodology for carrying out a computer-assisted mass appraisal
(CAMA) system for property valuation in Brcko District, consistent with the Real Estate
Tax Law.1 Broadly defined, CAMA is:

             A system of appraising property, usually only certain types of
             real estate, that incorporates statistical analysis, such as
             >>multiple regression analysis<< and >>adaptive estimation
             procedure<< to assist the appraiser in estimating value.
                        Property       Appraisal        and       Assessment
                        Administration, The International Association of
                        Assessing Officers, editor Joseph Eckert, 1990,
                        page 638.

In Brcko District, this CAMA system will be used during the initial phase of the real
estate tax in early 2006. The system allows quick and easy appraisal of registered
property based on information that the taxpayer provides the Tax Administration
Agency when the property is being entered into the real estate tax registry.


1
  In this document, we often substitute “property” for “real estate,” especially when referring to a specific
lot, house, home, garage or other property that is subject to the real estate tax.

COMPUTER-ASSISTED MASS APPRAISAL SYSTEM FOR BRCKO DISTRICT REAL ESTATE TAXATION
The CAMA system simulates, based on statistical analysis and probability theory, the
current methods used by the Brcko District Real Estate Assessment Commission, as has
been applied to the sale or transfer of property during 2004. The CAMA system is
essential to starting the real estate tax system while mimicking market valuation and
setting the stage for movement toward a full market value-based system.

The CAMA system shall be applied only to the registration of residences, both
apartments and houses, as well as garages. It will not be applied to industrial and
commercial properties, such as factories, gas stations and hotels. Commercial and
industrial properties should be assessed on an item-by-item basis.

   1.3. Organization of this report

This report begins with a discussion of the current framework and methodologies
regarding property taxation in Brcko District, followed by a discussion of the overall
approach to mass appraisal based on statistical and capital value models. It concludes
with an assessment of the availability of data and the feasibility of applying the CAMA
in Brcko District. The report also includes an Annex on regression techniques used in
the development of the statistical models.


2. Analysis of Current Methodologies

   2.1. Current taxes on real estate in Brcko District

This section discusses briefly the characteristics of the current set of taxes on property in
effect in Brcko District.

Taxes on Real Estate. Currently, only the transfer, sale, inheritance or gifting of real
estate is subject to taxation. There is, in effect, no tax on the ownership of real estate
during a tax period.

Tax Liability Occurrence. The tax liability occurs as of the day of entering into
agreement on the transfer of real estate. When the tax liability occurrence cannot be
determined based on an agreement, the tax liability is deemed to have occurred as of the
date on which the transferee came into possession of the property. If the property is
obtained through a court’s decision, the tax liability occurs as of the date of validity of
the decision letter.

Tax Rate. The tax rate on the transfer of real estate is 3% of the tax base. The Law sets
a rate of 6% for the resale of privatized state-owned apartments, if owned by the buyer
for less than five years from the date of privatization.

Tax Exemptions. The real-estate transfer tax is not paid in the following cases:




                                                                                           2
       the transfer represents the settlement of liabilities to the Brcko Tax
       Administration Agency (i.e. public revenues) in an enforced collection
       procedure;
       a diplomatic or consular mission of a foreign country is transferring ownership
       rights;
       the transfer involves the investment of real estate as capital into a company;
       it is for the privatization of state-owned apartments;
       it is for the acquisition of agricultural land for the purpose of enlargement of
       property; or,
       it is for the transfer of property rights under regulations governing life-long
       support.

   2.2. Formal approach

The Rulebook on the Method of Establishing Real-Estate Market Value (Official
Gazette of RS, no. 15/94) regulates the market valuation procedures for real estate
subject to taxation.

The Rulebook elaborates the legal treatment of real estate market valuation. Essentially,
real estate value is determined according to one of two methods. The first is “book
value” and is applied to those firms that maintain records and include their real estate
assets in their book value. The other method uses physical characteristics and location
as the basis for determining value.

Property values for taxpayers that do not maintain formal business records are
established in accordance with the basic and corrective elements of the building (real
estate). The basic elements to determine the value of the building are:

   1. useful area of the building;
   2. average market price per square meter of building; and
   3. age of the building.

The corrective elements are:

   1. building location; and
   2. building quality.

Each of these elements is explained in turn below.

Useful area constitutes the total number of square meters of the building surface
between the inner sides of the walls. To the useful area is added 30% of the area of
balconies and accessory rooms, 75% of the area of verandas, and 60% of basement and
attic space.

Average market price is the average sale price of a square meter of a new building as of
December 31 of the prior year. Average sale price is determined based on information


                                                                                       3
obtained from businesses engaged in construction and sale of buildings in Brcko District
and neighboring municipalities. The lowest average price among those businesses is
considered the average market price. If the average market price cannot be established
in this manner, it is established using the average sale price per square meter of a
representative building as of December 31 of the prior year.

The third basic element, building age, reduces the market price by 1% per year, up to
40% of the value established based on useful area and average price.

Once the basic elements are established, a quality ratio is then applied to correct the
market value for the quality of the building. This is expressed as a ratio between the
score for a given building and the maximum score that a building can attain (695), based
on the prescribed elements for establishing building quality. (See Table 1 below.)

If a building is located in an urban area, the market value established using the basic
elements and the quality ratio is adjusted using a ratio of 1.2. If the building is damaged
due to force majeure (more than 10%), the market value is reduced by the percentage of
the established damage.

Table 1: Elements related to building quality

 Item                          Description                          Unit of        Score
                                                                  Measurement
   1                               2                                   3              4
          1. Building construction
  1.1.    Buildings made of unbaked brick or barracks                                   50
  1.2.    Prefabricated buildings (wood, tin, metal, iron)                             150
  1.3.    Buildings made of mixed material                                             190
  1.4.    Buildings made of hard material                                              300
          2. Staircase
  2.1.    Wooden                                                                          15
  2.2.    Metal or concrete                                                               20
  2.3.    Marble                                                                          40
          3. Doors
  3.1.    Standard wooden                                         per                     30
                                                                  apartment.
  3.2.    Solid wood or iron doors                                per                     40
                                                                  apartment
          4. Windows
  4.1.    Wood, iron, plastic – single pane                       per                     20
                                                                  apartment
  4.2.    Wood, iron, plastic – double pane                       per                     30
                                                                  apartment
          5. Window blinds or shutters
  5.1.    “Eslinger” (wood, plastic)                              per                     10
                                                                  apartment


                                                                                          4
Item                         Description                  Unit of   Score
                                                      Measurement
5.2.    Rollers, shutters                             per              15
                                                      apartment
        6. Floors
6.1.    Brick, concrete, terrazzo tile, boards        per              10
                                                      apartment
6.2.    Beach, oak, and other wood parquet            per              25
                                                      apartment
6.3.    Artificial or natural fibers, “vinaz”, PVC    per              20
                                                      apartment
        7. Sanitary rooms
7.1.    Fully-fitted bathroom                         per              25
                                                      apartment
7.2.    Partially-fitted bathroom                     per              10
                                                      apartment
        8. Plumbing
8.1.    Connected to water supply network             per              30
                                                      apartment
8.2.    Connected to water-well                       per              20
                                                      apartment
        9. Sewage
9.1.    Connected to sewage network                   per              20
                                                      apartment
9.2.    Connected to manhole                          per              10
                                                      apartment
        10. Electricity and telephone installations
10.1.   Electricity installation                      per              40
                                                      apartment
10.2.   Telephone installation                        per              10
                                                      apartment
        11. Heating
11.1.   Central heating                               per              50
                                                      apartment
11.2.   Other types of heating                        per              15
                                                      apartment
        12. Building exterior
12.1.   Classic façade                                                 10
12.2.   Brick façade                                                   20
12.3.   Artificial façade                                              40
12.4.   Natural stone façade                                           50
12.5.   Copper roofing                                                 20
        13. Elements for increased value
13.1.   Gas installation                              per              25
                                                      apartment



                                                                        5
 Item                            Description                         Unit of        Score
                                                                   Measurement
 13.2.    Elevator in building                                                           25
 13.3.    Swimming pool                                                                  80

In accordance with the Rulebook on the Method of Establishing Real Estate Market
Value, the Tax Administration Agency’s Real Estate Assessment Commission
determines market value, applying the general and corrective elements described above.
Based on data from the Court-verified real-estate sales agreement submitted by the
taxpayer at the time of property transfer, the Commission establishes the status of the
basic and corrective elements of the property in question. The Commission then
prepares an official assessment, which, when completed, is signed by the taxpayer and
all Commission members.

   2.3. Valuation in practice

Brcko District currently imposes a tax on the transfer, sale, inheritance or gifting of real
estate. The tax is imposed at rates ranging from two percent to six percent of value,
depending on the nature of the transfer.

The value of the property is supposed to be its market value, which may be either the
book value of property for firms or an estimated value based on a number of specific
factors, including building size, land area, condition of building, age of building, and
location, as discussed in more detail above. Some taxpayers file a form with the Tax
Administration Agency providing these data, and the value of the property is assessed
based on these data plus exogenous information about construction costs. However,
many more taxpayers do not bother to file at all.

Despite the formal approach, our collection of the facts indicates that this system is not
being implemented in its entirety. Indeed, of the 750 transfers for which we collected
and analyzed data, the elements of building quality, listed in Table 1, were only
included in the formal assessments 52 times, i.e., for only 7% of the transactions.

To establish a market-simulating CAMA system, it is necessary to establish the
statistical parameters that mimic the present methods used to assess property values.

   2.4. Data description

We collected and analyzed data from 751 transactions (transfers, sales, inheritances,
gifts) in property for which the transfer taxes were applied. The following table
describes most of the data in the database.




                                                                                          6
Table 2: Transfer tax database – average values by property use

                                  BLDG
                         BLDG                   BLDG          LAND       LAND
                   #              COSTS                                               LANDV        TOTALV
                         SIZE                   VALUE         AREA       PRICE
                                  (per m2)

                                                        Average values

All                751      62          832        44,523       3,071            15      14,165     27,359
House               85      77          490        24,499         890            37      17,010     42,728
Apt                166      56          742        36,509                                50,400     36,473
Business            27      54         2142       135,809       3,961            31      67,190    144,409
Garage only          6      19          209         3,857                                            3,857
Other/Ag-related   467                                          3,457            11      13,117     13,042



The next table presents the descriptive statistics for some of the variables.

Table 3: Transfer tax database descriptive statistics
                          BLDG
                                   BLDG VALUE         LAND AREA          LAND VALUE        TOTAL VALUE
                           SIZE


Mean                        62               44,504           3,076           14,174                27,284

Median                      55               31,880            894               8,260              12,852

Maximum                     613        2,145,500            116,621        1,058,540              2,346,320

Minimum                     12                2,340             19                189                  189
Std Dev.                     56          135,239              7,355           47,137               100,317

Number                      274                279             553                553                  744

Total Value is the total value of a given property upon which the transfer tax has been
imposed. It is the tax base for the transfer tax and is equal to the sum of the value of the
building or facility (BLDG VALUE) and the value of the land (LAND VALUE).
Building Size is the useful area of any constructed premises on the property, as
described earlier.

The largest property transferred and included in this database was valued at KM
2,346,320, while the smallest was valued at only KM 189. This lowest valued property
only consisted of land and did not include the transfer or sale of a constructed facility.
Of particular concern for the proposed new real estate tax is the fact that the median
value property was only KM 12,860, which is below the minimum value upon which the
new tax will be applied. Extrapolating from the sample data, this means that less than
half of the District’s properties will actually be subject to the tax.

Four “dummies” are included in our analysis. A dummy is a binary indicator, where “1”
is entered to indicate that the property is of a certain type—a Business, for instance—or



                                                                                                         7
a “0” is entered to indicate that it is not. Table 2 shows then that 27 of the properties in
this database are business premises; 85 are houses; 166 are apartments, and only 6 are
garages.

Other data are included with these transactions, but are too few in number or are not
relevant for this analysis. One item that may be important in the future but does not yet
bolster this analysis is information concerning the condition of the building or other
constructed facilities; only 52 such observations were included in the database.

Table 4 presents the correlations amongst the main indicators.

Table 4: Correlations among variables
                BLDGSIZE      BLDGVALUE LANDAREA             LANDVALUE        TOTALVALUE
BLDGSIZE             1.00
BLDGVALUE            0.87          1.00
LANDAREA             0.58          0.62            1.00
LANDVALUE            0.80          0.78            0.61            1.00
TOTALVALUE           0.88          0.99            0.64            0.82             1.00

From Table 4, it is quite obvious that these variables are all highly related to each other.
Such high correlations imply that if the expert is knowledgeable about one of the
variables, he probably has a good indication of the values of other indicators. For
instance, it is clear that the larger the land area, the larger a building, the more value the
property is likely to have.

To illustrate this visually, the following graph shows a clear non-linear relationship
between building values and land value.




                                                                                            8
Figure 1: Log-linear relationship between building value and land value

              14
              13

              12
              11
 LOG(LANDV)




              10
              9

              8
              7

              6
              5
                   7   8   9   10   11   12   13   14    15
                               LOG(BLDGV)

Figure 1 illustrates that the greater the value of the building or other facilities
constructed on a site, the higher the value of land is likely to be. This is consistent with
the notion that developed land is more valuable than undeveloped land.

Figure 2 shows a clear log-linear relationship between building and plot size.




                                                                                          9
Figure 2: Log-linear relationship between building and land size

                 7


                 6
 LOG(BLDGSIZE)




                 5


                 4


                 3


                 2
                     2   4      6      8       10        12
                             LOG(LANDAREA)

This figure leads one to expect that the larger the land area, the larger the building is
likely to be that is on that land. That it is non-linear, or log-linear, means that one
should not expect the size of the building to increase in line with increases in land size.
For instance, a plot of land double the size of another plot of land is likely to have a
building on it that is less than double the size of the building on the smaller plot.

Figure 3 is a scatter diagram of land values and land areas for the sample data.




                                                                                        10
Figure 3: Log-linear relationship between land area and land value

              14
              13

              12
              11
 LOG(LANDV)




              10
              9

              8
              7

              6
              5
                   2   4      6        8        10       12
                           LOG(LANDAREA)
It is quite clear from Figure 3 that there is a positive relationship between the value of a
property and its size. At the same time, from the dispersion of the observation points, it
is also clear that many things other than land area add to the value of a land property.

3. Overall approach to CAMA

A number of methods are proposed for assessing property values.

Each of these tends to systematically undervalue properties, and it is highly unlikely that
any of them would results in an overvaluation of property. It is prudent to choose
methods that systematically under value property in order to not over tax anyone.

Application of the CAMA will tend toward under valuation as a strategy to avoid having
valuations challenged by taxpayers. Taxpayers have the right to challenge an appraisal
and request that the Brcko authorities undertake a physical inspection of the property.
Taxpayers should understand that, except for very unusual circumstances, such
reassessments and physical inspections will likely result in higher value assessments, not
lower.

The following chart illustrates the overall hierarchy of assessment methods for the
CAMA.




                                                                                         11
Figure 4: CAMA process


                        Self-
                        appraisal




                       Recent sale
                       price
   Registration




                                           Highest
                                           resulting
                                           value              Assessment issued



                       Statistical
                       model



                       Capital
                       value




The process laid out above has a number of steps. The first step is the registration of
real estate properties in Brcko District. We have developed forms for registration and
annexed them to this report, but the steps and procedures for registration are not
discussed here.

The second step is to enter the data from the real estate tax registration forms into the
CAMA system. The taxpayer enters data related to the property that is registered that
will provide sufficient information for a property valuation based on the statistical
model that is described later in this document. In addition, if the taxpayer had
purchased the property or otherwise received the property in a transfer, she will then
provide information about the purchase price of the property.

The taxpayer also has the opportunity to express her own opinion as to the market value
of the property. This is referred to as “self-appraisal.”

The capital value approach uses property information provided by the taxpayer to
determine a value for property that is rented to someone who is not a relation of the
taxpayer. The methodology for calculating the capital value of rented properties is also
developed later in this CAMA document.

In addition, a recent sale price can be a reliable basis for establishing the current market
value of property. Finally, the taxpayer has the opportunity to express her own opinion
as to the market value of the property, which is referred to as “self-appraisal.”


                                                                                         12
Since each of the above methods for determining value is likely to significantly under
value most properties, the highest property value resulting from these four
methodologies is taken as the final assessed value of the property. It is this final,
assessed value that serves as the official tax base upon which the real estate tax is levied.


4. The statistical value model

While the tables and figures above all demonstrate clearly the interrelationship among
the key property variables, they do not yet yield useful statistics that can help in defining
a CAMA system. To do this, we need to apply regression analysis, which will generate
both useful parameters for the CAMA system as well as provide us with indicators of
probability and “goodness of fit” of the analysis. “Goodness of fit” means how well the
model reflects reality.

For this analysis, total value of a property is merely the sum of land value and building
or facility value. Two separate regressions are generated to model land and building
values, which can then be aggregated to yield part of a CAMA valuation.


   4.1. Statistical value model for buildings

Our model holds that building values differ significantly based on whether the building
is a house, a business, an apartment, or a garage. To model this, we applied the “1, 0”
dummy variables in multiplicative form to the size of the building or facility. In this
way, all building properties are included in the analysis, but specific parameters are
generated for each of the four building types.

The following regression provides estimators for the building value model.




                                                                                          13
Table 5: Statistical value model for buildings

Dependent Variable: BLDGV
Included observations: 282 after adjustments

        Variable         Coefficient    Std. Error   t-Statistic   Prob.

   BLDGSIZE*HOUSE         367.6842      33.62791     10.93390      0.0000
    BLDGSIZE*APT          692.8567      30.21792     22.92867      0.0000
  BLDGSIZE*GARAGE         200.3526      458.5689     0.436908      0.6625
 BLDGSIZE*BUSINESS        3412.545      31.82477     107.2292      0.0000

R-squared                 0.975143
Adjusted R-squared        0.974875

These regression results indicate an extremely good fit for the model, based on the very
high R-squared value. This high R-squared value indicates that the regression can
explain 98% of the variance in the dependent variable (building value). The t-statistics
of the regression indicate very high confidence for building size for houses, apartments,
and businesses. The t-statistic for garages is not statistically significant, which at any
rate would be very difficult to establish with only 6 observations. However, discussions
with counterparts in the Revenue Agency and Tax Administration Agency of Brcko
indicate that the coefficient of KM 200 per square meter looks very reasonable.

This regression forces a zero coefficient, which is rather unusual in the practice of
econometric analysis. In this case, however, it is quite sensible. A regression with a
constant (or intercept) larger than zero (not from the origin) would indicate that
someone would be willing to pay for a building of zero size. This is clearly nonsensical.
On the other hand, a negative constant would indicate that any building below a certain
size would have a negative price, which also makes no sense.


   4.2. Statistical value model for land

We posit land value to be based on only two things: land area and building value. As
we generally know from developed markets, developed land generally has a higher
value than similar land that has not otherwise been developed. This is generally
illustrated in Figure 1, and is statistically shown in Table 6 below.




                                                                                       14
Table 6: Statistical value model for land, unadjusted for location

Dependent Variable: LANDV
Included observations: 87 after adjustments

        Variable        Coefficient    Std. Error   t-Statistic     Prob.

      LANDAREA           11.37922      2.679040      4.247501       0.0001
     LANDAREA^2         -0.000814      0.000424     -1.921990       0.0580
        BLDGV            0.077382      0.013077      5.917559       0.0000

R-squared                0.497440
Adjusted R-squared       0.485474

The regression results in Table 6 indicate that land, in general, is worth about KM 11 per
square meter. However, LANDAREA^2 is the squared value of land area. Its
coefficient is negative, indicating that the larger the plot, the lower the per square meter
value will likely be. Conversely, this implies that smaller properties will tend to have a
higher value per square meter than will larger properties. Clearly, this must have some
limits since properties of one million square meters, according to this model, would
have negative value.

The notion that developed land is worth more than undeveloped land is borne out by the
positive and statistically significant coefficient for building value (BLDGV). This
coefficient indicates that land’s value is enhanced by about 8% of the value of the
building that stands on it.

Table 7 below includes regression results for the statistical model for land with
adjustment for location.

Table 7: Statistical value model for land, results adjusted for location

Dependent Variable: LANDV
Method: Least Squares
Date: 06/14/05 Time: 16:50
Sample: 1 750
Included observations: 750

        Variable        Coefficient    Std. Error   t-Statistic     Prob.

    LANDAREA*BC          38.62627      1.280101     30.17439        0.0000
     LANDAREA            0.977902      0.145624     6.715263        0.0000
       BLDGV             0.081231      0.011408     7.120474        0.0000

R-squared                0.564329     Mean dependent var          10455.15
Adjusted R-squared       0.563163     S.D. dependent var          40935.58




                                                                                         15
In our model, the location factor (BC) refers to those properties that are within the city
limits of Brcko City. Some of these properties are in downtown Brcko, while others are
in the immediately surrounding area. This model indicates that properties within Brcko
City limits tend to have a basic, undeveloped land value of about KM 38 per square
meter, while those properties outside of Brcko City limits have a basic, undeveloped
land value of about only KM 1.0 per square meter.

Perhaps a more direct way to estimate the determinants of land value would be to
estimate the determinants of land prices. The next table presents such regression
estimates.

Table 8: Determinants of land prices

Dependent Variable: LANDP
Included observations: 749

         Variable          Coefficient      Std. Error      t-Statistic     Prob.

           C                 11.02380       1.096426        10.05430        0.0000
       LANDAREA             -0.000174       0.000109       -1.596346        0.1108
         RURAL              -4.117556       1.450089       -2.839520        0.0046
         HOUSE               26.75195       2.183793        12.25022        0.0000
         BLDGV              -4.58E-06       8.00E-06       -0.572583        0.5671

R-squared                    0.203657     Mean dependent var              11.37295
Adjusted R-squared           0.199376     S.D. dependent var              20.71393

From this regression, the contribution of a building’s value seems to add no explanatory
power. The regression is useful, however, as it indicates that rural land should be valued
at about KM 4 below the urban land value, while urban land with a house on it should
be valued at considerably higher values, namely at KM 26.75 above the value of urban
land without a house of KM 11.02 (from the constant, not the mean).

The regression estimate for the impact of land size on land price is not useful.2


    4.3. Adjusting the statistical value model for location

It is an old saw to say that the value of any product, good, or service, is determined by
the interaction of supply and demand. Unfortunately, it is very difficult to estimate
(econometrically, or any other way) the supply and demand curves in any market. It is
even more difficult to estimate supply and demand curves in a place such as Brcko

2
  From inspection of documents and review of the actual methodology applied, our team has come to the
tentative conclusion that property values are often set by the official involved, who then imputes a land
price consistent with the overall assessed property value.


                                                                                                      16
District, where complete information regarding supply and demand is simply not
available.

In many countries and localities around the world, assessors do not attempt to make
formal calculations of supply and demand curves; rather, they construct models, such as
those above, that have many of the components or elements that one expects to affect
supply and demand. In most of those models, location is a key factor. In our models
above, location is only handled in a very rudimentary way, i.e., in the incorporation of
the “BC” factor in Table 7.

The “BC” factor in Table 7 is very important to the operation of the statistical value
model. The coefficient generated here is an average for basic, undeveloped land values
only for properties either located inside of the urban center of Brcko City, or in the
surrounding areas of Brcko City. Moreover, it is only applied to the value of
constructed space, not to land.

We add a location factor to our statistical value model to account for higher property
values in the central part of Brcko City as compared to the surrounding urban area. To
do this, we propose to establish two Brcko City location zones:

Zone I would apply an adjustment factor of 1.0 to the land value of all urban properties
located in the central sections of Brcko City.

Zone II would apply an adjustment factor of 0.7 to all properties in the areas of Brcko
City not directly in the central area, i.e., not in Zone I.

For all homes outside of Brcko City, we treat properties located in the wealthier urban
village settlements differently from all other rural properties. In this case, we propose to
apply two additional adjustment zones:

Zone III would apply to the wealthier urban village settlements, which are Brezovo
Polje čaršija, Šatorovići, Potočari, Donji Brezik, Brka, Gornji Rahić, Maoča, Krepšić,
Cerik, Bosanska Bijela. Properties in these urban village settlements will apply an
adjustment factor of 1.0.

Zone IV properties are all those rural properties not included in Zone III. Indeed, any
properties not in any of Zones I to III would be Zone IV properties. The adjustment
factor for Zone IV properties is 0.5.

The following table presents the factors and applicable coefficients that would be used
in applying the statistical model to estimate land value when applying the CAMA
system.




                                                                                         17
Table 9: Location-adjusted land value coefficients

Zone                   Adjustment Factor      Land coefficient per Location-adjusted
                                              square meter         land coefficient
Zone I                          1.0                  38.0                  38.0
Zone II                         0.7                  38.0                  26.6
Zone III                        1.0                   1.0                  1.0
Zone IV                         0.5                   1.0                  0.5


   4.4. Adjusting the statistical value model for apartment story

Apartments throughout BiH, including those in Brcko, tend to command higher values if
they are on the first or second floor of a building. This is a result of problems with
unreliable elevators, electricity cutoffs, inadequate pressure for heating and water, and
other common inconveniences. First and second floor apartments, for these reasons,
tend to sell or rent at a 15% to 20% premium over apartments in the same building but
on higher floors.

Apartments on the first or second floor of a building will have their statistical model
value adjusted by a multiplier of 1.2, while apartments on higher floors will have their
statistical model value adjusted by a multiplier of 0.80.


5. Capital value of rental property

In cases where a property is rented, the market value can be estimated directly based on
the rent that is received by the property owner. Rents represent a payment, hence an
economic value, that is made for the temporary use of a property. By deducting a
reasonable expense rate, including an allowance for depreciation and maintenance, and
by applying the concepts of “required rate of return” and discounting to determine an
asset value, the Brcko District Tax Administration Agency can calculate a reasonable
estimate of the market value of the rented property.

The capital value of any asset can be assessed according to the future stream of income
divided by the appropriate discount rate. In this model, we essentially treat the rental
property as if it were an annuity.

The calculation is simply: V = I/r, where

V is the value of the property,
I is the net income stream, after allowance for maintenance and replacement for
depreciation, and
r is the discount rate, or the rate of return that investors would demand for investment in
rental property in Brcko District.



                                                                                        18
The methodology in this section should apply when the rental rate charged is an “arm’s
length” charge. The arm’s length concept means that the rent that is charged the renter
should actually be the rent that the rental market would charge in the case of two
unrelated parties. For instance, if a mother rents her house to her child for a very low
monthly charge, this would not constitute an arm’s length transaction, and therefore this
methodology could not be applied.

Determining whether a rental charge meets the arm’s length criterion may be difficult
for the first few years of the operation of the Real Estate Tax Law. However, a few
simple rules should apply. Namely, the rental value method should NOT apply IF:

   1. the renter and the landlord are related by blood, up to two degrees of sanguinity;
   2. the renter and landlord are related by marriage, up to two degrees;
   3. the landlord is a business that is owned by the renter;
   4. the landlord is a business that is owned by a relative of the renter, as per items 1
      and 2 of this listing;
   5. there is some other obvious relationship between the landlord and the renter; or
   6. the Tax Administration Agency official can otherwise ascertain that the rent
      charged is significantly lower than the rent that is charged for similar properties.

In the above cases, the rental methodology may still be applied, but only by using the
rental value of a very similar property, such as other apartments of the same size and
quality in the same apartment house.

   5.1. Capital value for rental apartments and garages

The capital value of an apartment that is rented out takes as the stream of income the
current annual rent—or twelve times the current monthly rent—less an allowance for
depreciation and maintenance of the constructed part of the property. Since apartments
and garages in Brcko have been sold without other land, their entire rental value must be
adjusted to allow for depreciation and maintenance.

A reasonable adjustment rate for depreciation and maintenance would be 20% of rents.
Therefore, the calculation of the capital value of a rental apartment or garage would be:

Va = (annual rent – 20%*annual rent)/r.

This expression can be simplified to:

Va = .8*Ra/r,

where
        Va is the value of the apartment or garage,
        Ra is the annual rent of an apartment or a garage, and
        r is the discount rate.




                                                                                       19
   5.2. Capital value for rental houses

Capital value for rental houses is established using a similar methodology to that for
rental apartments and garages described above. However, because land cannot be
depreciated and does not require general maintenance, depreciation and maintenance
should only be deducted from the value of the house and any other constructed facilities
on the property. The Brcko District database shows that land value comes to 30% of the
total value of properties with houses. Therefore, the valuation equation can be
expressed as:

Vh = (annual rent – 20%*70%*annual rent)/r

This can be simplified to

Vh = (1-.2*.7*Rh)/r, or
Vh = .86*Rh/r,

where
        Vh is the capital value of the house,
        Rh is the annual rent for the house, and
        r is the discount rate.


   5.3. Capital value for rented land

As explained above, land does not depreciate and needs little general maintenance. A
landowner who rents his land generally need neither set aside funds for depreciation nor
for maintenance. Therefore, the capital value of land is:

Vl = Rl/r,

where

        Vl is the capital value of the rented land,
        Rl is the annual rent received for the rented land, and
        r is the discount rate.


   5.4. Discount rate

Deciding on what is the most appropriate discount rate is complex. Essentially,
investors would need to state the rate of return they expect to receive and do receive for
their investments in real estate in Brcko. This is not really feasible. Investors do not
know, for sure, what the yield will be on their investments; nor can they exactly define
the rate of risk they are willing to accept; nor do they know how much risk is inherent in
any particular real estate investment in Brcko.


                                                                                       20
Nonetheless, there is an active financial market in BiH that provides guidance for
determining an acceptable discount rate for application to the Brcko property market.

The Central Bank of BiH publishes monthly interest rates that it pays to banks that are
required to maintain reserves with the Central Bank. This is referred to as the
“remuneration rate” and it is essentially a “risk-free” interest rate. In one sense, it is the
pure price of money in BiH. In recent years, this remuneration rate has declined from
almost 4% per annum to only 2% per annum, as of May 2005.

The Central Bank of BiH also publishes the interest rates that the banking sector pays its
depositors and charges its customers. Two rates of interest for bank loans are published:
one for long-term commercial loans and the other for short-term commercial loans. As
is the case with the remuneration rate, the long-term commercial lending rate has been
on the decline, from 11% in 2002 to only about 8% in May 2005. The spread between
the remuneration rate and the long-term commercial lending rate has also declined over
this period, from 8 points to only 6 points in May 2005. The following graph shows
these trends visually.

Figure 5: Interest rate spread and remuneration rates in BiH, 2002 – 2005


                             Interest rates in BiH
                         Spread and remuneration rate

  9.0                                                                                 4.50
  8.5
  8.0
  7.5                                                                                 3.00
  7.0
  6.5
  6.0                                                                                 1.50
        1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
                                         Months


 Notes on Figure 5:
 Spread is shown on the left-hand axis and the remuneration rate on the right-hand axis.
 Spread data are on the line with the squares.
 Remuneration rate data are shown on the line with the diamonds.


The discount rate, also know as the “required rate of return,” is developed by adding:

   1. The risk-free rate (in the BiH case, this would be the remuneration rate);
   2. An allowance for risk;
   3. An allowance for illiquidity; and


                                                                                             21
   4. An allowance for management of the asset.

It is impossible to define the allowance rates that ought be applied, but we propose that
the spread that the long-term commercial lending rate enjoys would be a good starting
point. At this writing, the spread is 6.0 percentage points.

Since commercial lenders in BiH are now quite sophisticated and since they only
provide this lending rate to their best commercial customers, it is clear that investors in
real estate will require a higher spread. This higher spread must accommodate the
greater risk that real estate investment entails, along with the greater management needs
of managing real estate investments.

We propose that the spread for real estate investment in Brcko District, for purposes of
developing the CAMA system, be set at the long-term commercial lending spread, plus
2 points for additional risk and asset management requirements.

In this case, the official discount rate for determining the value of rented property in
Brcko, as of this writing, would be 2.0% + 6.0% + 2% = 10%.


5.5.    Multipliers for rental real estate in Brcko District

       5.5.1. Apartments and garages

To assess the value of apartments and garages in Brcko District, based on the capital
valuation method, the assessor need merely multiply the declared monthly value of the
property by the multiplier for apartments and garages, Ma, which is:

12*(1-0.2)/.10 =

12(.8)/.10 = 96

This means that an apartment that rents for KM 300 per month would have an assessed
value of 300*96 = KM 28,800.


       5.5.2. Houses

To assess value of rental houses in Brcko District, based on the capital valuation
method, the assessor need merely multiply the declared monthly value of the property
by the multiplier for houses, Mh, which is:

12*(1-0.2*0.7)/.10 =

12(.86)/.10 = 103




                                                                                        22
This means that a house that rents for KM 500 per month would have an assessed value
of 500*103 = KM 51,500.


6. Data availability in Brcko District

    6.1. Introduction

Establishing a reliable real estate database is an integral part of creating a modern
property tax system in Brcko District. Such a database will greatly aid in the application
of the CAMA model by minimizing the deviations between market and appraised real
estate values. Furthermore, it will provide for fairer taxation, fewer taxpayer appeals,
and fewer tax re-assessments.

Currently, Brcko District has no comprehensive database that could serve as a reliable
basis for property appraisal and tax assessment. Instead, property records are
maintained by multiple government institutions for different purposes.

On September 8, 2005, we visited several institutions in Brcko District to determine
what real estate data they maintain and to evaluate the feasibility of using those data for
mass property appraisals. Those institutions included: the Public Records Department –
Cadastre Division; the Department of Agriculture, Forestry and Water Supply; the City
Planning Department; the Municipal Court – Property Registry; the Department of
Displaced Persons, Refugees and Housing; the Public Affairs Department – Housing;
and, the Tax Administration Agency.

Each of these institutions houses some form of real estate database, although the
information in each database varies depending on the function or legislative authority of
the institution. Clearly, data collection under the current system is too diffuse to be used
effectively in developing a modern property tax system in Brcko District. Since the Tax
Administration Agency is responsible for collection of the property tax, it is our
recommendation that a single, comprehensive real estate database for property appraisal
and tax assessment be located in this institution.


    6.2. The Status of Property Records

        6.2.1. Public Records Department – Cadastre

The Public Records Department houses the division in charge of Cadastre books and
Archives. In accordance with the regulations governing its operations,3 the division
performs the following services:

3
 The operations of the cadastre and archives division are governed by a number of District, Entity, and
BiH-level laws, including: the Law on Administrative Procedure of Brcko District; Law on Land
Registration and Land Rights; Law on Maintenance of the Land Survey and Land Cadastre; Law on the
Public Utilities Cadastre; Law on Charges for the Use of Land Survey and Land Cadastre Data and for


                                                                                                    23
        1. Prepares and implements decision letters on changes to land;
        2. Performs geodesic surveys for the purpose of recording changes to land;
        3. Receives and reviews contracts on changes of ownership and records the
           changes in the Cadastre database;
        4. Issues certificates and records from the database as well as copies of land-
           related plans;
        5. Performs land-related evaluations at the request of the courts;
        6. Maintains the property registry and synchronizes information in the registry
           with information in the Cadastre; and
        7. Takes care of the survey points network in Brcko District.

Brcko District comprises 59 cadastre municipalities. The most recent aerial screening of
the terrain is from 1987, but because real estate data based on this screening is organized
for only 11 cadastre municipalities (accounting for only about 25% of the total number
of real estate units in Brcko District), the Cadastre database established from a 1964
aerial screening remains in use even today.

The Cadastre maintains somewhat reliable data concerning ownership of real estate in
the area of Brcko District. For the areas of Brcko District for which data have been
processed (land consolidation), reliable data exist on land and buildings constructed on
that land. However, for the areas for which the data have not been systematized, the
land data are fairly reliable, but the building data are significantly less so. This is due, in
part, to illegal construction, where neither the original buildings nor subsequent changes
to them were recorded in the Cadastre.

For each real estate unit recorded in the Cadastre database, the following information is
available: first and last name of the owner; personal identification number (JMB) of the
owner; cadastre code of the land parcel (defining the location and identifying the
parcel); parcel location; parcel title; use of the parcel; parcel area; parcel class (1–8);
buildings on the parcel; and, any changes in ownership of the parcel. All data is
available in electronic format.

Apartment ownership is not recorded in the Cadastre. Land surrounding apartment
buildings that were built as “public apartments” is formally registered to Self-Governing
Interest Community of Housing, i.e. it is owned and regulated by the Brcko District
Government.

The Cadastre database contains 45,241 records for real estate units. Among these
records, there is the possibility of data repetition concerning a given owner-taxpayer
where that taxpayer owns several pieces of property. In addition, there is the possibility
of data repetition concerning a certain parcel of land due to changes that were not
properly recorded. However, we estimate that the repetition rate is, at a maximum, 5%


Performing Services relate to the Land Survey and Real Estate Cadastre; and the Law on Archiving
Activity of BiH and of the Republic of Srpska, and the respective bylaws.


                                                                                             24
for the parcel data. Furthermore, one can easily identify and correct repetitious or
incorrect owner data.

Given the status of the Cadastre database, IT specialists will be needed to evaluate
whether the existing database is adequate for CAMA application. Such an evaluation
would consider, among others:

   i)         the quality of electronic entry and the possibility of data transfer to another
              database;
   ii)        the ability to create reports according to ownership criteria (i.e. by JMB of
              the owner) to eliminate repetitions and establish a listing of potential
              taxpayers;
   iii)       the ability to create reports according to location, i.e. Zones I – IV from
              Section 4.3 above; and
   iv)        the ability to create reports according to type of real estate.


          6.2.2.   City Planning Department

The City Planning Department maintains information used for determining the property
location coefficients.

The city plan for Brcko encompasses the “narrow” and the “broad” urban areas. The
narrow urban area is divided into three zones. Zone borders are precisely delineated so
that every parcel of land and every building can be clearly identified as belonging to one
zone or another. Demarcation of the zones is based on and related to benefits for
performing business, benefits for housing, rents, public facility benefits, as well as other
factors.

The City Planning Department maintains data on all real estate owned by Brcko District.
The District Government uses only part of that property directly, while it rents the
remainder of its property to businesses. All rented property is distinguished by zone.
Thus, Brcko District has 156 business premises in the city center (zone I); 10 at the
border crossing (zone I); 22 at the greenmarket (zone I); 30 at the flea market (zone I);
19 in zone II; five in Zone III; and five in zone IV. Data on location, size, monthly
rental price, lease term, and other relevant information exist for each rented business
premises.

A Special Rulebook, issued by the Mayor, regulates the procedures for renting business
premises and other real estate in Brcko District. The starting monthly prices for real
estate rentals are provided in an attachment to the Rulebook (for land, business
premises, and other public spaces). Actual rental prices are obtained by means of
auction. Using the above information, it is not only possible to make an appraisal of the
market value of rented real estate, but also to derive the location coefficient for real
estate according to zones. For example, the rental price per square meter of business




                                                                                          25
space in Zone II comes to about 70%, and in Zone III around 30%, of the rental price of
business space in Zone I.

Aside from responsibilities for determining the property local coefficients, the City
Planning Department also manages the procedure for legalization of illegally
constructed buildings. The Department keeps data on all illegal structures and those
subsequently legalized. Merging the database of the City Planning Department with that
of the Cadastre would improve the data on buildings. However, this would only
partially solve the problem, since those illegal buildings for which legalization has not
been sought would remain unrecorded.

All the data maintained by the City Planning Department are in hard copy only. For
CAMA purposes, it would be necessary to reenter all the data into the new taxpayer
database.


       6.2.3. Department of Agriculture, Forestry and Water Supply

This Department maintains data from the “Agricultural Survey,” which was conducted
from late 2002 to early 2003. The primary goal of this survey was to help in the
creation of policy incentives to stimulate agricultural development in Brcko’s rural
areas.

The questionnaire used for data collection featured a number of questions related to the
activity of agricultural households and cultivation of their land. Approximately 9,600
households completed the questionnaire. However, Department officials consider the
data not to be fully accurate and suggest that respondents tended to understate property
size and incomes out of concern that the data might be used for tax assessment purposes.
Consequently, the data, which exist only in hard copy, are used merely for guidance, and
the Department continues to contact the Cadastre office when in need of precise data on
land parcels. In terms of application of these data to the CAMA model, therefore, the
Agriculture Survey may be of limited use.

Nevertheless, the Department does maintain two additional types of data that could
prove useful for the CAMA: (1) data on private and government-owned land in Brcko
District; and (2) data on mined land. Not all of these data are included in the Cadastre,
nor are they easy to differentiate in certain cases.


       6.2.4. Department of Displaced Persons, Refugees and Housing

This Department maintains very precise data on property returns, completed property
reconstruction, and donations for property reconstruction. Its database is in electronic
format and contains records for around 10,500 cases.




                                                                                      26
Each case contains data on the property (type of property, location, size, condition, etc.)
and the property owner (first and last name, JMB, address, etc.). Information is also
available concerning how the owner financed the reconstruction of his property, e.g., on
his own, with donations, or a combination of the two. This information can be useful if
the legislature chooses to provide certain benefits, such as temporary property tax
exemptions, to individuals who independently reconstruct damaged property. All data
from the Department’s database can be useful for comparing and updating information
on the same properties contained in other databases.


       6.2.5. Municipal Court – Property Registry

The Municipal Court’s Property Registry contains basic information on all apartments in
Brcko District. Since the Cadastre does not maintain records for apartments, data from
the Property Registry are presently used to supplement the information already
contained in the Cadastre database. Because it includes all apartments constructed in
accordance with the apartment building plan, as well as additionally constructed stories,
the Property Registry, with documentation for approximately 4,000 apartments,
represents the most complete set of records on apartments in Brcko District.

The Property Registry maintains the following data for each apartment: location; first
and last name of the owner; owner’s JMB; apartment area; apartment construction;
apartment story; type of ownership; use of apartment; construction year; and,
information on the building in which the apartment is located. Unfortunately, these data
are not kept in electronic format.

The Department is currently in the process of merging the data from the Cadastre and
the Property Registry, and will submit those data for public consideration. At this
writing, the Department has merged about 40% of the data for the City of Brcko. When
completed, the combined database will more provide reliable, complete and up-to-date
property information than is available in the existing, isolated databases. Therefore, the
merged data will be very useful for application to the CAMA model. IT specialists will
need to find a way to establish the database for application of CAMA model based on a
combination of “merged” and “unmerged” data, so that the new “merged” data can be
entered into the database and replace the “unmerged”.

The Property Registry also contains data on houses in Brcko District, but only on basic
dimensions. The Cadastre data on buildings provide a more complete and up-to-date
record for such properties.



       6.2.6.   Public Affairs Department – Housing

The Housing division of the Public Affairs Department maintains a database with
information on privatized apartments and garages as well as those in the process of


                                                                                        27
privatization. All data are in electronic format. At the end of August 2005, the
Department had 3,105 apartments and 128 garages registered in its database.

The following data are available for each apartment: first and last name of the owner;
owner’s JMB; apartment location; construction year; apartment size; privatization price
of the apartment; apartment construction; and story. The documentation serving as the
basis for privatization of an apartment includes additional information on the quality of
construction and installed equipment.

The Law on Privatization of State-Owned Apartments prescribes the methodology for
establishing the privatization price for an apartment. Table 10 shows the per-square
meter prices for privatized apartments, set according to year of construction:

Table 10: Valuation guidelines for Brcko District apartments

 Construction       Before                                                       After
                                 1950-60       1960-70     1970-80   1980-90
    Year             1950                                                         1990
    Price,                                                                       Market
                      40            80            100       115        135
 KM per m2                                                                       price
 Source: Law on Privatization of State-Owned Apartments.

To arrive at the overall apartment price, one would multiply the above unit prices by the
apartment’s total area and then adjust that amount using the location and age
coefficients. The location coefficient accounts for city zone and apartment story.

A comparison of privatization and market prices confirms that the privatization pricing
methodology undervalues apartments in Brcko District. Therefore, the apartment
privatization process cannot be reasonably used as a basis for property tax assessment.

Aside from its other deficiencies, the Housing division’s apartment database is narrower
in scope than that of the Property Registry, since it only includes privatized apartments
and those for which the privatization process is already underway. It does not include,
for example, annexed apartments, illegally constructed apartments, and apartments
subject to court proceedings to determine ownership.


        6.2.7.   Tax Administration Agency

In September 2000, the Brcko District Assembly adopted the regulations of the Republic
of Srpska on property taxation. However, Brcko District only applies that part of the
law which refers to the tax on the transfer of property. The District does not levy tax on
property ownership and does not enforce the regulations related to taxpayer
identification, property valuation, tax declaration filing, etc. Consequently, there is no
adequate real estate database within the Tax Administration Agency that could be
readily used for implementation of the new Property Tax.



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The Tax Administration Agency does maintain data on property transactions subject to
the tax on property sale, gift or inheritance, including data on appraised values. These
data are currently available for the years 2003, 2004, and part of 2005. Based on the
available data, there are roughly 1,000 real estate transactions in Brcko District each
year. The data are not organized well, nor are they available in electronic format.
However, in building the sample for the CAMA model, TAMP has already developed
an electronic database for 2004 transactions.


   6.3. Summary of findings regarding data and feasibility of the CAMA

Following is a summary of our findings concerning available data and feasibility of
applying the CAMA model using those data:

   1. Brcko District does not have a single property database.
   2. Data exist for most properties. However, the data are dispersed among various
      institutions, and the respective databases are not linked or compatible.
   3. Data are classified and processed according to a variety of different criteria.
   4. There are no data on property value from direct market transaction observations,
      other than those in the transfer tax database.
   5. The quality and currency of data varies from institution to institution.
   6. Some databases are more suitable than others for integration into a single
      database.
   7. There are data overlaps and repetition among the various institutions.
   8. Generation of data for institutions’ databases is not synchronized or
      standardized.

Regardless of the challenges highlighted above, it is possible to form a single,
comprehensive database from the various sources that would allow for application of the
CAMA model. In the beginning, of course, there will likely be errors due to data
deficiencies, but the whole model of introduction of property tax calls for error
correction mechanisms. In addition, one should bear in mind that a good part of the
database can be developed based on tax declarations from taxpayers. Proportions and
reliability of the data will depend on the approach to implementation of the new
Property Tax Law.




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