Final report 1(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Grant agreement number 20102.2011 .001-2011.181

Theme: 2.01 - National accounts methodological and technical improvements

Final report: ”National balance sheets for non-financial assets in Finland”

Final report 2(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

CONTENTS

Foreword…………………………………………………………………………………………………………...3

PART I: Evaluation of data sources, the present capital stock and inventories

1. Main data sources and possible need for new data sources…………………………………………………..…3 2. Evaluation of the present capital stock from the point of view of balance sheets 2.1 Description of the present capital stock ……………………………………………………….5 2.2 Improvement needs of the present capital stock from the point of view of balance sheets…....7 3. Changes to the IT-system……………………………………………………………………………………….9 4. Balance sheets by asset type and by sector……………………………………………………………………..10

PART II: Estimation of land value

1. Introduction…………………………………………………………………………………………………….12 2. Choice of the estimation method...... 12 3. Estimating the value of land by using the direct approach...... 15 4. Estimation of land area by land types 4.1 In general...... 15 4.2 Practice in Finland...... 16 5. Estimation of changes in the land type areas 5.1 In general...... 21 5.2 Practice in Finland...... 22 6. Estimation of representative unit prices for each relevant land type 6.1 In general...... 23 6.2 Practice in Finland...... 24 7. Modelling price changes for each land type 7.1 In general...... 26 7.2 Practice in Finland...... 26 8. Calculation of land value 8.1 In general...... 30 8.2 Practice in Finland...... 30 9. Specifying volume changes and price changes 9.1 In general...... 32 9.2 Practice in Finland...... 33 10. Separation of timber value and land improvements from the relevant land values 10.1 Forestry land...... 34 10.2 Land Improvements...... 36 11. Concluding the preliminary results...... 38 12. Land value by sectors...... 40

Appendices I SLICES Land use classification II SLICES Land cover classification III SLICES Soil classification IV Unit prices by local government and land use, year 2012 Final report 3(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Foreword The general objective of this project was the preparation for the implementation of the ESA 2010 in Finnish Na- tional Accounts. The objective of this particular action was to develop methods and make calculations for the regular production of balance sheets for non-financial assets (table 26). The main objective was calculations for new items in the transmission programme, particularly calculations for the asset AN.211 Land by sectors. In addition, the present capital stock was evaluated for the purposes of balance sheets. The project was part of the comprehensive ESA 2010 -project of Statistics Finland. The project can be divided into two parts from the practical point of view. First, the data sources and methodology for the present capital stock and inventories were analysed, and overall picture of the needs for high-quality non-financial balance sheets was created (part I). Secondly, the estimates and the calculation system for the land value were carried out in co-operation with the experts of the National Land Survey (part II). When reading this report one should bear in mind that all the numerical results are preliminary, and they should not be cited.

PART I: Evaluation of data sources, the present capital stock and inventories

1. Main data sources and possible need for new data sources Total value of non-financial assets in Finland is estimated to rise up to 900-1000 billion euro (2012), if all the assets defined in ESA2010 could be calculated. Fixed assets (AN.11) cover approximately 50-60% of that, which means that they are the most important subgroup of non-financial assets affecting the total wealth. Balance sheet values for the fixed assets can be estimated by PIM (perpetual inventory model), which is currently used in na- tional accounts mainly to produce estimates on consumption of fixed capital. Description of the present capital stock is given in chapter 2. A weakness in utilising the PIM-data in balance sheets is the definition of price: in the balance sheets, year-end price should be applied, and for the PIM, year-average price is applied. In addition, the net capital stock calcu- lated by PIM is strongly depending on the underlying assumptions such as service lives and depreciation profiles of the different assets. In spite of these weaknesses, the PIM can be seen as the best available method for most of the fixed assets which do not have effective markets. However, for assets such as dwellings we can observe market prices, which gives as a chance to make comparative calculations. These calculations and the conclusions to improve the PIM are shown in chapter 2. Inventories (AN.12) cover approximately 10% of the total value of the real assets in Finland. Compiling the balance sheets, stock data of national accounts was used as a source. Most of the stocks consists of growing trees in forests (AN.1221). The value for growing trees is calculated by Finnish Forest Research Institute and based on national forest inventory and market stumpage prices. The same concerns the timber inventory prices as to fixed assets; year-average prices are applied. Final report 4(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

For valuables (AN.13) the stock data can be used as well as for inventories, although there is very barely primary source data available. Non-produced assets (AN.2) are estimated to cover 30-40% of the total non-financial assets in value. Approxi- mately half of this amount is due to value of land (AN.211). For the land valuation, information from land use statistics (SLICES) and statistics on real estate transactions were used. The land estimates can be considered relatively reliable, taking into account that it is a very challenging task with all the exceptions and details. The procedure of land estimates is described in part II of this report in detail. The next-in-line development task for land valuation is the improvement of the sectorization method. In principle, the land estimates could be calcu- lated directly by sectors. However, at this stage sectorization is based on the owner distributions of the structures built on land for the built land. Besides land, mineral and energy reserves (AN.212) are a significant natural resource in Finland. They are es- timated to cover 10-20% of the total non-financial assets. Geological Survey of Finland (GSF) maintains data- bases on national mineral resources and reserves. Changes in volumes can be estimated based on new strikes (GSF) and mining statistics published by Ministry of Employment and the Economy. These data sources also cover the supply and use information of peat; the only economically significant energy reserve in Finland. The challenges in estimating balance sheet items for minerals and energy reserves are the defining of economically exploitable amounts of the articles as well as finding the relevant market prices (or net present value). However, the commodity statistics by industries publishes annual mining volumes and correspondent values by products, and the market prices could probably be estimated based on this information. At least price indices could be cal- culated based on this information, because the price changes of slightly processed products reflect mostly the price changes of the raw material. Only very preliminary estimations on the asset value has been done so far. Non-financial balance sheets also include assets such as “Contracts, leases and licenses” (AN.22) and “Goodwill and marketing assets” (AN.23). Not much effort was put to evaluate those assets so far. However, based on some recently published research it can be estimated that the total value of these assets is around five per cents of the total value of the non-financial assets.

Fixed assets

Inventories and valuables Land

Mineral and energy reserves Other non-produced assets

Picture 1. Distribution of non-financial assets values in Finland by asset type. The data sources and needs for new/improved data are shown in table 1 at rough level. Table 1. Data sources by assets Final report 5(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Asset Source data Need for new/improved data AN.11 Fixed assets Net capital stock (national Data on entertainment, accounts) literature and artistic ori- gins, data on mineral exploration AN.12 Inventories Inventory stocks (national Year-end price data for accounts, forest research standing timber (and for institute) other inventories as well) AN.13 Valuables Expert advice AN.211Land Land use statistics, statis- Information on land own- tics on real estate transac- ers by land types, infor- tions mation on land use changes annually AN.212 Mineral and Mineral and peat stocks Price data energy reserves by Geological Survey of Finland AN.22 Contracts, leases, Ad hoc surveys All licenses AN.23 Goodwill and Ad hoc surveys All marketing assets

2. Evaluation of the present capital stock from the point of view of balance sheets

2.1 Description of the present capital stock (source: Report on improving the Perpetual inventory Method (PIM) in Finland, 2010; Eurostat grant No 20101.2008.002-2008.185) The Perpetual Inventory Method (PIM) is a general way of calculating stocks of assets. Assets enter the stock when they are acquired or created with own resources and they are taken out of the stock when they are sold, scrapped or in other ways disposed of. In connection with the major revision process of the Finnish National Accounts finished in 2006 also the meth- ods of capital stock calculations (the PIM) were amended and revised. The revision included, among other things, introduction of the geometric depreciation method for certain fixed assets, derivation of the price indices used in the PIM calculations from the supply and use tables, some improvements to the processing of sector transfers and revision of average service life assumptions of capital goods used in mining, manufacturing and electricity and water supply (industries C, D, E, NACE Rev.1). Estimates on net stocks of fixed assets are also of interest in their own right when it comes to measuring wealth and when balance sheets are set up. In the past, in many statistical offices, the main purpose of measuring capital Final report 6(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

was to provide a basis for the calculation of consumption of fixed capital so that net measures could be derived in the national accounts. The measurement of consumption of fixed capital (CFC) remains a key reason for capital measurement but two additional objectives have increasingly gained in importance: establishing balance sheets for economic sectors and measuring capital services. Capital stock calculations in Finland follow the model that is quite common in statistical offices of the OECD countries. Long enough time series of gross fixed capital formation are constructed so that is possible to calculate capital stocks entirely by a perpetual inventory method. To estimate depreciation both straight line (linear) and geometrical age-price profiles are used depending on asset. On some asset types it is also assumed that their av- erage service life is changing over time. Individual GFCF series and resulting stocks are classified by sector, activity, producer type and asset type. Pro- ducer type is used to distinguish producers in the same sector and kind of activity that are considered to be in- volved mainly on market production, production for own account or other non-market production. In practice, the capital stock system contains around 1100 individual GFCF series and same number of stocks. Lengths of the time series allow estimation of stocks from 1960 onwards for most asset types, with the help of some assumptions for the earliest years (till 1920) for the assets with longest service lives. A geometric depreciation method was partly taken into use in the capital stock model with the revision of the National Accounts in 2006, while earlier only a linear consumption profile was utilised. The geometric consump- tion pattern is used for machinery, equipment and transport equipment and for intangible investments (mineral exploration, computer software and entertainment, literary and artistic originals, research and development). The linear method is still applied to all construction investments. Net capital stock (NCS) represents the cumulative value of past investments less the cumulated consumption of fixed capital. Net capital stock is the stock concept of the SNA08/ESA2010 accounting framework and it is used in balance sheets, use tables and input-output tables. The net capital stock in constant prices can be obtained by subtracting accumulated capital consumption from the previous year’s capital stock and adding current year’s investments. The average lengths of service lives of capital goods are based on data obtained with survey inquiries and from administrative sources, and on expert evaluations and practices in other countries. For public infrastructure, for example, the average service lives of rail tracks, roads and waterways are based on data provided by the Finnish Rail Administration, Road Administration and Maritime Administration. The average service life for dwellings is 50 years, mineral exploration ten years, computer software five years, originals ten years, R&D 5 to 20 years and improvements to land 30 to 70 years. Capital stocks can be valued using three different price concepts: – Constant replacement cost, i.e. capital goods are valued at the prices of a selected base year – Current replacement cost, i.e. capital goods are valued at the prices prevailing in the current year, and – Acquisition cost, i.e. capital goods are valued at the (“historical”) prices prevailing at the time they were purchased. The first two price concepts are used in the Finnish National Accounts. The calculations using the PIM are based on long time series of fixed capital formation at constant prices in absolute values. Constant-price capital stocks are inflated into current-price ones by using price indices of investments. Prices for gross fixed capital formation are obtained from the supply and use tables. Gross fixed capital forma- tion is made up of products of domestic production (market output P11 and production for own final use P12) and imported products (P7). In addition to product and type of output classifications the current price includes Final report 7(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

institutional sector and type of producer classifications. In SUT gross fixed capital formation consists of 345 NA products. Since there are no genuine investment price indices available, gross fixed capital formation is first deflated at basic prices mainly with producer price indices (PPIs). Basic price values are transformed to purchasers’ price values by adding constant price formation items to the basic price values. In general, price indices of GFCF have a major impact on capital stock and CFC figures since PIM figures are calculated in constant prices and then inflated to current prices.

2.2 Improvement needs of the present capital stock from the point of view of balance sheets The overall conclusion of the present capital stock is, that there are no obstacles to use the capital stock as a source for fixed assets in the balance sheet. There are no fundamental shortages in quality or differences in the definitions of coverage or valuing the assets between the two systems. For most of the fixed assets there is no chance to observe real market prices, and so the methodology used in valuing the capital stock can be used as a second best solution. However, there are effective markets for some assets like for dwellings (and to certain extent also for office and commercial buildings). The comparison data for real estate sales leads to a significantly higher value than the capital stock value of dwellings plus the value of land under dwellings (valued by the direct method; see part II of this report). This begs the question if the price index or service lives for dwellings should be developed. The average service life for dwellings currently used in the capital stock is 50 years. The price index currently used is based on con- struction costs. A possible reason for the difference in the values between two statistics could be the increase in value of existing dwellings near the city centres, which typically exceeds the increase rate of construction costs especially during the economic booms. Some analysis was made to examine the sensitivity of the net capital stock of dwellings to the service life and price index used. The current situation is shown in picture 2. It can be observed, that the difference between the two valuation methods has broadened during the last decade.

Final report 8(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Picture 2. Comparison of the market value of dwellings to the total value of capital stock for dwellings and value of the land under dwellings.

Test calculation were made with different combinations of service lives (from 50 to 70 years) and price indices. The results showed that changing only the service life does not change the slope of the curve named ”Net Capital Stock”. Therefore, even the change in service life from 50 to 60 years would close the gap between the curves for recent years, it would broaden the gap during the 1990s. However, examining the whole observation period, 60 year’s service life for dwellings would lead to a stock value, which would better reflect the market observations for real estates, when summed with the value of underlying land. Picture 3 shows the results, where the price index used in the capital stock valuation process was based on the transactions of real estates. It was concluded that by developing the price index the gap could be caught up better than just changing the service life for dwellings.

Picture 3. Comparison of the market value of dwellings to the total value of price adjusted capital stock for dwellings and value of the land under dwellings.

Changing the capital stock to better meet the reality naturally also leads to higher consumption of fixed capital (CFC). Some examinations were made to analyze the influences of different valuation methods on CFC. The results are shown in picture 4. Final report 9(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Picture 4. Sensitivity analysis of valuation methods to CFC.

Blue curve denotes the development of the CFC for dwellings, when linear depreciation model and a service life of 50 years are applied (the present situation). Red line stands for linear depreciation and service life of 60 years and green line geometric depreciation profile and service life of 50 years, respectively. Yellow curve (REPI) indicates the development of the CFC when the price index is based on real estate transactions, service life 50 years and depreciation model linear. The conclusion was, that changing the price index, ceteris paribus, significantly increases the CFC. After a care- ful consideration it was decided to change the service life for dwellings from 50 to 60 years. This solution was also strongly supported by the fact that in many European countries the applied service life for dwellings is more than 50 years. In addition to these developments to the value of dwellings in the capital stock, also some new capital stock as- sets due to ESA2010 were calculated: weapon system and research and development. These actions were carried out in separate projects, and the methodology is not repeated here. Annual changes were distributed to holding gains / losses and other changes in volumes by assets. This was done by adding other changes in volume when available and then producing the revaluations computationally as a residual (closing stock - opening stock + investments - CFC + other changes in volumes = revaluations).

3. Changes to the IT-system

The calculation system for the non-financial balance sheets was one of the two completely new subsystems that were needed due to the implementation of ESA 2010 (the other one was the pension table subsystem). The objec- tive was that as much data as possible would move automatically from other systems and sub-systems of national accounts database to the balance sheet sub-system. It is relatively easy to add such automatic transfers between different parts of the system. In this context, this apply for fixed assets, inventories and valuables which all can - at least partly - be transferred from the net capital stock and inventory stock subsystems to the balance sheet sub- system. Final report 10(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

It was agreed that the distribution of flows between opening and closing balance sheets is needed with the fol- lowing variables: -investments -CFC -changes in inventories -net acquisitions of non-produced assets -other changes in volumes -revaluations. Because such distinction is not available in the net capital stock or inventory stock subsystems, it was impossible to introduce automatic transfers inside our IT system for all the variables, but some open cells and calculation rules had to be added to the balance sheet subsystem.

The balance sheets for the non-produced non-financial assets (AN.211 Land, at the moment) are currently calcu- lated separately in excel and imported to the subsystem by SAS. In the end, the financial balance sheets were automatically brought to the balance sheet subsystem from the financial accounts in order to be able to calculate key ratios such as total wealth and net assets by sectors.

All the real assets at aggregate level and variables included in the subsystem can be seen in tables in chapter 4.

4. Balance sheets by asset type and by sector

Preliminary non-financial balance sheets by institutional sector are shown in tables 2-5. Table 2. Non-financial balance sheets for S11 in 2012, billion euro STOCK FLOWS STOCK TRANSACTIONS OTHER FLOWS Opening Investments Consumption of Changes in Net acquisi- Other Revaluations Closing balance fixed capital inventories tions of asset changes in balance ASSET sheet volumes sheet AN.1 Pro- 252 19 17 1 11 266 duced non- financial assets AN.11 Fixed 209 19 17 10 221 assets AN.12-13 43 1 1 45 Inventories and valuables AN.2 Non- 55,9 -0,1 0,4 1,5 57,7 produced non- financial assets AN.211 (of 55,9 -0,1 0,4 1,5 57,7 which) Land AN. Total 307,9 323,7

Table 3. Non-financial balance sheets for S12 in 2012, billion euro STOCK FLOWS STOCK TRANSACTIONS OTHER FLOWS Opening Investments Consumption of Changes in Net acquisi- Other Revaluations Closing balance fixed capital inventories tions of asset changes in balance ASSET sheet volumes sheet Final report 11(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

AN.1 Pro- 1,1 0,3 0,4 0,1 1,1 duced non- financial assets AN.11 Fixed 1,1 0,3 0,4 0,1 1,1 assets AN.12-13 Inventories and valuables AN.2 Non- 0,1 0 0 0 0,1 produced non- financial assets AN.211 (of 0,1 0 0 0 0,1 which) Land AN. Total 1,2 1,2

Table 4. Non-financial balance sheets for S13 in 2012, billion euro STOCK FLOWS STOCK TRANSACTIONS OTHER FLOWS Opening Investments Consumption Changes in Net acquisi- Other Revaluations Closing balance of fixed capital inventories tions of asset changes in balance ASSET sheet volumes sheet AN.1 Produced 93 5 4 0,1 3,1 97,2 non-financial assets AN.11 Fixed 91 5 4 3 95 assets AN.12-13 Inven- 2 0,1 0,1 2,2 tories and valu- ables AN.2 Non- 52,9 0 0,2 1,4 54,5 produced non- financial assets AN.211 (of 52,9 0 0,2 1,4 54,5 which) Land AN. Total 145,9 151,7

Table 5. Non-financial balance sheets for S14 in 2012, billion euro STOCK FLOWS STOCK TRANSACTIONS OTHER FLOWS Opening Investments Consumption of Changes in Net acquisi- Other Revaluations Closing balance fixed capital inventories tions of asset changes in balance ASSET sheet volumes sheet AN.1 Pro- 251 13 9 0,4 10,6 266 duced non- financial assets AN.11 Fixed 211 13 9 10 225 assets AN.12-13 40 0,4 0,6 41 Inventories and valuables AN.2 Non- 124,5 0,1 0,7 5,9 131,2 produced non- financial assets AN.211 (of 124,5 0,1 0,7 5,9 131,2 which) Land AN. Total 375,5 397,2

Table 6. Non-financial balance sheets for S15 in 2012, billion euro STOCK FLOWS STOCK TRANSACTIONS OTHER FLOWS Opening Investments Consumption of Changes in Net acquisi- Other Revaluations Closing Final report 12(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

balance fixed capital inventories tions of asset changes in balance ASSET sheet volumes sheet AN.1 Pro- 12,2 0,5 0,6 0,6 12,7 duced non- financial assets AN.11 Fixed 11,8 0,5 0,6 0,6 12,3 assets AN.12-13 0,4 0,4 Inventories and valuables AN.2 Non- 5,3 0 -0,1 0,2 5,4 produced non- financial assets AN.211 (of 5,3 0 -0,1 0,2 5,4 which) Land AN. Total 17,5 18,1

PART II: Estimation of land value

1. Introduction

Joint task force of OECD and Eurostat has evaluated different methods for land valuation during 2012-2014. Finland has participated in drafting a compilation guide on land estimates within this task force, particularly for estimating the value of land using so-called direct method. This part of the report describes the basis on method choice in Finland, the direct method in general as it was described by the task force, and particular solutions made in Finland to apply this method. Test calculations for land value in Finland are presented as well.

The expertise and data of National Land Survey of Finland (NLS) is utilized. Statistics Finland and NLS have agreed upon co-operation in achieving best solutions to estimate the land value for national balance sheets pur- poses. NLS is responsible on producing estimates on the value of land, which are calculated by land use type, by institutional sector, by year and by location.

When looking at the numerical results, it should be noticed that they represent preliminary test calculations. In addition, the value of timber is included in the value of forestry land, and the value of land improvements reflect in the values of agricultural and forestry land in these test calculation. Statistics Finland is responsible for separa- tion of the value of these two assets from the combined land value produced by experts in National Land Survey of Finland. The methods used to do these separations are described in chapter 10 of this part.

2. Choice of the estimation method for land valuation in Finland

When assessing the current state of the capital stock from the balance sheets point of view, comparative calcula- tions were made to estimate the value of dwellings. These calculations can as well be used to analyse land esti- mates and factors impacting the value of land, when the residual method is applied. There are effective markets for dwellings in Finland, and therefore the regional market prices per square meter are available. When we also have information on the surface area for different type of dwellings, the combined Final report 13(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

value can easily be calculated. On the other hand, the National Land Survey has evaluated land underlying dwell- ings for the national wealth purposes. Applying this data, comparative calculations between the direct land estimate and the residual land estimate can be made. The results are shown in picture 1. Blue line denotes the combined value of dwelling real estates, red line the value of capital stock for dwellings in national accounts (PIM), and yellow line denotes value of land calculated as residual. Green line stands for value of land underlying dwellings estimated by the direct method.

500000 450000 Combined value for 400000 real estates with 350000 dwellings 300000 Capital stock for dwellings 250000

200000 Land underlying Millioneuros 150000 dwellings, residual method 100000 Land underlying 50000 dwellings, direct

0 method

1985 1987 1989 1995 1997 1999 2001 2003 2009 2011 1993 2005 2007 1991 Picture 1.

In picture 2 the diagrams show how the value of land under dwellings has developed in period 1985-2011 esti- mated by different methods.

250000

200000

Land underlying 150000 dwellings, residual method Land underlying 100000 dwellings, direct Millioneuros method

50000

0

Picture 2. Final report 14(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

The results illustrate that the pattern of land value by the direct method is significantly smoother and less volatile over time than the pattern got by using the residual method. In addition, the residual method seems to overesti- mate the value of land for most of the years during the period under review. These observations can be reasoned by the assumptions in perpetual inventory model (PIM): Firstly, the slope of the capital stock curve (picture 1, red line) is dependent on the chosen price index. In Finland the price index is based on the construction costs, and it cannot perfectly reflect market price changes in dwelling prices. Secondly, the slope of the capital stock curve is also dependent on the chosen depreciation profile. Currently linear depreciation model is applied in Finland. Test calculations indicate that geometric profile could be more realistic for dwellings. Thirdly, the value of the capital stock is strongly dependent on the chosen service life for dwellings. Picture 3 illustrates a consequence of changing service lives for dwellings from 50 years (current practice) to 60 years. Based on this analysis it was decided to change to service life for dwellings from 50 to 60 years in Finland. The service life of 60 years is also closer to service life applied for dwellings in other European countries. This can be seen as a first step in PIM development, possible change of depreciation profile and price index are left for later stage, when the land estimates by the direct method are finalised. Changing the capital stock assumptions reflects in the consumption of fixed capital (CFC), and should therefore be made with careful consideration.

180000 160000 140000 120000 Land underlying 100000 dwellings, residual method 80000 Land underlying Millioneuros 60000 dwellings, direct 40000 method 20000

0

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 1985 Picture 3. Land value by the direct method and by the residual method with 60 years’ service life for dwellings

A possible reason for the difference in the land values between the two methods (picture 3) could be the increase in value of existing dwellings near the city centres, which typically exceeds the increase rate of construction costs especially during the economic booms. Final report 15(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

It can be concluded that the value of land by residual method is strongly dependent on the assumptions of PIM such as service life, depreciation profile and price index. As long as these assumptions can vary from country to another, comparing their land wealth may be very misleading. In addition, using the residual approach in land valuing leads the value of land to reflect the changes in real es- tate prices very strongly, although the most of the change in value should be allocated to dwellings in many cases instead of the underlying land. For example, during low interest rates investors start to seek for better still quite low risk yields from rented apartments. This increases the demand and prices for dwellings, despite they stand on rented land or not. In other words, in this case the most of the increase in value is towards the dwelling, not to- wards the underlying land. Based on this analysis, it was decided to apply the direct method to value the land in Finland

3. Estimating the value of land by using the direct approach

Generally, the direct approach can be described by

(1) , where is the total value of land in the observed year t. reflects the price for land type in the observed year t and the corresponding area measure. Summing up all land types yields the total value of land for that par- ticular year. Since the value of land is highly dependent on the location and land use, it is recommended that this calculation is done at the lower regional level by each land type. In the actual implementation, the direct ap- proach can be described by the following procedure with which the countries can conduct adjustments in a few steps, if needed.

a) Estimation of land area by land types in a single year or over a couple of years b) Estimation of changes in the land types annually to produce time series c) Estimation of representative unit prices for each relevant land type for a single year or a couple of years d) Modelling the price changes for each land type over time (specifying price indices) in order to produce unit price time series e) Bringing together the area and price information to produce time series on land value (balance sheet information) f) Specifying volume changes and price changes per year for the Other changes in the volume of assets account and the Revaluation account

4. Estimation of land area by land types

4.1 In general

Measuring the area of a country constitutes the basis for an economic valuation of land. Typically, this informa- tion is provided in square kilometres or any other surface measure. The process of estimating the area of land can generally be described in 3 steps: Step 1 consists of the registration of the total territory of a country to ensure the area of interest. In a second step, the economic territory of a country has to be determined according to the SNA definition of asset (see SNA 2008, § 1.46). In the third and final step, a differentiation of the economic territory according to the classification used in the balance sheets has to be established on the basis of land use statistics. Final report 16(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Countries might have a more detailed classification of land, which they can use in their estimations. To illustrate the general procedure of direct estimating, the proposed classification should be used as a minimal classification. Following these three steps countries shall be able to gather detailed information on surface area measures for one year or over a couple of years regarding the areas of interest. Table 1 provides an example of the classifica- tion of land and the corresponding areas for the different land use types:

Table 1. Estimated area data in km² by land types and years (an example)

Year Land underlying buildings and structures Land under cultivation Recreational land Other land Total Land underly- Land underlying other Agricultural Forestry Surface water ing dwellings buildings and structures land land used for aquaculture 2007 21,000 30,000 178,000 110,000 800 2,500 700 343,000 2008 22,000 31,000 177,000 109,000 900 2,500 600 343,000 2009 22,000 32,000 177,000 107,000 900 3,500 600 343,000 2010 23,000 32,000 176,000 107,000 1000 3,500 500 343,000 2011 24,000 33,000 174,000 106,000 1200 4,300 500 343,000 2012 24,000 34,000 174,000 105,000 1200 4,400 400 343,000

4.2 Practice in Finland The NLS has carried out a project, which aimed at developing an applicable sub-classification of land and data collection system in Finland. As a result, the Finnish classification of land use for statistical purposes was out- lined. The classification of land use is divided into sub-classes denoted by capitals, which are further sub-classified into first and second level sub-classes denoted by numbers (the numbers in parentheses represent the number of first/second level sub-classes).

A. Land under dwellings (3/8) B. Land for commercial, administr. and industrial purposes (2/2) C. Land for auxiliary activities (2/9) D. Land for abstraction of rock and soil (1/2) E. Agricultural land (2/5) F. Silvicultural land (2/2) G. Other land (2/0) H. Inland waters (2/0) I Special land areas (2/0) Some of the second-level classes are further divided into subgroups. The total number of subclasses in this classi- fication is nearly 50. The statistics of land use is composed utilising existing source data as broadly as possible. Several national geo- graphical information systems were usable source data in creating the land use statistics. Most of this source data consists of databases and maps occupied by the NLS. In addition, data from several national administrative sources were used. Final report 17(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

The land use statistics is updated every few years. Currently available statistics describe the land use situation approximately in 2010. Although the statistics describe the land use distribution a few years ago, the yearly changes in land use are so minor that this statistics can well enough be used as a basis for valuation of land. Classifying different land use areas is based on SLICES-procedure. The Finnish land use statistics (SLICES) consists of 10 x 10 meter pixels, which are summed up by land uses on municipality level. The total is equivalent to the surface area of Finland. The basic concepts in the estimation process are soil, land cover and land use (Picture 4). The estimation process is illustrated in detail in Picture 5.

(Land Use-) Restrictions

Soil

Land Cover

Land Use

Reality

Picture 4. Basic concepts in estimating land uses in the SLICES-system (source: Mikkola 2003)

Final report 18(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

The SLICES Project 1997-98 LCS -databases (Land use, Land cover, Soil, (LU-) restrictions)

Electric wire Digital Water database GIS- Railroad database data data- base Road database Data from Dump site Digitizing FLPIS high density register populated areas Mine Building database register

Digital NFI Nature soil conservation data database

Preprosessing and Preprosessing and Preprosessing and Preprosessing and classification classification classification classification

Classified data: First phase

Areas under Land Use Soil Land Cover restricted use

Merging and Merging and Merging and Merging and raster-generalization raster-generalization raster-generalization raster-generalization

Areas under Land Use Soil Land Cover restricted use Final databases

Other digital data Merging Customer's own data Raster-Vector conversations

Customer-dependent combinations CORINE LC

'Standard' Analyses combination

Results

Picture 5. LCS -databases (land use, land cover, soil) in the SLICES-system (source: Mikkola 2003)

Final report 19(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Land uses are derived from a vector-based land information system (the original data being points, curves and areas; the SLICES-system). Most important of those datasets are: a. the topographic database b. arable land parcel register c. building and apartment register d. Raster-based satellite images were also used to identify different classes of forest land.

There are some limitations in the methodology used: a. unbuilt lots could not be identified, so they where usually classified as arable or forest land b. parks and recreation areas could not be identified, so there where usually classified as arable or forest land c. large areas around factory buildings or public buildings where difficult to classify.

The real estate boundaries where included in the methodology. The land use boundary was forced to follow the real estate boundary, if they were close to each other. However, the maximum size of a rural housing site was restricted to 5000 m2.

Land use is divided into 42 categories in the SLICES procedure. In addition to this, there are 5 classes of water. The classification is based on the known usage of the buildings that are located on land, and real estate bounda- ries. In cases where there are no buildings, satellite images were used.

Those 42 basic land use categories are combined to produce 13 different subcategories and four main categories. These categories and their areas are shown in table 2. The total land area in table 2 is slightly less than the actual total land area of Finland. The error is insignificant, since effort was made to calculate the area of the most valu- able land classes as accurately as possible.

Table 2. Land areas in different land uses in 2005 and 2012 (km-2) Main class Subclass Explanation area (km-2) 2005 2012 Housing lot AK blocks of flats 126 129 AP single family 1116 1181 AR row or detached 131 136 A, rural areas 1133 1219 R rural second home 1298 1330

Commercial and K commercial, office industrial lots 67 79 T industrial 309 338 Y public 108 112

E technical services 2244 2244 Transport, parks, L transport technical services 5916 5916 V parks 36 36

low productivity forest low productivity forest 52986 52986 Arable and forest forest forest land 207616 207416 arable arable 25978 25942

Total 299064 299064 Final report 20(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Areas by land use and region are given in table 3. Detailed land use, land cover and soil classifications of SLICES are shown in appendices I, II and III.

Table 3. Areas in different land uses, different regions, year 2012 (km-2)

commercial and housing lots industrial lots

A, AK AP AR R K T Y rural

Total 129 1181 136 1413 1145 70 312 112

City of Helsinki 21 18 7 1 0 4 6 7 Other Helsinki region 18 124 18 43 96 8 20 12 Five other largest cities with regions 24 150 24 123 132 9 48 15 Other regions where population increases 27 288 29 315 273 17 79 26 Regions with more than 30.000 people where population decreases 16 208 20 263 196 11 59 17 Regions with less than 30.000 people where population decreases 23 393 38 668 448 21 100 35

Transport, parks, Arable and forest land technical services low total produc- E L V forest arable tive forest Total 2244 5916 36 52986 207616 25978 299064

City of Helsinki 2 32 2 13 67 9 188 Other Helsinki region 40 210 3 146 2122 924 3784 Five other largest cities with regions 111 443 4 695 8810 2168 12757 Other regions where population increases 354 1197 8 4627 33035 6712 46989 Regions with more than 30.000 people where population decreases 388 1064 6 2383 30048 4258 38938 Regions with less than 30.000 people where population decreases 1348 2970 13 45121 133534 11907 196618

CORINE is a European standard for land cover, and it will replace SLICES in Finland, too. However, CORINE is a cruder method and in that sense inferior. So far CORINE has not been used in land valuation in Finland.

Final report 21(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

5. Estimation of changes in the land type areas

5.1 In general

Area data are typically not published on an annual basis, and therefore it might be difficult to produce representa- tive time series illustrating the area changes (per year) between the different land types. If these data are not available on an annual basis but are provided for different time-cycle (e.g. 5-year cycle) the following example shall illustrate how these changes should be conducted:

Table 4. Estimated changes in area in km² by land type and years

Land type 2008 Changes (2008-2010) 2010 Changes (2010-2012) 2012 Land underlying dwellings 22,000 1,000 23,000 1,000 24,000 Land underlying other buildings and structures 31,000 1,000 32,000 2,000 34,000 Agricultural land 177,000 -1,000 176,000 -2,000 174,000 Forestry land 109,000 -2,000 107,000 -2,000 105,000 Surface water 900 100 1,000 200 1,200 Recreational land 2,500 1,000 3,500 900 4,400 Other land 600 -100 500 -100 400 Total 343,000 0 343,000 0 343,000

Table 4 shows data by land types for the years 2008, 2010, 2012. Columns 3 and 5 provide information on how area data have changed between the observation points subdivided by land types1. To produce representative time series a simple (linear) interpolation approach can be conducted here.

For many productivity analyses, the volume of land is usually assumed to be constant across years. In the SNA, however, differences in quality are, generally, treated as differences in volume. In other words, the change in value of stock of land due to changes in its economic use should be regarded as the appearance of additional amounts of the land and as changes in volume of land. As large changes in value of land are due to reclassifica- tion from agricultural land and forestry land into building sites, the result of reclassification should be measured as changes in volume.

If countries are able to identify a change of quality in one land type category, these changes have to be treated as volume changes as well. (E.g. a dry and infertile plot of agricultural land changed due to (land) improvements in quality but is still used as agricultural land. Another example might be parkland that was converted into building land and a factory was built on that plot of land. Land underlying dwellings located right next to the former park- land decreases in quality due to the newly built factory. Consequently, a differentiation between volume and price changes requires data on changes in area between several types of land.

It might be quite difficult to assign these changes to the different land types2. For the numerical example pre- sented here, the total area of land is held constant across time.

1 Ideally area changes between different land types should sum up to zero but this might not always be the case, since areas might be demolished by some sort of disaster and not cap- tured in the asset category anymore (which leads to negative numbers here) or new areas have entered the asset boundary and have to be valued now which leads to positive num- bers. For the purposes here these factors are held constant across time. 2 For instance, it might be very difficult to assign certain gains of land underlying dwellings or land underlying other buildings and structures to losses of, for instance, agricultural area or forestry area. Final report 22(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

5.2 Practice in Finland

Land use time series was constructed using land use data (SLICES) (for years 2000, 2005, 2010) and land sales data for period 1985-2012. As an ad hoc method it was assumed, that a sale of a lot implies a change in land use, by a certain multiplier. Although planning and building are certainly stronger indicators of land use, for our pur- poses a sale was good enough, as sale often occurs between planning and building.

Several rules of thumb very applied: a. a lot sale indicates a change in land use from forest land to lot land, the size of which is the size of lot sold. b. on residential sales, even if the same lot is sold several times, the change in land use is assumed to hap- pen every time the lot is sold. c. a land use where a lot sale is not involved is neglected. d. lot leases are neglected as an indicator of change in land use. e. planning and building are neglected as an indicator of change in land use.

These operational rules were followed:

A lot sale indicates a land use change with these restrictions in the maximum size of the lot: a. the lot size counts as a land use change up to 5000 m2 at maximum. b. In rural areas, the lot size counts as a land use change up to 2000 m2 at maximum, as in rural areas the lot is almost always for one family only. c. In rural areas only residential uses are counted.

These multipliers were used: a. the sum of sold residential lot sizes as such b. half of the sum of sold industrial, commercial and office lot sizes. This is because there are plenty of transactions in this category, very often involving the sale of the same lot several times.

The analysis of sold lots yields following time series (table 5):

Table 5. Change on lot area 1985-2012 (km-2) commercial and housing lots industrial lots A, AK AP AR R K T Y rural year 1985 113 950 108 982 1074 52 249 96 1990 116 996 118 1023 1152 54 262 100 1995 119 1021 121 1048 1214 56 273 103 2000 123 1063 126 1071 1276 60 287 106 2005 126 1116 131 1103 1341 64 297 108 2010 128 1165 135 1135 1398 69 308 111 2012 129 1181 136 1145 1413 70 312 112

change % 14 24 26 17 32 35 25 17 annual change% 0,5 0,8 0,8 0,5 1,0 1,1 0,8 0,6

Final report 23(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

The increase in lot area was correspondingly decreased in rural areas. In addition, because land rises in Finland as an after-effect of the ice age, the total land area of Finland increases slightly every year. This insignificant change is, however, ignored in this land valuation process.

In addition to lots, the area of all other urban land increases over time. This change between the observation years was ignored, because the error made is relatively insignificant taking into account the general difficulty of measurement of unit price and area of non-lot urban land (parks, roads etc). As a further development task, data on planning and building available for land estimates will be analysed.

6. Estimation of representative unit prices for each relevant land type

6.1 In general

The direct estimation of the value of land requires price information and data on surface areas. The price should reflect the actual market transaction price or its equivalents, at least as required in SNA 2008 §13.44. The actual market transaction price, if available, is the most preferred. If that price is not available, other sources may be used, such as: publicly-appraised market-price equivalent, property tax information converted to a market price, an adjacent land’s market price of similar use, generalized standard land values, an artificial price with an adja- cent market price multiplied by a certain conversion factor, etc.

Table 6 provides a simple example of how such price information differentiated by the various classes can look like:

Table 6. Estimated price data by years and land types in €/m²3 Year Land underlying buildings and structures Land under cultivation Recreational land Other land Land underlying other Surface water Land underlying buildings and struc- Agricultural Forestry used for aquacul- dwellings tures land land ture 2007 120.00 15.00 5.00 2.00 1.00 3.00 0.50 2008 115.00 13.00 4.50 2.00 1.00 4.00 0.40 2009 115.00 13.00 4.50 1.50 1.50 3.50 0.50 2010 120.00 14.00 4.00 1.50 1.00 4.00 0.40 2011 120.00 14.00 4.00 1.00 1.50 3.50 0.40 2012 125.00 15.00 3.50 1.00 1.00 4.00 0.50

Expert knowledge has shown that many issues may arise regarding adequate price information. For instance, price data can be quite old or even missing for some land types or years, since less frequent transactions of land may lead to data gaps. Furthermore, price information can be provided by different sources and it is necessary to match these different data sources to obtain reliable price data. In addition, regional aspects have to be taken into account when estimating prices (e.g. by using stratification) since different areas of the same land use type might have significantly different price values.

3 This price represents the price valid on balance sheet date since intra-annual price data might be difficult to obtain for some countries. Moreover the here proposed price might also be interpreted as the average price across the year. Final report 24(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

It can be concluded, that collecting reliable price information for the estimation of land can be very difficult. Depending on the sources and institutional circumstances in the different countries, arising issues may differ significantly between the countries. How to handle these issues depends on each country’s expertise, abilities, and data sources regarding these types of information. Nevertheless the representativeness of the price used for calculations here has to be guaranteed.

6.2 Practice in Finland

The unit price for each land use type from region to region is derived from the statistics of real estate transac- tions. The classification of land uses in the transaction statistics is based on land planning and slightly differs from the classification of the SLICES. In order to match the two statistics, some adaptations is made to the land use classification of the transaction statistics.

In the land valuation process, the land area is divided into 13 different land use categories which can be aggre- gated from the SLICES -data. Unit prices by land uses are calculated using all real estate transactions in period 1985-2012 to ensure satisfactory amount of transactions in every land use. There are 1,6 millions sales of unbuilt land areas during that period.

In principle, there are Ny * Nu *Nc =130 000 unit prices for the period 1085-2012, where

Nc = number of municipalities Nu = number of land uses Ny = number of years.

In practice, however, it’s meaningful to combine some land uses and plenty of municipalities with similar price development patterns. There are 16 different price indices specified by National Land Survey (see chapter 7.2) to manage the price movements throughout the country, which leads to significantly smaller amount of unit prices needed in the land estimation process.

The unit prices for land uses for one particular year were calculated using all the 1,6 million sales of land made in period 1985-2012 and multiplying them by relevant price index to make them comparable in one year.

Although the number of land sales is large, there are still problems in interpreting them as an indicator of land value for the whole land use category:

a. Lot sales usually occur in the fringe of a city or town, where new building occurs. In the already built-up central parts of towns the sales of lots are more scarce. As a result the mean prices may be downward bi- ased.

b. The road and other transport areas are large (much larger than lot areas). Those areas are seldom ac- quired by sale, but by expropriation. A small number of sales are made, however, of usually quite small but valuable pieces of land. These sales may be highly upward biased as an indicator of land value.

c. There are no markets for parks and roads in the same sense that there are markets for land for housing, farming or industry. Parks and roads serve the land that is using them, and their existence is reflected in the value of the land that is using them. Perhaps parks and roads do not have any independent value at all, only the increase in the value of surrounding lots. The purchase price on parks and roads, if there is any, is not a valid indicator of their land value.

d. Public building lots are of multiple of purposes and of a large range of value. Some of them are valuable offices, some are for education and social services. Public building lots are sometimes in central loca- Final report 25(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

tions and have often been built a long time ago. There are no “efficient markets” for public buildings, since they are usually build on sites that the government already owns. However, there are quite a lot of sales of public building sites, the purpose of which is not to provide public services, but to change the use of unneeded public building sites to other uses, most often housing. These sales are not very repre- sentative as an indicator of public building land value.

Because of the identified problems, the data on real estate sales had to be filtered and some manipulation made on some unit prices by National Land Survey experts in order to produce as representative results as possible.

Only sales of unbuilt land were accepted. Of forest land sales, a minimum of 90 % forest land share of the total area of sale was required. Of arable land sales, a minimum of 90 % arable land share of the total area of sale was required.

A large part of forest land in the very north of Finland is of a very low productivity. The value of this land was given a low artificial figure. The same applies to the value of transport areas, it was given a low artificial figure.

In order to improve the quality of land estimates, the City of Helsinki was examined separately transaction by transaction to eliminate outliers. District unit prices were applied for Helsinki instead of municipal unit prices. Certainly, some more ad hoc manipulation could have been done in some cases to improve the results.

Some examples of typical unit prices in different land uses and regions, year 2012 (euros/m2) are given in table 7. Detailed unit prices by land use and by location are shown in appendix V.

Table 7. Examples of typical and maximum unit prices in different land uses and regions, year 2012 (euros/m2) Commercial and Transport, parks, Arable and forest Housing lots industrial lots technical services land Median Max Median Max Median Max Median Max All 9 1815 28 3066 1,1 367 0,4 2,2

City of Helsinki 457 1815 1145 3066 5,1 77 1,3 1,6 Other Helsinki region 81 766 94 1273 5,1 38 1,3 1,6 Five other largest cities with regions 17 479 48 618 1,9 50 0,7 1,4 Other regions where popula- tion increases 14 463 42 888 1,4 53 0,5 1,5 Regions with more than 30.000 people where popula- tion decreases 7 162 27 1495 1,0 44 0,4 1,2 Regions with less than 30.000 people where population decreases 7 173 20 856 0,9 367 0,3 2,2

Final report 26(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

7. Modelling the price changes for each land type over time

7.1 In general

Availability of price data might differ significantly between the countries and thus general advice is very difficult to give. Depending on each countries data modelling the price changes for each land type over time (specifying price indices) in order to produce unit price time series remain in the hands of the different countries. Apart from this free choice of methodology countries have to bear in mind that the applied method should still meet the claim of representativeness concerning price information in this context.

7.2 Practice in Finland

There were 16 price indices used in the calculation. With these indices the price movements are managed all over the country in all land uses. If a separate price index were calculated to every community and main land use, 2300 different indices were needed. This is unnecessary, of course. The price movements are quite homoge- nous in most of the country, and between some different land uses, too. This was examined and verified with statistical tests.

In principle, only one sale in 28 years is needed to produce the land value for every year in 1985-2012 for a cer- tain land use. As the number of sales is far above that, the accuracy of the calculation does not depend on the circumstances of any particular sale.

The most important price indices from the land valuation point of view, the indices of housing lots, are published annually by National Land Survey of Finland. The specification of housing lot price indices can briefly be de- scribed as follows:

Realized sales prices are changed to meet the price of quality-standardized lot. In other words, the sales prices are quality-standardized. Multipliers for quality-standardization are calculated on the basis of transactions in years 2000-2009. Price indices are based on the average transaction prices of quality-standardized lots.

In the estimation of quality function, following variables are used separately for Helsinki metropolitan area and for the rest of the country: surface area for the lots in city plan area, surface area for the lots outside city plan area, lot ratio, detailed plan, seller (municipality or private), distance from Helsinki, distance from large town centre, distance from medium size town centre, distance from village centre, and waterside. In addition, munici- pal-specific constants are included in the function. Furthermore, outliers are filtered by a certain set of assump- tions.

The price indices for all other land uses than housing lots were calculated for the first time for this balance sheet purpose with a statistical model, where the index numbers are the dummy variables of the model. Other variables in these models are geographical subdivision, and as continuous variables different access variables and, in some models, lot size and lot ratio. Estimated nominal indices are shown in table 8 and real indices in table 9.

Final report 27(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Table 8. Nominal prices indices 1985-2012

with decreasing population decreasing with

ions with increasing population increasing with ions

population decreasing with Regions population increasing with Regions region Helsinki Regions population increasing with Regions population decreasing with Regions population increasing with Regions region Helsinki population decreasing with Regions population increasing with Regions population decreasing with Regions Reg population decreasing with Regions population increasing with Regions

rural second home commercial industrial transport, public forest arable housing lots lots and office lots lots parks buildings land land 1985 49 48 34 16 13 34 26 56 34 39 47 28 67 53 48 32 1986 37 17 14 36 27 48 37 39 47 30 70 55 47 32 51 49 1987 40 19 16 41 31 55 39 47 54 33 69 63 46 33 53 52 1988 47 25 26 51 38 56 47 62 55 37 80 53 42 36 57 55 1989 52 28 31 57 42 70 49 82 63 43 88 59 41 40 60 60 1990 55 33 33 63 44 69 63 106 63 50 87 62 53 51 62 60 1991 53 34 31 57 43 65 66 132 70 57 88 64 51 60 57 55 1992 51 31 26 47 36 59 76 110 70 61 72 58 44 70 50 46 1993 44 26 20 43 32 49 67 66 65 55 70 48 38 70 47 40 1994 46 26 20 41 30 43 66 46 57 47 64 46 48 64 47 38 1995 46 26 20 39 32 39 58 45 57 40 65 42 70 55 48 37 1996 47 26 20 37 30 38 49 50 62 40 66 39 65 51 49 37 1997 49 28 22 42 38 43 50 55 67 43 61 40 57 54 49 38 1998 53 30 26 43 36 55 44 52 68 46 65 40 39 58 52 40 1999 53 35 30 47 40 66 47 57 71 49 64 45 44 53 54 45 2000 57 39 35 50 43 64 48 54 71 53 73 52 41 45 56 48 2001 58 40 38 52 46 57 49 59 66 54 74 59 53 48 58 51 2002 61 43 42 57 51 54 50 64 68 55 79 67 55 50 60 53 2003 69 47 43 61 57 66 50 64 71 56 75 68 65 54 62 57 2004 72 55 59 68 62 79 61 68 74 61 69 76 54 52 65 62 2005 74 60 68 73 68 90 71 74 74 66 72 74 53 58 68 68 2006 77 71 79 79 75 94 78 80 79 72 84 77 53 63 74 74 2007 85 76 85 91 88 96 81 93 89 76 105 78 64 61 81 80 2008 91 79 86 92 83 94 85 87 98 81 103 90 73 70 84 83 2009 87 81 81 83 81 100 93 93 104 85 106 103 92 81 87 89 2010 97 92 92 97 91 94 94 99 106 89 104 110 107 94 90 94 2011 101 95 94 96 98 104 100 101 102 96 107 103 111 97 97 99 2012 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 change % 190 524 671 194 288 79 190 159 114 255 48 89 110 216 103 108 Final report 28(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

annual change% 4,0 7,0 7,9 4,1 5,1 2,2 4,0 3,6 2,9 4,8 1,5 2,4 2,8 4,4 2,7 2,7

Final report 29(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Table 9. Real prices indices 1985-2012

creasing population creasing

Regions with decreasing population decreasing with Regions population increasing with Regions region Helsinki population decreasing with Regions population increasing with Regions population decreasing with Regions in with Regions region Helsinki population decreasing with Regions population increasing with Regions population decreasing with Regions population increasing with Regions population decreasing with Regions population increasing with Regions

rural second home commercial industrial transport, public forest arable

housing lots lots and office lots lots parks buildings land land

1985 66 30 25 65 49 103 63 71 89 47 132 109 80 42 89 89 1986 68 32 26 66 50 87 67 71 87 49 135 111 78 42 90 89 1987 72 34 29 72 55 95 67 82 96 53 128 120 72 43 91 91 1988 79 41 43 86 63 93 77 102 94 57 138 96 62 47 92 92 1989 82 44 49 91 67 110 76 127 100 64 143 99 58 52 93 94 1990 82 49 50 95 66 103 94 157 95 71 134 98 74 66 90 89 1991 75 49 44 81 62 93 93 188 101 78 129 96 67 77 81 78 1992 71 43 36 66 51 81 104 153 98 82 102 85 56 90 69 63 1993 61 35 27 59 43 67 91 90 90 72 99 69 47 88 62 54 1994 63 35 27 56 41 57 88 61 76 60 89 66 61 79 62 50 1995 62 35 27 52 42 52 77 59 77 51 89 59 90 67 63 49 1996 63 35 27 49 39 50 65 66 82 50 89 54 81 61 64 48 1997 65 36 29 56 49 55 65 71 88 53 82 55 71 64 63 49 1998 69 39 33 55 47 71 56 67 88 57 86 55 47 69 65 51 1999 68 44 39 60 51 84 59 72 90 60 82 59 52 61 67 56 2000 70 48 43 62 53 79 58 67 88 64 91 67 47 50 68 59 2001 70 48 46 63 55 68 59 70 80 64 91 72 62 54 69 61 2002 72 52 50 68 60 64 59 75 81 63 94 81 62 55 69 62 2003 81 55 51 73 67 77 58 75 83 64 89 81 74 60 72 66 2004 84 64 69 80 73 91 71 79 86 69 81 90 61 57 75 72 2005 86 70 79 85 79 104 82 86 86 75 84 86 59 64 78 78 2006 89 82 91 91 87 106 87 90 89 80 96 89 58 68 83 83 2007 95 86 96 102 99 106 89 102 98 83 117 86 69 64 89 88 2008 98 85 93 99 89 102 92 94 106 87 112 98 78 74 90 90 2009 94 87 88 90 88 106 99 99 111 90 113 110 97 85 92 95 2010 103 98 98 103 97 99 98 103 111 93 109 115 112 98 94 98 2011 104 98 97 99 101 106 102 102 104 98 109 105 113 99 99 101 2012 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 change % 53 228 305 55 104 -3 58 41 13 113 -24 -8 26 135 12 12 annual change% 1,6 4,5 5,3 1,6 2,7 -0,1 1,7 1,3 0,4 2,8 -1,0 -0,3 0,8 3,2 0,4 0,4

Final report 30(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

Unit prices as of 2012 are documented in appendix V. The unit prices for the 28 preceding years are calculated by multiplying the unit price for one year by the index. Examples in the country level are given in table 10.

Table 10. Examples of typical and maximum unit prices in different land uses (euro/m2) Transport, Commercial and Arable and forest Housing lots parks, technical industrial lots land services Median Max Median Max Median Max Median Max vuosi 1985 3 499 9 1182 0,7 247 0,2 1,0 1990 5 635 15 3247 0,9 321 0,3 1,3 1995 4 707 13 1372 0,6 239 0,2 0,8 2000 5 764 13 1670 0,7 266 0,2 1,0 2005 7 1234 17 2284 0,8 264 0,3 1,5 2008 8 1561 21 2665 1,2 379 0,4 1,8 2010 9 1679 25 3026 1,2 382 0,4 2,0 2012 9 1815 24 3066 1,1 367 0,5 2,2 Final report 31(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

8. Calculation of land value

8.1 In general

As mentioned before, estimating the total value of land follows the idea of matching information about different land types and the corresponding prices. To determine the sub-values by classes and, subsequently, the total value of land, a simple multiplication and summation is used. The first step consists of multiplying the area size with the appropriate price for each type of land in the observed year. For example, the total value for land under- lying dwellings of the year 2009 is 22,000km² x 115.00€/m² = 2,530billion €. This procedure is conducted across all land use types. Secondly, the resulting values of all land use types are summed up to determine the total value of land. These steps are repeated for each year to establish a time series. The results for this example are presented in table 11.

Table 11. Estimation value ( ) of land across time (for each land type and total) in billion € Year Land underlying buildings and structures Land under cultivation Recreational land Other land Total For- Surface water Land underly- Land underlying other Agricultural estry used for aquacul- ing dwellings buildings and structures land land ture 2007 2,520.00 450.00 890.00 220.00 0.80 7.50 0.35 4,088.65 2008 2,530.00 403.00 796.50 218.00 0.90 10.00 0.24 3,958.64 2009 2,530.00 416.00 796.50 160.50 1.35 12.25 0.30 3,916.90 2010 2,760.00 448.00 704.00 160.50 1.00 14.00 0.20 4,087.70 2011 2,880.00 462.00 696.00 106.00 1.80 15.05 0.20 4,161.05 2012 3,000.00 510.00 609.00 105.00 1.20 17.60 0.20 4,243.00

8.2 Practice in Finland

Multiplying area and unit price yields the land value for Finland (table 12):

Table 12. Value of land by land use, years 2005 and 2012 (billion euro) value Main class Subclass Explanation (billion euro) 2005 2012 Housing lot AK blocks of flats 41 63 AP single family 28 45 AR row or detached 7 10 A, rural areas 9 11 R rural second home 10 14

Commercial and indus- K commercial, office trial lots 17 25 T industrial 14 23 Y public 9 16

Transport, parks, tech- E technical services 1 1 nical services L transport 6 8 Final report 32(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

V parks 0 1

low productivity forest low productivity forest 2 2 Arable and forest land forest forest 56 83 arable arable 15 22

Total 215 324

Land value time series by aggregate land uses and by location are given in tables 13 and 14.

Table 13. Land value time series by use, years 1985-2012 (billion euro) Transport, Arable Commercial Housing parks, and and indus- Total lots technical forest trial lots services land 1985 23 18 6 53 99 1990 49 34 7 66 157 1995 37 26 5 49 117 2000 56 29 6 59 149 2005 94 39 7 73 214 2008 120 48 10 90 268 2010 132 57 11 98 297 2012 144 60 10 107 321

Land value time series in different regions is given in table 14:

Table 14. Land value by region, years 1985-2012 (billion euro) City of Other Five Other re- Regions Regions Helsinki Helsinki other gions where with more with less region largest population than 30.000 than 30.000 Total cities increases people people with where popu- where popu- regions lation de- lation de- creases creases year 1985 99 14 8 13 18 13 35 1990 157 32 17 16 26 17 49 1995 117 20 12 14 21 13 37 2000 149 29 18 17 26 16 45 2005 214 49 30 24 35 20 56 2008 268 61 38 31 44 24 70 2010 297 68 41 35 50 27 76 2012 321 73 45 39 55 28 81

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9. Specifying volume changes and price changes per year

9.1 In general

It is necessary to decompose the differences in the value of land per year into differences in volumes and differ- ences in prices for the Other changes in the volume of assets account and the Revaluation account. In case of direct land valuation this decomposition can be conducted as described in the following paragraphs.

Depending on data availability in the particular country the value of change of land can be decomposed into Holding gains/ losses and volume changes in two different ways. In general Holding gains/losses are estimated by deducting from the total change in the value of assets those that can be attributed to transactions and to other changes in volumes. If information on the price developments of land is available, it might be possible to esti- mate the holding gains and losses autonomously and derive one of the other flow components as a residual. However, both principles lead to the same results for volume changes and the corresponding Holding gains/losses and vice versa.

Both principles have in common that in the first step the value change of land (per land type i) for period t+1 can be estimated by

(2) where reflects the value changes of land (per land type i) in the next observation period.

If information on the price developments of land is available Holding gains/losses (per land type) can be esti- mated by

(3) and the corresponding , volume changes (per land type i) can be deduced by

(4)

If information on the price developments of land is not available Holding gains/ losses can be deduced as the residual of the total change in the value of land and the corresponding volume changes. Therefore, in the first step the volume change can be estimated by

(5) and the corresponding Holding gains/losses can be deduced by

(6)

To illustrate the procedure of separating annual price-changes and the annual volume-changes, area data, price data and the corresponding value of land are needed.

Table 15. Estimation value changes of land ( ) across time (for each land type i and total) in billion € Final report 34(54)

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Year Land underlying buildings and structures Land under cultivation Recreational land Other land Total Surface water Land underly- Land underlying other Agricultural Forestry used for ing dwellings buildings and structures land land aquaculture 2008 10.00 -47.00 -93.50 -2.00 0.10 2.50 -0.11 -130.01 2009 0.00 13.00 0.00 -57.50 0.45 2.25 0.06 -41.74 2010 230.00 32.00 -92.50 0.00 -0.35 1.75 -0.10 170.80 2011 120.00 14.00 -8.00 -54.50 0.80 1.05 0.00 73.35 2012 120.00 48.00 -87.00 -1.00 -0.60 2.55 0.00 81.95

As mentioned before, the annual value changes presented in table 15 can be separated into holding gains/losses and volume changes. It is assumed that information on the price developments of land is available and, therefore, Holding gains/losses can be estimated autonomously.

For the numerical example the total holding gains/losses and annual holding gains/losses for each land type are estimated and presented in Table 16:

Table 16. Estimated holding gains/losses of land ( ) across time in billion € Years Recreational Other land Total Land underlying buildings and structures Land under cultivation land Land un- derlying Land underlying other Agricultural Forestry Surface water used for dwellings buildings and structures land land aquaculture 2008 -105.00 -60.00 -89.00 0.00 0.00 2.50 -0.07 -251.57 2009 0.00 0.00 0.00 -54.50 0.45 -1.25 0.06 -55.24 2010 110.00 32.00 -88.50 0.00 -0.45 1.75 -0.06 54.74 2011 0.00 0.00 0.00 -53.50 0.50 -1.75 0.00 -54.75 2012 120.00 33.00 -87.00 0.00 -0.60 2.15 0.05 67.60

Correspondingly, the estimated total volume changes and annual volume changes for each land type were esti- mated and are presented in Table 17:

Table 17. Estimated volume changes of land ( ) across time (for each land type and total) in billion € Years Recreational land Other land Total Land underlying buildings and structures Land under cultivation Land un- For- derlying Land underlying other Agricul- estry Surface water used dwellings buildings and structures tural land land for aquaculture 2008 115.00 13.00 -4.50 -2.00 0.10 0.00 -0.04 121.56 2009 0.00 13.00 0.00 -3.00 0.00 3.50 0.00 13.50 2010 120.00 0.00 -4.00 0.00 0.10 0.00 -0.04 116.06 2011 120.00 14.00 -8.00 -1.00 0.30 2.80 0.00 128.10 2012 0.00 15.00 0.00 -1.00 0.00 0.40 -0.05 14.35

9.2 Practice in Finland

Dividing annual changes in land values into volume changes and revaluations can be done applying the methodology described earlier in chapter 9.1. Value change due to transactions between institutional sectors and change in land use areas are deducted from the total change in value. The residual was interpreted as holding Final report 35(54)

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gains/losses. The results for Finland at aggregate level of AN.211 Land are shown in tables 2-5 in part I

10. Separation of timber value and land improvements from the relevant land values

10.1 Forestry land The value of land is estimated by the direct method as a rule in Finland. However, using data on real estate trans- actions to value forestry land leads to significant overestimation for the value of land, because transactions of forestry land always include the value of standing timber. As a matter of fact, vast majority of the value derived from those transactions should be allocated to timber (inventories) instead of the underlying land. How to record separately the value of forestry land and the value of standing timber? Can the value of underlying land be estimated by deducting the value of timber from the combined value? Or would it be possible to use a direct method to estimate the value of underlying land. The long history of Finnish forest research gives us tools to make comparative calculations by using different methods.

The residual method for forestry land Residual method can be applied for the cultivated land as well: if the produced asset standing on the land can be evaluated and subtracted from the combined value at market prices, the value of cultivated land can be obtained. The combined value for forestry land and timber for Finland can be obtained by the direct method described in general in chapter 3 of this part. The volume for standing timber in Finland can be obtained by the National Forest Inventory system carried out by the Finnish Forest Research Institute. The system has produced reliable estimates on Finnish forest resources since 1920. The total volume of standing timber is calculated using information on annual growth of forests and annual fell- ing. This information is available by institutional sectors. The value of standing timber can then be derived by multiplying the timber assortment volumes by correspond- ing stumpage prices (sale on the stump is by far the most popular form of sale). There are three commercially significant tree species in Finland (spruce, pine, birch), for which prices are registered separately for saw-timber and pulpwood. The relevant market prices are collected by the Finnish Forest Resource Institute, as well. The value of standing timber is already currently registered in national accounts as work-in-progress inventories for biological assets, so it is also part of produced non-financial assets in the balance sheets. Picture 6 shows the combined value of forestry land, the value of timber inventories, and the value for underlying land calculated as residual for the period 1985-2012 in million euro.

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80000 70000 60000 50000

Combined Value of 40000 Forestry land Timber inventory 30000

20000 Forestry Land (excl. Million euros Million 10000 timber)

0

-10000

2007 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2009 2011 -20000

Picture 6.

The graph indicates that the residual method leads to negative values for forestry land for some years. This is due to two reasons: Firstly, the combined value, which is based on transactions of forest real estates, is probably an underestimate. This follows from the fact that the forest real estate sales does not represent an average forest real estate, because most of the sales is towards real estate with young tree stand. In other words, the combined value may be down- ward biased. Secondly, the value of timber inventory is revised approximately five years backwards annually, when new in- formation from the National Forest Inventory is gathered. This leaves the value of timber inventory with high uncertainty for the recent years. In addition, the graph 6 indicates that the residual value for forestry land is quite volatile; i.e. the variance in timber prices is reflected also in the value of underlying land. This is not a reasonable result taking into account the long period of tree growth in Finland (from 50 to 90 years depending on the latitude and the wood species). The value of clean forestry land should develop smoother, and reflect more the productivity of the land than cy- clical changes in timber prices. Therefore, if the residual method is used to estimate forestry land, some kind of modifications should be done to the residual method model, at least to avoid negative land values.

The direct method There are active markets for forest estates in Finland. When an estate is evaluated for the trade, usually so-called summation approach is used. In this method, the total value of a forest estate is a total of land value and the tree stand value including expectation values for young growing stands. For this purpose, the Forestry Development Centre Tapio has published unit prices for different forest land types. Tapio has recently published new estimates for unit prices to be applied in the evaluation of forest estates. Final report 37(54)

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Area information for site fertility classes on mineral soils and on mires on forest land are available from the sta- tistics published by the Finnish Forest Research Institute. Applying this information, a total value for the total forest land in economic use can be calculated for a single year (2012). In order to estimate the value of forestry land for the period 1985-2012, price index for forest estates from the National Land Survey was applied. The results are shown in picture 7 (blue line).

80000 Forestry Land, direct 70000 method 60000 Timber inventory

50000

40000 Timber Inventory + 30000 Forestry Land by direct method 20000

Million euros Million Combined Value of 10000 Forestry land

0

Forestry Land,

-10000 residual method

1994 1985 1988 1991 1997 2000 2003 2006 2009 2012 -20000

Picture 7.

The results indicate that the value of forestry land by direct method is less volatile than the one obtained by the residual method. Because the direct method is not based on realized market prices but on the estimated produc- tive value of forest land types, it implicitly excludes the value of capitalized land improvements. The residual method instead includes the land improvements by definition. Therefore, if the residual method is applied, the stock of land improvements should be deducted to obtain the value of forestry land in accordance with ESA2010 (see chapter 10.2). It can be concluded that the direct method leads to more reliable results. It is known that the market prices of forest real estate are not that sensitive to timber prices than the results of the residual method would illustrate. The direct method based on forest productivity was chosen to evaluate the value of forestry land.

10.2 Land Improvements

Land improvements (AN.1123) are the result of actions that lead to major improvements in the quantity, quality or productivity of land, or prevent its deterioration (SNA 10.79, ESA Annex 7.1). Examples include the increase in asset value arising from land clearance, land contouring, creation of wells and watering holes. Land improve- ments, according to this definition, may apply to any type of land (and is not restricted to, for example, agricul- tural land).

Land improvements represent a category of fixed assets distinct from the non-produced land asset as it existed before improvement. Land before improvements remains a non-produced asset and as such is subject to holding gains and losses separately from price changes affecting the improvements. Final report 38(54)

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Land improvements can sometimes only be observed in combination with the land itself. However, as can be concluded from the above, in the ESA and SNA land and land improvements are classified as separate assets. In case land and land improvements cannot be separated from each other, ESA and SNA recommend to register the composite asset in the category representing the greater part of its value. In the national accounts of Finland this asset type (land improvements, AN. 1123) consists mainly of investments in land under cultivation (AN.2112), i.e. land improvement investments in agriculture and forestry. Land im- provements in other industries are mostly included in other structures (AN.1122). Major improvements in agriculture include fertilising and subsoil drainage. The value of fertiliser use is based on data to be found in agricultural enterprise and income statistics and the Farm Accountancy Data Network. The data sources can be considered quite reliable. Data on the sale of agricultural fertilisers and other land improve- ment materials are used for control purposes. For the subsoil drainage, the industry organisation Subsoil Drain- age Centre collects data about it by area and cost in hectares. Expenditures on land improvements can also be found in Statistics on the Finances of Agricultural and Forestry enterprises collected by Statistics Finland.

In the forestry industry, these investments consist of forest management and land improvement to be found in the Statistics on Forestry and Forest Improvement Activities collected by the Finnish Forest Research Institute. It contains: preparation of renewal area, artificial regeneration, seedling stand care, refining young forest, thinning of thicket and forest fertilisation.

Above mentioned land improvements are recorded as gross fixed capital formation by industries in the national accounts. Because they are part of fixed assets, consumption of fixed capital has to be estimated. This is done by using PIM. For the land improvements on agriculture, a service life of 30 years is applied, and for the forestry 50 years, respectively. Straight line depreciation function is used for both of the industries concerning land im- provements.

As a result of recorded investments and the PIM, the net capital stock for the land improvements (AN.1123) can be calculated. The PIM also includes a price index for the land improvements, and applying that the holding gains/losses can be calculated.

The net capital stock can then be utilised in land estimates by deducting the value of the net capital stock from the market value of land. In other words, it is assumed, that the estimated market value of land includes also the value of land improvements.

Following tables 18 and 19 show the registration of land improvements and corresponding land type in the ac- counts for agricultural land and forestry land in Finland in 2012.

Table 18. Registration of land improvements and land value on agricultural land (million euro)

Classification of IV.1 III.1 and III.2 III.3.1 III.3.2 IV.3 assets Opening Transactions Other Holding Closing balance sheet changes in gains and balance volume losses sheet Land improvements 1270 33 (P.51g) - 72 1286 (ditches) -89 (P.51c) (AN.1123) Final report 39(54)

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Market Value of 20976 22000

Agricultural Land

(AN.211 +

AN.1123) Ta- ble Land (AN.211) 19706 - -19 (K.1) 1027 20714 19. Registration of land improvements and land value on forestry land (million euro)

Classification of IV.1 III.1 and III.2 III.3.1 III.3.2 IV.3

assets Opening Transactions Other Holding Closing

balance sheet changes in gains and balance

volume losses sheet

Land improvements 3339 212 (P.51g) - 116 3438 (ditches) -229 (P.51c) (AN.1123)

Market value of 9407 9683 Forestry Land

Land (AN.211) 6068 - - 177 6245

11. Concluding the preliminary results

Value of land and timber has been more than the annual GDP in every single year (picture 8):

350

300

250

200 GDP 150 Value of land and 100 timber

50

0

1991 1987 1989 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 1985 Picture 8. Land and timber value and GDP (billion euro current prices)

The ratio of value of land and timber to annual GDP was at its lowest in 1995-2005. Finland suffered a severe depression at the early 1990s. After the depression the GDP rose fast, but it took several years for land values to follow (picture 9).

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2,0 1,8 1,6 1,4 1,2 1,0 0,8 0,6 0,4 0,2

0,0

1991 1998 2005 2012 1986 1987 1988 1989 1990 1992 1993 1994 1995 1996 1997 1999 2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2011 1985 Picture 9. Ratio of land and timber value to GDP

A significant change occurred in the structure of land value: in 1985 forest and arable land (including timber) was 55 % of total value of land (and timber). In 2012 this figure has fallen to only 33 %. Land values have con- centrated to urban uses, and to a smaller area.

Within the urban land use another structural change has taken place: residential land has increased in value com- pared to all other land uses, urban or rural. In 1985 residential land was 23 % of total land value (including tim- ber), but in 2012 a high number of 45 % was reached.

This has caused that the share of land value in some large city-regions has grown. The share of city of Helsinki has grown from 14 % in 1985 to 23 % in 2012, although the share of Helsinki land area is only 0,07 %.

How accurate are these results? Not so accurate we wished they were. They are based on average prices, and in large cities there is great variation around the average. A more accurate result could be achieved if basic unit of calculation were the real estate unit itself, instead of all the real estate of a particular land use in a particular community. Preliminary calculations, based on real estate units in Helsinki, give somewhat lower values than calculations based on Helsinki averages. This is surprising, because in a typical city averages are based on vacant lots on new developments, often on the fringe of the city. However, in Helsinki there are quite a few sales of residential lots even in central locations, which may explain the difference.

There is little ad hoc fine-tuning in results. In this preliminary calculation there are some obvious flaws. Most striking example is perhaps public building lots (Y) in Helsinki, the aggregate value of which is 8 billion euros and unit price 1145 euro/m2. These figures look too high. Most of the Y-lots are suburban schools, nursery homes, medical centres and even sport fields, which the local government has planned and built on land in al- ready owns. It has not paid a specific Y-lot -market price, a crude level of which is not easy to determine.

Most results, however, are reasonable estimates, but some of them are problematic. E.g. the obviously expensive level of residential land, compared to all other land, urban or rural, may not be a permanent situation, because there is no permanent lack of supply of land for housing. It is rather a temporary lack of supply, although this temporary situation has lasted several decennia in Helsinki region, if not in some other cities, too.

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On the other hand, there is no lack of supply of land for industrial and commercial buildings. On the contrary, there is an oversupply of land for those purposes. The statistics reveal a phenomenon, where land for industrial and commercial buildings has considerably increased in area, but the relative value of that land has decreased.

12. Land value by sectors

Previous chapters have examined how to produce an estimate of the value of land in total. However, there is also a need to provide estimates on sector basis. Statistics Finland produces estimates of different types of land: land underlying dwellings, land underlying other buildings, land underlying structures and land under cultivation (ag- ricultural and forestry land). To attain a division into sectors, a top down approach is used. This implies that the total value of each type of land is estimated first after which land is allocated to different sectors. The argument for using the top down approach is that the only available source data are on the national level. Data on land prices or surface areas by sector is not available for the time being.

The top down approach may be a little crude method, but it is still capable of catching medium- and long-term trends in land sector allocation and reflect economic reality. For the top down method, information from the capital stock is applied for land areas underlying buildings and structures. This assumes a relationship between the value of the buildings and structures and the underlying land. In the top down approach the total value of land is estimated first. Then, the distribution of the value of the structures from the capital stock is used to dis- tribute land underlying structures across sectors.

In the top down approach, land underlying structures is expected to be similarly distributed by industry and sec- tor as the value of the dwellings. A drawback of this approach is that it assumes equal land-structure ratios across sectors. However, land-structure ratios may vary due to differences in location and the type of structure. For instance, dwellings of social housing corporations are likely to be built on smaller plots of land since they are more often located in flats or apartment buildings. Nevertheless, because data about land-structure ratios across industries and sectors are not available, equal land-structure ratios are assumed.

When using the PIM data as an indicator for the land ownership distribution, it is also necessary to take into ac- count possible cases where the sector of the structure differs from the sector of the underlying land. This is true for some cases, e.g. when dwellings owned by households stand on a hired (e.g. from municipality) lot. This kind of exceptions were examined and corrected using tax data on real estates, where the surface area of hired lots is available.

Since no relation exists between cultivated land and the capital stock, the division of land under cultivation into sectors relies on a different method than the division of land underlying dwellings. Farm structure statistics was used to allocated the value of agricultural land by sectors, and statistics on forest ownership for forestry land. Institutional sectors for the rest of the land uses are judged mainly based on general information, e.g. recreational areas are allocated to public sector. Land estimates by land use and by sector are shown in tables 20.

Table 20. Land estimates by land use and by sector, billion euro, 2012 Sector / Land S1 S11 S1311 S1313 S14 S15 type AN.211 Land 248 58 6 48 131 5

AN.2111 Land 219 54 6 46 108 5 underlying buildings and structures AN.21111 Land 154 23 0 24 104 3 underlyind dwell- Final report 42(54)

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ings AN.21112 Land 65 31 6 22 4 2 underlying other buildings and structures AN.2112 Culti- 27 4 0 0 23 0 vated Land AN.2112 Agricul- 21 2 0 0 19 0 tural Land AN.2112 Forestry 6 2 0 0 4 0 Land AN.2113 Other 2 0 0 2 0 0 Land

The sectorization methodology is still under development in Finland, and the current practice may possibly change to production of land estimates by sectors using a bottom up approach. The bottom up approach estimates data at the bottom level, for instance producing estimates for all combinations of land by asset, sector and indus- try, and then aggregating up these values to the total economy level.

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APPENDICES

I: SLICES Land use classification

A. RESIDENTIAL AND LEISURE AREAS A1. Residential areas A11. Areas with block of flats A12. Small house areas A121. Chain house areas A122. Other small house areas (single small houses) A2. Holiday and tourist areas A21. Holiday home areas A22. Tourist service and holiday areas A221. Campgrounds and caravan areas A222. Private gardening areas (colony gardening) A3. Other leisure activity areas A31. Amusement and entertainment service areas A32. Sports and recreation service areas A33. Parks

B. BUSINESS, ADMINISTRATIVE AND INDUSTRIAL AREAS B1. Business and administrative areas B11. Business and office building areas B111. Business building areas B112. Office building areas B12. Public building areas B2. Industrial and storage areas B21. Industrial areas B22. Storage areas B221. Areas for storage buildings B222. Open storage areas

C. SUPPORTING ACTIVITY AREAS C1. Traffic areas C11. Road traffic areas C111. Public main roads C112. Streets and other local roads in population centres C113. Private roads C12. Railway and other rail traffic areas C13. Air traffic and aviation areas C14. Port areas C15. Other traffic areas C2. Infrastructure maintenance areas C21. Environmental management areas C22. Energy supply areas C23. Water supply areas C24. Other infrastructure maintenance areas

D. ROCK AND SOIL EXTRACTION AREAS D1. Rock and soil extraction areas Final report 44(54)

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D11. Rock extraction areas D111. Mines D112. Quarries

D12. Soil extraction areas D121. Peat extractions areas D122. Sand and gravel extraction areas D123. Other soil extraction areas

E. AGRICULTURAL LAND E1. Used agricultural land E11. Fields E12. Permanent grassland and pastures E13. Horticultural cultivation E131. Fruit tree and berry cultivation areas E132. Nurseries and greenhouse areas E2. Other agricultural land E21. Unused agricultural land E211. Fallows (long period) E212. Former fields E22. Agricultural built-up land E221. Farms E222. Built-up land for other agricultural purposes

F. FORESTRY LAND F1. Productive forest areas F11. Forest land (of normal or high productivity) F12. Forest land of low productivity

G. OTHER LAND G1. Waste land G2. Other land

H. WATER AREAS H1. Inland water areas H11. Natural (not artificial) water bodies H111. Non-regulated water bodies H112. Inland waters under regulation H12. Other (artificial) waters H121. Channels H122. Reservoirs and other regulated artificial waters H2. Sea areas

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II: SLICES Land cover classification

A. AREAS COVERED BY FORESTS (tree crown coverage at least 10 %) A1. Forests with high crown coverage (crown coverage over 30 %) A11. Coniferous forests (proportion of coniferous trees over 75% of crown coverage) A111. Volume 250 - m3/ha A112. Volume 150 - 250 m3/ha A113. Volume 50 - 150 m3/ha A114. Volume 10 - 50 m3/ha A115. Volume - 10 m3/ha A12. Deciduous forests (proportion of deciduous trees over 75% of crown coverage) A121. – A125. Volume classes like in class A11. A13. Mixed forests (proportion of deciduous and coniferous trees 25 – 75 % of crown coverage) A131. – A135. Volume classes like in class A11.

A2. Other forest covered areas (crown coverage 10 - 30 %) A21. Coniferous forests (proportion of coniferous trees over 75% of crown coverage) A221. – A125. Volume classes like in class A11. A22. Deciduous forests (proportion of deciduous trees over 75% of crown coverage) A221. – A125. Volume classes like in class A11. A23. Mixed forests (proportion of deciduous and coniferous trees 25 – 75 % of crown coverage) A231. – A135. Volume classes like in class A11.

B. FORESTS WITH LOW CROWN COVERAGE AND AREAS COVERED BY OTHER VEGETATION (crown coverage under 10%) B1. Young forests and grasslands B2. Highlands, bushlands

C. UNVEGETATED LAND AREAS C1. Built-up areas C2. Other open, unvegetated lands

D. WATERS D1. Lakes and sea D11. Open water D12. Water areas covered by vegetation D2. Rivers/streams

(E. NODATA: land cover information not available)

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III: SLICES Soil classification

A. ROCKY AREAS AND MINERAL SOILS A1. Rocks and rocky soil A11. Open rocks A12. Rocky soil A13. Rocks with only a slight mineral soil coverage (0 - 1 m)

A2. Mineral soils A21. Moraine soils and soils with moraine on bottom and only a slight mineral soil coverage (0 – 1 m) A22. Sorted soils and soils with sorted soil on bottom and only a slight mineral soil coverage (0 – 1 m) A221. Soils with medium and big grains (rough granularity) A222. Soils with small grains (fine granularity)

B. PEATLANDS AND WET MINERAL SOILS (areas turning to swamps) B1. Peatlands with thick peat coverage (at least 1 m) B11. Silt soils (former lake bottoms) B12. Peatlands with thick Carex –peat coverage B13. Peatlands with thick Sphagnum –peat coverage

B2. Peatlands with thin peat coverage (under1 m) B21. Peatlands with rocks on bottom B22. Peatlands with moraine on bottom B23. Peatlands with sorted soils on bottom A231. Bottom soils with medium and big grains (rough granularity) A232. Bottom soils with small grains (fine granularity)

C. OTHER SOILS (built-up lands, digged lands, filled lands) - Original soil type unknown

D. WATERS

(E. No data; soil information not available)

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IV: Unit prices by local government and land use, year 2012

unit price (euro/m2), median

industrial, com- roads, parks, arable and housing lots mercial and office technical forest land lots services AH AK AP AR R K T Y E L V l.pr for arabl 11 Espoo 114 766 226 366 25,0 516 373 128 1,8 5,1 23,7 0,1 1,6 1,3 Helsinki 948 1815 355 457 35,2 3066 1095 1145 1,8 5,1 76,8 0,1 1,6 1,3 Vantaa 95 400 147 222 24,4 302 235 101 1,8 5,1 37,7 0,1 1,6 1,3 Hyvinkää 23 202 68 111 15,3 1272 86 118 1,8 5,1 8,1 0,1 1,6 1,3 Järvenpää 78 241 107 176 6,5 242 105 79 1,8 5,1 9,6 0,1 1,6 1,3 Kauniainen 459 606 413 515 14,1 1273 486 94 1,8 5,1 28,9 0,1 1,6 1,3 Kerava 112 428 130 206 14,1 157 137 124 1,8 5,1 22,1 0,1 1,6 1,3 Kirkkonummi 23 222 101 83 22,1 134 107 41 1,8 5,1 4,7 0,1 1,6 1,3 Mäntsälä 15 119 46 46 5,9 63 42 99 1,8 5,1 27,3 0,1 1,6 1,3 Nurmijärvi 18 154 80 84 12,6 111 58 28 1,8 5,1 20,4 0,1 1,6 1,3 Pornainen 8 36 33 43 8,3 12 32 48 1,8 5,1 36,5 0,1 1,6 1,3 Siuntio 18 39 60 47 18,5 115 37 61 1,8 5,1 1,7 0,1 1,6 1,3 Tuusula 21 188 81 114 11,6 78 90 30 1,8 5,1 6,2 0,1 1,6 1,3 12 Karkkila 12 101 30 54 8,7 85 47 22 0,5 0,6 3,9 0,1 1,5 1,1 Lohja 10 165 45 46 9,5 154 46 98 0,5 0,6 7,9 0,1 1,5 1,1 Vihti 12 145 71 87 9,8 65 56 95 0,5 0,6 7,2 0,1 1,5 1,1 13 Hanko 13 129 25 24 11,7 208 42 76 0,4 0,9 53,2 0,1 1,1 1,0 Inkoo 9 33 35 33 9,5 93 14 134 0,4 0,9 4,7 0,1 1,1 1,0 835 9 232 46 82 12,9 223 302 191 0,4 0,9 7,6 0,1 1,1 1,0 22 Salo 9 104 23 23 14,8 184 59 45 0,2 1,4 20,7 0,1 0,6 1,1 Somero 4 101 19 23 9,9 123 31 112 0,2 1,4 11,4 0,1 0,6 1,1 23 Kaarina 20 77 50 43 10,1 192 52 90 0,6 1,5 12,7 0,1 0,7 1,4 Lieto 11 50 40 72 3,8 40 48 32 0,6 1,5 11,3 0,1 0,7 1,4 Masku 7 32 28 31 10,8 96 35 64 0,6 1,5 30,3 0,1 0,7 1,4 Mynämäki 5 39 12 25 3,2 165 12 29 0,6 1,5 9,4 0,1 0,7 1,4 Naantali 14 242 58 64 13,0 118 59 62 0,6 1,5 24,9 0,1 0,7 1,4 Nousiainen 5 47 17 30 2,6 28 21 4 0,6 1,5 4,3 0,1 0,7 1,4 Paimio 6 67 15 19 2,8 49 54 58 0,6 1,5 0,8 0,1 0,7 1,4 Raisio 32 260 64 63 15,3 146 87 49 0,6 1,5 19,7 0,1 0,7 1,4 Rusko 7 44 29 39 3,0 25 30 10 0,6 1,5 25,0 0,1 0,7 1,4 Sauvo 5 9 7 21 10,4 5 24 32 0,6 1,5 5,8 0,1 0,7 1,4 Turku 32 363 89 88 18,4 580 195 281 0,6 1,5 22,3 0,1 0,7 1,4 24 Kustavi 5 1 7 1 10,4 90 10 4 0,6 0,9 7,5 0,0 0,7 1,1 Laitila 7 29 7 7 3,8 80 21 71 0,6 0,9 2,6 0,0 0,7 1,1 Pyhäranta 4 3 3 3 7,2 16 14 3 0,6 0,9 1,3 0,0 0,7 1,1 Taivassalo 4 10 7 6 8,1 16 25 64 0,6 0,9 5,8 0,0 0,7 1,1 Uusikaupunki 4 49 18 17 8,1 140 38 68 0,6 0,9 6,2 0,0 0,7 1,1 Vehmaa 4 24 4 7 5,4 39 35 57 0,6 0,9 3,8 0,0 0,7 1,1 25 Aura 7 17 13 20 4,2 20 22 79 0,4 0,6 7,3 0,0 0,6 1,1 Koski Tl 5 43 5 7 2,5 42 17 38 0,4 0,6 1,4 0,0 0,6 1,1

Final report 48(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median industrial, roads, parks, arable and forest housing lots commercial and technical ser- land office lots vices AH AK AP AR R K T Y E L V l.pr for arabl Loimaa 3 32 9 11 3,8 127 28 21 0,4 0,6 6,8 0,0 0,6 1,1 Marttila 4 9 4 6 1,5 29 20 17 0,4 0,6 10,3 0,0 0,6 1,1 Oripää 2 6 3 5 9,3 2 4 55 0,4 0,6 0,6 0,0 0,6 1,1 Pöytyä 3 25 5 6 9,4 8 15 22 0,4 0,6 17,1 0,0 0,6 1,1 Tarvasjoki 3 3 5 5 1,6 12 17 12 0,4 0,6 6,7 0,0 0,6 1,1 41 Eura 4 18 8 12 23,4 101 16 36 0,0 1,0 8,5 0,1 0,7 1,1 Eurajoki 5 7 4 6 7,6 22 7 7 0,0 1,0 1,3 0,1 0,7 1,1 Köyliö 3 37 3 5 8,8 3 7 45 0,0 1,0 1,6 0,1 0,7 1,1 Rauma 7 65 25 33 10,8 211 31 130 0,0 1,0 8,2 0,1 0,7 1,1 Säkylä 8 25 13 23 9,6 1495 13 25 0,0 1,0 44,3 0,1 0,7 1,1 43 Harjavalta 4 23 9 9 16,3 65 23 10 0,3 1,5 7,1 0,1 0,6 1,2 Huittinen 6 55 10 19 4,0 35 20 55 0,3 1,5 8,3 0,1 0,6 1,2 Kokemäki 3 27 8 14 6,2 50 19 28 0,3 1,5 1,7 0,1 0,6 1,2 Luvia 5 7 5 5 11,7 6 12 25 0,3 1,5 6,9 0,1 0,6 1,2 Merikarvia 3 15 6 7 6,6 41 21 29 0,3 1,5 7,0 0,1 0,6 1,2 Nakkila 5 17 5 6 2,9 34 16 19 0,3 1,5 1,1 0,1 0,6 1,2 Pomarkku 3 18 5 6 8,8 17 16 20 0,3 1,5 2,6 0,1 0,6 1,2 Pori 14 112 23 24 11,5 268 42 142 0,3 1,5 25,5 0,1 0,6 1,2 Ulvila 13 26 9 9 5,7 47 18 13 0,3 1,5 17,4 0,1 0,6 1,2 44 Honkajoki 2 15 4 3 1,2 40 11 15 0,7 0,6 0,8 0,0 0,3 0,8 Jämijärvi 5 5 8 16 9,6 96 37 50 0,7 0,6 3,8 0,0 0,3 0,8 Kankaanpää 5 38 17 15 6,1 86 24 67 0,7 0,6 16,0 0,0 0,3 0,8 Karvia 2 7 3 5 2,7 74 11 11 0,7 0,6 1,3 0,0 0,3 0,8 Lavia 2 12 4 2 6,5 67 12 9 0,7 0,6 2,8 0,0 0,3 0,8 Siikainen 4 1 8 4 13,3 24 12 10 0,7 0,6 2,6 0,0 0,3 0,8 51 Hattula 10 33 30 28 11,8 85 27 37 0,3 1,8 4,5 0,1 0,7 0,9 Hämeenlinna 8 176 35 48 10,1 214 67 119 0,3 1,8 17,7 0,1 0,7 0,9 Janakkala 4 72 16 25 11,4 74 36 39 0,3 1,8 6,7 0,1 0,7 0,9 52 Hausjärvi 5 14 14 14 6,4 51 30 25 0,3 2,8 12,5 0,1 0,7 1,0 Loppi 5 26 17 17 8,1 74 22 40 0,3 2,8 7,0 0,1 0,7 1,0 Riihimäki 16 158 41 63 19,7 174 62 43 0,3 2,8 12,0 0,1 0,7 1,0 53 Forssa 5 43 29 15 3,3 95 85 113 0,2 1,1 15,1 0,1 0,7 1,0 Humppila 3 22 6 7 5,1 14 17 1 0,2 1,1 5,4 0,1 0,7 1,0 Jokioinen 4 24 8 7 3,5 12 20 35 0,2 1,1 2,6 0,1 0,7 1,0 Tammela 5 36 16 19 21,4 28 34 40 0,2 1,1 47,7 0,1 0,7 1,0 Ypäjä 2 50 4 16 1,9 11 16 11 0,2 1,1 2,6 0,1 0,7 1,0 61 Hämeenkyrö 10 27 14 17 8,8 21 12 7 0,2 0,7 14,7 0,0 0,4 0,7 Ikaalinen 8 47 14 18 7,8 66 20 100 0,2 0,7 14,9 0,0 0,4 0,7 Kihniö 3 7 8 17 5,9 34 31 60 0,2 0,7 11,2 0,0 0,4 0,7 Parkano 3 21 10 8 7,2 50 16 74 0,2 0,7 4,6 0,0 0,4 0,7

Final report 49(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median industrial, roads, parks, arable and for- housing lots commercial technical ser- est land and office lots vices AH AK AP AR R K T Y E L V l.pr for arabl 62 Pälkäne 9 34 12 9 12,1 14 34 14 0,0 0,4 9,0 0,0 0,5 1,0 63 Urjala 3 49 7 6 6,8 97 16 28 0,2 0,9 3,5 0,1 0,5 0,9 Valkeakoski 14 66 24 17 14,3 203 51 146 0,2 0,9 35,9 0,1 0,5 0,9 64 Kangasala 18 119 55 50 12,9 131 48 42 0,8 2,1 3,8 0,1 0,8 1,2 Lempäälä 27 116 53 47 11,2 88 45 146 0,8 2,1 15,7 0,1 0,8 1,2 Nokia 12 87 53 42 12,3 153 62 70 0,8 2,1 6,4 0,1 0,8 1,2 Pirkkala 26 115 84 48 17,1 75 62 31 0,8 2,1 50,0 0,1 0,8 1,2 Tampere 36 479 150 129 15,1 618 154 280 0,8 2,1 49,9 0,1 0,8 1,2 Vesilahti 7 18 13 10 13,5 26 20 5 0,8 2,1 1,3 0,1 0,8 1,2 Ylöjärvi 11 109 45 37 11,6 63 32 28 0,8 2,1 20,3 0,1 0,8 1,2 68 Punkalaidun 4 16 8 8 3,6 29 23 16 0,1 0,8 3,0 0,0 0,6 1,1 912 6 47 11 11 8,2 107 26 49 0,1 0,8 6,0 0,0 0,6 1,1 69 Juupajoki 3 12 4 4 6,2 19 19 14 0,2 1,2 3,6 0,0 0,5 0,7 Orivesi 8 43 11 10 7,6 93 22 21 0,2 1,2 5,5 0,0 0,5 0,7 Ruovesi 9 9 10 23 11,5 23 14 0 0,2 1,2 18,3 0,0 0,5 0,7 Virrat 4 30 11 22 8,2 101 23 33 0,2 1,2 3,4 0,0 0,5 0,7 71 Asikkala 6 45 28 31 12,9 32 16 75 0,7 1,8 28,5 0,1 0,6 0,9 Hollola 8 74 24 31 9,8 226 55 94 0,7 1,8 5,4 0,1 0,6 0,9 Hämeenkoski 4 68 12 31 5,9 31 18 39 0,7 1,8 9,5 0,1 0,6 0,9 Kärkölä 8 58 13 17 3,2 48 35 69 0,7 1,8 1,9 0,1 0,6 0,9 Lahti 63 463 46 81 10,1 311 89 207 0,7 1,8 18,4 0,1 0,6 0,9 Nastola 8 28 20 15 12,1 59 29 79 0,7 1,8 3,0 0,1 0,6 0,9 Orimattila 6 45 15 22 4,6 54 26 84 0,7 1,8 16,4 0,1 0,6 0,9 Padasjoki 5 39 11 10 15,4 5 15 17 0,7 1,8 4,4 0,1 0,6 0,9 72 Hartola 4 17 8 11 8,3 18 12 23 , 1,0 11,6 0,0 0,7 0,8 Heinola 9 82 17 23 9,4 138 22 146 , 1,0 22,0 0,0 0,7 0,8 Sysmä 8 19 10 11 10,3 54 15 37 , 1,0 2,6 0,0 0,7 0,8 81 Iitti 4 24 6 8 10,6 46 19 27 0,4 1,3 7,3 0,1 0,5 0,7 Kouvola 5 67 10 14 9,1 154 32 38 0,4 1,3 14,2 0,1 0,5 0,7 82 Hamina 4 57 10 18 6,3 160 25 67 0,7 0,8 8,7 0,1 0,4 0,6 Kotka 9 132 23 29 4,8 104 49 97 0,7 0,8 24,1 0,1 0,4 0,6 Miehikkälä 2 4 0 0 4,2 14 9 12 0,7 0,8 0,1 0,1 0,4 0,6 Pyhtää 4 4 7 8 10,1 23 11 63 0,7 0,8 8,1 0,1 0,4 0,6 Virolahti 3 11 4 6 5,6 16 18 25 0,7 0,8 24,1 0,1 0,4 0,6 91 Lappeenranta 5 195 27 46 10,8 150 44 160 0,7 1,4 15,0 0,1 0,5 0,7 92 Lemi 3 7 4 6 6,0 6 7 35 0,2 0,2 3,0 0,0 0,5 0,6 Luumäki 7 21 8 12 7,4 18 12 3 0,2 0,2 5,3 0,0 0,5 0,6 Savitaipale 5 36 8 13 8,4 2 11 39 0,2 0,2 6,1 0,0 0,5 0,6 Taipalsaari 7 18 14 14 8,6 24 19 46 0,2 0,2 20,0 0,0 0,5 0,6 93 Imatra 11 87 14 25 13,5 168 47 40 0,4 1,0 9,6 0,1 0,4 0,5

Final report 50(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median industrial, roads, parks, arable and forest housing lots commercial technical ser- land and office lots vices AH AK AP AR R K T Y E L V l.pr for arabl Parikkala 2 12 6 4 4,7 26 8 40 0,4 1,0 7,9 0,1 0,4 0,5 Rautjärvi 3 8 3 6 3,9 110 15 10 0,4 1,0 2,6 0,1 0,4 0,5 Ruokolahti 4 19 5 10 8,5 57 13 10 0,4 1,0 7,9 0,1 0,4 0,5 101 Hirvensalmi 5 36 11 9 7,4 16 10 45 0,4 1,3 12,5 0,1 0,5 0,5 Kangasniemi 5 25 10 8 5,8 14 21 12 0,4 1,3 4,8 0,1 0,5 0,5 Mikkeli 6 107 3 21 9,4 74 38 53 0,4 1,3 3,4 0,0 0,5 0,5 Mäntyharju 4 42 8 14 7,7 37 22 25 0,4 1,3 5,0 0,1 0,5 0,5 Pertunmaa 4 13 4 5 5,8 14 18 4 0,4 1,3 3,5 0,1 0,5 0,5 102 Joroinen 3 23 6 4 5,1 35 8 37 0,2 0,3 2,3 0,0 0,5 0,6 Juva 4 44 8 11 5,2 20 11 36 0,2 0,3 3,3 0,0 0,5 0,6 Puumala 8 34 14 9 9,3 91 16 18 0,2 0,3 5,3 0,0 0,5 0,6 Rantasalmi 3 56 4 25 5,5 25 9 21 0,2 0,3 2,0 0,0 0,5 0,6 103 Enonkoski 4 26 5 4 5,4 33 22 39 0,4 0,7 3,5 0,1 0,5 0,5 Heinävesi 4 52 8 13 4,9 23 35 15 0,4 0,7 6,2 0,1 0,5 0,5 Savonlinna 5 84 18 23 7,7 152 31 102 0,4 0,7 8,7 0,1 0,5 0,5 Sulkava 5 9 5 5 7,4 76 13 22 0,4 0,7 4,5 0,1 0,5 0,5 105 Pieksämäki 5 94 18 14 4,8 145 27 46 0,0 1,0 4,6 0,0 0,5 0,4 111 Iisalmi 5 162 26 27 3,7 80 33 73 0,1 0,7 3,3 0,1 0,3 0,5 Keitele 2 10 7 3 3,6 261 17 13 0,1 0,7 2,7 0,1 0,3 0,5 Kiuruvesi 2 29 10 8 2,9 86 24 60 0,1 0,7 1,6 0,1 0,3 0,5 Lapinlahti 3 34 6 11 3,1 47 26 46 0,1 0,7 9,5 0,1 0,3 0,5 Pielavesi 3 33 10 7 4,3 27 7 14 0,1 0,7 0,3 0,1 0,3 0,5 Sonkajärvi 2 8 6 7 2,9 32 10 13 0,1 0,7 2,9 0,1 0,3 0,5 Vieremä 2 11 7 14 2,6 58 11 35 0,1 0,7 1,7 0,1 0,3 0,5 112 Kuopio 4 62 8 13 30,2 217 11 20 1,0 4,9 3,1 0,0 0,5 0,6 Maaninka 5 111 7 14 6,9 42 0 28 1,0 4,9 4,2 0,1 0,5 0,6 Siilinjärvi 8 123 30 36 11,0 53 19 33 1,0 4,9 18,8 0,1 0,5 0,6 113 Juankoski 5 24 7 14 2,9 7 10 43 0,5 1,3 3,5 0,0 0,4 0,6 Kaavi 2 17 6 13 3,9 61 17 23 0,5 1,3 4,1 0,0 0,4 0,6 Rautavaara 1 2 4 2 2,9 9 8 19 0,5 1,3 1,1 0,0 0,4 0,6 Tuusniemi 5 18 7 10 4,4 9 8 4 0,5 1,3 1,3 0,0 0,4 0,6 114 Leppävirta 4 21 9 14 4,7 22 23 20 0,3 0,5 2,7 0,0 0,5 0,4 Varkaus 10 73 18 21 5,7 93 34 26 0,3 0,5 5,7 0,0 0,5 0,4 115 Rautalampi 4 10 10 10 4,8 23 13 13 0,0 0,8 6,3 0,0 0,4 0,4 Suonenjoki 5 27 12 18 4,5 290 12 35 0,0 0,8 1,3 0,0 0,4 0,4 Tervo 6 3 8 9 3,6 44 6 17 0,0 0,8 2,4 0,0 0,4 0,4 Vesanto 2 19 6 7 4,6 42 14 12 0,0 0,8 1,4 0,0 0,4 0,4 122 Ilomantsi 2 62 10 11 4,3 85 25 27 0,1 1,8 4,1 0,1 0,4 0,5 Joensuu 5 169 36 50 4,9 249 58 115 0,1 1,8 16,2 0,1 0,4 0,5 Kontiolahti 6 28 16 19 5,5 13 46 35 0,1 1,8 3,4 0,1 0,4 0,5

Final report 51(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median industrial, roads, parks, arable and forest housing lots commercial technical ser- land and office lots vices AH AK AP AR R K T Y E L V l.pr for arabl Outokumpu 3 34 9 10 6,3 2 29 53 0,1 1,8 13,8 0,1 0,4 0,5 Liperi 3 25 13 15 6,1 84 20 14 0,1 1,8 21,3 0,1 0,4 0,5 Polvijärvi 2 21 7 24 5,2 72 15 33 0,1 1,8 1,1 0,1 0,4 0,5 124 Kitee 3 51 8 9 5,8 66 20 71 2,6 0,8 6,2 0,0 0,4 0,4 Rääkkylä 4 38 6 4 5,5 0 16 5 2,6 0,8 0,4 0,0 0,4 0,4 Tohmajärvi 2 9 4 8 2,7 36 7 22 2,6 0,8 5,6 0,0 0,4 0,4 125 Juuka 3 39 7 7 4,4 119 8 54 0,2 0,5 1,7 0,0 0,3 0,3 Lieksa 3 44 7 9 6,0 219 46 42 0,2 0,5 8,0 0,0 0,3 0,3 Nurmes 3 42 17 12 3,8 147 17 89 0,2 0,5 2,4 0,0 0,3 0,3 Valtimo 2 5 5 3 2,2 16 6 41 0,2 0,5 2,9 0,0 0,3 0,3 131 Hankasalmi 4 43 10 9 6,8 127 12 9 0,7 1,9 3,1 0,1 0,7 0,7 Jyväskylä 11 271 42 48 9,3 391 65 175 0,7 1,9 11,3 0,1 0,7 0,7 Laukaa 6 39 20 17 9,3 18 22 22 0,7 1,9 6,3 0,1 0,7 0,7 Muurame 9 37 31 36 13,5 16 22 19 0,7 1,9 3,5 0,1 0,7 0,7 Petäjävesi 4 34 7 5 10,1 99 14 25 0,7 1,9 8,9 0,1 0,7 0,7 Toivakka 4 27 8 27 8,6 18 2 25 0,7 1,9 1,3 0,1 0,7 0,7 Uurainen 4 13 9 15 6,7 127 12 63 0,7 1,9 17,4 0,1 0,7 0,7 132 Joutsa 4 33 10 13 7,1 14 14 5 0,1 0,1 2,5 0,0 0,5 0,5 Luhanka 6 22 26 8 10,5 8 2 0 0,1 0,1 18,0 0,0 0,5 0,5 133 Keuruu 6 42 12 13 6,2 44 19 72 0,1 1,2 6,2 0,0 0,4 0,4 Multia 3 5 4 6 4,2 13 6 3 0,1 1,2 7,1 0,0 0,4 0,4 134 Jämsä 8 64 8 10 18,5 113 19 80 0,2 0,3 5,6 0,0 0,7 0,9 Kuhmoinen 6 32 7 7 9,6 12 19 63 0,2 0,3 6,9 0,0 0,7 0,9 135 Konnevesi 4 11 3 6 5,6 8 19 19 0,7 1,5 1,8 0,0 0,5 0,6 Äänekoski 6 42 10 14 6,4 73 16 21 0,7 1,5 5,9 0,0 0,5 0,6 138 Kannonkoski 4 23 8 5 6,9 31 9 26 0,4 0,9 2,1 0,0 0,3 0,5 Karstula 4 14 10 14 5,1 51 15 34 0,4 0,9 2,2 0,0 0,3 0,5 Kinnula 3 4 4 4 5,0 0 9 15 0,4 0,9 1,0 0,0 0,3 0,5 Kivijärvi 4 22 5 6 6,8 14 7 5 0,4 0,9 1,1 0,0 0,3 0,5 Kyyjärvi 3 23 2 4 2,1 28 9 7 0,4 0,9 4,1 0,0 0,3 0,5 Pihtipudas 3 22 9 13 5,1 30 19 23 0,4 0,9 7,3 0,0 0,3 0,5 Saarijärvi 4 20 11 24 4,6 15 14 92 0,4 0,9 3,8 0,0 0,3 0,5 Viitasaari 5 37 11 6 5,7 53 13 24 0,4 0,9 7,0 0,0 0,3 0,5 141 Isojoki 2 11 7 5 3,1 24 7 5 0,3 0,4 4,5 0,0 0,3 0,9 Karijoki 3 4 1 3 1,7 42 7 32 0,3 0,4 4,7 0,0 0,3 0,9 Kauhajoki 4 38 10 15 5,8 80 27 52 0,3 0,4 12,5 0,0 0,3 0,9 Teuva 5 24 10 6 5,1 28 26 62 0,3 0,4 1,5 0,0 0,3 0,9 142 Ilmajoki 5 34 11 17 6,1 52 30 26 0,5 2,3 9,5 0,1 0,5 1,1 Lapua 8 59 12 21 6,8 45 19 59 0,5 2,3 12,5 0,0 0,5 1,1 Seinäjoki 12 139 33 33 5,9 97 49 53 0,5 2,3 10,0 0,1 0,5 1,1

Final report 52(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median industrial, roads, parks, arable and forest housing lots commercial and technical ser- land office lots vices AH AK AP AR R K T Y E L V l.pr for arabl 143 Jalasjärvi 4 42 5 6 2,3 30 19 16 0,4 0,8 2,3 0,0 0,3 0,9 Kurikka 5 32 9 13 4,0 51 19 22 0,4 0,8 13,6 0,0 0,3 0,9 144 Alavus 4 23 8 10 5,9 47 18 24 0,1 1,1 6,9 0,0 0,3 2,2 Kuortane 5 26 14 6 4,9 50 13 19 0,1 1,1 2,7 0,0 0,3 2,2 Soini 3 9 6 4 5,7 8 10 8 0,1 1,1 2,2 0,0 0,3 2,2 Ähtäri 5 15 14 12 8,8 31 20 41 0,1 1,1 5,3 0,0 0,3 2,2 145 Kauhava 5 41 9 18 2,4 44 21 23 0,3 0,4 7,1 0,0 0,3 1,0 146 Alajärvi 5 32 8 10 6,4 34 19 15 0,2 0,0 10,4 0,0 0,3 0,9 Evijärvi 3 13 7 7 5,9 11 12 4 0,2 0,0 5,8 0,0 0,3 0,9 Lappajärvi 4 31 12 9 8,7 6 13 6 0,2 0,0 4,9 0,0 0,3 0,9 Vimpeli 4 14 6 5 10,4 59 23 18 0,2 0,0 1,8 0,0 0,3 0,9 151 Isokyrö 7 7 5 4 9,2 25 30 1 , 0,3 17,9 0,1 0,3 1,4 Laihia 6 29 9 29 2,2 80 32 51 , 0,3 6,2 0,1 0,3 1,4 152 Korsnäs 4 12 7 3 6,7 42 12 10 0,8 5,1 2,4 0,1 0,4 1,0 Maalahti 7 10 7 9 5,6 39 9 0 0,8 5,1 3,2 0,1 0,4 1,0 Mustasaari 9 119 27 36 13,6 35 34 42 0,8 5,1 3,7 0,1 0,4 1,0 Vaasa 18 256 45 56 12,1 135 116 258 0,8 5,1 9,1 0,1 0,4 1,0 153 Kaskinen 22 10 19 7 18,3 57 9 5 0,2 0,3 1,8 0,0 0,3 1,0 Kristiinankaupunki 6 35 12 5 8,1 175 15 14 0,2 0,3 4,3 0,0 0,3 1,0 Närpiö 5 19 8 8 9,5 9 8 48 0,2 0,3 3,7 0,0 0,3 1,0 154 Kruunupyy 6 33 9 30 6,4 242 7 5 0,6 1,0 3,8 0,1 0,4 0,8 Luoto 9 16 12 11 10,6 7 22 8 0,6 1,0 5,6 0,1 0,4 0,8 Pietarsaari 27 197 41 26 28,8 145 73 14 0,6 1,0 2,0 0,1 0,4 0,8 Pedersören kunta 6 7 14 7 6,9 20 18 47 0,6 1,0 16,1 0,1 0,4 0,8 Uusikaarlepyy 6 8 24 21 8,7 55 25 4 0,6 1,0 2,3 0,1 0,4 0,8 161 Halsua 5 8 6 6 1,4 22 7 16 0,3 , 2,6 0,0 0,2 0,7 Kaustinen 6 26 7 5 2,2 113 12 18 0,3 , 5,8 0,0 0,2 0,7 Lestijärvi 7 7 5 6 10,5 22 5 33 0,3 , 2,5 0,0 0,2 0,7 Perho 4 15 6 6 3,1 0 11 19 0,3 , 31,8 0,0 0,2 0,7 Toholampi 4 2 9 8 1,4 37 16 65 0,3 , 0,4 0,0 0,2 0,7 Veteli 5 7 6 6 2,1 38 12 15 0,3 , 2,1 0,0 0,2 0,7 162 Kannus 7 114 18 16 2,6 42 33 94 0,6 1,8 20,3 0,1 0,3 0,9 Kokkola 15 119 31 39 14,4 146 49 91 0,6 1,8 21,0 0,1 0,3 0,9 171 Hailuoto 7 121 2 42 4,9 53 22 4 0,7 2,1 3,3 0,1 0,5 0,7 Kempele 11 57 41 43 14,8 100 66 48 0,7 2,1 10,7 0,1 0,5 0,7 Liminka 7 33 15 15 0,7 18 20 86 0,7 2,1 3,4 0,1 0,5 0,7 Lumijoki 3 21 6 5 2,7 279 14 242 0,7 2,1 11,6 0,1 0,5 0,7 Muhos 8 64 16 25 5,2 240 21 53 0,7 2,1 10,3 0,1 0,5 0,7 Oulu 2 9 3 5 1,8 4 8 5 0,7 2,1 10,4 0,0 0,5 0,7 Tyrnävä 7 24 13 6 2,0 80 36 47 0,7 2,1 6,0 0,1 0,5 0,7

Final report 53(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median industrial, roads, parks, arable and forest housing lots commercial technical ser- land and office lots vices AH AK AP AR R K T Y E L V l.pr for arabl 173 Ii 6 35 8 4 4,3 59 15 28 3,4 0,3 2,3 0,0 0,7 0,6 Pudasjärvi 4 25 12 11 6,9 40 12 40 3,4 0,3 3,2 0,0 0,7 0,6 Utajärvi 3 20 5 5 3,5 26 3 19 3,4 0,3 7,5 0,0 0,7 0,6 174 Pyhäjoki 4 27 8 6 5,1 25 21 6 0,9 0,9 2,7 0,0 0,2 0,5 Raahe 4 61 14 11 2,7 87 24 56 0,9 0,9 7,2 0,0 0,2 0,5 Siikajoki 3 7 6 3 3,7 20 7 10 0,9 0,9 5,5 0,0 0,2 0,5 175 Haapavesi 4 25 13 11 2,6 47 7 25 0,0 0,9 15,4 0,0 0,2 0,5 Pyhäntä 3 16 2 3 6,9 3 4 9 0,0 0,9 2,6 0,0 0,2 0,5 176 Haapajärvi 4 35 12 13 2,3 169 18 17 0,3 1,1 9,0 0,0 0,3 1,0 Kärsämäki 2 17 3 6 0,9 35 12 3 0,3 1,1 #### 0,0 0,3 1,0 Nivala 3 41 9 10 1,3 50 15 37 0,3 1,1 3,4 0,0 0,3 1,0 Pyhäjärvi 5 19 6 3 7,7 12 18 19 0,3 1,1 1,7 0,0 0,3 1,0 Reisjärvi 4 11 5 6 7,2 14 29 29 0,3 1,1 4,5 0,0 0,3 1,0 177 Alavieska 2 25 6 8 1,2 3 19 5 0,3 0,4 11,3 0,1 0,7 0,7 Kalajoki 4 36 11 17 16,7 97 21 78 0,3 0,4 6,2 0,1 0,7 0,7 Merijärvi 1 2 2 3 1,4 39 3 11 0,3 0,4 6,2 0,1 0,7 0,7 Oulainen 3 47 19 20 2,1 53 20 66 0,3 0,4 7,9 0,1 0,7 0,7 Sievi 2 29 10 12 5,0 8 28 23 0,3 0,4 12,0 0,1 0,7 0,7 Ylivieska 4 82 19 30 5,7 75 41 94 0,3 0,4 7,9 0,1 0,7 0,7 178 Kuusamo 5 64 15 35 91,2 106 24 32 1,6 0,3 6,7 0,0 0,3 0,5 Taivalkoski 2 23 6 9 2,4 52 7 24 1,6 0,3 3,8 0,0 0,3 0,5 181 Hyrynsalmi 3 9 6 3 7,0 43 22 31 0,4 0,6 8,6 0,0 0,3 0,3 Kuhmo 5 91 17 24 3,2 165 4 21 0,4 0,6 3,7 0,0 0,3 0,3 Puolanka 4 18 14 15 4,7 119 15 32 0,4 0,6 2,2 0,0 0,3 0,3 Suomussalmi 4 39 8 5 2,9 44 30 23 0,4 0,6 1,5 0,0 0,3 0,3 182 Kajaani 7 118 25 21 5,1 114 23 51 0,5 1,5 5,3 0,1 0,3 0,5 Paltamo 3 6 5 18 5,1 185 12 7 0,5 1,5 6,6 0,1 0,3 0,5 Ristijärvi 3 5 4 18 3,1 38 20 2 0,5 1,5 0,6 0,1 0,3 0,5 Sotkamo 6 43 20 23 12,7 88 32 62 0,5 1,5 9,2 0,1 0,3 0,5 Vaala 4 81 9 9 5,8 37 2 10 0,5 1,5 2,3 0,1 0,3 0,5 191 Ranua 4 24 13 15 2,5 48 32 12 0,1 2,6 11,0 0,1 0,1 0,2 Rovaniemi 11 283 32 42 3,2 757 65 108 0,1 2,6 11,2 0,1 0,1 0,2 192 Kemi 6 75 16 42 5,3 96 52 124 0,5 1,0 7,6 0,1 0,2 0,4 Keminmaa 6 28 10 14 3,2 46 13 104 0,5 1,0 4,3 0,1 0,2 0,4 Simo 3 11 3 4 2,3 14 5 18 0,5 1,0 2,1 0,1 0,2 0,4 Tervola 3 5 7 5 1,8 27 11 18 0,5 1,0 8,4 0,1 0,2 0,4 Tornio 5 100 19 23 3,7 39 46 69 0,5 1,0 3,2 0,1 0,2 0,4 193 Pello 3 22 5 11 2,5 45 17 24 1,1 1,6 2,6 0,0 0,2 0,4 Ylitornio 4 11 7 11 2,1 297 19 20 1,1 1,6 2,7 0,0 0,2 0,4 194 Kemijärvi 5 58 9 4 3,7 104 18 34 0,5 1,8 1,7 0,0 0,2 0,3

Final report 54(54)

Department of Economic and Environmental Statistics 24.2.2014 Ville Haltia

National Land Survey of Finland Risto Peltola

unit price (euro/m2), median

industrial, roads, parks, arable and forest housing lots commercial technical ser- land and office lots vices AH AK AP AR R K T Y E L V l.pr for arabl Pelkosenniemi 8 4 8 17 10,2 20 5 2 0,5 1,8 2,0 0,0 0,2 0,3 Posio 4 14 10 10 2,4 15 3 20 0,5 1,8 1,8 0,0 0,2 0,3 Salla 4 44 10 15 4,1 23 20 15 0,5 1,8 1,7 0,0 0,2 0,3 Savukoski 1 14 7 2 1,4 52 7 23 0,5 1,8 0,9 0,0 0,2 0,3 196 Enontekiö 2 9 5 6 6,0 27 7 23 0,1 0,5 1,4 0,0 0,3 0,5 Kittilä 5 59 14 11 22,4 189 46 56 0,1 0,5 2,9 0,0 0,3 0,5 Kolari 6 31 13 20 17,9 40 5 21 0,1 0,5 10,6 0,0 0,3 0,5 Muonio 4 11 5 18 5,1 32 1 4 0,1 0,5 6,0 0,0 0,3 0,5 197 Inari 3 18 6 12 8,1 19 11 18 0,0 0,9 1,4 0,0 0,2 0,8 Sodankylä 4 29 9 11 4,2 122 73 45 0,0 0,9 2,5 0,0 0,2 0,8 Utsjoki 4 6 3 3 2,5 17 3 18 0,0 0,9 22,7 0,0 0,2 0,8 201 Askola 7 18 21 16 5,1 14 50 56 0,5 0,8 2,9 0,1 0,9 1,1 Myrskylä 4 34 12 15 3,2 50 22 24 0,5 0,8 1,3 0,1 0,9 1,1 Pukkila 5 17 10 14 4,7 9 29 86 0,5 0,8 16,9 0,1 0,9 1,1 Porvoo 18 326 73 66 11,4 888 86 303 0,5 0,8 11,8 0,1 0,9 1,1 Sipoo 21 120 109 96 12,7 58 36 32 0,5 0,8 10,2 0,1 0,9 1,1 202 Lapinjärvi 7 5 8 3 4,3 24 19 2 0,5 5,0 6,6 0,0 0,5 0,9 Loviisa 9 104 13 10 9,9 113 32 73 0,5 5,0 15,7 0,0 0,5 0,9 211 Maarianhamina 35 173 33 72 1,8 856 139 31 , 2,1 9,8 0,0 , , 212 Eckerö 6 22 3 3 3,2 18 19 19 , 0,3 3,3 0,0 0,7 1,1 Finström 4 16 9 5 2,2 37 6 , , 0,3 1,4 0,0 0,7 1,1 Geta 3 22 3 4 3,3 18 15 , , 0,3 2,2 0,0 0,7 1,1 Hammarland 3 22 5 5 2,8 4 2 , , 0,3 3,3 0,0 0,7 1,1 Jomala 11 47 24 27 5,8 62 51 0 , 0,3 2,3 0,0 0,7 1,1 Lemland 6 22 23 22 2,8 18 3 , , 0,3 1,7 0,0 0,7 1,1 Lumparland 4 22 6 13 3,0 18 11 , , 0,3 3,3 0,0 0,7 1,1 Saltvik 7 22 4 3 2,8 18 15 , , 0,3 0,0 0,0 0,7 1,1 Sund 3 2 8 13 2,7 18 15 , , 0,3 8,5 0,0 0,7 1,1 213 Brändö 6 8 13 3 1,1 , , , , 0,7 6,2 0,0 0,3 1,4 Föglö 5 10 5 4 2,2 , , 0 , 0,7 6,7 0,0 0,3 1,4 Kumlinge 3 8 5 3 0,7 , , , , 0,7 6,2 0,0 0,3 1,4 Kökar 2 8 5 3 3,1 , , , , 0,7 6,2 0,0 0,3 1,4 Sottunga 1 8 5 3 0,8 , , , , 0,7 6,2 0,0 0,3 1,4 Vårdö 4 8 5 3 2,0 , , , , 0,7 6,2 0,0 0,3 1,4