German experiences in estimating households’ non-financial assets

1. German experiences in estimating households’ non-financial assets OECD Working Party on National Accounts and Financial Statistics Paris, 2-5 October 2007 Presented by Oda Schmalwasser and Marc Peter Radke Federal Statistical Office Deutsche Bundesbank oda.schmalwasser@destatis.demarc-peter.radke@bundesbank.de

2. Content 1. Introduction 2. Compilation of households’ fixed assets by the Federal Statistical Office 3. Estimation of households’ stock of land underlying buildings and structures by the Deutsche Bundesbank 4. Example of use: Compilation of integrated financial and non-financial household sector balance sheets 5. Conclusion

3. 1. IntroductionAvailability of data on households’ non-financial assets

4. 2. Compilation of fixed assets

5. 2. Compilation of households’ fixed assets (2) Traditional German non-financial enterprises: S.11+S.14 Dwellings by sector available Basis for the further breakdown: PIM information in a cross classification of • other buildings and structures (including major improvements on land and costs of ownership transfer on land) • machinery and equipment • intangible assets for S.1 – S.12 – S.13 – S.15 = (S.11 + S.14) by 60 industries (A60 of ESA 95)

6. 2. Households’ fixed assets (3) Net stock at current replacement costs

7. 3. Estimation of households’ stock of land underlying buildings and structures

8. 3.1 Background information • Characteristics of the “old” approach to the estimation of land underlying buildings and structures by the Bundesbank • Approach was inextricably linked with the estimation of fixed assets • Approach was based on an updating procedure of former estimates of fixed assets by Destatis under ESA 1979 and former land estimates by the German Institute for Economic Research (DIW) and the Deutsche Bundesbank • Introduction of Destatis’ sectoral compilation of fixed assets required a “new” approach to the estimation of land underlying buildings and structures

9. 3.2 Data requirements Aim of the estimation procedure was to compile • Market value and real stock of land underlying buildings and structures (AN.2111) for households including non-profit institutions serving households (S.14+S.15) for the period from 1991 to 2006 • Breakdown of the results into • part of land (AN.2111) underlying dwellings (AN.1111) • part of land (AN.2111) underlying other buildings and structures (AN.1112)

10. 3.3 Data sources • Statistics on purchase values of building land published by Destatis • Transactions: sales volumes of building land (in sq.km) from 1964 up to 2007 (annual data) • Transaction/market prices (in €/sqm) • Breakdown by building areas (business area, mixed business and residential area, residential area, industrial area, village area) • No breakdown by sector and no breakdown into land underlying dwellings and land underlying other buildings and structures according to ESA 1995 • Statistics on the area of land classified by actual uses published by Destatis • Breakdown of the whole economy’s stock of land by kinds-of-use (in sq.km) (quadrennial data: 1992, 1996, 2000, 2004) • Definition of item “areas and open areas underlying buildings” corresponds to land underlying buildings and structures (AN.2111) according to ESA 1995. • No breakdown into land underlying dwellings and land underlying other buildings and structures according to ESA 1995; no information on land (market) prices

11. 3.4 Estimation procedure Step 1: Stock-flow calculation of land underlying buildings and structures at the total economy level (S.1) and breakdown by building areas (business area, mixed business and residential area, residential area, industrial area, village area)from 1991 to 2006 Step 2: Breakdown of the stock-flow calculation into land underlying dwellings and land underlying other buildings and structures at the total economy level (S.1) Step 3: Breakdown of land underlying dwellings and land underlying other buildings and structures by institutional sector

12. 3.5 Results (1) Notes: Net stock of fixed assets is valued at replacement costs; land underlying buildings and structures is valued at market prices.

13. 3.5 Results (2) Notes: Net stock of fixed assets is valued at replacement costs; land underlying buildings and structures is valued at market prices.

14. 3.6 Assessment • Estimation approach led to considerable improvements in data quality • But results have to interpreted with due care because • sectoral breakdown is based on assumptions (no sectoral data available) • estimates can be only considered as a lower limit of the “true“ market value (no information on market values of land which is already built-up)

15. 4. Example of use: Compilation of integrated financial and non-financial household sector balance sheets

16. 4.1 Compilation procedure • Compilation of balance sheets for households including non-profit institutions serving households (S.14+S.15) from 1992 to 2006 • Data sources and compilation: 1. Net stock of fixed assets (AN.11) from Destatis 2. Land underlying buildings and structures (AN.2111) from Bundesbank 3. Financial assets and liabilities (AF) from Bundesbank (financial accounts) 4. Net worth (B.90) compiled as residual

17. 4.2 Results (1) Notes: Net stock of fixed assets is valued at replacement costs; land underlying buildings and structures is valued at market prices. Financial assets and liabilities are predominantly valued at market prices.

18. 4.2 Results (2) Notes: Net stock of fixed assets is valued at replacement costs; land underlying buildings and structures is valued at market prices. Financial assets and liabilities are predominantly valued at market prices.

19. 4.3 International Comparison Source (except data for Germany): OECD, Economic Outlook, Vol. 2007/1, No. 81, June, Annex Table 58: Household wealth and indebtedness, p. 298. Notes: For Canada, Italy and the United States, data also include consumer durables. For Canada, Germany, France, Japan, the United Kingdom and the United States, data also include non-residential buildings and fixed assets of unincorporated enterprises and of non-profit institutions serving households, although coverage and valuation method may differ.

20. 5. Conclusion • Data quality and data availability regarding non-financial assets and respecting household sector balance sheets have been improved considerably by the latest work of Destatis and the Bundesbank • Potential fields of improvement: • Regarding data availability: for example, collection of data on non-financial assets which have not yet been covered by the current compilation approach • Regarding data quality: for example, development of reliable valuation methods for land taking into account regional differences