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Compilation of Commercial Property Price Indices for Germany Tailored for Policy Use

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  • Knetsch Thomas A.

    (Directorate General Statistics, Deutsche Bundesbank, Frankfurt am Main, Germany)

Abstract

The compilation of commercial property price indices (CPPIs) is challenging. Policymakers urge for timely, reliable and comprehensive data. In Germany, lack of data prevents the calculation of official figures by the national statistical authority. Different applications of price indices need different definitions of commercial real estate. CPPIs according to these definitions are constructed on the basis of existing data for 127 German towns and cities (that cover about one-third of German population). The overall price developments revealed by the various indices are rather similar in terms of central time series characteristics, while differences in detail can be explained by their specific compositions. Price increases for all definitions have been strongest in the seven largest cities. The definitions tend to lead to more marked differences for medium-sized towns.

Suggested Citation

  • Knetsch Thomas A., 2021. "Compilation of Commercial Property Price Indices for Germany Tailored for Policy Use," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 437-461, August.
  • Handle: RePEc:jns:jbstat:v:241:y:2021:i:4:p:437-461:n:1
    DOI: 10.1515/jbnst-2019-0072
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    References listed on IDEAS

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    1. Katharina Knoll & Moritz Schularick & Thomas Steger, 2017. "No Price Like Home: Global House Prices, 1870-2012," American Economic Review, American Economic Association, vol. 107(2), pages 331-353, February.
    2. Òscar Jordà & Katharina Knoll & Dmitry Kuvshinov & Moritz Schularick & Alan M Taylor, 2019. "The Rate of Return on Everything, 1870–2015," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1225-1298.
    3. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
    4. Mick Silver, 2013. "Understanding Commercial Property Price indexes," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 14(3), pages 27-42, July.
    5. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
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    More about this item

    Keywords

    commercial property price indices; private data; stock weighting; policy use; C43; E31; R33;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • R33 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Nonagricultural and Nonresidential Real Estate Markets

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