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.
Acknowledgements
The author thanks Julian Barnikol, Alexandra Hock, Mario Schimmelpfennig and Patrick Schwind for excellent research assistance. The paper benefitted from intensive discussions with Christine Schlitzer on this topic. Comments by Edgar Brandt, Andrew Kanutin, Malte Knüppel and the participants of sessions at the 62nd World Statistical Congress in Kuala Lumpur and the 2019 Workshop “Recent trends in the real estate market and its analysis” held by Narodowy Bank Polski are gratefully acknowledged. I thank two anonymous referees and the editor for helpful suggestions. Of course, all remaining errors are mine. The views expressed in this paper are solely mine and should not be interpreted as reflecting the views of the Deutsche Bundesbank and the Eurosystem.
Definition of city-groups and towns and cities covered
| City-group | Explanation | List of towns and cities |
|---|---|---|
| Seven largest cities | Originally introduced by bulwiengesa; meanwhile adapted by other compilers (e.g. Destatis, Deutsche Bundesbank, vdp) | Berlin, Cologne, Düsseldorf, Frankfurt (Main), Hamburg, Munich, Stuttgart |
| Large cities excluding seven largest cities | BBSR definition of a large city: at least 100.000 inhabitants | Aachen, Augsburg, Bergisch Gladbach, Bielefeld, Bochum, Bonn, Bottrop, Braunschweig, Bremen, Bremerhaven, Chemnitz, Darmstadt, Dortmund, Dresden, Duisburg, Erfurt, Erlangen, Essen, Freiburg (Breisgau), Fürth, Gelsenkirchen, Göttingen (City), Hagen, Halle (Saale), Hamm, Hannover, Heidelberg, Heilbronn, Herne, Hildesheim (City), Ingolstadt, Jena, Karlsruhe, Kassel, Kiel, Koblenz, Krefeld, Leipzig, Leverkusen, Lübeck, Ludwigshafen, Magdeburg, Mainz, Mannheim, Moers, Mönchengladbach, Mülheim (Ruhr), Münster, Neuss (City), Nürnberg, Oberhausen, Offenbach (Main), Oldenburg, Osnabrück, Paderborn (City), Pforzheim, Potsdam, Recklinghausen (City), Regensburg, Remscheid, Reutlingen (City), Rostock, Saarbrücken, Siegen (City), Solingen, Trier, Ulm, Wiesbaden, Wolfsburg, Wuppertal, Würzburg |
| Medium-sized towns | BBSR definition of a medium-sized town: at least 20.000, but below 100.000 inhabitants | Albstadt, Aschaffenburg, Bamberg, Bayreuth, Brandenburg (Havel), Coburg, Cottbus, Dessau-Roßlau, Detmold, Düren (City), Eisenach, Flensburg, Frankfurt (Oder), Friedrichshafen, Fulda (City), Gera, Gießen (City), Görlitz, Greifswald, Gütersloh (City), Halberstadt, Hanau, Kaiserslautern, Kempten (Allgäu), Konstanz (City), Landshut, Lüdenscheid, Lüneburg (City), Marburg, Minden, Neubrandenburg, Neumünster, Offenburg, Passau, Plauen, Ratingen, Ravensburg (City), Rosenheim, Salzgitter, Schweinfurt, Schwerin, Stralsund, Suhl, Tübingen (City), Villingen-Schwenningen, Weimar, Wilhelmshaven, Witten, Zwickau |
Annual CPPI changes.
| In per cent or percentage points | |||
|---|---|---|---|
| Mean | Standard deviation | ||
| 1995–2019 | 2005–2019 | 1995–2019 | |
| 127 towns and cities | |||
| CRR definition | 2.44 | 5.75 | 6.24 |
| ESRB definition | 3.39 | 6.84 | 6.26 |
| Broadest definitiona | 3.31 | 6.31 | 5.50 |
| Memo items: | |||
| Office | 2.58 | 6.25 | 7.26 |
| Retail | 1.88 | 4.01 | 3.45 |
| Multi-family dwellings | 4.37 | 7.98 | 6.79 |
| Houses and apartments | 2.10 | 3.79 | 3.34 |
| Seven largest cities | |||
| CRR definition | 4.51 | 8.60 | 10.01 |
| ESRB definition | 5.10 | 9.01 | 7.87 |
| Broadest definitiona | 4.75 | 8.17 | 6.51 |
| Large cities excluding 7 largest cities | |||
| CRR definition | 1.30 | 4.15 | 4.81 |
| ESRB definition | 2.40 | 5.64 | 5.67 |
| Broadest definitiona | 2.62 | 5.45 | 5.17 |
| Medium-sized towns | |||
| CRR definition | −0.66 | 2.12 | 4.33 |
| ESRB definition | 1.56 | 4.34 | 5.15 |
| Broadest definitiona | 2.05 | 4.57 | 4.84 |
-
aAccording to Eurostat (2017).
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