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Disentangling the Short and Long-Run Effects of Occupied Stock in the Rental Adjustment Process

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Abstract

In the current stand of literature on the rental adjustment process starting with Hendershott et al. (Real Estate Economics, 30, 165-183, 2002a, Journal of Real Estate Finance and Economics, 24, 59-87, 2002b) it has become practice to treat the compound variable “occupied stock” as a supply variable. In this study we show that this variable deserves a more critical investigation and that the general view of a supply variable may be misleading. Using panel data covering 30 urban areas for 17 years, we investigate the rental adjustment process in the German office market. The application of recently developed cointegration techniques for non-stationary panel data in conjunction with the corresponding error correction model (ECM) enables us to overcome the data limitations, particularly existent for most European real estate markets. Hence, our primary motivation is (a) to demonstrate how “occupied stock” should be interpreted correctly and (b) to provide useful insights into the long-term relationships and short-run dynamics of real office prime rents. The empirical evidence suggests that a one percent rise in office employment increases real rents on average by 1.64% through higher demand for office space. On the other hand, a one percent increase in the supply of office space decreases real rents in the long run by 2.25%. The results from the error correction model show that deviations from the long-run equilibrium lead to an adjustment process which restores equilibrium within approximately 3 years.

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Notes

  1. Numerous empirical studies have analyzed the mechanisms and determinants of the construction cycle in the U.S. office market with the main contributions among others being Wilson and Zurbruegg (2003), Matysiak and Tsolacos (2001), Wilson and Okunev (1999a, 1999b), Wilson et al. (2000) and Scott and Judge (2000). For a general comprehensive literature review for the U.S. and U.K see also Tsolacos et al. (1998).

  2. It seems to be the general finding that variables used to explain changes in real rents are more relevant in larger markets, or to be more precise, larger markets are better modelled using standard methods. See e.g. Pollakowski et al. (1992).

  3. This model goes back to labour economics and was introduced for housing rents by Blank and Winnick (1953). In the labour market, real wages are considered as responding to differences between natural and observed unemployment rates. A gap between the natural and observed unemployment (vacancies) leads to a rise in real wages (rents).

  4. See e.g. Hendershott (1997).

  5. Since the demand for office space or use is directly reflected in occupied space, this equation is fulfilled at all times. In the long run, however, the vacancy rate is replaced by its natural value, \( {D_t}\left( {{E_t},{R_t}} \right) = \left( {1 - \upsilon_t^*} \right) \cdot {S_t} \).

  6. In practice, however, it is reasonable to assume that an increase in stock will not leave the occupancy rate constant but will lead to a decrease in the occupancy rate, so that the majority of the newly added stock is in fact vacant stock.

  7. The suitability of this approach was firstly proposed by Simes (1986). Here α = 0.05 and N = 30.

  8. We are aware of the ongoing discussion if variables such as the vacancy rate which is bounded from both sides can in fact be non-stationary even if the null hypothesis of a unit root cannot be rejected. We believe that the vacancy rate can have a unit root insofar as random shocks in the past have a permanent influence on future values even though this random walk behaviour lies in a certain range. However, as pointed out by Gonzales and Pitarakis (2006), the concept of I(0) and I(1) variables is in this case not well defined.

  9. Common first generation tests such as the IPS test lead to the same result with the exception of rents which are indicated as I(0) at the 5% level.

  10. Although it is obvious that most variables have time trends, it is less clear if these trends have a common source.

  11. Regionales Immobilien Wirtschaftliches InformationsSystem (Regional Property Market Information System). Within their Regional Property Market Information System the BulwienGesa AG calculates rents and prices for representative commercial and residential real estate. The information is collected from own appraising activities as well as from secondary sources such as building societies, research institutes, appraiser committees, real estate broker associations, chambers of industry and commerce, and independent experts. The data base covers planning data for all 439 German districts and market data for 125 cities, and provides essential data for market comparisons and quantitative analyses in key sectors.

  12. For an A classification the stock must be more than 5 million square meters and the long-term average of turnover must be more than 100,000 square meters. For B-cities the stock of office space must lie between 1.5 and 4 million square meters and the turnover has to be at least 35,000 square meters. C-cities are of regional and limited national relevance with an important vibrancy on the surrounding region. In contrast, D-cities are regionally focused locations with a central function for the direct hinterland with low market volume and turnover. Cities with less than 100,000 inhabitants are disregarded in the database of RIWIS.

  13. For the A cities for instance, the outliers are Frankfurt (36 EUR on average) and Berlin in the early nineties (39 EUR from 1990 to 1995).

  14. The moderate bimodality that is present in the data is due to differences in overall population rather than the existence of “office clusters”.

  15. For example, the eastern German B-cities Magdeburg and Leipzig have vacancy rates of around 15% and 25%, respectively.

  16. In the course of the German unification, office buildings in East Germany were granted special depreciation rights in order to foster investments in this area. However, any effects that might have arisen due to this event are ultimately reflected in our demand or supply variables. Consequently, a t-test for comparing the coefficient means between East and West German cities yields T = 0.051 for employment and T = 0.047 for occupied stock. Therefore, the null hypothesis of equal means cannot be rejected.

  17. This also becomes evident from Fig. 2.

  18. It is important to note that the dotcom bubble and its burst were directly reflected in office employment and occupied stock. A dotcom dummy for the periods 1999–2000 and a post dotcom dummy for the periods 2001–2005 turned out to be insignificant. Therefore, the dotcom period is likely to have increased standard errors but is less likely to have biased coefficient estimates.

  19. The estimated time effects also show little variation over time making their inclusion unnecessary.

  20. If the gap in the vacancy rate is replaced by the log of the vacancy rate the effect increases in absolute terms to -1.22 which is more in line with the findings of Wheaton and Torto (1988).

  21. An F-test with test statistic F = 13.12 indicates that the coefficients are jointly significant. The full results including the estimates for the rest of the coefficients are not shown but are available from the authors upon request.

  22. At first glance one might expect a high degree of collinearity between employment and the occupancy rate (and thus occupied stock) as both variables are interpreted as demand variables in this short-run model. However, the two effects mentioned above show that employment and the occupancy rate do not necessarily need to be highly correlated. In fact, the correlation between both variables is only 0.29 on average.

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Acknowledgments

The authors are grateful to Thomas Voßkamp from BulwienGesa AG for providing the data and Peter Pedroni for the RATS code for cointegrating vectors in panel data and helpful comments. Furthermore, we thank Heinz Rehkugler, Tobias Rombach, Nico Rottke, Matthieu Stigler, Anthony Strittmatter, Marcel Tyrell, Franziska Wenzel, Joachim Zietz, an anonymous referee, and the participants of the 2009 ARES conference and 2009 IREBS conference on real estate finance and economics for helpful suggestions on various earlier versions. We bear of course responsibility for all remaining errors.

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  1. European Business School, Oestrich-Winkel, Germany

    Zeno Adams & Roland Füss

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  1. Zeno Adams
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Adams, Z., Füss, R. Disentangling the Short and Long-Run Effects of Occupied Stock in the Rental Adjustment Process. J Real Estate Finan Econ 44, 570–590 (2012). https://doi.org/10.1007/s11146-010-9250-7

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