Residential construction in Germany - Deutsche Bank Research

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Findings of panel regressions depending Tobin’s q partly distorted by government intervention Findings for German conurbations Estimates for all cities Current Issues The findings of the panel analysis are documented in Table 6, categorised according to BulwienGesa‗s ABCD city classification system. 7 It contains the estimates from calculations in which investment activity with building permits including measures in existing stock were set against the total existing housing stock. Cities All A-rated B-rated C-rated D-rated Number of cities 95 7 14 21 53 Observations 1,425 105 210 315 795 Within-R² 0.20 0.49 0.10 0.20 0.23 Overall-R² 0.17 0.29 0.08 0.20 0.19 Sources: BulwienGesa, Destatis, IW Cologne Institute for Economic Research. Notes: The coefficients of q are significant at the 1% level in all regressions; all variables (in the total sample and in the subsamples) are stationary, i.e. they have a constant mean value and a constant variance over the entire observation period. This is shown by the Levin-Lin-Chu test, which rejects the null hypothesis of the existence of a unit root for all variables at all significance levels. 6 To assess the ability of Tobin‘s q to describe investment volume we use R² statistics. If the coefficient of determination is equal to zero, there is no correlation between Tobin‘s q and building activity, while if it is equal to one, the model can explain the investments in full. Since variances emerge between cities as well as within cities in panel analyses, two values are given altogether. The most important value for fixed-effects estimates, ―within-R²‖, shows how the changes in building activity can be explained by Tobin‘s q of a city. ―Overall-R²‖ explains the meaningfulness of the overall model. The estimated coefficients and their significance show that, in line with expectations, Tobin‘s q exerts a positive, significant influence on investment activity. 8 The findings show that, in line with expectations, Tobin‘s q exerts a positive and significant influence on investment activity. The investment fluctuations in a city can be explained to around 20% altogether on the basis of Tobin‘s marginal q. 9 The explanatory power is considerably higher for A-rated cities, at 49%, while it is considerably lower for B-rated cities at 10%. For the smaller C and D-rated cities it is around 20%. This suggests that the explanatory power of the regressions does not increase or decrease with the size of the city. The low coefficients of determination are probably not solely attributable to quality problems with the data, but mainly to the fact that Tobin‘s q cannot possibly contain all the information on the market situation of a given city. Note in this case in particular the abolition of the home-ownership grant at the end of 2005. Thanks to 7 A city is categorised with an A, B, C or D rating according to its functional importance, largely according to its population, but also according to office turnover and office stock (www.riwis.de). 8 Because of endogeneity problems the coefficients are not interpreted further. 9 The estimates differ only marginally depending on the proxies chosen for construction investment.By contrast, the regressions with the average q deliver noticeably poorer results. For the estimates with time-lagged q values it emerges that current q, as a mirror of the market situation, can give the best reflection of investment activity. Furthermore, the explanatory power of the models declines when the years 1990 through 1994 are included. This may be interpreted as a gradual return to normality in the real estate markets following the heavy degree of government intervention during the German unification phase. 6 August 22, 2011
Residential construction in Germany Surrounding communities need to be factored in this subsidy, newbuild prices could rise independently of pure building costs. At the same time, though, there was a tangible drop in building activity. Even if only owner-occupiers and not all investors were affected, this structural break would have to be factored into the model. At the same time, though, there was a tangible drop in building activity. This break would have to be factored into the model. Furthermore, the effects of the boom years following German unification are still to be seen at the beginning of the observation period in 1995, when very sizeable amount of money continued to be invested in the housing market despite a lack of demand. Estimates for individual markets The preceding section examined Germany‗s total market in a panel analysis. Now we shall look at the extent to which the correlation between Tobin‗s q and investment activity is also visible in the individual cities. Our focus zeroes in on the ten most populous cities in Germany, that is all cities from Berlin (3.44 million inhabitants) to Bremen (0.55 million). These major cities differ very considerably in some cases in their demographic and economic development. Between 1990 and 2010, Bremen, Essen und Dortmund suffered a decline in population. The other cities recorded slight gains or strong growth, such as Hamburg and Munich at roughly 8%. Our analysis of the individual markets also factors in the building activities in the counties surrounding the central cities. So far, investments in the direct catchment area have been disregarded, even though the price and rent ratios of a large city also have a substantial influence on developments in the surrounding municipalities (see Nitsch 2011). A high q value in the city prompts investors to look for suitable locations for new real estate. Since the supply of building land in an agglomeration is low and the price of building land is more favourable in the periphery, the search for locations is not only conducted within the city limits. Spill-over effects emerge which probably prove all the greater, the less that demand for building land can be met by the supply within a city and the tighter the political definition of the central city. Therefore, we formed two rings around every city in order to include the building activity in the surrounding counties. — The first ring only factors in the directly adjacent counties. 10 — The second ring corresponds to the metropolitan region in which the respective major city is located. Because of problems of definition, we refrained from including a second ring for the four cities of the Rhine-Ruhr metropolitan region (Dortmund, Dusseldorf, Essen and Cologne). This simple method for defining the rings was chosen primarily for handling reasons. Moreover, the prices and rents, and thus the q values, of the included counties in the vicinity were ignored, since our aim was to analyse whether price dynamics in the central city also induce building activity in the surrounding area. 10 There are two exceptions: 1) Berlin: The county of Brandenburg a.d. Havel is also categorised in the first ring because it is fully integrated into the territorial bodies of the adjoining counties. 2) Stuttgart: While the county of Göppingen does not directly border on the city of Stuttgart, it does belong like the other four adjacent counties to the Stuttgart region and is therefore categorised as part of the first ring. August 22, 2011 7
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