Residential construction in Germany - Deutsche Bank Research

Germany Current Issues
August 22, 2011
Authors
Ralph Henger
IW Köln
+49 221 4981-744
henger@iwkoeln.de
Tobias Just
+49 69 910-31876
tobias.just@db.com
Michael Voigtländer
IW Köln
+49 221 4981-741
voigtlaender@iwkoeln.de
Editor
Bernhard Speyer
Technical Assistant
Sabine Kaiser
Deutsche Bank Research
Frankfurt am Main
Germany
Internet: www.dbresearch.com
E-mail: marketing.dbr@db.com
Fax: +49 69 910-31877
Managing Director
Thomas Mayer
Residential construction in
Germany
Tobin‘s q pointing to regional recovery
Consolidation over in residential construction sector. Residential
construction is a key pillar of economic activity; residential buildings account for
roughly 6% of German GDP. This is why it is good news that in the first five
months of 2011 the number of residential building permits increased by over 30%
on the (very low) year-earlier level. Our analysis shows that Germany has many
regions where house prices are sending clear signals that more building
completions will follow as a result.
Tobin’s q as measure of propensity to invest. Tobin‘s q measures the value
of newbuild investments in comparison with existing stock and increases when the
prices of existing houses and/or newbuild rents go up – ceteris paribus in each
case. Up until recently, the q value for Germany as a whole has been on the
decline – this indicates that building demand is not increasing at the national level.
However, the picture changes if one looks at Germany‘s conurbations and major
metropolitan regions.
In conurbations, Tobin’s q is on the increase – residential
construction is rightly picking up there. For not only Munich and Hamburg
but also Heidelberg, Bamberg and Potsdam are in some cases seeing significant
increases in their q values. So building demand is concentrated on certain regions
and is fundamentally warranted by the respective price trends.
Price developments in central cities have spill-over effects far beyond
the city limits. Tobin‘s q and building activity in a given region correlate even
more closely if one does not focus solely on building activities in a central city but
also on those in its environs. There are evidently strong filter processes taking
place between the central cities and the surrounding vicinity in (most) German
conurbations. This is where the regional housing markets are obviously functioning
very well.
Tobin's q still decreasing
– for Germany as a whole
1995=100
115
110
105
100
95
90
85
80
75
70
1990 1994 1998 2002 2006 2010
Marginal q
Rent (newbuild)
Rent (existing stock)
Price (newbuild)
Price (existing stock)
Sources: BulwienGesa, Destatis, IW Cologne
Institute for Economic Research
Tobin's q increasing in
Munich
1995=100
120
110
100
90
80
70
1990 1994 1998 2002 2006 2010
Marginal q
Rent (existing stock)
Rent (newbuild)
Price (existing stock)
Price (newbuild)
Sources: BulwienGesa, Destatis, IW Cologne
Institute for Economic Research

10% of GDP
Construction investment as % of GDP
(nominal)
1970 1980 1990 2000
18
16
14
12
10
8
6
4
2
0
2010
Residential construction
Non-residential construction
Source: Destatis 1
Correlation between
construction and GDP
Nominal growth, % yoy
1970 1980 1990 2000
35
30
25
20
15
10
5
0
-5
-10
-15
2010
GDP
Residential construction
Non-residential construction
Source: Destatis 2
Moderate pick-up in building
permits
Number of approved housing units ('000)
450
400
350
300
250
200
150
100
50
0
1992 1995 1998 2001 2004 2007 2010
Single and two-family dwellings
Multi-family dwellings
Source: Destatis 3
Introduction
Current Issues
In 2010, nominal investment in construction totalled nearly
EUR 250 bn in Germany. Residential construction alone accounted
for over EUR 140 bn of this total, with the rest divided between nonresidential
commercial construction and civil engineering projects.
Construction investment thus generates around 10% of Germany‘s
gross domestic product (GDP); this roughly equals the mean for
Western Europe. Significantly higher readings are often a reflection
of catching-up processes, as is currently the case in Eastern
Europe, or of overheating, as in Spain and Ireland before 2007.
The absolute volume of residential construction alone does not
determine its overall economic significance, though. The political
and social value attached to housing as a good as well as the
pronounced volatility of the sector also play a part: in recession
years residential construction experiences a particularly strong
slump, whereas in boom phases the sector outstrips the economy
as a whole. This is why the standard deviation for the growth rate of
residential construction has been over twice as high as for GDP over
the past 40 years. These characteristics also make residential
construction a popular target for economic and (re-)distribution
policies. In Germany, very expansionary residential-construction
policy measures triggered sizeable growth stimuli in the early 1990s,
but as special depreciation charges, accelerated depreciation
schedules and the home-ownership grant were gradually abolished
or at least curtailed over the subsequent years, the result was a
lasting recession in residential building and weak house prices. The
development of residential building permits and completions gives a
very good illustration of this adjustment burden: in the multi-family
segment the number of housing units approved fell by over 80%
within ten years, while for single and two-family dwellings the
decline came to 60%.
Over the past few years many housing markets have stabilised. A
resumption of income growth and still comparatively low mortgage
rates are currently driving up the price of rental accommodation and
house prices. 1 In 2010, rents and prices increased by 2-3% in
Germany‘s major cities. While this represents an only modest climb
by international standards, for Germany it translates into the biggest
jump since 1994.
This development raises several key questions: how close is the
correlation between house prices and building activity? Does a
recovery in the housing markets also trigger a significant resumption
of building activity? Which regions are likely to see the most
pronounced recovery?
There are tentative signs of reviving activity in the residential
construction sector – building permits are up by around 10% since
the 2008 low. And, in the first 5 months of 2011 the number of
residential building permits actually jumped by over 30% yoy. Does
this provide clear evidence of a trend reversal?
1 See Just, T. (2010).
2 August 22, 2011

Residential construction in Germany
Tobin‘s q: Formal representation
General:
Real estate market:
If
MB = Rent for existing stock
MN = Newbild rent; as a simplification MB=MN.
PB = Price of existing stock
L = Supply price for capital (acc. to Tobin)
RK = Reproduction costs
R = Return on newbuild
and it holds that:
Then it follows for the average Tobin‘s q:
And for the marginal Tobin‗s q:
With
And because
it follows that:
Sources: Nitsch (2011), Tobin (1969)
Quotient of existing house prices and
newbuild rents signals demand for
construction
In this article we pursue two objectives: first of all we conduct a
systematic analysis of whether an empirical relationship exists in the
Housing completions per 1,000 inhabitants
2009 (% yoy)
Wohnungsfertigstellungen pro 1000 Einwohner (2009)
27, 84 11, 36 8, 63
Sources: Destatis, DB Research
Qu e lle : D e u ts ch e B a n k R e s e a rch
8, 63 Keine Daten
first place between price dynamics in the housing markets and
residential construction activity. To this end we estimate the link
between building activity and Tobin‘s q for various regional property
markets. Secondly, we shall attempt to draw conclusions on the
development of building activity in Germany and in key submarkets
from our empirical findings.
Tobin’s q: What does it mean?
Tobin‘s q was developed over 40 years ago by James Tobin, a
winner of the Nobel Prize for Economics. Taken from the word
―quotient‖, it is a ratio that first found application in his portfolio
theory for company valuations. It is calculated as the quotient of
market value (equity plus debt capital) and the price that would have
to be paid in the market to purchase all of a company‗s real assets
anew (replacement price). If the market value exceeds the
replacement price, i.e. if q is greater than 1, it is worthwhile investing
in real capital, in other words in ―replicating‖ the company. This
makes Tobin‗s q an ideal indicator for potential investment activity,
for the market value contains all the estimates of the market
participants and the replacement price embraces all the costs of
investment activity. 2
This approach can be applied to investments in real estate: it holds
by the same token that it is worthwhile to invest in new buildings
precisely when the discounted future rents to be generated are
2 In portfolio theory, q reflects the risk-adjusted reconciliation of returns on real and
monetary capital (Tobin, 1969).
No data
August 22, 2011 3
> 8.6
< 8.6
< -11.4

Difficulty of capturing data seriously
hampered analysis
Current Issues
higher than the reproduction costs. Given a sufficiently long useful
life for a piece of real estate, the discounted rents of a property
equal the property price in the existing stock. So Tobin‗s q for real
estate is calculated as being the quotient of the price of existing
stock and the respective reproduction costs (for its formal derivation,
see the box in the margin on page 3). 3 Furthermore, Hayashi (1982)
argues that the ratio of the existing stock price to the reproduction
price does not reflect the actual investment strategy since stock
prices show the profit expectations for past investment projects and
not for future ones. To this extent, the Tobin q described above is an
average q (qD). This is why Hayashi suggests that it is better to
choose marginal q (qM) as a guideline.
For real estate markets, Hayashi‘s marginal qM can be calculated by
a simple additional ratio of newbuild rent and existing stock rent
(Nitsch, 2011). The higher the prices of existing houses and the
higher the rents for newbuild houses, the greater is this qM, ceteris
paribus.
Empirical findings
Empirical findings on Tobin‗s q to date have not been
unambiguous – neither at company level nor for real estate
markets. 4 This is frequently explained by the difficulty of capturing
the appropriate data. There are indeed substantial difficulties to
determine the exact reproduction costs for real estate in particular,
owing to its heterogeneity. After all, every piece of real estate is
ultimately unique, making it impossible to reproduce and re-offer it
on the market. Nailing down market prices is not an easy task either.
If a given building has not been put up for sale of late, its market
value can only be approximated via rules-based procedures.
In economic literature there have been only few studies hitherto
focusing on the correlation between Tobin‗s q and investments in
construction. Jud and Winkler (2003) were the first to apply Tobin‗s
and Hayashi‗s theories to the real estate sector in detail. Furthermore,
using US data they demonstrated that Tobin‗s q can explain a
large part of real estate investment. Berg and Berger (2006)
reaffirmed these findings for the Swedish market. For Germany,
Nitsch (2011) determined that around 75% of real estate investment
can be described by Tobin‗s q for some markets.
Data and model used
Our study analyses investment in residential construction in 127 of
Germany‘s major cities. The data was sourced from BulwienGesa‘s
RIWIS database, which stores largely seamless annualised
statistics both on prices and rents for existing stock and newbuild
houses 5 , as well as from the German Federal Statistical Office‘s
statistics on building activity.
To ascertain Tobin‗s q we take the price and rent ratios between
stock and newbuild housing. As explained in the previous section,
newbuild prices are used to approximate reproduction costs. The
average price and rent developments for all of the cities are
depicted together with Tobin‗s q in chart 4. All the time series
3
For a detailed discussion of Tobin‗s q for real estate see Nitsch (2011).
4
See Schulz & Werwatz (2008) for an overview of the literature for real estate
market analyses.
5
The variables are called: ―Purchase price owner-occupied home, resale average‖,
―Purchase price owner-occupied home, newbuild average‖, ―Re-let homes,
average‖, ―Rent newbuild, average‖. For a more detailed description of the
variables and list of the cities, see www.riwis.de.
4 August 22, 2011

Residential construction in Germany
Tobin's q still decreasing
– for Germany as a whole
1995=100
115
110
105
100
95
90
85
80
75
70
1990 1994 1998 2002 2006 2010
Marginal q
Rent (newbuild)
Rent (existing stock)
Price (newbuild)
Price (existing stock)
Sources: BulwienGesa, Destatis, IW Cologne
Institute for Economic Research 4
Fewer newbuilds
Building permits and completions, in %
of housing stock
1990 1993 1996 1999 2002 2005 2008
Completions
Completions incl. measures in
existing stock
Building permits
Building permits incl. measures in
existing stock
Average for 127 German cities
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Sources: Destatis, DB Research, IW Cologne
Institut for Economic Research 5
indicate a very strong increase in prices and rents following German
unification, even though there was a significant correction starting in
the years 1993/1994. The rent ratio remained relatively constant
over time. For prices, the gap widened considerably. This is because
after the recession in the second half of the 1990s newbuild prices
have remained relatively constant to this very day, while prices of
existing stock have constantly trended downward since then. In the
case of rents, the differences narrowed towards the end of the
1990s when newbuild rents noticeably eased relatively. All in all, this
has resulted in a steady decline in Tobin‗s q in Germany.
The estimate of investment activity was based on the number of
newly built dwellings. The official statistics offer four different time
series with housing completion and permit data, both including and
excluding measures in the existing stock, respectively. 6 Building
permit statistics are used for the following regressions since these
are the starting basis for the other statistics on building activity;
moreover, they coincide very closely with the start of the investment
process. The measures in existing stock were added in order to also
factor in investments in the interior of existing stock.
Since the focus of interest is not the absolute volume, but the
relative change in the capital stock, these data are set in relation,
respectively, to the housing stock data (see Meier 2011). The
relative growth of the housing stock from 1990 to 2009 is displayed
in chart 5. All in all, the housing market expanded by roughly 1% per
year in the 1990s, with the rate slowing to not quite 0.5% over the
past ten years.
For our empirical evaluation we took recourse to panel data sets on
95 major cities in Germany. The panel analysis only includes the
data from the years 1995 through 2009, since the price and rent
data before 1994 show certain quality-related shortcomings on
account of differing data-collection procedures in the individual cities
and since the building-activity statistics for 2010 were not yet
available at the time of this analysis. Moreover, in this period the
data set includes the entire information necessary for every unit
under review. On the basis of these data empirical tests were
performed to establish the correlation between investment activity I
(building activity) in relation to the capital stock K (existing housing
stock) and Tobin‗s q with the following estimating equation in a fixedeffects
model:
�Iit Kit
� � �i � �itqit
� � it
The variable q is the q value of a city i in period t. it
� and i � are it
coefficients to be estimated. � is an error term. An applied fixed-
it
effects model, unlike with the pooled-regression model introduces a
dummy or a city-specific constant for every city. This enables us to
control for potential systematic unobserved differences between the
cities.
6 The individual time series are called: 1) Completions in residential buildings; 2)
Completions incl. measures in existing stock: total houses; 3) Housing permits in
residential buildings; 4) Housing permits incl. measures in existing stock: total
houses.
August 22, 2011 5

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

Tobin's q increasing in
Munich
1995=100
70
1990 1994 1998 2002 2006 2010
Marginal q
Rent (existing stock)
Rent (newbuild)
Price (existing stock)
Price (newbuild)
120
110
100
90
80
Sources: BulwienGesa, Destatis, IW Cologne
Institute for Economic Research 8
Correlation of Tobin's q for building activity in
individual cities and their surrounding counties
Current Issues
Ring 1 Ring 2
The results of the individual regressions for the cities reveal stark
differences. For half of the cities, Tobin‘s q correlates very heavily
with building activity. For these cities the correlation coefficient is
greater than 0.5. For the other half there is virtually no correlation
between investment and Tobin‘s q. A host of causes may provide the
explanation for the differences. However, the main reason is
probably that the demand for construction investment expressed by
Tobin‘s q cannot be satisfied within a major city by a corresponding
supply of attractive locations.
The inclusion of the surrounding counties can therefore significantly
boost the explanatory power of Tobin‘s q. In 8 out of 10 cities the
coefficient of determination climbs in some cases very significantly if
the building activity in the counties of the first ring is factored in
(Bremen, Düsseldorf, Essen, Hamburg). These values can be
boosted further for the metropolitan region as a whole. So price
dynamics in one city have spill-over effects far beyond the city limits.
(see table 7).
By contrast, there does not appear to be any improvement in the
meaningfulness of Tobin‘s q when the counties surrounding the two
cities of Munich and Stuttgart are included. However, there is no
explanatory power to be recognised for these two cities from the
outset. On account of its valley basin location, Stuttgart faces heavy
restrictions on how much land can be made available for building
purposes, a fact which may not be reflected in market prices. There
are no such obvious explanatory approaches for Munich. The
Bavarian capital is an attractive residential location despite high land
prices, as evidenced by the price and rent trends (see chart 8). At
the same time, there have been sharp increases in land prices
which can never be fully stripped out of the prices and rents.
Furthermore, distortions might also emerge because of pronounced
demand overhangs, since Munich takes a very conservative
approach to residential construction.
What this all boils down to is that Tobin‘s q can offer very good
explanations for housing growth. Even though the significance of
investment activity in the surrounding counties varies for all the
cities to a very considerable degree on account of their size, political
structure and significance for a region, the explanatory power of
8 August 22, 2011
City
Directly adjacent
counties
Metropolitan
region
Berlin 0.902 0.946 0.957
Bremen 0.685 0.846 0.859
Dortmund 0.402 0.349 --
Dusseldorf 0.719 0.844 --
Essen 0.306 0.958 --
Frankfurt 0.255 0.904 0.920
Hamburg 0.726 0.809 0.835
Cologne 0.775 0.859 --
Munich 0.059 0.379 -0.021
Stuttgart -0.286 -0.110 -0.116
Sources: BulwienGesa, Destatis, DB Research, IW Cologne Institut for Economic Research 7

Residential construction in Germany
Tobin's q jumps in Hamburg
1995=100
120
115
110
105
100
95
90
85
80
75
70
1990 1994 1998 2002 2006 2010
Marginal q
Rent (newbuild)
Rent (existing stock)
Price (newbuild)
Price (existing stock)
Sources: BulwienGesa, Destatis, IW Cologne
Institute for Economic Research 9
Newbuilds are popular
Growth differentials, owner-occupied
housing, (Newbuild-Existing stock), pp, p.a.
q increases q decreases
-4 -2 0 2 4 6 8
2000-2005 2005-2010
Frequency distribution of growth diffentials in 127
German cities. A positive value means newbuild prices
rose faster than prices of existing stock.
The x-axis designates the upper limit of the respective
interval, e.g. a value of -2 stands for the interval
[-4 bis -2].
Sources: BulwienGesa, DB Research
60
50
40
30
20
10
0
10
Tobin‘s q increases rapidly when the surrounding counties are
factored in.
Implications for building activity
Our analysis has shown that the concept of Tobin‘s q may also be
applied to real estate markets. Even though the investment
processes there evolve on a very long-term horizon and therefore
the supply of new houses remains relatively constant, Tobin‘s q may
provide an explanation for part of the building activity. In this context,
Tobin‘s q seems to have an immediate impact and leads to changes
in residential construction investment in the very same year.
Since it is plausible that adjustment processes, first of all, are not
constant for all cities and, second, do not necessarily occur
simultaneously, the fundamental mechanics of Tobin‘s q may also be
illustrated by means of a comparison of two longer time intervals: to
this end we shall now compare the interval from 2000 to 2005
(period 1) with 2005 through 2010 (period 2).
If the prices of existing houses climb considerably faster than
building prices (using newbuild prices as a proxy) and/or the rental
prices for newbuilds pick up considerably faster than rents for
existing stock, the increase in marginal qm points to higher demand
for newbuild houses (under respective ceteris paribus conditions).
This enables us to demonstrate that, firstly, construction demand at
the beginning of the millennium was low and, secondly, that it is now
(moderately) rising.
To this end, we look separately at the development of relative prices
and rents in the two time frames for 127 German cities: on average,
newbuild prices picked up faster than prices of existing stock in both
periods. For this reason, Tobin‘s q steadily declined. The widening
price gap has narrowed again by nearly half, however. This also
shows the low demand for residential building at the start of the
millennium.
Since demand may differ by region, it is necessary to look at the
regional distribution of this development. Chart 10 shows the
frequency distribution of the cities‗ growth differentials. A positive
growth differential in a city indicates that newbuild prices have
increased faster than stock prices. As can be seen, there were many
more cities with faster increases in stock prices than newbuild prices
during the second period from 2005 to 2010. There is a distinct shift
to the left in the frequency distribution of the 127 cities in period 2.
This goes hand in hand with rising q values and, in keeping with
Tobin‘s theory, with higher demand for construction. Some of the
cities with a negative growth differential, i.e. an uptrend in marginal
qm, include markets such as Munich, Frankfurt am Main, Cologne,
Hamburg and several university cities such as Bamberg, Darmstadt,
Würzburg and Heidelberg. It is a familiar pattern that demand for
construction tends to grow particularly in the robust business
clusters as well as in popular university towns. Of course, it is also
worth noting that the list includes several east German cities:
Rostock, Cottbus and Potsdam, for instance.
However, the picture changes if we look at the development of
relative rent dynamics: according to the design of marginal q
additional building activity will be induced if rents for newbuilds climb
more quickly than rents for existing stock. On average, though, the
increase in rents for existing stock slightly outstripped newbuild
August 22, 2011 9

Tenants are (nearly)
indifferent
Growth differentials, rental housing,
(Newbuild-Existing stock), pp, p.a.
q decreases
q increases
-2 0 2 4 6
2000-2005 2005-2010
Frequency distribution of growth differentials in 127
German cities. A positive value means newbuild rents
rose faster than rents for existing stock.
The x-axis designates the upper limit of the respective
interval, e.g. avalue of -2 stands for the interval
[-4 bis -2].
Sources: BulwienGesa, DB Research
70
60
50
40
30
20
10
0
11
Current Issues
rents both in the 2000 to 2005 period as well as in the 2005 to 2010
period. As the regional distribution in chart 11 shows, the rent
differentials decline slightly in roughly 60 cities, by up to 2% (interval
-2 to 0), and increase in roughly 50 cities by up to to 2%. The growth
differential in the second period slightly contracted vis-à-vis the first
period. Nevertheless, the development of relative rents between
newbuild houses and existing stock does not point to growing
demand for newbuild housing.
Since there was a regional shift in price growth between period 1
and period 2 (see chart 10), it follows that construction demand in
Germany is on the increase. However, it remains true that this
additional demand is not arising across the nation, but instead is
concentrating on a nonetheless increasing number of metropolitan
regions.
This phenomenon is also to be seen in the still decreasing marginal
qm for Germany as a whole (see chart 4) as well as in the recently
increasing q for Hamburg and Munich (see chart 8 and 9).
Conclusions
In this note we analysed important ways in which the German real
estate markets function, aided in our investigation by the use of
Tobin‘s q, a measure of relative price development in housing
markets for newbuild and existing stock.
We demonstrated that the market prices emit key scarcity signals,
with an increase in Tobin‘s q indicative of increasing building activity
in a given region. However, the correlations were less clear than
might have been presumed in light of the theoretical stringency of
the concept. Real estate markets simply are not perfect markets:
real properties are very heterogeneous; there is a lack of
transparency; effects kick in with significant time lags; and heavy
regulatory intervention is not uncommon. This is compounded by the
fact that the proxy used – the substitution of newbuild prices for
building costs – can simply be nothing else but an approximation. 11
Nonetheless, by factoring in the surrounding municipalities we were
clearly able to show that the relevant real estate markets very often
do not just stop at city limits; we were very frequently able to boost
the explanatory power of Tobin‘s q by extending the boundaries of
the market.
All in all, the development of Tobin‘s q for many metropolitan regions
and university towns (not only in west Germany) point to an increase
in building demand. This is, however, by no means a nation-wide
phenomenon, but rather it is confined to those regions that are
marked by specific migration patterns and a paucity of building
activity in the past.
Ralph Henger, IW Köln (+49 221 4981-744, henger@iwkoeln.de.)
Tobias Just (+49 69 910-31876, tobias.just@db.com)
Michael Voigtländer, IW Köln (+49 221 4981-741, voigtlaender@db.com)
11 In fact, one also has to factor in the obstacles of relatively short time series and the
gap caused by German unification.
10 August 22, 2011

Residential construction in Germany
Literature
Berg, Lennart and Tommy Berger (2006). The Q Theory and the
Swedish Housing Market – An Empirical Test. Journal of Real
Estate Finance and Economics. 33 (4). 329‐344.
Hayashi, Fumio (1982). Tobin‘s Marginal q and Average q: A
Neoclassical Interpretation. Econometrica. 50 (1). 213-224.
Jud, G. D. and D.T. Winkler (2003). The Q theory of housing
investment. Journal of Real Estate and Economics. 27 (3).
379-92.
Just, T. (2010). German residential property: Back in fashion – with
good reason? In Deutsche Bank Research. Current Issues.
Frankfurt am Main.
Meier, Carsten-Patrick (2011). Bauinvestitionen und Immobilienpreise.
In Rottke, Nico and Michael Voigtländer(Ed.). Immobilienwirtschaftslehre.
Vol. 2. Cologne. To be published shortly.
Nitsch, Harald (2011). Tobins Q: Vom Analyserahmen zum
praktischen Tool. In Francke, H-H., Rehkugler, H. [Ed.]
Immobilienmärkte und Immobilenbewertung. 2. Aufl. Verlag
Franz Vahlen. To be published soon.
Schulz, Rainer and Axel Werwatz (2008). House Prices and
Replacement Cost: A Micro-Level Analysis. Discussion Paper
2008-013. Humboldt-University of Berlin.
Tobin, James (1969). A General Equilibrium Approach to Monetary
Theory. Journal of Money, Credit, and Banking. 1 (1). 15-29.
August 22, 2011 11

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