Accentro-Iw Housing Cost Report 2019

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ACCENTRO-IW HOUSING COST REPORT 2019

An Analysis of Rents and Owner-Occupied-Housing Costs in 401

ACCENTRO-IW HOUSING COST REPORT 2019

Third-Party Sponsored Expertises Expert Opinions

An Analysis of Rents and Owner-Occupied-Hous- ing Costs in 401 Districts

Prof. Dr. Michael Voigtländer, Pekka Sagner

Client: ACCENTRO Real Estate AG Cologne, 12 April 2019 CONTACT DETAILS

Prof. Dr. Michael Voigtländer +49 (0)221 / 4981 - 741 [email protected]

Pekka Sagner +49 (0)221 / 4981 - 881 [email protected]

Cologne Institute for Economic Research (IW) PO box 10 19 42 D-50459 Cologne

2 ACCENTRO-IW Housing Cost Report 2019

CONTENT

Summary ...... 4

1 Introduction ...... 5

2 User costs ...... 5

2.1 Notes on the Methods Used ...... 5

2.2 Findings for Germany ...... 8

2.2.1 Long-Term Owner-Occupied-Housing Costs ...... 8

2.2.2 Latest Owner-Occupied-Housing Costs and Rents ...... 9

2.2.3 Regional Evaluations ...... 11

2.2.4 Interest Rate Sensitivity on the District Level ...... 15

3 Trend in Property Prices and Property Financing ...... 16

4 Responses among Property Buyers ...... 21

5 Conclusion ...... 25

6 Bibliography ...... 26

List of Tables ...... 27

List of Figures ...... 27

Annex ...... 28

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SUMMARY

The user cost approach makes it possible to compare rental costs and the expenses periodically arising for homeowners. The approach is used to determine the relative economic benefit of homeownership versus renting your home.

Homeownership remains attractive. At present, owner-occupying your home is more affordable than renting in 94 percent of Germany’s districts and independent cities. The average economic benefit of homeownership nationwide is nearly 40 percent. Even in the country’s metropolises, where condominium prices experienced particularly brisk growth in recent years, homeownership retains its economic benefits. Owner-occupancy in Berlin, for instance, is currently about 27 percent more affordable than renting, while being 35 percent more affordable in Hamburg. This is explained by the combination of simultaneous rental growth and the still highly favourable conditions on the market for mortgage loans. While interest rates have registered a modest increase lately, they are not expected to go up sharply any time soon. Moreover, an interest rate sensitivity analysis revealed that a large number of districts would be resilient to an interest rate tightening cycle. The fact that owner-occupied-housing costs are lower than the costs of renting almost everywhere also suggest that prices for freehold residential property are more likely to keep rising, defusing concerns that the housing market might be overheating.

Meanwhile, the fact that the number of first-time buyers has declined overall while their age average and— most recently—their income levels have increased indicates that few households in Germany benefit from the favourable terms of financing in the sense that they take advantage of them to acquire homes for owner-occupancy. Capital adequacy requirements, which go up in sync with rising prices, represent one of several reasons for this. In order to enable more households to opt for homeownership and, not least, to bolster their retirement schemes by doing so, the body politic should review the entry thresholds on the market for freehold residential property and lower them for the sake of preserving the country’s financial stability.

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1 INTRODUCTION

The user-cost-of-housing approach or user cost approach, for short, is an internationally wide-spread meth- od for evaluating developments on the housing market (cf. Poterba [1984] or Himmelberg et a.. [2005]). The approach is used, on the one hand, to derive possible indications of overvaluations and thus of a spec- ulative bubble, but, on the other hand, it also permits observations regarding the advantage of home ownership over renting. For several years now, the Accentro-IW Housing Cost Report has applied this approach to Germany as a whole and to each of its 401 administrative districts.

As with any empirical analysis, it is of the essence to keep verifying the data used and the underlying assumptions and to adjust them as needed. In the case of the Accentro-IW Housing Cost Report, this process prompted a number of adjustments. For one thing, we no longer use asking prices but instead rely on transaction data made available by vdpResearch (2019), a research firm focused on the German real estate market. In addition, we put the process of deriving anticipated property appreciation figures on a broader data basis in order to avoid possible distortions among the districts due to an unduly short period of time. While these adjustments compromise the comparability of the findings with previous analyses, they help to make the profiling of developments more accurate. Moreover, the repayment calculation previously used was replaced by an affordability calculation that is generally more intuitive.

In addition to these methodological adjustments, we also mapped the long-term development this time, based on OECD data. Looking back all the way to the 1970s is making it easier to put today’s situation on the housing market in perspective. The analyses moreover covered the performance of first-time buyers in the housing market and differentiated between urban and rural regions.

Despite the methodological adjustments, the overall picture remains robust: In most of the districts, homeownership is still more attractive than renting a home even though the owner-occupied-housing costs have begun to rise because of the price trend and rebounding interest rates. However, the stats for first-time buyers show that few households actually take advantage of the opportunities in the housing market. It is high time German policymakers made an effort to lower the threshold to homeownership.

2 USER COSTS

2.1 Notes on the Methods Used

The user-cost-of-housing approach that is subsequently applied to Germany and that is also referred to as “user cost approach” was introduced by Poterba (1984) and Himmelberg et al. (2005). The approach is based on the assumed premise that households are principally indifferent to the choice between buying or renting a given property. The premise is assumed to be valid if the relative costs of either option are identical. If, for example, the costs were to shift in favour of homeownership, this would boost the relative appeal of buying a home and with it demand for homes. Increased demand in the market for freehold residential property will raise the selling prices in the respective regions while rents will become more affordable, relatively speaking, restoring the balance. Over short periods of time, the residential property market is rigid, meaning that new-build construction will respond to a surge in demand for housing in a given region with considerable delay. More important yet is the fact that people are loath to relocate, so that the pace of the market adjustment is very slow. The slow response rate, including to declining demand, explains why owner-occupied-housing costs and rents may drift apart for limited periods of time.

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Comparing the costs of tenants with those of property owners is no mean feat because rental costs are a flow variable whereas the purchase price represents a one-time payment. This is where the concept of owner-occupied-housing costs comes in: The purchase price, including incidental acquisition costs, and with financing costs and lost income on the equity employed for the property purchase all taken into account, is converted into a flow variable. The variable permits a comparison between rental costs and the costs borne by owner-occupiers.

According to Schier/Voigtländer (2015), the annual owner-occupied-housing costs can be determined on the district level at any given time as follows:

SNKkt = Pkt ∙ (1 + gkt + mkt + e + n) ∙ [b ∙ iF,t + (1 - b ) ∙ iA,t ∙ (1 - τt ) + s + a -∆Pk ] The letter P represents the property purchase price in euros per square metre of dwelling floor area. For the first time, we used data obtained from vdpResearch (2019). The term in the subsequent bracket combines the incidental costs of a property acquisition: g represents the amount of the real estate transfer tax, whose rate varies from one federal state to the next, anywhere from 3.5 to 6.5 percent of the purchase price. Moreover, we assume that the property is acquired through an estate agent, with the agent’s fee m also varying from one federal state to the next and generally ranging from 3.57 to 7.14 percent of the purchase price. For the entry in the land register e and the notarial charges due n, we impute a flat rate of 1.525 percent. The purchase price is normally financed in the form of a mortgage loan. In recent years, the borrowed capital share or leverage b approximated 78 percent of the purchase price (Dr. Klein, 2019). As variable borrowing rate iF we assume the mean effective interest rate that German banks charge for housing loans to private households with an initial fixed interest period of more than 10 years (Deutsche Bundesbank, 2019a). In addition to the actual payments to be made for the property bought, opportunity costs are in- curred for the equity employed (on average, 22 percent of the purchase price). As opportunity interest rate, we impute the average current yields of domestic bearer bonds as iA (Deutsche Bundesbank, 2019b). The income generated from the investment on the capital market must be taxed at the rate τ. To this end, we use the average tax rate as delineated in the official finance statistics (BMF, 2018). Homeowners also shoulder annual costs in the form of maintenance and depreciation a. To cover these, we impute a flat rate of 3 percent (Clamor et al., 2013). They represent costs that should be considered on an opportunity basis. If a condominium owner skipped these expenditures, for instance by failing to execute necessary refurbishment or modernisation measures, the property would depreciate year by year. Finally, the expected long-term price growth rates for residential real estate in a given district are factored in as ΔP with a negative sign. The long-term price expectations in this context are based on the average annual price growth rate for the years 2005-2018. To defuse the significance of excessive recent price hikes, if any, we limit the maximum annual price growth rate to 3 percent.

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Table 2.1: Variables and data sources

Variable Explanation Source Purchase price in euros per sqm of vdpResearch (2019) Pkt dwelling floor area b Debt capital share Dr. Klein (2019)

i F,t Mortgage interest rate Deutsche Bundesbank (2019a)

i A,t Current yield on bearer bonds Deutsche Bundesbank (2019b)

τt Tax rate BMF (2018)

ΔPk Purchase price change F+B (2019) Source: IW Economic Institute

Example This section will illustrate the calculation of the owner-occupied-housing costs by determining them for a model city as follows. Let us say that the purchase price per square metre of dwelling floor area is 4,000 eu - ros The incidental acquisition costs break down into the real estate transfer tax (in this case 6 percent), the agent’s fee (in this case 3.57 percent), the costs of the land register entry and the notarial charges (in this case 1.525 percent), adding up to a sum total of 444 euros per square metre or about 11 percent of the purchase price. The leverage is 78 percent and subject to an interest rate of 1.96 percent. The equity ratio is 22 percent, while an opportunity interest rate of 2.48 percent is assumed for an investment in this amount in the capital market, which matches the average current yields that German bearer bonds generated in 2018. Such investment income is taxable, the imputed tax rate being 22.4 percent (average tax rate as delineated in the financial statistics for 2017 and adopted for 2018). The expected annual price growth rate is imputed as 2.5 percent.

The above assumptions return owner-occupied-housing costs in an amount of c. 9.08 euros per square metre of dwelling floor area and month. If these housing user costs happen to be lower than the monthly rent for a comparable dwelling, owner-occupying your home has relative economic benefits over renting, and vice versa.

Critical Discussion of the Assumptions One of the key factors influencing the determination of owner-occupied-housing costs are the exceptionally low interest rates on the mortgage loan market. Lower interest rates on the mortgage lending market reduce housing user costs because they decrease the total loan amount to be financed. Since they are currently based on a very low mortgage rate level, residential user costs are much more sensitive to an interest rate hike. If, to use the above example again, mortgage rates were to rise by one percentage point, it would raise the owner-occupied-housing costs to 11.97 euros or 31.8 percent. If all other assumptions continued to apply, an increase in mortgage rates from 3 percent to 4 percent would raise the owner-occupied-housing costs from 12.08 euros to 14.97 euros – an increase by 23.9 percent only.

The expected long-term price growth rates are also of key significance for the owner-occupied-housing costs. Whenever the expected annual price growth rates go up, the owner-occupied-housing costs will decline. The average price growth rates in Germany’s districts and independent cities have fluctuated sub- stantially in recent years. In order to include both current trends on the real estate market and to exclude price exaggerations in particularly strained housing markets, the expected price growth rate is determined here as a long-term price trend based on the average annual price growth rates of the years 2005 through 2018 and capped at an annual 3 percent. The negative impact of the price growth rate on owner-occu-

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pied-housing costs could actually cause the rate to be overestimated in some of the districts. As a conse- quence, the economic benefit could be underestimated in these districts while homeownership is more advantageous than apparent. While the expected price changes by no more than 3 percent (nominal) per year may seem very moderate—at least when compared to the development of recent years—they need to be contextualised with the historical price trend. Nationwide, average German selling prices have only increased by an annual 0.23 percent (in real money terms) since 1970 (cf. Abbildung 3.1).

2.2 Findings for Germany

2.2.1 Long-Term Owner-Occupied-Housing Costs

Before studying the latest findings of the user cost approach below, let us have a look at the long-term trend of rental costs and owner-occupied-housing costs, respectively.

The OECD provides data on the growth of rents and prices in Germany since 1970. The data are based on figures provided by Deutsche Bundesbank. However, the OECD only offers indices, no absolute figures. They are therefore unsuitable for deriving actual owner-occupied-housing costs. The idea in the present context, however, is only to outline the long-term trends of owner-occupied-housing costs relative to rental costs. To do so, we proceed as follows: In a first step, the index of house prices is linked in a multiplicative relation with the long-term interest rate, which is also provided by the OECD. The long-term interest rate reflects the returns on long-term government securities, whose interest rates tend to be slightly lower than long-term mortgage rates; still, their performance is comparable. The changes in interest rates tend to be parallel, subject to minor differences in the respective levels.

By linking house prices and interest rates, you get the simplest version of owner-occupied-housing costs (DiPasquale/Wheaton, 1992). To be able to compare this calculated owner-occupied-housing cost index with the rent index, the second step references the central assumption of the user cost approach: In the long term, owner-occupied-housing costs and tenant costs are the same. It is therefore assumed that the costs between 1970 and 2018 are identical on average. This means that the differences in costs between the two types of use across the entire period return a zero sum. The index of owner-occupied-housing costs is shifted exactly as much as needed to satisfy this premise. For the outcome of the calculation, see Figure 2.1.

The figure shows that the economic benefit of homeownership has clearly shifted. Between 1970 through the end of the 1990s, tenants were better off than homeowners. This phase was characterised by very high interest rates and occasional price hikes that compromised the appeal of residential property relative to rents. But since the Zero Years, homeownership has been the cheaper option because the interest rate development more than offset the price trend. Another factor was that rents rose much faster during the 1990s, partly due to strong demand in the early 1990s in the wake of Germany’s reunification and due to immigration, but to some extent also because of the abolition of the limited-profit housing status in the late 1980s. Only recently have owner-occupied-housing costs started to perk up again, after the gap between renting and buying had steadily widened for years. Due to the index scores used and the assumptions made (equivalence of rental costs and owner-occupied-housing costs), one should not read too much into the analysis: Particularly the differences between the two cost types are strongly dependent on the underlying assumptions. Some may find fault with the use of interest rates on long-term government securities because their interest rates have fallen so much faster, particularly in recent years, than mortgage rates after the ECB’s interventions (cf. Demary/Voigtländer [2018]). Nonetheless, the figure is very revealing. On the one hand, it confirms prior analyses, such as the Expert Commission on Housing Policy (1995), which found that renting generally offers an economic advantage. On the other hand, however, it also shows that

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the appeal of real estate in general has increased significantly as a result of declining interest rates over several decades. Historic multipliers, meaning price-to-rent ratios, are therefore useful only to a certain extent since the drop in interest rates implies higher multipliers. Looking at the slow adjustment processes, the benefits of homeownership could stay in place for a long time to come. The section below will now take a closer look at the latest developments.

Figure 2.1: Index1) of long-term rent rates and owner-occupied-housing costs

Rent index Owner-occupied-housing costs

140

120

100

0 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

1) Index 1997 = 100, nominal prices

Source: OECD (2019); IW Economic Institute

2.2.2 Latest Owner-Occupied-Housing Costs and Rents

The average owner-occupied-housing costs in Germany are close to 5.58 euros per square metre of dwelling floor area and month. These contrast with average monthly rental costs of 9.24 euros for acompa- rable flat rented on new tenancy. Thus, the costs of owner-occupancy are almost 40 percent lower than the rental costs. By 2018, owner-occupying a home was more affordable even when compared to passing rents, the average rental costs on unexpired leases being 6.72 euros and therefore 17 percent higher than owner-occupied-housing costs. Figure 2.2 illustrates the development of owner-occupied-housing costs and rents between 2010 and 2018. No extended time series are available for the transaction data provided by vdpResearch (2019). At the start of the current decade, owner-occupied-housing costs and rental costs were still on the same level. But since 2012, owner-occupied-housing costs have undercut even passing rents. One of the drivers of owner-occupied-housing costs is the interest rate development. Annual interest

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on mortgage loans with maturities of over 10 years was around 4 percent as recently as 2010 and 2011 (Deutsche Bundesbank, 2019a). During the period under review, owner-occupied-housing costs bottomed out in 2016. Up to then, interest rate had kept tumbling until they hit 1.8 percent. In 2017 and 2018, the owner-occupied-housing costs began to nudge up again in sync with the interest rate development. Lately, the growth of owner-occupied-housing costs has outpaced both passing rent and new tenancy rents, implying an emerging convergence of rental costs and owner-occupied-housing costs. Nevertheless, the relative attractiveness of owning your home versus renting it remains high. Nationwide, a comparison of average passing rents and average owner-occupied-housing costs also shows that even terminating an unexpired lease to buy a home is generally associable with economic benefits.

Figure 2.2: Trend in owner-occupied-housing costs and rents1) Population-weighted German2) average, in euros per square metre of dwelling floor area and month

Owner-occupied-housing cost New tenancy rents Passing rents

2 2010 2011 2012 2013 2014 2015 2016 2017 2018

1) Passing rents (F+B, 2019) refer to a dwelling of standard fit-out specifications and state of repair. Rents on new leases (vd- pResearch, 2019) and selling price are based on transaction data and refer either to first-sale prices or to resale prices of fully refurbished flats in good locations and with good interior specification.

2) With no population data for 2018 available yet, the population weightings of 2017 were adopted for 2018, too. To do the census reset of 2011 justice, retrograde calculation was used for 2010 as defined by the BBSR Federal Institute for Research on Building, Urban Affairs and Spatial Development (2018).

Source: IW Economic Institute based on data by vdpResearch (2019); F+B (2019)

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2.2.3 Regional Evaluations

The situation in Germany’s metropolises, the so-called “Big Seven” cities, more or less matches the national average, which is explained not least by the fact that the sheer number of residents living in these cities definitively influences the country’s population-weighted average. In all of the metropolises, owner-occupied-housing costs undercut the rental costs of a new tenancy. In Berlin (27 percent), Hamburg (35 percent) and Munich (38 percent), the advantage of owner-occupied-housing costs falls short of the population-weighted nationwide average. Inversely, the advantages of homeownership actually exceed the German average in Düsseldorf (54 percent), Frankfurt am Main (50 percent), Cologne (54 percent) and Stuttgart (44 percent).

Yet the major German cities seem to be in a process of converging owner-occupied-housing costs and rental costs. The recent surge in purchase prices relative to rental growth, combined with slightly higher interest rates for mortgage loans, have led to an increasing equalisation of owner-occupied-housing costs and new tenancy rents, especially in Berlin. Here, owner-occupied-housing costs went up by 19 percent annually over the past two years. The economic benefits of owner-occupancy are now drastically diminished.

Figure 2.3 shows moreover that the owner-occupied-housing costs exceed passing rents in Berlin, Ham- burg and Munich. This means that in these cities, living in a rented flat with standard fit-out specification on an average passing rent tends to be cheaper than moving into a refurbished condominium with a good fit-out specification in a good location. As said before, no passing rent stats are available for directly comparable assets. Still, drawing the comparison makes sense insofar as it will be an attractive proposition for many prospective property buyers to buy a home with superior fit-out specifications. In Berlin, owner-occupied-housing costs have lately exceeded passing rents by 21 percent, the latter rising noticeably slower because the city has a very large rental housing market where tenants make up around 82 percent of the total (Henger et al., 2019). Owner-occupied-housing costs similarly top passing rents by 10 percent in Hamburg and by 3 percent in Munich. Still, it is safe to say even for these cities: If you plan to move and have the choice of two comparable flats, one to let, one for sale, owner-occupancy will be more affordable.

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Figure 2.3: Owner-occupied-housing costs and rents in Germany’s metropolises In euros per square metre of dwelling floor area and month

erlin Owner-occupied-housing costs Owner-occupied-housing costs New tenancy rents Passing rents New tenancy rents Passing rents 15 15

10 10

5 5

0 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018

ranfurt a amurg Owner-occupied-housing costs Owner-occupied-housing costs New tenancy rents Passing rents New tenancy rents Passing rents 20 20 15 15 10 10 5 5 0 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018

Cologne unich Owner-occupied-housing costs Owner-occupied-housing costs New tenancy rents Passing rents New tenancy rents Passing rents 15 30

10 20

5 10

0 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018

Stuttgart Owner-occupied-housing costs New tenancy rents Passing rents 20 15 10 5 0 2010 2011 2012 2013 2014 2015 2016 2017 2018

Source: IW Economic Institute based on data by vdpResearch (2019); F+B (2019)

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A comparison between owner-occupied-housing costs and rents in all of the German districts and independent cities shows that homeowners have a lower overhead than tenants in virtually all districts. Indeed, owner-occupied-housing costs are lower than the rental costs of a new tenancy in 94 percent of the districts. Figure 2.4 depicts the relative economic benefit of homeownership over renting as a geographic spread. Most of the districts with very low economic benefits of homeownership lie in the Ruhr and Sauerland regions of North Rhine-Westphalia and in parts of East Germany. In 26 districts, the owner-occupied-housing costs top rental costs, and 17 of these are located in the Ruhr, southern Westphalia, the Sauerland region or in surrounding areas. The situation is explained by the slow and in some cases negative price dynamic for freehold residential property in these districts. Some of these regions are moreover associated with a poor growth outlook (cf. ZDF [2018] and Kempermann et al. [2019]). Since the user-cost-of-housing approach includes these development trends in the form of anticipated price trends, it comes as no surprise that these regions are characterised by elevated owner-occupied-housing costs relative to rents. In the Annex, you will find a schedule listing the owner-occupied-housing costs in euros per square metre and month along with the economic benefit over renting, covering all of Germany’s 401 districts and independent cities.

On the whole, a comparison of the differences in economic benefit of homeownership over renting across Germany reveals a heterogeneous picture. A look at Brandenburg shows that Berlin’s suburban periphery has become a geographic agglomeration where homeownership has distinct economic benefits. One possible reason to explain this is the still rather low purchase price level in the metro region, coupled with sound growth prospects because of their proximity to the German capital. Owner-occupiers in the districts Od- er-Spree and Oberhavel, for instance, spend about 60 percent less on housing than tenants do. The district of Sömmerda boasts the highest economic benefit at a ratio of 69 percent, whereas owner-occupancy in the city of Hagen in the southern Ruhr is 38 percent more expensive than renting a home.

The findings of prior years are therefore borne out by the new database. But, homeownership remains attractive in virtually all districts. Tracing the owner-occupied-housing costs over time both for Germany as a whole and for its metropolises shows, however, that owner-occupied-housing costs have bottomed out lately. Again, the underlying economic theory assumes a long-term equivalence between the costs of owner-occupying and renting. For the time being, there are no signs suggesting a significant rise in mortgage rates (cf. Demary/Voigtländer [2018]). Accordingly, the findings imply that the rental equivalence in regions where the economic benefit of homeownership is most pronounced will only be restored if rents soften or if selling prices keep climbing. That being said, the market for freehold residential property is more likely to adjust through continued price growth.

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Figure 2.4: Comparative view of owner-occupied-housing costs and rents1) 2018, in percent

Economic benefit of homeownership versus renting

< 14.5 % 14.5 - 32.4 % 32.4 - 46.0 % 46.0 - 51.3 %

1) New-tenancy rents 51.3 - 55.5 %

Source: IW Economic Institute based on data by vdpResearch > 55.5 %

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2.2.4 Interest Rate Sensitivity on the District Level

The data basis of vdpResearch (2019) that was used for the first time this year as well as the modest recent rise in interest on mortgage loans necessitate a reassessment of the findings in regard to interest rate sensitivity (cf. Seipelt/Voigtländer [2016]). Although recent surveys based on the demographic trend rule out a sharp rise in interest rates in Germany in the years to come (Demary/Voigtländer, 2018) and although mortgage loans in Germany tend to have rather long fixed interest periods, the section below examines how high interest rates could go without eroding the economic benefit of homeownership in each district. To this end, we determine the interest rate for long-term mortgage loans that would put owner-occupied-housing costs on a level with rental costs for comparable flats in 2018.

The actual average interest rate on mortgage loans with maturities of more than 10 years was 1.96 percent in 2018 (Deutsche Bundesbank, 2019a). Figure 2.5 shows the neutral interest rate for Germany’s 401 districts and independent cities. In keeping with the above elaboration regarding the economic benefit of homeownership in the various districts, the neutral interest rate implying equivalence of owner-occupied-housing costs and rents was undercut in 26 districts in 2018. In the city of Hagen, for instance, interest would have to drop to an annual rate of 0.3 percent.

Figure 2.5: Neutral interest rate1) on the district level 2018, in percent; number of districts on the abscissa

Neutral interest rate Mortgage interest rate with a term of more than 10 years 10.0

9.0

8.0

7.0

6.0

5.0 Frankfurt a.M.

Munich Cologne 4.0

Berlin Stuttgart Economic benefit of 3.0 homeownership versus Hamburg renting 2.0

1.0

Hagen 0.0 1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 307 316 325 334 343 352 361 370 379 388 397

1) Mortgage rate at which the owner-occupied-housing costs match the rental costs (new tenancy)

Source: IW Economic Institute based on data by vdpResearch

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But on the whole, the figures paint very robust picture with respect to the interest rate sensitivity of the economic benefits of homeownership versus renting. In 346 districts, owner-occupying your home would still be of advantage even if interest rates were to rise to 3 percent p.a., and in 249 districts this would apply even if the interest level rose to 4 percent. An interest rate hike to 5 percent, with all other parameters un- changed, would push the number of districts in which owner-occupied-housing costs would still be lower than rents down to 60.

Figure 2.5 shows the position of the metropolises relative to the other districts. At 2.9 percent, Berlin is on record with the lowest neutral interest rate of all major cities, followed by Hamburg (3.3 percent) and Munich (3.5 percent). The relatively low interest rate sensitivity in Berlin could be explained, among other things, by the rather modest price growth projection of 3 percent annually at the most. The forecast is at odds with the significant price hikes the city actually registered in recent years. What makes the conserva- tive projection sensible nonetheless is the intention to map the long-term trend.

3 TREND IN PROPERTY PRICES AND PROPERTY FINANCING

The user cost approach permits a fair comparison of the respective costs of tenants and owner-occupiers. It is fair in the sense that capital formation through amortization payments is expressly ignored and that solely the costs of use are taken into account. Decisive for private households, however, is probably the absolute financial burden of interest and amortisation. We therefore supplemented the calculation of the owner-occupied-housing costs with a calculation of the financing costs. Unlike previous analyses, it factors in the annual costs of a full repayment loan. This let us study affordability over time and put the price trend in context.

The political and public debates on the subject of Germany’s housing market are usually preoccupied with the latest developments, especially those in popular metro regions. It pays, however, to look at the long- term picture. Figure 3.1 shows the price trend on the residential property market since 1970 and the annuity trend for full repayment loans with a 25-year maturity. Underlying the calculation is the interest on long-term securities, which generally determines the development and level of long-term mortgage rates. The chart puts current developments on the real estate markets in perspective in several ways while high- lighting them at the same time.

For starters, it traces the cyclical progression of the price trend in real-money terms on the residential property market. One reason for choosing real-term prices is that deflated selling prices let you take the development of purchasing power in the wake of rising income levels into account. Real-term prices have been fluctuating around the base level, indexed here to 100, since 1970. In the early 1970s, prices initially rose slightly, to just under 6 percent above the base level before dropping back down to the base level in 1975/1976. Next, property prices went up by 12 percent above the baseline value during a boom cycle of five years. Between the mid-1980s and Germany’s reunification in 1990, prices largely followed a lateral trend. In the wake of the reunification, prices rallied steadily until the mid-1990s. This was followed by a 13-year period of steady decline which brought prices down to 87 percent of the base level by the end of the Zero Years. Since 2010, prices have been steadily pushing up and have lately topped the base level by nearly 10 percent. The national average of real estate prices is evidently nearing an all-time high, but has not (quite) reached it yet.

16 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

There are all sorts of reasons for price fluctuations in the real estate market. One needs to differentiate between endogenous and exogenous causes. Exogenous factors include economic and structural developments. The current developments in the market can be aptly described as a combination of endogenous and exogenous influencing factors. An important factor in endogenous mechanisms, which trace the re- action to exogenous shocks, are time lags. In the case of a price mechanism lag, an unexpected surge in demand for housing is matched by a fixed supply that is impossible to adjust on short notice. The time that passes before prices are fully adjusted is called price mechanism lag. To restore the market equilibrium, you either need to raise prices or to expand the supply in residential accommodation. The unexpected rise in demand for housing during the past years has mainly affected the major German cities and university towns where prices have soared both on the market for owner-occupied property (cf. also Abbildung 3.2) and in rental housing (GdW/Empirica [2015], Oberst/Voigtländer [2018]). A key factor for the demand-driven price growth is the unforeseen demographic trend of recent years. Germany has benefited from structural immigration changes that are prompted by the increasing European integration and fuelled by the robust performance of the German economy. Overall, the gradual introduction of the free movement of workers increased internal migration in the EU, while the eastward enlargement of the EU increased immigration from these countries, followed by a major influx of asylum seekers from North Africa and the Middle East (Malteser Foundation Migration Report, 2017). Accordingly, the decline in property prices before 2010 are attributable not least to the steady decline in population. Inversely, Germany has experienced a steady upward population increase since 2011, averaging around 400,000 persons annually (0.5 percent). De- mographic growth in the cities of Berlin (1.4 percent p.a.), Hamburg and Munich (1.1 percent p.a. each) was particularly brisk (Federal Statistical Office, 2018) and is reflected in corresponding price hikes on the residential property market. Indeed, both recent market evidence and the historical context illustrate that population trends and real real estate price trends are closely intertwined.

Another endogenous mechanisms which is discussed in relevant publications, and which triggers a short- term fluctuation in prices, involves decision-making lags and construction lags. Planning and executing large-scale developments to provide relief to the real estate market take time. The period required to com- plete them is referred to as “construction lag.” Germany’s proverbial red tape, which includes norms, building regulations and standards of every sort, has led to lengthy planning and licensing processes (BMUB, 2016).

In addition to the demographic trend, which acts as an exogenous shock that causes property prices to rise via endogenous mechanisms, there are other medium-term influences based on economic developments and long-term factors that play a key role. The upward trend on the German labour market, the historic low in unemployment that coincides with it and increases in earned income, all act as drivers of the price growth on Germany’s real estate market. Many Germans invest some of their increased income in the con- sumption of residential floor space, and the per-capita floor-space requirements of people have gone up as a result.

The interest rate development also plays a decisive role for real estate prices and is particularly important to property buyers. The majority of property purchases are debt-financed. As mentioned in several places above, the interest rate on long-term mortgage loans are on an historically very low level. The fact is reflected in the annuity trend shown in Figure 3.1. The average annuity paid across Germany has lately been on an exceptionally low level, and market evidence shows that the interest rate development made up for price hikes until recently.

17 Cologne Institute for Economic Research

Figure 3.1: Long-term price trend1) on the German real estate market Trend in property prices and annuity

Real estate price index Annuity

140

130

120

110

100

1) Index 1970 = 100, price trend in real money terms (adjusted for inflation)

Source: OECD (2019); IW Economic Institute

However, the above discussions regarding the average long-term price trend in Germany are not to suggest that the latest price hikes in major cities can be safely ignored. Figure 3.2 shows both the annuity trend and the annuity level for a condominium of 100 square metres debt-financed to 80 percent on a full repayment loan with a 25-year maturity. While the interest rate development largely offset the purchase price increases in the major cities until 2015, the steep price increases in combination with a slight rise in interest rates lately has in some cases necessitated significantly higher annual expenses on interest and redemption than were required as recently as 2010.

The annuity trend is particularly dynamic in Berlin, where the sum to be expended was more than 48 percent higher by 2018 than it had been in 2010. But a look at the absolute level shows that Berlin is still less pricey than Frankfurt am Main, Hamburg, Stuttgart and Munich. Then again, Berlin has been catching up quickly to the most expensive cities in Germany because of its faster price growth. Another metropolis with very fast price growth since 2010, and second only to Berlin, is Munich. The slowest annuity growth rates were registered in Cologne and Düsseldorf, where expenses toward interest and redemption increased by 14 and 11 percent, respectively, in 2018.

18 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Figure 3.2: Trend1) and level2) of the annuity3)

Berlin Düsseldorf Frankfurt a.M. Hamburg Cologne Munich Stuttgart Germany

35,000 155

30,000 145

25,000 135

20,000 125

15,000 115

10,000 105

5,000 95

0 85 2010 2011 2012 2013 2014 2015 2016 2017 2018

1) Index 2010 = 100, right axis showing scale; lines in chart

2) Euros in real money terms (2018), inflation-adjusted using the harmonised consumer price index (Federal Statistical Office, 2019)

3) Annual costs of interest and repayment for a condominium of 100 square metres and assuming a 25-year loan maturity, full repayment loan, 20 percent equity capital

Source: IW Economic Institute based on data by vdpResearch

Germany’s major cities are far more expensive than the national average. Judging by the population-weighted average and based on the above assumptions, barely 11,000 euros would have to be spent in Germany on interest and amortisation to fully pay off a 100-square-metre flat within 25 years. Yet in Berlin, Frankfurt and Stuttgart, you pay 1.8 times as much, in Hamburg 210 percent and in Munich an actual 280 percent. Even in the two most affordable metropolises, Cologne and Düsseldorf, homeownership still costs 1.3 times the national average. Figure 3.3 shows the geographic spread of the annuity expended on real estate financing in each of the German districts and independent cities. Annual expenses in the dark-shaded regions are at least 13,460 euros. The most affordable regions nationwide are clustered mainly in West Germany, the big cities and surrounding districts being most expensive, with the greater Munich area topping the list.

19 Cologne Institute for Economic Research

Figure 3.3: Differences in annuity level in real estate financing, 2018.

Annuity

< €5,554 €5,554 - €7,040 €7,040 - €8,653 €8,653 - €10,565 €10,565 - €13,460 > €13,460

1) Annual costs of interest and repayment for a condominium of 100 square metres and assuming a 25-year loan maturity, full repayment loan, 20 percent equity capital

Source: IW Economic Institute based on data by vdpResearch

20 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

4 RESPONSES AMONG PROPERTY BUYERS

Even if the most auspicious window of opportunity for buying property may have closed, the parameters for the acquisition of residential property remain excellent. Considering the sometimes wide gaps between owner-occupied-housing costs and rental costs, the potential for setbacks is low. On the contrary, there is reason to expect further price hikes. In addition, the need for private retirement planning has increased.

A closer look at first-time buyer figures below will help us assess to what extent households (are able to) take advantage of housing market opportunities. The analysis will not be limited to absolute figures but also look at the income trend and the age of prospective buyers. There are no official figures on first-time buyer households, meaning on private households buying their first home. According to estimates by the Federal Statistical Office, around 500,000 residential properties change hands every year (BMJV, 2019).

To track the numeric trend in first-time buyers over time, data of the Socio-Economic Panel (SOEP), an annual household and personal survey, were used (Wagner et al., 2007). First-time buyer households are iden- tified on the basis of the households that change the residential ownership status from tenant to owner between two survey periods, adjusted to exclude those households that previously owner-occupied homes in one of the prior periods. To minimise annual fluctuations and to avoid giving them too much weight, this and subsequent evaluations show rolling four-year averages. This is sufficient insofar as we are interested in long-term development trends.

21 Cologne Institute for Economic Research

Figure 4.1: First-time buyer households1) in Germany Rolling four-year averages; broken down into municipalities with populations of more than 100,000 and those with smaller populations, plus Germany as a whole.

Germany Towns > 100,000 Towns < 100,000

800000

700000

600000

500000

400000

300000

200000

100000

0 199 0 199 1 199 2 199 3 199 4 199 5 199 6 199 7 199 8 199 9 200 0 200 1 200 2 200 3 200 4 200 5 200 6 200 7 200 8 200 9 201 0 201 1 201 2 201 3 201 4 201 5 201 6 201 7

1) First-time buyer households are defined as households that owner-occupy their homes for the first time in a given survey year, adjusted to exclude households that previously registered as owner-occupiers in a prior poll.

Source: SOEP v34 (2019); IW Economic Institute

Figure 4.1 shows the trend in first-time buyer households between 1990 and 2017, broken down into municipalities with populations of more than 100,000 (cities), municipalities with populations of less than 100,000 as well as the sum total of the two. For one thing, it shows that the majority of properties were acquired in smaller towns. The observation is borne out by the fact that the homeownership rate is much higher in small towns than in the major cities. Nationwide, the homeownership rate in municipalities with populations of 100,000 to 500,000 was only 35 percent in 2017 while in metropolises with more than 500,000 residents, a mere 28 percent of the households owned their homes outright. Conversely, two out of three residents in the smallest towns and villages owner-occupy their homes, while the ratio in midsize towns is one out of two. Particularly striking to note is that the number of first-time buyers is declining, which suggests that few households are in a position to take advantage of the favourable terms of financing for the purpose of buying a home. While the absolute number of first-time buyers in the major cities has remained stable, market evidence shows that the share of first-time buyers among the total number of households has become very low lately. In 2017, their share across the major German cities dropped to just 0.8 percent, which is 0.3 percentage points below the long-term mean (1990-2017). But generally speaking, the decline in the number of first-time buyers must be blamed on the decrease in first-time buyers in the countryside. It is explained, inter alia, by the negative migration balance of smaller municipalities that are losing residents to larger independent cities. With the exception of a minor spike in the number of first- time buyers in the early years of this decade, the rock-bottom level of interest rates does not seem to have

22 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

raised the absolute number of first-time buyers.

Other meaningful aspects apart from the sheer number of first-time buyers include their age average, de- picted in Figure 4.2. It, too, shows an unambiguous trend over time: First-time buyers keep getting older. In addition, it suggests that first-time buyers in the major cities tend to be older than those in smaller towns – by an average of roughly 2.5 years between 1990 and now.

Figure 4.2: Average age1) at time of property acquisition Rolling four-year averages; broken down into municipalities with populations of more than 100,000 and those with smaller populations, plus Germany as a whole.

Germany Towns > 100,000 Towns < 100,000

30 199 0 199 1 199 2 199 3 199 4 199 5 199 6 199 7 199 8 199 9 200 0 200 1 200 2 200 3 200 4 200 5 200 6 200 7 200 8 200 9 201 0 201 1 201 2 201 3 201 4 201 5 201 6 201 7

1) Median age of head of household in year of first-time acquisition of owner-occupied dwelling

Source: SOEP v34 (2019); IW Economic Institute

First-time buyer households not only seem to get older but also earn higher incomes. Here, the survey focused specifically on the development of real-term (inflation-adjusted) net household incomes of first- time buyers. The evidence clearly shows an upward trend, especially in the years since 2012. In 2017, first- time buyers earned c. 3,100 euros in net income, up from a long-term income average of barely 2,900 euros for first-time buyer households between 1990 and 2017.

The findings regarding first-time buyers may come as a surprise, but only at first glance. Although financing costs have admittedly gone down, the costs of entering the market, including the incidental acquisition costs and the equity capital, have gone up. All of these costs are pro-rata items pegged to the purchase price and ineligible for financing. Banks have lately become more willing to waive their equity requirements for blue-chip borrowers, but a substantial cost item remains that includes real estate transfer tax, agent’s fee, notary and land register fee, which can amount to as much as 15 percent but varies from one

23 Cologne Institute for Economic Research

German state to the next (Voigtländer, 2019). Access to home ownership is therefore considerably more difficult in Germany than in other countries, especially in neighbouring countries like the Netherlands or the Nordics (Voigtländer/Bierdel, 2017). As a result, the acquisition of property hinges increasingly on the wealth that households already own or on the ability of parents to lend money. Specifically low- and middle-income households are deprived of their the opportunity to buy property and build capital, which in turn exacerbates the unequal distribution of wealth.

Figure 4.3: Median household income1) at time of property acquisition Rolling four-year averages; broken down into municipalities with populations of more than 100,000 and those with smaller populations, plus Germany as a whole.

Germany Towns > 100,000 Towns < 100,000

3400

3200

3000

2800

2600

2400

2200

2000 199 0 199 1 199 2 199 3 199 4 199 5 199 6 199 7 199 8 199 9 200 0 200 1 200 2 200 3 200 4 200 5 200 6 200 7 200 8 200 9 201 0 201 1 201 2 201 3 201 4 201 5 201 6 201 7

1) Median household income in the year of the first-time acquisition of the owner-occupied home, 2017 prices in real terms. Adjusted for inflation using the general consumer price index.

Source: SOEP v34 (2019); IW Economic Institute

24 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

5 CONCLUSION

Compiling this latest edition of the Housing Cost Report involved substantial revisions. The use of transaction data as well as the improved representation of the anticipated price growth by taking into account a broader data basis has returned more differentiated findings. That said, the latest analyses also reconfirm the situation outlined by previous reports: Homeownership remains as attractive as ever because the interest rate development more than compensates for the price growth.

Just how attractive homeownership is today becomes evident when looking at long-term data. Compared to prior generations, first-time buyers spend much less on interest and repayment today even if they seek to clear their mortgage debt within a relatively short period of time.

The fact that the number of households opting for homeownership is nonetheless low should probably be blamed on societal changes. More volatile careers, fewer persons per households and the desire for an urban lifestyle diminish the appeal of homeownership. The situation is exacerbated by the short housing supply in general and the shortage in freehold residential property in particular, especially in the big cities (Accentro, 2018). Nevertheless, one would generally expect a higher number of people to acquire residential property, but hurdles in the form of high capital requirements seem to keep many average earners without assets or high-net-worth parents from becoming homeowners. Accordingly, these households not only bear higher financial burdens than comparable tenants and have no way to build up a pension pot, but they also have a growing sense of social unfairness since only a minority of the population is able to take advantage of the ongoing real estate price boom. It is high time the body politic took a hard look at the situation to identify and dismantle any barriers blocking access to homeownership.

25 Cologne Institute for Economic Research

6 BIBLIOGRAPHY

Accentro, 2018, Wohneigentumsreport 2018, Berlin, https://www.accentro.ag/publikationen/wohneigentumsreport/

BMF (Federal Minstry of Finance), 2018, Entwicklung der Steuer- und Abgabenquoten, https://www.bundesfinanzministe- rium.de/Monatsberichte/2018/11/Inhalte/Kapitel-6-Statistiken/6-1-11-entwicklung-der-steuer-und-abgabequoten.htm [08/04/2019]

BMJV (Federal Ministry of Justice and Consumer Protection), 2019, Entwurf eines Gesetzes zur Stärkung des Bestellerprinzips bei der Vermittlung von Kaufverträgen über Wohnimmobilien

BMUB (Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety), 2016, Bericht zum Bündnis für bezahlbares Wohnen und Bauen und zur Wohnungsbau-Offensive, Berlin

Clamor, Tim / Brügelmann, Ralph / Voigtländer, Michael, 2013, “Abschreibungsbedingungen für den Mietwohnungsneubau,” in: IW-Trends, vol. 40, no. 2, pp. 63-79

Demary, Markus / Voigtländer, Michael, 2018, “Reasons for the Declining Real Interest Rates,” IW-Report, 47/18

Deutsche Bundesbank, 2019a, Zeitreihe BBK01.SUD119: Effektivzinssätze Banken DE / Neugeschäft / Wohnungsbaukredite an private Haushalte, anfängliche Zinsbindung über 10 Jahre [08/04/2019]

Deutsche Bundesbank, 2019b, Zeitreihe BBK01.WU0022: Umlaufsrenditen inländischer Inhaberschuldverschreibungen / Anleihen von Unternehmen (Nicht-MFIs), https://www.bundesbank.de/Navigation/DE/Statistiken/Zeitreihen_Datenbanken/ Makrooekonomische_Zeitreihen/its_details_value_node.html?tsId=BBK01.WU0022 [08/04/2019]

DiPasquale, Denise / Wheaton, William, 1992, “The Markets for Real Estate Assets and Space: A Conceptual Framework,” in: Journal of the American Real Estate and Urban Economics Association, vol. 20, no. 2

Dr. Klein, 2019, Mittlerer Beleihungsauslauf, https://www.drklein.de/dtb-baufinanzierung [08/04/2019]

F+B, 2019, F+B Marktmonitor

GdW / Empirica, 2015, Schwarmstädte in Deutschland. Ursachen und Nachhaltigkeit der neuen Wanderungsmuster, Berlin

Henger, Ralph / Sagner, Pekka / Voigtländer, Michael, 2019, Milieuschutz in Berlin, Köln, https://www.iwkoeln.de/fileadmin/ user_upload/Studien/Gutachten/PDF/2019/Gutachten_Milieuschutz_Berlin.pdf [19/03/2019]

Himmelberg, Charles / Mayer, Christopher / Sinai, Todd, 2005, “Assessing High House Prices. Bubbles, Fundamentals and Misperceptions,” in: Journal of Economic Perspectives, vol. 19, no. 4, pp. 67–92

Kempermann, Hanno / Ricci, Agner / Sagner, Pekka / Lang, Thorsten, 2019, Wohnen in Deutschland 2019. Sparda-Studie, https://www.iwkoeln.de/fileadmin/user_upload/studie-gutachten-wohnen-in-deutschland-2019.pdf [10/04/2019]

2017, Malteser Migrationsbericht 2017. Fakten statt Stimmungslage, Cologne

Oberst, Christian A. / Voigtländer, Michael, 2018, “IW-Studentenwohnpreisindex 2018 – Mietpreisunterschiede zwischen Hochschulstandorten weiten sich,” IW-Report, 36/18, Cologne

Poterba, James M., 1984, “Tax Subsidies to Owner-Occupied Housing: An Asset-Market Approach,” in: The Quarterly Journal of Economics, vol. 99, no. 4, pp. 729–752

Schier, Michael / Voigtländer, Michael, 2015, “Immobilienpreise. Ist die Entwicklung am deutschen Wohnungsmarkt noch fundamental gerechtfertigt?,” in: IW Trends, vol. 42, no. 1, pp. 55–73

Seipelt, Björn / Voigtländer, Michael, 2016, “Droht eine Überhitzung des deutschen Wohnungsmarktes? Eine Analyse von Mieten und Wohnnutzerkosten für 402 Kreise,” ACCENTRO-IW-Wohnkostenreport, Cologne

Statistisches Bundesamt, 2018, Regionalstatistik, www.regionalstatistik.de [10/04/2019] vdpResearch, 2019, transaction database, https://www.vdpresearch.de/transaktionsdatenbank/ [08/04/2019]

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26 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

LIST OF TABLES

Table 2.1: Variables and data sources ...... 7

LIST OF FIGURES

Figure 2.1: Index1) of long-term rent rates and owner-occupied-housing costs ...... 9

Figure 2.2: Trend in owner-occupied-housing costs and rents1) ...... 10

Figure 2.3: Owner-occupied-housing costs and rents in Germany’s metropolises ...... 12

Figure 2.4: Comparative view of owner-occupied-housing costs and rents1) ...... 14

Figure 2.5: Neutral interest rate1) on the district level ...... 15

Figure 3.1: Long-term price trend1) on the German real estate market ...... 18

Figure 3.2: Trend1) and level2) of the annuity3) ...... 19

Figure 3.3: Differences in annuity level1) in real estate financing, 2018 ...... 20

Figure 4.1: First-time buyer households1) in Germany ...... 22

Figure 4.2: Average age1) at time of property acquisition ...... 23

Figure 4.3: Average age1) at time of property acquisition ...... 24

27 Cologne Institute for Economic Research

ANNEX

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 Schleswig-Holstein 01001 Flensburg 3.64 46.5 4.1 01002 Kiel 4.57 50.5 4.5 01003 Lübeck 4.34 55.6 5.1 01004 Neumünster 4.47 35.2 3.6 01051 Dithmarschen district 4.92 25.7 3.1 01053 Herzogtum Lauenburg district 4.65 45.9 4.2 01054 Nordfriesland district 6.46 13.3 2.3 01055 Ostholstein district 4.63 52.4 4.7 01056 Pinneberg district 4.69 54.1 4.9 01057 Plön district 4.04 48.7 4.3 01058 Rendsburg-Eckernförde district 3.75 51.9 4.7 01059 Schleswig-Flensburg district 3.48 50.3 4.5 01060 Segeberg district 4.29 57.2 5.3 01061 Steinburg district 4.99 32.1 3.7 01062 Stormarn district 5.03 52.7 4.7 Hamburg 02000 Hamburg 10.26 35.0 3.3 Lower Saxony 03101 Braunschweig 4.25 52.2 4.7 03102 Salzgitter 6.11 2.3 2.1 03103 Wolfsburg 3.74 59.5 5.6 03151 Gifhorn district 3.69 46.9 4.2 03153 Goslar district 6.95 -14.3 1.2 03154 Helmstedt district 4.82 21.8 3.1 03155 Northeim district 5.21 9.8 2.5 03157 Peine district 3.83 43.0 4.3 03158 Wolfenbüttel district 5.62 21.0 3.0 03159 Göttingen district 5.86 32.3 3.6 03241 Hanover district 4.48 50.4 4.5 03251 Diepholz district 3.33 59.2 5.6 03252 Hameln-Pyrmont district 5.99 4.0 2.1 03254 Hildesheim district 4.44 35.0 3.9 03255 Holzminden district 5.87 -5.5 1.7

28 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 03256 Nienburg (Weser) district 3.09 48.9 4.8 03257 Schaumburg district 6.28 1.4 2.0 03351 Celle district 5.17 29.8 3.5 03352 Cuxhaven district 4.09 45.6 4.1 03353 Harburg district 4.65 54.4 4.9 03354 Lüchow-Dannenberg district 3.55 29.2 3.4 03355 Lüneburg district 4.38 56.2 5.2 03356 Osterholz district 3.70 51.0 5.0 03357 Rotenburg (Wümme) district 2.67 62.9 6.2 03358 Soltau-Fallingbostel district 4.03 38.4 4.2 03359 Stade district 4.15 55.1 5.0 03360 Uelzen district 3.35 47.1 4.7 03361 Verden district 3.45 58.4 5.5 03401 Delmenhorst 3.77 48.8 4.6 03402 Emden 4.39 49.5 5.5 03403 Oldenburg 4.55 52.4 4.7 03404 Osnabrück 3.88 57.4 5.3 03405 Wilhelmshaven 4.44 25.4 3.2 03451 Ammerland district 3.75 50.7 4.5 03452 Aurich district 3.69 49.7 4.4 03453 Cloppenburg district 2.69 63.3 6.3 03454 Emsland district 3.01 57.4 5.3 03455 Friesland district 3.93 41.0 3.7 03456 Grafschaft Bentheim district 3.64 54.9 5.3 03457 Leer district 4.16 46.0 4.1 03458 Oldenburg district 3.69 55.1 5.0 03459 Osnabrück district 3.33 52.1 4.7 03460 Vechta district 3.66 48.4 4.3 03461 Wesermarsch district 3.19 52.1 4.8 03462 Wittmund district 5.30 15.9 2.4 Bremen 04011 Bremen 4.89 52.1 4.7 04012 Bremerhaven 4.36 23.9 3.1 North Rhine-Westphalia 05111 Düsseldorf 6.37 53.8 4.9 05112 Duisburg 8.44 -18.2 1.1 05113 Essen 9.41 -14.6 1.4

29 Cologne Institute for Economic Research

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 05114 Krefeld 8.98 -8.5 1.6 05116 Mönchengladbach 6.93 7.5 2.3 05117 Mülheim an der Ruhr 8.55 3.0 2.1 05119 Oberhausen 7.89 -7.5 1.6 05120 Remscheid 8.96 -30.6 0.6 05122 Solingen 6.54 19.2 2.8 05124 Wuppertal 9.86 -30.4 0.7 05154 Kleve district 5.68 22.8 3.2 05158 Mettmann district 6.57 33.9 3.6 05162 Rhein-Kreis Neuss district 5.45 45.2 4.0 05166 Viersen district 7.17 12.5 2.5 05170 Wesel district 7.03 14.5 2.7 05314 Bonn 5.87 52.1 4.7 05315 Cologne 6.53 53.6 4.8 05316 Leverkusen 6.64 29.3 3.3 05334 Städteregion Aachen district 4.69 53.5 4.8 05358 Düren district 5.63 20.1 2.9 05362 Rhein-Erft-Kreis district 5.46 46.3 4.6 05366 Euskirchen district 5.73 26.1 3.2 05370 Heinsberg district 5.00 31.9 3.7 05374 Oberbergischer Kreis district 7.59 -6.1 1.7 05378 Rheinisch-Bergischer Kreis district 6.67 32.2 3.5 05382 Rhein-Sieg-Kreis district 5.20 44.1 3.9 05512 Bottrop 6.50 17.8 2.8 05513 Gelsenkirchen 7.93 -25.5 0.8 05515 Münster 5.81 53.4 4.8 05554 Borken district 5.22 27.1 3.3 05558 Coesfeld district 4.98 31.3 3.5 05562 Recklinghausen district 8.50 -18.8 1.1 05566 Steinfurt district 3.91 44.6 4.2 05570 Warendorf district 3.93 46.6 4.5 05711 Bielefeld 5.01 43.0 4.0 05754 Gütersloh district 4.28 43.7 4.0 05758 Herford district 5.15 25.2 3.3 05762 Höxter district 5.55 -12.2 1.4 05766 Lippe district 5.70 19.6 3.0 05770 Minden-Lübbecke district 5.11 25.6 3.3

30 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 05774 Paderborn district 4.03 47.8 4.2 05911 Bochum 8.41 -5.2 1.7 05913 Dortmund 6.03 27.0 3.4 05914 Hagen 9.08 -37.8 0.3 05915 Hamm 4.41 31.0 3.6 05916 Herne 7.22 -13.0 1.3 05954 Ennepe-Ruhr-Kreis district 7.26 -0.4 1.9 05958 Hochsauerlandkreis district 6.55 -6.3 1.7 05962 Märkischer Kreis district 7.99 -18.3 1.1 05966 Olpe district 6.81 14.5 2.7 05970 Siegen-Wittgenstein district 5.29 36.2 4.0 05974 Soest district 4.49 36.2 3.8 05978 Unna district 6.97 -1.8 1.9 Hesse 06411 Darmstadt 6.43 51.5 4.6 06412 Frankfurt am Main 9.08 50.1 4.5 06413 Offenbach am Main 5.79 49.4 4.4 06414 Wiesbaden 7.13 45.8 4.1 06431 Bergstrasse district 4.93 47.9 4.3 06432 Darmstadt-Dieburg district 5.25 48.4 4.4 06433 Gross-Gerau district 4.88 56.6 5.5 06434 Hochtaunuskreis district 7.47 43.3 3.9 06435 Main-Kinzig-Kreis district 5.14 45.2 4.2 06436 Main-Taunus-Kreis district 6.82 46.4 4.1 06437 Odenwaldkreis district 4.38 40.1 4.2 06438 Offenbach district 5.51 51.2 4.6 06439 Rheingau-Taunus-Kreis district 5.91 41.1 3.7 06440 Wetteraukreis district 5.15 49.6 4.4 06531 Giessen district 4.40 52.4 4.7 06532 Lahn-Dill-Kreis district 4.90 30.1 3.4 06533 Limburg-Weilburg district 6.09 14.3 2.6 06534 Marburg-Biedenkopf district 4.09 58.6 5.5 06535 Vogelsbergkreis district 4.02 29.3 3.6 06611 Kassel 3.71 56.0 5.1 06631 Fulda district 3.50 47.5 4.2 06632 Hersfeld-Rotenburg district 5.87 7.4 2.3 06633 Kassel district 3.47 49.5 4.4

31 Cologne Institute for Economic Research

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 06634 Schwalm-Eder-Kreis district 4.79 20.4 2.9 06635 Waldeck-Frankenberg district 5.11 16.1 2.8 06636 Werra-Meissner-Kreis district 3.24 43.0 4.8 Rhineland-Palatinate 07111 Koblenz 4.58 49.7 4.4 07131 Ahrweiler district 3.84 51.5 4.6 07132 Altenkirchen (Westerwald) district 4.21 39.0 4.6 07133 Bad Kreuznach district 4.62 38.3 3.8 07134 Birkenfeld district 5.60 -2.5 1.8 07135 Cochem-Zell district 3.58 34.6 3.3 07137 Mayen-Koblenz district 4.20 36.8 3.7 07138 Neuwied district 3.58 45.3 4.0 07140 Rhein-Hunsrück-Kreis district 6.20 -5.6 1.7 07141 Rhein-Lahn-Kreis district 5.02 24.4 3.1 07143 Westerwaldkreis district 3.78 40.1 4.2 07211 Trier 4.70 59.3 5.6 07231 Bernkastel-Wittlich district 4.03 41.3 3.7 07232 Eifelkreis Bitburg-Prüm district 3.75 55.1 5.0 07233 Vulkaneifel district 6.14 -6.1 1.7 07235 Trier-Saarburg district 4.60 47.4 4.2 07311 Frankenthal (Pfalz) 4.36 47.8 4.3 07312 Kaiserslautern 4.57 40.2 4.1 07313 Landau in der Pfalz 3.88 56.9 5.3 07314 Ludwigshafen am Rhein 4.49 49.2 4.4 07315 Mainz 6.18 54.3 4.9 07316 Neustadt an der Weinstrasse 4.53 45.1 4.0 07317 Pirmasens 4.68 8.8 2.4 07318 Speyer 4.27 56.4 5.2 07319 Worms 4.17 50.7 4.5 07320 Zweibrücken 4.58 32.2 3.8 07331 Alzey-Worms district 3.34 54.6 5.0 07332 Bad Dürkheim district 4.20 48.3 4.3 07333 Donnersbergkreis district 3.73 40.7 4.3 07334 Germersheim district 4.05 48.1 4.3 07335 Kaiserslautern district 5.11 26.3 3.6 07336 Kusel district 4.03 32.5 3.7 07337 Südliche Weinstrasse district 4.41 44.4 4.0

32 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 07338 Rhein-Pfalz-Kreis district 4.27 49.5 4.4 07339 Mainz-Bingen district 3.95 58.8 5.5 07340 Südwestpfalz district 4.57 27.9 3.5 Baden-Württemberg 08111 Stuttgart 8.77 44.0 3.9 08115 Böblingen district 6.73 42.9 3.8 08116 Esslingen district 6.45 46.7 4.1 08117 Göppingen district 5.47 42.7 3.9 08118 Ludwigsburg district 6.36 46.8 4.2 08119 Rems-Murr-Kreis district 6.30 42.4 3.8 08121 Heilbronn 5.32 46.4 4.1 08125 Heilbronn district 4.51 50.5 4.5 08126 Hohenlohekreis district 3.80 52.0 4.7 08127 Schwäbisch Hall district 4.09 46.5 4.1 08128 Main-Tauber-Kreis district 4.12 36.5 3.6 08135 Heidenheim district 4.13 44.0 3.9 08136 Ostalbkreis district 4.65 46.5 4.1 08211 Baden-Baden 5.51 51.3 4.6 08212 Karlsruhe 6.03 51.9 4.7 08215 Karlsruhe district 4.82 48.6 4.3 08216 Rastatt district 4.50 49.2 4.4 08221 Heidelberg 9.02 41.9 3.8 08222 Mannheim 5.38 52.9 4.8 08225 Neckar-Odenwald-Kreis district 2.98 55.4 5.1 08226 Rhein-Neckar-Kreis district 4.89 50.2 4.5 08231 Pforzheim 4.32 51.1 4.6 08235 Calw district 4.41 44.1 4.3 08236 Enz district 4.97 41.6 3.7 08237 Freudenstadt district 6.16 17.3 2.8 08311 Freiburg im Breisgau 7.59 48.9 4.3 08315 Breisgau-Hochschwarzwald district 6.04 42.2 3.8 08316 Emmendingen district 5.24 44.2 3.9 08317 Ortenaukreis district 4.81 40.7 3.7 08325 Rottweil district 4.42 41.6 4.1 08326 Schwarzwald-Baar-Kreis district 4.18 46.7 4.1 08327 Tuttlingen district 3.62 55.7 5.3 08335 Constance district 6.75 45.2 4.0

33 Cologne Institute for Economic Research

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 08336 Lörrach district 5.20 53.0 4.8 08337 Waldshut district 4.36 47.0 4.2 08415 Reutlingen district 5.46 48.8 4.3 08416 Tübingen district 6.12 50.2 4.5 08417 Zollernalbkreis district 3.49 54.0 4.9 08421 Ulm 5.06 57.6 5.4 08425 Alb-Donau-Kreis district 3.82 56.3 5.2 08426 Biberach district 3.71 55.5 5.1 08435 Bodenseekreis district 5.58 55.5 5.1 08436 Ravensburg district 4.65 53.6 4.8 08437 Sigmaringen district 3.71 49.1 4.4 Bavaria 09161 Ingolstadt 6.68 50.9 4.5 09162 Munich 13.01 37.6 3.5 09163 Rosenheim 6.59 45.4 4.0 09171 Altötting district 4.12 45.4 4.0 09172 Berchtesgadener Land district 5.42 43.0 3.8 09173 Bad Tölz-Wolfratshausen district 7.31 42.7 3.8 09174 Dachau district 7.78 42.9 3.8 09175 Ebersberg district 7.70 45.8 4.1 09176 Eichstätt district 5.53 42.3 3.8 09177 Erding district 6.57 46.6 4.1 09178 Freising district 7.34 45.8 4.1 09179 Fürstenfeldbruck district 7.98 44.8 4.0 09180 Garmisch-Partenkirchen district 8.05 32.1 3.1 09181 Landsberg am Lech district 5.72 48.2 4.3 09182 Miesbach district 8.76 35.7 3.3 09183 Mühldorf am Inn district 3.80 52.4 4.7 09184 Munich district 11.18 32.5 3.2 09185 Neuburg-Schrobenhausen district 4.67 46.0 4.1 09186 Pfaffenhofen an der Ilm district 5.10 46.0 4.1 09187 Rosenheim district 5.91 46.3 4.1 09188 Starnberg district 9.10 43.2 3.9 09189 Traunstein district 6.07 38.2 3.5 09190 Weilheim-Schongau district 5.79 46.1 4.1 09261 Landshut 5.77 46.7 4.1 09262 Passau 3.86 60.3 5.8

34 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 09263 Straubing 3.99 51.1 4.6 09271 Deggendorf district 3.64 52.7 4.7 09272 Freyung-Grafenau district 3.97 35.2 3.8 09273 Kelheim district 3.96 54.1 4.9 09274 Landshut district 4.96 40.7 3.7 09275 Passau district 3.82 46.1 4.1 09276 Regen district 4.24 31.9 4.0 09277 Rottal-Inn district 3.02 54.3 4.9 09278 Straubing-Bogen district 2.98 54.7 5.0 09279 Dingolfing-Landau district 3.82 43.9 3.9 09361 Amberg 4.04 47.5 4.2 09362 Regensburg 8.09 39.0 3.6 09363 Weiden in der Oberpfalz 3.02 59.1 5.6 09371 Amberg-Sulzbach district 3.07 53.0 4.8 09372 Cham district 3.38 40.3 3.6 09373 Neumarkt in der Oberpfalz district 4.14 48.9 4.3 09374 Neustadt an der Waldnaab district 3.31 47.4 4.2 09375 Regensburg district 4.94 43.5 3.9 09376 Schwandorf district 2.94 57.6 5.4 09377 Tirschenreuth district 3.91 16.0 2.6 09461 Bamberg 5.21 53.4 4.8 09462 Bayreuth 4.41 52.3 4.7 09463 Coburg 3.79 49.6 4.4 09464 Hof 3.08 48.1 4.8 09471 Bamberg district 3.96 40.3 3.6 09472 Bayreuth district 3.58 45.4 4.0 09473 Coburg district 3.48 47.6 4.7 09474 Forchheim district 3.97 49.8 4.4 09475 Hof district 3.31 35.7 4.1 09476 Kronach district 6.73 -8.7 1.5 09477 Kulmbach district 2.66 61.2 5.9 09478 Lichtenfels district 3.34 44.4 4.0 09479 Wunsiedel im Fichtelgebirge district 3.84 21.0 2.9 09561 Ansbach 4.26 52.5 4.7 09562 Erlangen 6.95 46.3 4.1 09563 Fürth 4.84 56.8 5.2 09564 Nuremberg 5.66 51.3 4.6

35 Cologne Institute for Economic Research

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 09565 Schwabach 5.13 48.3 4.3 09571 Ansbach district 3.99 44.3 3.9 09572 Erlangen-Höchstadt district 5.00 46.4 4.1 09573 Fürth district 4.86 46.9 4.2 09574 Nürnberger Land district 4.84 46.2 4.1 09575 Neustadt a. d. Aisch-Bad Windsheim district 3.44 47.9 4.3 09576 Roth district 4.23 45.3 4.0 09577 Weissenburg-Gunzenhausen district 3.49 44.5 4.0 09661 Aschaffenburg 5.44 46.3 4.1 09662 Schweinfurt 4.05 47.8 4.2 09663 Würzburg 5.52 48.7 4.3 09671 Aschaffenburg district 4.23 51.7 4.6 09672 Bad Kissingen district 4.44 35.1 3.9 09673 Rhön-Grabfeld district 3.08 47.6 4.2 09674 Hassberge district 3.05 40.8 3.7 09675 Kitzingen district 3.05 50.6 4.5 09676 Miltenberg district 4.91 34.5 3.6 09677 Main-Spessart district 3.70 44.9 4.0 09678 Schweinfurt district 3.28 48.0 4.3 09679 Würzburg district 3.88 54.3 4.9 09761 Augsburg 5.69 49.1 4.4 09762 Kaufbeuren 3.43 56.5 5.2 09763 Kempten (Allgäu) 4.36 53.1 4.8 09764 Memmingen 4.53 54.8 5.0 09771 Aichach-Friedberg district 4.88 46.6 4.1 09772 Augsburg district 5.01 46.3 4.1 09773 Dillingen an der Donau district 3.27 51.5 4.6 09774 Günzburg district 3.94 51.0 4.6 09775 Neu-Ulm district 4.50 56.4 5.2 09776 Lindau (Bodensee) district 5.83 48.8 4.3 09777 Ostallgäu district 4.98 46.6 4.1 09778 Unterallgäu district 3.79 54.5 4.9 09779 Donau-Ries district 4.00 49.2 4.4 09780 Oberallgäu district 5.52 42.4 3.8 Saarland 10041 Stadtverband Saarbrücken district 5.46 30.7 3.4 10042 Merzig-Wadern district 3.95 56.1 5.3

36 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 10043 Neunkirchen district 4.79 23.9 3.3 10044 Saarlouis district 3.63 51.8 5.1 10045 Saarpfalz district 5.22 26.4 3.3 10046 Sankt Wendel district 3.45 48.3 4.5 Berlin 11000 Berlin 8.96 26.9 2.9 Brandenburg 12051 Brandenburg an der Havel district 3.71 36.8 3.4 12052 Cottbus 3.03 53.3 4.9 12053 Frankfurt (Oder) 5.00 24.5 3.0 12054 Potsdam 6.28 46.6 4.1 12060 Barnim district 2.91 59.6 5.6 12061 Dahme-Spreewald district 3.13 61.5 5.9 12062 Elbe-Elster district 3.79 28.4 3.3 12063 Havelland district 3.15 59.7 5.7 12064 Märkisch-Oderland district 3.15 58.4 5.5 12065 Oberhavel district 3.21 61.3 5.9 12066 Oberspreewald-Lausitz district 4.11 27.8 3.9 12067 Oder-Spree district 2.84 64.2 6.4 12068 Ostprignitz-Ruppin district 2.61 54.2 4.9 12069 Potsdam-Mittelmark district 4.77 48.8 4.3 12070 Prignitz district 3.61 38.3 4.7 12071 Spree-Neisse district 2.18 61.8 6.0 12072 Teltow-Fläming district 3.51 53.8 4.9 12073 Uckermark district 2.05 64.6 6.5 Mecklenburg-Western Pomerania 13003 Rostock 5.14 45.0 4.0 13004 Schwerin 3.41 55.0 5.0 13071 Mecklenburgische Seenplatte district 3.50 43.7 4.7 13072 Rostock district 3.47 50.8 4.5 13073 Vorpommern-Rügen district 3.73 49.5 4.4 13074 Nordwestmecklenburg district 3.64 47.8 4.2 13075 Vorpommern-Greifswald district 5.00 35.0 3.7 13076 Ludwigslust-Parchim district 2.44 60.9 5.8 Saxony 14511 Chemnitz 7.80 -28.2 0.6 14521 Erzgebirgskreis district 3.85 30.2 4.2

37 Cologne Institute for Economic Research

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 14522 Mittelsachsen district 5.66 -0.6 1.9 14523 Vogtlandkreis district 4.76 8.7 2.6 14524 Zwickau district 5.10 12.3 2.8 14612 Dresden 4.99 38.5 3.5 14625 Bautzen district 3.49 43.1 5.5 14626 Görlitz district 1.84 66.0 7.5 14627 Meissen district 4.65 28.8 3.5 Sächsische Schweiz-Osterzgebirge 14628 3.53 42.8 4.8 district 14713 Leipzig 4.38 41.8 3.9 14729 Leipzig district 3.63 41.5 4.4 14730 Nordsachsen district 2.89 53.1 5.7 Saxony-Anhalt 15001 Dessau-Rosslau district 4.47 25.1 3.6 15002 Halle/Saale district 3.38 53.2 4.8 15003 Magdeburg 3.56 44.1 3.9 15081 Altmarkkreis Salzwedel district 2.79 46.5 5.3 15082 Anhalt-Bitterfeld district 4.37 28.8 3.9 15083 Börde district 2.51 53.7 5.9 15084 Burgenlandkreis district 4.23 27.2 4.0 15085 Harz district 3.66 36.1 4.4 15086 Jerichower Land district 3.41 38.4 4.9 15087 Mansfeld-Südharz district 2.97 44.1 5.0 15088 Saalekreis district 3.43 43.2 5.6 15089 Salzlandkreis district 4.02 28.4 3.8 15090 Stendal district 3.97 29.2 4.0 15091 Wittenberg district 2.40 54.9 5.7 Thuringia 16051 Erfurt 4.32 47.1 4.2 16052 Gera 4.63 16.0 2.9 16053 Jena 4.73 55.0 5.0 16054 Suhl 3.97 37.6 4.5 16055 Weimar 4.49 44.2 4.1 16056 Eisenach 6.71 -4.3 1.7 16061 Eichsfeld district 2.38 52.4 4.7 16062 Nordhausen district 2.03 62.4 6.1 16063 Wartburgkreis district 3.38 38.6 4.7

38 Survey compiled for ACCENTRO Real Estate AG ACCENTRO-IW Housing Cost Report 2019

Owner-occupied- Cost advantage Neutral Region housing costs 2018, over renting, interest rate in euros per sqm in percent, 2018 16064 Unstrut-Hainich district 3.95 28.3 4.0 16065 Kyffhäuserkreis district 3.81 25.2 3.6 16066 Schmalkalden-Meiningen district 2.17 62.0 6.6 16067 Gotha district 3.56 41.8 4.9 16068 Sömmerda district 1.90 69.4 8.8 16069 Hildburghausen district 4.17 30.6 4.1 16070 Ilm-Kreis district 5.31 17.5 3.1 16071 Weimarer Land district 2.89 49.2 5.3 16072 Sonneberg district 5.13 4.6 2.2 16073 Saalfeld-Rudolstadt district 3.42 42.3 4.7 16074 Saale-Holzland district 3.81 40.7 4.6 16075 Saale-Orla district 2.55 56.2 6.6 16076 Greiz district 5.36 0.9 2.0 16077 Altenburger Land district 4.49 18.5 3.1