Investment opportunities in Germany s mid-size cities

Transcription

1 Residential, office, and retail property Investment opportunities in Germany s mid-size cities

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3 Investment opportunities in Germany s mid-size cities Residential, office, and retail property The No. 1 for Real Estate in Eastern Germany: Office. Retail. Commercial. Residential. 3 3

4 Contents Editorial 5 Foreword Prof. Dr. Tobias Just, academic director of the IRE BS International Real Estate Business School and professor of real estate at the University of Regensburg 6 Management Summary 8 1 Starting position and problem definition 10 2 Selecting the cities 11 3 Wüest & Partner civic rating Structure of the rating Results of the residential civic rating Results of the office civic rating Results of the retail civic rating 18 4 Risk/return profiles Methodology Residential Office Retail Returns 38 5 Summary 40 6 List of references 42

5 Editorial One of Germany s primary strengths compared with many other business locations is its decentralised structure with a large number of centres with different economic emphases. Traditionally, there has been a high proportion of medium-sized manufacturing and processing industries. This state of affairs is a key reason why the German economy has remained very robust thus far despite the stricken environment elsewhere in Europe. This applies in particular to the cities that have not traditionally attracted a great deal of attention from professional national and international investors. Apart from what are known as the seven A locations, the metropolises of Berlin, Hamburg, Düsseldorf, Cologne, Frankfurt, Stuttgart and Munich, there are 73 other cities in Germany with more than 100,000 inhabitants. These cities are home to around 16 million people roughly one fifth of the German population. From an investor s perspective, these medium-sized cities, referred to as B and C locations, are extremely exciting. Over the past few years, many of them have shown that they have their own sustainable economic structure. Some of them proved to be even less sensitive to fluctuations in economic activity than several of the economic conurbations that have been established for several decades. What applies to the general economy also applies in the same way to the property markets, albeit with a certain time delay. Whereas the returns in the A cities continue to fall as a result of increasingly intense competition surrounding the most desirable properties, the markets in the B and C cities still offer a range of positive surprises for property investors. Adequate transparency in the markets is an important prerequisite, as it allows investors to recognise and take advantage of these opportunities based on a calculable level of risk. This study conducted by TLG IMMOBILIEN and Wüest & Partner analyses the property markets of 25 selected B and C locations throughout the Federal Niclas Karoff Republic. It is motivated by a desire to help make information, particularly regarding these smaller investment locations, more transparent. It also aims to stimulate interest in these diverse and exciting markets that otherwise tend not to attract a great deal of attention. We would like to express our gratitude to Wüest & Partner for their work in preparing the data, carrying out complex analyses and for their constructive cooperation in compiling this study. If there is one result we can mention at the outset, it is that the gap between the major metropolises and the smaller cities is narrowing. Furthermore, the lines of division that sometimes exist between northern and southern or western and eastern Germany are becoming increasingly blurred. Investment opportunities in medium-sized German cities are primarily dependent on the respective local economic features and not on their geographical location. Berlin, May 2012 Jochen-Konrad Fromme Niclas Karoff Managing Director Managing Director TLG IMMOBILIEN GmbH TLG IMMOBILIEN GmbH TLG WOHNEN GmbH TLG WOHNEN GmbH Jochen-Konrad Fromme 4 5

6 Foreword Even B and C cities can offer AAA opportunities By Prof. Dr. Tobias Just from such an upturn would be confined to the few large A cities in western Germany and Berlin, allegedly because only these cities can guarantee longterm demand for property as well as lasting upward potential with regard to prices and rents. The categorisation into A, B or C cities certainly invites comparisons with the kind of quality-based evaluations issued by the rating agencies. However, this notion is misleading in several respects. Prof. Dr. Tobias Just Photo: IREIBS Thomas Plettenberg The euro crisis has severely affected the trust of many investors. In seeking stability, they are turning their backs on capital market products and reevaluating the concept of real estate investment. Indeed, there are many arguments in favour of such a strategy at present: financing rates are low, unemployment is falling and the low level of construction activity over the last few years has led to a shortage of supply in the residential markets in many cities. Apartment rents and prices are rising in many places. Nevertheless, there appears to be a very widely held view that the opportunities resulting To begin with, B cities are smaller than A cities. Consequently, the property markets in the B and C cities are less liquid than those in the large A cities and data is not as readily available. Large-scale investment properties are difficult to find. There are risks associated with these markets in a downturn, in the event that one was forced to sell a property at short notice. By the same token however, illiquidity and low transparency also offers opportunities for experienced investors who have a long-term focus, because it s exactly these things that prevent large investment firms with extensive borrowing from driving up prices during periods of cheap credit. Particularly in the case of the office markets and many residential markets, it is clear that smaller cities are exposed to significantly lower price fluctuations than large cities. The fact that this only applies to a limited extent to the eastern German cities during the first ten years following reunification is

7 due neither to their insufficient size nor their location in eastern Germany certainly over the long term but primarily to the historically unique challenge and associated special government incentives for the property industry. Looking beyond the classification based purely on size, the categorisation into A, B or C cities hides more than it shows in many cases because neither all medium-sized cities nor all the properties in them are the same. Those seeking a low-risk, city-centre property will find one not just in the six large western German cities, but also in most of the 100 next smaller ones. It is often more useful to classify cities based on their sector structure or their core function within the division of labour at national level, rather than by their size alone. University cities such as Heidelberg, Regensburg or Karlsruhe as well as state capitals such as Hanover, Mainz or Dresden are subject to low market fluctuations because a large part of the demand for property comes from the less volatile sectors of education and public administration. When looking at the medium-sized cities, it is also useful to distinguish between cities such as Gelsenkirchen, Saarbrucken or Rostock that lost out as part of the structural change, and those that managed to retain their strong industrial base, such as Wolfsburg or Ludwigshafen. And this still does not take into account the very stable small markets which contain the head offices of hidden champions of the mechanical and electrical engineering sectors. And to clarify the situation: Many eastern German cities have successfully completed their economic transition and now qualify for inclusion in the stable medium-sized cities of Germany in just the same way as western German cities of comparable size. Examples of such cities include Dresden, Leipzig or Jena. However, it is not our contention that all mediumsized cities offer properties of exceptional value stability. Naturally, this cannot be the case in locations where the sector structure is being eroded and which are unable to offer high-quality, sustainable employment conditions; such locations are seeing an exodus of young people. The unique risk of small markets is therefore their heavy dependence on a small number of companies. A medium-sized city that falls victim to structural change is less able than a metropolitan region to offset this burden with its strengths in other sectors. However, this risk can be countered by adopting a targeted portfolio strategy, and the medium-sized cities in particular are worthy of closer inspection as is this report. The author is the academic director of the IRE BS International Real Estate Business School and professor of real estate at the University of Regensburg 6 7

8 Management Summary Problem definition In Germany, most property investors concentrate on the A locations of Berlin, Düsseldorf, Frankfurt, Hamburg, Cologne, Munich and Stuttgart. The B and C cities are either completely ignored or merely mentioned in passing in regular market publications by even the major property consultancies. In addition to the top seven locations, there are a further 73 medium-sized cities in Germany with more than 100,000 inhabitants. Around 16 million people live in these B and C cities, which equates to roughly 20 percent of the German population. They are important regional centres of economic activity and, therefore, of property. Knowledge of their property markets is relatively limited and the restricted level of transparency complicates the process of collecting and formatting data. TLG IMMOBILIEN commissioned Wüest & Partner (W&P) to carry out an investigation of 25 selected cities of this scale across all areas of the Federal Republic with regard to the opportunities and risks for property investments in selected usage types. Study In this study, the statistical and property market data from the 25 selected cities is analysed in two stages, and their respective strengths and weaknesses highlighted and separated according to the residential, office and retail uses. The aim is to identify the unique prerequisites and to extract an overview of the opportunities and risks associated with property investments. The first step involves determining the location quality of the 25 selected cities in terms of property investment risk. The starting point is the Wüest & Partner civic rating, which covers all of the approximately 12,000 municipalities in Germany. The rating incorporates statistics on demographics, the economy, the labour market, accessibility and construction activity. The major cities in the south of the Federal Republic regularly appear close to the top of the civic rating. These include Nuremberg, Karlsruhe and Regensburg. However, various cities in eastern German states, such as Potsdam and Dresden, also score highly on a regular basis. Alongside Chemnitz and Halle/Saale, cities such as Duisburg and Wuppertal were also found towards the bottom of the table. This order also reflects the opinions of many market participants. Secondly, data on the property market is gathered and condensed to produce risk/return profiles. These figures are not included in the civic rating because they are neither available nationwide nor at a comparable level of quality. The figures and the risk/ return profiles developed from them provide an indepth insight into the respective markets and supplement the civic rating. Overall result As a result, clear distinctions can be drawn between investment opportunities and risks in B and C cities. Neither their geographical location nor their prevailing economic circumstances reveal any clear tendencies. Only when returns are added does a picture of a particular location from an investor perspective emerge. In this context, low returns are always associated with low risks. The study comes to the conclusion that B and C cities often have a better risk/return ratio than A cities, as they either generate higher returns on comparable risks or offer lower risks for equivalent returns. The detailed study of B and C locations when summarised reveals interesting and, in some cases, surprising results. Results by usage type For residential use, the study revealed a much more heterogeneous picture for the B and C locations analysed than for the A locations. The cost of living ranges between 17.8 percent and 28.3 percent. On average, tenants have to pay 22.1 percent of their household spending capacity on rent plus heating. However, the average in the top seven cities is significantly higher at 25.3 percent. The rent

9 potential is therefore often higher in the smaller locations. The analysis of the risk/return profiles reveals that significantly different returns are achieved across a range of cities despite their comparable risk profile. These returns fluctuate between 6.1 percent (Regensburg) and a maximum of 9.9 percent (Halle/Saale and Chemnitz). The average risk is at a similar level to that of the top seven locations. The best risk/return ratio for residential use was found in Bielefeld, followed by Brunswick, Aachen, Bochum, Kassel and Magdeburg. Regensburg, Hanover, Lübeck and Potsdam achieve the poorest risk/ return ratio. Here, the returns possible are relatively low compared to the calculated risk. For further results, read from page 22. The study also found significant differences in the returns on office properties with similar risks in B and C cities. The returns are higher compared to A locations as major investors focus primarily on the larger and more liquid markets of the A locations. The greater competition that results exerts a downward pressure on returns. Office vacancies tend to be higher in the top seven cities than in smaller locations. The B and C locations with low vacancy rates of less than 4 percent are Heilbronn, Aachen, Duisburg, Lübeck and Bielefeld. The lowest investment risk for office properties can be found in Kassel, particularly on account of the low rental costs, low vacancies and good prospects. Erfurt came last among the cities looked at. In spite of the good outlook, productivity here is low and vacancies are (still) high. For further results, see from page 27 on. be found in Dortmund. Adjusted retail space per capita is very low here with high peak rents and an adequate return. Rostock has an attractive risk/return ratio thanks to its average return of 8.5 percent, putting it in the top third of the 25 cities analysed. The southern centres of Nuremberg and Augsburg are in the low mid-table for risk/return profiles owing to their high levels of retail space per capita. For further results, see from page 32 on. The figures and risk/return profiles developed in this study do not purport to be complete or totally accurate. Rather, they should be seen as encouragement to challenge one s initial impressions, to question popular opinion and to discover the hidden qualities of these locations. Returns from retail use in the B and C cities analysed also reveal similar differences that sometimes exceed three percentage points for comparable risks. Retail space per capita also varies significantly between 0.96 m² (Duisburg) and 3.40 m² per capita (Augsburg). Peak rents in prime locations begin at 40 per m² (Magdeburg) and extend to up to 210 per m² (Dortmund, Hanover). The high-priced locations match the level of the top seven cities. The lowest investment risk for retail properties can 8 9

10 1 Starting position and problem definition Berlin, Düsseldorf, Frankfurt, Hamburg, Cologne, Munich and Stuttgart are the biggest property locations in Germany and attract most of the investor interest. Based on their property markets, these cities are classified as A locations. The leading property consultancies issue regular reports on market trends in these top seven cities. The B and C cities are either completely ignored, or merely dealt with in passing by the regular market publications. Both investors and property experts are frequently biased against these locations. In addition to the seven top locations, there are a further 73 cities in Germany with more than 100,000 inhabitants. These cities are home to approximately 16 million inhabitants, thus accounting for around 20 percent of the population of the Federal Republic (of approximately 81.8 million). They are important regional centres of economic activity and, therefore, of property. Knowledge about B and C cities is low in many cases, although limited transparency admittedly makes the collection and processing of data extremely time-consuming and complex. In this study, the statistical and property market data from the 25 selected cities is analysed, their respective strengths and weaknesses highlighted and separated according to the residential, office and retail uses. This study provides an overview of the opportunities and risks of investments in the B and C cities and contains suggestions for analysing these locations. Since the rating is based on figures that are available and, more importantly, comparable on a nationwide basis, it excludes any information that does not meet these criteria. For this reason, we supplement the rating by taking another look at the property markets in the selected cities. This involves evaluating and condensing a range of property metrics to create risk/reward profiles for the respective submarkets. This deeper examination yields detailed insights that supplement the civic rating and lead to interesting results. Sometimes, a city offers a generally favourable level of property investment risk only for a closer examination to reveal that the potential in sub-markets is already exhausted. Although the investment risk remains low, the opportunities may also be low as well. This applies all the more to direct comparisons with other cities. The risk/reward profiles certainly demonstrate the wisdom of taking a supplementary look at the locations. We begin by presenting the ratings for the selected cities from the Wüest & Partner civic ratings. The civic rating includes all of the approximately 12,000 municipalities in Germany. The civic rating presents the location quality in terms of the risk associated with property investment. This is based on statistics on demographics, the economy and the labour market, accessibility and construction activity. These statistics for each municipality are condensed to create rating figures ranging from 1.0 to 5.0 for the residential, office and retail segments. This produces a rating that describes the general level of opportunity and risk in the respective market from a (professional) property investor s perspective.

11 2 Selecting the cities The classification into A, B or C cities is not based on any standardised criteria. The general consensus in the property sector is that the cities of Berlin, Düsseldorf, Frankfurt, Hamburg and Munich represent the top five locations in Germany. They are followed by Cologne and Stuttgart, which means that these seven cities are collectively regarded as A cities. There are different opinions as to whether the 73 other large German cities are B or C locations. Bonn, Bremen, Dortmund, Dresden, Erfurt, Essen, Hanover, Leipzig, Nuremberg and Wiesbaden are generally acknowledged as B locations. But even at this stage, opinions differ depending on the respective use. The information service for property market data Riwis-online, for example, includes Dresden and Leipzig among the A cities in the retail segment while Essen qualifies as an A city in the residential segment. This study makes no distinction between B and C locations. The selection criteria were a representative position in the respective region and approximately equal distribution of cities throughout the Federal Republic. The following individual cities were selected for the study: Region State City Population North Schleswig-Holstein Kiel 239,526 North Schleswig-Holstein Lübeck 210,232 North Lower Saxony Hanover 522,686 North Lower Saxony Brunswick 248,867 East Mecklenburg-Western Pomerania Rostock 202,735 East Brandenburg Potsdam 156,906 East Saxony Dresden 523,058 East Saxony Leipzig 522,883 East Saxony Chemnitz 243,248 East Saxony-Anhalt Halle/Saale 232,963 East Saxony-Anhalt Magdeburg 231,525 East Thuringia Erfurt 204,994 East Thuringia Jena 105,129 South Bavaria Nuremberg 505,664 South Bavaria Augsburg 264,708 South Bavaria Regensburg 135,520 South Baden-Württemberg Karlsruhe 294,761 South Baden-Württemberg Heilbronn 122,879 West NRW Dortmund 580,444 West NRW Duisburg 489,559 West NRW Bochum 374,737 West NRW Wuppertal 349,721 West NRW Bielefeld 323,270 West NRW Aachen 259,030 West Hesse Kassel 195,530 Total 11 of 13 area states 25 cities 7,540,575 List of cities selected for analysis 10 11

12 3 Wüest & Partner civic rating The starting point for the analysis is the civic rating by Wüest & Partner (W&P). This rating determines the property risks for the residential, office and retail uses in each of the approximately 12,000 municipalities in Germany. The civic rating provides information about the location quality resulting from demographics, accessibility and the labour market. It also describes the local property market, for example, by taking construction activity into account. The ratings are created from an (institutional) investor s perspective. This point of view may, but does not necessarily, coincide with that of all customers, for example, of tenants or those seeking apartments. For instance, tenants regard low rents as a positive whereas owners seek to maximise prices. 3.1 Structure of the rating From raw data to points The raw data contains the most diverse units such as millions of people, scorings in risk ratings, rents in /m²/month, or variables for which there is either a 1 or a 0 (major city: yes or no). Each variable is assigned 0 to 100 points. The points are generally distributed in the same way as the distribution of original data. In other words, if the original values are normally distributed, then the points assigned are also between 0 and 100. The following categories and weightings were applied according to usage type: Category Residential Office Retail Demographics 30.0% 25.0% 16.0% Labour market 9.0% 25.5% 10.3% Tourism 13.3% Vacancy rate 5.0% Centrality 12.0% 20.0% 12.3% Municipal type 7.0% 2.0% 2.2% Rent 11.0% 5.0% 2.2% Economy 2.0% 11.5% 8.3% Taxes 3.0% 4.0% 0.7% Construction 14.0% 4.0% 3.1% Purchasing power 7.0% 3.0% 31.6% Total 100.0% 100.0% 100.0% Categories and weightings in W&P civic rating For each category, data on different variables was gathered, weighted in the respective category and summarised to create an overall value. Up to seven variables are incorporated into the rating for each category. Result controlling The raw data was subjected to an incoming check to eliminate errors. The rating itself is subjected to ongoing stress tests. In conducting the tests, the locations whose point number is to be investigated are deliberately moved upwards or downwards by massive adjustments to the weights. If the location cannot be shifted to any significant degree or only by using extreme weightings, then the location is deemed to be correctly ranked in principle. Data completeness Variables for which a large number of locations are unable to supply data were not integrated. These include variables of undoubted importance such as office space vacancy levels and the stock of office space. However, both of these variables are only available for very few locations. If the data was only missing for a few locations, either reasonable estimates were made or a lower value applied as a form of penalty (the minimum value or 10 percent or 30 percent quantile).

13 Data quality The data originates from various official sources such as the Federal Statistical Office, the statistical offices of the federal states, the Federal Institute for Research on Building, Urban Affairs and Spatial Development as well as private organisations, estate agents and our own research activities. It is not possible to exclude the possibilities of errors in the data. To minimise the effect on the rating, we incorporated as many variables as possible and, in some cases, redundant variables. Weightings The weightings are not based on any scientific principles. The weightings were allocated to the best of our knowledge and belief and were checked and adjusted for plausibility. Interpretation of the points and marks The results of the rating are firstly a score expressed as a total of the weighted individual points from each variable. Minor differences in the score lead to different rankings, although these do not necessarily indicated significant differences between the locations. The distribution of points is in turn used to calculate a mark for each location, which groups the locations more closely together. The marks are a conversion of the scores into a standardised system of 1.0 to 5.0. The distribution of marks (i.e. the number of locations awarded a 1.0 and the number that are awarded a 5.0) can be influenced by various methods: In this case, only a small number of locations score a 1.0 (the highest mark). The same applies for the lowest mark 5.0. The majority of locations fall within the range 2.5 to 3.5. The advantages is that this one of the most widely known distributions that most closely resembles the original distribution of the points. The disadvantage is that the majority of locations fall into the middle range and there may not be sufficient differentiation. Distribution skewed to the left In the case of left-skewed distribution, the number of top as well as average locations is relatively low. A large number of locations are classified as poor or very poor. The advantage of this distribution is that it most accurately reflects the preferences of investors, who tend to focus on a few locations. The disadvantage is that this distribution does not reflect the distribution of points and therefore shows sudden transitions from one mark to the other even though the score is virtually identical. The published distribution of marks is equivalent to the effective points distribution and therefore approximates a normal distribution. The procedure outlined above was used to create a rating for all municipalities in Germany for each usage type. This rating creates a ranking based on investment risk for the residential, office and retail uses in each of the approximately 12,000 municipalities in Germany. Normal distribution 12 13

14 3.2 Results of the residential civic rating The W&P civic rating produces the following risk distribution in the area of rented apartments for the more than 12,000 municipalities in Germany: for residential use among the 25 cities. Cities such as Dresden, Jena and Hanover fall into the mid-table category. Four out of the total of ten categories are examined more closely below to gain a better understanding of the rating structure and the strengths and weaknesses of the respective locations: Population Key Highways No allocation Excellent Good Medium risk Above-average risk Very high risk Based on this rating, the corresponding data for the selected cities was extracted and one mark per usage type derived proportionally from the pool of 25 cities. The table on the following page contains the mark for each city, the mark as part of the overall rating, and the mark in a rating of just the 25 cities. The key factors for the residential market in a city are the population and its development. Consequently, this category is weighted strongly in the overall rating. Positive population trends over the previous five years and clearly positive forecasts for the future indicate locations with low demand risks. Potsdam receives the highest rating in this context. By comparison, the majority of cities in the Ruhr area and in eastern Germany are rated significantly lower. Negative population projections in particular are responsible for higher risk levels. Having suffered significant losses of economic power and employment opportunities, these cities not only have to contend with natural population decline, but also increasingly with losses due to migration. These effects are reflected in the strongly negative population projections. 1 The labour market The distribution of partial values for the labour market is similar to the results of the overall rating and of the population category. Cities such as Karlsruhe and Heilbronn receive the highest marks among the 25 selected cities. Also worthy of mention are the cities of Jena and Bochum, which appear in the second half of the overall rating. However, they appear mid-table in the labour market category. Relatively low unemployment rates coupled with a significant downward trend in these rates have a positive impact on these locations. Among the selected cities, Regensburg receives the highest rating. Duisburg is awarded the lowest rating and is therefore allocated the highest investment risk

15 Future vacancy risk In the future vacancy risk category, the lowest scores are achieved particularly by cities that currently report excess supply and expect negative population forecasts. Numerous cities are aware of this possible problem and are already developing countermeasures and gradually adjusting the supply of residential property in line with demand. The residential market The eastern German cities score particularly well in the residential market category. Here, the high proportion of apartments in multiple family dwellings, which are of interest to investors, has a positive effect. The rents category (in the residential market) clearly shows that the cities in the south are most attractive for investors. In contrast to many cities in eastern and western Germany, the last few years have seen rents here rise across the board. Furthermore, the absolute rent level in these cities is significantly higher than in cities such as Duisburg, Wuppertal or Magdeburg. However, it is important to take account of the fact that the rent level in relation to income represents yet another figure, which is not included in the rating, but presented in Chapter 4.2. This creates consequences that sometimes appear contradictory because high rents in southern Germany also translate into a relatively high rental cost, thus limiting the potential for rent increases. The residential rating for the selected cities produced the following results: City Overall rating Top 25 Population Labour market Future vacancy risk Residential market Regensburg Heilbronn Karlsruhe Nuremberg Potsdam Augsburg Dresden Kiel Hanover Brunswick Jena Dortmund Lübeck Erfurt Wuppertal Aachen Bochum Bielefeld Rostock Leipzig Kassel Magdeburg Halle/Saale Chemnitz Duisburg Results of W&P civic rating for residential use in selected cities Source: own account 1 In some cities, the actual population development in recent years was markedly better than forecast. In view of this, the population forecasts are subject to a transformation that has been accounted for, including in the regular updates of the civic ratings

16 3.3 Results of the office civic rating The W&P civic rating for offices provides the following overall risk distribution for the more than 12,000 municipalities in Germany: In the office rating, Regensburg and Karlsruhe achieve the highest scores of the 25 selected locations, closely followed by Potsdam and Nuremberg. However, Augsburg, Heilbronn and Dresden are also rated as locations with a low investment risk for office properties. Bringing up the rear are Wuppertal, Duisburg, Halle/Saale and Chemnitz, all with comparatively high investment risk. Closer analysis of three of the total of nine categories clarifies the structure of the rating and reveals the locations strengths and weaknesses: The economy Cities such as Jena achieve positive ratings in the economy category. They have a high proportion of service industries with the potential for further development, and the trend for value creation in the service sector is clearly positive. Cities such as Wuppertal with a low proportion of services, a negative trend in this area and less development of servicebased value creation do not fare as well. Another criterion is the number of commuters. Hanover has the highest number of commuters among the cities examined, whereas Wuppertal is less attractive to workers and has the lowest number of commuters among the cities analysed. Taxes Key Highways No allocation Excellent Good Medium risk Above-average risk Very high risk The table on the following page contains the mark for each city, the mark as part of the overall rating, and the mark in an evaluation of just the 25 cities. In the sub-category of taxes, Hanover scores the highest number of points. This is due to high trade tax revenue and the high basic rates of property tax B. High tax revenues indicate a large number of industrial estates, which in turn points to strong economic performance. This also leads to an indirect increase in demand for office and commercial space. In contrast, Potsdam or Jena record low tax revenues. Construction activity Cities such as Potsdam and Jena achieve positive ratings for the sub-criterion of construction activity. New construction activity for non-residential property is showing positive development while the level of newly constructed non-residential space per inhabitant is comparatively good. In cities such as Halle/Saale, Chemnitz or Duisburg, the level of construction activity is relatively low.

17 The following results were recorded for the office rating: City Overall rating Top 25 Economy Taxes Construction Regensburg Karlsruhe Potsdam Nuremberg Augsburg Heilbronn Dresden Leipzig Kiel Hanover Jena Brunswick Kassel Erfurt Magdeburg Rostock Lübeck Dortmund Bielefeld Bochum Aachen Wuppertal Chemnitz Halle/Saale Duisburg Results of W&P civic rating for office use in selected cities Source: own account 16 17

18 3.4 Results of the retail civic rating The W&P civic rating for the retail sector results in the following risk distribution for the more than 12,000 municipalities in Germany: Three out of the total of ten categories are more closely examined to gain a better understanding of the rating structure and the strengths and weaknesses of the respective locations: Centrality Centrality was determined by taking into account travel times to the motorway, to the railway station, to the airport, to the nearest medium-sized regional centre and to the nearest major regional centre. In general, the selected cities in the rating of 12,000 municipalities are much closer to one another than the rating marks suggest. Lübeck heads the pack because travel time from the city centre to the motorway is the shortest and because the nearest airport can also be reached very quickly from the centre. The main reason for the poor rating received by Heilbronn is its lack of a long-distance railway station. Purchasing power Key Highways No allocation Excellent Good Medium risk Above-average risk Very high risk The table on the following page contains the mark for each city, the mark as part of the overall rating, and the mark in an evaluation of just the 25 cities. Once again, Nuremberg tops the table for retail use, closely followed by Karlsruhe and Dresden. The lowest rankings and the associated higher risks for retail investments are awarded to Magdeburg, Halle/Saale and Chemnitz. The purchasing power category incorporates the criteria of average income, change in average income, purchasing power of households in the community, purchasing power of households regionally, purchasing power per person, purchasing power per thousand and overall purchasing power per municipality. In this category, Nuremberg, Hanover and Dortmund achieve the highest scores overall thanks to their high purchasing power per capita and purchasing power per municipality. Tourism Tourism creates purchasing power streams that are important for retail investments. For this reason, it is assigned a heavy weighting in the retail area. Arrivals (tourists), the number of hotels, the number of hotel beds, the number of overnight stays per inhabitant and the total number overnight stays by visitors are taken into account. Dresden, a UNESCO world heritage city, achieved the highest score in this cat-

19 egory. It was followed by Nuremberg (tourism and trade fairs) and Hanover (trade fairs). Leipzig and Rostock are also tourist attractions and score highly in this category. Cities such as Heilbronn, Halle/ Saale and Duisburg are at the bottom of the table in the tourism category. Nevertheless, Heilbronn s high purchasing power enables it to offset the location s second-tier tourism ranking (and weak centrality score) in the overall rating. The following results were recorded for the retail rating: City Overall rating Top 25 Centrality Purchasing power Tourism Nuremberg Karlsruhe Dresden Regensburg Potsdam Hanover Augsburg Dortmund Leipzig Lübeck Heilbronn Brunswick Bielefeld Aachen Bochum Kiel Kassel Erfurt Wuppertal Duisburg Jena Rostock Magdeburg Chemnitz Halle/Saale Results of W&P civic rating for retail use in selected cities Source: own account 18 19

20 4 Risk/return profiles To supplement the civic rating by Wüest & Partner, data about the property market was gathered and condensed to produce risk/reward profiles. These figures are not included in the civic rating for various reasons including the fact that they are not available from all areas of the country and, more importantly, not of comparable quality. (Despite intensive research, it was not always possible to obtain the kind of figures used here). Since the figures and the risk/return profiles developed from them enable a much more detailed view of the respective markets, they are important when it comes to analysing the markets. They constitute an ideal supplement to the civic rating. To avoid distortions created by the euphoria surrounding reunification, particularly in the eastern German cities, the study excluded the (available) time series dating back to 1990, but focussed instead on each of the previous 10 years from 2002 to This period covers two cycles: The upturn and downturn of the dot-com era through to the recession during the years 2003 to 2004, the boom from 2005 to 2007 that was initiated primarily by international investors and the renewed downturn in the wake of the sub-prime and financial crises, and the (so far) soft landing. Although the choice of period under review may in some cases create distortions, these apply equally to all the cities analysed and can therefore be evened out. The figures created here do not represent the only possible approach. It is quite possible to create a range of other figures, which lead to different results under certain circumstances. However, the figures used here do form core areas and are of decisive significance for the respective market segments. One problem, however, was related to the different sources of original data and the resulting contradictory figures (see for example the low productivity per unit area for offices in Munich and Stuttgart). These contradictions have been resolved as far as possible. Where it was not possible to do so, this is indicated accordingly.

21 4.1 Methodology The analysis is conducted using combined relative figures. In deriving these figures, it is important to note that simply comparing inventory or periodic figures does not reveal a great deal about the risk/ reward weighting in individual cities. Accordingly, past rent price trends are not a reliable indicator of future rent development. From our perspective, we were forced in some cases to make justifiable simplifications of content to ensure the comparability of the figures. Furthermore, the necessary data had to be available for all or at least for the clear majority of cities involved. What applies to the rating applies equally to the figures themselves: Figures for the entire macrolocation were used. In selected micro-locations, conditions and therefore the figures can deviate significantly. This applies in particular to the retail sector. City-centre locations in particular can perform significantly better than one might imagine from looking at the city as a whole. A second step involved using the figures to derive a specific risk for the respective segment. The figures from the 25 cities were compared in a ratio to one another and awarded marks ranging from 1.0 (excellent) to 5.0 (very poor) based on their magnitude. In addition, the future outlook for each city based on the Prognos Future Atlas 2010 was used for each segment to take into account the risk/ reward mix for the respective city. This produces a specific investment risk in each segment of each city, which is also rated with marks from 1.0 to 5.0. Only the 25 selected cities were used as reference quantities and not as in the Wüest & Partner civic rating all 12,000 municipalities. A risk of 5.0 therefore is only regarded as high for the group of 25 cities and would certainly still compare very favourably in the context of all 12,000 municipalities. Variable 1 Variable 2 Variable 3 Figure 1 per city Variable 4 Variable 5 Figure 2 per city Prognos future prospects per city Grade 1 5 Grade 1 5 Grade 1 5 Specific investment risk for type of use Calculation of specific investment risk Source: own account 20 21

22 4.2 Residential HH income ø rent ø utility costs Cost of living rate per city Number of apartments Number of households Supply ratio per city Prognos future outlook Demographics per city Three figures were calculated for the residential use (rented apartments) and applied in this study: the cost of living for households in percent the penetration rate of the markets expressed as the number of apartments per household the specific residential investment risk The specific residential investment risk was derived on the basis of the adjacent diagram. Grade 1 5 Grade 1 5 Specific investment risk for residential Grade 1 5 Source: own account The cost of living ratio indicates how much of a burden rent payments impose on households. A low ratio would indicate a relative potential for rent increases, while a higher ratio points to a higher than average burden on households and limited scope for additional rent increases. The cost of living ratio is derived from household income 2 and the average number 3 of rental properties in the city. The figures used for net rent excluding heating costs assume an additional heating cost of 2.50/m² (unless otherwise indicated, rents and costs are presented as monthly amounts) and an average apartment size of 70 m². Consequently, the cost of living ratio for the 25 cities ranged from 17.8 percent (Heilbronn) to 28.3 percent (Rostock). The mean value is 22.1 percent. The following graph shows the individual values. For comparison: The cost of living ratios in the top seven locations range between 21.5 percent (Düsseldorf) and 28.3 percent (Frankfurt/Main), with 25.3 percent as the average value. (This and subsequent graphs show the top seven locations marked in grey; they are also excluded from the rating.) Percent Heilbronn Wuppertal Duisburg Bochum Dortmund Brunswick Kassel Chemnitz Bielefeld Halle/Saale Augsburg Düsseldorf Magdeburg Aachen Nuremberg Karlsruhe Leipzig Cologne Dresden Hanover Erfurt Stuttgart Lübeck Regensburg Potsdam Kiel Berlin Jena Munich Hamburg Rostock Frankfurt/Main Rate of living costs in selected cities Source: own account

23 There is a clear tendency for a fall as one moves from the south-west to the north-east. Cities in the west are located primarily in the left-hand area of the graph and the middle area is dominated by cities in the south. And with the exception of Regensburg and the top seven cities, the right-hand area is made up exclusively of cities in the north and east of the Federal Republic. This is obviously due to the trend for low rents and average incomes in the west. Higher rents in the south are offset by greater purchasing power there, whereas the north and east (exceptions: Brunswick, Chemnitz, Halle/Saale and Magdeburg) must reconcile rents that are already high with low incomes. This geographical trend does not apply to the A cities. The market equilibrium is characterised in particular by the penetration rate of the respective markets with apartments. In this context, the number of apartments 4 was compared with the number of households 5. This ranges from significant excess demand and a penetration rate of 91 percent (Aachen and Bielefeld) to a substantial excess in supply of 118 percent (Chemnitz). The graph below shows the corresponding distribution. Percent Bielefeld Aachen Hamburg Karlsruhe Stuttgart Bochum Brunswick Berlin Hanover Kiel Jena Cologne Lübeck Frankfurt/Main Potsdam Rostock Regensburg Augsburg Nuremberg Munich Dresden Erfurt Kassel Düsseldorf Heilbronn Duisburg Dortmund Leipzig Wuppertal Magdeburg Halle/Saale Chemnitz Degree of supply in selected cities Source: own account There is not necessarily a correlation between supply and the cost of living ratio. Brunswick, for example, has a lower than average cost of living ratio despite excess demand. Conversely, cities with significant excess supply such as Wuppertal, Halle/Saale and Chemnitz do have the expected higher than average cost of living ratios. In order to include the aspect of future expectations into this snapshot as well, the Prognos Future Atlas 2010 was used. The corresponding ranking from the sub-area demographics was allocated to the residential segment because population development represents the relevant variable for the residential market. 2 Source: GfK 3 Own calculations, source: IDN Immodaten, BulwienGesa 4 Source: Destatis 5 Source: GfK 22 23

24 The future outlook (lower values indicate greater opportunities) is presented as follows for the cities evaluated: Rank Dresden Jena Potsdam Regensburg Leipzig Kiel Frankfurt/Main Karlsruhe Rostock Erfurt Magdeburg Augsburg Heilbronn Kassel Cologne Brunswick Hamburg Aachen Stuttgart Berlin Munich Düsseldorf Nuremberg Halle/Saale Bielefeld Duisburg Chemnitz Dortmund Wuppertal Hanover Lübeck Bochum Prognos Future Atlas 2010, Demographics, selected cities Source: own account The three individual figures with equal weighting were summarised to create the specific residential investment risk. This risk was compared with the average returns 6 achievable in the respective markets: Return 10.00% Halle/Saale Chemnitz Magdeburg 9.00% 8.00% Bielefeld Brunswick Bochum Kassel Erfurt Duisburg Wuppertal Rostock Leipzig Kiel Dortmund Lübeck 7.00% Aachen Dresden Heilbronn Augsburg Jena Nuremberg Hanover 6.00% Karlsruhe Potsdam Regensburg Berlin Düsseldorf Cologne Frankfurt 5.00% Stuttgart Hamburg Munich 4.00% 1.00 Risk Risk/return profile for residential properties in selected cities Source: own account; own survey; IDN Immodaten, RC Analytics, BulwienGesa

25 Even for a comparable, specific risk, the returns corresponding to the risks anticipated by the market vary significantly. Thus, a comparable risk of 3.0 for the cities of Regensburg, Potsdam, Nuremberg, Kiel, Erfurt and Duisburg is rated by the market with returns ranging from 6.1 percent (Regensburg) to 9.5 percent (Magdeburg). This means that the market risk in Regensburg is considered to be significantly lower than in Kiel, Erfurt and above all Magdeburg. The results of the top seven locations are also shown in the graphic for comparison (see page 24). With regard to the calculated figures, the top seven cities offer a similar level of risk to the B and C cities analysed, but at significantly lower returns. Purchase prices in Munich, for example, are significantly higher than prices in cities with comparable risks. However, rents have already reached very high levels so the already high cost of living could stand in the way of further rent increases of the scale achieved in previous years. However, it is important to bear in mind that the markets in the top seven cities are significantly bigger and more liquid, and that returns are correspondingly lower due to the higher demand and demand pressure. According to the selected figures, B and C cities offer a lower level of risk for the same returns as in A cities, or significantly higher returns than in A cities for comparable risks. In other words, the risk/ return profile in the B and C cities is better. City Living costs Supply Demographics Investment risk Ø return Risk/return ranking Bielefeld % 1 Brunswick % 2 Aachen +/ % 3 Bochum % 4 Kassel % 5 Magdeburg +/ % 6 Heilbronn % 7 Duisburg + +/ % 8 Halle/Saale +/ % 9 Augsburg +/ +/ % 10 Dresden +/ % 11 Rostock +/ % 12 Erfurt % 13 Kiel +/ % 14 Karlsruhe % 15 Chemnitz % 16 Wuppertal % 17 Dortmund % 18 Leipzig % 19 Jena +/ % 20 Nuremberg +/ % 21 Potsdam +/ % 22 Lübeck +/ % 23 Hanover +/ % 24 Regensburg +/ % 25 Key Living costs ++ Very low costs + Low costs +/ Average costs Above-average costs High costs Supply ++ Significant shortage + Shortage +/ Balanced Oversupply Significant oversupply Demographics ++ Excellent prospects + Good prospects +/ Balanced Risk High Risk Overview of residential results Source: own account 6 Source: own survey; IDN Immodaten, RC Analytics, BulwienGesa 24 25