The economic relevance of the real estate sector und its recent dynamics
In most countries, the real estate sector plays a dominant role—as measured by volume,
share of the economy, and workforce; but its turnover ratio and therefore its ability
to generate profit for traditional banks or transaction and consultancy companies
is lower than it is for the stock or bond market. For example, in the US, the total
value of the real estate market amounted to about $46.4 trillion in 2018 (Savills
2017, 2019) which is around half of American households’ overall net wealth of $98.2
trillion in 2018 (Credit Suisse 2018) and about 2.3 times the US GDP of 2018; around
4.5% of the US workforce1 works in the construction sector in 2018 (BLS 2019). Similar
figures can be obtained for Germany with a total value of the real estate market of
about $8.3 trillion in 2018 (Savills 2017, 2019) which is more than half of the households’
overall net wealth of $14.5 trillion in 2018 (Credit Suisse 2018) and about 2.1 times
Germany’s GDP of 2018; around 6.8% of Germany’s workforce2 works in the construction
and real estate sector in 2018 (Statistisches Bundesamt 2019). However, not only the
sheer volume and workforce of the real estate market contributes to its economic relevance,
but also its interconnection via financial instruments like mortgages and asset-backed
securities with the capital markets, as has been revealed during the subprime crisis
from 2007 on. This crisis started in the US real estate sector, but also affected
the capital markets and eventually the real economy of many other countries around
the world leading to a worldwide recession in 2008/09. The higher utilization of derivatives
on financial real estate products used to increase the turnover of the business with
real estate and amplified this crisis.
Besides this apparently already high general economic importance of the real estate
sector, there are several reasons why to expect rather a highly dynamic development
in this segment even for the near future. According to personal interviews and surveys
with 905 influential real estate experts (PWC and ULI 2020) and market analyses of
real estate consultant firms (e.g., JLL 2020; Savills 2020), there are the following
megatrends at work which will change business in the real estate industry in the long
term and to a large extent: (1) Urban expansion creates further megacities, (2) regulatory
changes increase housing affordability in the cities, (3) demographic change affects
demand, (4) developers must use new technology to be fast and smart, (5) labor and
material costs for construction will continue to rise, and (6) sustainability considerations
change the design for buildings. Apparently, not all of these impact factors are also
relevant for the special case of Germany. However, certainly demographic change is.
In addition, as pointed out by Cajias et al. (2020b), Germany has experienced a long-lasting
economic expansion for the last ten years while interest rates – due to the politics
of quantitative easing applied by the European Central Bank – are at historically
low levels. Induced by these favorable economic conditions in Germany and the general
geopolitical situation, high numbers of migrants have come to Germany especially since
2015. Moreover, at least partly as a consequence of an aging population, urbanization,
and a growing tendency to single-households, the number of households has been increased.
All these aspects have led to a booming demand for housing in the major cities in
Germany. Similar trends can be observed for other major European countries (Battistini
et al. 2018; Pittini et al. 2019); Germany has a total housing stock of 41.97 million
units with 305,659 housing starts in 2017; for France and the UK, the corresponding
figures are 35.80 million units with 430,000 housing starts, and 28.74 million units
with 193,390 housing starts, respectively (Pittini et al. 2019).
Real estate conferences and the real estate sector in Germany
Against this background, there is no doubt that a closer look at the German real estate
market may be of special interest. However, “business is local” and this insight applies
to the real estate sector in a particular way. Therefore, the development of prices
and liquidity on the German housing market is not homogenous throughout the country,
but rather varying from region to region (Cajias et al. 2020b). In fact, increasing
prices can primarily be observed in the most populous cities and their surroundings
as well as certain economically strong regions while other parts of Germany, e.g.
in the east, may even face decreasing housing prices. As a consequence, a regional
analysis of the German housing market is necessary. Three out of the four articles
of this special issue address this topic, while the fourth is related to the field
of real estate finance.
To put these topics in relation to the working papers currently presented at real
estate conferences, we analyze the titles of the last papers discussed at annual meetings
of the American Real Estate Society and the European Real Estate Society, as well
as at the international conference of the American Real Estate and Urban Economics
Association between 2015 and 2019. Since we do not have access to the abstracts of
all working papers, we rely on the titles to discover the most common topics currently
researched. By using a machine learning method, we extract which words commonly occur
together in the titles and plot network figures of the words for their co-occurrences
(see Fig. 1). The plot of these co-occurring words identifies the relationships better
than a table. To focus on the keywords, we ignore stop words such as ‘and’ or ‘or’
and swapped all plural words to the singular. For the entire time period (Panel a),
the word family is clustered around real estate in the center. The terms market, house,
and housing build a second word family with fewer ties to other words than real estate.
Nevertheless, the network has a clear center and not various clusters with word families.
Surprisingly, the word mortgage is not in the focus of research. The pair energy and
efficiency is isolated and the pair green and building is connected via office to
the center. In Panels b, c, d, we analyze a shorter period of time and find that the
pairs energy and efficiency as well as green and building were connected to the center
for the years 2015–2016 (Panel b), whereas mortgage and default were isolated in the
years 2017–2018 (Panel c). For the recent year 2019 (Panel d), the map shows a similar
network as for the entire sample, but we see that the UK and market may be more in
the focus due to the Brexit.
Fig. 1
Word Pairs in Titles. This figure shows the most common word pairs in the titles of
working papers presented at real estate conferences (annual meeting of the American
Real Estate Society, international conference of the American Real Estate and Urban
Economics Association, annual meeting of the European Real Estate Society) in Panels
a–d and of real estate related working papers at finance conferences in Panel e between
2015 and 2019. For a description of the finance conferences, see Table 1. The number
of co-occurrence (n) is indicated by the thickness of the connection line; the most
common pairs are located in the center
Taking now a closer look at the articles of this special issue that aim at the analysis
of the German real estate market we indeed find that none of them is concerned with
mortgage issues, but all of them address general characteristics and developments
of the German real estate market.
According to the study “Transformation of the real estate and construction industry:
empirical findings from Germany” by Andreas Pfnür and Benjamin Wagner, the specific
drivers of structural change on the German real estate market are more important than
the general mega-trends mentioned above (Pfnür and Wagner 2020). Based on a comprehensive
survey, the following main determinants of the transformation process in the German
real estate industry are identified: Massive changes in space requirements and the
way space is provided induce occupiers to search for holistic and flexible solutions
to fight increasing uncertainties regarding their requirements for space. As a consequence,
property developers concentrate on their development activities more on the occupiers.
While investors are also recognizing this rising relevance of occupiers, for the time
being, they often refrain from reacting to this in an adequate way. Instead of questioning
their existing business models in a general way, service providers try to optimize
already existing processes and activities. Summarizing, up to now, the players in
the real estate industry have failed to satisfy the altered needs of occupiers—an
issue which may constitute a threat to the transformation process in Germany in general.
Marcelo Cajias, Philipp Freudenreich, Anna Freudenreich, and Wolfgang Schaefers are
looking at “Liquidity and prices: a cluster analysis of the German residential real
estate market”, thus investigating also in a differentiated way regions instead of
the country as a whole (Cajias et al. 2020b). Based on a dataset that comprises in
total more than 4.5 million observations in 380 German regions from the beginning
of 2013 to the end of 2018, the authors first build on a regional basis quality- and
spatial-adjusted price indices as well as a liquidity index for the most important
German residential investment and rental markets. A cluster analysis leads to the
result that the optimal number of clusters is two for price as well as liquidity.
For both price and liquidity on the investment and rental market, the first cluster
is characterized by higher growth rates with respect to population, working population,
and real GDP leading to a more pronounced demand for space. Moreover, this first cluster
generally exhibits lower unemployment rates and higher disposable income. As another
consequence of this study, it seems that a large part of the German population has
turned into professional real estate investors, because the regions belonging to the
first cluster appear to be chosen with the help of a very sophisticated market analysis
in order to identify those areas with the best fundamental data.
In their article entitled “Exploring the determinants of real estate liquidity from
an alternative perspective: censored quantile regression in real estate research”,
Marcelo Cajias, Philipp Freudenreich, and Anna Heller present the first study to investigate
the time on market for rental dwellings (i.e. the inverse of their liquidity) by applying
a censored quantile regression (CQR) in real estate research (Cajias et al. 2020a).
By this approach, the variation of time on market is explained as a function of dwelling
features and other spatial and socioeconomic characteristics. In particular, CQRs
make it possible to model any quantile of the distribution of the dependent variable.
The underlying dataset consists of 482,196 observations on the rental market of Germany’s
seven largest cities between the beginning of 2013 and the end of 2017. The relevance
of explanatory variables for time on market differs across these cities and between
time on market quantiles in the cities under consideration highlighting the relevance
of this special empirical approach and one more time of a detailed market assessment.
Against this background, landlords of dwellings should be better able to infer how
fast they can let them or what measures could be taken to increase their marketability.
Moreover, the conclusions of this article hint at the problems connected with nationwide
or statewide policy measures instead of addressing particular regions, cities, or
neighborhoods.
Real estate finance as a special subset of the real estate literature
As a second pillar of our empirical analysis, we want to get a better feeling of the
relevance of real estate related topics in finance. Therefore, we conduct an examination
of the most relevant finance conferences (annual meetings of the American Finance
Association, the Western Finance Association, the European Finance Association, the
Financial Management Association (US Meeting), and the European Financial Management
Association) for the years 2015 to (May) 2020 and the most important finance journals
(Journal of Finance, Journal of Financial Economics, Review of Financial Studies,
Review of Finance, Journal of Financial and Quantitative Analysis, Journal of Banking
and Finance, and Journal of Corporate Finance). We searched for articles and papers
with keywords containing at least one out of the following list: agricultural, housing,
land, mortgage, real estate, residential, REIT, rural, spatial, and urban. The overall
number of such real estate related finance papers and articles are described by Table
1. About 5.6% of all papers presented at the most relevant finance conferences are
related to real estate issues. While this figure is rather stable over time, there
are marked differences across conferences with the highest shares of real estate topics
for the American Finance Association and the Western Finance Association. The smallest
numbers of such papers are presented at the annual meetings of the European Financial
Management Association. A similar average portion of about 6.1% results when we look
at the journal articles. Once again, the overall average does not exhibit much intertemporal
variation. Figures are remarkably high for the Journal of Financial and Quantitative
Analysis (about 19%) whereas (with the exception of the Journal of Finance, about
8%) for the other journals, they are mostly in a range of 4–6%.
Table 1
Real estate related finance articles and papers
2015
2016
2017
2018
2019
May 2020
Panel a: Conferences
American Finance Association
29
32
28
31
31
24
14.7%
12.7%
11.9%
12.6%
12.9%
10.0%
Western Finance Association
19
19
22
26
17
20
13.1%
13.1%
15.3%
18.1%
11.8%
13.7%
European Finance Association
20
22
11
8
3
8.3%
9.1%
5.0%
3.3%
1.2%
Financial Management Association (US Meeting)
17
24
17
25
22
2.3%
2.8%
2.2%
3.3%
3.2%
European Financial Management Association
4
3
4
7
1
1.4%
1.0%
1.2%
2.3%
0.4%
Total (Conferences)
89
100
82
97
74
44
5.5%
5.6%
4.8%
5.7%
4.7%
11.4%
Panel b: Journals
Journal of Finance
7
3
8
4
3
4
9.6%
4.1%
12.9%
6.2%
4.3%
10.5%
Journal of Financial Economics
4
5
4
4
7
5
3.3%
4.1%
3.3%
3.5%
5.1%
5.8%
Review of Financial Studies
6
6
6
10
7
4
6.9%
6.5%
5.4%
8.3%
5.7%
4.9%
Review of Finance
3
2
2
4
2
2
5.2%
2.9%
2.8%
6.5%
6.1%
10.5%
Journal of Financial and Quantitative Analysis
12
12
18
15
8
11
22.2%
17.6%
19.8%
17.9%
9.9%
27.5%
Journal of Banking and Finance
12
7
6
10
4
6
4.0%
4.1%
3.5%
4.2%
2.1%
4.0%
Journal of Corporate Finance
6
10
9
3
5
5
5.4%
8.2%
5.7%
2.2%
4.8%
3.8%
Total (Journals)
50
45
53
50
36
37
6.2%
6.3%
6.7%
6.1%
4.8%
6.8%
This table shows the absolute and relative number of real estate related finance working
papers in relation to all papers for various conferences (Panel a) and journals (Panel
b) for the years 2015 to (May) 2020. The respective key words to be incorporated in
this list are: agricultural, housing, land, mortgage, real estate, residential, REIT,
rural, spatial, and urban
Not very surprisingly, results with respect to the frequency of our keywords are rather
unambiguous as well. When first looking at the conference papers, from all references
to this keyword list, about 83% belong to the following terms: real estate, mortgage,
and housing. If papers are weighted according to their respective downloads in the
Social Science Research Network, almost the same result applies: About 81% of all
downloads refer to papers which contain at least one of the three terms real estate,
mortgage, and housing. When turning to the results for the finance journals, the ranking
slightly differs, but one more time, the terms mortgage, real estate, and housing
perform best. However, only about 65% of all real estate related references stem from
these keywords. With articles being citation-weighted, this figure decreases slightly
to 61%. The next two most frequent keywords are residential and rural with an overall
share of about 14 to 18%.
Apparently, real estate-related finance papers are typically addressing issues in
the sphere of mortgages. Comparing the results with a pair word network as in Fig. 1,
we are able to identify a center around evidence which is closely and strong connected
to market, housing, credit, and mortgage. The term real estate is not in the focus
of finance related conferences and the terms mutual pension fund is isolated (see
Panel e).
To take a somewhat more differentiated and state-of-the-art view, we additionally
performed a machine learning method to identify the common “topics” in the abstracts
of the conference papers and publications under consideration (see Figs. 2, 3). To
find the topics, we apply the text mining approach “Latent Dirichlet Allocation” (LDA)
(Blei et al. 2003); this unsupervised machine learning method classifies each abstract
as a mixture of topics and each topic as a mixture of words. This approach mimics
the natural language processing for dividing documents into natural groups without
any pre-specified topics. The mathematical approach is conceptually similar to a factor
analysis, where a large number of variables is reduced to fewer factors. The same
idea applies to LDA where the large dimensionality of linguistic data is reduced from
words to topics (Dyer et al. 2017). In line with the word pairs of Fig. 1, we use
word co-occurrences within documents (in our case abstracts), but topics are defined
as a collection of words and each word is linked with a probability of belonging to
a topic. Thus, LDA connects documents with probability distributions belonging to
topics, so that one document can contain several topics. Based on the number of observations,
we set the number of topics to eight. For an overview how to apply text mining with
the programming language R, see Silge and Robinson (2016).
Fig. 2
Probability of a word belonging to a topic. This figure shows the top 10 words for
each of the 8 topics found by the LDA (Latent Dirichlet Allocation) in the abstracts
of the finance conferences (Panel a) and finance journals (Panel b) for papers with
real estate related subjects. For a description of the conferences and journals, see
Table 1. Each word is connected with a probability (β) of that word belonging to that
topic
Fig. 3
Probability of an abstract belonging to a topic. This figure shows the probability
(γ) that a real estate related abstract belongs to specific topic. The y-axis counts
the number of abstracts and is log(10)-scaled. Panel a depicts the finance conferences
and Panel b the finance journals; see Table 1 for a description of the conferences
and journals
In Fig. 2, we show the probability (β) of the top 10 words for each topic belonging
to that topic—conferences are in Panel a and journals in Panel b. Due to space considerations,
we refrain from a detailed analysis of the results. For conferences and journals,
in six or seven, respectively, out of eight topics we find at least one of our most
important keywords mortgage, real estate, and housing, and apparently most clusters
are focusing on financing problems with respect to real estate properties for private
households and firms as well as the risks connected with such transactions. Moreover,
in this regard, the role of mortgages as collateral as well as the role of banks and
mortgage-backed securities (MBS) in secondary-market transactions are discussed. Even
crisis aspects are addressed. Additionally, though of somewhat minor importance, there
is some reference to real estate transactions from an investment point of view e.g.
with more emphasis on return issues (see especially topics 2 and 8 for conferences
and topics 2 and 3 for journals). Overall, clustering results for our finance conferences
and finance journals are rather similar, so that at least for the last five years,
there does not seem to be a special development in real estate topics anticipated
by finance conferences in comparison to the subjects currently addressed in finance
journals. Thus, we will see similar topics for the next year(s), since conference
papers have an average lead time of at least 1 to 2 years before they are published.
However, the “unique allocation” of the topics is different between the finance conferences
and finance journals as shown in Fig. 3 with the probability (γ) that a given abstract
belongs to a given topic. In Panel b (journals) we see with topics 5 and 8 that the
majority of the articles does not cover this topic (0%), whereas most of the remaining
abstracts cover this topic for 100%—it is an either-or topic. The other articles have
a similar U-shaped distribution among their corresponding topics. Contrary for Panel
a (conferences), the probability that a given abstract belongs to a given topic is
here more dispersed. For example, topics 4 and 7 express a bulk of occurrences with
low probabilities (left-hand side of the axis).
Thus, conference papers have—currently or in general—a more diluted allocation to
the topics than articles, whose abstracts have a clearer distinction among the topics
and are higher discriminated as belonging to or not to a topic. Maybe, this is a consequence
of the journals’ reviewing process with referees demanding a clearly defined focus
of the submitted papers.
Against the background of our results so far, it does not come as a surprise that
Florian Manz, Birgit Müller, and Dirk Schiereck are analyzing the special role of
real estate collateral in their article “The pricing of European non-performing real
estate loan portfolios: evidence on stock market evaluation of complex asset sales”
(Manz et al. 2020). Their study is built upon a unique transaction database of 476
non-performing loan deals during the years 2012 and 2018. This leads to the analysis
of the value implications of 317 divesture announcements at 58 listed banks during
2012 and 2018. Two-thirds of all collateral are real estate loans, while the other
third consists of consumer loans, corporate loans, and mixed loan pools. Since the
latter is a mixture of the other three types, the actual real estate proportion will
most probably be even higher. The aim of the study is to reach a better understanding
of the characteristics and the functioning of the secondary market for such loans
under distress. The findings may also lead to regulatory implications since the European
Central Bank deems the issue of non-performing loans to be of high importance.
The results identified by Manz et al. (2020) can be interpreted as robust evidence
for a significantly positive stock market reaction at vendor banks following non-performing
loan sales with the most important determinants of this positive reaction being a
size effect and real estate collateral in these transactions. Apparently, the capital
markets consider the sale of real estate non-performing loan portfolios to be relatively
more attractive than other kinds of non-performing loan portfolios. The tangibility
of real estate loans makes it easier for the contractual partners to determine the
value of the loan than, e.g., in the case of consumer loans. In addition, real estate
collateral requires specific and interdisciplinary knowledge to be handled adequately
leading to high opportunity costs for the creditor. When selling these special non-performing
loans with real estate collateral, these demanding tasks are transferred from the
vendor bank to the buyer releasing valuable resources which now can be allocated to
other fields of operation.
Outlook: real estate trends and COVID-19
When publishing the call for papers for this special issue, the world had never seen
before the Coronavirus SARS-CoV-2 and no corresponding disease called COVID-19. Since
then, our daily life has changed dramatically and certainly, the consequences for
real estate markets will be tremendous as well. Some effects and implications are
clear and already irreversible. For example, the long-existing trend of “death of
retail” or “retail apocalypse” (Rasmusson 1999) has been exacerbated; first surveys
show that many consumers may permanently change their shopping behavior and stick
to shopping online (US: 29%; UK: 42%; Competera 2020). The sales of e-commerce shops
will continue to rise and traditional shops have expanded their e-commerce channels
in answer to the current situation. This will shift the demand for space of retail
shops and shopping centers to storage houses and decentralized distributions centers
and consequently their rents will rise. The hotel, leisure, and catering industry—hopefully—may
only undergo a short economic drop so that there are lower long-term consequences
with respect to real estate issues to be expected in these fields, unless the impact
of a reduction in business trips does not become too grave. Hospitals and nursing
homes must adjust their hygiene concepts and may need to make technical and structural
modifications in their buildings.
Other effects and implications are unclear and further developments are not yet foreseeable.
For example, if the trend to work (partly) at home or in decentralized co-working
buildings will continue, the necessity for office spaces in the city centers will
decrease. This would change the demand for traditional office concepts and commuting
patterns and consequently the city and traffic planning.
Even if the implications of the articles in this special issue are not directly affected
by the long-term social and economic distortions of the “new” Coronavirus, the consequences
of this pandemic for real estate related issues are worthwhile to be examined.