Paper presented at the 35th International Conference of the System Dynamics Society,
July 16-20, 2017, Cambridge, MA, U.S.A.
"Boom Without Limits?" —
An Analysis of the Stuttgart Real Estate Market
Eleftheria-Eleni Kapourani Florian Kapmeier*
ESB Business School, Reutlingen University ESB Business School, Reutlingen University
AlteburgstraBe 150 AlteburgstraBe 150
72762 Reutlingen 72762 Reutlingen
Germany Germany
NOVA School of Business and Economics
Campus de Campolide
1099-032 Lisbon
Portugal
email: eleftheria.kapourani@gmail.com email: florian. en-university.de
*corresponding author
Version 1.0
June 2017
Prior research for this paper was conducted at the ESB Business School, Reutlingen University as part of
Eleftheria Kapourani's Bachelor thesis under the supervision of Florian Kapmeier.
Abstract
Real estate markets are known to fluctuate. The real estate market in Stuttgart, Germany, has been booming for
more than a decade: square-meter price hit top levels and real estate agents claim that market prices will
continue to increase. In this paper, we test this market ‘standing by developing and analyzing a system
dynamics model that depicts the Stuttgart real estate market. Simulating the model explains oscillating behavior
ar
ising from significant time delays and feedback structures — and not necessarily
interest rates, as market experts assume. Scenarios provide insights into the system's behavior reacting to
changes exogenous to the model. The first scenario tests the market development under increasing interest rates.
The other scenario deals with possible effects on the real estate market if the regional automotive economy
suffers from intense competition with new market players entering with alternative fuel vehicles and new
technologies. With a policy run we test market structure changes to eliminate cyclical effects. The paper
confirms that the business cycle in the Stuttgart real estate market arises from within the system's underlying
structure, thus hasizing the imp e Of | ‘ feedback s
Keywords: cyclical markets, real estate market, real estate cycles, oscillation, simulation
Introduction: The Stuttgart Real Estate market
Housing prices have been skyrocketing in many major German cities in the last few years. Among those,
Stuttgart, the capital of the state of Baden-Wiirttemberg, has experienced particularly aggressive price
movements (Buchenau, 2015; 2016; Reichel, 2014). Average prices for apartments have reached almost 3,250
Euro per square meter (€/m”), while the premium segment has even hit peak values of approximately 15,400
€/m? (Gutachterausschuss fiir die Ermittlung von Grundstiickswerten in Stuttgart, 2016, p. 5). According to the
head of the city’s Real Estate Expert Committee, there is not any sign of end of the price increase because of
number of hold:
popul low interest rates, and good economic conditions
(Jager, cited in Buchenau, 2016).
However, historical price developments reveal that today's prices of the Stuttgart real estate market have not
grown gradually over time. Instead, the price index in Figure 4 illustrates a cyclical behavior with booms and
busts over more than 30 years. The current real estate boom observed in Stuttgart challenges the question
whether real estate prices are continuing to increase or whether there will be a bust. If the latter is possible, the
question arises where the cyclical dynamics originate from.
Cyclical behavior is common in commodity markets, where the interplay of supply and demand determines the
market (Sterman, 2000). However, these markets, including real estate, are highly complex as they consist of
p Cc q ly, it is difficult to understand and analyze them. Still, literature provides
different analysis approaches to overcome such difficulties (Pyhrrn et al., 1999). While some involve
macroeconomic perspectives (as cited in Pyhrrn et al., 1999), others relate the oscillating behavior to the
endogenous structures of the real estate market (DiPasquale & Wheaton, 1992). The latter studies imply causal
relationships among influencing variables as well as feedback structures consisting of the interaction between
supply and demand.
In this paper we analyze the Stuttgart real estate market and expose the main drivers of its actual cyclical
behavior to achieve a better understanding of the underlying structure. We test whether market prices will
continue to grow, as the city’s Real Estate Expert Committee (Jager, cited in Buchenau, 2016) and other experts
(Haar, 2017) claim. Policy design and scenario settings will deliver profound insight in order to experience the
long-term ramifications of the systems current behavior.
In the following, we first explain the research approach. q
about the Stuttgart real estate market. We then explain and test the system dynamics model on the Stuttgart real
estate market. Afterwards, we test scenarios on an increase of interest rates as well as on a serious decline in
ly, we provide information
households and a policy run on considering underway construction and discuss the insight. The paper closes with
conclusions.
Research Approach
In order to analyze the Stuttgart real estate market, we combine designing a case study (Yin, 2003) with
a itative system d: ics model (Forrester, 1961; Sterman, 2000). Although simulation models
are mainly based on quantitative data, qualitative input is also essential throughout the modeling process (Luna-
Reyes & Andersen, 2004, p. 271; Sterman, 2000, p. 853). Therefore, the research approach of this paper entails a
mixed methods research, which uses both qualitative and data ding to (Creswell, 2003) (Figure
1.
Quantitativ Qualitative Data
Se
Figure 1: Applied research methodologies in the paper (authors' representation).
We collected quantitative data through secondary research, primarily from the Statistical Offices of the federal
state of Baden-Wiirttemberg as well as of the city of Stuttgart. Since quantitative data account only for a fraction
of all the information available that is "crucial for understanding and modeling complex systems" (Sterman,
2000, p. 853), qualitative research serves as an additional methodological basis for the study.
Instead of an isolated use, several methods have been triangulated within the qualitative research approach in this
study. According to its original form, triangulation implies different qualitative research methods being applied
in one study thus diminishing limitations of single methods when used separately (Denzin, 2012; Patton, 1999;
Yin, 2003). From the different types defined by Denzin (2012) and Patton (1999), we combined multiple
qualitative research methods - literature review and expert interviews - with system dynamics. First, the
literature review provided profound background on commodity markets in general and on real estate markets in
particular. Thus, it provided a firm basis for the system dynamics modeling process as it helped to frame the
problem and to develop the dynamic hypothesis.
Second, in addition to secondary research, we carried out primary research in order to obtain in-depth
information about the market. We collected qualitative data through expert interviews conducted with employees
of one eof the Stuttgart-based real estate ies. Interviews, with other research methods of
and qualitative nature, supp: the outline of the dynamic hypothesis as well as the actual design
of the aiamilation 4 model (Sterman, 1987; 2000).
As semi-structured interviews offer an effective way for the purpose of this study (Sterman, 2000), we conducted
the interviews as "guided ions rather than queries" (Yin, 2003, p. 89) to underpin certain
information retrieved from secondary research and to confirm assumptions for the system dynamics model (Yin,
2003, p. 90). For this reason, the design of the interview guideline was based on open-ended questions. Before
the actual interviews, we conducted 'dry runs' with a person of similar background who is not involved in the
study.
We interviewed four real estate experts, among those a director of the private real estate sector and a managing
general partner of a renowned Stuttgart-based real estate company in the fall of 2015. Interviews ran about 60-90
minutes. We transcribed the interviews and analyzed them to explain and verify the model and investigate
scenario and policy runs.
Literature Review of System Dynamics on Real Estate
Real estate cycles have been observed and analyzed with system dynamics in various regions and cities around
the world from Netherlands (Eskinasi, 2014), over Istanbul (Barlas et al., 2007) to Boston (Genta, 1989) and
Taiwan (Hu & Lo, 1992). Eskinasi (2014), for example, aims at systematically linking real estate economics and
system dynamics, by presenting several projects and case studies from the Netherlands. Among those, the
Haaglanden project describes the entire "model building project about new housing construction, urban renewal
and the impact of both processes on a regional social housing market" (Eskinasi et al., 2009, p. 182). In a further
s housing policy effectiveness for the Dutch housing market (Eskinasi, 2014). Eskinasi (2014)
transfers DiPasquale and Wheaton’s (1992) model into a simple system dynamics model and extends it by
case, he addres:
adding "institutional aspects like land use Planning, t rent regulation, fiscal mortgage support and residual land
pricing policy" (p. 18). Two descendants of Eskinasi’s second case are the Middle Incomes and the Mortgage
model. The former model evaluates the Dutch state support regulation while the latter analyzes the dynamics of
mortgage debts of Dutch households (Eskinasi, 2014).
Further, Mashayekhi et al. (2009) address the cyclical dynamics in the owner-occupied real estate market in Iran.
By comparing it with the properties of Wheaton's (1999) rental market model, Mashayekhi et al. (2009) simulate
the differences in cyclicality that arise from the vacancy structure employed in the owner-occupied model only.
Furthermore, an integrated model combines both markets, thus demonstrating cyclical effects resulting from the
of both s' structures. N hi et al. (2009) conclude that the interrelationship of
different cycle-producing mechanisms increases complexity and deliver different dynamic behavior.
Atefi et al. (2010) base their model on Mashayekhi et al. (2009) to study the real estate market in Iran with a
focus on housing affordability. Their main conclusion draws on supporting financial structures that may regulate
the fluctuations in the Iranian market.
Barlas et al. (2007) aim at enhancing the ing of cyclical d ics of the real estate market in Istanbul.
The model is developed from a major construction firm's perspective, assuming an oligopoly in the construction
sector. The analysis reveals that time delays in the supply chain strongly influence the cyclical behavior of the
real estate market. Considering this effect, one of the proposed policy designs takes into account the houses
under construction with the result of reducing price oscillations. In their sensitivity analysis, Ozbas et al. (2008)
point out the variables construction time and sales time having the highest impact on the period and amplitude of
the oscillations, while profit margins determine the price level.
Similar to Barlas et al. (2007), Hu and Lo (1992) study the structure of the real estate market in Taiwan. Their
simulation demonstrates "that the cyclical behavior pattern is influenced by the structure of the housing market
itself" (Hu & Lo, 1992, p. 256). The limitation of the model is the assumption of unlimited land supply, which
however is fairly unrealistic in the specific case of the island of Taiwan.
As one of the first system dynamics works in the field of real estate is Genta's (1989) analysis of the real estate
market in Boston. The model takes into account a broad set of variables includi: i growth,
regional economic factors as well as interest rates to explain the rapid rise in Boston's housing wees (Genta,
1989). It is further used for foresight and policy design with the aim of studying falling prices to balance demand
and supply.
Thornton's (1992) analysis provides insights into the field of real estate by identifying "organizational learning
that influence developers' mental models and cause less optimal development decisions" (p.2).
Thornton (1992) illustrates the difficulties that real estate professionals have in understanding the complex
system of the real estate market despite their expertise. In addition, his work demonstrates how systems thinking
can improve strategic planning and decision making in the real estate sector.
Finally, Sterman (2000) addresses cycles in real estate markets in general and emphasizes the impact of the
endogenous structure of the system thereby (pp. 698-708). The model we developed for our analysis draws
profoundly on Sterman’s (2000) general model structure.
System Dynamics Analysis of the Stuttgart Real Estate Market
Similar to the markets described above, times of boom and bust have also shaped numerous German real estate
markets. Being rather "calm for many years" (Deutsche Bundesbank, 2013, p. 14), the German real estate market
has become especially attractive for international investors as alternative investment options after the bursting
housing bubble in the middle of 2000s in the United States and several European markets in contrast to
"optimistic economic expectations" in Germany (Deutsche Bundesbank, 2013). Consequently, Germany's so-
called big seven, the cities of Berlin, Cologne, Diisseldorf, Hamburg, Frankfurt am Main, Munich, and Stuttgart,
have been in particular focus in recent years. These cities have experienced the largest increases in housing
prices compared to other German cities or rural areas (Deutsche Bundesbank, 2014b; 2014b, p. 64;
Gutachterausschuss fiir die Ermittlung von Grundstiickswerten in Stuttgart, 2015; Reichel, 2017).
Stuttgart’s real estate market cons of many attractive prime locations in residential real estate, commercial
real estate and industrial real estate. The former makes up the largest share of Stuttgart’s real estate segments
(Gutachterausschuss fiir die Ermittlung von Grundstiickswerten in Stuttgart, 2015), which is where our focus of
analysis lies. Market experts expect the prices in this area to increase further (Haar, 2017) because of the city’s
characteristics (Jager, cited in Buchenau, 2016). We shed light on the drivers the experts identify: population and
economy, and p
D of Por ion and He ld:
Stuttgart's population has experienced a rapid increase in recent years, surpassing 600,000 inhabitants in 2013
(Statistisches Landesamt Baden-Wiirttemberg, 2017). Although one would assume that such a large city has
ped with a steady i ing p ion over decades, a closer look on Stuttgart's population development
shows ups and downs over a 40+ years period between 1970 and 2014 (Figure 2, blue).
640.000 5 Population ; 340.000
620.000 - - 320.000
600.000 - + 300.000 +
§ 2
2 GB
2 ®
& 580.000 - - 280.000 >
3 co
& &
560.000 - + 260.000
Households
540.000 - + 240.000
520.000 + 220.000
1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 2: Popul: and Pp in Stuttgart (adapted from Statistisches Amt Stuttgart, 2015b;
Statistisches Landesamt Baden-Wiirttemberg, 2014c).
In 1970, more than 630,000 people lived in Stuttgart. , the p i to less than
560,000 people in 1987, followed by a sharp increase over 10 years to more than 600,000 people. The number of
inhabitants slowly decreased in order to increase again to slightly more than 600,000 people in 2013. The fact
that Stuttgart's population today is smaller than in the early 1970s can be explained by the post-war baby boom
in the 1960s and the increasing wealth after the second world war resulting in a rising population, which reached
its peak in the early 1970s (Schmitz-Veltin, 2009, p. 326). Afterwards, birth rates decreased due to the economic
crisis in the beginning of the 1970s, new forms of birth control and changing society's values, leading to negative
population balance and a declini pulation (Schmitz-Veltin, 2009, p. 326). We observe another upward trend
during the 1990s due to an "echo boom" of births, i.e. the children of the first baby boomer generation, as well as
declining death rates (Schmitz-Veltin, 2009, p. 326).
The unsteady populati tempts to the lusion that demand in housing has developed
accordingly. However, demand in housing is primarily generated by the number of households. In contrast to its
population, the growth rate of households has been almost linear over the examined period (Figure 2, red).
Increasing wealth and social changes have transformed the hi hold ition so that over one
third of the households consist of only one person (Heilweck-Backes & Straub, 2007). At the same time, it can
be observed that with a declining number of persons per household, the living space per capita continues to rise
(Eskinasi et al., 2009). Whereas in 1990, Stuttgart's inhabitants had on average less than 35 m, today the
average living space per capita exceeds 40 m? (Statistisches Amt Stuttgart, 2015a; 2015b). Concluding, despite
the irregular population development of Stuttgart, the number of households has increased nearly linearly since
1970, thus increasing demand in housing.
Regional Economy
A driver of the lately increasing number of people living in Stuttgart, according to the market experts (Jager,
cited in Buchenau, 2016), is also the booming economy in the region since the mid-1980s. The economy of the
greater Stuttgart area is dominated by the automotive industry, including the two OEMs Daimler AG and Dr.
Ing. h.c. F. Porsche AG, and the 1" tier supplier Robert Bosch GmbH, that all have their headquarters and major
production facilities in Stuttgart. In addition, many world-leading as well as icall small and
medium-sized enterprises are present in the region (Gutachterausschuss fiir die Ermittlung von
Grundstiickswerten in Stuttgart, 2015). Therefore, the city has emerged to be one of the most prospering
economic areas among Germany's larger cities, granting its path to the top five leading economic regions in
Germany and even top 15 within the European Union (Statisti: L Baden-Wiirttemberg, 2016).
Consequently, Stuttgart is highly ive for many prospecti p! from all over the world — and thus
another stimulus of increasing housing demand in the real estate market.
Geographic Scope of Study
In addition, many inhabitants find the city’s location attractive. As illustrated in Figure 3, the city’s downtown
(‘Mitte’) is located in a basin between hills and vineyards. In the context of Stuttgart's real estate market, the
characteristic topographical structure constrains Stuttgart's territorial expansion, thus setting a natural limit to
new real estate development. Hence, increasing demand cannot be satisfied with new housing construction, since
building land is scarce (Reichel, 2014). Several districts, in particular those situated on the hillsides and offering
panoramic views over the city center, enjoy increased popularity. This is why real estate prices have been
skyrocketing especially in these areas in recent years (Gutachterausschuss fiir die Ermittlung von
Grundstiickswerten in Stuttgart, 2011; 2016; Real Estate Advisor 2, personal communication, September 23,
2015).
207m to 549m
Height above sea level
Figure 3: The city of Stuttgart with its districts. ‘Mitte’ is where the city center is located (Gutachterausschuss
fiir die Ermittlung von Grundstiickswerten in Stuttgart, 2015).
Considering the three aspects described above, the trend of an increasing number of households, the prospering
economy, increasing demand for more space per resident, and the city’s attractive topography shape Stuttgart's
real estate market. Stuttgart's regional economic strength increases the city’s attractiveness and attracts
increasingly more people to move into the city, who demand more space per capita, thus increasing demand for
housing. The city’s topography, however, reinforces the lack of available housing space, since new construction
is restricted by Stuttgart's basin. Market experts refer to the term ‘Kessellage' in this context (Heilweck-Backes
& Straub, 2007; Real Estate Advisor 1; Real Estate Advisor 2, personal communication, September 23, 2015).
Or, as one interviewee stat
‘the power, ined with its proximity to nature and the
topographical position makes the city so appealing to me and many of our customers" (Real Estate Advisor 2,
personal communication, September 23, 2015).
The of ii ing demand and si shortage in supply are skyrocketing housing prices
(Eskinasi et al., 2009; Heilweck-Backes & StrauB, 2007). In 2015, Stuttgart’s prices per square meter hit an all-
time high of 3,250 € for purchased apartments on average, and 5,075 €/m? for newly constructed apartments,
while peak values surpassed 15,400 €/m? and are expected to reach a record level of 17,000 €/m? for certain
properties (Gutachterausschuss fiir die Ermittlung von Grundstiickswerten in Stuttgart, 2016; Hahn, 2016).' One
interviewee states that "the market is crazy. Yes, these are prices that we have never seen before. They had never
been realized before" (Real Estate Director, personal communication, September 23, 2015).
140 5
120 5 Price
= 100)
B
8
Price Index (@ 2005:
io) ot ot -
1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 4: Reference Mode I: Average prices for owner-occupied apartments in Stuttgart (adapted from
Gutachterausschuss fiir die Ermittlung von Grundstiickswerten in Stuttgart, 2011, p. 13).
While high demand and increasing prices have determined the Stuttgart real estate market for the past years, the
city had previously experienced persistent cyclical instability (Eskinasi et al., 2009; Heilweck-Backes & Straub,
2007). Figure 4 i the oscillating price since 1970. The price index for owner-occupied
apartments in Stuttgart exhibits reoccurring cycles, yet, with an overall rising price level. Bust times, in general,
are ascribed to weak economic conditions, leading to low demand as it happened around the turn of the
millennium in Stuttgart (1995 - early 2000s). At that time, high unemployment rates, a weak economy in
particular affecting the strongly export-driven region of Stuttgart, as well as the rather uncertain future in the
context of the European Union and the Maastricht Treaty (i.e. change of national currency to Euro) made it
difficult to place real estate objects on the market (Gutachterausschuss fiir die Ermittlung von
Grundstiickswerten in Stuttgart, 1995). Real estate was still regarded as secure investment opportunities, not
with a very attractive return on i though (G fiir die Ermittlung yon
' To put this into perspective, comparing prices in real terms among Germany and United States might lead to the false
conclusion that German housing prices are on a much lower level. However, considering prices against average income in
Germany and the United States results in almost converging ratios (The Economist Data Team, 2016).
ickswerten in Stuttgart, 2001). Our interviewees confirm the cyclical movements, i.e., when saying that
like a wave trough. And it is exactly like this: you can observe it every seven years. Now, we are maybe
on a higher price level" (Real Estate Advisor 1, personal communication, September 23, 2015). The market
experts’ understanding of the market is obviously not coherent: while some acknowledge cycles in the real estate
market — but primarily because of oscillating interest rates — they only see continuously rising prices, With our
model we analyze Stuttgart's real estate cycle and try to better understand its price behavior and shed light on the
underlying structure as the origin of the behavior.
System Dynamics Model of the Stuttgart Real Estate Market
Our model of the Stuttgart real estate market iders the theoretical back d on real estate in general as
well as Stuttgart's individual and characteristics. Various concepts used in our analysis approach
comply with Rahmandad & Sterman's (2012) reporting guideline as well as Sterman's (2000) modeling tools.
Model Scope
In contrast to traditional econometric models that primarily rely on exogenous variables, our system dynamics
model p largely end to the aim to seek "endogenous explanations of
phenomena" (Sterman, 2000, p. 95). Endogenous factors are the pillars of the model structure and drive model
behavior. They include housing price, demand, the real-estate supply chain, and the formation of expectations.
Exogenous inputs influence the endogenous factors, yet latter arise outside the model boundary. Among those
uch as the number of households, construction
variables di
model parameters are some that remain constant, whereas othe:
costs and interest rates were retrieved as real data from statistical time series and added to the model. In addition,
there are variables that are relevant for understanding the real estate market in general, but lie outside the
boundary of our model as we seek to keep the model as simple as possible but as adequate as necessary to
analyze and understand the system's behavior (Table 1).
Endogenous Exogenous Excluded
+ Price » Households + Regional employment
+ Demand - Construction costs - National economy (e.g., GDP)
+ Supply Chain including + Interest Rates - Consumption and disposable
- Buildings under - A set of parameters income of households
construction (i.e., adjustment times, - Financial sector
ildi sensitivity values) (e.g., mortgages)
- Buildings occupied + Aset of table functions - Rest of construction industry
- Formation of Expectations (in (i.e., for effects) (e.g., capacities, labor force)
price, costs, excess demand and - Available building land
profit)
Table 1: Model boundary chart.
Time Horizon and Reference Modes
According to Sterman (2000), "two of the most useful processes are establishing reference modes and explicitly
setting the time horizon" (p. 90). The time horizon is set from 1970 to 2045, enabling us to capture a few cycles
of Stuttgart’s real estate market in the past 47 years. Extending the horizon to 2045 provides us with an outlook
long enough to understand future behavior of the market in our simulation runs.
Secondary research and personal interviews helped to identify housing price as the central key variable. All
interviewees stated that Stuttgart has experienced the hog cycle effect in the give time horizon and thus,
oscillating prices as well (Real Estate Advisor 1; Real Estate Advisor 2; Real Estate Director; Real Estate
General Manager, personal communication, September 23, 2015). The city’s statistics confirm the cyclical price
devel in housing as i in Figure 4. For simplicity matters, we take median prices of owner-
occupied apartments as indicator throughout the present analysis, without distinguishing newly constructed and
old buildings. Note that data for the price as illustrated in Figure 4 is not deflated, as it is directly taken on from
Stuttgart's 'Stadi '(G fiir die i von Gr ii ten in Stuttgart, 2011,
p. 13). Data for construction costs are similarly retrieved. We use both datasets to calibrate the model.
Unfortunately, time series with absolute numbers on both, real estate prices and construction costs, are not
available. Note further that deflated prices and costs, however, increase oscillations attitudes by their own
cyclical behavior. Figure 5 illustrates the available total stock in housing buildings in Stuttgart, captured by the
variable 'Buildings occupied’ in Table | and our model. In contrast to price, it shows a nearly linear growth.
80.000
Buildings in Stuttgart
70.000
60.000
50.000
40.000
30.000 1
1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 5: Reference Mode II: Housing buildings in Stuttgart (Statistisches L Baden-Wiirt
2014d).
Simulation Model
Figure 6 shows the basic structure of the Stuttgart real estate market model in a highly aggregated causal loop
diagram (CLD), depicting only the most important variables and the two balancing feedback loops that capture
common macroeconomic assumptions (Mankiw, 2010, p. 9): the balancing loop B1 - ‘Demand response’
describes that prices decrease if supply exceeds demand. Lower prices stimulate demand and consequently the
supply/demand ratio decreases. This again, causes prices to rise — contrary to the initial price movement. Hence.
the balancing loop B1 regulates demand and prevents it from increasing infinitely. At the same time, when
prices decrease, profits decline in the balancing loop B2 - ‘Supply response’. Thus, less new construction is
supplied, resulting in a decreasing stock of supplied housing. This will also reduce the supply/demand ratio,
which in turn results in an increase of prices.
Housing Stock
x ‘
Supply/Demand
New Construction Ratio
ts)
4
Supply
Response = Demand
Response.
Price
Pott ee
Figure 6: Causal loop diagram for the Stuttgart real estate market (author's representation following Sterman
(2000)).
Thus, the real estate market creates two negative feedback loops that attempt to balance both demand and supply
through the price. They are characterized by several time delays, as indicated on the respective arrows. In the
balancing loop B1 - 'Demand response’, price movements stimulate demand. Yet, due to a low elasticity of
demand, the response time is fairly long (Muth, 1988, p. 351; Sterman, 2000, p. 708). On the supply side (B2 -
‘Supply response'), profit developments stimulate new construction, which requires planning time before
construction can actually start. Moreover, construction takes its own time until new housing is completed, thus
creating a second delay in B2.
Generally speaking, balancing feedback loops cause variables to adjust discrepancies in quantities supplied and
demanded. Hence, the system undertakes corrective actions, such as supplying additional housing to satisfy
increased demand, to bring the market back to equilibrium. However, the presence of the indicated time delays
in both negative loops "cause corrective actions to continue even after the state of the system reaches its goal,
forcing the system to adjust too much, and triggering a new correction in the opposite direction" (Sterman, 2000,
p. 114). Consequently, the system constantly over- and undershoots its equilibrium, resulting in an oscillating
behavior.
We developed the simulation model of the STuttgart Real Estate Market (STREM-model) based on the model
depicted in Figure 6. It entails several sub-structures from existing system dynamics works, including Barlas et
al. (2007), Eskinasi (2014), Eskinasi et al. (2009) and Sterman (2000). The complete simulation report is
provided in the appendix, including the complete model structure (Appendix A), simulation settings (Appendix
B and C), as well as all model equations (Appendix D). Key equations are presented along with corresponding
substructures below.
Aging Chain of Supplied Buildings
On the supply side, we expand the housing stock variable to a clas ging chain structure of the real estate
market, following Sterman (2000). As shown in Figure 7, it begins with the inflow of the 'Construction start
rate'. We assume an 'Average planning time' for new construction projects to start as a constant parameter of four
years, considering planning to meet future demand and the average time it takes for selecting building sites and
for the formal construction permission process. Our interviewees underline these long time delays, stating that "it
is very difficult to receive a building permit. Sometimes, it takes months to get one" (Real Estate Director,
personal communication, September 23, 2015). The flow accumulates in the first stock 'Buildings under
construction’. Note that all initial values of the stocks, indicated with the prefix IN, are based on statistical data
of the Stuttgart real estate market (Statistisches Amt Stuttgart, 2015a; Statistisches Landesamt Baden-
Wiirttemberg, 2014a; 2014b).
Average
Planning fime
Avera
Ave Average
‘ arate Time of
IN Buildings under Constructish Time IN Buildings sales fine IN Buin em
Construction Completed Occupie i]
=p Jeuitcings under 3 Buildings Buildings
i Completed ig Occupied
Sanction P| Constucton | oan PShg, Be] compltes [— AF Denali rae
Start Rate Completion Rate Nw
NS A
e Demolished
Space
Figure 7: Substructure: Aging chain structure for buildings in Stuttgart.
CSR = MAX (0, DNC/APT) (1) where CSR Construction start rate
DNC _ Desired new construction
APT Average planning time
BUC = IN BUC + CSR CCR (2) where BUC Buildings under construction
CCR Construction completion rate
After an assumed average construction time of 1.75 years, 'Construction completion rate! adds new buildings into
the stock of 'Buildings completed’, which represents the number of vacant buildings. After the buildings are sold,
they flow into the third stock Buildings occupied’ via the 'Sales rate’. Here, the 'Average sales time’ is set at a
nine-month period.
CCR = BUC/ACT (3) where CCR Construction completion rate
ACT Average construction time
(C= INBC +CCR-SR (4) where BC Buildings completed
SR Sales rate
SR = PS/AST (5) where PS Potential sales
AST Average sales time
We further assume that Buildings occupied’ are demolished after an 'Average lifetime of buildings' of 100 years.
When there are not enough apartments available, people who are forced to move out, start to look for new
housing. Consequently, 'Demolished space' will generate new demand, and thus needs to be linked with the
demand structure of the model.
=INBO+SR-—DR (6) where BO Buildings occupied
DR _ Demolition rate
DR = BO/ALTB (o) where ALTB Average life time of buildings
Demand Creation
“Demolished space’ and the “Hi Net Growth Rate’ determine the ‘Potential Demand’ on real estate in
Stuttgart (Figure 8). The 'Hi hold net growth' rate agg the inflow and the outflow of the stock, thus
comprising both growth and decline in Stuttgart's households.
{racial
aS easy ‘Assumption
won Future Household
LUIS tm fractional rowtn Rate <=="
fesumgtion
Constructign Time Sales Time suilding ime of Buildings Household Fractional
a wee "ecued™ Growth Rate RATA
= J
completion Rate A,
owseotds Net
Apartments *
Hodsehola Time to Smaoth
Demand from
y. HH Growth
v
A demand in
Supply Demand ulin increase Rote Potential Dean <—j-——<"
Delay Time :
in Demand Attractivty of Real
Creatior Estate Purchase
Figure 8: Substructure: Demand formation.
PD = HNGR’/TSD + DS/APH (8) where PD Potential demand
HNGR Households net growth rate
DS Demolished space
APH Apartments per household
The exogenous input for future household growth rate assumptions, indicated in dark green in Figure 8, is set for
the period 2017-2045. We estimated its value on the average growth rate of real data of the last twenty years. It
may be changed for scenario runs. The net growth in households determines 'Potential demand' — if it is positive,
‘Potential demand’ increases. So, both, the higher the natural household growth and the more space is
demolished, the higher is the ‘Demand increase rate’, one h hold d one
(‘Apartments per household’). 'Attractivity of real estate purchase' is a further variable affecting demand by
incorporating demand reactions to varying price levels of housing as well as exogenous drivers like interest rates
(see explanation of balancing loop B1 below). Note that the stock 'Demand' considers a 'Delay time in demand
creation’.
DIR = (AREP*PD*APH)/DTDC (9) where DIR
AREP = Attractivity of Real Estate Purchase
DTDC Delay Time in Demand Creation
Demand increase rate
‘Demand’ in apartments is d in Demand in b ‘ with an average of four 'Apartments per building’
according to Stuttgart's data (Heilweck-Backes & Straub, 2007, p. 118). On the one hand, 'Demand in buildings’
and the stock of ‘Buildings completed! induce the 'Supply demand ratio’, i.c., the ratio of empty houses to
demand. On the other hand, ‘Demand in buildings’ defines 'Potential sales’. This closes the balancing feedback
loop BS - 'Sales generation’, that regulates that completed buildings are only sold if demand is available.
Price Setting
Considering the balance between supply and demand, the 'Supply demand ratio! affects the price setting
mechanism of the model (Figure 9). The structure, partially adopted from Barlas et al. (2007) and Sterman
(2000), comprises the two expectation formation structures on price expectations and expectation of future
construction costs. Adaptive expectations signify information delays and refer to perceived values that gradually
adjust to the actual value of the corresponding variables while the adjustment time determines how fast
expectations are corrected (Sterman, 2000, p. 428).
IN Demand
y
lls,
Supp Demand Bung
Table for Effect of
‘Demane Reto so Supply Demand :
Minimum
Tabs for Effect of
ye Price
Aecopted Price z
ak Price on Demand
Rs eee
28
Accepted
Proft Margin N
Time to Agia Construction
MO eri Nocmal Profit Time te Adjust,
ost en pts 4
Future Construction Conststion Cost a
Cost Assumption Index Dt ean
Figure 9: Substructure: Price setting and feedback loop 'Demand response".
First, regarding the formation of construction costs, the 'Construction costs index', actual construction cost data,
determines the actual average construction costs in Stuttgart's real estate market. Real estate suppliers form an
expectation on construction costs on the basis of the actual ‘Construction costs’ with an information delay,
following Sterman (2000). This delay, the 'Time to adjust expected costs’, represents the time it takes to gather
and process new construction costs data.
CECC = (CC - ECC)/TAEC (10) where CECC Change in exp. construction costs
cc Construction costs
ECC Expected construction costs
TAEC Time to adjust expected costs
The costs depend on various factors, including fixed and variable costs, such as wages and materials — which are
outside the model boundary and are thus excluded from the model. Based on 'Expected construction costs',
suppliers set a 'Minimum accepted price' for housing prices. We model this with an ‘Accepted profit margin' and
assume a ‘Normal profit margin’ of 25%. In addition, suppliers also take into account the actual interest rates
when making a decision on whether to build new housing (Real Estate Advisor 1, personal communication,
September 23, 2015). If the interest rate decreases, so does the ‘Accepted profit margin’. With current interest
rates of close to zero percent, investors also accept profit margins of about 3% (Dalcomo, 2016).
The interest rate structure influences the profit margin as follows: the actual 'Interest rate! is determined by the
exogenous variable ‘Interest rate data’, which contains the actual interest rates since 1970 in Germany.
The 'Smoothed effect for interest rate on profit margin’ converts the interest rate development in such a way that
low interest rates have a decreasing effect on the 'Accepted profit margin’, while higher interest rates claim an
increased profit margin. Thus, the varying "Interest rates' influence the ‘Minimum accepted price' of the market,
which in turn affects the actual 'Price'.
'Price' is determined by both, the 'Minimum accepted price' and the ‘Supply demand ratio’. A decri
ing non-
linear table function generates an 'Effect of supply demand ratio on price’. It captures that people are willing to
pay higher prices when supply is short, while oversupply reduces prices (Mankiw, 2010, p. 9). ‘Sensitivity of
price to supply demand ratio' adjusts the degree of how sensitive the 'Price' responds to changes in the 'Supply
demand ratio'. Accordingly, the price for housing is affected by the minimum epted price, the linked
construction costs and interest rates as well as the balance between supply and demand at any point of time.
‘Price! affects both, supply and demand. Both react according to their expectations of price developments
(Deutsche Bundesbank, 2013). Thus, the model structure to capture price and price expectation is similar to the
structure that captures “Construction costs’ and ‘Expected construction costs’. As 'Price' varies according to the
availability of supplied housing, it differs from the price perceived by both, suppliers and potential buyers.
Hence, suppliers and buyers form their expectations on price by adjusting the 'Expected price' to eliminate the
'Price variation' between the current and perceived value. Since real prices are not reported on a regular basis, the
‘Change in expected price' adjusts with an information time delay. There is an inflow to the stock when 'Price' is
higher than 'Expected price’. Vice versa, an outflow exists when ‘Price’ drops under 'Expected price’. Because of
its structural formulation, the 'Time to adjust expected price' results into a smoother and lagging development of
‘Expected price’ as reaction to variations in 'Price'.
For simplicity, we assume demand and supply determine prices adjustments with the same time delay. Yet, we
are aware that in reality, real estate experts, such as developers and consultants, might form their expectations
differently compared to potential customers that plan to purchase an apartment.
Eventually, market players on the demand side respond to changes in price by as: ing ‘Expected price to price
ratio’, i.e. the change in 'Expected price' relative to the current 'Price'. When 'Expected price’ over 'Price' changes,
there needs to be an opposing effect on demand. Hence, the non-linear, increasing S-shaped table function
determines the 'Effect of price on demand’, and regulates the 'Demand increase rate': Demand decreases when
the 'Expected price to Price Ratio! increases; vice versa, demand rises when a decline in prices is perceived. The
parameter Sensitivity of demand to price' determines the degree of changes in the behavior of demand. Muth
(1988) mes demand to be rather inelastic to changes in real estate prices.
EPPR = EP/P ay where EPTPR Expected price to price ratio
EP Expected price
P Price
EPD = (TEPD*EPPR)*SDP (12) where EPD Effect of price to demand
TEPD — Table for effect of price on demand
SDP Sensitivity of demand to price
TEPD = f(x); f 20; f'xex,.)203 f'xsx,0; (13)
The price setting mechanism of the STREM-model closes the first balancing feedback loop B1 - 'Demand
response’. On the one hand, assuming an increase in demand, initiated by a positive 'Households net growth rate!
and a subsequent increase in ‘Potential demand’, the 'Supply demand ratio! declines. The latter has a diverging
effect on 'Price', so that ‘Price’ increases when supply becomes scarce. On the other hand, a price increase creates
a 'Price variation’, so that 'Expected price' is adjusted respectively, yet with a time delay. Hence, an initial
increase in price translates into a declining 'Expected price to price ratio’ at first. Then, the effect of price
variation on demand is modeled with the variable 'Effect of price on demand’, which involves a lookup table and
a sensitivity parameter to determine the price elasticity of demand. In general, demand in real estate is found to
be not very elastic (Sterman, 2000; Muth, 1988). Further, the effect of price determines the 'Attractivity of real
ite purchase’. The latter variable affects demand by incorporating not only the demand side's reactions to
es
varying price levels of housing but also the effect of interest rates. 'Attractivity of real estate purchase’
increasing with lower prices, and vice versa, declining with an increasing price level. In addition, interest rates
is
determine real estate attractiveness as an investment option in contrast to other financial products, such as bank
account savings, bonds or stocks. Low interest rates make real estate a promising investment opportunity, thus,
raising its attractiveness — and vice versa. Consequently, demand declines because prices have increased,
contrary to the starting point. As already indicated in the CLD (Figure 6), demand is regulated by a balancing
feedback loop that adjusts demand and prices towards market equilibrium (Figure 9, B2 - 'Demand response').
Profit Generation
The price setting mechanism, and in particular 'Expected price’ determines the supply response to changes in
'Price' (Figure 10). For this reason, the supply side calculates its 'Expected profit' by taking into account
‘Expected price! as well as 'Expected construction costs'. Increasing prices boost profitability while construction
costs lead to a reduction. Profitability is the key driver of the supply side, as suppliers’ investment decisions are
determined by financially aspects. Therefore, when prices rise above construction costs, investors’ expected
profitability increases, too. Thus, higher 'Expected profit’ stimulates more new construction. We model this with
a table function that causes a change in 'Expected profit' to have a positive 'Effect of expected profitability’ on
‘Desired new construction’. 'Desired new construction' feeds into the inflow of the aging chain of the model, thus
pushing the 'Construction start rate’ to rise. Thereby, it closes the second balancing feedback loop B2 - 'Profit-
driven supply’.
pronning’ fim Avarage Average
janine’ me N Buildings under Constcian Time IN Bulings sales Tine yBulaings
male ec
Buildings Buildings
Completed Occupied
‘Gonstraction Sales Rate
Desired Now Start Rate: Completion Rate
2
erate a sma
eran gt eares Sart
‘ Sw opane cst cn
Ratio on Price seston Demand,
ATS, Segal
Construction Costs,
Expected Profit
Change in Expected
Construction Costs
Price Variation
Minimum iba
PF nccepted Price
x Expected Price to
+ Price Ratio
Accepted
Profit Margin
+ 4
Time to Adjust onstruction
Expocted Costs orga etime>
Future Construction Construction Cost Margin:
ost Assumption Index DATA
nang in
Expect Price
Figure 10: Substructure: Profit generation.
New construction in the stock ly, as i ingly more
available housing is supplied to the Stuttgart real estate market, the ‘Supply demand ratio’ increases as well. As
described above, the balance between demand and supply has a negative effect on 'Price'. Hence, more supply
results in decreasing prices, as people are not willing to pay as much as before. Here again, as the price
movement is opposite to the initial assumption (B2 - 'Profit-driven supply’), it regulates the supply side, in terms
eking equilibrium in the Stuttgart real estate
of the quantity supplied (i.e., new construction) and prices, s
market.
Furthermore, the supply side aims at satisfying demand. Thereby, constructors are aware of the oversupply
resulting in increasing vacancy rates and newly completed buildings cannot be sold. Unsold buildings or new
apartments implicate no revenues, so that suppliers are left with uncovered costs and insolvency risk. For this
reason, they try to forecast excess demand in the real estate market as accurately as possible. The according
structure in the STREM-model is described in the following.
Excess Demand
Investors on the supply side react to demand as
for many business decisions" (Lyneis, 2000, p. 3). Yet, as they cannot know the precise actual excess demand,
umptions about future demand and performance are essential
they estimate it (Figure 11). We model the structure similar to the price and cost expectations, as described
above. Once again, it takes time to form expectations, so 'Expected excess demand' is adjusted by an average
delay time (‘Time to form expectation of excess demand’).
The aim of the supply side is to meet 'Expected excess demand". 'Desired new construction' emerges from
‘Excess demand in buildings' and initiates new construction to be planned, thus determining the inflow into the
aging chain, ‘Construction start rate’. If Excess demand! increases, so does 'Expected excess demand’ after a time
delay, boosting 'Desired new construction’. After construction has begun and finally ends up as new ‘Buildings
completed’, the number of 'Vacant apartments' grows likewise. These, in turn, reduce 'Excess demand’, which is
contrary to the starting point. Hence, this substructure entails the balancing feedback loop B3 - 'Supply line
control’, which keeps the supply line under control in terms of construction activity. Thus, new construction is
only started according to given excess demand in the market.
Time to Form
Expectation 0
IN Expectea excess Bi
Excess Demand
ied
excess
ao cman
— pe Expected Excess,
Apartments
Buildings spre
Average Average Average Beas ym
Planning Time wildings under Consitucion Time yy Bui sales Tene INButdings TH" oF vito
ree naafe seuior
uitsings under Buliings Buliings
+ Craton P| Consiucton oan, Me] Completes [AP PP occupied [bemoiion rage
Desired Newer Start Rate ‘Completion Rate
* ‘Sele Goneraton
Potentia
Bales,
' Demand
demand in eman ean
Buildings satisfaction Rate increase Rate
Figure 11: Substructure: Excess demand.
Ce ining the ab p of the STREM-model, Figure 12 illustrates the complete model
structure. As already explained with the CLD (Figure 6), two balancing feedback loops B1 and B2 primarily
determine the structure. On the one hand, demand responses to changes in price, on the other hand, the supply
side reacts with new construction if it is profitable. Next, the model is tested and validated. An extended
overview of the STREM-model is also provided in Appendix A, including all variable types specified.
onsite Ban Soe rete ae AI
pects Came rece gh
Bais ‘Seay g_ Sing rt
pry seri a 5 = ae
vs . SE ange
s .o > 5 ee
7 orgs BA +
Figure 12: Stuttgart real estate market model (STREM) ~ full model.
Model Validation and Testing
Sterman (2000) describes model validation as a "continuous process of testing and building confidence in the
model" (p.81). Nonetheless, models can never be validated in the sense of verified, as they are simplified
representations of the real world's systems (Sterman, 2000, p. 846). Still, model testing is a crucial step in the
system dynamics modeling process. For this purpose, there is a wide range of testing methods that can be applied
in order to increase confidence in the model (Sterman, 2000, pp. 859-861). They are categorized into behavior
and structural testing.
Structural Testing
Model Boundary Adequacy Test
The model boundary has been laid out above in the form of a model boundary chart (Table 1) and a causal loop
diagram. By reviewing adequate literature and from the insights gained from the expert interviews, we identified
variables and causalities relevant for the system's behavior. In doing so, we modeled important variables
endogenous to the system, while others needed to be left as exogenous input or even omitted.
Structure Assessment Test
Structure assessment testing refers to the consistency between the model and the real system in the context of the
model's purpose (Sterman, 2000, p. 863-864). Tools used in boundary assessment can be applied likewise.
Existing literature and expert's statements have helped to model the system's structures and formulate valid
equations compared to the real system. An important question to ask is whether the model adopts basic physical
laws (Sterman, 2000, p. 846). The Stuttgart real estate market is especially constrained by its topographical site,
yet the simulation model does not capture this aspect of natural limits to expansion. As the supply chain of the
model involves a source and sink, it implies infinite in- and outflow. However, adjustment times and other
variables control accumulation and depletion of stocks and prevent them from generating surrealistic behavior.
For instance, ‘Construction start rate' can only start to flow when 'Desired new construction’ is available.
Dimensional Consistency Test
We tested the model for di ional i th ‘hout the entire modeling process. All variables are
specified with units. We distinguish between the units buildings and apartments since supply constructs
whereas | demand ap . In order to link the variables, the units are transformed where
necessary with the parameter ‘Apartments per Building’. With an average of four apartments per buildings, the
parameter is set as an actual value of the Stuttgart's housing market (Heilweck-Backes & Straub, 2007, p. 104).
The variables 'Price' and ‘Construction costs' are specified as indexes, and thus are consistent. Yet, the base value
is set at two different years, creating a limitation of the model. However, as they are not directly linked in the
model, the limitation is mitigated. For instance, ‘Accepted minimum price' takes into account only 'Expected
construction costs' and computes 'Accepted profit margin’ only based on costs.
Parameter Assessment Test
Several parameter values of the model are estimations based on qualitative data retrieved from literature and
personal interviews. Where available, they have been adopted as numerical data from other references. In those
cases, their sources are cited either in the text when described or in the model equations. Statis
ical methods, as
proposed by Sterman (2000) to estimate parameters should be considered in future research and optimization of
the model. Despite some weakne: of the model assessed through structural testing, e.g., capacity constraints,
price indexes, p imati the followi i testing helped to further build confidence in the
model.
Behavioral Testing
Extreme Conditions
Behavioral testing involves assessing the system's behavior under extreme conditions. It reflects whether the
system's behavior and the model equations still make sense when the model is exposed to extreme values of
inputs (Sterman, 2000, p. 869-870). This way, one extreme condition te: sumption that
demand drops to zero. A physical reaction would be, for example, that supply does not initiate any new
construction projects, so that the inflow ‘Construction start rate! falls to zero as well. A simulation under this
condition shows that the model responds plausibly to unavailable demand (Figure 13).
the model under the a
500
375
lings/year)
250
125
Construction Start Rate (bui
0
1970 1980 1990 2000 2010 ©2020» 20302040
Figure 13: Extreme condition: zero demand.
We conducted further tests under likewise extreme conditions, such as shocks in supply or extremely high profit
margins that result in price is lhening the confidence in the model.
Partial Testing
Partial testing helps reducing complexity in a model's behavior by cutting feedback loops or 'switching off
certain variables (Morecroft, 1985; 1988). This way, we tested several substructures of the model when
developing the model on whether they behave in a reasonable manner. In order to be able to analyze the drivers
17
of the real estate cycles, we conducted several partial tests. This also increased our understanding of the behavior
of the full model. For instance, to analyze the origins of the cyclical 'Price' behavior, the substructures
encompassing interest rates and construction costs were cut off. The effects on prices are shown in Figure 14:
Regularly recurring oscillations are visible throughout the simulated time horizon (dashed and dotted-dashed).
The partial test thus confirms that oscillatory behavior arises from within the system, and is not caused by
irregular developments in costs and interest rates. Including exogenous inputs rather mitigates oscillations (base
run, solid), whereas partial testing without interest rates result in intensified cycles (dashed). In addition, feeding
the model with actual data on construction co:
s over the given time period, accounts for a likewise upward
development in housing prices. Thus, cutting off these costs results in an almost constant average price index (an
equilibrium), around which oscillations reoccur in approximately ten-year intervals (dotted-dashed).
300
No interest rate
No interest rate + constant
225 construction costs oo x
!
3 cN rt
Bi .
2 1so |- ese Vv,
7 toy
78
Base run
°
1970 1980-1990 2000-2010 «= 202020302040
Figure 14: Partial testing: Oscillating price behavior continuous.
Behavior Reproduction
The behavior testing method as
ses whether the model reproduces the real system's behavior as illustrated in
the reference modes (Figure 4 and 5). "The good fit between simulation run and real world data is an important
step in ensuring that the model structure correctly estimates short-term and long-term interdependencies between
variables and depicts realistically the development of (...) market data" (Kapmeier et al., 2011, p. 16).
Considering the mode of price in the Stuttgart real estate market, the base run of the
STREM-model reproduces the real price movement well (Figure 15, left graph), yet, overestimating actual price
since the beginning of the 1990s.
250
historical \
1875
simulated
Price (index)
historical
Buildings occupied (# buildings)
1970 1980 1981 2001 2011 1370 1979 1988 1997 2006 2015
Figure 15: Behavior reproduction for 'Price' (left) and ‘Buildings occupied’ (right).
Likewise, the construction activity of the supply side results in a simulated growth rate of housing stock similar
to the real data, yet not with a same good fit as the price behavior (Figure 15, right graph). The simulation is
underestimating actual data. An explanation might be that the aging chain is too simplified and omi
factors of the construction side, which might result in the observed inaccuracy.
several
Sensitivity Analysis
Since various parameters needed to be estimated when no real data are available, a sensitivity analysis helps to
determine to understand how sensitive the model reacts to certain parameters. Parameters included in the model
18
encompass adjustment times, such as ‘Average planning time' and 'Average construction time’ as well as
elasticity of demand and supply (‘Sensitivity of demand to price’ and 'Sensitivity of supply to price’). The
sensitivity analysis has been conducted by assessing the sensitivity of the several parameters on the key variable
Price’.
As Figure 16 illustrates, selected parameters cause different behavior of 'Price’. 'Price', for example, exhibits
highest sensitivity when the p sitivity of price to demand supply ratio' takes on values ranging from
zero to ten. Consequently, the parameter strongly influences the oscillating behavior of Price’, in particular the
amplitudes. Compared to that, the ining sensitivity itivity of demand to price’ and
‘Sensitivity of supply to price’, result in a lower sensitivity of 'Price'. In addition, the lower left chart of Figure 16
indicates that time delay in the aging chain, i.e., supply chain of the model, influences the cyclicality in price
movement. Testing the sensitivity of price on a ranging 'Average planning time! between zero and ten years, the
chart shows the sensitivity of price in terms of its oscillatory behavior.
Sensitivity: ‘Sensitivity of Price to Demand Supply Ratio’ on ‘Price’ Sensitivity: ‘Sensitivity of Demand to Price’ on ‘Price’
Runs: 200, Min: 0, Max: 10, Noise seed: 1234 Runs: 200, Min: 0, Max: 10, Noise seed: 1234
Sensitivity: ‘Average Planning Time’ on ‘Price’ Sensitivity: ‘Sensitivity of Supply to Price’ on ‘Price”
Runs: 200, Min: 0, Max: 10, Noise seed: 1234 Runs: 200, Min: 0, Max: 10, Noise seed: 1234
Figure 16: Sensitivity analysis: Sensitivity of price to demand supply ratio on Price, Sensitivity of demand to
price on Price, Sensitivity of supply to price on Price, Average planning time on Price (clock-wise from upper
left).
After conducting several tests according to recommendations given by Sterman (2000) and Morecroft (1985;
1988), including structural and behavior testing, the model can be assessed as valid. In the following section, its
behavior is analyzed in a simulation base run.
Model Base Run
The base run is conducted for the predefined time horizon of 1970 to 2045 and comprises available historical
data. A 'business as usual' (BAU) strategy is applied, which adopts a continuation in several parameters’ behavior
as usual. Accordingly, it is assumed that the household growth rate continues to rise with its average growth rate
of the last 20 years. Similarly, the exogenous construction costs take on the average value of the last 20 years.
The interest rate remains constant as of its last available data in 2016 (Osterreichische Nationalbank, 2017).
Future ink
costs and interest rates will play a significant role in scenarios,
so that their assumed values are comprised as separate variables in the model (highlighted in green in the
STREM-model; Figure 12): 'Future household growth rate assumption’, 'Future construction cost assumption’,
and 'Future interest rate assumption’. Settings of the base run, scenarios and policy are provided in Appendix C.
19
The following figures depict the behavior of selected variables in the STREM-model over the time horizon. As
shown in the left graph in Figure 17, 'Demand' oscillates despite an almost linear increase in 'Households of
Stuttgart’. Thus, the oscillation originates not from 'Households' but from another source: The balancing
feedback loop B1 is the driver that makes 'Demand' to respond to changes in 'Price' and vice versa, resulting in a
cyclical behavior. As the balancing loop B1 - 'Demand response' is strongly interacting with the second one on
the supply side, B2 - 'Profit-driven supply’, it correspondingly affects the demand cycles as well.
20000 ap
400008 ovo tes
300 inden
soo Dernandin
00008 ‘semble walle Ny
Households in
15,000 ap Stuttgart | ™s
3000 bides
80 index
300,000 hh
7500 a9 500 bidgs
250,000hh
75 index
Demand
Hovseholdsin Stutteart (households)
ents)
Demand (apa
oa | Buildings completed
200,000 hh“ bide! ings compl
1970 1980 199020002010 «2020 20302080 ‘70 1980 1990 + 2000«2010:«=«2020«=« 20302080
Figure 17: Base run: Behavior in 'Households' and 'Demand' (left) and Behavior in 'Price', Demand in buildings’,
and ‘Buildings completed’ (right).
The right graph in Figure 17 depicts that the supply side responds with a lag to the demand development, due to
the time delays in the stock and flow structure of the model, i.e., in the supply chain, as well as in the forecasting
process of expected excess demand. With increasing demand, prices also increase. Consequently, expected
profitability increases, which makes new real estate construction attractive. Due to the time delays in the supply
side's response to satisfy new demand, buildings are only completed years later, which is reflected in lagging
cycles (see Buildings completed’ in Figure 17). After reaching a peak in 2020, high prices make demand fall
again according to the macroeconomic law of demand. This reverse behavior translates into declining prices,
which in turn offset declining demand. Consequently, demand starts to increase again, starting a new cycle.
The delays in the aging chain are illustrated in Figure 18: When 'Desired new construction' is assessed (blue), the
behavior of the aging chain is initiated. However, the 'Average planning time' delays new construction, so that
the 'Buildings under construction’ are lagging behind (red). After construction projects are started, it takes the
‘Average construction time! until the buildings are completed and ‘flow’ into the stock 'Buildings completed’
(green). One can clearly observe that the peaks as well as the lowest turning points of the three variables are
lagging one after the other. Thus, completed buildings are placed on the market relatively late to satisfy the prior
increased demand. In contrary, when apartments are ready for sale, demand is in decline again. Consequently,
the balancing loop B2 inhibits ‘Desired new construction’ in response to lower demand, lower prices and lower
expected profitability and turns the cyclical behavior in the aging chain into a reverse movement. The variations
in amplitudes of the supply chain result from the varying demand cycles (Figure 17, red), as well as from
differences in feasible construction capacities.
20,000
15,000
2 Desired new
2 construction \
2 10,000 a
= Buildings under
e construction
3 Buildings
& 5000 completed
0
1970 1980 1990 2000 2010 2020 2030 © 2040
Figure 18: Base run: Behavior in Aging Chain.
20
The oscillating behavior of supplied buildings is also present in reality as indicated with the red line in Figure 19.
Since the STREM-model is only a simplified structure of the actual supply chain and is not constrained by
significant factors, such as construction capacity or available building land, the amplitudes of the simulated
cycles do not reproduce the behavior of real data.
2,000
= 1,500
2
=
= 1,000
2 simulated
e
8
2 500
2 . Peace
3 wo
historical
oO
1970 1980 1990 2000 + +-2010+~=«2020:~«=«080~=~=« 0D
Figure 19: Base run: Behavior in 'Buildings completed’.
Consequently, the observed real estate cycles arise due to significant delays and a diverging interplay between
overshooting and undershooting variables. These time delays include the supply side responding to changing
demand by forecasting excess demand. The time delay it takes for the completion of new buildings, including
both 'Average planning time' and 'Average construction time', causes prices to increase in that period, as demand
in the
cannot be satisfied and shortage in supply dominates the market, or, as the Real Estate Director states
interview, "there is just little supply" (personal communication, September 23, 2015). Another interviewee
confirms that "the supply of high quality real estate is really short" (Real Estate Advisor 1, personal
communication, September 23, 2015).
When construction is finished, the stock ‘Buildings completed’ accumulates. However, since demand in the
meantime has asa toi d prices, supply excesses demand. This way, the variables
of the STREM-model undertake corrective actions that result in a new cyclical period.
The base run based on a BAU setting results into a model behavior that reproduces the behavior of the Stuttgart
M ', by comparing it to the reference
real estate market as observed and described by the interviewees.
modes, i.e., real data, the confidence in the model is increased. Hence, it can be continued with experimental
runs that comprise different scenarios as well as policy design.
Scenario 1: Increasing Interest Rates
As our interviewees stress that interest rates play a significant role for the Stuttgart real estate market, we analyze
the impact of changing interest rates in more detail in the first scenario. Interest rates in Germany have reached
an all-time low in the observed time ° horizon (Osterreichische Nationalbank, 2017). The downturn in financial
markets has resulted in an i of fi jal assets (real estate)" (Deutsche Bundesbank,
2014a, p. 46). ‘Cement gold’ as Real Estate Advisor 1 (personal communication, September 23, 2015) states,
determines the current trend in capital investment. Notably, our interviewees all agree that interest rates do have
a large impact on the development of the real estate market. The Real Estate General Manager (personal
communication, September 23, 2015), for example, emphasizes that the demand side is strongly driven by the
interest situation. He continues that in situations of low interest rates, clients tell him that:
*Lam not selling.” And when many more people act likewise, supply will collapse. Supply is just very
short. And when you have hundreds [of people] waiting in the waiting line who say ‘I want to buy a real
estate; I don’t want to have my money laying around on my bank account. And anyway, the credit
conditions are so attractive! Now or never! Now it is just great!” And what happens then? Then, demand
will continue to increase. If supply is decreasing and demand increasing, what happens with prices?
21
They will increase. This is the mechanism that has occurred since 2010/2011. (Real Estate General
Manager, personal communication, September 23, 2015)
He continues explaining that clients call him saying "’Ah, I have money on my bank account. Don’t you have
some nice real estate for me? It could also be a house, or an apartment building, or two or three apartments or
whatever™ (Real Estate General Manager, personal communication, September 23, 2015). So, it seems as if
people just continue buying to whatever price, which makes the boom stronger. Real Estate Advisor 1 further
explains that:
People always live in some real estate. And if it is not nec
rily a castle which will collapse in the
near future, people just buy it immediately. For example, there is really high demand on investment
property for speculation as there are currently no investment alternatives. These days, you can offer
property for four or five percent [per year] only. (Real Estate Advisor 1, personal communication,
September 23, 2015)
Consequently, while the market gets short, demand is increasing also at low return rates. Real Estate Advisor 2
(personal ication, Sep 23, 2015) also i the demand side when saying that "the second
player in this market that have led to real estate scarcity and to this high demand are those who can, with these
low interest rates, afford to buy their own place instead of paying rent".
When asked for the real ite supply, the Private Real Estate Director (personal communication, September 23,
2015) characterizes it as "a market with low supply". Real Estate Advisor | (personal communication,
September 23, 2015) is convinced that "(...) it will always stay like this, supply is just too tight." It seems as if
the expert believes the situation is out of control, as he admits that "usually, the market is determined by supply
and demand". And this
September 23, 2015).
all due to the interest level (Real Estate General Manager, personal communication,
At the end of 2015 however, the Federal Reserve raised the interest rates in the United states for the first time in
nearly a decade (Wiebe, 2015) and just recently for the third time since the financial crisis (The Economist
Newspaper Limited, 2017). One could assume that the European Central Bank will follow eventually. Contrary
to the base run, where interest rates remain as low as of 2015, the first scenario assumes a gradually increase in
interest rates after the end of 2016. Hence, 'Future interest rate assumption’ ent:
s an increase up to seven
percent, leaving the current low-interest environment behind. Regarding the insights gained from the interviews,
it is expected that prices will decline along with increasing interest rates (Real Estate Advisor 2, 2015; Real
Estate General Manager, personal communication, September 23, 2015). Figure 20 illustrates real estate losing
attractiveness (dotted-dashed), which into declining demand to the base run (solid).
E
= Aaeeran Base run
£15] B a
A ~ .
3° 2 © 3000
i senor ~ \ | 2
$s Bu
§ E scenstio1
£
+ 0.
197019601990 2000 2010-2020 ~—-2080~—~—«2000 ism 19801980 2000 2010 2020 2030 3040
Figure 20: Scenario 1: Increasing interest rates resulting in declining attractiveness of real estate (left) and
demand (right).
Figure 21 presents the simulated behavior of model under the defined conditions of Scenario 1 exemplified by
the variable 'Price' (dotted-dashed).
22
300
Scenario 1
225
150
Base run
Price (index)
historical
0
1970 1980 1990 2000 ©2010 2020 2030 2040
Figure 21: Scenario 1: Increasing interest rates and Behavior of 'Price'.
Contrary to the initial expectation, ‘Price’ in this scenario (dotted-dashed) first behaves similar to the base run
(solid). The drop in 2025, however, is a little steeper, but it increases at a higher rate after 2025. Yet, in this
scenario, there is another sharp drop in the mid-2030s. After that, price recovers and increases on a higher level
than in the base run. Possible explanations for its behavior is the 'Minimum accepted price' that is determined by
the interest rates. As explained earlier, increasing interest rates cause suppliers
thus r:
to claim higher profit margins and
e the minimum accepted price level. Consequently, the market price increases likewis
drop in demand (between 2030 and 2040) is reflected by a decline in price as well. Despite the interviewee:
statement that interest rate affect demand in real estate, a study by the Deutsche Bundesbank (201 4a) indicates
that "interest rates seem to play a subordinate role in determining house prices (...), instead, the recent price
. However, the
increases are more likely the result of households’ productivity and income expectations" (p. 23). Hence,
modeling causal relations between interest rates and variables endogenous to the STREM-model needs to be
assessed carefully.
Scenario 2: Decline in Households
The second scenario is based on the previous one, assuming a light increase in interest rates. In addition, we
assume a drastic decline in h hold:
Stuttgart's attracti highly depends on its regional economic
strength, as laid out above, in particular on its cluster of automotive manufacturers and suppliers. In this scenario
we assess the effect of a declining economic importance of the automotive industry over a period of 30 years
(2015-2045). This might happen, for instance, consequently to possible future competitors like Tesla, Google, or
Apple, capturing higher market share with highly innovative, self-driving, and elect rs and making the
resident automotive manufacturers less important (Afhiippe et al., 2016). In this scenario, latter cut back jobs.
Although the STREM-model does not comprise any variables representing Stuttgart's regional economy directly,
this scenario can be modeled by a rapid decline in 'Future household fraction growth rate assumption Scenario 2'.
Naturally, as the city becomes less attractive and many inhabitants are not employed anymore, people are
moving away, resulting in a negative 'Household net growth rate' which gradually depletes the stock ‘Households
in Stuttgart’. A weak regional economy may also affect the construction industry, so that we assume a slight
decline in construction costs as well, modeled by ‘Future construction cost as
sumption Scenario 2'.
The following figures illustrate the resulting behavior of the STREM-Model of scenario 2 compared to the base
run. In Figure 22, after 2020, when the number of households decline (dashed), demand decreases faster
(dashed) than in the base run (solid) and continues to decline even further, while demand in the base run is
already turn upwards (around 2025). The rapid drop in demand leaves the supply side immensely overshooting
its equilibrium, which is reflected in Figure 23.
23
6,000 bldgs
400,000 hh
Demand in buildings Households
Base run >, ase run
= 4,500 bldgs
2 350,000 hh Demand in buildings
Scenario 2
3,000 bldgs
300,000 hh
Households
1,500 bldgs Scenario 2
250,000 hh .
oy
~ v3”
Households in Stuttgart (# households)
--
O bldgs
200,000 hte70 1980 1990 2000 2010 2020 2030 2040
Figure 22: Scenario 2: Declining households leading to a drop in ‘Demand’
i 20,000
e n 15,000
3 seenario2 S S
£ et g Desired new
a 2 construction - Sc 2
in) 2 >
2 10,000
z 14 = Buildings under
2 i s construction Sc 2
E rot S Buildings
21 et % 5000 completed~Sc2
3 H
1
° a g 0
197-1980 -~—«*1990~—=«2000-=«O10-=S=«0D-—«2030—~=S=—«OA 9701980 990 -—«2000=«ao10=«020~=—=—«2080 «2040
Figure 23: Scenario 2: Declining households leading to supply overshooting.
In this scenario, 'Supply demand ratio’ exhibits intensified amplitudes to the decline in households and its
corresponding demand (Figure 23, dashed in left chart). Moreover, the supply side responds with a rapid
decrease in 'Desired new construction' to almost zero after reaching its peak in 2020 (Figure 23, blue in right
chart) with lagging cycles in the aging chain (see 'Buildit
in green). Consequent to the high oversupply of housing, prices drop accordingly (Figure 24, dashed). Low
prices make real estate an attractive investment though, so that demand rises eventually and subsequently prices
under ion’ in red and 'B
as well. as Figure 24 i the decline in households in Scenario 2 leave price on a lower level
in average (dashed) compared to the base run (solid).
300
Base run
225
150
Price (index)
historical Scenario 2
0
1970 1980-1990 2000 ©2010 «= 2020.» -2030S 2040
Figure 24: Scenario 2: Declining households leading to a drop in 'Price'.
The behavior of the model though needs to be assessed with care since several variables, in particular
construction capacities take on surrealistic values ly to leave out ints and natural capacities of
the Stuttgart real estate market.
24
The second scenario simulates possible effects of a demand shock on the Stuttgart real estate market. In reality,
Real Estate Advisor 2 described a similar scenario, when referring to a weak economy affecting the Stuttgart real
estate market:
I do remember well that during a time when Daimler Benz was in financial struggle way back, the
entire region of Béblingen and Sindelfingen, where many Daimler Benz employees live, was
struggling. Prices did suddenly go down. As many former employees needed to leave the region, they
threw their houses on the market. (Real Estate Advisor 2, personal communication, September 23,
2015)
Interestingly, this statement came from the most experienced market expert. His younger colleagues have not
experienced a former bust market situation. So, this might lie outside of their range of thoughts.
Policy: Consideration of Underway Construction
The last experimental run implies a policy design, which is partially adopted from Barlas et al. (2007). Although
it is difficult to change the feedback structure of the STREM-model, this experimental setup aims at reducing the
1 s in certain variables that are ascribed to
strong cyclical ics. To the cycli
substantial time delays as described above, underway construction is considered and incorporated it into
planning when estimating 'Desired new ion’. This way, the supply side should be able to better respond
to changes in demand in order to avoid extreme over-/undershooting of its equilibrium that come along the time
delays within the aging chain. Therefore, the structure of the model needs to be adjusted by linking the two
stocks ‘Buildings under ion' and ‘Buildings ' directly with 'Desired new construction’ as
highlighted in Figure 25.
Expected Excess
Demand in Buildings B38
= ° Supply Li
~ pe ppl Line
Boral
a
Average _—
Planning time yy Buildings under Construction Time
Construction
Desired New Construction Genet Construction _
4 Construction Start Rei Completion Rate
Figure 25: Policy: Consideration of underway construction in the model.
Accordingly, the equation of the variable 'Desired new construction’ needs to be changed to:
DNC =(EEDB-BUC-BC)*EPDNC (14) where DNC Desired new construction
EEDB _ Expected excess demand in buildings
BUC Buildings under construction
BC Buildings completed
EPDNC Effect of Expected Profitability
on Desired New Construction
The resulting behavior is illustrated in Figure 26, where, indeed, the cycles are reduced. 'Desired new
construction’ (Figure 26, dashed in left chart) is significantly flatter than in the base run (solid), while the stock
‘Buildings completed’ respectively exhibits almost linear behavior (policy in dashed vs. base run in solid). The
policy prevents excess supply that arises when underway construction is ignored. Consequently, the 'Supply
demand ratio’ is fairly balanced, following an almost stable development until the end of the simulation. It is
indicated with a dotted line while the solid line shows the base run (Figure 26, right chart). Since cycles in the
aging chain are almost balanced out, the model does not exhibit major lagging booms and bust any longer. This
25
behavior is transferred to the demand side accordingly with the result of the almost linear 'Supply demand ratio!
offsetting cyclical price movements as presented in Figure 27.
20,000 4
Desired new
construction Base
Desired new
wa, Buildings under eatuction
construction - Base Paley
Scenario2 ~,
(dal)
5 ildings under fa Scenario 1
2 reco Builingsunde EB,
= serene fae 2
2 Buildings é
@ 3000 a1
0 o =i = 2 wai
‘sro 1980 1980 —~200~—«7010~=«7020~«=030~=~«OO ‘s7a 1980390 200020102020 208 2040
Figure 26: Policy: Consideration of underway construction reducing cycles in the construction stock-and-flow
(left) as well as in ‘Supply demand ratio’ (right).
The effect of offset cycles in key variables as described above lead to smoothened amplitudes in price (Figure
27, dotted). Since supply is not over- and undershooting demand time after time, price reacts with likewise
balanced developments over the simulated time horizon.
300
Scenario 1
Base run
225 ;
Policy
3
z 150
g
a
75
historical
0
1970 1980 1990 2000 2010 2020 2030 2040
Figure 27: Policy: Consideration of underway construction affecting price cycles.
Given the current design of the STREM-model, this policy accomplishes smoother behavior with almost offset
cycles, on both demand and supply side. Hence, this experimental run indicates that policy design in general can
be leveraged far more on the system's dynamics. Therefore, the dominant feedback loops of the model should be
changed by "redesigning the stock and flow structure, eliminating time delays, changing the flow and quality of
information available at key decision points, or fundamentally reinventing the decision processes of the actors in
the system" (Sterman, 2000, p. 104).
Discussion and Conclusions
Although often not i a dity, real estate belongs to those construction industries in
which long manufacturing times and asset lifetimes determine the market behavior. Similar to other markets,
e.g., copper, aluminum and coffee, and also aircraft and shipbuilding, real estate markets
in general exhibit cyclical dynamics (Sterman, 2000). However, the market’s complexity oftentimes prevents
decision-makers from capturing the underlying structure and thus understanding the origins of cyclical behavior.
such as raw material
The real estate market of the city of Stuttgart has seen an unprecedented increase in prices for the last decade.
Market experts expect prices to further increase. We have transferred findings from secondary data in the
26
literature and from conducting semi-structured interviews into a simulation model to gain further insights in how
market experts perceive the market and increase validity.
Yet, we acknowledge several limitati of our simul
model when ing the results of the paper. For
example, our model omits several structures that may be relevant, i.e., further aspects of the construction side
(e.g., capacities and available building land) or financial factors affecting demand, such as mortgages and loans
but also disposable income.
In addition, the model comprises weaknesses in numerical data and its equations. The utilized indices of price
and construction costs are valid for the Stuttgart real estate market, yet, they are not scaled to the same base
years. As a further limitation, we needed to estimate several model parameters, as secondary data was not
available for the specific case of Stuttgart.
Despite the limitations, our analysis reveals some valuable insights for decision makers and academics, which
in the following. First, the model reproduces the behavior of the reference modes, indicating a decent
fit between simulation and real world data on price, for example. The model structure might not be as
sophisticated as it could be. Yet, it succeeds in simplifying a complex system, and thus enabling an
understanding and further analysis of accordingly complex structures.
we di
Second, our analysis shows that cyclical dynamics do exist in the Stuttgart real estate market, like in many other
real estate markets that have been analyzed before. The market experts we interviewed acknowledge the
comparison with the hog cycle — but when asked about possible future price development, they seem to neglect
possible market cycles. Instead, they point to the cyclical behavior of interest rates as the driver for an oscillating
real estate market: According to their mental model, the real estate market oscillates because interest rates go up
and down. Yet, our analysis reveals that the origins of oscillating behavior are created endogenously. Housing
prices react to changes in related variables, i.e., the balance between demand and supply. As both sides, demand
and supply, imply negative feedback loops characterized by significant time delays, the system is constantly
over- and undershooting its equilibrium. Thus, the system exhibits persistent cyclical instability. In particular,
the lagging supply of desired new construction leads to unsatisfied demand, hence increasing prices. When new
construction is completed, demand has decreased again as a response to the previously increased prices. The
subsequent supply surplus reduces prices and thus expected profits, so that construction activity is
lower in the
following period. It is the ongoing interplay of over- and undershooting variables results in the oscillating
behavior — which explains the hog cycle effect in the real estate market and not the interest rate’s ups and downs.
Third, the two scenarios provide additional insights into the system's behavior responding to exogenous changes
that are not influenced by the real estate market itself. The scenario in which we assume that interest rates will
increase again shows oscillating and increasing prices. In the second scenario we test, how the market will
behave when there is a period of economic downturn because of a change in the structure of the automotive
industry — which is discussed in the media but seems to be considered as an extreme scenario by many people.
Here, the market will go down — until finally demand and supply meet after a long period of excess demand.
Afterwards, prices will again increase. By designing a policy on how to manage the market differently, real
estate cycles could be almost eliminated. While cycles originating from the supply chain could be reduced
significantly, the policy design did not accomplish a similar effect on prices. Yet, the price level did not decrease
due to increasing construction costs accompanied by high profit margins — an assumption which could be
challenged in future research.
Fourth, Thornton (1992) as well as the personal interviews conducted show that real estate experts have
difficulties in ass
ing the impact of the endogenous feedback structure dominating real estate markets. This is
ascribed to both the complexity itself, but also to the physical time period between each cycle, so that many
have not yet experienced a plete cycle. Although all our interviewees are aware of cycles in the
real estate markets, they do not ascribe as much significance to endogenous causalities, rather, their focus
remains on macroeconomic factors, exogenous to the model.
27
Concluding, the paper has illustrated that endogenous structures play the critical role in dynamic system, and
thus should not be underestimated. With this analysis approach, difficulties in human information processing can
be overcome, resulting in more effective decision-making in any business environment.
Although the paper presents various relevant aspects of the real estate market, future research suggestions
involve a further extension of the model to a more sophisticated structure. Therefore, the model boundary could
be expanded by additional structures, i.c., capturing more accurately demand creation, capacity constraints and
interest rates for demand. Also, model equations could be redefined. In particular, the model's parameters as well
as the table functions could be tested and analyzed further.
The future outlook of the paper also reflects aspects of the problem statement that have been completely left out.
Considering the current situation with regard to concerning migration, the tremendous rise in the number of
refugee seeking asylum in the European Union boosts housing demand. Thus, the resulting increased housing
needs must be reassessed for the upcoming years. Appropriate policy design and effective decision-making today
will pay off in the near future.
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30
Appendix
Appendix A. Full Model and Legend of Variable Types
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Figure Al: Full STREM-model.
Time to Realize Effect of ‘Smoothed Effect of ‘Smoothed Effect of
Interest Rate on Profit ——®= Interest Rate on Imarest rate On ag Time'te Realize Effedt of
Interest Rate on Demand
Profit Margin
+
+
Table for Effect of Effect of Interest Effect of Interest Table for Effect of
Interest Rate on Profit gs Rate on Profit Margin Rate on Demand “—— Interest Rate on
Margin 4 ae Demand
Interest Rate
—— a
<Time>
oF
Interest Rate DATA Future Interest
wee Aesuaneten ge
SWITCH Interest
Rate Scenario 1
Future Interest Rate Future Interest Rate
‘Assumption BAU Assumption Scenario 1
Figure A2: Substructure of STREM-model: Interest rate effects on supply and demand side.
Color Variable Type
| Endogenous variables, determined by the system's behavior as well as table
functions of effects
Exogenous input added (e.g., real data of times series and initial values)
Constant parameters implicating information delays (e.g., time adjustments)
Constant parameters implicating sensitivity of variables
Shadow variables: defined elsewhere and used to avoid clutter and overlapping of
structures
oO Variables i ing future ions and
it) Real data, which simulated variables are compared to
Table Al: Legend of STREM-model.
Appendix B. General Simulation Settings
The model was implemented in Vensim PLE for Macintosh, Version 6.3, and Vensim DSS for Windows
Version 6.3 Double Precision (x32). Model settings are described below (Table A2):
Initial Time 1970
Final Time 2045
Time Step 0.125
Units for Time Year
Integration Type Euler
Table A2: Model settings in Vensim.
The experimental runs were executed in Vensim PLE for Macintosh, Version 6.3 with a MacBook Air and in
Vensim 6.3 Double Precision with a ThinkPad. Exogenous data was imported through vdf-files and respective
variables are indicated in red and with the suffix 'DATA' in STREM-model (Figure A1).
Appendix C. Experimental Runs Settings
The settings described in Table A3 were used to execute all runs — base, scenarios and policy runs.
Base Run: Scenario 1: ; ; Policy: Consideration of
i Scenario 2: Decline in :
Business as Increasing Underway Construction
Households
usual Interest Rates
~ Scenario 1, plus Policy
Interest rate Scenario 1, plus simulation of eae y
Design: Supply side
increases again a collapsing regional
considers underway-
assuming a automotive industry leading
Description construction, thus being able
step-by-step toa sharp decrease in
to estimate more accurately
desired new additions in
order to meet excess demand.
increase by the demand, i.e. growth rate of
households declines.
SWITCH "Interest Rate Scena
= Increase in interest rates as of 2017 i i
1 = Interest rates remain as low as of
2015 at 0.005
SWITCH ‘Scenario 2"
0 = Future Household Fractional Growth
Rate assumed constant at 0.004
1 = Sharp decline in households between
2017-2021, followed by constant
Fractional Growth Rate at -0.005
Table A3: Run settings.
Appendix D. Equations
Equations and Comments Units
(On) ‘Accepted Profit Margin= Normal Profit Margin*Smoothed Effect of Interest Rate on Profit Margin Dimensionless (Dmnl)
‘The accepted profit margin is determined by the annual interest rate level. Therefore, the effect variable adjusts the normal profit margin as follows: Supply side
accepts lower profit margins when interest rates are respectively low. They claim higher profit margins during periods of high interest rates. Eventually, the accepted
profit margin determines the minimum acceptable price by taking into account the construction costs.
(2) ‘Apartments per Building=4 apartments/building
In average each building is constructed in such way as to yield four apartments, This average number is based on real data of the real estate market Stuttgart. Source:
hup://service stuttgart.de/ths-s 7251_1\ Stuttgart_2006.PDF (retrieved October 29, 2015)
(03) ents pet Household=1 ‘apartments/household
Tris assumed that each household ‘occupies one apartment. This variable helps to equal units, i.e. 1 household = 1 apartment.
(4 “Attractivity of Real Estate Purchase=SMOOTH( Smoothed Effect of Interest rate on Demand* Effect of Price on Demand, Dial
Time to Smooth Attractivity trom Price Effect )
vane expresses the Attractivity of purchasing real estate by taking into account the effect of changing prices as well as interest rates on demand.
75
‘Average Construction Time=I Year
he average time to construct a building.
‘Average Life Time of Buildings=100 Year
The average life time of buildings until the building is fully demolished.
) ‘Average Planning Time=4 Year
‘The average time to plan the construction of a new building before the actual construction start
‘Average Sales Time=0.75 Year
‘The average time it takes to sell a building
09) Buildings Completed= INTEG (Construction Completion Rate-Sales Rate, IN Buildings Completed) buildings
The stock accumulates the fully constructed buildings.
(10) Buildings Completed DATA :INTERPOLATE: buildings
Jata was inserted into model for all buildings completed retrieved November 2015 from http://www.statistik.baden-
wuerttemberg.de/SRDB/Tabelle.asp?H=ProdGew&U=01&T=07015011&E-KR&R=KRI 11
i) Buildings in planning DATA -INTERPOLATE: buildings
Data was inserted into model for all buildings in planning retrieved November 2015 from hp! /Iwoww statistik baden-
wuerttemberg.de/SRDB/Tabelle.asp?H=ProdGew&U=01&T=07015011&E=KR&R=KRI1
(2 Buildings Occupied= INTEG (Sales Rate-Demolition Rate, IN Buildings Occup D buildings
‘The stock accumulates all buildings sold and thus occupied. The buildings being demolished are deducted through the outflow Demolition Rate
(3) Buildings Occupied DATA-INTERPOLATE: buildings
Data was inserted into model for all buildings in planning retrieved November 2015 from stuttgart /jb193.php
(5) Buildings under Construction= INTEG (Construction Start Rate-Construction Completion Rate,IN Buildings under buildings
rruction)
The stock of building that are constructed.
(15) Change in Expected Construction Costs=(Construction Costs-Expected Construction Costs)/Time to Adjust Expected Costs price index/Year
The flow adjusts the stock of Expected Construction Costs based on the input of the average construction costs per unit.
16) ‘Change in Expected Price=Price Variation/Time to Adjust Expected Price price index/Year
This inflow changes the expected price in response to the gap between the indicated price and the current expected price.
17) Construction Completion Rate=Buildings under Construction/A erage Construction Time bull ngs Year
This variable transforms the stock of buildings under construction into the stock of completed buildings. The higher the value the faster are buildings constructed.
(8) Constretion Cost Index DATA (197002016200) (1970 31.2)(1971342(1972.364, (197339. (1974, O9TS 412), price index
3), (1977,44.2), (1978.47.3),(1979,51.8),1980,57.5), (1981,60.2) 7)(1983,61.5), (1984,63.1),
{U98r55 1) 1088 66.9; 980.682 (1980-73 9) U99I-7E (105% a2 a ioH4es) (1904 ¥S 4) (998863) O86 HA 8 OSTEO,
(1998,84),(1999,84,2),(2000,85.2), 2001.85 9), (2002,86.1), (2003,85.6), (2004,86.7), (2005,87.3), (2006,89.4), (2007,95.7), (2008,98.5),
(2009,99.1), 2010, 100),201 1,103), (2012,105.5), 2013,107.4),(2014, 109.6), (2015,111.9),2016,114.5))
is table contains the index of construction costs for the federal state of Baden-Wiirttemberg for 1970-2016, retrieved from: hhttp://www.statistik.baden-
‘wWuerttemberg.de/GesamtwBranchen/K onjunktPreise/BPI_LR jsp (February 28, 2017) / Banana! BW --- After 2017: Data is calculated based on average growth
rate of previous 10 years (1996-2016) and is returned through the variable "Future Constru Assumptions’
33
0) Construction Cost
Assumption)
This is the exogenous input of Construction Costs per apartment indicated as an index. For 1970-2016 real data is returned via the variable "construction Cost Index
DATA", whereas, after 2017, Data is calculated based on average growth rate of previous 10 years (1996-2016) and is returned through the variable "Future
Construction Costs Assumptions"
IF THEN ELSE( Time<2017 , Construction Cost Index DATA(Time) , Future Construction Cost price index
(20) ee Costs DATA INTERPOLATE: price index
Dat ul ie ay del fo ne of corto i Ti Foil ado de W Ueno Wr 19702016 eye i
hhtip://wvww.statistik.bad e/BPL_LR jsp (February 28, 2017)
en Construction Start Rate=MAX< (0,Desired New Construction/Average Tae Time) buildings/Vear
‘The Construction Start Rate is the first inflow into the supply aging chain and takes into account the amount of desired new construction projects. The MAX function
usts the inflow not to become negative at any point of time.
i
(22) Delay Time in Demand Creation=1 ‘Year
This is the time people need to create actual demand in housing, meaning that people do not react immediately to changes in price, rather it takes time to decide to
move into a new apartment,
(23),
Demand= INTEG (Demand Increase Rate-Demand Satisfaction Rate, IN Demand) ‘apartments
This is the stock that accumulates demand in housing, i.e. how many buildings are demanded in total in a certain point of time.
(24) Demand in Buildings=IF THEN ELSE (D. per Building>0, ‘per Building, O) buildings
Demand of apartments transformed into number of buildings demanded
(25) increase Rate=SMOOTH(Attractivity of Real Estate Purchase*Potential Demand* Apartments per Household , apartments/Year
Delay Time in Demand Creation)
This is the net growth rate of demand. When positive, demand is created, otherwise, when negative demand is satisfied. The smooth function reflects the delay in
demand formation, since people do not react to price changes immediately.
(26) Demand Satisfaction Rate=Sales Rate* Apartments per Building apartments/Vear
‘The Demand Satisfaction Rate decreases the stock Demand along with the Sales Rate, i.e. when an apartment is sold/occupied.
en lished Space=Demolition Rate* Apartments per Building apartments/Year
‘This variable caleulates the total demolished space in units of apartments. Demolished space results in new demand for apartments.
(28) Demolition Rate=Buildings Occupied/Average Life Time of Buildings buildings/Vear
The outflow adjusts the stock Buildings Occupied since buildings need to be demolished after the average life time of a building.
29) Desired New Construction=Expected Excess Demand in Buildings*Effect of Expected Profitability on Desired New buildings
‘onstruction
Desired New Construction to satisfy Expected Excess Demand is supplied depending on Profitability High Profitability = high supply of new construction Low
profitability = decreases supply Equation for Policy 1: (Expected Excess Demand in Buildings-Buildings under Construction-Buildings Completed) *Effect of
Expected Profitability on Desired New Construction
G0) Effect of Expected Profitability on Desired New Construction=Table for Effect of Profit on Desired New Dial
mnstruction(Expected Profit)’Sensitivity of Supply to Price
Desired capacity is adjusted above or below current capacity in response to the expected profitability of new investment
GD Effect of Interest Rate on Demand=Table for Effect of Interest Rate on Demand( Interest Rate* 100) Dial
This is the effect that interest rates have on the demand. Low interest rates make Real Estate an attractive investment opportunity, thereby increasing the attractiveness
of purchasing Real Estate. Whereas, the higher interest rates increase the attractiveness of alternative investment options for potential real estate purchasers, such as
financial assets, savings on bank account.
fect of Interest Rate on Profit Margin=Table for Effect of Interest Rate on Profit Margin(Interest Rate* 100) Dmal
(2)
This is the effect that interest rates have on developer's Profit Margins. Low interest rates reduce profit margins, higher interest rates result in higher profit margins,
@3) ae ‘of Price on Demand=Table for Effect of Price on Demand(Expected Price to Price Ratio) Sensitivity of Demand to mn
Prict
Expected Price higher than current rice > Demand decreases
Expected Price lower than current Price --> Demand increases
G4) Effect of Supply Demand Ratio on Price=Table for Effect of Supply Demand Ratio on Price(Supply Demand Dial
Ratio)Sensitivity of Price to Supply Demand Ratio
The effect of the supply demand ratio on price is a power function of the demand/supply ratio. The Sensitivity of Price to the demand coverage controls the magnitude
of the response. The higher the sensitivity of price to the demand/supply ratio, the greater the change in price induced by any imbalance. Price rises when
demand/supply ratio is less than normal, and falls when it is greater.
35) Excess Demand=MAX(0 , Demand-Vacant Apartments ) ‘apartments
This is the gap in housing, i. the discrepancy between the supplied space (the stock ‘Buildings Completed’) and the demanded space (the stock ‘Demand!). It is a goal
seeking function. If positive, it is the excess of space demanded over space supplied. If negative, it is the excess in supply over demani
) Expected Construction Costs= INTEG (Change in Expected Construction Costs, IN Expected Construction Costs) price index
Expected Construction Costs represent beliefs among market participants about the unit costs of production (variable and fixed, including normal profit margins)
Expected Costs therefore represent beliefs about what a 'fair' price would be, or the long-run equilibrium price. Expected costs adjust to the actual costs with a delay
representing the time required for gain information and adjust beliefs about costs. Exogenous for partial model test
on) Expected Excess Demand= INTEG (Expected Excess Demand Net Growth Rate, IN Expected Excess Demand) ‘apartments
It isa first order information delay stock that represents the supply side’s expectation of real desired sp:
G8) ted Excess Demand in Buildings=Expected Excess Demand/Apartments per Buildin, buildings
The value sim the value of the stock Expected Excess Demand in such a way as to yield the unit of buildings
9) ted Excess Demand Net Growth Rate=(Excess Demand-Expected Excess Demand)/Time to Form Expectation of apartments/Year
Enoes Demund
It takes into account both the vacant space (=the gap between supplied and demanded space) and the demolished space. Since it cannot be accurately known, it is,
estimated by the supply side through an information delay structure,
(40) Expected Price= INTEG (Change in Expected Price, IN Expected Price) price index
‘The price market makers and traders believe would clear the market if demand and supply were in balance, and no other pressures to change price existed.
ai Expected Price to Price Ratio=ZIDZ(Expected Price, Price) Dial
When expected price increases the effect on demand shall decrease. When expected price decreases the effect on demand shall increase, having a positive effect on
demand,
(42) Expected Profit=(Expected Price-Expected Construction Costs)/Expected Price Dmnl
This is the expected profit of the supply side based on the expected price and expected costs,
(@) FINAL TIME = 2045 Year
‘The final time for the simulation.
(44) Future Construction Cost Assumption=IF THEN ELSE (SWITCH Scenario 2=0, Future Construction Cost Assumption price index
Scenario 1 (Time), Future Construction Cost Assumption Scenario 2 (Time))
The variable delivers the construction cots price index under each specified scenario.
Fur Construstion Cost Assumption Senaro (2017102045200 201711609 201817 5), 219,182) (200 20.70, price index
(ama 12231), 2022125 94, (03,125 '27.25),2025,128.95)(2026,130.66), (2027,132.4),(2028,134.16), 229,135.94),
37.73), 031.139.5208, 1413), (2085145 31) (2084 148.22) 2085 14918), 2086, 491
137,151.09 53.1), (2039, 155.13), (2040, 157.19) 2041, 159.28), (2042,161.), (2043,163.55), (2044, 165.72),(2045,167.92))
This i indicates the assungiion for future construction costs for the period from 2017 until the end of the simulation run (204s) for the Base Run, Scenario 1
oli uming "Business as usual" - i.e. Construction costs increase with a rate of the last 10
34
75) Tass Construction Cost Assumption Scenario 2 (((2017,100)(2045 200)),2017,116.02),2018,117.56), (209,119.13), (2020,120.7), price index
(2021 22.31, 2022,120.7, 2023 120.7,(2025, 120.7 (2025.119-7 (2026119. (20271192), 20281183), 2029118), 2031175),
(2035,115), (2086, 114.5), (2037,114), (2038,113.5), (2039,113), (2040,112.5), (2041,112),
(2031117), (2032,116.5) 2034,
(2042 1152083 111 fo088 T10.9)2088 110)
This table indicates the assumption for future construction costs for the period from 2017 until the end of the simulation run (2045) in Scenario 2: assuming a drop of
construction costs along with decline in HH, due to weak economy in Stuttgart area,
47) Future Household Fractional Growth Rate Assumption=IF THEN ELSE (SWITCH Scenario 2=0, Future Household Year
Fractional Growth Rate Assumption Scenario 1(Time), Future Household Fractional Growth Rate Assumption Scenario 2
(Time)
This variable takes on values of variables Future Household Fractional Growth Rate Assumption’, that change in Scenario 1 and 2
(48) Future Houschold Eracional Growth Rate Assumption Scenario I (217 )2045.)1(2017.0.00), (2048 000,219.00), Year
(2020,0.004) 0.004), (2022,0.004), (2023,0.004),(2024,0.004), (2025,0,004), 2026, 0.004), (2027,0.004), (2028,0.004) (2029.0.004),
(2030.0.008, 2031.0 008) (20324008), 2033 0.008) (20340.04),2038,0.008) (2036.0 04), (20370008), (2038,0.008),
(2039,0.004),(2040,0,004), (2041,0.004),(2042,0.0037), (2083,0.004), (2044,0.004), (2045,0.004))
This table indicates the assumption for future household growth for the period from 2017 until the end of the simulation run (2045) in the Base Run, Scenario 1 and
Policy 1: assuming a Steady growth in the Households of Stuttgart.
(49)
Futre HoursboldFraetional Growth Rats Asstipion Scenario 2 (017-417 O045 05% GO17 AOE 2H18 0000), Year
0.001), (2019.97,-0.004), (202 20 3), (2024, 0.005), 2025,-.005),(2026,-0.005),(202
(0.005),2029,-0.005), 2030, $2 05).2038<0 008), (2084-0008), “ans, “us 99)20 0,005), (2038,
1039-0,005)(2040 -0.005), (2041 -0.005),(2042,-0.005),2042.-0,005) (20430, 005) (2044 -0.005)2048 0.005)
‘This table indiates the assumption for future household provrth forthe peviod ftom 2017 unt the end ofthe simulation run (2088) under SCENARIO 2(a Drop in
Households Growth Rate due to decline in market's economy).
(50) Future Interest Rate Assumption=IF THEN ELSE (SWITCH Interest Rate Scenario 1=0, Future Interest Rate Assumption Dial
BAU(Time), Future Interest Rate Assumption Scenario I(Time))
‘The variable delivers the interest rate assumption under each specified scenario
a) Future Interest Rate Assumption BAU(@017, 2017,0.005), (2018,0.005} 2020,0,005),2021,000% Dial
(2022,0.005), (2023,0.005), (2024,0.003),(2025,0.00: 2029,0.005), (
(2032,0.005), (2033,0.005), (2034,0.008), cn {2036.0.003(2039.0.00) (2038,0.05) 2089.00
(2042,0.005), (2043,0.005)(2044,0.005), (2045,0.005)
This table indicates the assumption for future interest rate cal for the period from 2017 until the end of the simulation run (2045) for the Base Run assuming
0.005),
(2040,0.008), (2041,0.005),
“Business as usual" - i.e, interest rates remain as low as of 2015 (as defined in the variable "Interest Rate DATA").
() ote Interest Rate Assvnpion Scenario 1 (017 145,0.08)],(2017,0.006), (201 7020,0.021 Dinnl
(2021,0.032), (2022,0.037), (2023,0.039),(2 0.055), (2028,0.057),
(2030.17,0.068019), (2032,0.071),(2035,0.075), (2037,0.0755),(2040,0.078),(2042,0.08),(2045,0.08))
This table indicates the assumption for future interest rate development for the period from 2017 until the end of the simulation run (2045)
In Scenario 1, 2 and Policy 1: an increase in interest rates as of 2017.
(53)
Household Fractional Growth Rate DATA ({((1970.0.03)-(2016,0.03)],(1970,0.0041), (1971,0.0041), (1972,0.0041), (1973,0.0041), Year
(48740004), 995,041, 19%60.004 (1977, 0.0041 1978 0.041)(1979.0.004), (19809041), (1981 0.081), (1982.8.0040,
(1983,0.0041), (1984,0.0041), (1985,0.0041), (1986,0.0041),(1987,0.0041), (1988,0.0049), (1989,0.0049),(1990,0.0049), (1991,0.0049),
{1992.0.0088) 1988 0.0049 (19940 0049){0998 6.0089) (1996.0.008), 19970 0008) (1998 0.0008) {1999 0.0008) (20000 005)
(2001,0.0043), (2002,0.0043),(2003,0.0043), (2004,0.0043), (2005, 0.0043),(2006,0.0064) (2007,0.0012), (2008,0.0069), (2009,-0.0008), (2010,-
(0.0217),(2011,0.0125),(2012,0.0189), (2013,0.0122),2014,0.0095),(2015,0.0158),(2016,0.0116))
This is the lookup of the fractional household growth rate in Stuttgart from 1970 until 2030 calculated on basis of the households data of Stuttgart. For missing data,
the annual compound method is used to calculate fractional growth rates between two given values. Source Households 1970-1995: http://www.statistik.baden-
‘wuerttemberg.de/SRDB/Tabelle.asp?H=1&U=07&T=99025080&E=GE&K=I 11&R= GE111000 (Retrieved October 3, 2015)
Source Households since 1995: http://statistik stuttgart. 199/jb4699 php (Retrieved February 27, 2016).
(4) Households in Stuttgart= INTE tous Net Growth Rate,IN Households) households
This stock accumulates all households in Stut
65) Households in Stuttgart DATA “NTERTOTATE households
Source Households 1970-1995: http://www.stati
(Retrieved October 3, 2015)
de/SRDB/Tabelle.asp?H=1 &U=07&T-990250808
SE&K=111&R= GE111000
(56) Hourchols Net Growth Rate=IF THEN ELSE Time=2015 , Household Fractional Growth Rate DATA(Time)*Households households/Year
Stuttgart , Future Household Fractional Growth Rate Assumption*Households in Stuttgart )
This flow shige the stock of households in Stuttgart.
G7)
Buildings Completed= INITIAL(S66) buildings
The stil value of Buildings completed is estimated based on the number of buildings existing in Stuttgart in 1970 and 1971. Data is retrieved from
http://www statistik baden-wuerttemberg.de/SRDB/Tabelle.asp?H=ProdGew&U=05&T=07055011&E=KR&R=KR 1 (November 14, 2015).
(58) IN Buildings Occupied= INITIAL(59036) buildings
‘The initial value, 59036 buildings in 1970, is retrieved from Stuttgart data. From stuttgart, /jb193.php (November 16, 2015).
(59) IN Buildings under Construction= INITIAL(570) buildings
The initial value of Buildings under Construction is estimated based on the number of buildings completed after two years (in 1972) considering the fact that the
constructions turn into completed buildings after 2 years on average. The data for buildings completed is retrieved from http://www. statistik.badet
wuerttemberg.de/SRDB/Tabelle asp? H=ProdGew8&U=05&T=07055011&E=KR&R=KR 11 (November 14, 2015)
(60) TN Demand= INITIAL(1000) ‘apartments
The initial value of demand is assumed at 1000.
(61) IN Expected Construction Costs= INITIALGI.2) price index
‘The initial value of the Construction Cost index for 1970 retrieved from http://www statistik.bad tIndex.asp
IN Eepeited tcisg Demin INETTAT3000) ‘apartments
The initial value of expected ex: mand is assumed at 3000.
(63) IN Expected Prices INIHIALGT) price index
The initial value of expected price is assumed at 37, close to actual price in beginning of time horizon.
(64) TN Households= INITIAL (264312) households
The initial value of households is retrieved from http://www.statistik.baden-wuerttemberg.de/SRDB/Tabelle.asp?H=1 &U=07 &T=99025080&E=GE&K=111&R=
GE111000 (Retrieved October 3, 2015)
INITIAL TIME = 1970 Year
‘The initial time for the simulation.
(66) Interest Rate=IF THEN ELSE( Time=2016 , Interest Rate DATA(Time) , Future Interest Rate Assumption) Dinnl
The function returns the values of real interest rate data for 1970 until 2015. After 2016 the Table for Interest Rate Assumption is returned.
(a) Tnterest Rate DATA ((1970,0)-(2015,0.2)), (1970,0.082), (1971,0.083),(1972,0.082)(1973,0.095){1974 0.106), (1975,0.0868), (1976,0.0804), Dini
(197700653), (1978,0.0613),(1979,0.0758),(1980,0.0843), (1981,0.1013),(1982,0.0894),(1983,0.0808),(1984,0.0798), (1985,0.0704),
{488600617 (1987.0.624, (198.0958) (19880070), (190 0.88, 1910.05), (982.0079), (995.0681, 1994.0. 068,
(4995,0.0685), (1996,0.0622),(1997,0.0564),(1998,0.0457), (1999,0.0449), (2000,0.0526).(2001,0.048), (2002,0.0478), (2003,0.0407),
{20030 080s) 200s 0.0838), 200600876), (2007400822) (20080398), 200800322) 2010100298), Oot l/ob2et, O12 001%,
2013,0.0157)(2014.0.0116), (2015 0.005), (2016,0.0009))
Exogenous input: interest rates for Germany from | od from http://de.statista di
Hipialinaiteacestzey ch euacitend/ “Dai fo we 1974 retrieved from hap: //www.digitalis.uni-koeln.de/Geldwesen/geldwesen279-284.pdf and
http://w 083
68) Minimum Accepted Price=Expected Costa Costs*(1-+Accepted Profit Margin) price index
This is the minimum price that the supply side accepts, based on expected costs plus an accepted profit margin.
(69) Normal Profit Margin=0.25 Dial
This is the normal profit margin for the construction side. It determines the accepted profit margin on basis of the annual interest rate level. See variable Accepted
Profit Margin.
35
(0) Potential Demand=SMOOTH( Households Net Growth Rate , Time to Smooth Demand from HH Growth)*(Demofished households/Year
Space/Apartments per Household)
Variable delivers potential demand calculated by the net growth in Houscholds as well as Demolished space
a) Potential Sales-MAX(0, MIN(Demand in Buildings, Buildings Completed )) idings
‘The MIN-Function returns the smaller value of either Demand or Buildings Completed. The function prevents Potential Sales, i.e. the number of buildings sold con
exceeding the existing demand in any point of time as naturally buildings can only be sold until total demand is satisfied.
(72) Price=Minimum Accepted Price*Effect of Supply Demand Ratio on Price price index
Trader's set prices by adjusting their belief about the underlying equilibrium price in response to market pressures such as the supply/demand balance, here represented
by inventory coverage relative to the normal level, and unit costs.
(3) Price Index Stuttgart DATA -INTERPOLATE: price index
Price index with 2010=100 retrieved from 2011, Stuttgart
(74) Price Variation=Price-Expected Price price index
The difference between Price and Expected Price adjusts the change in expected price.
(75) Sales Rate=Potential Sales/Average Sales Time buildings/Vear
The Sales Rate is the outflow that reduces the stock of Buildings completed. However, it is calculated on basis of Potential Sales
completed that can be sold must not exceed the given demand.
since the number of buildings
SAVEPER = TIME STEP Year
The frequency with which output is stored,
(a) Sensitivity of Demand to Price=0.5 Dial
This is the demand elasticity, which adjusts the effect ice on demand. Demand in real estate market is found to be rather inelastic (see Sterman, 2000; Muth,
1988). The lower the value, the less price sensitive is dem
(78) Sensitivity of Price to Supply Demand Ratio=0.75 Dinnl
Controls the response of price to the supply/demand coverage. Must be positive for high demand to lead to higher prices. Higher absolute values lead to greater price
changes for any given demand coverage
(79) Sensitivity of Supply to eal Dini
This is the supply elasticity, which adjusts the effect of price on supply. Supply in real estate market is found to be pretty elastic (DiPasquale,1999; Muth, 1988). The
higher the value, the more price sensitive is supply. The effect of elasticity is determined by this variable together with the lookup "Table for Effect of Profit on
Smoothed Effect of Interest rate on Demand=SMOOTH( Effect of Interest Rate on Demand , Time to realize effect of interest, Dmal
rate on demand)
This is the smoothed effect that interest rates have on the demand side. See also 'Table for Effect of Interest Rate on Demand’
(cH) Smoothed Effect of Interest Rate on Profit Margin=-SMOOTH( Effect of Interest Rate on Profit Margin , Time to realize Dial
effect of interest rate on profit margin)
This is the smoothed effect that interest rates have on developer's Profit Margins. Low interest rates reduce profit margins, higher interest rates result in higher profit
margins.
(82) Supply Demand Ratio=XIDZi( Buildings Completed, Demand in Buildings, 30) Dial
‘The supply demand ratio is the balance between demand and supply, expressed as a ratio. Supply is equal to the stock of buildings completed
SWITCH Interest Rate Scenario 1=0 Dini
This is a switch 0 = BAU 1 = Scenario 1, Scenario 2, Policy 1
SWITCH Scenario Dial
This is a switch 0= Base Run, Scenario 1, Policy 1 1 = Scenario 2
(85) ‘Table for Effet of Interest Rate on Demand (0.0}(1225}(03.18), (06354381495), (1297351 46667), (1.85936,45714) Dmal
(280424 41908) (37271-13743), (4.057031 276194 7169118099) 35234108571, (6) (668766089!
{Gast 40790475 48 2608301404579 (8969430.7087), (8.65993, 0 68971, 10587, 068714, 1 8ST S3HI8Sy(12.049,0615088)
Table determines the effect of interest rates on the attractiveness of purchasing real estate (i.e. demand) based on Germany's interest rates. Low interest rates generate
higher demand since investing into Real Estate seems as a better option, compared to high interest rates that moderate demand, since alternative investment options
become more attractive with higher interest rates (e.g. financial assets).
(86) Table for Effect of Interest Rate on Profit Margin (((0,0)-(13, (0.0571429)(1.29735, 7 Dinnl
(2.19756,0,0809524), (3.09776,0.12),(4.26273,0.209524),5,13646,0.31 9048) (5.85132,0.504762), (6.83096,0,795238), (7.96945,0.933333),
(©.29328,0.992857),11,1))
Table determines the acceptable profit margin based on Germany's interest rates. Low interest rat
requests a higher profit margi
decrease the profit margin acceptable, whereas higher interest rate
7) Table for Effent of Price on Demand ((0-(2),(0.03 0:25) (013482 024761903 027/05 0370.7 055400177 075280 Dinnl
(1,1,G.1,1.21), (.2,1.45), (1.33608, 1.70476),(1 491184762). (1.6,1.92),(1.8,1.98),
The S-shaped Table function adjusts demand as follows: When Expected Price increases -> effect decreases demand When expected price to price ratio decreases >
effect increases demand
(88) Table for Effect of Profit on Desired New Construction ([C05,0)(1,8)), -0.5,0:1),-0.2,0.15),0.13,0.26),-0.0997963,0.419048)(- Dini
(0.0448065,0.647619), (0.00101835,0.876191), (005,1.25), (0.098778, 1.67619), (0.15.21), (0.202648,2.81905),(0.25,3.3),
(0.294297,3.73333)40.35,3.9)40.404277.4),(0.45,4),0.54)1.4))
Table for Effect of Profit on Desired New Buildings. Depending on the expected profits, suppliers adjust the desired new constructions, with the assumptions that high
profits stimulate an increase in desired new while lower profits decrease the desired new
(89) Table for Effect of Supply Demand Ratio on Price ({(0.0)-6.2)|(0,1.49), (0.385321,1 42105), (0.7.1.28)(0.844037,1 14035)(1,1), Dini
(1.24771,0.815789), (1.72477 0.614035), (2.47706,0.535088), (3.37615,0.517544),(5,0.5),6,0.5))
When Supply > Demand = Effect on Price less 1 = decreases Price due to oversupply (low demand) When Supply < Demand = Effect on Price greater | = increases
rice due to supply shortagehigh demand, based on Barlas, 2007.
(0) TIME STEP Year
The time step for te si aoa
Time to Adjust Expected Costs=0.5 Year
The time to form expectations about the construction costs.
(2) Time to Adjust Expected Price=1.5 Year
The expected price adjusts to actual prices over this time period.
Time to Form Expectation of Excess Demand=2 Year
timated time that is needed in order to form the expectation of how much additional space is desired. It generates an information delay.
4
94) Time to realize effect of interest rate on deman Year
This is the time it takes until the effect of interest rates reach demand side.
(05) Time to realize effect of interest rate on profit margin=2 Year
This is the time it takes until the effect of interest rates reach supply side
96) Time to Smooth Attractivity from Price Effect=0.75 Year
This is the time that smooths Attractivity: Attractivity of houses resulting from price changes (Effect of price on demand) does not translate into demand immediately
but with the given time delay.
) ‘Smooth Demand from HH Growth=2.5 Year
‘his dly/ldne smooths tha amplitudes in Potential Demand
(98) Vacant Apartments=Buildings Completed* Apartments per Building ‘apartments
Variable calculates the number of vacant apartments based on buildings assumed being vacant once completed.
Table A4: Equations and comments of STREM-model.
36
Appendix E. Supporting Material
A Vensim file of the STREM-model is attached, including all experimental runs conducted and described in the
present paper. In addition, a vdf-file entails data time series that were used as real data input for validation
purposes.
Attachments:
e Vensim file of the "STREM-model", including
© Experimental runs:
Base Run: Business as Usual
Scenario 1: Increasing Interest Rates
Scenario 2: Decline in Households
Policy: Consideration of Underway Construction
37
