Price Bubbles in the Real Estate Markets - Analysis and Prediction
PAWEŁ DEC
Institute of Corporate Finance and Investment
SGH Warsaw School of Economics
Al. Niepodleglosci 162, 02-554 Warszawa, Poland
POLAND
GABRIEL GŁÓWKA
Institute of Corporate Finance and Investment
SGH Warsaw School of Economics
Al. Niepodleglosci 162, 02-554 Warszawa, Poland
POLAND
PIOTR MASIUKIEWICZ
Scientific Society of Praxeology
ul. Madalińskiego 31/33, 02-554 Warszawa, Poland
POLAND
Abstract: The article concerns the issue of price bubbles on the markets, with particular emphasis on the
specificity of the real estate market. Up till now, more than a decade after the subprime crisis, there is no
accurate enough method to predict price movements, their culmination and, eventually, the burst of price and
speculative bubbles on the markets. Hence, the main goal of the article is to present the possibility of early
detection of price bubbles and their consequences from the point of view of the surveyed managers. The
following research hypothesis was verified: price bubbles on the real estate market cannot be excluded,
therefore constant monitoring and predictive analytics of this market are needed. In addition to standard
research methods (desk research or statistical analysis), the authors conducted their own survey on a group of
randomly selected managers from Portugal and Poland in the context of their attitude to crises and price
bubbles. The obtained results allowed us to conclude that managers in both analysed countries are different
relating the effects of price bubbles to the activities of their own companies but are similar (about 40% of
respondents) expecting quick detection and deactivation of emerging bubbles by the government or by central
bank. Nearly 40% of Polish and Portuguese managers claimed that the consequences of crises must include an
increased responsibility of managers for their decisions, especially those leading to failures.
Key-Words: price bubble, real estate market, crisis, managers
Received: June 21, 2021. Revised: December 16, 2021. Accepted: January 14, 2022. Published: January 16, 2022.
1 Introduction
The problems of prediction and socio-economic
consequences of a price bubble on the housing
market are an important area of research dealing
with the volatility of creation factors and shock
events. The international subprime crisis of 2007-
2009, when the real estate price bubble burst, was
an example of dramatic social and economic
consequences. The time of acquisition of
information about the risk of a price bubble is
crucial for two reasons – the earlier the bubble can
be detected, the easier it is to avoid its growth; the
ordinary economic policy tools become less
effective when the housing market enters the phase
of boom. The authors primarily aim to present a
possibility of early detection of price bubbles and
their consequences from the point of view of the
surveyed managers. The verified hypothesis is that
price bubbles on the real estate market cannot be
excluded, which is why constant monitoring and
predictive analytics of this market are indispensable.
It should be noted that the authors reject the
hypothesis of the efficiency of financial markets and
consider the irrationality of the real estate market
investors possible. The research methods used in
this publication include analysis of domestic and
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foreign literature, CAVI research as well as
statistical analysis of the obtained results.
2 Problem Formulation
Observations of the processes taking place in the
modern economy show that one of the basic
manifestations of crisis shocks is the fluctuation of
asset prices on every market. Hence, numerous
studies on the subject [1, 2, 3, 4, 5]. The key issue
regarding the asset market is the problem of the
convergence of asset prices with their fundamental
value [6, 7, 8, 9]. Significant disproportions between
the price and value of assets usually lead to the
emergence of a phenomenon on the market, which
is referred to as price bubble. This term very aptly
reflects the impermanence and instability of this
process. A price bubble grows when the market for
a specific type of asset rises in the current prices of
assets above their fundamental values. The
fundamental value of an asset is its value resulting
from the usefulness of the asset, expressed by the
price level in the conditions of equilibrium. A price
bubble on the asset market means a deviation from a
dynamic equilibrium of the market, which results
from its very character [10].
2.1 Theoretical Aspects of the Development
of Price Bubbles on the Markets
The phenomena of growing and bursting of price
bubbles on asset markets are becoming a direct
cause of disturbances in the stability of economies.
It is based on at least two reasons. The first is that
excessively high asset prices do not properly fulfil
the informative function of prices, which contributes
to the inadequate allocation of resources, since too
many of them are allocated in assets characterised
by much stronger dynamics of price growth than
implied by fundamental factors. Secondly, the burst
of the asset price bubble most often makes
significant losses in the financial sector, causing at
the same time a threat to financial stability and a
deep collapse in economic growth [11, 12].
However, it should be emphasised that not
every increase in asset prices must mean a price
bubble growth. A progressive increase in asset
prices may be determined by fundamental factors.
An asset market bubble occurs when an increase in
asset prices is not based on fundamental factors. P.
Garber, characterising this phenomenon, defines it
as "part of asset price movement that is
unexplainable based on what we call fundamentals"
[13]. And J. Stiglitz states that "if the reason that the
price is high today is only because investors believe
that the selling price will be high tomorrow—when
"fundamental" factors do not seem to justify such a
price—then a bubble exists." [14]. On the other
hand, according to Mr Kindleberger, a price bubble
is "a sharp rise in price of an asset or a range of
assets in a continuous process, with the initial rise
generating expectations of further rises and
attracting new buyers" [15].
The essence of the price bubble is therefore
manifested in the behaviour of asset market players,
consisting in undertaking transactions in overvalued
assets. Thus, it is important to answer the question
of what makes investors willing to accept asset
prices on the market that are significantly different
from their fundamental value. To some extent, one
can try to answer this question on the basis of the
models of asset market price bubbles presented in
the literature. [10, 16]. According to the research
assumptions made to analyse speculative bubbles on
asset markets, we may talk about the rationality of
speculation and then we have to do with a rational
bubble. Then, investors rationally anticipate the
expected value of dividends to be discounted and
prices to rise as a result of speculative behaviour,
and they predict the probability of a price bubble
burst within a specific period of time. There may
also be bubbles on the asset market described as
irrational [17]. In this case, investors make decisions
in an irrational way, on non-economic grounds, for
example as an element of herd behaviour.
Eventually, information bubbles may appear on
asset markets due to asymmetry or misinterpretation
of information [17]. The reason for the formation of
a price bubble is then that asset prices do not
discount all information or there is some
overinterpretation of it. The observation of real
estate markets shows that there are cyclical
fluctuations in the level of prices and the number of
buildings put into use. These fluctuations may be
determined by fundamental factors, but they may
also come as a result of various types of external
shocks [18].
Price bubbles of various character may appear
on many kinds of asset markets; the phenomenon is
not confined to financial markets only. Increasingly,
it also applies to real estate markets. A considerable
significance of the real estate market in the modern
economy becomes an important motivation for a
deeper analysis of the processes taking place on it,
also in this area. A real estate market is subject to
fluctuations in activity and changes in the dynamics
of development, which is a natural phenomenon in
the market economy. The most important
manifestation of its cyclical development is
primarily changes in the price level [19, 20, 21].
Price fluctuations on the real estate market have an
important impact on the way it operates and on the
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related sectors of economy. First of all, they
significantly affect the condition and stability of the
financial sector, the financial results of large groups
of investors, and consequently the economic
situation in the entire economy [22, 23].
2.1 Theoretical Aspects of the Development
of Price Bubbles on the Markets
The phenomena of growing and bursting of price
bubbles on asset markets are becoming a direct
cause of disturbances in the stability of economies.
It is based on at least two reasons. The first is that
excessively high asset prices
According to M. Friedman [24], scientific theories
cannot be judged by the truth of their assumptions,
but by their usefulness for making accurate
predictions. For crisis prevention, it is important to
monitor the market, including price dynamics [23,
25]. Typical instruments for measuring price
changes include analysis of price dynamics and
dispersion, analysis of correlation with incomes
(e.g., households), analysis of the price scissors
opening and fundamental value of the asset, etc. A
relatively new method of assessing the risk related
to price movements is the use of stress tests. For
example, some central banks (such as the National
Bank of Poland) conduct stress tests in the area of
bank resistance to the deterioration of the quality of
housing loans and the decline in real estate prices
[26, 27]. To this end, estimates are made of how a
decline in real estate prices could affect bank
sensitivity to the deterioration of the loan portfolio
quality. The results of the test showed a high
sensitivity of banks to the deterioration of the
quality of housing loans. The usefulness of stress
tests has also been confirmed by independent
researchers [28, 29].
In theory and practice, the methods of price
analysis on the housing market are most
comprehensively developed. Generally known
measures of imbalance on this market include the
following indicators [30]:
the ratio of the average price of one sqm of an
apartment to the average annual household
income (P/I ratio), informing about the number
of years necessary to buy an apartment,
the cost and profit structure of the developer
with regard to the construction of an apartment
on a local market (national, regional),
price dispersion index against a competitive
country or region and price dispersion index
between bear market and bull market,
comparative analysis on the basis of rent, i.e.,
comparison of the market price of the apartment
with its price determined by discounted income
from rent (discounting the stream of future rents
and the resale value of the apartment); a
significant positive difference is a signal about
the expectation of price increases included in
the market price,
analysis of the ratio of the cost of possessing an
apartment to the cost of its rental (P/R ratio),
where the cost of renting may be close to the
amount of rent, and the cost of ownership is
increased by the cost of interest and commission
on the housing loan (renting an apartment as a
substitute for its ownership),
examination of changes in the Case-Shiller
house price index (USA).
However, these indicators may not be reliable
when rental prices and rents begin to rise above
average, along with housing prices rise in the short
term. If apartment sellers make above-average
profits from apartment trading on an annual basis
(or on average over a period of several years), this is
the first sign of a growing price bubble. A separate
issue is the analysis of arbitrage between capital
investment submarkets, e.g., the relationship
between price changes in the market of bank
deposits, gold, securities, and real estate [31].
Predictive analytics is the next step in price
bubble detection, following descriptive and
diagnostic analytics. In the case of descriptive and
diagnostic analytics, it is the historical data that are
primarily used to explain events in the past.
Predictive analytics goes beyond this framework;
i.e., it uses data to predict the future. Thanks to this,
consulting companies or financial directors are able
to base their decisions on real numbers, and not only
on their own intuition [32]. The basic role of
predictive analytics in planning and predicting the
future is indicated, for example, by J. Waupsh [33],
S. Łobejko [34], Lepenioti et al. [35].
Econometric models used in prediction can be
divided by goal:
classification – discrete target (e.g., decision
trees, logistic regression),
approximation – continuous target (e.g., linear
regression, neural networks),
association – co-occurrence of values (e.g.,
Apriori algorithm, associative networks),
segmentation – division into segments (e.g., k-
means algorithm, Kohonen networks).
Unfortunately, most of their component
measures were quite often based on historical data
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and old research. Today, the availability of basic
tools for conducting the prediction process allows
for advanced analytics:
data mart (thematic data warehouse) – a
logically separated range of data stored in the
organisation, focusing on one topic, e.g., sales
support, created to support decisions on this
subject.
data warehouse – a central repository
integrating data collected and produced by
various units of the entity, as a result of various
business processes.
ETL (Extract, Transform, Load) – a process in a
data warehouse responsible for downloading
data from a single source, transforming them to
fit the processes in which they will be applied,
and then loading them into the target database.
data quality verification applications – processes
and techniques related to ensuring the reliability
of data and the efficiency of their use. Data are
of high quality if they reliably reflect the
business processes to which they refer and fit
the intended uses in operation activities,
decision-making and planning.
tools for predicting company bankruptcies (e.g.,
discriminatory, logit, probit, neural networks,
etc.).
Another method that can be useful when
forecasting bubbles is foresight methods. They are
divided into heuristic and econometric, quantitative,
and qualitative (descriptive). Below are some of the
most characteristic of these methods that can be
used in international, national, corporate as well as
regional research. The foresight methodology
includes a number of predictive analytics methods
(Table 1).
Table 1. Foresight methods
No.
Heuristic methods
No.
1
2
3
4
5
6
7
8
9
10
Delphi method
SWOT analysis
PEST Analysis
Prioritization
(brainstorming,
others)
Modified workshop
method
Expert panels,
specialist - modified
Social Consultation
Decision tree
Bayesian model
Others
1
2
3
4
5
6
7
8
9
10
Cross-analysis of
impacts
Key technologies
Neural networks
Others
Source: [32].
A. Czerniak and B. Witkowski [36] presented an
early warning econometric model (EWS) against
price bubbles on the housing market in 18 OECD
developed countries. The explanatory variables for
this model were selected with the use of the
Bayesian Averaging (BMA) method from an
extensive set of potential determinants of price
bubbles on housing markets, i.e., economic,
demographic, institutional and socio-cultural. The
statistics used by the authors show that the EWS
model constructed in this way shows significantly
better prognostic properties than the models used so
far in the literature. This result was obtained due to
econometric modelling variables illustrating the
institutional and socio-cultural conditions of the
functioning of the housing market; they have as a
rule been ignored in research so far.
Simple methods of extrapolation and interpolation
of price trends are hardly useful in forecasting price
bubbles, because they do not account for
extraordinary shock phenomena. Instead, the PEST
factor analysis may be useful for forecasting price
changes, as shown in Table 2.
Table 2. Factor analysis of housing prices - PEST
Demand factors
of price
rise/decline
Impact
+/-
Supply factors of
price rise/decline
Impact
+/-
A. Political and
economic
factors
1. Increase in
potential demand
2. Low price
elasticity of
demand
3. High income
elasticity of
demand
4. High
availability of
credit
5. Low mortgage
loan price
6.Customer
prepayment
flexibility for
developers
7. Low interest
rates on deposits
and gold prices
+
+
+
+
+
+
+
A. Political and
economic factors
1.Government
affordable
housing
programmes
(financing)
2. Increase in
housing supply
3. Monopolistic
practices of
developers
4. Tax reliefs for
construction
materials and
services
5. Costs of legal
regulations
6. Reduction in
the average
apartment area
7. Increase in
exchange rates
-
-
+
-
+
_
+
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(housing as a
substitute for
deposits)
8.Flipping -
increase in
speculative
investments
regarding
housing
9.Tax reliefs for
apartment
purchases
10. Inflation rise
11.Significant
decline in
household
incomes
12. Significant
decline in
employment
13. Increase in
rent and fee costs
B. Social and
technical factor
14. High
sensitivity of
some assets to
speculation
(price
speculation level
coefficient)
15. Contagion
effect; including
increase in
housing prices in
neighboring
countries
16. Susceptibility
to herd behavior
+
+
+
_
_
_
+
+
+
8. Increase in
prices of
materials, raw
materials and
others
9. Deflation
B. Social and
technical factors
10. Location
fashions (e.g.,
mountains)
11. Decrease in
the share of own
work in house
construction
12. Low
availability of
building land
13. Significant
emigration of
young people
14. Cheap
construction
technologies
15. Other
extraordinary
factors
(e.g., influx of
immigrants)
+
-
+
+
+
_
_
+/-
Source: Authors’ own work.
Usefulness of the PEST analysis when forecasting
home prices, as it provides more additional
information than a simple extrapolation of the
current development trends and changes. This, in
turn, may contribute to faster detection of price
bubbles in such a market. The current possibilities
of using modern technologies, machine learning and
artificial intelligence create additional value in this
area.
One of the important channels of contagion of the
crisis on an international scale was the behavioural
channel, creating price bubbles [30, 37]. Distortions
of perception of reality through heuristics and the
so-called mental accounting (the work of Khaneman
and Tversky) when making decisions by investors
and clients must be taken into account in anti-crisis
activities. The basic methods of such research are
demoscopic methods. The lack of broader analyses,
the need to use psychological knowledge, a small
spectrum of socio-psychological tools for
deactivating bubbles and few studies in this area
justify broader research of behavioural factors.
However, it goes beyond the scope of this article
[30].
To recapitulate, no economic model is able to
account for all the events, because, for example,
beyond the horizon of forecasts, there is a terra
incognita of cognition, i.e., what we do not know
that we do not know. A pandemic or another
extreme event that is very unlikely to occur, but
when it occurs, it has a significant impact on reality
(it is referred to as a "black swan"). The
disadvantage of the models used is the risk of
identifying, as a price bubble, those boom periods
on the housing market in which investors did not
succumb to irrational enthusiasm [38]. N. Taleb [39]
observed that the knowledge contained in models
may be meaningless when confronted with an
extreme phenomenon (i.e., a factor of bubble
creation). According to the authors, black swans
may be an increasingly common phenomenon in
world economies. Hence the need for a greater
opportunity to explore the uncertain and volatile
environment of enterprises in order to minimize the
potential negative effects of such non-obvious
events.
3 Problem Solution
The research was conducted in November 2019 on a
random group of 200 managers (CEOs, board
members, CFOs, business owners) from Poland and
Portugal (100 people from each country).
Computer-aided interviews (CAVI) were conducted
in Poland by renowned research company Indicator,
and in Portugal in cooperation with a local partner
company. The ordering, interpretation and
evaluation of the research results were completed by
the authors of this article.
It is very difficult to predict the occurrence of
further economic crises, the crowning example of
which was the subprime credit crunch of 2007/2008;
and recently also typical health phenomena, such as
the coronavirus pandemic. Meanwhile, as history
shows, they have a huge impact on the functioning
of the vast majority of companies. Hence, our first
question was about the lessons resulting from the
experienced crises as well as the consequences
managers see for themselves in connection with
them (Figure 1).
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Fig. 1: Contribution of economic crises (economic,
financial, real estate) to various types of situations.
Source: Authors’ own study.
Portuguese (56%) and Polish (53%) managers
reported that the occurrence of economic crises
(economic, financial, real estate) contributes to the
necessity for changes in restructuring and recovery
procedures. Therefore, it can be seen how many of
them must have experienced difficulties when going
through recent crises (probably local recessionary
phenomena as well) so that now they can see the
need for legislative corrections in this area. More
than a third of the responses (in Poland – 36%, in
Portugal 37%) concerned increased responsibility of
managers for failures emerging in the company
operations. Thus, the problem of the guilt of
company managers for their business failures and
their proper subsequent settlement is conspicuous.
Although 16% of Portuguese and 17% of Polish
managers indicated the opposite direction of action
in favour of limiting the responsibility of managers
for possible failures, exactly due to the existing
crises, i.e., there may be situations that cannot be
predicted and it is difficult to blame the managers
for everything. Such reasoning may be well founded
with regard, for example, to the current pandemic,
where many actions of state authorities had an
adverse impact on the company operations and
increased the risk of bankruptcy. Eventually, a fairly
large group of respondents (26% each in either
country) indicated that the lesson from the existing
crises in the world should result in changes in the
principles of sustainable development. Further
research on specific types of crises and their impact
on changes in managerial behavior definitely
requires a longer research perspective.
Economic crises, changes in people's attitudes,
new trends and phenomena in societies and
economies (e.g., sharing economy) raise a question
whether individual states or organised groups of
states should conduct policies aimed at increasing
regulations and procedures or rather limit and
deregulate economies; should we then expect deeper
and broader actions, such as deactivating
speculative bubbles? The research indicates that
46% of Polish and 36% of Portuguese managers
claim that they are a threat to the activities of any
company. The opposite opinion, i.e., that they do
not affect the activities of all companies, was
expressed by 35% of respondents in Poland and
48% in Portugal. Thus, there is a fundamental
difference between the two countries, Polish
managers are more cautious and feel more respect
for such phenomena than their Portuguese
colleagues. Although the attitude of both groups to
their deactivation is very similar. In Portugal, 37%
of respondents, and in Poland, 36% believe that
speculative bubbles should be quickly and
efficiently deactivated by relevant state institutions
in accordance with the implemented economic and
monetary policies. However, 27% of managers in
Poland think that there is no need for the state to
deactivate them in order not to interfere with the
free market (for comparison, in Portugal there were
17% of such responses).
Fig. 2: Price bubbles in the economy, including the
real estate market.
Source: Authors’ own study.
The attitude of managers to the threats resulting
from the emerging speculative bubbles and the
potential direction of activities in this area shows the
need for the state to be present in this area and to
implement appropriate measures. So, state
institutions can be expected not only to act as free
market guardians, but also firm animators and
initiators of specific actions.
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Fig. 3: Price bubbles – threat and deactivation
determined by the size of company revenues.
Source: Authors’ own study.
The authors also decided to examine in detail
how managers, depending on the revenues obtained
by their companies, approach threats and possible
interventions in the area of price bubbles (Figure 3).
Thus, 48% of Portuguese managers in companies
with revenues above 2 million euros said that price
bubbles do not pose a threat to the company
activities, which is comparable to companies with
revenues below 2 million – 49%. The situation is
slightly different in Poland, where in companies
with revenues up to 2 million euros, according to
44% of managers price bubbles pose a threat to the
company activities (33% indicated otherwise), 40%
of respondents from entities with higher revenues
were of a similar opinion, and 44% of managers did
not consider them a threat to the activities of the
company they manage. These answers are
interesting when we compare them with the
question about the need to deactivate price bubbles
through interventions (made e.g., by the government
or central bank). At that time, both in Poland and
Portugal, regardless of the group of companies with
specific revenue volumes, respondents pointed to
the necessity for the price bubble deactivation.
Strong supporters of deactivation of price bubbles
were managers in companies with revenues above 2
million euros, in Portugal there were as many as
62% of them, while in Poland 59%. It is possible
then to explain no fear on the part of managers of
speculative bubbles, as they expect a reaction from
the government in this respect. This determines the
need to develop tools for early forecasting and
detection of potential price bubbles in the economy,
especially those on the real estate market, the
observation of which seems to be less complex than
the phenomena referred to as black swans
Table 3. Statistical test results - t-test and Chi
squared
Question 1
t-test
p-value
a1
-0,8605
0,3895
a2
-1,7452
0,081
a3
-0,1469
0,8832
a4
0,195
0,849
a5
-0,426
0,670
a6
0
1
Question 2
t–test
p-value
a1
-0,2536
0,7998
a2
-1,0102
0,3124
a3
1,707
0,0878
a4
0,1461
0,8839
a5
-1,8656
0,0621
a6
1,4377
0,1505
Question 3
Chi square
p-value
a1
2,4439
0,655
a2
5,4177
0,247
a3
4,1681
0,384
a4
5,440
0,245
Source: Authors’ own work.
The statistical tests of the obtained results of the
survey of managers in Poland and Portugal showed
no grounds for rejecting the hypothesis about
differences in the answers received (a1, a2, etc.) in
the case of a question about crises (Question 1) or
strictly price bubbles (Question 2). Therefore, it
seems advisable to expand the research sample in
the future to other countries, which on the one hand
may provide valuable new results, but on the other
hand, requires significant financial outlays for such
research. Using the chi-square test for the case of
the data from the third question, it was found that
there was no reason to reject the hypothesis that the
variables were independent. Therefore, the key goal
seems to be to obtain results in this area from other
countries and even continents, as the human factor
may play a decisive role here.
4 Discussion
Our research shows that economic crises may lead
to increased responsibility of managers for potential
business failures, which was confirmed by the
Covid-19 pandemic [40]. In the case of upcoming
crises, it is still very important to study the causes of
price bubbles (especially those on the real estate
market, which are often the main trigger for the
following economic crises). Many determinants of
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Volume 19, 2022
price bubbles and the related processes leading to
crises have not yet been fully explained, and an
appropriate time horizon is also necessary for this
sort of research [41]. Moreover, each case of the
phenomenon of price bubble on the real estate
market seems to be different to a certain extent and
has its own specificity. Therefore, according to the
authors, it is difficult to fully agree with J.
Brzezicka [42], proposing a new typology of price
bubbles, because they are constantly changing and
their causes may be unexpected. The observation of
real estate markets shows that these processes take
place differently in every country as real estate
markets, despite the progressing processes of
globalisation, still largely retain their local and
national character.
It is related to the conducted economic policy,
including housing policy in every country, different
real estate registration systems as well as diversity
of legal systems with regard to real estate. The
degree of development of the banking system and
capital market are also important. And there is also a
question here whether we can study the
phenomenon of price bubbles with one method in
different cities/countries as proposed by S. Oh, H.
Ku, D. Jun, [43], or try to use methods that account
for the local specificity of the market (as the PEST
method suggested by us). Experience also shows
that in the case of the real estate market, we can
generally talk about several groups of basic factors
which, when overlapping each other, cause cycles
and crises on this market [44, 45]. The first of these
is related to the structural feature of the real estate
market, i.e., relatively rapid changes in demand,
which, with a rigid supply reacting with a
significant delay, cause an increase in prices [46].
And this triggers adjustment processes on the
market, which after a certain period of time lead to
increased supply of real estate.
An important problem, however, is that this
supply usually appears when the demand on the
market begins to fade, which means that increased
supply often turns out to be too large. It manifests a
certain specificity of the mechanism of price
bubbles in the real estate market in relation to other
types of markets, such as the financial asset market,
where the process of adjusting demand and supply is
much shorter due to the possibility of a relatively
fast reaction of the supply of this type of asset.
Secondly, speculative factors play an important role
in the level of price fluctuations on the real estate
market, the source of which is the widespread
expectation of a further increase in prices. This
belief motivates speculative purchases, which leads
to increased demand and a growth in prices becomes
a fact. Thus, a self-driven mechanism of price
growth, price expectations and demand may be
launched. It is facilitated by a clearly changing, for
some time now, character of flats as goods,
increasingly perceived as capital goods [47].
Therefore, the share of real estate purchased f9r
investment purposes, also speculative is growing,
both by natural persons and more and more often by
investment funds. The effect of this mechanism is,
as a rule, the detachment of prices from the
economic reality and emergence of a price bubble
on the real estate market. It should be emphasised,
however, that in the case of the real estate market,
there is an extremely important role in the formation
of price bubbles played by the banking system,
which through an excessively liberal way of lending
can finance these dynamic price increases [48].
Here, too, a question arises as to whether our
research results, indicating the expectation on the
part of managers of an active role of the state in
deactivating price bubbles, will be confirmed by the
implemented policies, or whether the aspect of an
early detection of price bubbles will still dominate
[49]. Even more so because, as J.P. Rodrigue [50]
pointed out, speculative bubbles have four phases,
i.e., hidden, conscious, phase of mania and collapse
(although there are, of course, other approaches to
this problem – [51]). In the first of them, there is a
situation in which people who know how to invest
perfectly, begin to buy a hitherto unknown product,
which, nevertheless, has great potential (the hope is
growing) and in the future may bring them a large
profit [50].
Then, along with the demand for it, its price also
increases, and the product becomes more and more
popular (desirable). This situation may occur
especially in countries with significant tourist values
[52]. In the next phase, there is a situation in which
the investor world is already convinced that it is
good to invest in this specific asset. The price of the
asset is definitely higher than in the first phase, but
still attractive and good to invest in. Demand is
growing more and more; the price continues to rise
even more until the first sell-off (bear trap) occurs.
The third phase is the herd behaviour (mania) - in
which even an average saver knows that it is worth
investing in such a product, even if you have to
pledge your own house or excessively borrow.
Consumers go on thinking that the price of the asset
is still attractive, which is deceptive, because a real
profit could be expected by those who decided to
buy in the first phase. Eventually, prices reach their
apogee, and the market quickly approaches a crash -
i.e., the fourth phase occurs. In fact, some time ago,
J. Stiglitz [53] emphasised that the problem of the
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real estate bubble should be approached not as an
accidental market error, but as a consequence of bad
legislation and inappropriate economic signals
spread around the world. Currently, what can
definitely be added to this is the black swan factor,
i.e., the Covid-19 pandemic [54]. The example of
Poland shows that in the first months of 2020, it was
predicted that the following months would present a
range of dynamics of property values between -5%
and +5% (year on year, in transaction prices). It was
to result from, for example, the further development
of flipping in Poland, which has already established
its position and covers a significant part of the
secondary market of apartments in the largest cities.
According to data published by the National
Bank of Poland in the first quarter of 2021, on the
largest housing submarkets, the average transaction
price of an apartment bought from a developer was
7.7% higher than a year before, and in the second
quarter of 2021 this increase shot up even more – up
to 10%. This growth group includes the major
Polish cities: Gdańsk, Gdynia, Kraków, Łódź,
Poznań, Warsaw and Wrocław. In the case of
secondary housing, price increases were lower.
Prices per 1 meter of used premises increased at the
beginning of 2021 by 6.5% more than a year before.
Such an increase in housing prices was affected by
many factors [55]. A large increase in COVID-19
cases in Europe and the protective measures taken
by every country caused negative economic effects
which may also affect the real estate market in the
area of supply as well as prices.
The pandemic, contrary to last year’s fear, did
not overturned the housing market; although the
price increase was clear. In addition, there is still a
high housing demand and the supply is limited. A
significant positive impact on demand was exerted
by very low interest rates, traditions of
thesaurisation of savings in real estate, low
propensity to risk (e.g., to invest in the capital
market), easier credit terms, and also high potential
demand of young families. According to the data of
the key player on the credit information market in
Poland – BIK, the value of housing loans granted in
the first quarter of 2021 amounted to about PLN 26
billion. Importantly, developers could build even
more apartments, but to some extent it is hindered,
apart from constantly rising land prices, prolonged
administrative procedures, no attractive plots put up
for sale, as well as financing barriers. This is
confirmed by the fact that the number of vacant
plots in Warsaw is lower than a few years ago, and
most new investments are made in areas far from
the centre; a quickly developing public transport
network is their advantage. There are also
transactions for the purchase of post-industrial areas
by developers (such as the purchase of 62 hectares
by one of such companies in August 2021. It will be
possible to build up to ten thousand apartments in
the area located closer to the centre, but this kind of
land is hardly available at present. According to the
authors, such large increases in housing prices
already show signs of a price bubble; however, the
pandemic situation with subsequent potential
lockdowns make it difficult to forecast its further
development or deactivation. Importantly, the
symptoms of price bubbles are also noticed on other
markets [52, 56, 57, 58, 59].
There is no turning back from the digital
economy, which is increasingly entering new areas
[60]. Digitization is already ubiquitous on the real
estate market [61], as in the area of housing prices
[62, 63], property value estimation [64,65], and
ending with virtual real estate agents or virtual tours
[66, 67]. Technological challenges, as well as the
issue of quantitative easing (QE) policy recently
conducted by some countries, such as Japan, the
United States or the European Union (which,
according to experts, is contributing to the increase
in housing prices [68]), were not at the heart of our
research. Which does not exclude that the surveyed
managers from our groups did not have such
knowledge in this regard [69], which could make
their answers more thoughtful.
5 Conclusion
Not every imbalance of supply and demand causes a
bubble. Polish experience shows that the greatest
opportunities occur when there is a large opening of
supply and demand scissors, along with the
coincidence of various shock factors. Declining
production is not able to meet a rapidly growing
demand, causing a sharp increase in prices. On most
large markets, changes in both of these parameters
are quite calm and usually parallel. The
identification and measurement of bubbles are the
basis for a possible crisis early warning system,
which should be run by the central bank or a
government forecasting centre. Predictive analytics
and constant monitoring of the housing market can
make it possible to predict and prevent price
bubbles. The demand for information on the housing
market situation, including in particular
macroeconomic imbalances, led to the development
of procedures to detect and warn against price
bubbles, in particular within central banks.
However, can such procedures be sufficient? The
Thucydides Trap, which may refer to Poland and
Portugal (and to many other similar countries)
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whose future depends on EU countries, but also to
other countries, experiencing growths as well as
crises, requires systematic studies on the future with
the use of predictive analytics tools, especially in
the area of speculative bubbles.
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