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Housing Market in Capital Cities - the Case of Poland and Portugal

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The real estate markets in capital cities are considerably different from the other regions of their respective countries. Due to the higher level of investment , business activity and income, increased tourism, and demand for real estate (and the subsequent higher prices), the markets in capital cities are usually more developed and more valuable; however, they describe the degree of development of the national real estate market to a certain extent. The aims of this paper are to conduct a comparative study of the housing prices in Warsaw and Lisbon (the capital cities of Poland and Portugal, respectively) using some macroeconomic variables and to measure the influence of factors including wages , supply of houses, and unemployment on the housing prices of the cities of Warsaw and Lisbon. We analyzed data from the period of 2009 through 2015. The following research methods were used in the research: a literature review, secondary data sources research, and statistical methods (the correlation, simple , and multiple regression techniques). We determined which factors affect the housing prices in the cities of Warsaw and Lisbon and to what extent this occurs.
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GEOMATICS AND ENVIRONMENTAL ENGINEERING • Volume 12 • Number 3 • 2018
http://dx.doi.org/10.7494/geom.2018.12.3.75
Izabela Rącka*, Saira Khalil ur Rehman**
Housing Market in Capital Cities –
the Case of Poland and Portugal
Abstract: The real estate markets in capital cities are considerably dierent from the
other regions of their respective countries. Due to the higher level of invest-
ment, business activity and income, increased tourism, and demand for real
estate (and the subsequent higher prices), the markets in capital cities are usu-
ally more developed and more valuable; however, they describe the degree of
development of the national real estate market to a certain extent. The aims of
this paper are to conduct a comparative study of the housing prices in Warsaw
and Lisbon (the capital cities of Poland and Portugal, respectively) using some
macroeconomic variables and to measure the inuence of factors including wa-
ges, supply of houses, and unemployment on the housing prices of the cities of
Warsaw and Lisbon. We analyzed data from the period of 2009 through 2015.
The following research methods were used in the research: a literature review,
secondary data sources research, and statistical methods (the correlation, sim-
ple, and multiple regression techniques). We determined which factors aect
the housing prices in the cities of Warsaw and Lisbon and to what extent this
occurs.
Keywords: housing, housing market, price dynamics
* The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Kalisz, Poland
** Polytechnic Institute of Bragança, Bragança, Portugal
76 I. Rącka, S. Khalil ur Rehman
1. Introduction
The real estate market is of vital importance for an economy, being one of the
most important markets in the developed economies on provisional rent and invest-
ment levels on the one hand and as a source of guarantee for bank loans on the other
hand.
The real estate market is an interesting eld of research, both at the macro and
micro levels. On the macro level, real estate is closely related to the nancial and
business sector; therefore, the real estate market is an important part of any country’s
economic analysis. It is believed that the integration of real estate markets around
the world is one of the key features of globalization. Also, it has been seen that the
real estate markets are highly correlated during times of crisis.
The real estate market is a dynamic system that is subject to changes taking
place in the environment; it is characterized by its surroundings and functional
bonds. The nancing subsystem includes the availability of mortgage loans for buy-
ers (mortgages) or investment loans for developers, for example [1]. The political
environment consists of various forms of local and governmental authority, polit-
ical risk, constitutional principles, participation in international organizations, and
political climate at the national level. The economy aects the real estate market
through a resource allocation system, interest rates, ination rate, exchange rates,
the industrial and spatial structure of the country, the structure of economic activity,
the organization and development of capital and money markets, market opening
levels, and tax system (reliefs and other investment incentives). The social envi-
ronment is determined by race, culture, social relationships, class structure, and
behavior. The legal environment consists of legal regulations such as rights to real
estate, rules of real estate turnover, spatial development, construction, inheritance,
and taxation. The real estate market is inuenced by the provisions of the constitu-
tion, interpretation of regulations, dierences in the application of law on a regional
basis, and impact of international law (e.g., European Union directives) on national
and local regulations.
An important segment of the real estate market is the residential real estate
market, which generates a large share of turnover. Real estate is important for any
type of business. Land is one of the three basic factors of production. Those proper-
ties that are not directly used for production (such as residential properties) support
production and give people a place where they can live.
2. Location Theory
Based it on the economic theory of land rent, the rst spatial location theory was
authored by J.H. von Thünen [2], who presented a rational model of the intensity of
agricultural land use. Around the city, which is a market, there is an intensive culti-
77
Housing Market in Capital Cities – the Case of Poland and Portugal
vation of vegetables, soft fruit, and milk production in the rst concentric zone (i.e.,
perishable and fresh products). In subsequent zones, the intensity of agricultural
land use decreases and further circles of forest management occur. As the distance
from the city increases, we can notice: forests, extensive cultivation of cereals and
potatoes, animal husbandry, and cereal crops for farmers’ own needs.
Then, M. Weber presented the theory of the optimal location of an industrial
enterprise, distinguishing three basic factors of its location: transport costs, labor
costs, and agglomeration benets. This theory explained the location of heavy
industry based on the exploitation of mineral and energy resources as well as the
development of industrial districts. The criterion of optimal location was to mini-
mize transport costs in relation to the costs of production and consumption.
The theory of the location also includes Christaller’s theory of central centers [3],
which is based on the principle of centrality. It allows us to identify cities that play
the roles of main centers (central markets) in relation to the surrounding spaces. The
areas of impact of individual cities are hierarchically positioned, creating a hexag-
onal model (based on a regular hexagon system). Christaller has identied seven
levels of hierarchical cities: capital, provincial, transregional, regional, district, com-
mune, and trade fair cities.
Most theoretical models of housing location start from the monocentric mod-
el associated with W. Alonso [4] and R.F. Muth [5]. The basic model assumes that
households work in a single location in the city but choose between living in the city
center and out-of-town (access versus space). Since commuting to the city center
involves both time and transport costs, the price of housing and land must fall when
distance from the city center increases.
In economic geography [6–8], aention is paid to the non-economic factors of
the location of economic activities undertaken by people who do not always act
in accordance with rational and complete knowledge about their environment.
Despite the popular opinion on independence from spatial location due to the
progress in the eld of information and communication, it has been shown that
the location of nancial services shows a strong concentration in metropolises.
Metropolises usually are service centers. The largest towns bit by bit abandon
purely productive proles. For this reason, metropolises (especially state capitals)
are dierent from other local markets. They are characterized by dierent terms
of concluding transactions and a higher level of investment that cause a greater
demand for real estate, resulting in a higher price level. The locality of real estate
markets is a key issue when analyzing the market. Residential real estate is much
more related to location than investment real estate, for example. When making
an investment decision, investors often go beyond the local market [9]. The subject
of local real estate markets was dealt with by Lund [10] and Ranci, Brandsen, and
Sabatinelli [11]. The locality of the real estate market has also been conrmed by
research carried out by Dziauddin, Ismail, and Othman [12] as well as Bełej and
Kulesza [13], who demonstrate the diversication of the strength of the inuence
78 I. Rącka, S. Khalil ur Rehman
of signicant factors on the levels of housing prices on the markets of individual
cities. The factors aecting the housing market include the population, age struc-
ture of the population, family situation (number of households), employment,
wage level, income stability, propensity to save, availability of loans, condition
and structure of the housing stock in a given area, rents, vacancy rate, availability
and prices of land for new investments, prices and availability of building mate-
rials, and others. The real estate market remains sensitive to social, demographic,
political, and economic changes in a given area [14]. In the case of capital cities, the
situation on the real estate market is rst of all aected by macroeconomic factors
such as CACs, state investment aractiveness, share of foreign investments in total
investments, aractiveness of the labor market, etc.
In connection with the above, the situations in the housing markets of Warsaw
and Lisbon was analyzed during the period of 2009–2015 in order to identify the
similarities and dierences between the residential property markets in the Polish
and Portuguese capitals as well as the factors aecting the real estate markets in
these cities.
3. Analysis of Residential Real Estate Markets
in Selected Countries
3.1. Housing Market in Poland
The macroeconomic environment of the housing market reects the economic
situation of a country. Similar to other European Union countries, the economic sit-
uation in Poland is assessed by means of the so-called business climate test, whose
results (business climate indices) allow us to determine the business climate cycle
that reects the state of the economy [15]. The globalization of economic processes as
well as the growing role of the ow of capital, goods, and services within the inter-
national system make it impossible to separate the processes on the Polish housing
market from international inuence.
Like in many other countries, the housing market in Poland has gone through
a period of rapid systemic changes connected with the global economic crisis. In
Poland, the dynamic growth of market mechanisms focusing on satisfying the pub-
lic housing started in 1989 along with the transformation of the political system,
which proves the opinion that the housing market is a function of structural and
political factors. Poland started the period of transformation with a relatively low
level of housing in 1991; some 3.6 new housing units (houses or ats) were built per
1000 inhabitants. The main suppliers of ats were housing cooperatives that built
more than 83,000 ats. Most of the ats in multi-family construction were built using
large-panel technologies [16].
The high global competitiveness index of Poland (GCI = 4.5669) can contribute
to the development of its housing market by evaluating the quality of the macroeco-
79
Housing Market in Capital Cities – the Case of Poland and Portugal
nomic environment, condition of the public institutions, and technological level of
a given country. The Index of Economic Freedom (IEF = 68.5) measures the level
of the property rights protection and for Poland it is quite high. [17].
According to preliminary data, 162,727 dwellings were completed in 2016;
i.e., 10.2% more than in 2015, when the number of dwellings increased by 3.2%. Dur-
ing the 12-month period of 2016, the number of dwellings for which building per-
mits were granted or that were registered with a construction project amounted to
211,565; i.e., 12.0% more than in 2015 (against an increase by 20.5% the year before).
The number of dwellings in which construction began also increased to 173,932;
i.e., 3.3% (against an increase by 13.7% the year before) [18]. The pace of changes in
the supply of apartments in Poland was the greatest between 2007 and 2009, when
the number of new apartments oered, new constructions started, and building per-
mits issued reached their peak and boom values in 2007 and 2009, respectively.
One of the main causes of the changes in the housing market was the dramatic
decrease in lending by the banking sector, resulting mainly from the turmoil in the
global nancial market and deterioration of the socio-economic and demographic
situation of the country [19]. During the 12-month period of 2016, there were more
dwellings completed than the year before. The number of dwellings for which per-
mits were granted or that were registered with a construction project as well as the
number of dwellings in which construction began also increased [18].
At the end of 2016, the primary market in Warsaw saw a transaction price at
PLN 7686/m2, while Krakow and Poznan saw prices of PLN 6469 and 6285/m2,
respectively. The secondary market observed the highest transaction price as of
the end of 2016 in Warsaw (PLN 7231/m2), Krakow (PLN 5942/m2), and Wroclaw
(PLN 5480/m2). House prices in Warsaw changed at a very minimal rate from 2012
to 2015. Although the economy of Poland recovered slowly, housing prices contin-
ued to fall. The price declined drastically from 2010 until the end of 2011 because
of the economic downturn, oversupply of housing, and limited supply of credit [20].
The prices increased again during the period of 2012 through 2015.
3.2. Housing Market in Portugal
In terms of square meter prices, Portugal has some of the lowest prices in Europe
for a city center property. Property cap rates and market values dier not only
within the two largest main cities in Portugal (Lisbon and Porto) but also between
them [21]. The recovery of the Portuguese economy depends on the success of scal
consolidation, which means a change in government consumption and expenditure.
Moreover, the downturn of the Portuguese housing stock is not a consequence of the
typical cyclical downturn but is rather the result of a high level of debt that, added
to the increased budget constraints of households and high employment rate, jeop-
ardizes the recovery of the housing market [22].
Between 1985 and 1998, house prices rose by about one per cent a year in Portu-
gal. Between 1999 and 2006, house prices registered zero-annual growth. The housing
80 I. Rącka, S. Khalil ur Rehman
prices grew less than one percent per year on average until the beginning of the nan-
cial crisis in 2007. In Portugal, residential investment had been declining since the end
of the 90s. Between 2007 and 2013, residential investment fell at an annual average rate
of about 12%.
In the Portuguese case, the unit price is obtained from bank appraisals through
Statistics Portugal and the Bank of Portugal, which are collected through a month-
ly survey, gathering information on homes that are objects of bank nancing. In the
third quarter of 2016, the House Price Index (HPI) in Portugal increased by 7.6%
when compared to the same period of 2015 (6.3% in the previous quarter). This
was the highest price increase ever observed and the third consecutive quarter in
which the HPI recorded an annual rate of change above 6%. In the third quarter
of 2016, the house sales indicator totaled 31,535 transactions, 26,341 of which were
purchases of existing dwellings, with this last gure representing a new maxi-
mum in the available series. During this period, the value of transacted dwellings
exceeded 3.6 billion euros, which represents a 17.6% increase when compared with
the same quarter of the previous year.
The construction industry in Portugal has been called the engine of the
economy, and the housing market is a non-negligible portion of the construction
industry. Construction can represent the main driver behind economic recovery,
as the crisis in the economy is directly linked to the crisis in the construction
industry.
The housing market in Lisbon had been in depression since 2010; however, it
started to recover in 2013. Housing prices fell continuously from the end of 2010
until the third quarter 2013, with an accumulated drop of 14%. Since then pric-
es had been rising. After the recession, house prices had some recovery in 2009
and then started to fall again in the last quarter of 2010. But then again, at the
end of 2013 and throughout 2014, there was a visible recovery in this sector, after
three consecutive years of house price declines. If ination was considered, price
declines would be larger. This gives a more meaningful guide to how house prices
have increased compared to typical prices in the economy.
3.3. Comparison of Housing Market in Lisbon and Warsaw
We compared the housing markets in both capital cities, summarizing the
results in Table 1.
Table 1. Comparison of Warsaw and Lisbon housing markets
City 2009 2010 2011 2012 2013 2014 2015
Housing prices [EUR]
Warsaw* 1749 1973 1834 1627 1704 1755 1788
Lisbon 1412 1425 1355 1232 1198 1205 1255
81
Housing Market in Capital Cities – the Case of Poland and Portugal
Change in housing prices [%]
Warsaw 4−4 −10 532 4
Lisbon 1−5 − −3 141
Real GDP growth rate
Warsaw 2.8 3.6 5.0 1.6 1.3 3.3 3.6
Lisbon −3.0 1.9 −1.8 −4.0 −1.1 0.9 1.5
Housing development intensity
Warsaw Poland is listed in the countries with above average housing develop-
ment intensity
Lisbon The residential market in Portugal appear to be rather saturated as evi-
denced by the relatively low housing development intensity
Aordability
Warsaw Housing in Poland falls into a less aordable category
Lisbon Relatively aordable housing can be found in Portugal
New dwelling price
Warsaw Average size of new dwelling in Poland for EUR 200,000 is 181 m2**
Lisbon Average size of new dwelling in Portugal for EUR 200,000 is 194 m2
*
The data collected for housing prices in Warsaw was in PLN; but, if we convert it to euros, it
can be noted that the prices per square meter are higher in Warsaw than in Lisbon.
** [23].
4. Methods and Models
A quantitative approach with secondary sources of data has been used in this
study, and all of the data has been collected from dierent national databases and
websites. The databases used to collect the data for Poland are Narodowy Bank Pol-
ski (National Bank of Poland, NBP) and the Polish Local Data Bank (BDL). Most
of the data was collected on a yearly basis from 2009 to 2015. The house prices for
Poland have been collected from NBP (Poland). The NBP database is created by the
voluntary sharing of data by real estate agents and the developers of NBP, whereas
the data about housing prices in Lisbon is based on bank appraisals (nominal pric-
es). The other variables used in this study are ination, annual wages, population,
unemployed population, and number of dwellings in the respective cities.
The variables used in this study are mentioned in Table 2 along with a short
description of each variable. All explanatory variables were presented as mean or
relative values.
Table 1. cont.
82 I. Rącka, S. Khalil ur Rehman
Table 2. Description of data
Variable Description
Ination General prices increase of goods and services
Income Average monthly net income and salaries of households
Unemployment Registered unemployed persons per population [%]
Number of houses Number of dwellings in residential and non-residential buil-
dings per population
Average unit house price Dependent variable
A strong correlation between the average unit house price and only three of
the four dependent variables can be observed. For this reason, the outlying varia-
ble (which is the ination rate) was not accepted as an explanatory variable in the
model.
Regression models for two countries were built, and the results for Poland are
shown in Table 3. In case of three independent variables, the model explain quite
well (coecient of determination R2 69%) the changes in housing prices. Next, we
have examined the adjusted R2, which considers missing data, deleting, adding data,
and adding or removing independent factors. The adjusted R2 is only 38%. When the
rst multiple regression was conducted with the three independent variables, the
slopes of the regression did not depict their expected signs. For example, note that
income had a negative slope when it in fact should have had a positive one; that is,
when the income increases, house prices should also increase. The same applies to
the no. of houses, which is positively related to the dependent variable but should
have been negative. The reason for the incorrect slopes could be explained by the
high multicollinearity of the test. The results show the Signicance F value and the
p-values are larger than a signicance level of 0.05, which means that the model is
not reliable.
More regressions were conducted with dierent combinations of the chosen
variables, and none of the models were reliable; this means that the chosen inde-
pendent variables are not signicant enough to determine the housing prices in
Warsaw.
Table 3. Regression statistics, dependent variable – house price
Regression
statistics Three variables
Multiple R0.831
R20.690
Adjusted R20.380
Standard error 3.568
Observations 7
83
Housing Market in Capital Cities – the Case of Poland and Portugal
Anova df SS MS F Signicance F
Regression 3 85.031 28.344 2.226 0.264
Residual 3 38.197 12.732
Total 6123.229 –
Characteristics Coe-
cients Standard
error t stat. p-value Lower
95% Upper
95%
Intercept −8.117 93.062 −0.087 0.936 −304.290 288.047
Net income −0.667 0.501 −1.333 0.275 −2.261 0.926
No. of houses 1.921 1.375 1.397 0.257 −2.455 6.298
Unemployment −0.176 0.079 −2.239 0.111 −0.427 0.074
The results for Portugal are shown in Table 4. Coecient of determination R2 in the
case of three independent variables explains 95% of the variation in the housing prices.
Next, we have examined the R2 with adjusted R2, which considers missing data, deleting,
adding data, and adding or removing independent factors. The adjusted R2 is 90%; this
shows that 5% of the data is lost with the use of these factors. The slopes of all of the var-
iables in this regression had the correct signs. The results show the signicance F value
is smaller than a signicance level of 0.05, which means that the model is t. However,
the p-values of the variables are not reliable, which can be due to the multicollineari-
ty between the independent variables. It is common that, when conducting a multiple
regression analysis, some of the independent variables are correlated with each other.
Table 4. Regression statistics, dependent variable – house price
Regression
statistics Three variables
Multiple R0.975
R20.950
Adjusted R20.901
Standard error 2.183
Observations 7
Anova df SS MS F Signicance F
Regression 3 273.246 91.082 19.104 0.019
Residual 3 14.303 4.768
Total 6287.549 –
Characteristics Coe-
cients Standard
error t stat. p-value Lower
95% Upper
95%
Intercept 602.278 361.588 1.666 0.194 −548.455 1753.012
Net income 0.690 0.560 1.231 0.306 −1.093 2.472
No. of houses −5.649 3.776 −1.496 0.231 −17.665 6.366
Unemployment −0.047 0.162 −0.290 0.790 −0.564 0.469
Table 3. cont.
84 I. Rącka, S. Khalil ur Rehman
Regression
statistics One variable
Multiple R0.941
R20.885
Adjusted R20.861
Standard error 2.577
Observations 7
Anova df SS MS F Signicance F
Regression 1254.354 254.354 38.312 0.002
Residual 533.195 6.639
Total 6287.549 –
Characteristics Coe-
cients Standard
error t stat. p-value Lower
95% Upper 95%
Intercept 1126.319 167.125 6.739 0.001 696.711 1555.927
No. of houses/
population −10.252 1.656 −6.190 0.002 −14.510 −5.994
When the purpose of a multivariable regression analysis is to explain the indi-
vidual eects of the predictors on an outcome variable, it is important that a poten-
tial multicollinearity between the predictors be investigated and the correlated
variables (which convey the same information) should be removed. Due to the mul-
ticollinearity between the variables, more regressions were conducted using dier-
ent combinations of independent variables as well as using each independent varia-
ble individually. Table 4 also shows the results of the regression analysis using only
the number of houses.
The results indicated that the Signicance F value was reduced to 0.002 and
the p-value was also reliable, which means that, out of all three of the independent
variables used in this study, the number of houses is the most signicant one. The
slope of the variable is also theoretically correct. The results show that, if the number
of houses increases by one unit, housing prices decrease by 10.252 units. The com-
puted R2 was 88.5%. Adjusted for its degrees of freedom (adjusted R2), it remained
very high (86.1%).
5. Conclusions
For most people around the world, their home is the primary investment they
make, so understanding the residential market is far more important than any other
kind of real estate market. The demand for houses is directly connected to variables
Table 4. cont.
85
Housing Market in Capital Cities – the Case of Poland and Portugal
such as income and unemployment, which then inuence the prices. The house pric-
es are also aected by the quantity of newly constructed houses and their respective
building costs.
The purpose of this study was to compare the housing prices in Warsaw and
Lisbon during the period of 2009–2015 and determine which of the three selected mac-
roeconomic variables play a signicant role in aecting housing prices in both cities.
We investigated the following factors: wages, number of houses/population,
and unemployment/population, and our ndings conrm that these chosen mac-
ro factors indeed inuence housing prices. An increase in wages appears to have
a positive eect on housing prices, while increases in the number of houses and the
unemployed population have a negative inuence on the prices. The results of our
simple regressions revealed that there were some similarities and some dierences
regarding how each of the chosen factors inuenced the housing prices in both cities.
Our regression models helped us identify which factors were more signicant
in aecting the housing factors in both cities. After multiple regressions, we nally
managed to choose one factor (namely, the no. of houses) that was considered the
most inuential factor in determining the prices in Lisbon. Meanwhile, the regres-
sion model appeared to be invalid in the case of Warsaw, which means that none of
the chosen factors apparently inuence housing prices in Warsaw.
In conclusion, this study helped us develop a model to determine how and to
what extent the chosen macro factors aect housing prices in Lisbon. However, in
the case of Warsaw, the model is not reliable, so more research should be done to
construct a reliable one.
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Housing Market in Capital Cities – the Case of Poland and Portugal
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Rynek mieszkaniowy w stolicach państw –
przykład Polski i Portugalii
Streszczenie: Rynki nieruchomości w stolicach państw znacznie różnią się od tych w innych
regionach krajów. Rynki w stolicach ze względu na wyższy poziom inwe-
stycji, działalności gospodarczej i dochodów, wzrost turystyki i popyt na nie-
ruchomości, a w konsekwencji wyższe ceny – są zazwyczaj bardziej rozwinięte
i wartościowe, ale w pewnym stopniu decydują o poziomie rozwoju krajowego
rynku nieruchomości. Celem artykułu jest przeprowadzenie analizy porównaw-
czej cen mieszkań w Warszawie i Lizbonie, stolicach Polski i Portugalii, z wyko-
rzystaniem niektórych zmiennych makroekonomicznych oraz pomiar wpływu
czynników, w tym płac, podaży domów i bezrobocia, na ceny mieszkań w obu
miastach. Analizą objęto lata 2009–2015. W badaniach wykorzystano następujące
metody badawcze: przegląd literatury, badania źródeł wtórnych danych, meto-
dy statystyczne (korelacja, regresja prosta i wielokrotna). Ustalono, które czynni-
ki i w jakim stopniu wpływają na ceny mieszkań w Warszawie i Lizbonie.
Słowa
kluczowe: mieszkalnictwo, rynek nieruchomości mieszkaniowych, zmiany cen
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