Januário, João Fragoso; Cruz, Carlos Oliveira
Article
The impact of the 2008 financial crisis on Lisbon's housing
prices
Journal of Risk and Financial Management
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Suggested Citation: Januário, João Fragoso; Cruz, Carlos Oliveira (2023) : The impact of the 2008
financial crisis on Lisbon's housing prices, Journal of Risk and Financial Management, ISSN
1911-8074, MDPI, Basel, Vol. 16, Iss. 1, pp. 1-18,
https://doi.org/10.3390/jrfm16010046
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Citation: Januário, João Fragoso, and
Carlos Oliveira Cruz. 2023. The
Impact of the 2008 Financial Crisis on
Lisbon’s Housing Prices. Journal of
Risk and Financial Management 16: 46.
https://doi.org/10.3390/jrfm
16010046
Academic Editor:
Rafael González-Val
Received: 12 December 2022
Revised: 3 January 2023
Accepted: 5 January 2023
Published: 12 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Journal of
Risk and Financial
Management
Article
The Impact of the 2008 Financial Crisis on Lisbon’s
Housing Prices
João Fragoso Januário and Carlos Oliveira Cruz *
CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
* Correspondence: [email protected]
Abstract:
Real estate markets are frequently affected by growth and contraction cycles. Given the
social and economic impacts of changes on real estate prices, the understanding of these cycles is
crucial from a socio-economic perspective, but also, and more importantly, from a public policy
view. The literature has provided several contributions focusing on the deconstruction of the main
determinants of housing prices. This research focuses on the analysis of housing prices variation
with a particular emphasis on the analysis of the impacts of the 2008 financial crisis. Within the
existing body of knowledge, few studies have focused on this particular issue, and even fewer have
focused on countries where the financial crisis led to an external bailout, as was the case in Portugal.
The analysis confirmed that the 2008 financial crisis had a negative impact on real estate prices, and
the ex-post growth in GDP and low interest rates had a positive impact. The paper also provides a
long-term analysis of housing price trends over the last decades.
Keywords: economic cycles; financial crisis; housing; real estate
1. Introduction
The real estate market has traditionally been subject to cycles of boom and bust, led
by speculative behavior, construction booms, financial crisis or external economic events
(Shiller 2007). Over recent decades, several authors have studied the main determinants of
real estate prices, motivated by the fundamental importance of understanding real estate
prices, their dynamics and driving factors. The vulnerability of the real estate market to
economic cycles assumes a significant relevance, given the social and economic impacts of
real estate prices on both corporate firms and individuals. In fact, in most countries, real
estate is the most important source of financial saving, and its (in)stability is critical for the
financial balance of households.
The 2007–2008 subprime crisis, which started in the US, resulted from an excessive
purchase of real estate properties by individuals led by easy access to credit and leveraged
by high levels of debt. The willingness to buy property, not just for a housing function, but
also as a way to make a quick profit, led to a peak in property prices. In total, in the years
prior to the crisis, over a trillion dollars was channeled into the US subprime mortgage
market. The crash of the housing bubble that followed had worldwide global consequences,
impacting real estate markets across the globe. The lack of quality in the Mortgage-Backed
Securities (MBS) products held by financial institutions, and their overall exposure to the
real estate market, led to a financial crisis spreading all across the globe (Reinhart and
Rogoff 2008; Valadez 2011). As discussed in this paper, the financial crisis severely affected
the fragile Portuguese economy, and, particularly, public finances, leading the Government
to require external financial assistance.
What was the impact of the financial crisis on real estate prices in Lisbon? This is the
starting point for this research. Building upon existing literature, presented in detail in the
next section, the authors expected to find a negative impact, i.e., the occurrence of the crisis
would have led, directly and indirectly, to a decrease of prices. The paper also provides
J. Risk Financial Manag. 2023, 16, 46. https://doi.org/10.3390/jrfm16010046 https://www.mdpi.com/journal/jrfm
J. Risk Financial Manag. 2023, 16, 46 2 of 18
a historical overview on the variations of real estate prices in Lisbon, providing some
economic and social contexts to help read the data and trends identified by the authors.
Furthermore, the authors also controlled for other economic, demographic and social
variables, to grasp the main determinants of real estate prices so as to conclude on how
the determinants (and the corresponding impacts) compared with the existing body of
knowledge.
As expected, the research concluded that it was possible to find a correlation between
GDP growth in the ex-post period of the crisis, and an increase in real estate prices. Low
interest rates also seemed to play a role in the increase of real estate prices. On the other
hand, the number of overnights, used as a proxy for touristic volumes, had a negative
impact. The paper provides a detailed discussion of these effects.
This paper is organized as follows: after this introduction, the authors present an
extensive literature review focusing on the main determinants of housing prices, with a
particular emphasis on economic determinants, and the main findings provided by the
literature; Section 3 presents some contextual background on the dynamics of the market;
Section 4 contains the data and methodology; Section 5 presents the results and discussion;
and, finally, Section 6 presents the main conclusions.
2. Literature Review
Several papers identified macroeconomic variables as determinants of housing prices,
such as the following: population and household income (Duca et al. 2010; Xu 2017),
housing supply (Glaeser et al. 2008; Grimes and Aitken 2010; Caldera and Johansson 2013;
Glaeser and Gyourko 2018), GDP (Rodrigues and Lourenço 2017; Ismail and Nayan 2019),
unemployment rates (Ismail and Nayan 2019), interest rates (Goodhart and Hofmann 2008;
Duca et al. 2010; Xu 2017; Ismail and Nayan 2019; Afxentiou et al. 2022) and tourism
(Garcia-López et al. 2020; Cocola-Gant and Gago 2021; Yang et al. 2023). Nevertheless, it is
noteworthy that, despite some commonalities, their effects on the prices of residential real
estate differed depending on the observed environment. In fact, the full list of economic
variables impacting housing prices may be hard to find, given the complexity of the
economy, as stated by Grum and Govekar (2016). In the next subsection, the authors
provide a detailed discussion of the contributions found in the literature regarding each
main determinant.
2.1. Population and Household Income
Leamer (2007) stated that the housing market is a result of a consumer cycle, not of
a business cycle. Hence, the consumer plays a significant role in housing dynamics. The
study of the population and its financial context is, therefore, crucial in understanding the
market cycles.
The importance of household income in the real estate market has justified numerous
studies of price-to-income ratios as proxies for the affordability and overall market “health”
(Leamer 2007; Mostafa et al. 2006; Yates 2008; Meen 2018). Sharp increases in median
price-to-income ratios usually signal an unsupported increase in valuations, lacking the
fundamentals, and, thus, creating conditions for a higher probability of housing bubbles.
Duca et al. (2010) pointed out that, in Spain, housing prices during the 2000s were
strongly propelled by household income growth. Xu (2017) studied the Chinese housing
market and concluded that increases in household income would lead to an increase in
demand for housing and, thus, to an increase in housing prices. Additionally, the author
argues that this increase in prices would promote speculative behavior, increasing the risk
of a housing bubble, and causing a vicious circle.
A higher household’ income can also have secondary effects on valuation. As shown by
DiPasquale (1999), higher-income homeowners are more likely to improve their property,
which further increases their property’s price and future sale value. Nevertheless, the
author also noted that, as income rises, homeowners were more likely to move to a new
house than to improve their current unit.
J. Risk Financial Manag. 2023, 16, 46 3 of 18
It is possible to argue that there is more to population and household income than
what is suggested by the traditional economic theory, based on the rational “homo eco-
nomicus”. For example, Shiller (2007) pointed out that people’s vague expectations for the
future, which are constantly changing based on their experience and as new information is
provided, may largely influence their long-term decisions, like buying a house. Fears of war
or terrorism, for instance, may influence their buying decisions and, thus, housing prices.
Additionally, if people perceive prices to continue to go up this may lead to a “psychological
expectations coordination” further pushing the momentum of increases in home prices. Only
a housing supply response tends to bring prices down. However, one should note the
existing lag between the increase in demand and the response on the supply side. This may
lead to a prolonged period of increasing prices. Furthermore, the distribution of housing
shocks may be unevenly distributed across the territory. For example, Li and Wei (2020)
found that the value of housing enjoying proximity to green spaces, jobs and good public
schools was more resilient compared with a market lacking these amenities. Therefore, the
market’s volatility may be amplified, due to uneven spatial distribution of physical and
service amenities or residential segregation.
The current economic conditions, which have resulted from the COVID-19 pandemic
and war in Ukraine, have also increased the financial stress on household incomes. The
increasing costs of energy add to the rest of the inflating costs (e.g., food, transportation),
leaving households with tighter budgets and fewer chances to increase their consumption
in the housing market (Cermáková and Hromada 2022). Furthermore, the recent interest
rate hike has also decreased accessibility to the housing market (Venhoda 2022). This is
especially concerning for single-parent and younger households, considering that, in many
cities across Europe, these groups have already been priced out of most real estate markets
due to soaring housing prices in recent years (McKee 2012; Hromada and Cermakova 2021).
2.2. Housing Supply
The housing market, as with any other market, follows the laws of supply and demand.
Therefore, housing supply is of utmost important for price determination. For a given
constant demand, the lower the volume of supply, the higher the price one could expect for
a given property. The inverse relationship also holds true: the higher the supply available
in the market, the lower the price of each individual unit. According to Glaeser et al. (2008),
the responsiveness of the market to housing price changes also influences future prices. In
supply-constrained markets, the adjustment occurs primarily in the price of housing rather
than in expanding housing supply, while markets with a higher supply responsiveness
tend to have smaller price rises following demand shocks and are less prone to “bubbles”
in housing prices. In summary, the more inelastic the supply side, the more susceptible the
market is to price increases and housing “bubbles”. Nevertheless, the authors warn that
the oversupply of housing could also be a problem, leading to larger welfare losses at the
end of boom–bust cycles.
Grimes and Aitken (2010) also noted that increases in housing supply, relative to
population, reduce the long-run impact of upward shifts in demand. However, the authors
added a land value dimension to their analysis, concluding that the responsiveness of
housing supply is closely linked to land elasticity. Shortage of available land for construc-
tion leads to higher prices and lower elasticity in housing supply. This is especially the
case where land values immediately reflect increases in prior housing prices, leading to
diminishing developers’ returns and less supply of new housing.
Caldera and Johansson (2013) studied the responsiveness of housing supply to price
changes in 21 OECD countries and concluded that the market response varied substantially
across countries, depending on national geographical and urban characteristics, land
use policies and planning regulations. The authors claimed that North America and
some Nordic countries had higher supply elasticity than countries like Switzerland, the
Netherlands and Austria. This meant that the former countries were less susceptible to the
creation of housing “bubbles” on a supply–demand basis, as housing investment adjusted
J. Risk Financial Manag. 2023, 16, 46 4 of 18
more rapidly to significant changes in demand. However, this rapid adaptation led to
more cyclical swings in economic growth derived from the housing market. According to
the authors, the elasticity of a market could be addressed through policy reforms, notably
in the areas of housing regulations and taxation. Efficient licensing processes were also
considered crucial to address housing supply responsiveness issues.
According to Glaeser and Gyourko (2018), housing supply even impacted population
growth with lower housing supply levels leading to higher prices and less population
growth relative to demand.
Despite the proven relationship between housing supply and demand, we can argue
that there is more to it than just matching numbers of overall supply and demand. The
quality of supply and the housing features demanded are also at play. Thus, an overall
large number of available houses does not necessarily mean that the overall market reflects
this in the pricing of all properties, given that vacant properties may not be the ones desired
to meet demand.
2.3. Gross Domestic Product (GDP)
The relationship between housing prices and GDP growth has long been studied by
scholars (Quigley 1999; Case et al. 2005; Leamer 2007; Valadez 2011; San Ong 2013; Jaeho
and Joohyung 2014; Xu 2017). Leamer (2007) even considered residential investment offers
as “by far the best early warning sign of an oncoming recession”. Vigna and Ferrara (2009)
also found a strong correlation between housing prices and GDP in France, suggesting
housing values were an important factor when forecasting GDP growth and business cycles,
strengthening the empirical evidence on the strong relationship between housing and GDP.
Xu (2017) found that GDP growth influenced building values and, thus, housing prices. Is-
mail and Nayan (2019) studied the interaction between housing prices and macroeconomic
variables in East Asian countries
1
and found GDP, interest rates, unemployment rate and
stock price indices to be among the most significant determinants. Jaeho and Joohyung
(2014) studied the housing markets in G7 countries (U.S., U.K., Canada, Germany, France,
Italy, and Japan) and found a procyclical relationship between housing prices and real
growth of output (GDP), even during shock periods and in post-shock periods, such as the
oil shocks during the 70s, 80s and 90s.
Memisevic and Jalloul (2022) studied the impacts of the 2008 and Covid crisis on
the Swedish housing market and found that the real GDP and unemployment were both
significant and affected housing prices in the short run.
2.4. Unemployment Rate
The unemployment rate also plays a significant role in housing prices. Grum and
Govekar (2016) studied real estate markets in Slovenia, Greece, France, Poland and Norway
and found statistically significant impacts of unemployment on housing prices. Agnew
and Lyons (2018) analyzed the impact of employment on housing prices in Ireland and
concluded that job creation (or destruction) was associated with increase (decrease) in rent
and sale prices, though the effects varied by economic sector. According to their findings,
1–2 years after the creation of 1000 jobs, nearby house rents had risen by 0.5–1%, while
sale prices rose by at least 2%. However, it should be noted that there was a negative
relationship between the distance to employment centers and housing prices, with the
latter decreasing as distance to firms increased. The authors also concluded that, while
rental prices reacted to any employment activity, sale prices reacted only to employment
changes in the presence of medium or large start-ups and shutdowns.
Much of this relationship can be explained by the increase/decrease in household
income relatively to the proximity/distance to productivity hubs. As explained by the
Phillips curve (Phillips 1958; Phelps 1967), there is a strong negative relationship between
the money wage rates, the level of unemployment and the rate of change of unemploy-
ment. This means that a lower level of unemployment leads to higher wage inflation and,
consequentially, to overall inflation in the economy. We can argue that this phenomenon
J. Risk Financial Manag. 2023, 16, 46 5 of 18
affects the market in two ways: (1) the overall inflation affects labor and material costs
for construction, naturally leading to higher valuation of newly developed properties and,
therefore, of overall properties; (2) wage increase resulting from a higher employment rate
leads to an increase in demand for property, further increasing valuations.
2.5. Interest Rates
Interest rates are also considered to play a significant role in setting housing prices.
Seyfried (2010) studied the relationship between interest rates (through Taylor’s rule
(Taylor 1993)) and housing prices in countries that experienced housing bubbles, such as
Ireland, Spain, the United Kingdom and the United States. The author concluded that
a loose monetary policy by Central Banks, which translated to lower interest rates, had
a significant impact on housing bubbles. According to his results, a tighter monetary
policy by Central Banks, following Taylor’s rule, would have reduced the growth rate of
housing prices by 38% in Spain, 50% in the United States and 57% in Ireland. Goodhart
and Hofmann (2008) studied the markets of 17 industrialized countries, from 1970 to 2006,
and found a “significant multidirectional link between house prices, broad money, private credit
and the macroeconomy”. They also pointed out that the relationship between these variables
appeared to be stronger in recent years, possibly as a consequence of the liberalization of
financial markets during the 1970s and 1980s, highlighting the importance of monetary
policy in the stability of housing markets. Some authors even argue that a weakening of
credit standards, which allows one per cent more of the population to have access to credit,
would boost the demand for housing by 20% (Duca et al. 2010). However, this may induce
over-optimism about the downside risks of nonprime loans, as was the case in the US
housing bubble.
Xu (2017) found that interest rates affected both supply and demand for houses. Real
estate market booms were usually followed by an increase in interest rates, which reduced
investment in real estate to a certain extent, due to an increase in investment costs.
2.6. Tourism
The relationship between tourism and housing prices was subjected to analysis by
several scholars in recent years (Füller and Michel 2014; Schäfer and Braun 2016; Blanco-
Romero et al. 2018; Garcia-López et al. 2020; Cocola-Gant and Gago 2021; Yang et al. 2023).
Several capital cities have been flooded by tourism leading to a rapid increase in short-term
rentals, especially in city center areas, driving up affordability issues for local populations
(Schäfer and Braun 2016; Mikuli´c et al. 2021). Wu et al. (2021) stated that there were two
possible arguments for tourism driving up housing prices: (1) lack of accommodation
facilities for the number of tourists may increase the demand for limited accommodation,
increasing housing prices; consequently, the higher demand further increases pressure in
the market, and (2) some wealthy tourists may try to purchase their own houses in their
preferred destination city or country, therefore increasing prices.
Biagi et al. (2015) analyzed the Italian case, from 1996 to 2007, across 103 cities, and
concluded that although tourism presents an opportunity for local economies to grow,
it also drives housing prices up, leading to serious social effects in terms of affordabil-
ity, displacement, and gentrification, though the effects varied across locations. It was
also noteworthy that a composite tourism index, accounting for the number of tourism
accommodations, number of second homes, museums’ revenues and total nights of stay of
tourists, was found to be more significant to the model of housing prices than each of its
individual components.
Mikuli´c et al. (2021) studied the influence of tourism in various Croatian municipalities
and found lower affordability to be associated with a higher share of rental housing within
the total housing stock, in addition to higher tourist concentrations and vulnerability to
tourism. Cities with a higher percentage of private rentals also had more inelastic supply
prices than those holding more collective accommodation facilities. Furthermore, the
authors found that it was not the locational concentration of tourism but its seasonality
J. Risk Financial Manag. 2023, 16, 46 6 of 18
which primarily drive housing prices up leading to affordability issues for local inhabitants.
This happened due to the inelastic supply of housing, leading the market to be leveled by
peak summer month prices. High levels of seasonality
2
were also detrimental to housing
affordability, due to employment rates and economic fluctuations.
Yang et al. (2023) analyzed the housing market in G7 countries and found that the
relationship between tourism and housing prices was nonlinear and dependent on the
environment. The author also concluded that the impact depended on the economic
development phase of the country and that. in certain conditions, it might even have a
negative contribution to housing prices.
In the next section, we analyze the case of the city of Lisbon under the recent historical
context of the previous five components identified as significantly impactful in housing
prices.
3. Lisbon’s Case
3.1. The Years Prior to the Crisis
Before addressing the impacts of the 2008 crisis, it is important to understand the
context in which it arrived. The Portuguese reality has been characterized, especially
during the last century, by a high preference for home ownership over private rentals, due
to cultural factors and public policies incentivizing the former (Braga 2013; Azevedo 2020).
The rent cap
3
in the cities of Lisbon and Porto imposed by Law 2030 of 1948, discouraged
investors in the rental market in the two largest cities in the country leading to an overall
degradation of its building stock (particularly in the city’s center) and to a decrease in the
private rental market. In 1984, Portugal started the liberalization of its financial system,
allowing the creation of new private banks (Santos et al. 2014). Until this point, and since
the 1974 revolution, most banks were nationalized, offering interest rates fixed by the
government, with strict policies on access to the credit market. In 1986, Portugal acceded to
the European Economic Community and, in the years that followed, expanded its financial
system. Access to the EEC’s funds and the lessened restrictions on credit allowed many
families to fulfil their long-awaited wishes of buying a property, which, over the years,
became synonymous with adulthood and success. The creation of subsidized special
credit conditions for young adults
4
, and the high inflation felt in the country during the
70s and 80s
5
, also increased demand for home ownership. For example, in 1981, 82%
of Lisbon’s inhabitants were renters, while in 2001 that number had decreased to 52%
(Câmara Municipal de Lisboa (CML) 2022). This increase in demand led to an increase in
new construction all over Lisbon’s Metropolitan Area (LMA), reinforcing the trend started
in the 1970s, as shown in Figure 1. This trend adopted an upward slope until the country
was hit by the financial crisis.
However, it is interesting to note that, after the 2000s, this trend was mainly driven
by Lisbon’s neighboring municipalities, as the tendency in the city of Lisbon saw a sharp
decline in total number of yearly concluded new dwellings in the 2000s, reaching its peak
in 2003 (2870 new dwellings) and plummeting by 98% to only 37 new dwellings in 2006
(Figure 2). This may have come about, in part, as the consequence of the cessation of public
fund grants to house purchasing in 2002 (Santos et al. 2014).
The decrease in the total number of households living in the city of Lisbon (Figure 1)
illustrated this dynamic, with many families opting to live in the nearby municipalities,
with newer and more affordable properties. This was also justified by the macroeconomic
context. After the 2000s, the Portuguese economy entered a period of lower growth in
real GDP, creating additional difficulties for families to cope with housing costs (Figure 3).
However, the willingness to acquire property had not vanished. Since the early 1990s
real estate developers and brokers started to price properties based on the indebtedness
capacity of Portuguese families, which fueled valuation increases (Braga 2013). This was
well represented by the mean value of traded real estate in Lisbon, which rose from 137,755
€
per property in the year 2000 to 321,697
€
in 2007, a 133.52% increase in just seven years. The
indebtedness levels rose accordingly: in 1995, household debt represented 35% of income
J. Risk Financial Manag. 2023, 16, 46 7 of 18
reaching a peak of 130% in 2009
6
. According to Santos et al. (2014) this rise in indebtedness
was easily explained by the increase in home loan values.
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 7 of 19
Figure 1. Number of dwellings and households in the city of Lisbon. Source (CML 2022).
However, it is interesting to note that, after the 2000s, this trend was mainly driven
by Lisbon’s neighboring municipalities, as the tendency in the city of Lisbon saw a sharp
decline in total number of yearly concluded new dwellings in the 2000s, reaching its peak
in 2003 (2870 new dwellings) and plummeting by 98% to only 37 new dwellings in 2006
(Figure 2). This may have come about, in part, as the consequence of the cessation of public
fund grants to house purchasing in 2002 (Santos et al. 2014).
Figure 2. Total number of completed dwellings in new constructions for family housing in Lisbon.
Source: INE.
The decrease in the total number of households living in the city of Lisbon (Figure 1)
illustrated this dynamic, with many families opting to live in the nearby municipalities,
with newer and more affordable properties. This was also justified by the macroeconomic
context. After the 2000s, the Portuguese economy entered a period of lower growth in real
GDP, creating additional difficulties for families to cope with housing costs (Figure 3).
However, the willingness to acquire property had not vanished. Since the early 1990s real
246000
320000
200000
243000
150,000
170,000
190,000
210,000
230,000
250,000
270,000
290,000
310,000
330,000
350,000
1960 1970 1981 1991 2001 2011 2021
Dwellings Households
2870
502
0
500
1000
1500
2000
2500
3000
3500
Total Number of Dwellings
Figure 1.
Number of dwellings and households in the city of Lisbon. Source (Câmara Municipal de
Lisboa (CML) 2022).
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 7 of 19
Figure 1. Number of dwellings and households in the city of Lisbon. Source (CML 2022).
However, it is interesting to note that, after the 2000s, this trend was mainly driven
by Lisbon’s neighboring municipalities, as the tendency in the city of Lisbon saw a sharp
decline in total number of yearly concluded new dwellings in the 2000s, reaching its peak
in 2003 (2870 new dwellings) and plummeting by 98% to only 37 new dwellings in 2006
(Figure 2). This may have come about, in part, as the consequence of the cessation of public
fund grants to house purchasing in 2002 (Santos et al. 2014).
Figure 2. Total number of completed dwellings in new constructions for family housing in Lisbon.
Source: INE.
The decrease in the total number of households living in the city of Lisbon (Figure 1)
illustrated this dynamic, with many families opting to live in the nearby municipalities,
with newer and more affordable properties. This was also justified by the macroeconomic
context. After the 2000s, the Portuguese economy entered a period of lower growth in real
GDP, creating additional difficulties for families to cope with housing costs (Figure 3).
However, the willingness to acquire property had not vanished. Since the early 1990s real
246000
320000
200000
243000
150,000
170,000
190,000
210,000
230,000
250,000
270,000
290,000
310,000
330,000
350,000
1960 1970 1981 1991 2001 2011 2021
Dwellings Households
2870
502
0
500
1000
1500
2000
2500
3000
3500
Total Number of Dwellings
Figure 2.
Total number of completed dwellings in new constructions for family housing in Lisbon.
Source: INE.
J. Risk Financial Manag. 2023, 16, 46 8 of 18
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 8 of 19
estate developers and brokers started to price properties based on the indebtedness ca-
pacity of Portuguese families, which fueled valuation increases (Braga 2013). This was
well represented by the mean value of traded real estate in Lisbon, which rose from
137,755€ per property in the year 2000 to 321,697€ in 2007, a 133.52% increase in just seven
years. The indebtedness levels rose accordingly: in 1995, household debt represented 35%
of income reaching a peak of 130% in 2009
6
. According to Santos et al. (2014) this rise in
indebtedness was easily explained by the increase in home loan values.
Figure 3. Real GDP growth in Portugal (1986–2018). Source: INE.
In 2008 Lisbon was a city experiencing a decrease in households and total number of
inhabitants, which, due to a stagnant economy in the 2000s and the ever-increasing prices
of real estate properties in the city, were moving out to nearby municipalities, offering
newly built dwellings with better conditions at much lower prices. However, the levels of
indebtedness were also increasing, leaving households in much harder financial positions
than in the 1990s. The rental market was also stagnant, due to the government’s rent freeze
policies, leaving Lisbon with an old and unappealing city center.
3.2. The 2008–2013 Crisis
The economic crisis trigged by the US subprime crisis of 2007–2008 had started to be
slowly felt in the country by 2008. From 2008 to 2013, the annual GDP growth was nega-
tive, reaching a minimum of −4.1% in 2012. The country experienced a large decrease in
investments and companies presented higher mortality than birth rates (Carreira et al.
2021). This led to significant increases in job losses. The unemployment rate almost dou-
bled over five years, from its December 2007 value of 9.4% to18.4% by December 2012.
The country’s economic outlook led to the intervention of the “troika”
7
in 2011. The dis-
cussions between the “troika” and the Portuguese government led to a memorandum of
understanding between the two parties, on 17 May 2011 (Rodrigues 2011). This memoran-
dum provided the adoption of some legislative measures affecting the housing market. It
started by defining “measures to amend the New Urban Lease Law, Law n.º 6/2006
8
, in order to
guarantee balanced obligations and rights of landlords and tenants, taking into account the most
vulnerable groups”. This led to approval of Law nº31/2012
9
which came into effect on 12
November, 2012. It is important to note that, due to political unrest, although the first
discussions with troika were taken under a socialist prime-minister, he presented his res-
ignation in March 2011, leading to the election of social-democrat prime-minister, Pedro
Passos Coelho, who was in charge of the political enforcement of the memorandum. The
-6
-4
-2
0
2
4
6
8
10
Real GDP Grouth (%)
Figure 3. Real GDP growth in Portugal (1986–2018). Source: INE.
In 2008 Lisbon was a city experiencing a decrease in households and total number of
inhabitants, which, due to a stagnant economy in the 2000s and the ever-increasing prices
of real estate properties in the city, were moving out to nearby municipalities, offering
newly built dwellings with better conditions at much lower prices. However, the levels of
indebtedness were also increasing, leaving households in much harder financial positions
than in the 1990s. The rental market was also stagnant, due to the government’s rent freeze
policies, leaving Lisbon with an old and unappealing city center.
3.2. The 2008–2013 Crisis
The economic crisis trigged by the US subprime crisis of 2007–2008 had started to
be slowly felt in the country by 2008. From 2008 to 2013, the annual GDP growth was
negative, reaching a minimum of
−
4.1% in 2012. The country experienced a large decrease
in investments and companies presented higher mortality than birth rates (Carreira et al.
2021). This led to significant increases in job losses. The unemployment rate almost doubled
over five years, from its December 2007 value of 9.4% to18.4% by December 2012. The
country’s economic outlook led to the intervention of the “troika”
7
in 2011. The discussions
between the “troika” and the Portuguese government led to a memorandum of understanding
between the two parties, on 17 May 2011 (Rodrigues 2011). This memorandum provided
the adoption of some legislative measures affecting the housing market. It started by
defining “measures to amend the New Urban Lease Law, Law n.
º
6/2006
8
, in order to guarantee
balanced obligations and rights of landlords and tenants, taking into account the most vulnerable
groups”. This led to approval of Law n
º
31/2012
9
which came into effect on 12 November,
2012. It is important to note that, due to political unrest, although the first discussions
with troika were taken under a socialist prime-minister, he presented his resignation in
March 2011, leading to the election of social-democrat prime-minister, Pedro Passos Coelho,
who was in charge of the political enforcement of the memorandum. The center-right
government implemented strict financial austerity measures and put tourism and foreign
investment as the cornerstones to the country’s economic recovery. As part of this strategy,
a Residence Permit for Investment Activity (ARI) program was created, commonly known
as the Golden Visa Program. This allowed foreign nationals to obtain a temporary residence
permit for investment activity with the exemption of a residence visa to enter national
territory, providing free access to travel in 26 countries of the Schengen area. The program
also entitled beneficiaries of the ARI/Golden Visa to apply for permanent residence or
even for Portuguese citizenship
10
. As part of the criteria to be accepted into this program,
applicants should fulfill certain requirements. Among the options, one could:
J. Risk Financial Manag. 2023, 16, 46 9 of 18
(1) purchase real estate property with a value equal to, or above, 500 thousand Euros;
(2) purchase real estate property, with construction dating back more than 30 years or
located in urban regeneration areas, for refurbishing, for a total value equal to, or above,
350 thousand Euros.
From 2012 to 2022, the Golden Visa Program raised over 6 billion euros, from over ten
thousand investors, with a strong share of Chinese and Brazilian nationals. A total of 90.3%
of this investment was made through real estate
11
purchases. This had a significant impact
on the Portuguese real estate market, particularly in the luxury property sectors of Lisbon
and Porto. The influx of capital then trickled down to the rest of the housing market, due
to demand displacement and equity effects (Gordon 2020).
The creation of short-rental platforms, such as AirBnB in 2008, also led to deep trans-
formations in the housing supply, especially in the capital city’s center (see, for example,
Cocola-Gant and Gago 2021). During this period, the lower ECB rates stimulated the
economy, with Euribor 3-month rates lowering from 4.68% in 2007 to 0.29% in 2013.
This economic context lay the foundations for the years that followed in the Portuguese
real estate market.
3.3. The Years That Followed
The year 2014 was a turning point in the Portuguese economy. Despite a slow start,
the country’s GDP returned to a growth rate of over 2% in 2017 (Carreira et al. 2021). This
economic recovery was recognized in the median banking valuations for properties in the
Lisbon municipality (Figure 4):
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 9 of 19
center-right government implemented strict financial austerity measures and put tourism
and foreign investment as the cornerstones to the country’s economic recovery. As part of
this strategy, a Residence Permit for Investment Activity (ARI) program was created, com-
monly known as the Golden Visa Program. This allowed foreign nationals to obtain a
temporary residence permit for investment activity with the exemption of a residence visa
to enter national territory, providing free access to travel in 26 countries of the Schengen
area. The program also entitled beneficiaries of the ARI/Golden Visa to apply for perma-
nent residence or even for Portuguese citizenship
10
. As part of the criteria to be accepted
into this program, applicants should fulfill certain requirements. Among the options, one
could:
(1) purchase real estate property with a value equal to, or above, 500 thousand Euros;
(2) purchase real estate property, with construction dating back more than 30 years
or located in urban regeneration areas, for refurbishing, for a total value equal to, or above,
350 thousand Euros.
From 2012 to 2022, the Golden Visa Program raised over 6 billion euros, from over
ten thousand investors, with a strong share of Chinese and Brazilian nationals. A total of
90.3% of this investment was made through real estate
11
purchases. This had a significant
impact on the Portuguese real estate market, particularly in the luxury property sectors of
Lisbon and Porto. The influx of capital then trickled down to the rest of the housing mar-
ket, due to demand displacement and equity effects (Gordon 2020).
The creation of short-rental platforms, such as AirBnB in 2008, also led to deep trans-
formations in the housing supply, especially in the capital city’s center (see, for example,
Cocola-Gant and Gago 2021). During this period, the lower ECB rates stimulated the econ-
omy, with Euribor 3-month rates lowering from 4.68% in 2007 to 0.29% in 2013.
This economic context lay the foundations for the years that followed in the Portu-
guese real estate market.
3.3. The Years that Followed
The year 2014 was a turning point in the Portuguese economy. Despite a slow start,
the country’s GDP returned to a growth rate of over 2% in 2017 (Carreira et al. 2021). This
economic recovery was recognized in the median banking valuations for properties in the
Lisbon municipality (Figure 4):
Figure 4. Median banking valuation (€/m
2
) of properties in Lisbon Municipality, from 2011 to 2021.
Source: INE.
1474€
3113€
1000
1500
2000
2500
3000
3500
€/m2
Median Banking Valuation (€/m2)
Figure 4. Median banking valuation (€/m
2
) of properties in Lisbon Municipality, from 2011 to 2021.
Source: INE.
From 2014 to 2019, the median banking valuation of properties in Lisbon municipality
doubled from 1474
€
/m
2
to 2963
€
/m
2
in 2019, over a period of five years. Despite the
COVID-19 pandemic, and against the initial expectations of a decrease in housing values,
valuations rose another 5% from 2019 to 2021. As of August 2022, banking valuations
in the city reached 3471
€
/m
2
. So, could this significant growth be solely attributed to
GDP growth? In fact, data shows there were a lot of factors contributing to the rise in
valuations. However, some of the fundamentals seem not to have been supporting this
upward slope. For instance, analyzing the average monthly earnings of employees in
Lisbon, it was possible to observe a loss in purchasing power since 2010. Figure 5 shows
the growth of earnings and compares it with the Consumer Price Index (CPI) from 2009 to
2018. We see that, despite an increase in average monthly earnings, these were not on a
par with inflation, which rose 7% during this period, contrasting with a 12% increase in
inflation.
J. Risk Financial Manag. 2023, 16, 46 10 of 18
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 10 of 19
From 2014 to 2019, the median banking valuation of properties in Lisbon municipal-
ity doubled from 1474 €/m
2
to 2963 €/m
2
in 2019, over a period of five years. Despite the
COVID-19 pandemic, and against the initial expectations of a decrease in housing values,
valuations rose another 5% from 2019 to 2021. As of August 2022, banking valuations in
the city reached 3471 €/m
2
. So, could this significant growth be solely attributed to GDP
growth? In fact, data shows there were a lot of factors contributing to the rise in valuations.
However, some of the fundamentals seem not to have been supporting this upward slope.
For instance, analyzing the average monthly earnings of employees in Lisbon, it was pos-
sible to observe a loss in purchasing power since 2010. Figure 5 shows the growth of earn-
ings and compares it with the Consumer Price Index (CPI) from 2009 to 2018. We see that,
despite an increase in average monthly earnings, these were not on a par with inflation,
which rose 7% during this period, contrasting with a 12% increase in inflation.
As previously mentioned, scholars have often identified a positive correlation be-
tween household income and housing prices, concluding that higher levels of income lead
to higher levels of housing consumption. However, despite the GDP increase, there was
a decrease in the purchasing power, which, assuming a positive correlation, would lead
to a decrease in housing values.
Figure 5. Average earnings per employee in Lisbon and Consumer Price Index (CPI). Base = 100
(2009). For CPI, we used the 12-month average as of 31 December. Source: PORDATA, INE.
The housing supply had not significantly changed, with few new dwellings being
built, as seen in Figure 2. In fact, the number of total dwellings decreased by approxi-
mately 2% from 2011 to 2021 (see Figure 1), compared with a decrease in the number of
households of less than 1%. From a purely statistical point of view, there were far more
dwellings than families (an excess of 77,000 dwellings, as per Figure 1). However, looking
at housing prices, there were no signs of oversupply of housing. Rather, the opposite was
visible in the data, suggesting a mismatch between the living requirements of the Lisbon
households and the offers existing in the market, which was in in line with previous study
findings (see, for reference, Garha and Azevedo 2021).
Euribor 3-month rates continued their downward trend, lowering from 0.29% in 2013
to −0.55% in 2020. This decrease in financing costs might have contributed to an increase
in housing valuations, offering better loan conditions to creditworthy homebuyers. In
+7%
+12%
100
102
104
106
108
110
112
114
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Average Earnings CPI
Figure 5.
Average earnings per employee in Lisbon and Consumer Price Index (CPI). Base = 100
(2009). For CPI, we used the 12-month average as of 31 December. Source: PORDATA, INE.
As previously mentioned, scholars have often identified a positive correlation between
household income and housing prices, concluding that higher levels of income lead to
higher levels of housing consumption. However, despite the GDP increase, there was a
decrease in the purchasing power, which, assuming a positive correlation, would lead to a
decrease in housing values.
The housing supply had not significantly changed, with few new dwellings being
built, as seen in Figure 2. In fact, the number of total dwellings decreased by approximately
2% from 2011 to 2021 (see Figure 1), compared with a decrease in the number of households
of less than 1%. From a purely statistical point of view, there were far more dwellings
than families (an excess of 77,000 dwellings, as per Figure 1). However, looking at housing
prices, there were no signs of oversupply of housing. Rather, the opposite was visible in
the data, suggesting a mismatch between the living requirements of the Lisbon households
and the offers existing in the market, which was in in line with previous study findings
(see, for reference, Garha and Azevedo 2021).
Euribor 3-month rates continued their downward trend, lowering from 0.29% in 2013
to
−
0.55% in 2020. This decrease in financing costs might have contributed to an increase
in housing valuations, offering better loan conditions to creditworthy homebuyers. In
addition, low interest rates may even have encouraged speculative behavior, as history
shows us that several central banks used increases of interest rates as a way to deter both
inflation and speculation (Drazen 2003). It is noteworthy that, due to rising inflation mainly
derived from the energy crisis and the war in Ukraine, central banks have been rapidly
increasing interest rates. However, the effects on the real estate market are still to be seen.
Unemployment rate also decreased from a peak of 18.6% in January and February
2013, to as low as 5.6% in July 2022. This fact also increased pressure on the housing market,
allowing more households to consider increasing their housing consumption (e.g., moving
to a larger unit or to a better location).
One of the factors commonly debated, both by scholars (Mendes 2011, 2017; Franco
and Santos 2021; Cocola-Gant and Gago 2021; Cunha and Lobão 2022) and in the public
arena (see, for reference, Pereira 2022), was the effects of tourism on Lisbon’s housing
market.
J. Risk Financial Manag. 2023, 16, 46 11 of 18
In the wake of the financial crisis, the country has seen, and relied on, a large economic
recovery based on tourism (Figure 6).
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 11 of 19
addition, low interest rates may even have encouraged speculative behavior, as history
shows us that several central banks used increases of interest rates as a way to deter both
inflation and speculation (Drazen 2003). It is noteworthy that, due to rising inflation
mainly derived from the energy crisis and the war in Ukraine, central banks have been
rapidly increasing interest rates. However, the effects on the real estate market are still to
be seen.
Unemployment rate also decreased from a peak of 18.6% in January and February
2013, to as low as 5.6% in July 2022. This fact also increased pressure on the housing mar-
ket, allowing more households to consider increasing their housing consumption (e.g.,
moving to a larger unit or to a better location).
One of the factors commonly debated, both by scholars (Mendes 2011, 2017; Franco
and Santos 2021; Cocola-Gant and Gago 2021; Cunha and Lobão 2021) and in the public
arena (see, for reference, Pereira 2022), was the effects of tourism on Lisbon’s housing
market.
In the wake of the financial crisis, the country has seen, and relied on, a large eco-
nomic recovery based on tourism (Figure 6).
Figure 6. Nights (No.) in collective tourist accommodations in Lisbon’s Metropolitan Area. Source:
INE.
The growth in tourism was so significant that it made Portugal the EU country with
the highest increase in tourist arrivals between 2010 and 2018
12
(UNTWO 2019). This was
reflected in the country’s economy. In 2011, only 4.8% of the total Gross Fixed Capital
Formation (GFCF) was due to accommodation and food service activities, but by 2019 the
sector already accounted for 8.6%.
While studying the Portuguese reality, Franco and Santos (2021) found that each one
percent increase in the share of Airbnb properties translated into a 4.5% increase in house
prices and a 2.26% increase in rent values.
Cunha and Lobão (2022) also found a significant impact of short-term rental (STR)
activities on housing prices, leading to price increases, especially in municipalities where
a higher percentage of housing was transferred to tourism. The shock of tourism was ad-
justed in the market by increasing house prices and not by increasing supply quantities.
The authors found that each percentage point increase in the share of STR resulted in an
increase of 27.4% and 16.1% in housing prices of the Lisbon and Porto metropolitan areas,
in the upper quartile of tourism STR, respectively.
9,411
20,166
6,138
0
5,000
10,000
15,000
20,000
25,000
Thousands
Nights (No.)
Figure 6.
Nights (No.) in collective tourist accommodations in Lisbon’s Metropolitan Area. Source:
INE.
The growth in tourism was so significant that it made Portugal the EU country with
the highest increase in tourist arrivals between 2010 and 2018
12
(UNTWO 2019). This was
reflected in the country’s economy. In 2011, only 4.8% of the total Gross Fixed Capital
Formation (GFCF) was due to accommodation and food service activities, but by 2019 the
sector already accounted for 8.6%.
While studying the Portuguese reality, Franco and Santos (2021) found that each one
percent increase in the share of Airbnb properties translated into a 4.5% increase in house
prices and a 2.26% increase in rent values.
Cunha and Lobão (2022) also found a significant impact of short-term rental (STR)
activities on housing prices, leading to price increases, especially in municipalities where
a higher percentage of housing was transferred to tourism. The shock of tourism was
adjusted in the market by increasing house prices and not by increasing supply quantities.
The authors found that each percentage point increase in the share of STR resulted in an
increase of 27.4% and 16.1% in housing prices of the Lisbon and Porto metropolitan areas,
in the upper quartile of tourism STR, respectively.
Nevertheless, it is important to note that this influx of tourists and increase in housing
prices also created incentives for the rehabilitation and renovation of Lisbon and Porto’s
historical city centers (Franco and Santos 2021).
Since 2016, the municipalities of Porto and Lisbon have taken some containment
measures to mitigate the effects of tourism, such as the introduction of tourism taxes
and the suspension of new STR accommodations in certain zones of the city. Rodrigues
et al. (2022) studied the impact of this measures and concluded that restricted areas saw a
decrease of 20% in sales and a decrease of 9% in prices, indicating a strong impact of STR on
housing. The issuance of Golden Visas was also suspended in the two largest metropolitan
areas in an effort to prevent further significant increases.
4. Data and Methods
The main focus of this section is understanding the impact of the economic crisis on
the real estate market, measured through property sale prices. The data was collected from
the National Statistics Institute (INE) and a public statistics database called PORDATA
13
,
J. Risk Financial Manag. 2023, 16, 46 12 of 18
with a total of 44 quarterly observations, ranging from the first quarter of 2008 to the fourth
of 2018. The price variables were collected from a Lisbon City Council (CML) database,
containing over 8000 property sales between 2008 and 2018, aggregated into quarters. All
property values were standardized, based on the 2008 mean value. It is noteworthy that
the standardization of property values has often been used in hedonic price modeling (see,
for reference, Quigley 1999). We did not include the unemployment rate in our models,
given the strong correlation with household income, as previously mentioned. The basic
variables are shown in Table 1.
Table 1. Base variables.
Variable Description Units Mean SD Min Max
POP Resident population individuals 525,119.16 18,988.17 504,471.00 550,934.00
NIGHTS
Overnight stays (No.)
in hotel establishments
— 2,710,980.57 855,250.91 1,410,553.00 4,472,052.00
GDP
Gross Domestic
Product
million € 45,106.35 2488.13 41,690.80 50,908.10
RATE Euribor 3-months % 2.05 1.31 1.01 5.29
EARNINGS
Average monthly
earnings
€ 1559.20 29.35 1508.80 1616.10
CRISIS Period under crisis dummy 0.55 0.50 0.00 1.00
PRICE Sale price of properties
normalized
values
0.96 0.16 0.74 1.45
Note that the crisis dummy variable allowed us to identify events which occurred in
the 2008–2013 crisis period. The definition of the “crisis” period was based on the work of
Carreira et al. (2021).
We then adjusted the values of GDP, earnings and price for inflation, based on the
fourth quarter of 2008, and implemented a Multiple Linear Regression (MLR) model, as
follows:
∆PRICE
t
= β
O
+ ∆β
1
POP
t− 1
+ ∆β
2
NIGHTS
t− 1
+ ∆β
3
GDP
t− 1
+ ∆β
4
RATE
t− 1
+ ∆β
5
EARNINGS
t− 1
+ β
6
CRISIS
t− 1
+ ε
t
All the variables are defined in Table 1. Notably,
β
i
are the regression coefficients,
∆
is
the usual first difference operator and
ε
t
is the error term. This allowed us to analyze the
relationship between the set of explanatory, or independent, variables, and the dependent
variable (PRICE) (Grum and Govekar 2016). However, we should note that this method
only captured linear relationships between variables.
We followed the most recent literature and implemented a Feasible Generalized
Least Squares (FGLS) technique to estimate the regression coefficients (see, for reference,
Cunha and Lobão 2022). In this work, we used a Feasible Generalized Least Squares python
implementation with autoregressive covariance. This method, we felt, might offer efficiency
gains over Ordinary Least Squares (OLS), especially in the presence of heteroskedastic
errors, over large samples, as it is asymptotically unbiased. However, it could also be
biased for small samples, thus not BLUE
14
, and the efficiency gains were limited to the
type of heteroskedastic errors derived from variables explicitly recognized by the model
or derived from unknown variables (Miller and Startz 2018). Additionally, one should
note that the OLS model assumes that errors are homoscedastic and uncorrelated which, in
most practical applications, is not true. If errors do not meet these criteria, the estimated
variance of estimated coefficients might be incorrect (implying incorrect standard errors
and confidence intervals, and p-values
15
) and, therefore, while it might still be unbiased
and consistent, the estimated coefficients obtained by OLS under these circumstances
are inefficient. In order to address this issue, we also estimated the coefficients using
J. Risk Financial Manag. 2023, 16, 46 13 of 18
the basic Ordinary Least Squares (OLS) estimators, using a White’s Heteroskedasticity
Consistent estimator
16
(White 1980; Miller and Startz 2018; Kiefer n.d.), which proved
to be more misleading than the results obtained by the usual OLS, as demonstrated by
MacKinnon and White (1983). Nevertheless, one should note that White’s estimator factors
in heteroskedasticity, and not correlation, between errors. The use of the Generalized
Least Squares estimation of the coefficients would solve this issue leading to a BLUE
estimator. However, in practice, it would be unfeasible to implement such a solution
given that we did not know a prior the true value of the covariance matrix. Therefore, as
previously mentioned, we introduced a Feasible Generalized Least Squares estimation,
with autoregressive coefficients, by which the covariance matrix was estimated based on
the sample (Hayashi 2011). The FGLS estimator was defined as (see Equation (1)):
ˆ
β
FGLS
=
X
T
ˆ
Ω
−1
X
−1
X
T
ˆ
Ω
−1
X (1)
where
ˆ
Ω =
ˆ
Ω(θ) is the parameter estimation of the true unknown covariance matrix Ω.
The use of an autoregressive covariance explicitly addressed the issues arising from
autocorrelation of variables at t − 1.
Although FGLS might still not remove heteroskedasticity completely, nor is it able
to completely remove the correlation between variables, it should provide a more robust
result, and a better understanding of the significance and dispersion of each coefficient.
5. Results and Discussion
The results showed GDP, interest rates, number of residents and their average monthly
earnings to be the most significant in regard to housing prices. The crisis dummy variable
also proved to be significant (Table 2).
Based on Table 2, it is possible to observe that the financial crisis had a negative impact
on housing prices, evidenced by the negative regression coefficient in our dummy variable.
GDP growth and lower interest rates promoted the increase of housing values, which
was consistent with previous findings (see, for reference, Rodrigues and Lourenço 2017).
This accorded with the authors’ initial hypothesis, as higher productivity levels and lower
costs of financing led to higher levels of housing consumption. The low interest rates
environment allowed homebuyers to have access to lower monthly mortgage payments
and investors to have higher returns than in risk-free assets (e.g., treasury bonds), while
ensuring relative safety to returns, using real estate investment as an alternative saving
option (Rodrigues and Lourenço 2017).
The negative coefficient on the number of overnights in hotel establishments was not
in accordance with our expectations. The significant increase in tourists in the city of Lisbon
had deep and longstanding impacts on its housing market, as reported in our literature
review. Thus, the authors expected a positive coefficient in our regression, signaling the
positive impact of tourist activity in increasing housing prices. The negative coefficients
on the population and earnings, despite being contrary to the widespread belief held in
the literature, must be interpreted accordingly to the Portuguese reality. By analyzing the
trend in population and earnings in recent years, we could see that both decreased while
housing prices increased (average monthly earnings have decreased since 2010, if adjusted
to inflation). These findings suggested that the housing price growth was not sustained by
an increase in population, nor by an increase in the purchasing power of local inhabitants.
Thus, the increase in prices must have been fueled by institutional and private investors,
both national and foreign, contributing to the financialization of real estate properties in
the city and to the decrease in affordability. This was consistent with previous findings,
as, according to Rodrigues and Lourenço (2017), housing investment by non-residents has
been increasing in the country since the 1990s, and, despite a decrease between 2011 and
2014, from 2014 to 2017 it grew at an average of 9% per year. Additionally, it was consistent
with the findings of Schiffmann (2019 as cited in Cunha and Lobão 2022), according to
J. Risk Financial Manag. 2023, 16, 46 14 of 18
which 34% of the houses sold in Lisbon during the first half of 2019, were purchased by
foreigners from 80 different countries.
Table 2.
Regressions output—determinants of real house price growth. Ordinary Least Squares
Regression with robust standard errors (OLS (HC0)) and Feasible Generalized Least Squares with
first order autoregressive covariance matrix. The standard error of each coefficient can be seen in
parentheses below each coefficient.
Variables OLS (HC0) FGLS
INTERCEPT
102.9110 ***
(3.505)
100.0736 ***
(2.820)
POP
−29.7019 ***
(9.467)
−27.225 ***
(9.431)
NIGHTS
0.0065
(0.034)
−0.0097
(0.034)
GDP
9.0911 ***
(1.989)
7.8101 ***
(2.242)
RATE
−23.7773 **
(10.933)
−23.3798 *
(12.470)
EARNINGS
−9.5210 ***
(2.867)
−7.5919 **
(3.230)
CRISIS
−10.7764 **
(4.368)
−8.4970 *
(4.768)
R-squared 0.564 0.564
Adj. R-squared 0.474 0.471
F-statistic 8.412 6.043
Prob (F-statistic) 0.000 0.000
AIC 275.9 259.2
Durbin-Watson 1.464 1.709
* p-value < 10%; ** p-value < 5%; *** p-value < 1%.
By comparing the results from OLS, with heteroskedasticity-consistent estimators,
and from FGLS we could conclude that there were no significant differences between the
two results, yielding very similar outputs. This might have been the case because our
series was relatively small (44 observations) and might not have been sufficient for FGLS to
present efficiency gains. However, there was a decrease in the Akaike Information Criterion
(AIC), which might indicate a comparatively better result than the one yielded by the OLS
(HC0) model. It is also possible to see an increase in the Durbin–Watson values, signaling a
decrease in the autocorrelation of residuals. There was a decrease in the coefficient of all
variables and an increase in the standard errors, which might hint at an undervaluation of
standard errors and confidence intervals by the OLS estimation. There was also a decrease
in the significance of the EARNINGS, RATE and CRISIS variables. Nonetheless, they
remained significant at 5% and 10%, respectively
17
.
In light of these results, governments, and especially central banks, face a tough
challenge. On the one hand, they should aim to increase credit restrictions by tightening the
conditions to be considered a credit-worthy applicant (e.g., decreasing LTV or DSTI limits)
or increasing interest rates. This, in turn, would limit access to the market through higher
financing costs. However, as Dietsch and Welter-Nicol (2014) pointed out, this should be
done considering the specificities of the local market, as a one-size-fits-all solution might
not be appropriate. This should increase the robustness of the real estate market, making it
less vulnerable to volatile economic conditions, such as the ones experienced during the
2008 financial crisis. It is noteworthy that Banco de Portugal has taken some measures in
J. Risk Financial Manag. 2023, 16, 46 15 of 18
this direction (see, for reference, Banco de Portugal 2022). On the other hand, the economic
conditions tightening household budgets are significantly harming accessibility to the
housing market, leaving governments responsible for tackling affordability issues of the
local population. In this regard, measures should be taken to increase the housing supply
targeted at middle class and low-income families. Therefore, there is no simple solution for
this issue, as policies should take into account both the demand and supply sides.
6. Conclusions
This research provides a unique perspective on the dynamics of the real estate market
in Lisbon (Portugal). The analysis of this case study provides valuable academic and
professional insights considering the economic and social dynamics that have impacted
Portugal in the last two decades and, in particular, Lisbon. These dynamics involve the
financial crisis and sets of measures to leverage the economic rebound, such as the Golden
Visas targeted at increasing foreign investment in the country.
The authors’ initial hypothesis, that the financial crisis had a negative impact in real
estate prices, was confirmed. As discussed by Quigley (1999), one of the main outcomes
of a financial crisis is the decrease in available income and overall levels of employment,
which directly affects the demand levels for real estate. Therefore, it comes as no surprise
that the growth of GDP led to an increase in real estate prices in the post-crisis period.
The low interest rates also contributed to the increase in real estate prices. Several authors
discussed the effect of credit cost on real estate prices (e.g., Taylor 1993; Seyfried 2010), and
found similar patterns. The reduction of credit costs allows an increase in the volume of
demand, consequently increasing real estate prices.
More surprisingly, the number of overnights, used as a proxy for touristic volumes,
had a negative coefficient. In the authors view, this might be linked to two dimensions.
First, the number of overnights is not necessarily a proxy for touristic activity because
there has been an exponential growth in supply of other forms of touristic housing, as
discussed by Yang et al. (2023). Second, these other housing solutions for tourists are the
ones most impacting the availability of housing in city centers. In fact, with growth in
tourism, investors and homeowners have incentives to move their housing properties from
traditional housing to short-term rentals. Vizek et al. (2022) discuss the fact that short-terms
rentals provide higher returns, and, therefore, there are two effects: first, a willingness to
pay more given the higher expected return; and second, a reduction in traditional housing
supply, creating additional pressure on the demand side.
Future studies should expand the number of variables and analyze more disaggregate
data, ideally, at the monthly level. Unfortunately, this granularity was not available at the
time the authors conducted the research.
Author Contributions:
Conceptualization and Methodology, J.F.J.; Writing—original draft prepara-
tion J.F.J. and C.O.C. All authors have read and agreed to the published version of the manuscript.
Funding:
João Fragoso Januário acknowledges financial support from InfraRisk PhD Program
through research grant PD/BD/150402/2019. The authors are grateful for the Foundation for Science
and Technology’s support through funding UIDB/04625/2020 from the research unit CERIS.
Data Availability Statement: Data is available on request.
Conflicts of Interest: The authors declare no conflict of interest.
Notes
1
Malaysia, Singapore, Indonesia and Thailand.
2
Meaning shorter tourist seasons.
3
In fact, it was not the rent itself that was frozen but the valuation of rental properties, leading to a freeze in rent increases.
4
The first regime of subsidized credit was in fact created in 1976 and updated during the 80s and 90s (Santos et al. 2014).
5
Inflation during mid-80s almost peaked at 30%, therefore, investing in a house was one of the best ways to hedge against inflation
(Braga 2013).
J. Risk Financial Manag. 2023, 16, 46 16 of 18
6
See, for reference, Santos et al. (2014), chart 15, p. 33.
7
Decision group created by the European Commission (EC), the European Central Bank (ECB) and the International Monetary
Fund (IMF).
8
The New Urban Lease Law (NRAU), Law n.
º
6/2006, presented in 2006, was set to revitalize the rental market, especially in
Lisbon and Porto. However, it still presented large restrictions to rent increases, especially in the case of lease contracts prior
to 1990, rehabilitation of rented properties and eviction procedures. The original document can be seen at https://dre.pt/dre/
legislacao-consolidada/lei/2006-34578375; accessed on 6 January 2023.
9
For more information refer to https://www.portaldahabitacao.pt/pt/nrau/home/apresentacao_nnrau.html; accessed on 6
January 2023.
10
For more information refer to https://www.sef.pt/en/pages/conteudo-detalhe.aspx?nID=21; accessed on 9 January 2023.
11
For more information refer to https://www.globalcitizensolutions.com/pt-pt/estatisticas-golden-visa/; accessed on 9 January
2023.
12
During this period, the country saw a 234% increase, according to Cunha and Lobão (2022).
13
https://www.pordata.pt/home/; accessed on 9 January 2023.
14
Best Linear Unbiased Estimators (BLUE).
15
Note that covariance matrices are used for determining significance of regression coefficients (p-value) and constructing confidence
intervals for each coefficient.
16
Heteroskedasticity-consistent (HC) estimators, HC0 in this case. For more information, see Zeileis (2004).
17
RATE and CRISIS variables are both significant at 10% under the FGLS estimation.
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