Viktorija Cohen, PhD
Department of Economic Policy,
Faculty of Economics
Vilnius University, Lithuania
E-mail: viktorija.cohen@ef.vu.lt
Lina Karpavičiūtė
Department of Economic Policy, Faculty of Economics
Vilnius University, Lithuania
E-mail: linanes@gmail.com
Submission: 25/07/2016
Accept: 01/08/2016
ABSTRACT
Fundamental
determinants of housing prices which affect housing demand and supply are the
most common in developed countries. These are economic and financial
determinants as well as demographic indicators. However, housing price analysis
in less developed countries submit controversial and not sufficient results
about the impact of interest rate, inflation and unemployment. Moreover, it
does not investigate the influence of demographic variables and the means of
economic policy. In this article the effect of GDP, unemployment, inflation,
interest rate, emigration and the means of macroprudential policy on housing
prices in Lithuania was evaluated. The results showed that inflation, interest
rate and emigration are not causal determinants of housing prices, which mostly
depend on GDP, unemployment, the means of macroprudential policy and the
average housing prices in the previous period.
Keywords: housing price determinants,
housing market, macroprudential policy, emigration, transition economy.
1. INTRODUCTION
The
analysis of the determinants of housing prices is important because of the
housing impact on economic and social factors. Firstly, homeownership has a
positive effect on residential mobility, residents’ health and other social
consequences (DIETZ et al., 2003).
Secondly,
housing is a good, which is closely connected with other markets and the whole
economic status of the country. As houses can be purchased by the mortgages as
well as the own funds of the residents; the housing market is especially related
to financial sector (JUREVIČIENĖ; OKUNEVIČIŪTĖ; NEVERAUSKIENĖ, 2008).
Finally,
the changes in housing prices influence the construction market and other
economic variables such as unemployment and inflation (AZBAINIS, 2014).
Because
of this, a lot of analysis have been done in developed countries. It mostly
specified economic and financial determinants of housing prices, such as GDP,
unemployment, interest rate and credit conditions (ADAMS; FÜSS, 2010; AGNELLO;
SCHUKNECHT 2011; JACOBSEN; NAUG, 2005; CROWE et al., 2011), and more rarely – demographic determinants such as
population, ageing and migration (TAKÁTS, 2012; CHEN et al., 2012).
However,
the determinants of housing prices in Lithuania do not always coincide with
those in developed countries, mostly because of existing historic variable of a
planned economy and transition processes to a developing economy. For this
reason, the housing market in Lithuania is more similar to transition
economies.
Still,
researches for the housing market in Lithuania do not submit sufficient
results. Firstly, different analysis showed controversial results about the
impact of interest rate and inflation (IVANAUSKAS et al., 2008; KANAPECKIENĖ,
2009; LEIKA; VALENTINAITĖ, 2007).
Secondly,
the effect of unemployment on housing prices in Lithuania has been investigated
only twice (LEIKA; VALENTINAITĖ, 2007; TUPĖNAITĖ; KANAPECKIENĖ, 2009).
Furthermore, the impact of demographical variables and the means of economic
policy on housing prices has not been evaluated, as there is a problem of short
time series in Lithuania, which is especially important for demographic
determinants.
This
study was performed by adapting several approaches. Firstly, literature
analysis, synthesis and generalization were accomplished to investigate the
theoretical background of the determinants of housing prices. Secondly, the
Granger causality test was applied to reduce the causal determinants of housing
prices in Lithuania. Finally, regression analysis was performed to evaluate the
influence of the educed causal determinants.
The
analysis investigated the impact of GDP, unemployment, inflation, interest
rate, emigration; and the means of macroprudential policy on housing prices in
Lithuania in the period from 2001 to 2014.
2. THE FUNDAMENTAL DETERMINANTS OF HOUSING PRICES
Housing
prices can be explained mostly by fundamental determinants, which affect
housing demand and supply. The demand side depends on the households ability to
pay for a house or for a mortgage. Furthermore, the higher construction costs
lead to decrease in construction and thus to a lower level of housing stock
(ADAMS; FÜSS, 2010).
The
most common determinants of house prices are macroeconomic determinants such as
GDP, disposable income, and unemployment. An increase in economic activity
increases the demand for space and since the housing stock cannot change in the
short run, rents increase which leads to higher housing prices (ADAMS; FÜSS,
2010).
Moreover,
the persistence of growth in per-capita real GDP may lead to the perception of
higher life-time income growth and the willingness of agents to spend a larger
share of income on housing and related debt service. Because of this, we may
see higher growth of personal income being positively associated with a higher
probability of a housing boom and reversely lower growth with a higher
probability of a bust (AGNELLO; SCHUKNECHT, 2011).
The
decrease of unemployment also has a positive effect on disposable income and
causes agents to move to more economical but also more expensive housing
(LEIKA; VALENTINAITĖ, 2007). However, increased unemployment results in
expectations of lower wage growth and increased uncertainty concerning future
income and ability to repay debt. This reduces the willingness to pay for
owner-occupied dwellings (JACOBSEN; NAUG, 2005).
While
macroeconomic determinants mostly affect the ability to pay for a house,
financial determinants such as interest rate and credit conditions influence
mortgage accessibility. A higher long-term interest rate increases the return
of other fixed-income assets such as bonds relative to the return of real
estate, thus shifting the demand from real estate into other assets. A higher
long-term interest rate is furthermore reflected in higher mortgage rates,
which reduce demand and further decrease housing prices (ADAMS; FÜSS, 2010).
The
other important financial determinant is credit conditions such as: down
payment requirements, loan-to value (LTV) ratio and debt-to-income (DTI) ratio.
Chu (2014) finds that housing prices are sensitive to the changes of the down
payment requirements if owner-occupied and rental houses are inelasticity
supplied. Besides, Crowe et al. (2011) points out that the LTV ratio reduces
the pool of borrowers that can obtain funding and thus reduces demand pressures
and contains the boom. Similar to the LTV ration, the DTI ratio limits rein in
the purchasing power of individuals, which reduces the pressure on real estate
prices. Hence, macroprudential measures may limit mortgage credit and tackle
the risks of housing prices booms.
Finally,
demographic determinants such as population, ageing and migration also
determine housing prices. Takáts (2012) states that a larger population is
associated with higher real housing prices. Moreover, house prices might come
under pressure, if the relative size of the older population compared to
working population increases. Still, Chen et al. (2012) finds that population
ageing is not likely the main determinant of housing prices.
To
sum up, the most important determinants of housing prices are economic and
financial indicators, however, we can see there is a smaller but significant
influence of demographic determinant in a long run.
3. THE DETERMINANTS OF HOUSING PRICES IN LITHUANIA
Because
of different markets, the determinants of housing prices, which are significant
in developed countries, do not perfectly fit for housing markets in countries
with transition economy. Although Lithuanian housing market has achieved vast
developments and shifts towards the perception of more developed market, the
history of a planned economy has its impact on the country’s housing market,
and thus embodies principles of transition economies. However, different
researches show rather different results, depending on data period and the
method of the analysis.
The
most important economic determinant in Lithuania is GDP, as showed Leika and
Valentinaitė (2007), Simanavičienė and Keizerienė (2011), Tupėnaitė and
Kanapeckienė (2009). Still, Ivanauskas et al. (2008) argued that neither GDP
nor disposable income were causal determinants of housing prices in the period
from 1998 to 2014. They explained that the negative results on the possible
causality of housing costs and GDP might indicate a housing costs bubble.
Moreover,
there have been only two studies of the impact of unemployment in Lithuania
(LEIKA; VALENTINAITĖ, 2007; TUPĖNAITĖ; KANAPECKIENĖ, 2009), which showed that
there was no effect of unemployment on house prices. Such results could be
explained by the steady decline of unemployment in the period of research
(TUPĖNAITĖ; KANAPECKIENĖ, 2009). Finally, Tupėnaitė and Kanapeckienė (2009)
showed that inflation had negative impact, while Simanavičienė and Keizerienė
(2011) showed a positive effect of inflation on housing prices.
The
impact of financial determinants on housing prices is also not clear in
Lithuania. Although Ivanauskas et al. (2008) did not identify a causal relation
between interest rate and housing prices, Leika and Valentinaitė (2007) showed
that real interest rate and credit supply were significant determinants of
housing prices. Moreover, Tupėnaitė and Kanapeckienė (2009) supported the
result that credit supply has a strong influence on housing prices.
Finally,
the impact of demographical variables and the means of economic policy on
housing prices has not been evaluated. Although fiscal and monetary policy is
limited in the area of the regulation of house prices in Lithuania,
macroprudential policy has been introduced in 2011 (LIETUVOS..., 2011). As it
set the credit conditions such as the LTV ratio (85 percent) and the DTI ratio
(40 percent), it is important to evaluate the impact of the introduction of
macroprudential policy on housing prices.
To
sum up, researches of the housing market in Lithuania showed controversial
results about some variables, such as impact of interest rate and inflation.
This could happen because most of these researches were based on regression and
correlation analysis (LEIKA; VALENTINAITĖ, 2007; SIMANAVIČIENĖ; KEIZERIENĖ,
2011; TUPĖNAITĖ; KANAPECKIENĖ, 2009). Although regression analysis deals with
the dependence of one variable on other variables, it does not necessarily
imply the causation (GUJARATI; PORTER, 2009), hence, causal determinants of
housing prices are not clear in Lithuania.
4. METHODOLOGY
The
analysis investigated the impact of GDP, unemployment, inflation, interest
rate, emigration and the introduction of macroprudential policy on housing
prices in Lithuania in the period from 2001Q1 to 2014Q4. Data sources: average
house prices – the State Enterprise Centre of Registers; GDP, unemployment and
emigration – Statistics Lithuania; interest rate (6 month VILIBOR) – Bank of
Lithuania. Seasonality from the data was removed using the multiplicative
method. As the main means of macroprudential policy has not changed since 2011,
the introduction of macroprudential policy was included into the model as a
qualitative variable.
As
housing prices also have an impact on economic variables (AZBAINIS, 2014), the
Granger causality test was applied to reduce the quantitative causal
determinants of housing prices. The simple causal model is (GRANGER, 1969):
where
εt and ηt are taken to be two uncorrelated white-noise series, m will be
assumed finite and shorter than the given time series.
As it
is assumed that all variables are stationary in Granger causality test, the
Augmented Dicky-Fuller test was performed to check for stationarity. The ADF
test consists of estimating the following regression (GUJARATI; PORTER, 2009):
(2)
where
εt is a pure white noise error term and where . If
the hypothesis that δ = 0 is rejected, the time series is stationary. Sometimes
taking the first differences of the variables makes them stationary, if they
are not already stationary in the level form.
The
number of lagged terms was introduced in the causality test based on Akaike
information criterion (AIC), as AIC is a better choice for a smaller than 120
observations sample (LIEW, 2004). AIC is defined as (GUJARATI; PORTER, 2009):
(3)
where
k is the number of regressors and n is the number of observations.
To
evaluate the influence of the educed causal determinants, regression analysis
was performed. The basic form of the model is:
(4)
where
Y is an average housing price; xj – the determinants of housing
prices; u – residual.
To
measure the goodness of fit of the multiple regression model, the adjusted
coefficient of determination () was
used. gives the proportion of the variation in Y
explained by the variables Xj and can be specified as:
(5)
where R2 – multiple
coefficient of determination; k – the number of parameters in the model
including the intercept term; n – the number of observations.
Because the model (4) is the multiple regression, it must fit these
assumptions:
·
There is no multicollinearity among the regressors
included in the regression model. Multicollinearity can be seen with the
variance-inflating factor (VIF), which is defined as:
(6)
When , the
coefficient of determination in the regression of regressor Xj on
the remaining regressors in the model, increases, the VIF also increases. As a
rule of thumb, if the VIF of a variable exceeds 10, that variable is said to be
highly collinear.
·
The disturbances appearing in the population
regression function are homoscedastic. To detect heteroscedasticity White’s
test was used: the squared residuals from original regression are regressed on
the original X variables or regressors, their squared values, and cross
products of the regressors:
(7)
Under
the null hypothesis that there is no heteroscedasticity, it can be shown that
nR2 from the (7) regression asymptotically follows the chi-square
distribution with df equal to the number of regressors
(excluding the constant term). If exceeds the critical chi-square value, there
is heteroscedasticity.
·
There is no autocorrelation in the error terms, which
was detected using the Breusch-Godfrey (BG) test. In the BG test, the following
regression is estimated:
(8)
where
the term ut follows pth – order autoregressive scheme. If
the sample size is large, (n–p) R2 obtained from (8) asymptotically
follows the chi-square distribution . If
(n – p) R2 exceeds the critical chi-square value, we reject the null
hypothesis that there is no serial correlation of any order.
·
The residuals from (4) model are normally distributed.
Jargue-Bera (JB) Test of Normality was applied, which uses the following test
statistic:
(9)
where
n – sample size; k – the number of parameters in the model; S – skewness
coefficient and K – kurtosis coefficient. Under the null hypothesis that the
residuals are normally distributed, asymptotically the JB statistics follows
the chi-squared distribution with 2 df.
According
to these testing procedures, the model, which satisfies all these assumptions,
was constructed. Based on this model, the impact of the determinants of housing
prices were evaluated.
5. THE RESULTS AND THEIR INTERPRETATION
The
Granger causality test was performed to educe causal quantitative determinants
of housing prices in Lithuania in the period from 2001Q1 to 2014Q2. ADF test
showed that the p value of t statistic is higher than 0,05 for each variable,
thus all variables are nonstationary. Still, the p value of t statistic for the
first differences of each variable, is lower than 0,05. For this reason, the
first differences were used for the Granger causality test. Moreover, different
numbers of lagged terms were introduced in each causal model, according to AIC:
14 lags for the model of house prices and GDP, 12 – for unemployment (u), 3 –
for inflation (π), 12 – for interest rate (i), and 1 – for emigration (e).
The
Granger causality test showed that both GDP and unemployment are causal
determinants of housing prices (Table 1 and Table 2). As GDP and unemployment
also have influence on disposable income, we can see that residents‘ ability to
obtain a housing on their own funds is very important in Lithuania.
Table
1: The results of Granger causality test for housing prices and GDP
Table
2: The results of Granger causality test for housing prices and unemployment
There is also a causal relation
between housing prices and inflation, however, causation is from housing prices
to inflation, not vice versa (Table 3). This means that Tupėnaitė, Kanapeckienė
(2009) and Simanavičienė, Keizerienė (2011), who had adapted only correlation
and regression analysis, could show the incorrect result that inflation affect
housing prices.
Table
3: The results of Granger causality test for housing prices and inflation
Interest rate is not a causal
determinant of housing prices (Table 4). This is because only a small fraction
of housing is purchased by housing mortgages in Lithuania. For example, in the
first quarter of 2015 only one-fourth of housing transactions were made using
mortgages (LIETUVOS BANKAS, 2015).
Moreover, although the Granger
causality test showed that house prices cause interest rate, this does not
prove causality in this case, as the Granger causality test firstly requires a
logical foundation. Because a currency board existed in Lithuania till 2015,
the Bank of Lithuania could not influence interest rate independently (KOPCKE,
2000), thus it is not likely that housing prices could determine interest rate
in Lithuania.
Table
4: The results of Granger causality test for housing prices and interest rate
Finally, there is no causal relation
between housing prices and emigration (Table 5). However, this result could be
explained by several reasons. Firstly, the analysis was made only for the
period of 14 years, while literature analysis showed that demographic
determinants influence housing prices only in a long term (TAKÁTS, 2012; CHEN et
al., 2012).
Furthermore, the effect of
emigration on housing prices could be dual: on one hand, emigration has a
negative impact on population and reduces housing demand and housing prices. On
the other hand, emigration increases the transactions for the residents in
Lithuania and this leads to higher housing prices (LEIKA; VALENTINAITĖ, 2007).
Because of this, different sides of emigration could offset each other.
Table
5: The results of Granger causality test for housing prices and emigration
According to the Granger causality
test, inflation, interest rate, and emigration were not included into the
multiple regression model. As a result, Table 6 describes the model, where
house prices depend on GDP, unemployment and the introduction of the means of
macroprudential policy (D). Although all the variables in this model are
statistically significant (p value of t statistic is less than 0,05 for each
variable) and the value of is high, there are two problems in this model.
Firstly, BG test showed that p value of χ2(2) is 0,00 < 0,05,
hence there is autocorrelation in the error terms. Secondly, WT test showed
that p value of χ2(8) is 0,01 < 0,05, thus the model is
heteroscedastic.
Table
6: The results of the primary regression model of house prices
In order to remove heteroscedasticity
from the model, the new model was specified, where all quantitative variables
(house prices, GDP and unemployment) were introduced in logarithmic form.
Moreover, autocorrelation in the error terms means that housing prices depend
not only on defined independent variables, but also on lagged housing prices.
Because of this, the lagged housing price (Yt – 1) was introduced
into the model as an independent variable.
The description of this revised
model is shown in Table 7. It can be seen that all the variables are
statistically significant and that 98,76 percent of the variation in house
prices can be explained by this model. Finally, the further analysis showed
that the problems from the previous model were successfully removed.
Table
7: The results of the revised regression model of housing prices
First
of all, there is no multicollinearity among the regressors included into the
model, as the values of the estimated VIF are less than 10 for all independent
variables (Table 8). Moreover, the BG test showed that p value of χ2(2)
is 0,85 > 0,05, hence there is no serial correlation of any order.
Furthermore, the WT test showed that p value of χ2(13) is 0,20 >
0,05, thus the model is homoscedastic. Finally, the residuals from the model
are normally distributed (Fig. 1), as p
value of the JB statistic is 0,76 > 0,05.
Table 8: The
coefficients of determination for the regression analysis of independent
variables and the values of the VIF
Figure 1: The distribution of the residuals from the
revised model
As the model, described in the Table
7 meets all the requirements for the multiple regression model, it can be
written as:
(10)
where
Y – an average house price (EUR/m2); t – period; GDP – gross
domestic product (M EUR); u – unemployment (%); D – the introduction of the
means of macroprudential policy; u – residual.
The
(11) model shows that average housing prices in Lithuania mostly depend on GDP,
unemployment, the means of macroprudential policy and the average housing
prices in the previous period. GDP and the average housing prices in the
previous period have the strongest impact: when each of these variables rises
by 1 percent, housing prices rise by 0,46 and 0,66 percent respectively, while
other variables are unchanged.
Unemployment
has smaller but significant effect on housing prices: when unemployment rises
by 1 percent, house prices fall by 0,14 percent, while other variables are
unchanged. Finally, the introduction of the means of macroprudential policy had
a negative impact on housing prices: after introduction of the means of
macroprudential policy, housing prices fell on average by 0,10 percent. Because
macroprudential policy is the only source of economic policy, which has an
effect on housing prices in Lithuania, it is important to investigate not only
the impact of introduction of macroprudential policy, but also the impact of
separate means of macroprudential policy, such as the LTV and DTI ratios in
further researches.
6. CONCLUSION
The
analysis evaluated the influence of GDP, unemployment, inflation, interest
rate, emigration and the introduction of the means of macroprudential policy on
housing prices in Lithuania in the period from 2001 to 2014.
The
Granger causality test showed that inflation, interest rate and emigration are
not causal determinants of average housing prices. Although, there is a
statistical relation between inflation and housing prices, inflation is a
dependent variable. This means that researches of other authors, who had
adapted only correlation and regression analysis, could show the incorrect
result that inflation affects housing prices. Because of this, it is
recommended to test causal relations of the variables before including them
into the regression model.
The
multiple regression model showed that housing prices can be determined by GDP,
unemployment, the introduction of the means of macroprudential policy and the
average housing prices in the previous period. By these variables 98,76 percent
of the variation in housing prices can be explained.
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