CHALLENGES FOR MONETARY POLICY MAKERS
3. DATA AND METHODOLOGY
Although the differences of residential real estate prices in advanced and emerging market could be large, in most economies real house prices have significantly increased since 2013. The environment of easy financial conditions could contribute to accumulation of vulnerability in the housing sector and could result in unfavourable cycle between the financial and real economic sectors (International Monetary Fund, 2019).
In order to reach the research objectives, in this study we used the data obtained from the Central Bank of Croatia, European Central Bank, Eurostat and Statistical Bulletin and The Institute of Economics Zagreb. The quarterly data which was used in this paper for the time period of 2006Q1-2018Q4 are:
• Housing loans (HL)
• Country level of financial stress (CLFS) • Indicator of financial conditions (IFIS) • Real estate price growing (RE) and • Average nominal net payment (ANNPI)
Chart 1: Residential real estate prices, Average nominal net payment, Indicator of financial conditions (Authors` calculation)
Chart 1 indicates the developments of residential real estate prices, that are affected by increase of nominal net payment and easing financial conditions at financial markets. The correlation analysis is conducted for period after 2014. The analysis indicates the restoration of pre-crisis trends after 2014 in residential real estate prices and other macroeconomic variables what could stimulate new macroeconomic imbalances. The correlation analysis gives information about the magnitude of the association, or correlation, as well as the direction of the relationship. This analysis is the best method of measuring the association between variables of interest because it is based on the method of covariance. Pearson correlation shows the extent to which residential real estate prices are linearly related to the other variables. It is calculated according to the formula
𝑟 = 𝜎𝑥𝑦 𝜎𝑥𝜎𝑦
where 𝜎𝑥 and 𝜎𝑦 are standard deviations of variables X and Y, and 𝜎𝑥𝑦 is covariance. (Pearson,
1920) Correlation coefficients for all variables are presented in Table 1.
-200,000 -150,000 -100,000 -50,000 0,000 50,000 100,000 150,000 200,000 250,000 0,00 20,00 40,00 60,00 80,00 100,00 120,00 140,00 1Q 2006 3Q 2006 1Q 2007 3Q 2007 1Q 2008 3Q 2008 1Q 2009 3Q 2009 1Q 2010 3Q 2010 1Q 2011 3Q 2011 1Q 2012 3Q 2012 1Q 2013 3Q 2013 1Q 2014 3Q 2014 1Q 2015 3Q 2015 1Q 2016 3Q 2016 1Q 2017 3Q 2017 1Q 2018 3Q 2018
Table 1: Pearson Correlation Coefficient (authors` calculation)
IPRE HL CLFS ANNPI IFIS
IPRE 1
HL 0,896807 1
CLFS 0,559771 0,463177 1
ANNPI 0,941176 0,956469 0,532608 1
IFIS -0,8073 -0,95192 -0,49329 -0,9009 1
The correlation analysis is made for the period of last two years; from 2016 till 2018. The possible problem in recovery period could be accumulation of systemic risk and forming new residential real estate price bubbles. The residential real estate prices have positive and strong correlation with nominal net payment and strong negative correlation with financial conditions. The increase in nominal net payment and easing of financial conditions contribute to increase of residential real estate prices. At the same time, increase of nominal net payment affects the banks` credit activity because of increased housing credit demand. Lower interest rates on loans and deposits, low level of short-term interests on the inter-bank market, and high liquidity of the financial system have positive impact on economy. The negative correlation indicates the easing of financial institutions credit requirements what contributes to upturn in credit cycles. Furthermore, the Granger test confirmed the causality relationship between residential real estate prices and other economic variables. The research is carried out through two fundamental steps by this order, in first step the order of integration was tested by using the Augmented Dickey-Fuller (ADF) unit root tests in order to establish stationarity of the time series. With ADF unit root tests we determined the order of integration of variables that was included in the analyses. Augmented Dickey-Fuller Test Dickey and Fuller (1979) is the simplest single root test and it is most common in economic practice. Table 2 presents the results of ADF Test on selected variables in levels and their first and second differences.
Table 2: Augmented Dickey- Fuller test (authors` calculation)
Variable Lag
Length
t-statistic ADF
p Test for unit root:
HL (AIC) 6 -5.784623 0.0000 In level with Intercept
CLFS (AIC) 1 -3.942984 0.0035 In level with intercept
IFIS (SIC) 1 -5.915051 0.0000 In 1st difference with intercept
ANNPI (AIC) 2 -5.270500 0.0001 In 1st difference with Intercept
IPRE (AIC) 3 -12.31661 0.0000 In 2nd difference with intercept
After model specification, we conducted test for stationarity determination. According to the ADF Test, variables HL and CLFS are stationary in their levels with intercept included while variable IFIS and ANNPI become stationary when they are differentiated once with intercept included. Variable IPRE was stationary in 2nd difference also with intercept included in equation. Considering that data used in this research are at quarterly level, it was necessary to determine lag for each variable from 1 to 8. The lag is determined by using Estamet Vector Autoregression Estimates and Lag structure precisely Lag length criteria. According to the Bahovec and Erjavec (2009), Granger Causality Test is a popular method and is often applied in econometric analyses. Although the term causality is most often used in time series analysis, it is a common term in research in other areas of the economy. Although Granger (1969) started from the assumption that the future cannot cause the present or the past, the term causality implies the possibility of a variable to predict the dynamics of another variable. Hoover (2006) states in his paper that Granger Causality is actually the most influential approach to causality
in the economy. The simplest form of the test is performed based on the regression equation defined by the formula:
REt = α0+ ∑ αixt−1 n i=1 + ∑ βjyt−1 m j=1 + εt
where RE refers to residential real estate price growth and α and β refer to variables that predict the dynamics of residential real estate prices trends.
After determining the stationarity of the time series, we created the first probability check with Regressors t-statistics through Vector Autoregression Estimates. The results of Vector Autoregression Estimates and the Granger/Wald Test are presented in Table 3.
Table 3: Vector Autoregression Estimates and Granger/Wald Test (authors` calculation)
Dependent Variable IPRE
Vector Autoregression Estimates Granger/Wald Test t-statistics p ANNPI -1.775755 0.0837 CLFS -1.43470 0.1534 HL -3.22350 0.0038 IFIS -1.65465 0.0137
Due to the Vector Autoregression Estimates, the most significant are variable ANNPI, HL and IFIS. The strongest significance was noticed at variable HL with t-statistics -3.2235 which means that the volume of housing loans will have huge impact on residential real estate prices. According to the t-statistics, nominal net payment and financial conditions affect the residential real estate prices developments. Based on the Wald's test and the results obtained, we conclude that financial conditions, nominal net payment and the banks` credit activity toward housing loans affect the residential real estate prices in Croatia.
4. CONCLUSION
New developments in the economy could have negative effect on the macroeconomic balances. The strong residential real estate prices increase contributes to accumulation of systemic risks and may initiate new negative trends in the economy. The correlation analysis for the period after 2015 in Croatia showed the strong positive correlation between residential real estate prices, nominal net payment and banks` credit activity for housing purpose. At the same time there is strong negative correlation between residential real estate prices and financial conditions, what means that in the period of financial conditions easing the residential real estate prices are increasing. The Granger test confirmed that the better financial conditions cause rise in residential real estate prices at the significance of 0,05, and at the level of 0,1 the nominal net payment also causes the rise in residential real estate prices. According to the development that preceded the last global financial crisis, regulators (central banks) should carefully monitor the real estate price developments and introduce appropriate monetary and macroprudential measures for maintain long run stability of the financial and whole economic stability.
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