An assessment of house loans cointegration with macro variables in selected euro zone countries

Kupčinskas, Kazys; Paškevičius, Arvydas Article An assessment of house loans cointegration with macro variables in selected euro zone countries Ekonomika Provided in Cooperation with: Vilnius University Press Suggested Citation: Kupčinskas, Kazys; Paškevičius, Arvydas (2020) : An assessment of house loans cointegration with macro variables in selected euro zone countries, Ekonomika, ISSN 2424-6166, Vilnius University Press, Vilnius, Vol. 99, Iss. 2, pp. 39-58, https://doi.org/10.15388/Ekon.2020.2.3 This Version is available at: https://hdl.handle.net/10419/323081 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 39 Ekonomika ISSN 1392-1258 eISSN 2424-6166 2020, vol. 99(2), pp. 39–58 DOI: https://doi.org/10.15388/Ekon.2020.2.3 An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone Countries Kazys Kupčinskas Faculty of Economics Vilnius University, Lithuania E-mail: [email protected] Prof. dr. Arvydas Paškevičius Faculty of Economics Vilnius University, Lithuania E-mail: [email protected] Abstract. This paper performs an empirical study on house loans, interest rates, unemployment, and house rent prices relationship in Germany, France, Spain and Italy from the year 2003 to 2018. We look for the cointegration and causality relationship between the house loans and macro variables with the help of the Vector error correction model (VECM) and Granger causality methods. We investigate whether variables with monthly data explain better the relationship and causal effects between the variables. We find a long term cointegrating relationship between the real house loans and interest rates, unemployment and house rent prices for France, Spain, and Italy, but not for Germany. On average the equilibrium in house loan development is reached from 4 to 8 years, meaning that long term equilibrium exists, but the variables reach it in a rather long time period. The ECB deposit facility rate included as an exogenous variable in four countries gained no significant power in explaining the short term changes of house loans in any of the country. We reveal a complex interaction between the bank’s credits and unemployment, interest rates, house rental prices in the paper. Keywords: cointegration, vector error correction model, house loans 1. Introduction The development of the private credit market during the last two decades is as of interesting study, because the period between 2003 and 2018 covers full business cycle – two expansions, one financial crisis, and one recovery period just before the COVID-19 crises. We are interested in whether high-frequency data, like monthly time series, can establish more fruitful results throughout the business cycle for our research object – house loans to private individuals. We check if the hypothesis is valid across the four largest mortgage markets in Euro zone – Germany, France, Italy, and Spain. For that purpose, Received: 30/04/2020. Revised: 28/06/2020. Accepted: 29/06/2020 Copyright © 2020 Kazys Kupčinskas, Arvydas Paškevičius. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Contents lists available at Vilnius University Press ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 40 we have established a vector error correction model (VECM) to find the cointegrating relationships between house loans, cost of borrowing, unemployment and house rent prices. Most of the studies in the field are using either yearly or quarterly data. Annual and quarterly data used in modeling can produce different results for the same countries depending on the frequency of the data (Constantinescu and Lastauskas, 2018). As one of the main economic activity indicators – GDP is not available on monthly data. Hence, we have decided to omit the GDP and substitute it with a very lagged and inverse variable for economic development as - unemployment. Unemployment is measured and available in monthly frequency. It is widely used by other authors (Acemoglu, 2001; Dromel, Kolakez, and Lehmann, 2010; Petrosky, 2014; Bethune et al., 2015), who analyzed private credit or house loans. We have chosen VECM approach because of the non-stationary data, possible long term cointegration between variables and less strict restrictions over economic dependencies of variables in the model. Also in VECM analyses we have included ECB deposit facility rate as an exogenous variable in 4 countries to find out the short term effects of the ECB instrument impact upon the house loan development. The credit availability due to technological information spread and access through online channels became much more available and affordable, thus we are particularly interested on how the interaction of the housing market and unemployment, interest rates and costs of the housing has emerged during the last decades in four largest European Union countries, in terms of total house loan portfolio. There is number of studies of the interaction between the credit market and the macroeconomic variables, covering long term periods of time since the last century, however the banking sector is gradually transforming and old theories and empirical results needs to be tested in the new environment. The main objectives of the paper: a) explain how house loans are modeled based on existing economic models and ideas; b) classify key macroeconomic factors affecting private credit and house loans; c) present a methodology that is most suitable to investigate house loans with a high frequency of data; d) Explain short term and long term relationship between house loans, unemployment, interest rates and price of real estate e) measure Granger causality of the variables. The paper is organized as follows: a review of the literature section, data and descriptive analyses section, methodology and finally results sections. 2. Literature review 2.1. Factors Affecting Housing Market Mortgage market research is traditionally split into credit supply and demand models. Recent studies show that credit supply theory stands better than credit demand theory because private households tend to borrow extensively even if there are obvious signs of a downturn in the economy (Mian et al., 2016). The most noticeable channels are the wealth and the balance sheet. The wealth effect for households creates more borrowing and spending abilities due to increased collateral value when prices of real estate go up. Kazys Kupčinskas, Arvydas Paškevičius. An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone... 41 The channel of balance sheet is a monetary policy transmission process through the bank’s balance sheet. When prices of real estate, which is the most common collateral for the bank, increases - the bank’s balance sheet becomes stronger and credit supply is not restricted due to lower collateral risk. The Interest rates channel – is a mechanism of monetary transmission policy, when depository institutions have to pay a certain deposit facility rate for funds held within ECB. Short term interest rates affect the long term interest rates that are the main determinants of the borrowing costs for households. Channels of wealth, balance sheet, and interest rates explain how market forces and regulatory institutions can influence credit supply and demand in the market. One of the key functions of the central bank is to follow up on the development of the private credit. Since the last financial crises in the year 2008 economists and supervisory bodies have recognized the drawbacks of rapid housing market developments for the economy. There are a number of reasons why excessive borrowing is harmful for the economy - first of all, it creates house price booms, increases interest rates and debt burden for households, lowers affordability of housing for new low-income society members, increases rental costs, creates cycles in the economy and productivity shocks and slowdown of economic activity (Egert and Mihaljek, 2007). The main determinants of the housing market are considered the following: GDP growth, unemployment, interest rates, household’s income growth, GDP per capita, liquidity in the market for real estate sales; taxes; trust in the legal system and property rights; demographic factors, house market supply and demand trends and development. Further, we will discuss some of the determinants and their interactions with economic variables. 2.2. Unemployment Openness of the credit channel for new technology and innovations is very important for credit growth and level of unemployment. Credit market difficulties do not necessarily lead to higher unemployment as found in an empirical study of post-World War II era. Acemoglu (2001) found that countries, like Belgium, Italy and Denmark, with low debt level industries on average had two times higher employment level when compared to USA. European industries with high credit level had on average 30% higher employment level then USA. Other studies show that asymmetric information results in business cycles between the capital and labour markets (with free floating labour costs and prices), when business firms strive for maximization of their profits (Greenwald and Stiglitz, 1986). However other studies (Dromel, Kolakez and Lehmann, 2010) show that credit constraints, such as market imperfections, do not necessary increase the level of unemployment rate, but significantly reduce unemployment persistence to change. Besides the asymmetric information and openness of the credit channel, the unrestricted borrowing has also effect upon the unemployment. The maximum borrowing limits of the households depends on the complexity of the financial system, frequency of the liquidity shocks in the economy, and liquid household assets that serve as collateral. The theoretical model claims that unconstrained borrowing reduces the alternative costs of new hiring when market pressure for higher wages emerges. The elasticity of the job ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 42 market improves when costs of financing for new hires are lower because it encourages recruitment by the firms. Unemployment, through the vacancy costs and wage channels, match the volatility and credit market persistence (Petrosky, 2014). Multiple steady states or equilibriums between the credit and labor markets exist, because both markets are sustaining each other. For example, in the USA during 1980 and 2008 the decline of the long term average unemployment rate jumped after 3 quarters of the rise in unsecured household lending (Bethune, Rocheteau and Rupert, 2015). In other economic sectors, credit growth is related with real productivity increase, but this is not the case for the construction industry. Rapid growth in the financial sector usually coincides with a boom in the construction industry (Cecchetti and Kharroubi, 2015). It is because real estate and construction industries are rich in terms of collateral and low in productivity growths. Labor division between the skilled and unskilled workers creates pressure for the financial sector. The manufacturing industry is a lot dependent on the financial sector due to heavy need for R&D or external financing and sector’s heavy growth is at the expense of real economy growth. It leads to cycle development of the economy and ups and downs in the unemployment rate. 2.3. Cost of borrowing and interest rates During the last decade the environment of decreasing interest rates in the Euro area did not allow to establish models that would account for a sharp increase in interest rates and consequently lower demand for credit. While in periods of volatile interest rates innovations of short-term interest rates were suppressing the development of bank credit and GDP. Hoffman (2001) found that house property prices established long-term relationships between the credit, short and long-term interest rates. Mian et al. (2016) found that lower interest rates spread encourage growth of debt to GDP ratio and afterwards higher debt to GDP ratio increased unemployment. Factors as exchange rate regime, monetary policy, share of foreign trade (current account minus or surplus) had effect on the household debt and consumption. When household debt increases due to increased debt burden the consumption decreases and imports decreases. 2.4. Prices of real estate and house rent If rents for housing increases more than housing pricing, than in the long term there would be a upward pressure on house prices, if we believe a long term equilibrium exists. In such situation, it would be more favorable to buy a house instead of paying an increased rent. House rent index in the harmonized consumer price index is positively related with house rents (Jacobsen and Naug, 2005). Lagged shocks in the commercial real estate prices are the main contributors to the variation of the asset prices, while GDP and bank credit variables had less significant effects for the results (Davis and Zhu, 2011). There is no common consensus on what variables and methods should be applied for modeling the demand for house loans also it is difficult to strictly differentiate which factors affect only the supply or the demand side. Apart from the macroeconomic, demographic Kazys Kupčinskas, Arvydas Paškevičius. An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone... 43 and legal factors, there is a number of financial stability indicators used in the analyses of private credit development to relate to a number of macro prudential policy implications. When demand and supply for credit is in disequilibrium, there are additional costs for the economy (higher interest rates, higher prices). A mixture of ratio or level variables is used for the assessment of imbalances. Modeling sustainable credit development means identifying a credit gap or excess volume of credit. Excess of credit creates an additional need for credit provisions for the banking system. Shortage of credit increase interest rates and decrease house prices although the relationship between the credit prices and credit volume are complicated and in different directions. 2.5. Summary of econometric methods for the housing market Overall, the dynamic stochastic general equilibrium (DSGE) models are based on fundamental economic principles. However, DSGE model has problems in explaining empirical case studies when it comes to house price dynamics and population growth, construction costs, interests and house rental costs (Diks and Wang, 2016, Shiller, 2015). The same falls for the consumption of households and industrial production levels that are unable to explain the instability of house prices (Davis and Heathcote, 2005). When the cointegrating coefficients are subject to possible non-linear relationship and cross-equation restrictions Long run structural modeling is a solution for modeling. Autoregressive distributed lag model was used by Constantinescu and Lastauskas (2018), Pesaran and Shin (1998), and other authors. An additional test of Granger causality is widely used to define the direction of the effect for structural models between the variables (Constantinescu and Lastauskas, 2018; Panagiotidis and Printzis, 2016). Structural models for macro variables suffer from a number of endogeneity problems; thus vector error correction (VECM) model without such restrictions is an attractive choice. VECM models are very useful for the long-term relationship establishment, but the other benefit is the ability to include exogenous variables and estimate short-term effects (Panagiotidis and Printzis, 2016). There are a number of authors (Anundsen et al., 2016; Arestis et al. 2014; Davis and Zhu, 2011; Hoffman, 2001) who performed cross country analyses for a large group of countries, to find out private credit interaction with aggregated macro variables. Panel vector error correction model for cross-country credit empirical analyses is advantageous of the single country analyses due to ability to aggregate the data when single country yearly data are too short for multivariate regression analyses. 3. Methodology 3.1. Data used in analyses We have used a monthly time series data of four countries (Germany, France, Spain, Italy) covering period of from year 2003 to end of 2018. The variables (unemployment rate, costs of funding loans and costs of renting real estate) were selected based on credit demand and supply theories to recent empirical studies. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 44 3.2. Short overview of the credit market and selected variables development The largest house loan market in euro area was Germany with a 1231 trillion euro home loan portfolio; the second was France - 1046 trillion euro; third - Spain 517 trillion euro, and Italy 380 trillion euro. Since 2003 the average annual grow rate of house loan portfolio (in nominal values) was 2% in Germany, and 12% in France and Italy. The scenario for Spain was different - peak of house loan portfolio equal to 658 trillion euro was reached at the end of year 2008. Afterwards slow deleveraging happened and by the end of year 2018 house loans in Spain reached the lowest value of 517 trillion euro (See APPENDIX 2). Home ownership rate at end of year 2018 in selected European countries varied across the countries: in Germany - 52%, in France 65%, in Italy – 72% and in Spain - 75%, while the rest of tenants rented the flat. In Germany, France and Spain about a half of home owners had house loans, while in Italy only 25% of tenants had a mortgage. Level of home ownership with mortgages corresponds to the average households debt ratios in the countries: In France 96%; In Spain 91%; In Germany 87% and in Italy 62%. Table 1. Descriptive statistics of the variables in Germany and France Country Germany France Log of real house Loans volume Households cost of borrowing,% Unemployment rate,% House rent price, % Log of real house Loans volume Households cost of borrowing,% Unemployment rate,% House rent price, % Mean 7.178 3.53 6.82 1.94 7.126 3.41 9.21 2.63 Median 7.180 3.77 6.44 2.00 7.143 3.67 9.15 2.70 Std. Dev. 0.014 1.24 2.45 1.81 0.045 1.06 0.83 1.71 Variance 0.000 1.55 5.98 3.26 0.002 1.12 0.68 2.93 Range 0.061 3.87 7.99 7.60 0.188 3.86 3.31 7.60 Min. 7.145 1.61 3.25 -1.80 7.001 1.48 7.24 -1.50 Max. 7.206 5.48 11.24 5.80 7.190 5.34 10.55 6.10 Count 192 192 192 192 192 192 192 192 Source: summary made by authors Annual changes in house rent prices averaged: 1.94% in Germany, 2.63% in France, 2.99% in Spain, and 2.68% in Italy. Households cost of borrowing followed a downward trend due to the ECB low interest rate policy after 2008 financial crises. Average nominal interest rates for house loans were similar: in Germany - 3.5%, in France - 3.4%, in Spain - 3.20% and in Italy - 3.6%. Average unemployment rate was traditionally highest in Spain – 16.7%, than in Italy – 9.3%, France 9.2% and lowest in Germany 6.8% (see TABLE No.1 and No.2). Kazys Kupčinskas, Arvydas Paškevičius. An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone... 45 Table 2. Descriptive statistics of the variables in Spain and Italy Country Spain Italy Log of real house Loans volume Households cost of borrowing,% Unemployment rate,% House rent price, % Log of real house Loans volume Households cost of borrowing,% Unemployment rate,% House rent price, % Mean 7.123 3.20 16.77 2.99 6.977 3.55 9.32 2.68 Median 7.135 3.10 17.71 3.45 6.997 3.68 8.49 2.75 Std. Dev. 0.057 1.07 6.05 3.34 0.048 1.07 2.13 2.81 Variance 0.003 1.14 36.56 11.14 0.002 1.15 4.55 7.87 Range 0.232 4.23 18.38 15.60 0.194 4.12 7.22 11.20 Min. 6.957 1.84 7.93 -7.20 6.831 1.79 5.83 -2.40 Max. 7.190 6.07 26.31 8.40 7.025 5.91 13.05 8.80 Count 192 192 192 192 192 192 192 192 Source: summary made by authors 3.3. Description of the variables: •Log of Real House Loans volume (abbreviation: RHL) – describes loans to euro area households for house purchase, including all currencies and all maturities that are denominated in euro. The initial data was not seasonally adjusted. We have adjusted for seasonality component, used the Harmonized Index for Consumer prices to derive the real house loans and afterwards converted into logarithm values. Figure No.1 depicts the development of the real house loans after the adjustments. There are some authors who used nominal loan and interest values without the adjustment for the inflation because nominal interest rates are the actual rates paid for the actual/nominal value of mortgage. However, absolute majority of the studies prefer to use variables adjusted by the consumer price index because it better reflects the economic reality, real economic development and real prices of assets that serves as a collateral. FIG. No.1. Log of Real house loans volume in selected countries Source: Prepared by authors, based on ECB data  Households cost of borrowing (abbreviation: RR1) is equivalent to interest rates of new house loans. It is a total floating rate or an initial fixed rate to euro area households on euro-denominated loans for house purchase. Rates are in percentages per annum and reflect rates on new business. Again, we have adjusted the cost of borrowing for Harmonized Index for Consumer prices changes for every respective country. We expect a negative relation in the long term equation with the house loans. Although a negative interest rates environment and low variation in real rate might neglect the fundamental principle in demand curve between the credit and interest rates.  Unemployment (abbreviation: U) – harmonized unemployment rate (as a percentage of total labor force), standardized across countries, all ages, male and female, seasonally adjusted, not working day-adjusted. We expect a negative sign in the long term equation of the real house loan demand.  House rent price (abbreviation: P) – is a subgroup of the harmonized index of consumer prices (HICP) for housing, including rents and other housing related costs from Eurostat. It measures the average change of prices paid by households for house rents. Housing rent costs increase the demand for acquisition of the real estate and demand for mortgages. Based on the wealth channel for the house prices we expect a positive relation to the real house loan variable in the long term. 6,80 6,85 6,90 6,95 7,00 7,05 7,10 7,15 7,20 7,25 2003-01 2004-09 2006-05 2008-01 2009-09 2011-05 2013-01 2014-09 2016-05 2018-01 Germany France Spain Italy Fig. 1. Log of Real house loans volume in selected countries Source: Prepared by authors, based on ECB data ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 46 • Households cost of borrowing (abbreviation: RR1) is equivalent to interest rates of new house loans. It is a total floating rate or an initial fixed rate to euro area households on euro-denominated loans for house purchase. Rates are in percentages per annum and reflect rates on new business. Again, we have adjusted the cost of borrowing for Harmonized Index for Consumer prices changes for every respective country. We expect a negative relation in the long term equation with the house loans. Although a negative interest rates environment and low variation in real rate might neglect the fundamental principle in demand curve between the credit and interest rates. • Unemployment (abbreviation: U) – harmonized unemployment rate (as a percentage of total labor force), standardized across countries, all ages, male and female, seasonally adjusted, not working day-adjusted. We expect a negative sign in the long term equation of the real house loan demand. • House rent price (abbreviation: P) – is a subgroup of the harmonized index of consumer prices (HICP) for housing, including rents and other housing related costs from Eurostat. It measures the average change of prices paid by households for house rents. Housing rent costs increase the demand for acquisition of the real estate and demand for mortgages. Based on the wealth channel for the house prices we expect a positive relation to the real house loan variable in the long term. • ECB Dummy (abbreviation: dum_ecb1) – interest rates on the EUR deposit facility; it is one of the three interest rates that helps ECB to implement monetary policy. Deposit facility rate reflects the overnight interest rate received (in positive interest rates environment) or paid (in negative interest environment) for the placed deposits within the central bank. We have selected the variable to measure the policy effect for the real house loans. Due to the deposit costs or no return for the euro deposits in a bank, we expect that negative interest rates will encourage households to spend or invest the excess funds. 3.4. Time series tests for stationary While performing any regression analyses on the macroeconomic time series the key importance is to check for the stationary of the variables. We ran the Augmented DickeyFuller test and the Phillips-Perron test, to investigate if unit roots are present in the variables or checking if data are non-stationary. Dickey-Fuller test results are in Table No. 3. The more negative values are the stronger rejection of the null hypothesis of the tests. The results indicate that values in levels of log real house loans, real interest rates, unemployment rate are non-stationary in all of the countries. The first-order differences became stationary - were not increasing over time and, had no trend or drift. The only exception is the log of real house loans in Spain where we rejected the null hypothesis of the test with the 10 pct confidence level and confirmed I(1). Tests confirmed that coefficients of the variables with first differences became consistent and had no unit roots. Kazys Kupčinskas, Arvydas Paškevičius. An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone... 53 Tabble 7. Durbin-Watson test for residuals stability Countries / Variables Italy Spain France rhl DW = 1.99, p-value = 0.47 DW = 1.96, p-value = 0.38 DW = 2.01, p-value = 0.53 rr1 DW = 2.04, p-value = 0.59 DW = 2.04, p-value = 0.63 DW = 1.98, p-value = 0.46 uDW = 2.04, p-value = 0.60 DW = 2.01, p-value = 0.55 DW = 2.06, p-value = 0.67 pDW = 2.07, p-value = 0.68 DW = 1.92, p-value = 0.30 DW = 2.02, p-value = 0.56 Source: Authors’ calculation 4.5. Granger causality tests Even though the VECM relationships with cointegrating vectors have been found for France, Spain and Italy equations, further analyses is needed on the direction of the causal effects between the variables. Granger causality does not imply/require cointegration as two or more series can have Granger causality among themselves if they are both integrated of the same order I(0) or I(1) or cointegrated. However, the presence of the cointegration suggests that there must be a Granger causality in at least one direction between the variables. Based on Lütkepohl (2013) the results of the Granger causality can depend on the frequency of the data chosen for the analyses. Granger causality does not reflect the real causality relationship of the variables, but indicate the direction. 4.6. Results of Granger causality for single equations We have found that none of the first differences of variables (interest rates, unemployment, house rent prices and ECB dummy) in analyses did not have Granger causality towards the log of real house loans in any of the four countries (see APPENDIX 5). However, the opposite Granger causality effect of real house loans towards the variables was found in France with a 10% significance level only and in Spain with a 5% significance level. France real house loans had Granger causality for Interest rates (p=0.09), unemployment (p=0.09) and for exogenous variable ECB deposit facility rate (p=0.08). In Spain real house loans had Granger causality for interest rates (p=0.03) and for exogenous variable ECB deposit facility rate (p=0.009). We can conclude that only one way Granger causality effects were found and no bi-directional or two-way Granger causality effects were found for any of the country. Interest rates, unemployment and ECB deposit facility rate had the Granger causality with the house loans. Results suggest that house rent prices did not contribute for better forecasts of real house loans in any of the country model. Log of real house loans variable in France and Spain improves the prediction capabilities of the singular equations of interest rates, unemployment and ECB deposit facility rate. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 54 4.7. Results of instantaneous causality for multivariate VAR system Granger causality and instantaneous causality helps to explain the prediction capabilities of a multivariate VAR system, if adding certain argument can improve the prediction capabilities of the whole system. The Granger causality in multivariate systems rejected the null hypothesis of no Granger causality for unemployment in France (p=0.0005), for real interest rates in Spain (p=0.08) and in Italy (p=0.02), meaning that the named variables contributed for a better forecasting of the combined variables in the VAR models (see APPENDIX 6). Unemployment variable in all VAR system equations did not reject the null hypothesis of the instantaneous causality effect for the variables across all the countries. 5. Conclusions Vector error correction (VECM) models are widely used in economic literature to model non-stationary, integrated of the same order and cointegrated economic variables. We have found that log of real house loans, real interest rates, unemployment and house rental costs were non-stationary in levels and integrated of the same first order. Also and we have rejected the structural breaks in the time series data of 2003 until 2018. Empirical results suggests that in France, Spain and Italy there is a long term cointegrating relationship between the real house loans and interest rates, unemployment and house rent costs. On average the equilibrium is reached in 4 to 8 years, meaning that long term equilibrium exists, but the variables reach it in rather long term period. We did not find any cointegration relationship for Germany house loans and selected variables. High-frequency monthly data did not show any significant co-movements of the variables in the short run. Correction speed to equilibrium house loans was estimated for a long period, thus quarterly and annual data of variables are better choices for the analyses of the variables. The ECB deposit facility rate included as an exogenous variable in four countries gained no significant power in explaining the short term changes of house loans in any of the country. Results of short-term equations indicate that log of real house loans in disequilibrium were driven by different factors: most significant for the shortterm variation were lagged values of the log of real house loans and change of interest rates. We found no bi-directional and only one-way Granger causality effects for the countries analyzed. Log of real house loans variable in France and Spain improved the prediction capabilities of the singular equations of interest rates, unemployment, and ECB deposit facility rate. While in opposite direction interest rate for mortgages, unemployment rate and house rent prices did not Granger cause the log of the house loans in any country’s model. The Granger causality in multivariate VAR systems rejected the null hypothesis of no Granger causality for unemployment in France, in Spain, and in Italy, meaning that unemployment contributed for better forecasting of the combined variables in the VAR models. Additional research on house loans may cover more euro zone countries and tools for better short term prediction of the demand for house loans and related variables. Kazys Kupčinskas, Arvydas Paškevičius. An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone... 55 References Acemoglu, D. (2001). Credit market imperfections and persistent unemployment. European Economic Review, 45(4-6), 665-679. https://doi.org/10.1016/s0014-2921(01)00107-6. Anundsen, A. K., Gerdrup, K., Hansen, F., & Kragh-Sørensen, K. (2016). Bubbles and crises: The role of house prices and credit. Journal of Applied Econometrics, 31(7), 1291-1311. https://doi.org/10.1002/jae.2503. Arestis, P., & González, A. R. (2014). Modelling the housing market in OECD countries. International Review of Applied Economics, 28(2), 131-153. https://doi.org/10.1080/02692171.2013.828683. Baum, C. (2015). ZANDREWS: Stata module to calculate Zivot-Andrews unit root test in presence of structural break. Bethune, Z., Rocheteau, G., & Rupert, P. (2015). Aggregate unemployment and household unsecured debt. Review of Economic Dynamics, 18(1), 77-100. https://doi.org/10.1016/j.red.2014.08.002. Cecchetti, S. G., & Kharroubi, E. (2015). Why does financial sector growth crowd out real economic growth?. Constantinescu, M., & Lastauskas, P. (2018). The knotty interplay between credit and housing. The Quarterly Review of Economics and Finance, 70, 241-266. https://doi.org/10.1016/j.qref.2018.05.013. Davis, E. P., & Zhu, H. (2011). Bank lending and commercial property cycles: some cross-country evidence. Journal of International Money and Finance, 30(1), 1-21. https://doi.org/10.1016/j.jimonfin.2010.06.005. Davis, M. A., & Heathcote, J. (2005). Housing and the business cycle. International Economic Review, 46(3), 751-784. https://doi.org/10.1111/j.1468-2354.2005.00345.x. Diks, C., & Wang, J. (2016). Can a stochastic cusp catastrophe model explain housing market crashes?. Journal of Economic Dynamics and Control, 69, 68-88. https://doi.org/10.1016/j.jedc.2016.05.008. Dromel, N. L., Kolakez, E., & Lehmann, E. (2010). Credit constraints and the persistence of unemployment. Labour Economics, 17(5), 823-834. https://doi.org/10.1016/j.labeco.2010.04.005. Egert, B., & Mihaljek, D. (2007). Determinants of house prices in central and eastern Europe. Comparative economic studies, 49(3), 367-388. https://doi.org/10.1057/palgrave.ces.8100221. Greenwald, B. C., & Stiglitz, J. E. (1986). Externalities in economies with imperfect information and incomplete markets. The quarterly journal of economics, 101(2), 229-264. https://doi.org/10.2307/1891114. Greenwald, B. C., & Stiglitz, J. E. (1986). Imperfect information, credit markets and unemployment (No. 2093). National Bureau of Economic Research. Hofmann, B . 2001: The determinants of private sector credit in industrialised countries: do property prices matter? BIS Working Paper No. 108. Jacobsen, D. H., & Naug, B. E. (2005). What drives house prices?. Lütkepohl, H. (2013). Introduction to multiple time series analysis. Springer Science & Business Media. Mian, A., Sufi, A., Verner, E. (2016) Household Debt and Business Cycles Worldwide Kreisman Working Paper Series in Housing Law and Policy Nunes, L. C., Newbold, P., & Kuan, C. M. (1997). Testing for unit roots with breaks: evidence on the great crash and the unit root hypothesis reconsidered. Oxford Bulletin of Economics and Statistics, 59(4), 435448. https://doi.org/10.1111/1468-0084.00076. Panagiotidis, T., & Printzis, P. (2016). On the macroeconomic determinants of the housing market in Greece: A VECM approach. International Economics and Economic Policy, 13(3), 387-409. https://doi.org/10.1007/ s10368-016-0345-3. Pesaran, M. H., & Shin, Y. (1998). An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31, 371-413. Petrosky-Nadeau, N. (2014). Credit, vacancies and unemployment fluctuations. Review of Economic Dynamics, 17(2), 191-205. https://doi.org/10.1016/j.red.2013.10.001. Shiller, R. J. (2015). Irrational exuberance: Revised and expanded third edition. Princeton university press. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 56 Appendix 1. Germany, France, Spain and Italy dynamics of key model’s variables over time, % APPENDIX 1 Germany, France, Spain and Italy dynamics of key model’s variables over time, % Source: European Central Bank (ECB) -5 0 5 10 15 2003-01 2004-09 2006-05 2008-01 2009-09 2011-05 2013-01 2014-09 2016-05 2018-01 Households_cost_of_borrowing_Germany Unemp_Germany InflHousing_Germany -5 0 5 10 15 2003-01 2004-09 2006-05 2008-01 2009-09 2011-05 2013-01 2014-09 2016-05 2018-01 Households_cost_of_borrowing_France Unemp_France InflHousing_France -10 0 10 20 30 2003-01 2004-09 2006-05 2008-01 2009-09 2011-05 2013-01 2014-09 2016-05 2018-01 Households_cost_of_borrowing_Spain Unemp_Spain InflHousing_Spain -5 0 5 10 15 2003-01 2004-09 2006-05 2008-01 2009-09 2011-05 2013-01 2014-09 2016-05 2018-01 Households_cost_of_borrowing_Italy Unemp_Italy InflHousing_Italy Source: European Central Bank (ECB) Kazys Kupčinskas, Arvydas Paškevičius. An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone... 57 Appendix 2. Descriptive statistics of nominal house loans, in billion euro House loans Germany France Spain Italy Mean 1010 726 544 299 Median 973 776 575 353 Standard Deviation 79 195 122 78 Range 318 695 426 247 Minimum 913 351 239 132 Maximum 1231 1046 665 380 Count 192 192 192 192 Source: Prepared by authors, based on European Central Bank (ECB) data Appendix 3. Zivot-Andrews Unit Root Test (1992) with a single structural break Country Germany France Spain Italy Variables Null hypothesis Alternative hypothesis t-stat PSB t-stat PSB t-stat PSB t-stat PSB RHL I (1) Stationary -3.06 2009/9 -3.25 2006/1 -3.91 2005/4 -3.39 2010/4 I (2) I (1) -5.32 2008/3 -5.10 2016/9 -4.32 2008/3 -5.22 2014/3 RR1 I (1) Stationary -3.42 2011/4 -4.16 2007/1 -4.78 2005/9 -3.90 2008/7 I (2) I (1) -5.55 2008/5 -5.82 2008/9 -4.75 2008/6 -6.00 2008/5 U I (1) Stationary -5.36 2009/5 -3.25 2010/1 -2.97 2008/3 -3.38 2011/7 I (2) I (1) -6.24 2005/3 -5.24 2008/1 -5.12 2007/3 -5.22 2011/4 PI (1) Stationary -3.39 2013/8 -3.17 2010/8 -3.10 2012/8 -2.54 2014/3 I (2) I (1) -5.68 2016/1 -6.05 2006/3 -5.99 2016/3 -6.63 2012/8 Note: PSB (Potential break point at position); Lag=12; Intercept only; Critical values for test statistics: 1 pct -5.34; 5 pct -5.08, and 10pct = -4.82; Source: Authors’ calculations Appendix 4. Lag selection criteria for VAR systems for different countries Country / Lag 1234567891011 12 Germany AIC -23.7 -25.2 -25.2 -25.2 -25.2 -25.1 -25 -25 -25 -25 -24.9 -24.9 HQ -23.2 -24.7 -24.5 -24.4 -24.2 -24.1 -23.9 -23.7 -23.6 -23.5 -23.3 -23.1 BIC -22.6 -23.8 -23.5 -23.2 -22.9 -22.5 -22.2 -21.9 -21.6 -21.3 -20.9 -20.6 France AIC -24 -24.9 -24.8 -24.9 -24.9 -24.9 -24.9 -24.7 -24.7 -24.7 -24.6 -24.6 HQ -23.5 -24.3 -24.1 -24.1 -24 -23.9 -23.7 -23.5 -23.3 -23.2 -23 -22.9 BIC -22.9 -23.5 -23.1 -22.9 -22.7 -22.4 -22 -21.6 -21.3 -21 -20.7 -20.4 Spain AIC -19.2 -20.6 -20.5 -20.6 -20.5 -20.5 -20.4 -20.4 -20.3 -20.2 -20.1 -20.1 HQ -18.7 -19.8 -20 -19.8 -19.6 -19.4 -19.2 -19.1 -19 -18.7 -18.5 -18.4 BIC -18 -18.8 -19.1 -18.6 -18.2 -17.9 -17.6 -17.3 -16.9 -16.5 -16.2 -15.8 Italy AIC -19.8 -20.3 -20.3 -20.3 -20.3 -20.3 -20.2 -20.1 -20 -19.9 -19.8 -19.8 HQ -19.4 -19.6 -19.6 -19.5 -19.4 -19.2 -19 -18.8 -18.6 -18.4 -18.2 -18.1 BIC -18.7 -18.7 -18.8 -18.3 -18 -17.7 -17.3 -17 -16.6 -16.2 -15.8 -15.5 Note: Bold font denotes the lowest value of lag selection criteria. Akaike’s Information Criterion (AIC). Hannan-Quinn Criterion (HQ); Schwarz Criterion (SC)/ BIC; Source: Authors’ calculations ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2020, vol. 99(2) 58 Appendix 5. Singular Granger causality tests Variables F-test Variables F-test One way Germany ∆rhl~∆rr1 1.0625 {0.3956} ∆rr1~∆rhl 1.2450 {0.2574} No ∆rhl~∆u 0.6463 {0.7999} ∆u~ ∆rhl 1.3848 {0.1785} No ∆rhl~∆p 1.1186 {0.3489} ∆p~ ∆rhl 0.7748 {0.6756} No ∆rhl~∆r_ecb 0.4848 {0.9213} ∆r_ecb~∆rhl 1.0664 {0.3922} No France ∆rhl~∆rr1 1.4515 {0.1484} ∆rr1~∆rhl 1.6261 ***{0.08945}Yes ∆rhl~∆u 0.9319 {0.5168} ∆u~ ∆rhl 1.6358 ***{0.08688}Yes ∆rhl~∆p 0.6331 {0.8116} ∆p~ ∆rhl 0.8557 {0.5932} No ∆rhl~∆r_ecb 0.2333 {0.9964} ∆r_ecb~∆rhl 1.6675 ***{0.07896}Yes Spain ∆rhl~∆rr1 1.2941 {0.2270} ∆rr1~∆rhl 1.9712 ** {0.03028} Yes ∆rhl~∆u 1.3378 {0.2025} ∆u~ ∆rhl 1.2651 {0.2445} No ∆rhl~∆p 1.1699 {0.3093} ∆p~ ∆rhl 1.0179 {0.4352} No ∆rhl~∆r_ecb 0.4889 {0.919} ∆r_ecb~∆rhl 2.3260* {0.0092} Yes Italy ∆rhl~∆rr1 0.5147 {0.903} ∆rr1~∆rhl 0.7769 {0.6735} No ∆rhl~∆u 1.0533 {0.4035} ∆u~ ∆rhl 0.9733 {0.4768} No ∆rhl~∆p 0.7108 {0.7394} ∆p~ ∆rhl 1.2465 {0.2564} No ∆rhl~∆r_ecb 0.3996 {0.9621} ∆r_ecb~∆rhl 0.9416 {0.5073} No Note: Signs *,**, and *** indicate the significance at 1%, 5%, and 10% levels respectively; p-values are reported in {}; ∆ denotes first difference of the variables; Source: Authors’ calculations Appendix 6. Multivariate Granger causality test for VAR system H0: selected variable does not Granger-cause variables in brackets F-Test p-value H0: No instantaneous causality between variables Chisquared p-value Germany ∆rhl ~ ∆ (rr1, u, p) 1.1081 0.3095 ∆rhl and ∆(rr1, u, p) 31.613*0.0001 ∆rr1 ~ ∆ (rhl, u, p) 1.0529 0.3884 ∆rr1 and ∆(rhl, u, p) 35.384*0.0000 ∆u ~ ∆ (rhl, rr1, p) 1.0281 0.4268 ∆u and ∆(rhl, rr1, p) 4.098 0.2511 ∆p ~ ∆ (rhl, rr1, u) 1.0342 0.4172 ∆p and ∆(rhl, rr1, u) 8.765** 0.0326 France ∆rhl ~ ∆ (rr1, u, p) 0.1385 0.2703 ∆rhl and ∆(rr1, u, p) 35.738*0.0000 ∆rr1 ~ ∆ (rhl, u, p) 1.2074 0.1938 ∆rr1 and ∆(rhl, u, p) 34.864*0.0000 ∆u ~ ∆ (rhl, rr1, p) 5.9699* 0.0005 ∆u and ∆(rhl, rr1, p) 7.7631 0.0512 ∆p ~ ∆ (rhl, rr1, u) 1.0827 0.3556 ∆p and ∆(rhl, rr1, u) 10.27 ** 0.0164 Spain ∆rhl ~ ∆ (rr1, u, p) 1.1019 0.3179 ∆rhl and ∆(rr1, u, p) 10.697** 0.0135 ∆rr1 ~ ∆ (rhl, u, p) 1.3652*** 0.0800 ∆rr1 and ∆(rhl, u, p) 7.1998*** 0.0658 ∆u ~ ∆ (rhl, rr1, p) 1.2391 0.1644 ∆u and ∆(rhl, rr1, p) 4.899 0.1793 ∆p ~ ∆ (rhl, rr1, u) 1.0046 0.4643 ∆p and ∆(rhl, rr1, u) 11.073** 0.0113 Italy ∆rhl ~ ∆ (rr1, u, p) 0.8407 0.7331 ∆rhl and ∆(rr1, u, p) 5.9961 0.1118 ∆rr1 ~ ∆ (rhl, u, p) 0.7063 0.9004 ∆rr1 and ∆(rhl, u, p) 9.9973** 0.0185 ∆u ~ ∆ (rhl, rr1, p) 1.5586** 0.0222 ∆u and ∆(rhl, rr1, p) 3.7272 0.2925 ∆p ~ ∆ (rhl, rr1, u) 1.0643 0.3714 ∆p and ∆(rhl, rr1, u) 7.1489*** 0.0673 Note: Signs *,**, and *** reject the null hypothesis at 1%, 5%, and 10% levels of significance respectively; ∆ denotes first difference of the variables; Source: Authors’ calculations