Property Bubbles and the Driving Forces in the PIGS Countries 10 February 20161Philipp Klotz Philipp Klotz 1 Tsoyu Calvin Lin 2 Shih-Hsun Hsu 3 1 Ph.D. student, International Doctoral Program in Asia-Pacific Studies, National ChengChi University, Taiwan Professor, Department of Land Economics, National ChengChi University, Taiwan. 3 Assistant Professor, Department of Economics, National ChengChi University, Taiwan. Agenda 10 February 2016Philipp Klotz2 Introduction Framework and Methodology a) Bubble b) Monetary policy and bubble formation Data Empirical Analysis a) Long-run dynamics b) Short-run dynamics Conclusion & Discussion Introduction- PIGS countries 10 February 2016Philipp Klotz3 Source: Standard & Poors (June 2013)BBB- BB BBB+ B- Introduction- PIGS countries and construction sector 10 February 2016Philipp Klotz4 Construction industry as an important sector in the PIGS countries Source: Eurostat (June 2013) Introduction- Research Questions Due to strong reliance on the construction sector, the PIGS countries were severely hit by the downturn in the housing market The literature and policy makers frequently identified monetary policy as a relevant factor in the formation of asset bubbles. (ECB, 2011) Simple deviations of the money and credit aggregates from a trend that exceed a given threshold provide a useful predictor of costly boom and bust cycles (ECB, 2012) Ballooning credit and spending excesses overheated the economy in Ireland and misdirected resources during the booming years before the crisis This paper addresses two central questions 1) To which extent did the PIGS countries experience real estate bubbles throughout the period from 1999 to 2012? 2) What is the role of the monetary policy of the ECB in the formation of property bubbles? 10 February 2016Philipp Klotz5 Framework & Methodology- Bubble 10 February 2016Philipp Klotz6 Framework & Methodology- Monetary policy and bubble formation 10 February 2016Philipp Klotz7 10 February 2016Philipp Klotz8 Data- Bubble 10 February 2016Philipp Klotz9 Data- WACC Data- Key Interest Rate & Euribor 10 February 2016Philipp Klotz10 Data- Lending for house purchase-to-GDP 10 February 2016Philipp Klotz11 Data- Unit Root 10 February 2016Philipp Klotz12 Level Difference Variable t-valuep-valueLags t-valuep-valueLags Result HL ti I(1) IR c I(1) B Portugal c I(1) B Ireland c I(1) B Greece c I(1) B Spain c I(1) Note: The number of lags included in the ADF test is decided by the automatic lag length selection criteria based on SIC with maximum lag length of 10. c indicates that a constant term and ti indicates that a constant term as well as a linear time trend have been included in the model. Empirical analysis- Long-run relationship 10 February 2016Philipp Klotz13 Trace Test Maximum-Eigenvalue Test CountryLagH0trace 5% critical valuep-Value H0max 5% critical valuep-ValueResult Portugal2r= r= r=0 r r= Ireland4r= r= r=1 r r= Greece2r= r= r=1 r r= Spain4r= r= r=1 r r= Note: r is the rank of cointegration. trace is the Trace statistic, testing the null hypotheses r=0 and r1 against the alternative hypotheses r>0 and r>1. max is the Maximum-Eigenvalue statistic, testing the null hypothesis r=0 and r=1 against the alternative hypotheses r=1 and r=2. Ireland Greece Spain BIRHL BIRHL BIRHL Coefficient *-0.98* * *-1.90* Std. error t-statistic Adj. speed0.37*0.02*0.07* -0.20* * Std. error t-statistic Note: * denotes significance at the 95% confidence interval. The critical value for the t-test is Coefficient is the normalized cointegration coefficient; Adj. speed is the speed-of-adjustment coefficient and Std. error the respective standard error. Empirical analysis- Short-run relationship 10 February 2016Philipp Klotz14 Empirical analysis- Short-run relationship: IRF 10 February 2016Philipp Klotz15 Empirical analysis- Short-run relationship: IRF 10 February 2016Philipp Klotz16 Empirical analysis- Short-run relationship: Variance Decomposition 10 February 2016Philipp Klotz17 Portugal Ireland Greece Spain PeriodIRHL IRHL IRHL IRHL Conclusion & Discussion 10 February 2016Philipp Klotz18 1) To what extend did the PIGS countries experience real estate bubbles throughout the period from 1999 to 2012? Spain and Ireland experienced the largest bubble Followed by Portugal with a small positive bubble and Greece with a negative bubble 2) What is the role of the monetary policy of the ECB in the formation of property bubbles? Why are there differences? Country/ RelationshipLong-runShort-run PortugalN/AIR+, HL+ GreeceIR+, HL+ IrelandIR+, HL+IR-, HL+ SpainIR+, HL+IR-, HL+ Conclusion & Discussion 10 February 2016Philipp Klotz19 Differences in the financial system Interest rate channel: Interest rates set by the ECB are transmitted differently Credit channel: Monetary policy affects domestic credit supply differently Diverging Fiscal & Macro prudential policies 1) Countries with low interest and tax rates as well as relatively high LTV- ratios have the potential to experience large positive property bubbles 2) Central banks policies are crucial to trigger the boom & burst of housing bubbles Country/ PolicyMax. tax rate applicable on capital gains LTV-ratio requirement Portugal42%71% Ireland20%83% Greece0%73% Spain18%72.5% Source: ECB (2008); *if capital gains have been or will be reinvested in another permanent residence within certain time limits Thank you! 10 February 2016Philipp Klotz20