Property Bubbles and the Driving Forces in the PIGS Countries

Property Bubbles and the Driving Forces in the PIGS Countries

21 April 2023 1Philipp Klotz

Philipp Klotz1

Tsoyu Calvin Lin2

Shih-Hsun Hsu3

1 Ph.D. student, International Doctoral Program in Asia-Pacific Studies, National ChengChi University, Taiwan. Email: [email protected] Professor, Department of Land Economics, National ChengChi University, Taiwan. Email: [email protected] Assistant Professor, Department of Economics, National ChengChi University, Taiwan. Email: [email protected].

mailto:[email protected]



Agenda

21 April 2023 Philipp Klotz 2

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

21 April 2023 Philipp Klotz 3Source: Standard & Poor’s (June 2013) http://www.standardandpoors.com/ratings/sovereigns/ratings-list

BBB-

BB

BBB+

B-

Introduction- PIGS countries and construction sector


Construction industry as an important sector in the PIGS countries

Source: Eurostat (June 2013) http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/

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?


Framework & Methodology- Bubble


Framework & Methodology- Monetary policy and bubble formation



Data- Bubble

-80

-40

0

40

80

120

160

99 00 01 02 03 04 05 06 07 08 09 10 11 12

PortugalIrelandGreeceSpain


Data- WACC

1

2

3

4

5

6

7

8

9

10

99 00 01 02 03 04 05 06 07 08 09 10 11 12

PortugalIrelandGreeceSpain

Data- Key Interest Rate & Euribor


0

1

2

3

4

5

6

99 00 01 02 03 04 05 06 07 08 09 10 11 12

3-month EuriborKey interest rate on refinancing operation rates of the ECB

Data- Lending for house purchase-to-GDP


100

110

120

130

140

150

160

170

99 00 01 02 03 04 05 06 07 08 09 10 11 12

Lending for house purchase-to-GDP (%)

Data- Unit Root


Level Difference

Variable t-value p-value Lags t-value p-value Lags ResultHL ti 1.07 1.00 0 -5.44 0.00 0 I(1)IR c -1.85 0.35 1 -3.89 0.00 0 I(1)

BPortugal c -2.78 0.07 1 -3.51 0.01 0 I(1)

BIreland c -1.84 0.36 1 -4.15 0.00 0 I(1)

BGreece c -1.86 0.35 1 -5.39 0.00 0 I(1)

BSpain c -2.61 0.10 1 -3.52 0.01 0 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


Trace Test Maximum-Eigenvalue Test

Country Lag H0 λtrace 5% critical

value p-Value H0 λmax 5% critical

value p-Value ResultPortugal 2 r=0 19.93 29.80 0.43 r=0 13.89 21.13 0.37 r=0

r≤1 6.04 15.49 0.69 r=1 4.30 14.26 0.83 Ireland 4 r=0 60.54 29.80 0.00 r=0 46.17 21.13 0.00 r=1

r≤1 14.37 15.49 0.07 r=1 10.83 14.26 0.16 Greece 2 r=0 36.26 29.80 0.01 r=0 35.14 29.80 0.01 r=1

r≤1 10.55 15.49 0.24 r=1 13.36 15.49 0.10 Spain 4 r=0 36.26 29.80 0.01 r=0 25.71 21.13 0.01 r=1

r≤1 10.55 15.49 0.24 r=1 7.81 14.26 0.40 Note: r is the rank of cointegration. λtrace is the Trace statistic, testing the null hypotheses r=0 and r≤1 against the alternative hypotheses r>0 and r>1. λmax 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 B IR HL B IR HL B IR HLCoefficient 1 -26.31* -0.98* 1 -7.54* -0.18 1 -38.21* -1.90*Std. error -1.09 -0.07 -1.40 -0.11 -1.51 -0.10t-statistic -24.22 -14.31 -5.39 -1.61 -25.27 -19.61 Adj. speed 0.37* 0.02* 0.07* -0.20* 0.00 0.01 0.10 0.01 0.07*Std. error 0.13 0.01 0.02 0.07 0.01 0.02 0.22 0.01 0.02t-statistic 2.86 3.80 4.27 -2.97 -0.07 0.70 0.44 1.33 3.92Note: * denotes significance at the 95% confidence interval. The critical value for the t-test is 1.96. 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


Empirical analysis- Short-run relationship: IRF


0.0

0.2

0.4

0.6

0.8

1.0

2 4 6 8 10 12 14 16 18 20

Response of B_Portugal to IR

0.0

0.2

0.4

0.6

0.8

1.0

2 4 6 8 10 12 14 16 18 20

Response of B_Portugal to HL

Response to Cholesky One S.D. Innovations

0

1

2

3

4

5

2 4 6 8 10 12 14 16 18 20

Response of B_Greece to IR

0

1

2

3

4

5

2 4 6 8 10 12 14 16 18 20

Response of B_Greece to HL


Empirical analysis- Short-run relationship: IRF


-15

-10

-5

0

5

10

15

2 4 6 8 10 12 14 16 18 20

Response of B_Ireland to IR

-15

-10

-5

0

5

10

15

2 4 6 8 10 12 14 16 18 20

Response of B_Ireland to HL


-20

-10

0

10

20

30

2 4 6 8 10 12 14 16 18 20

Response of B_Spain to IR

-20

-10

0

10

20

30

2 4 6 8 10 12 14 16 18 20

Response of B_Spain to HL


Empirical analysis- Short-run relationship: Variance Decomposition


Portugal Ireland Greece Spain

Period ∆IR ∆HL IR HL IR HL IR HL

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2 0.09 0.01 0.15 2.39 8.36 1.98 0.00 0.10

3 0.13 0.04 3.79 3.13 15.42 2.31 0.00 2.17

4 0.15 0.06 4.30 2.47 20.71 2.75 0.08 2.19

5 0.15 0.07 5.68 4.45 24.05 3.01 0.30 1.82

6 0.16 0.07 10.73 8.90 26.22 3.24 1.37 1.59

7 0.16 0.07 18.81 16.38 27.64 3.42 4.04 1.41

8 0.16 0.07 24.76 24.92 28.64 3.57 7.27 1.23

9 0.16 0.07 28.46 33.13 29.37 3.70 11.32 1.19

10 0.16 0.07 30.85 39.54 29.93 3.80 16.22 1.25

11 0.16 0.07 31.77 44.95 30.36 3.88 21.14 1.44

12 0.16 0.07 31.28 49.64 30.71 3.96 25.29 1.75

13 0.16 0.07 30.16 53.44 31.00 4.02 28.76 2.09

14 0.16 0.07 29.16 56.17 31.25 4.07 31.54 2.40

15 0.16 0.07 28.31 58.19 31.45 4.12 33.61 2.67

16 0.16 0.07 27.62 59.78 31.63 4.16 35.13 2.89

17 0.16 0.07 27.12 61.04 31.79 4.19 36.28 3.05

18 0.16 0.07 26.82 62.00 31.92 4.22 37.19 3.18

19 0.16 0.07 26.63 62.78 32.05 4.25 37.95 3.28

20 0.16 0.07 26.45 63.46 32.16 4.28 38.66 3.36



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/ Relationship Long-run Short-run

Portugal N/A IR+ , HL+

Greece IR+ , HL+ IR+ , HL+

Ireland IR+ , HL+ IR- , HL+

Spain IR+ , HL+ IR- , HL+



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 bank’s policies are crucial to trigger the boom & burst of housing bubbles

Country/ Policy Max. tax rate applicable on capital gains

LTV-ratio requirement

Portugal 42% 71%

Ireland 20% 83%

Greece 0% 73%

Spain 18% 72.5%

Source: ECB (2008); *if capital gains have been or will be reinvested in another permanent residence within certain time limits

Thank you!


Property Bubbles and the Driving Forces in the PIGS Countries

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