Juan-Carlos Cuestas, Merike Kukk. 19th June 2017

1. Motivation
Literature
Data
Results
Final comments
Asymmetries in the interaction between housing
prices and housing credit in Estonia
Juan-Carlos Cuestas Merike Kukk
19th June 2017
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2. Motivation
Literature
Data
Results
Final comments
Table of contents
1 Motivation
Theoretical background
2 Literature
Research question
3 Data
4 Results
5 Final comments
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3. Motivation
Literature
Data
Results
Final comments
Theoretical background
Motivation
Figure: The dynamics of real house prices
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4. Motivation
Literature
Data
Results
Final comments
Theoretical background
Motivation
Figure: Estonia: Household sector’s debt-to-disposable income ratio (%)
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5. Motivation
Literature
Data
Results
Final comments
Theoretical background
Motivation
The big question we try to answer is:
What is the interrelation between housing prices and domestic
credit in Estonia?
Estonia is an interesting case study: one of the countries with the
highest growth and drop in housing prices, real terms, during
2000-2015, one of the countries with the highest growth in
debt-to-disposable income ratio, and one of the countries with the
highest degree of banking sector concentration in Europe.
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6. Motivation
Literature
Data
Results
Final comments
Theoretical background
Theoretical background
House prices → credit:
The higher the prices, the larger amounts are needed to
purchase real estate
Housing wealth effect → borrow to smooth consumption
(Carroll et al., 2011)
Collateral effect → housing equity withdrawal (Muellbauer,
2010)
Asset allocation choice: Residential investment → higher
demand for real estate (Piazzesi and Schneider, 2016).
Credit supply channel: Via the balance sheet of banks (Chen
2001)
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7. Motivation
Literature
Data
Results
Final comments
Theoretical background
Theoretical background
Credit → House prices:
Increase in the availability of credit increases demand for
housing if households have constraints in borrowing
(Muellbauer et al., 2015)
Increase in credit supply → lower interest rate → higher
expected returns on property and an increase in the demand
of housing (Goodhart & Hofmann, 2008)
⇒ the effect on house prices depends on the elasticity of housing
supply
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8. Motivation
Literature
Data
Results
Final comments
Research question
Brief literature review
Papers on house prices ⇔ credit
Most papers on one country: Finland (Oikarinen, 2009),
Ireland (Fitspatrick & McQuinn, 2007), Spain
(Martinez-Carrascal, 2010, and Cuestas, 2017), Greece
(Brissimis & Vlassopoulos, 2009), Norway (Anundsen &
Jansen, 2013), Sweden (Turk 2015), Hong Kong (Gerlach &
Peng, 2005), China (Liang & Cao, 2007).
Long time series (20-30 years) → VECM framework
In all studies the sample period is until the recession, the only
study covering more recent period is Turk (2015) on Sweden.
Although there is mixed evidence, still more evidence for
housing prices → credit growth than for credit growth →
housing prices
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9. Motivation
Literature
Data
Results
Final comments
Research question
Research question
Literature focuses mainly on developed countries (particularly
OECD countries) → What is the empirical evidence for countries
with high debt accumulation?
What is the relationship between house prices and new loans
for Estonia?
Are there other factors/fundamentals which may have fueled
housing prices?
Are there any asymmetries in the effect from new loans to
house prices?
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10. Motivation
Literature
Data
Results
Final comments
Data
Variables:
Log of real house price index deflated by the HICP.
Log of new housing loans in real terms, deflated by the HICP
(Fitspatrick & McQuinn, 2007).
Log of labour income deflated by HICP (Gimeno &
Martinez-Carrascal, 2010). The main alternative is GDP as a
proxy for economic activity (Hofmann, 2004, Liang & Ciao,
2007, Gerlach & Peng, 2005, Oikarinen, 2009, Brissimis &
Vlassopoulos, 2009). We have also tested with disposable
income and average wage.
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11. Motivation
Literature
Data
Results
Final comments
Data
Nominal interest rate for housing loans divided by 100
(Gimeno & Martinez-Carrascal, 2010). Real interest rate has
also been tested.
Log of completed new dwellings (Gerlach & Peng, 2005,
Fitspatrick & McQuinn, 2007). An alternative variable is
stock of dwellings (Anundsen & Jansen, 2013, Turk, 2015)
Consumer confidence indicator to capture expectations
(Anundsen & Jansen, 2013)
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12. Motivation
Literature
Data
Results
Final comments
Empirical analysis
Data: 2000/Q1-2015/Q4. Some variables have been
seasonally adjusted using the X13 procedure.
Method: (Structural) Bayesian VAR, we have also tested
VECM and VAR.
Cholesky, ordering as follows: labour income, confidence, new
dwellings, house prices, new loans, interest rates. (This has
been borrowed from the literature, Hofmann, 2004, and
Goodhart and Hofmann, 2008)
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13. Motivation
Literature
Data
Results
Final comments
Figure: Estonia: The dynamics of new loans and house prices (in real
terms)
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14. Motivation
Literature
Data
Results
Final comments
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15. Motivation
Literature
Data
Results
Final comments
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16. Motivation
Literature
Data
Results
Final comments
1 Splitting the sample in 2008 and testing the relationships
before and after the crisis gives unreliable results due to the
the small number of observations and the corresponding
reduction in degrees of freedom.
2 We propose to split the credit TURNOVER variable in two:
increases and decreases of turnover as follows (Goodhart &
Hofmann, 2008, Shin et al, 2014, Schorderet, 2001)
turnover+
t =
T
j=1
max(∆turnovert, 0)
turnover−
t =
T
j=1
min(∆turnovert, 0)
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17. Motivation
Literature
Data
Results
Final comments
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18. Motivation
Literature
Data
Results
Final comments
Conclusions
In Estonia, house prices had a positive impact on housing
credit in 2000-2015, and viceversa.
The effect from housing credit to housing prices is found to be
stronger for the period of declining turnover of housing credit.
The reaction of credit turnover to shocks to house prices
seems to be stronger in periods of increasing turnover.
House prices and housing credit are also affected by real
labour income and confidence.
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19. Motivation
Literature
Data
Results
Final comments
Conclusions
In line with the results obtained in the papers analysing fiscal
and monetary policy shocks on GDP during expansions and
recessions.
Message: during good times increase the credit buffers so
they can be released during bad periods.
(Similar pattern in Lithuania. However, it is difficult to detect
asymmetries.)
(In Latvia, an increase in the loan stock related to the
increase in house prices...)
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20. Motivation
Literature
Data
Results
Final comments
Thank you!
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