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Experience with DSTI in
Lithuania
Tomas Garbaravičius
3 July 2015
Vilnius
Content
►
Recent credit and housing market developments
►
Reasons behind changes in housing lending standards
►
Challenges/ issues/ policy questions
2
Responsible lending standards (RLS)
►
Introduced in Nov 2011 as a preventive measure and
as a response to the lessons of the financial crisis
►
Amended in May 2015
►
Why changes?
3
A creditless economic recovery
Credit growth in Baltic countries
Real GDP and credit growth in Lithuania
Annual percentage change
60
Annual percentage change
60
50
50
40
40
Lithuania
Latvia
Estonia
Real credit*
30
30
20
20
10
10
0
0
Real GDP
–10
–10
–20
–20
2005
2007
2009
2011
Sources: Statistics Lithuania and Bank of Lithuania calculations.
* Credit to private non-financial sector from all creditors.
2013
2015
2005
2007
2009
2011
2013
2015
Sources: national banks, Eurostat, and Bank of Lithuania calculations.
* Credit to private non-financial sector from all creditors.
4
Low household debt
EUR billions
8
percentages of GDP
40
70%
RLS
RLS
7
60%
annual growth
30
6
50%
5
40%
4
30%
3
20%
2
10%
1
0%
Household debt
20
Housing debt
10
Housing loans
0
0
2005
2007
2009
2011
2013
2015
Sources: Statistics Lithuania, Bank of Lithuania and Bank of Lithuania calculations.
-10%
2005
2007
2009
2011
2013
2015
Sources: Bank of Lithuania and Bank of Lithuania calculations.
5
No excesses in the residential property market
House prices do not seem to be overvalued
Deviation from a long-term equilibrium value
80
Positive expectations have lost momentum
Net percentage
80
60
60
40
40
min-max range
median
20
0
20
–20
0
–40
–60
–20
–80
–40
2000 2002 2004 2006 2008 2010 2012 2014
Source: Bank of Lithuania calculations.
Note: measures include price-to-rent ratio, price-to-income ratio, deviations from HP-filtered
trend and estimates from an econometric model.
Latvia (SEB data)
Lithuania (SEB data)
Lithuania (Bank of Lithuania data)
–100
2008 2009 2010 2011 2012 2013 2014 2015
Sources: SEB and Bank of Lithuania.
Note: Net percentage is defined as the difference between the percentage of respondents
expecting an increase and those expecting a decrease in residential property prices.
6
Nov 2011: Responsible lending standards (RLS)
►
DSTI ≤ 40% (no mandatory interest rate test/ shock)
►
LTV = 85%
►
Maximum loan term = 40 years
7
DSTI and DTI are linked through the interest rate
►
DTI = f (DSTI , interest rate, maturity, income)
►
Excel function:
Debt
%
,
∗
,
∗
,
∗
Annualincome
►
Countries that use DSTI are in fact also using a DTI
ratio that varies with interest rates !
–
Explains strong DSTI impact on credit growth (Kuttner and Shim, 2015)
8
Housing loan interest rate and
maximum loan amount in terms of DTI
percentages and times of annual income
12
RLS
10
LITHUANIA
Max DTI (debt-to-income)
DSTI=
8
40%
1.85%
Rate
5%
6%
6
Years
25
30
35
40
8.0 9.2 10.3 11.3
5.7 6.2 6.6 6.9
5.2 5.6 5.8 6.1
4
Interest rate
2
0
2000 2002 2004 2006 2008 2010 2012 2014
Sources: Bank of Lithuania and Bank of Lithuania calculations.
9
DSTI needs to be combined with interest rate test
►
DSTI allows increasing DTI ratios in a declining interest rate
environment and thus …
►
… needs to be combined with an interest rate test to prevent
excessive indebtedness
►
Interest rate test calibration
–
Current interest rate plus x% sensitivity test
Procyclical, still allows increasing indebtedness when interest rates decline
–
High absolute (long-term) interest rate level
Provides an effective DTI cap
10
Macro-prudential risk assessment
►
Risks for banks (weak lending standards)
NO
►
Risks for households(indebtedness)
YES
►
Excessive credit/ house price fluctuations
?
–
No concerns about housing prices, but
–
Stagnant housing credit, declining as % of GDP
11
May 2015: New lending standards
►
►
Regular loans:
–
DSTI ≤ 40%
–
DSTI ≤ 50% with r = 5% (or actual rate, if higher)
–
Loan maturity cap shortened to 30 years
Exemptions:
–
DSTI ≤ 60% for no more than 5% of new
mortgages issued
12
Changes in maximum DTI
NEW
DSTI=
Rate=
Regular
loans
50%
5%
1.85%
Rate
5%
6%
DSTI=
Exemptions
60%
1.85%
Rate
5%
6%
Years
25
30
35
40
10.0 11.5 12.9 14.1
7.1 7.8 8.3 8.6
6.5 6.9 7.3 7.6
Years
25
30
35
40
12.0 13.8 15.5 16.9
8.6 9.3 9.9 10.4
7.8 8.3 8.8 9.1
OLD
DSTI=
40%
1.85%
Rate
5%
6%
Years
25
30
35
40
8.0 9.2 10.3 11.3
5.7 6.2 6.6 6.9
5.2 5.6 5.8 6.1
No exemptions
5% of new mortgages
13
Impact on housing credit flows
Calculations based on housing loans
granted in 2014
By number of
loans
By volume of
loans
Share of loans which would have exceeded
new limits
14 %
20,9 %
Share of loans which would not have been
issued with the same amount
1,0 %
1,8 %
►
Exemption of 5% more than compensates estimated potential reduction
in credit flows
►
Based on housing loans issued in 2014, total increase in household
monthly mortgage payments would amount to less than EUR 1 million
per year.
–
►
Insignificant effect on consumption
Changes to become effective as of 1 November 2015
14
Challenges/ issues/ policy questions
►
Should DSTI vary with income?
►
What is an appropriate level of DTI?
►
Should macro-prudential limits vary by county/city?
►
Unlevel playing field: unregulated non-bank lenders
►
House purchases by foreigners and/or emigrants
►
When and how to use LTV?
►
What to do with activation lags?
Should macro-prudential policy-making emulate
monetary policy?
►
15
Source: IMF.
South Korea
Brunei Darusalam
Hong Kong
Serbia
Singapore
Algeria
Bahrain
China
Ecuador
Hungary
Israel
Pakistan
Poland
UAE
Bahamas
Canada
Mongolia
Netherlands
USA
Kuwait
Lithuania
Romania
Columbia
Brunei
Saudi Arabia
Q1: Should DSTI vary with income? DSTI levels
DSTI limit, percentages
70
60
50
Average DSTI = 47%
40
30
20
10
0
16
Q1: DSTI and LTI as predictors of NPLs
LTI at origination
DSTI at origination
Share of households with overdue mortgage payments
18%
Share of households with overdue mortgage payments
18%
16%
16%
14%
14%
12%
12%
10%
10%
8%
8%
6%
6%
4%
4%
2%
2%
0%
0%
DSTI at origination
Sources: NŪFSIS and Bank of Lithuania calculations.
Notes: Only loans that were granted in 2006-2008. Loan was categorised as overdue if a
payment was overdue for more than 60 days during any quarter up to Q4 2013.
(0-1] (1-2] (2-3] (3-4] (4-5] (5-6] (6-7] (7-8] (8-9] (1015]
LTI at origination
Sources: NŪFSIS and Bank of Lithuania calculations.
Notes: Only loans that were granted in 2006-2008. Loan was categorised as overdue if a
payment was overdue for more than 60 days during any quarter up to Q4 2013.
17
Q1: Higher DSTI + High income = Higher NPLs
High wages were much less reliable in a downturn
Share of households with overdue mortgage payments
15%
Share of households with overdue mortgage payments
30%
DSTI >60%
25%
12%
20%
9%
6%
DSTI > 40%
15%
DSTI (50‐60%]
DSTI < 40%
10%
DSTI (40‐50%]
3%
5%
DSTI (30‐40%]
All households
0%
0%
(1-2] (2-3] (3-4] (4-5] (5-6] (6-8] (8-10] (10- >15
15]
Household income, LTL thousands
Sources: NŪFSIS and Bank of Lithuania calculations.
Notes: Only loans that were granted in 2006-2008. Loan was categorised as overdue if a
payment was overdue for more than 60 days during any quarter up to Q4 2013.
DSTI (0‐30%]
(1-2] (2-3] (3-4] (4-5] (5-6] (6-8] (8-10] >10
Household income, LTL thousands
Sources: NŪFSIS and Bank of Lithuania calculations.
Notes: Only loans that were granted in 2006-2008. Loan was categorised as overdue if a
payment was overdue for more than 60 days during any quarter up to Q4 2013.
18
Q1: Should DSTI vary with income?
►
Higher DSTI + High income = Higher NPLs
Requests to apply higher DSTI for high-wage earners should be assessed
cautiously as income of such borrowers can drop precipitously in a
downturn
►
Why was this the case for Lithuania:
–
–
–
–
Volatile macroeconomic environment
Most companies SMEs by European standards
Aggressive borrowing/ high indebtedness
Low wealth buffers
19
Q2: What is an appropriate level of DTI/ LTI?
►
Lithuania:
Estonia:
UK:
Ireland:
7.8
6.9
4.5
3.5
(embedded)
(embedded)
(explicit)
(explicit)
►
Could be higher for “converging” economies with
higher potential growth rates, but by how much?
►
What should be income growth assumptions?
20
Q3: Should macro-prudential limits vary by
county/city?
►
Euro area vs. countries ≈
Country vs. its counties/cities?
►
Example: South Korea and Seoul
►
Housing price bubbles are probably more likely to start
in capital cities
21
Q3: Housing credit and GDP by county in 2013
GDP structure, %
Housing credit-to-GDP, %
Vilniaus apskritis
39
20
Vilniaus apskritis
Kauno apskritis
20
16
Kauno apskritis
Klaipėdos apskritis
12
24
Klaipėdos apskritis
Šiaulių apskritis
7
10
Šiaulių apskritis
Panevėžio apskritis
6
10
Panevėžio apskritis
Telšių apskritis
4
8
Telšių apskritis
Marijampolės apskritis
3
8
Marijampolės apskritis
Alytaus apskritis
3
10
Alytaus apskritis
Utenos apskritis
3
6
Utenos apskritis
7
Tauragės apskritis
16
LIETUVA
Tauragės apskritis
2
0
10
20
30
40
0
5
10
15
20
25
22
Q4: Unlevel playing field: Unregulated non-bank
lenders
►
Macro-prudential lending standards apply to credit
institutions only
Not a significant issue in bank-dominated financial systems
►
Forthcoming transposition of the EU Mortgage
directive should change the situation
–
Non-bank housing lenders will have to register
with the Bank of Lithuania and to comply with RLS
–
As is now the case for consumer lenders
23
Q5: House purchases by foreigners and/or
emigrants
Money transfers from abroad and housing transactions (Q1 2004 – Q4 2014)
Thousands of transactions
15.0
until 2009
12.5
10.0
7.5
after 2009
5.0
2.5
0
100
200
300
400
500
Transfers from abroad, EUR millions
Sources: Registrų centras and Bank of Lithuania calculations.
24
Q6: When and how to use LTV ratios?
LTV ratios and changes in housing lending
standards
Procyclicality of LTV ratios in Lithuania
Percentages
80
Percentages/ percentage points
15
Annual real GDP change (rhs)
60
10
Net percentages
100
LTV ratio, percentages
84
Easing of lending conditions
70
82
LTV (rhs)
40
5
40
20
0
10
78
0
–5
–20
76
–10
–50
74
–15
–80
72
–20
Annual LTV
change (rhs)
Annual change
in house prices (lhs)
–40
80
Tightening of lending conditions
–20
–60
2005
2007
2009
2011
2013
Sources: Department of Statistics, Registrų Centras, PRDB and Bank of Lithuania
calculations.
–110
2005
2007
2009
2011
2013
70
Sources: Department of Statistics, Registrų Centras, PRDB and Bank of Lithuania
calculations.
25
Q6: LTV and DTI changes in Hong Kong
Source: McDonald C., „When is macroprudential policy effective?“, 14 November 2014
26
Q7: What to do with activation lags?
►
Long activation/ implementation periods
–
–
–
Draft proposals revealed to public:
Final standards announced:
Activation:
9 April 2015
2 June 2015
1 November 2015
►
Banks need to prepare IT systems
►
Unnecessary flurry in the lending market before
activation
►
Hong Kong – changes effective as of next day
27
Final question: Should macro-prudential policymaking emulate monetary policy?
►
“General documentation” for macro-prudential
instruments
Banks fully prepared, no need to adjust IT systems after every decision
►
Regular macro-prudential meetings and decisions
–
At least quarterly (countercyclical capital buffer
decisions)
–
Decisions on lending standards effective immediately
28