This document analyzes mortgage risk in the Dutch market using data from the European Central Bank on over 1.7 million Dutch home mortgages. It finds that current loan-to-value (LTV) ratio is a dominant driver of mortgage defaults, with higher LTVs correlated with increased risk. The analysis uses a survival model to identify default risk factors over time, showing current LTV and debt-to-income ratios as the most impactful variables. Region, property type, loan size, and interest rates are also examined but do not significantly influence default probabilities.

1. Analytics for the Dutch Mortgage Market
Tonko Gast
Page 1

2. Analyzing Dutch mortgage risk
• The European Central Bank has collected data on Dutch
home mortgages
• The data is constantly updated and filled as more mortgage
information becomes available.
• We explore the characteristics of the Dutch mortgage
Market from the available data and present a preliminary
model for Drivers of Mortgage default in the Dutch market.
• Goal: Ranking and Segmentation of performing mortgages
Page 2

3. Data Summary
Snapshot Data
Number of Loan parts
1,702,589
Number of Unique Borrowers
911,741
Average Loan size
€ 181,663
Fixed Loans Percentage
88 %
Total Current Amount
€ 165.63 bn
WA Seasoning
~6 Years
WA Coupon
4.63 %
Delinquencies (%) (0+/30+/60+)
4.17/3.01/1.51
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4. Region Distribution
Extra
ND
Z-H N-B
N-H GLD
UT
LB
OV
GR
DR
FR
FV
ZL
Region
Page 4

5. Property Distribution
Page 5

6. Loan Vintage Distribution
Page 6

7. Current Loan Size Distribution
Page 7

8. Current Interest Rate Distribution
Page 8

9. Current Indexed LTV Distribution
ND
< 40%
40-
60-
70-
80-
60%
70%
80%
90%
90-
100-
110-
120-
130-
>
100% 110% 120% 130% 140% 140%
Page 9

10. Indexed Total Income Distribution
Page 10

11. Current Indexed LTI Distribution
Page 11

12. Current Indexed DTI Distribution
Page 12

13. Default Drivers : Our Approach
• Goal:
Ranking and Segmentation of performing mortgages
• Method: Survival analysis framework
• Data:
25 contemporaneous and time-invariant indicators
of borrower, loan, and collateral
Page 13

14. Default Drivers
• Based on the current data: the best predictive model uses a
non-linear form with combinations of:
•
•
•
•
Current DTI / Current LTI
Current LTV
Borrower Age
Remaining Fixed Rate Period
Page 14

15. Continuing Development
• Improving predictive accuracy of the model
• The model is continuously being refined as more data becomes
available.
• Alternative Soft-computing and data-mining models being implemented
• We are currently adding these variables to the model:
•
•
•
•
Net monthly income buffers
Number of borrowers
Distribution channel
etc.
Page 15

16. Questions?
Page 16

17. Net Monthly Income Buffers
Page 17

18. Willingness-to-pay
Bij de huidige rest schuld doet 15% er meer dan 10 jaar over om te kunnen
terug te betalen
Page 18

19. Conclusion
• We presented an overview of the Dutch Mortgage Market with a snapshot
examples from our ‘Transparency Tool’
• We presented a model to analyze drivers of default in the Dutch market
• We observe that Current LTV, is a surprisingly dominant driver of defaults
Page 19

20. Original LTV Distribution
ND
< 40%
40-
60-
70-
80-
60%
70%
80%
90%
90-
100-
110-
120-
130-
>
100% 110% 120% 130% 140% 140%
Page 20

21. Average Monthly Income Buffer Distribution
Page 21

22. Minimum Monthly Income Buffer Distribution
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23. Idea of the Hazards Model
t0 Mortgage Origination
t1 Mortgage entry in pool
i
t2 followup period (we only
observe up to this point
h
in time)
g
Mortgage h has defaulted in
f
The observation period.
e
d
c
b
a
t0
t1
t2
time
Loan Age (months)
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24. Delinquency (90 days+) Distribution in Netherlands (%)
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25. Delinquency (60 days+) Distribution in Netherlands (%)
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26. Delinquency (30 days+) Distribution in Netherlands (%)
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27. Delinquency (0 days+) Distribution in Netherlands (%)
Page 27

28. Hazard Model for Defaults
The proportional hazards model with time varying coefficients
has the form :
From the data we estimate a hazard model of the form :
F(t) is the baseline hazard and in our case follows a power-law
form.
Page 28

29. Default Drivers
G[X(t)] has several time varying and time-invariant variables
Most impact on probabilities of default is seen from the
variables Current Indexed LTV and Indexed DTI.
Among these , Current Indexed LTV has a non-linear relation
with probabilities of default , following a square root
transformation
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30. Default Drivers
All other variable remaining constant, we have observed the
following sensitivities:
Time to reset fixed rates: Every month the closer a mortgage
gets to its reset date, the hazard (not PD*) decreases by 1%
Indexed DTI: Every month the hazard of Indexed DTI
increases by 3.5%
Current Indexed LTV: Every month the hazard of Current
Indexed LTV increases by ~ 29%
* The actual change in PD depends on the baseline hazard
Page 30

31. Observations
Current LTV is a much greater driver of default than
ability to pay (reflected by Current DTI)
National Guarantee and surplus incomes may not
have much impact on defaults
Refinancing and the opportunity to do so, impact
defaults (another indicator of willingness to default)
Page 31

32. Current Indexed LTV evolution over Reporting Dates
A shift in density mass of
Current LTVs is observed
over time , with a greater
shift in the period from
January through June, a
period where we also
observe a relatively higher
number of delinquencies
Jul-2013
Jun-2013
May-2013
Apr-2013
Mar-2013
Feb-2013
Jan-2013
Dec-2012
Nov-2012
Oct-2012
Page 32

33. DTI evolution over Reporting Dates
The DTI in the time series
does not show any
discernable visual impact
on default. However, the
long tails correspond to
Mortgages where the
main borrower has had a
loss of income, increasing
DTI and risk of default
Jul-2013
Jun-2013
May-2013
Apr-2013
Mar-2013
Feb-2013
Jan-2013
Dec-2012
Nov-2012
Oct-2012
Page 33

34. Number of Defaults at each reporting date
662
609
429
421
314
297
34
89
68
19
Jul-2013
Jun-2013
May-2013
Apr-2013
Mar-2013
Feb-2013
Jan-2013
Dec-2012
Nov-2012
Oct-2012
Page 34

35. Driving Defaults
Current Indexed
LTV
Indexed DTI
As the density mass of Indexed
LTVs increase, we see
increasing number of defaults in
the pool.
Greater density mass in lower
LTV regions, corresponding to
lower defaults in the pool.
Similar trend holds for DTI. Relatively lower
sensitivity, shows less of a visual impact
Page 35

36. Average Monthly Income Buffer distribution by Reporting Dates
We observe a steady
mass distribution of
Average Monthly income
buffer , indicating stable
surplus incomes and not
much impact on defaults.
Jul-2013
Jun-2013
May-2013
Apr-2013
Mar-2013
Feb-2013
Jan-2013
Dec-2012
Nov-2012
Oct-2012
Page 36

37. Indexed LTI distribution by Reporting Dates
We observe a steady
mass distribution of LTI,
and no discernable impact
on defaults
Jul-2013
Jun-2013
May-2013
Apr-2013
Mar-2013
Feb-2013
Jan-2013
Dec-2012
Nov-2012
Oct-2012
Page 37