Embed Size (px)
DESCRIPTION
Tonko Gast van Dynamic Credit over actief, preventief en bijzonder beheer.
Citation preview
Page 1
Analytics for the Dutch Mortgage Market
Tonko Gast
Page 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 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
Page 4
Region Distribution
Extra
RegionND Z-H N-B N-H GLD UT LB OV GR DR FR FV ZL
Page 5
Property Distribution
Page 6
Loan Vintage Distribution
Page 7
Current Loan Size Distribution
Page 8
Current Interest Rate Distribution
Page 9
Current Indexed LTV Distribution
ND < 40% 40-
60%
60-
70%
70-
80%
80-
90%
90-
100%
100-
110%
110-
120%
120-
130%
130-
140%> 140%
Page 10
Indexed Total Income Distribution
Page 11
Current Indexed LTI Distribution
Page 12
Current Indexed DTI Distribution
Page 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 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 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 16
Questions?
Page 17
Net Monthly Income Buffers
Page 18
Willingness-to-pay
Bij de huidige rest schuld doet 15% er meer dan 10 jaar over om te kunnen terug te betalen
Page 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 20
Original LTV Distribution
ND < 40% 40-
60%
60-
70%
70-
80%
80-
90%
90-
100%
100-
110%
110-
120%
120-
130%
130-
140%> 140%
Page 21
Average Monthly Income Buffer Distribution
Page 22
Minimum Monthly Income Buffer Distribution
Page 23
Idea of the Hazards Model
timet0 t1 t2
t0 Mortgage Origination
t1 Mortgage entry in pool
t2 followup period (we only
observe up to this point
in time)
Mortgage h has defaulted in
The observation period.
ab
c
d
e
f
h
g
i
Loan Age (months)
Page 24
Delinquency (90 days+) Distribution in Netherlands (%)
Page 25
Delinquency (60 days+) Distribution in Netherlands (%)
Page 26
Delinquency (30 days+) Distribution in Netherlands (%)
Page 27
Delinquency (0 days+) Distribution in Netherlands (%)
Page 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 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
Page 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 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 32
Current Indexed LTV evolution over Reporting Dates
Jun-2013
Oct-2012Nov-2012
Dec-2012Jan-2013
Feb-2013Mar-2013
May-2013Apr-2013
Jul-2013
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
Page 33
DTI evolution over Reporting Dates
Jun-2013
Oct-2012Nov-2012
Dec-2012Jan-2013
Feb-2013Mar-2013
May-2013Apr-2013
Jul-2013
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
Page 34
Number of Defaults at each reporting date
19
68
89
297
314
421
662609
429
34
Jun-2013
Oct-2012Nov-2012
Dec-2012Jan-2013
Feb-2013Mar-2013
May-2013Apr-2013
Jul-2013
Page 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 36
Average Monthly Income Buffer distribution by Reporting Dates
Jun-2013
Oct-2012Nov-2012
Dec-2012Jan-2013
Feb-2013Mar-2013
May-2013Apr-2013
Jul-2013
We observe a steady mass distribution of Average Monthly income buffer , indicating stable surplus incomes and not much impact on defaults.
Page 37
Indexed LTI distribution by Reporting Dates
Jun-2013
Oct-2012Nov-2012
Dec-2012Jan-2013
Feb-2013Mar-2013
May-2013Apr-2013
Jul-2013
We observe a steady mass distribution of LTI, and no discernable impact on defaults
