M AY 2 0 1 6
METHODOLOGY
European RMBS Insight:
Spanish Addendum
European RMBS Insight: Spanish Addendum
DBRS.COM
2
Contact Information
Table of Contents
Keith Gorman
Global Structured Finance
+44 20 7855 6671
[email protected]
Introduction3
Sebastian Hoepfner
Global Structured Finance
+44 20 7855 6663
[email protected]
Rehanna Sameja
Vice President, EU RMBS
Global Structured Finance
+44 20 7855 6677
[email protected]
Kali Sirugudi
Vice President, EU RMBS
Global Structured Finance
+44 20 7855 6609
[email protected]
Asim Zaman
Assistant Vice President, EU RMBS
Global Structured Finance
+44 20 7855 6626
[email protected]
Executive Summary
3
Data3
Spanish Mortgage Scoring Model
3
Spanish Segmentation and Delinquency Migration Matrices
7
Spanish Correlation Assumption
8
Spanish Home Price Model
9
Spanish Distressed Sale Discount
10
Spanish Foreclosure Costs
10
Spanish Constant Prepayment Rate (CPR) Assumptions
10
Spanish Cash Flow Assumptions
11
Belen Bulnes Meneses
Senior Financial Analyst, EU RMBS
Global Structured Finance
+44 20 7855 6699
[email protected]
Davide Nesa
Senior Financial Analyst, EU RMBS
Global Structured Finance
+44 20 7855 6697
[email protected]
Claire Mezzanotte
Group Managing Director
Global Structured Finance
Tel. +1 (212) 806 3272
[email protected]
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Executive Summary
This report is the Spanish Addendum to the DBRS European RMBS Insight Model and Rating Methodology (published 17 May 2016).
The report outlines the country-specific aspects of the methodology to estimate defaults and losses for Spanish RMBS and Covered
Bonds. This addendum should be read in parallel with the DBRS European RMBS Insight Model and Rating Methodology. The
addendum includes an overview of:
1. Data used for building the Spanish Mortgage Scoring Model and Spanish Delinquency Migration Matrices
2. Spanish Mortgage Scoring Model
3. Spanish Dynamic Delinquency Migration Matrices
4. Spanish Correlation Assumption
5. Spanish House Price Model
6. Spanish Distressed Sale Discount
7. Spanish Foreclosure Costs
8. Spanish CPR Assumptions
9. Spanish Cash Flow Assumption
Data
The Spanish Mortgage Scoring Model (Spanish MSM) and Spanish Dynamic Delinquency Migration Matrices (DMM) were
constructed using Spanish loan-level data from the European Data Warehouse (EDW).
Home price data from Instituto Nacional de Estadística (INE) and TINSA where used to generate a base house price forecast and
MVDs. Additionally, the monthly Spanish Harmonised Consumer Price Index from Eurostat was used to calculate real house
prices.
Spanish Mortgage Scoring Model
Spanish MSM Modelling Sample
In the first step for the estimation of portfolio defaults and portfolio losses for Spanish mortgages, loans are scored dynamically
over a forecast horizon in the Spanish MSM. The technology used to build the Spanish MSM is logistic regression. To build
a score in this framework, a loan’s risk characteristics at a point in time (the “as-of” date) and a binary outcome variable that
represents the loan’s performance over a period of time subsequent to the as-of date are needed.
A set of data cleaning rules were applied to the universe of Spanish loans in the EDW to identify a dataset to develop the Spanish
MSM. The dataset included loans with the following characteristics:
• For each reporting date, there was a 12-month performance window of data available subsequent to that reporting date;
• The account is not in arrears at the as-of date;
• The current balance is greater than zero;
• The current loan to value (LTV) is less than 125%; and,
• The current interest rate margin is between 0% and 7%.
Given the universe of loans, the data on each loan is assembled with the risk characteristics at the as-of date, and the binary
outcome variable calculated from its subsequent performance. The outcome variable is determined based on the loan’s
performance over the subsequent 12 months from the as-of date. A loan is considered “bad” if during the 12-month period it
either i) entered into 90+ days arrears, ii) was foreclosed, iii) was repurchased, or iv) prepaid from a delinquency status of two
months or more. Additionally, this status persisted for at least three or more observations during that time window.
The modelling sample for the Spanish MSM included approximately 84,000 loans of which 12,000 were considered “bad” and
72,000 were considered “good”. Loans with multiple as-of dates were subject to entering the modelling sample only once. “Bad”
loans did not represent a very high proportion of the population and, as a result, were over-sampled to increase their presence
in the modelling sample.
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Spanish MSM Parameters
The Spanish MSM was built with objective variables (loan and borrower characteristics) and judgmental variables (Underwriting
Score and Adverse Performance). The Spanish MSM consists of 19 model parameters from 15 variables (Figure 1). Figure 1 also
reports the odds ratio for each parameter. The odds ratio measures the effect of each parameter in the Spanish MSM.
Figure 1 – Spanish MSM Parameters
Parameter
Loan Age Between 6-15 Years
Odds Ratio
Variable Change
Variable Type
0.91
6 Years to 8 Years
Continuous
Indexed LTV
1%-40%
1.16
30% to 40%
Continuous
40%-80%
1.20
70% to 80%
Continuous
80%-125%
1.56
110%-120%
Continuous
0.30
0.3% to 0.5%
Continuous
Margin
0.0%-0.5%
0.5%-0.9%
1.47
0.7% to 0.9%
Continuous
0.9%-1.2%
1.57
1.0% to 1.2%
Continuous
Borrower Not Employed
1.79
Binary
Maturity > 30 years
1.15
Binary
Foreign Borrowers
1.46
Binary
Property Type, Not a House, Flat, Condo or Multi-Family
Unit
1.20
Binary
Credit Line
1.53
Binary
VPO Guarantee
0.20
Binary
Purchase Loan
0.70
Binary
Loan Missing Income
0.88
Total Income Between EUR 1-100,000
0.96
60k to 80k
Continuous
Current Balance < EUR10,000
1.22
10K to 5K
Continuous
Binary
Underwriting Score
1
0.66
Binary
2
0.90
Binary
3
1.00
Binary
4
1.19
Binary
5
1.62
Binary
6
1.79
Binary
Adverse Performance (for Underwriting Scores 1-3 only)
1.20
Binary
Given the nature of the scoring model, the most direct way to measure the effect of a variable is by examining the odds ratios. Take,
for example, the binary variable Foreign Borrower; the odds ratio comparing Foreign Borrower to non-Foreign Borrower is:
P[Foreign Borrower]/(1-P[Foreign Borrower])/P[non-Foreign Borrower]/(1-P[non-Foreign Borrower])
Here, P[Foreign Borrower] is the probability of a Foreign Borrower becoming ‘bad’ in the 12-month time horizon. For logistic
regression, the odds ratio constructed on the values of one explanatory variable does not depend on the values of any of the other
explanatory variables. Variables included in the Spanish MSM are either binary, where the loan includes the relevant parameter
or does not, or continuous, where the odds ratio represents a specific change in the variable. For binary variables, the odds
ratio is calculated for each value as being a loan characteristic versus not being a loan characteristic. For continuous variables,
the odds ratio can be calculated for a specific change in the variable. Furthermore, for the continuous variables Indexed LTV
and Margin the change in odds ratio is not constant across the spectrum. As can be seen, the odds ratio for a 10% Indexed LTV
change is greater from 110% to 120% versus 70% to 80%.
Spanish Underwriting Score and Adverse Performance
The Spanish MSM includes two judgmental variables to assess the credit risk of Spanish residential portfolios: Underwriting
Score and Adverse Performance. Underwriting Score is a scaled scoring classification with values of 1 to 6 where 1 is considered
the least risky and 6 considered the highest risk. Adverse Performance is a binary variable which is reserved for a Spanish
Underwriting Score between 1 and 3.
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Spanish Underwriting Scores are assigned to each loan in a portfolio based on multiple factors including current underwriting
guidelines; changes in underwriting criteria; product types; and, length, consistency and relative quality of historical performance.
As seen in Figure 1, 3 is the base Underwriting Score where if a value of ‘3’ is assigned there is no risk adjustment to the loan
score in the Spanish MSM (Odds Ratio = 1.00). As a starting point, Spanish Underwriting Scores of ‘3’ or lower are reserved for
loans where DBRS observes established origination practices accompanied by historical data which is consistent with market
performance given the underlying product types. DBRS would typically assign an Underwriting Score of ‘3’ where a stable
relationship is observed between the originator’s strategy and the performance is deemed to be at least average. This typically
entails at least five years of performance history with average performance relative to the market in terms of 90 days arrears,
repossessions, recoveries or other metrics DBRS considers relevant. A score lower than ‘3’ would be assigned to loans where
above average performance data is observed for a substantial period of time.
Spanish Underwriting Scores of ‘4’ and higher are assigned to loans where DBRS finds it difficult to establish a consistent relationship
between an originator’s strategy and historical performance. This may be determined in situations where i) the originator provides
insufficient historical performance data, ii) performance data has underperformed relative to the market, or iii) DBRS believes the
origination or servicing strategy for the respective loans to be riskier than market standards. Although there have not been new lending
platforms established in the Spanish market, there have been mergers and acquisitions which have led to re-branding of previous
lenders. These rebranded platforms may have defined updated underwriting criteria which are consistent with market standards,
however the performance of a portfolio under the re-defined criteria may be considered riskier than a lender with an established track
record leading to a higher Underwriting Score. Spanish Underwriting Scores between ‘4’ and ‘6’ may also be assigned in situations
where an established lender introduces one or several new products, DBRS observes expanding underwriting criteria or historical
performance has deviated significantly compared to the market average. High Underwriting Scores may also be assigned to loans
which have been subjected to previous loan modifications or payment holidays as a result of loss mitigation strategies.
Although each loan is assigned a Spanish Underwriting Score, in practice the score is generally assigned at the portfolio level. In
circumstances where DBRS determines that a pool consists of sub-portfolios which would entail multiple scores being assigned,
the sub-portfolios would be defined with a different respective Underwriting Score assigned to each.
Generally, DBRS would expect the assigned Underwriting Score to remain constant over time. However, given the on-going evolution
of the Spanish market the Underwriting Score may change following acquisitions, servicing transfers or changes to servicing strategies.
The Spanish Underwriting Score was constructed based on an assessment of the originating entities for loans in the EDW. Two
qualitative variables were considered based on an analysis of the Spanish mortgage banking sector:
• Type – three classifications based on a bank’s market share as of 2011
• Strategy – two classifications based on a bank’s being going concern as of 2012
“Type” consists of three classifications – National, Regional and Local. BBVA and Banco Santander SA, the two largest issuing
entities in the EDW, are classified as National. Banks with a market share between 1% and 10% are classified as Regional and all
other originators are classified as Local. “Strategy” consists of two classifications: Risky and Average. Risky originators are those
which received financial support from Fondo de Reestructuración Ordenada Bancaria (FROB) or were acquired by another
entity.1 Each of the qualitative variables were assessed in the logistic regression individually. The combination of Type and
Strategy results in six possible Spanish Underwriting Scores (Figure 2).
Figure 2 – Spanish Underwriting Score Scale
Score
Type
Strategy
Odds Ratio
1
Local
Average
0.66
2
National
Average
0.90
3
Regional
Average
1.00
4
Local
Risky
1.19
5
National
Risky
1.62
6
Regional
Risky
1.79
1. Entities receiving support from FROB or acquired by another entity as detailed in the IMF report “Spain: Financial Stability Assessment”, June 2012.
The Spanish Underwriting Score is driven by the evolution of the banking sector through the financial crisis and how each of
these two variables potentially impacted the performance of loans in the data set. Annual Spanish mortgage origination grew
from EUR 142 billion in 2003 to a peak of almost EUR 300 billion in 2007 (Instituto Nacional de Estadística, INE). Volumes
dropped on average 20-35% per year before bottoming out at just EUR 37 billion in 2013. Regional savings banks represented
almost 50% of total volumes in 2003 and gained market share reaching approximately 57% in 2007. Savings bank originations
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dropped off at an even faster rate before disappearing in 2014, as a result of mergers and acquisitions.
Spanish home purchase non-performing loans (NPLs) increased from 0.37% at the beginning of 2006 to a peak of 6.10% at the
beginning of 2014 (Bank of Spain) driven by increased unemployment, falling home prices and a pull-back in mortgage lending.
All Spanish originators were impacted by increasing residential NPLs although the severity of the NPL impact differed across
institutions. The increase in NPLs at the originator level was most likely affected by each a bank’s Type and Strategy. Local banks
(mainly cooperative or specialised banks in the data sample) performed better than the overall market as their originations did not
grow at the same rate as the market or other types of originators gaining market share. Banks with aggressive strategies expanded
underwriting guidelines to chase market share and suffered from ambitious and growing exposure to the housing market2.
Figure 3 – Spanish NPLs, Mortgage Originations and Unemployment
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Home Loan NPLs
Quarterly Change Mortgages
06-2015
06-2014
06-2013
06-2012
06-2011
06-2010
06-2009
06-2008
06-2007
06-2006
06-2005
06-2004
06-2003
06-2002
06-2001
06-2000
06-1999
06-1998
-5.0%
Unemployment
Source: Bank of Spain and INE.
The Spanish Underwriting Score for transactions issued prior to 2012 is assigned based on the classification of the bank within the
relevant type and strategy. For transactions issued after 2012, DBRS assesses the origination guidelines, post-2012 performance
of the issuer and vintage distribution of the portfolio to assign the Spanish Underwriting Score.
The analysis of transaction performance for the development of the Spanish Underwriting Score also showed that despite
on average better underwriting the actual performance of transactions within one originator can differ. This performance
differentiation was observed for the Spanish Underwriting Score between 1 and 3. The Adverse Performance variable is applied
to portfolios where a Spanish Underwriting Score between 1 and 3 is applied, and portfolio performance may be expected to
deviate from the average.
2. Ostensibly, this overview is a simplification of what happened in the Spanish banking and housing sector; however, this approach yielded a reasonable level
of explanation of asset performance.
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Spanish Segmentation and Delinquency Migration Matrices
The Spanish MSM was applied to approximately 825,000 loans in the EDW to score each loan and assess the distribution of
scores. 16 risk segments were identified for the purpose of calculating DMMs with a corresponding DMM for each risk segment.
Figure 4 below shows cumulative percentiles for the the distributions of scores within each risk segment. Additionally, Figure 4
shows cumulative percentiles for the distributions of scores within each risk segment. Additionally, Figure 4 shows the low and
high Spanish MSM score for each of the 16 risk segments.
Figure 4 – Spanish MSM Score Distribution
Segment
Cumulative Percentile
Low
High
1
5%
0.0%
3.3%
2
10%
3.3%
4.3%
3
15%
4.3%
5.3%
4
20%
5.3%
6.4%
5
30%
6.4%
8.7%
6
40%
8.7%
11.0%
7
50%
11.0%
13.5%
8
60%
13.5%
16.5%
9
70%
16.5%
20.5%
10
80%
20.5%
26.0%
11
85%
26.0%
30.0%
12
90%
30.0%
35.3%
13
92%
35.3%
38.3%
14
95%
38.3%
42.0%
15
97%
42.0%
50.5%
16
100%
50.5%
100.0%
DMMs are calculated for each risk segment by computing the average roll rates observed in the loan level data between Q1 2013
and Q2 2015. For Spain, the roll rates are quarterly and measure the periodic transition rates between the six states described in
the DBRS European RMBS Insight Model and Rating Methodology.3
The risk segmentation exhibits separation in the default risk across the distribution of loan scores. This can be observed in two
ways. First, in Figure 4 the loan scores increase within each risk segment when moving from lower to higher risk segments (left
to right). Second, when iterating the DMM within each risk segments over time, higher cumulative default rates are calculated
for higher risk segments. Figure 5 which shows the cumulative defaults forecasted per risk segment over 50 iterations for a nonamortising pool4 of current loans in period zero.
3. The six states are DQ0, DQ1, DQ2, DQ3 Default, and Redeemed.
4. Assumed 0% CPR.
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Figure 5 – Cumulative Default Expectation per Spanish MSM Risk Segment
30%
25.8% 26.3%
Cumulative Defaults
25%
22.2%
20%
14.9%
15%
9.9%
10%
5%
3.8%
2.3% 2.3% 2.6% 3.0%
1.1% 1.3% 1.9%
4.9%
11.8%
6.7%
0%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Risk Segment
Looking at Figure 4 and Figure 5 together, for a portfolio of current non-amortising Spanish loans the following could be expected:
• High quality portfolio where all of the loans have an initial Spanish MSM score less than 3.3%, risk segment 0, the expected
default rate is approximately 1.1%.
• Average quality portfolio where all of the loans have an initial Spanish MSM score between 13.5% and 16.5%, risk segment 7,
the expected default rate would be approximately 3.0%.
• Low quality portfolio where all of the loans have an initial Spanish MSM score greater than 50.5%, risk segment 15, the
expected default rate would be approximately 26.3%.
Figure 5 illustrates a simplified estimate of potential cumulative default rates for each of the 16 different segments. In this
example the respective DMM for each risk segment is applied to a portfolio of performing non-amortising loans for 12.5 years.
Ultimately, forecasted portfolio default rates will be a function of the underling loan characteristics and each loan’s amortisation
profile. The loan amortisation profile determines the exposure at default (EAD) to calculate the periodic loan level defaults.
For Spain, the EAD for a given period is equal to the outstanding loan balance 18 months5 prior to loan the defaulting which is
reflective of market practice of recognising defaults.
Spanish Correlation
The correlation applied to each portfolio for estimated rating scenario default rates is a function of the expected default rate
of the portfolio. Correlations assigned to each portfolio range from 12% to 24% with higher default portfolios being assigned
lower correlations and lower default portfolios being assigned higher correlations (Figure 6). The scale of correlations for each
expected portfolio default rate is based on the Basel III framework.6
5. Defined as ‘k’ in the Base Case Default Rates section of European RMBS Insight Methodology.
6. Asset correlation assumption per Basel III – BIS, Basel III: A global regulatory framework for more resilient banks and banking system, December 2010, pg. 39.
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Figure 6 – Correlation Versus Expected Portfolio PDR
25%
Correlation
20%
15%
10%
9.75%
9.25%
8.75%
8.25%
7.75%
7.25%
6.75%
6.25%
5.75%
5.25%
4.75%
4.25%
3.75%
3.25%
2.75%
2.25%
1.75%
1.25%
0.75%
0%
0.25%
5%
Expected Portfolio Default Rate
DBRS estimated a correlation across the Spanish risk segments between 10% and 12% over a period of two years. Although
relatively low correlations were estimated for the lower risk segments, higher correlations are applied to lower default portfolios
due to potential exposure to future idiosyncratic risks not reflected in the available data.
Spanish House Price Model
The Spanish House Price7 model generates MVDs for each rating scenario. The Spanish MVDs are estimated at the national level
and for the 19 autonomous regions. There are two publicly available data sources which are commonly used to analyse Spanish
home prices: INE and TINSA. Each data source has advantages and disadvantages for analysing Spanish home prices. Data from
INE is quarterly covering the period from Q1 2007 to Q2 2015. Additionally, the INE data is reported for the 19 autonomous
regions which is advantageous to map to data in the EDW template. Data from TINSA is monthly covering the period from
January 2001 to October 2015. TINSA data does report sub-indexes, however these sub-indexes are not easily mapped to loan
level data. Given the shorter time period for the INE data, DBRS analysed the time periods where data was available for both
datasets to model each INE series as a function of the TINSA data to back fit missing INE values to 2001. Real home prices are
calculated using CPI data with June 2003 as the base year). MVDs for each of the autonomous regions and the national level are
shown below in Figure 7. MVDs are applied to the updated property value to discount the sales price of a property to calculate
periodic losses. This allowed a more granular data set over a longer period of time.
Real home prices are calculated using harmonised CPI data with June 2003 as the base year. MVDs for each of the autonomous
regions and the national level estimated using the Spanish Home Price Model are shown below in Figure 7. MVDs are applied to
the updated property value to discount the sales price of a property in the periodic loss calculation.
7. See EU RMBS Roll Rate Methodology, Appendix 2 for further details.
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Figure 7 – Spanish MVDs
Region
AAA
AA
A
BBB
BB
B
Volatile
Overheated
Andalucía
-33.0%
-30.4%
-25.5%
-21.6%
-17.5%
-12.6%
Yes
Yes
Aragón
-16.3%
-13.4%
-9.8%
-6.2%
-2.5%
0.0%
Yes
No
Asturias
-44.6%
-40.8%
-35.0%
-30.0%
-24.7%
-19.4%
Yes
Yes
Balearic Islands
-38.5%
-35.3%
-29.9%
-25.2%
-20.3%
-15.7%
Yes
Yes
Canary Islands
-43.0%
-39.9%
-34.2%
-29.6%
-24.7%
-18.5%
Yes
Yes
Cantabria
-41.7%
-38.2%
-32.3%
-26.8%
-21.3%
-16.7%
Yes
Yes
Castile-La Mancha
-47.4%
-44.3%
-38.5%
-33.3%
-27.7%
-22.0%
Yes
Yes
Castile and León
-36.3%
-32.9%
-27.6%
-22.6%
-17.8%
-13.7%
Yes
Yes
Cataluña
-17.4%
-14.5%
-10.4%
-6.6%
-2.8%
0.0%
Yes
No
Ceuta
-25.0%
-20.8%
-15.0%
-10.5%
-5.8%
-2.4%
No
No
Communidad Valencia
-31.7%
-29.1%
-24.1%
-19.9%
-15.7%
-11.8%
Yes
Yes
Extremadura
-43.2%
-40.0%
-34.2%
-29.5%
-24.7%
-18.2%
Yes
Yes
Galicia
-49.6%
-46.0%
-40.3%
-35.4%
-30.0%
-22.0%
Yes
Yes
Madrid
-16.2%
-13.5%
-9.5%
-6.1%
-2.6%
0.0%
Yes
No
Melilla
-36.4%
-33.6%
-28.5%
-24.6%
-20.2%
-14.5%
Yes
Yes
Murcia
-45.4%
-42.0%
-36.1%
-31.2%
-26.2%
-19.6%
Yes
Yes
Navarra
-16.7%
-14.2%
-10.4%
-6.5%
-2.7%
0.0%
Yes
No
Pais Basque
-33.2%
-29.9%
-24.1%
-18.4%
-13.0%
-9.1%
Yes
Yes
Rioja
-34.6%
-30.5%
-24.6%
-18.7%
-13.1%
-9.1%
Yes
Yes
National
-31.1%
-28.0%
-23.2%
-18.6%
-13.7%
-10.2%
Yes
Yes
Figure 7 also includes the market status for purposes of the MVD simulation where markets are considered overheated if the real
house price index (June 2003 = 100) has increased above 150 in the most recent run-up and has yet to drop below 85. Volatile
indicates if a market has ever been overheated.
Spanish Distressed Sale Discount
The Spanish Distressed Sale Discount (DSD) is 40%. This is based on analysis of over 6500 repossessed properties which were
sold between 2007 and 2015. Over 95% of the sales occurred after 2010. DSDs are applied to the expected property value after
applying the MVD and meant to address a property sale in a liquidation scenario.
Spanish Foreclosure Costs
DBRS estimates Spanish foreclosure costs to be 8.0% of the outstanding loan balance at the time of default and a fixed cost of
EUR 5,000. DBRS’s estimates of the variable and fixed costs are based on consultation with external counsel.
DBRS has implemented minimum loss given default (LGD) rates for certain rating scenarios in Spain. DBRS recognises in
periods of high economic stress it may be difficult or even impossible to find a market clearing price for residential real estate
resulting in potential losses which may be greater than those implied by stressed liquidation values. The minimum LGDs are set
for the AAA, AA and A rating scenarios at 25%, 20% and 15%, respectively.
Spanish Constant Prepayment Rate (CPR) Assumptions
Between 2004 and 2007 CPRs averaged approximately 6.5%. CPRs steadily declined until 2012 in tandem with the drop in house
prices and decrease in mortgage lending (Figure 8). Spanish CPRs have been averaging around 2% since 2012. Mortgage rates
have continued to drift lower. However, the decline in mortgage rates is not incentivising borrowers to refinance given most
mortgages tend to be floating rate indexed to 12-month Euribor. As Euribor trends downward, borrowers monthly payments
reset lower. As monthly payments remain low, the incentive to refinance is absent without substantial equity in the property.
House prices have begun to recover (up 6.5% between Q1 2014 and Q3 2015) but not to a level which would be expected to have
a significant impact on CPRs. DBRS expects the current observed trend in CPRs to continue and assumes a 2% CPR for the
Spanish MSM.
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35.0
12.0%
30.0
10.0%
25.0
8.0%
20.0
6.0%
15.0
4.0%
10.0
Mortgage Volumes (left)
Avg Mortgage Rate (right)
2015-07
2015-01
2014-07
2014-01
2013-07
2013-01
2012-07
2012-01
2011-07
2011-01
2010-07
2010-01
2009-07
2009-01
2008-07
2008-01
2007-07
2007-01
2006-07
2006-01
0.0%
2005-07
0.0
2005-01
2.0%
2004-07
5.0
2004-01
Monthly Lending (billions EUR)
Figure 8: Spanish CPRs, Mortgage Rates and Lending Volumes
3 Mo CPR (right)
Source: Band of Spain, Intex and INE.
Spanish Cash Flow Assumptions
Prepayments
Three prepayment stresses are generally run for the DBRS cash flow stresses. The stresses for Spain are 0% - Slow, 10% - Mid
and 20% - Fast.
Recovery Timing
The Spanish recovery timing for cash flow analysis is 48 months. This is based on further analysis of the repossessed loans
used to estimate the Spanish DSD. The recovery timing is the time between first period a loan stops contributing principal and
interest payments to the collections and receipt of the recoveries on a loan.
Structured Finance: European RMBS
May 2016
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