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Methodology or Forecasting andStress-Testing ABS and RMBS Deals

August 5, 2010

Prepared By

ECONOMIC & CONSUMER CREDIT ANALYTICS

Juan Carlos CalcagnoSenior [email protected]

Anthony HughesSenior [email protected]

Ioannis StamatopoulosAssistant [email protected]


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Methodology or Forecasting andStress-Testing ABS and RMBS Deals

The nancial crisis has demonstrated the urgency o developing new tools to improve nancial

decision-making. The need or research and development is especially great in the structured

securities market, which was at the epicenter o the crisis. Structured nance originations have

now virtually dried up, to the detriment o not only investors, but also consumers. I investors can ac-

curately value and assess the risks o their holdings, bonds can be priced correctly, and the benets

o securitization can then fow to them as well as to consumers in the orm o lower interest rates on

mortgages, auto loans, student loans and credit cards.

In general, structured nance deals are put together by pooling groups o consumer loans and plac-

ing the principal and interest paid by borrowers into a kitty to be distributed to investors who ownthe rights to dierent tranches representing dierent degrees o risk. A deal is oten composed o

numerous pools o loans, each with distinct characteristics. Complex rules govern how the money in

the kitty is distributed to investors. Some investors may be more exposed to a particular pool within

a deal than others who hold bonds carrying the same rating. Conversely, the pools may all pay into a

common kitty, in which case the division into pools serves a purely administrative unction. The value

o the bonds is thus determined by the extent to which the loan pools are able to contribute cash to

the kitty. The accuracy o orecasts o these cash streams should thereore be the objective o any

model used to assess bond value.

In this paper, we outline the approach Moodys Analytics uses to model, orecast and stress-test

collateral backing ABS deals, concentrating on U.S. RMBS. Since the Moodys Analytics Structured

Finance Workstation already precisely handles the waterall modeling problem, our task here is to

develop a methodology that accurately projects the cash fow rom the collateral backing the deals.

Our approach considers economic conditions at loan origination, loan characteristics, past pool per-

ormance, and dynamics in the macroeconomic environment over time to explain changes in pool

perormance. We will describe, in detail, our methodology, and the models developed will be vali-

dated against simple and similar alternative orecasting approaches. The same methodology has been

successully applied to other types o asset-backed securities such as international RMBS, auto ABS,

student loans, credit cards, and business leases. In all cases, the methodology is immediately andglobally available, allowing any type o security, provided data are available, to be accurately valued

and stress-tested. The output rom the models described in this paper provides all necessary data to

run waterall valuation engines and thus compute air value and expected loss under baseline as well

as stressed conditions.

MOODYS ANALYTICS / METHODOLOGY FOR FORECASTING AND STRESS-TESTING ABS AND RMBS DEALS


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MOODYS ANALYTICS / METHODOLOGY FOR FORECASTING AND STRESS-TESTING ABS AND RMBS DEALS 2

The MoodysAnalytics Approach

We eel that a pool-level approach is like-

ly to provide greater utility to end users than

competing methodologies. Data at this level

are available on securities rom all parts o

the world and on any product, meaning that

the pool-level approach is universally appli-

cable. Pool-level models are also more likely

to provide accurate orecasts o pool-level

aggregates than are competing approaches.

The data used or this study are drawn

primarily rom two sources: The Moodys

Analytics U.S. macroeconomic orecast

databases and the Moodys Analytics Peror-

mance Data Services product.

Moodys Analytics maintains one o thelargest repositories o macroeconomic,

demographic and nancial data rom a mul-

titude o government and private sources.

The data set covers the national accounts,

banking and nance, demographics, personal

income, prices, retail sales, labor markets,

energy, nancial markets and many other

indicators. Moodys Analytics each month

produces projections, both baseline and

stressed, or economies and nancial mar-

kets worldwide using large-scale structural

macroeconometric models. In the broadestterms, the models are specied to refect the

interaction between aggregate demand and

aggregate supply. In the short run, fuctua-

tions in economic activity are primarily de-

termined by shits in aggregate demand, in-

cluding personal consumption, gross private

investment, net exports, and government

expenditures. The level o resources and

technology available or production is, in the

short term, taken as givens. Prices and wages

then adjust slowly to equate aggregate de-

mand and supply and thus move the econo-my toward equilibrium. In the longer term,

changes in aggregate supply determine the

growth potential o the economy. The rate

o expansion o the resource and technology

base is the principal determinant o overall

economic growth, which eeds, interactively,

into other actors in the model. The model

and subsequent orecast are overseen by a

team o about 60 economists who cover the

perormance o the economy in real time.

The orecasts undergo continual revision

and adjustment to refect new trends and

changing data. Recent advances in macroeco-

nomic theory, new econometric techniques,

and increased computing power also govern

development o the models. The system has

been changing to accommodate increased de-

mand by clients who want to use the models

to generate alternative scenarios and to un-

derstand the sensitivity o the macroeconomy

to changing economic and nancial condi-

tions. Moodys Analytics produces one upside

and several downside alternative scenarios

each month or each economy it covers. A de-

tailed description o some recent alternative

scenarios is included in Appendix 2.

In terms o the ABS credit data, mean-

while, PDS is a comprehensive and standard-ized data set that includes raw inormation

on all active and inactive ABS and RMBS

deals rated by Moodys Investors Service in

the U.S. and globally. For a more compre-

hensive picture o the U.S. RMBS market

during the model development stage, and

to avoid any selection bias, we have supple-

mented the PDS data set with inormation

rom deals that are not rated by MIS; this

supplemental database is available only in-

ternally. Given that MIS-rated deals cover

about 90% o the U.S. RMBS universe, thePDS product alone is close to comprehen-

sive, given the aims o this paper.

The data set is composed o deal inorma-

tion at origination, augmented by continu-

ously updated perormance inormation

rom servicer and trustee monitoring reports.

The data les are cleaned and validated

by the MIS monitoring team as part o the

ongoing ratings surveillance process. The

data are o excellent quality and o a perect

structure or the orecasting models con-

sidered in this article. Included in the lesare over 100 perormance statistics at the

deal, pool, and tranche level or over 10,000

ABS and RMBS deals in the U.S., Europe, the

Middle East and Arica as well as in the Asia-

Pacic region. PDS coverage in each region

matches the market share o MIS in rating

the underlying deals. The data are not only

consistent within each region or country,

they are also consistent across and between

regions, allowing or international analysis

i so desired. Our analysis typically con-

centrates on key pool perormance metrics

such as delinquency rates as a proportion o

original balance, longer-term indicators o

deault, prepayment rates, and severity o

losses or loss given deault.

For the purposes o this study, because

o the lack o data on a ew key variables,

we restrict our attention to U.S. RMBS deals

originated ater December 2001. Many

perormance measures, however, are avail-

able back to 1995. Table 1 provides basic

descriptive statistics about the pools in the

sample used or modeling purposes. The

pools employed are concentrated in sub-

prime, alt-A and jumbo categories. Given

that originations grew so quickly rom 2004

to 2007, unsurprisingly, these cohorts arealso very well represented. Data or other

vintages and product types are suciently

available or their determining actors to be

accurately estimated.

Once all data les are merged, the nal

data set used or the analysis is a multi-

dimensional, longitudinal panel data set.

Our estimation sample is composed o

12,148 unique asset pools observed over, in

most cases, several years. In total, our es-

timation sample or most dependent vari-

ables amounts to about 660,000 uniqueobservations with a ew categories, most

notably loss severities, having access to

only about 140,000 separate observations.

The data cover a ull business cycle, includ-

ing two recessions, allowing us to correctly

measure the impact o cyclical actors and

to weigh these actors against internal

pool-specic inormation.

A. The econometric model

The panel nature o the data on struc-

tured securities provides a rich tapestry withwhich to construct high-quality orecasting

models. In this paper, we consider a model

o the orm

or, decomposing into its constituent

components:


(1)


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healthy dose o economic principle. Models

built this way enjoy the additional benet o

ease o interpretation.

The selection o the explanatory macro-

economic variables was also based on our

ability to orecast them easily and suciently

accurately. In econometric orecasting, the

uture realizations o the dependent variablerely on accurate orecasts or the independent

variables. Forecast errors can easily be multi-

plied i input variable orecasts are not closely

monitored or i the data backing the orecasts

are noisy and thus inherently dicult to

project. Identiying macro variables that are

useul in this context is straightorward: They

are the actors that oten have the power to

change the direction o nancial markets and

that are thus routinely debated in the public

square. Variables such as unemployment

rates, home price indices, key interest rates,and retail sales numbers are generally bet-

ter candidates than industry-level mortgage

oreclosure rates, or instance, which are o

interest only during certain brie periods. We

nd that a combination o such core variables

generally act as a reasonable proxy or these

more peripheral actors. Since core variables

are generally easier to predict, we nd them

more useul in building optimal orecasts o

ABS pool perormance.

Model Results

A summary o the variables included in

each regression or the vectors we modeled

is presented in Table 2. The rst group o vari-

ables, the pool origination actors ( in (2)),

include average LTV and FICO, a set o dum-

mies or vintage origination year, dummiesdening loan or collateral type (i.e. whether

the pool represents subprime or jumbo

mortgages, or instance) and a set o dum-

mies indicating whether the originator o the

pool in question was in the top 10 in terms o

total number o pools originated during the

in-sample period. The vintage year, collateral

type and originator dummies are included

in most regressions in order to control or

group heterogeneity in the data. Weighted

average LTV and FICO at origination are

key pieces o inormation not only becausethey determine pool quality and hence

perormance, but also because we seek to

retain the ability o the models to project the

perormance o newly originated mortgage

pools using inormation that is readily avail-

able at deal origination.

The second group o variables includes

the economic condition actors at deal origi-

nation, which is our set o instrumental vari-

ables, . In this category, we ound that gen-

eral measures o economic activity, like GDP

and the unemployment rate, are correlated

with changes in pool perormance; we also

nd good support or actors specic to the

mortgage market such as home prices and

interest rates. Broadly speaking, house price

changes and an average benchmark interest

rate at origination tend to have a counter-cyclical impact on measures o perormance

observed early in the deault process. Labor

market conditions at deal origination, how-

ever, become more important when model-

ing later stages in the deault process, includ

ing oreclosure, REO, and losses.

The third set o variables is the macro-

economic series or those actors related to

the pools exposure to the business cycle.

We ound that the types o macroeconomic

actors that aect delinquency are standard

are labor and housing market variables,income, and overall economic activity with

close attention paid to lag structures and

transormations applied to the economic

data being used. House price changes and

renancings become much more important

drivers at later stages in the oreclosure pro-

cess. We use both current home price chang-

es and price changes since origination as key

housing market actors. This second variable

allows or a dierential negative equity e-

TABLE 1

Number o pools by collateral type and closing year

Pools Collateral Type

Deal Closing Year

Total2002 2003 2004 2005 2006 2007 2008 2009 2010

HELOC 14 23 61 50 46 20 2 216High LTV 14 10 6 7 6 1 44

Home Equity/Closed End 2nds 8 8 6 6 19 14 61

Subprime 287 526 749 795 777 386 4 3,524

Alt-A 155 463 1,080 1,335 904 676 12 1 4,626

FHA-VA 9 17 17 25 20 2 90

Jumbo 207 583 534 452 315 252 30 2 1 2,376

Prime Conorming 5 1 6

Scratch & Dent 10 25 63 43 49 41 231

Subprime 2nds 2 13 27 51 83 41 217

Option Arms 5 71 221 284 173 3 757Total Pools 706 1,673 2,619 2,985 2,503 1,606 51 3 2 12,148

Average Time-Series Available 92 82 70 58 46 36 23 10 2


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ect to be explicitly modeled. The models o

oreclosure, REO, charge-os, and severity

are tied largely to home prices and various

other actors aecting housing and legal cost

structures. We also ound prepayment rates

to be more sensitive to interest rates, re-

nancing activity, and existing-home sales.

As the structure o the model is recursive

in nature, the next set o covariates consti-

tutes the so-called pipeline connections. We

model delinquencies largely independently

o one another but model subsequent events

prepayments, oreclosures, charge-os, and

severity conditionally on the delinquency

perormance the pools attain. We also take

advantage o our panel data estimation

techniques to measure the unobserved pool

heterogeneity embedded within several earlyperormance measures and then use these

estimates as an exogenous quality metric in

the subsequent net charge-o, prepayment,

and principal payment models.

Finally, the models include a cubic spline

baseline liecycle with our knots5 and inter-

action terms between the spline variables,

current economic conditions, and collateral

type dummies. By introducing these interac-

tions, we measure the dierential impact o

economic drivers on dierent types o assets.

For example, we allow the sensitivity to un-employment rate changes to vary between

pools classied as subprime and those dened

as jumbo deals. Similarly, by interacting the

spline with collateral type, we allow the shape

5 Three in the case o the LGD model.

o the liecycle to depend upon the underlying

quality o the pools under consideration.

Table 2 summarizes the specications

used to model each o the 11 vectors studied

in this article. All the variables (or groups

o variables) are signicant at the 5% level,

and all variables have appropriate signs con-

sistent with economic theory. The models

have been tested rigorously to ensure that

they are optimal, among the set considered,

or orecasting purposes. In Table 3 we pres-

ent the results or one regression, that or

the oreclosure rate, as an example o the

estimation results achieved or all models.

Other specications are, naturally, available

rom the authors on request.

Given that our general model specica-

tion included actors rom all the subcom-ponents described above, we can reasonably

conclude that actors specic and internal

to the pool, together with external mac-

roeconomic actors, are given appropriate

loadings in the calculation o perormance

orecasts. Once we eed in orecasts o

macroeconomic variables derived rom our

structural macroeconomic model, generating

pool-level orecasts is straightorward. Ex-

tending this principle to stress tests which

really constitute little more than pessimistic

economic orecasts is equally simple. Thisprocess delivers a set o vectors under each

alternative macro scenario, including a base-

line projection. The models can be applied

to custom scenarios provided all necessary

macroeconomic variables are covered by the

scenario. The particular shape o these ore-

casts and scenarios depends on the estimat-

ed elasticity o the risk vector to the included

macroeconomic series. As mentioned earlier,

the elasticities have been ound, in many

cases, to be heterogeneous across dierent

collateral groups. Moreover, we nd that

the slopes vary depending on the underlying

quality o the pool in question and the time

at which the pool was originated.

To illustrate these eatures, we present

orecasts and scenarios or a small number o

pools drawn rom the subprime, alt-A, jumbo,

and option ARM collateral types under two

alternative macroeconomic scenarios the

current baseline orecast (a recovery) and an

alternative, depression-like event. These pro-

jections are depicted in Charts 1 and 2. Under

baseline assumptions, we nd that oreclosurerates will stay fat or another quarter as the

real estate market stabilizes and then steadily

decrease as bad accounts move out o the

oreclosure state and are fushed rom the sys-

tem; the vector then converges to the steady-

state baseline liecycle. A depression scenario,

however, generates a sharp double dip, with

gures across collateral types reaching histori-

cally high levels in 2011, similar to the peaks

observed in mid-2009.

A. Out-of-sample validationOne o the most important ndings in the

orecasting literature is that the model that

best ts the data is not necessarily the one

that will provide the most accurate out-o-

sample orecast (Fildes and Makridakis (1995))

Typically, to assess accuracy, the data will be

0

5

10

15

20

25

30

35

07 08 09 10 11 12 13 14 15

Baseline

Alt. Scenario

Chart 1: Projections for Alt-A and Subprime Pools

Foreclosure rates, %

Source: Moodys Analytics

Subprime

Alt-A

0

5

10

15

20

25

07 08 09 10 11 12 13 14 15

BaselineAlt. Scenario

Chart 2: Projections for Jumbo and Option Arm pools

Foreclosure rates, %

Source: Moodys Analytics

Option ARM

Jumbo


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split into two data sets: The rst set, a devel-

opment sample, is used to speciy the model

and estimate its coecients; the second set

is used to evaluate orecast accuracy and is

known as the hold-out sample. To validate

our preerred models, we hold out the last six

available data points at the end o the time

series or each pool. The models were tted

to the remaining historical data. The orecasts

generated using the models were then com-

pared with actual values observed during the

hold-out sample, and aggregate root mean

squared orecast errors were computed.

Underlying our orecast accuracy evalua-

tion was the need to test our preerred mod-

el against reasonable alternatives (Baltagi

(2008)). For this purpose, the out-o-sample

perormance o the HT estimator was com-pared against that o three benchmark mod-

els. Our rst benchmark was a nave orecast

that assumed no change rom the most

recently observed value in the development

sample. The other two benchmarks were

specied similarly to our preerred specica-

tion but were estimated using pooled OLS

and the xed-eects estimator, respectively.

These approaches are described in previous

sections; both should provide ar stier com-

petition to the preerred specication than

the corresponding nave orecast.

Errors or each approach were aggregated

and results presented in Table 4. We present

the perormance o the alternative models

relative to the preerred HT specication.

Numbers greater than unity thereore indi-

cate cases in which the alternative approach

is able to beat the preerred method in a

head-to-head competition.

The results o the validation exercise are

clear-cut. The preerred HT estimated panel

data model suers less squared orecast error

than each o the three considered alternative

specications or all 11 estimated vectors.

The one exception is or charge-os, in which

the xed-eects estimator is able to eke out

a small victory by just a couple o percentage

points. We eel that any test applied enough

times will yield contrary results and placethis nding in that category. Folding in the

act that the xed-eects estimator reduces

model utility, we can easily conclude that a

2% improvement is not enough to warrant a

change in technique.

In terms o the three primary inputs to

the Structured Finance Workstation CDR,

CPR and severity the HT estimated model

easily dominates its competitors and can

thus be recommended. Vectors derived

rom this approach are likely to be optimal in

terms o deriving accurate bond valuations.

Final Remarks

These results demonstrate that pool-leve

perormance aggregates are well orecast us-

ing aggregate panel data specications that

accurately capture the impact o macroeco-

nomic dynamics on the behavior o loans.

The models described are simple, parsimo-

nious structures in which every coecient

estimate can be readily understood using

simple economic intuition. The subsequent

models orecast the aggregate perormance

characteristics accurately and easily lend

themselves to the construction o relevant

stress-testing scenarios.

Structured inance is a complex busi-

ness. Ater the recent recession exposed

the ailure o the models used in the in-dustry, many sought to solve the problem

with even more complex solutions and

models. Though it may seem counterintui-

tive, the correct response to the ailure o

complex models in 2007-2009 was a light

to simplicity and unctionality. The mod-

els that orm the backbone o the Moodys

Analytics system are simple yet compre-

hensive and, importantly, are designed

speciically to tackle the problem o ore-

casting and stress-testing the collateral

underlying ABS deals.



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TABLE 2

Summary o Models inputs

Group Variable

Vector

30 day

delinquency

60 day

delinquency

90+ day

delinquency CDR Bankruptcy Foreclosure REO LGD

Net

Chargeo CPR Principal

Origination

Conditions

LTV X X X X X X X

FICO X X X X X X X X X

Top Originators X X X X X X X X X X X

Vintage Year X X X X X X X X X

Loan Type X X X X X X X X X X X

Economic

Conditionsat

Origination

Fed Funds X X XX (relativeto WAC)

Home Prices X X X X

GDP X

Unemployment Rate X X X X

CurrentEconomicConditions

Disposable Income t, t-3 t, t-3 t-3

Home Prices (HP) t, t-6 t, t-6 t-6 t-3 t, t-6

Change in HP since 0 t t t

Negative Equity Dummy t t t

Unemployment Rate t-3 t-3 t-3 t-1 t-6 t

Avg. Hourly Earnings t-6 t-6 t-6 t-6

GDP t t

Personal Bankruptcies t-6

REFI Volume t-12 t t

Fed Funds tDebt Service Burden t-6 t

Existing Home Sales t-12

Mort. REFI Originations t-12 t

PipelineConnections

30 day delinquency t

90+ day delinquency t-3 t-3 t-3

Bankruptcy t-3

Foreclosure t-1

REO t

Unobserved eect (CDR) X X X

OtherVariables Liecycle * Loan Type X X X X X X

Liecycle * Economic X X X

Loan Type*Economic X X X X

Dec 2005 Bankruptcy Law X

Lifecycle

Number o knots 4 4 4 4 4 4 4 3 4 4 4

Notes: All models include seasonality actors (month) and dummies or zeros



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TABLE 3

Partial Regression Results or Foreclosure Rate

Coefcient Standard Error t-statistic

LTV 0.053 0.004 11.809

FICO (non-linear spline 1) -0.006 0.001 -8.402FICO (non-linear spline 2) -0.001 0.001 -1.889

Unemployment Rate, % at origination -0.443 0.014 -30.842

House Prices, % change year ago lagged 12 months -0.028 0.000 -119.093

GDP, % change year ago -0.015 0.001 -27.163

Dummy indicator or Negative Equity 0.108 0.004 29.156

90+ day delinquency lagged 3 months 0.110 0.001 173.511

Bankruptcy lagged 3 months 0.081 0.001 151.339

Observations 576651

Number o unique pools 11727

Note: model includes liecycle, seasonality, and dummies or zeros, originators, negative equity and loan type and a constant term

TABLE 4

Out o Sample Forecasting Results

Ratio between Hausman-Taylor and benchmark models RMSE

Vector Nave Forecast Pooled OLS Fixed Eects

30 day delinquency 0.935 0.742 0.941

60 day delinquency 0.871 0.853 0.904

90+ day delinquency 0.688 0.479 0.531

CDR 0.830 0.802 0.875

Bankruptcy 0.948 0.720 0.711

Foreclosure 0.572 0.636 0.777

REO 0.973 0.645 0.758

CPR 0.884 0.957 0.993

Principal 0.668 0.586 0.667

Severity 0.911 0.928 0.850

Chargeo 0.762 0.905 1.020


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Reerences


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Publishers.

3. Baltagi Badi H. (2001). Econometric Analysis o Panel Data. Wiley and Sons, Chichester, UK.

4. Baltagi, Badi H. (2008). Forecasting with panel data, Journal o Forecasting, 27(2), pp 153-173.

5. Hendry, David F. and Hubrich, Kirstin, (2009). Combining Disaggregate Forecasts or Combining Disaggregate Inormation to Forecast an

Aggregate. ECB Working Paper No. 1155.

6. Fabbozzi, Frank and Kothari, Vinod, (2008). Introduction to Securitization, Wiley.

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tical Review, 63, pp. 289-308.

8. Green, Richard and Wachter, Susan M. (2005), The American Mortgage in Historical and International Context. Journal o Economic Per

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Appendix A: Defnition o Variables

Field Description

Pool Perormance

30 day delinquency Amount o receivables that are 30-59 days past due divided by the original collateral balance (%).60 day delinquency Amount o receivables that are 60-89 days past due divided by the original collateral balance (%).

90+ day delinquency Amount o receivables that are 90 or more days past due divided by the original collateral balance (%).

CDR Constant deault rate based on amount o receivables in deault in the current month, annualized (%)

Bankruptcy Amount o receivables in which the obligor has declared bankruptcy divided by the current collateral balance (%)

Foreclosure Amount o receivables in oreclosure divided by the current collateral balance (%).

REO Amount o receivables that are real estate owned divided by the current collateral balance (%).

Loss-Given Deault (LGD) Monthly net losses during the related monthly period divided by gross losses during the related monthly period (%).

Net Chargeo Net losses during the related monthly period divided by the prior months ending collateral balance (%).

CPR Constant prepayment rate based on unscheduled principal paid by obligors rom the current month, annualized (%).

Principal Total principal collected during reporting period divided by the current collateral balance (%).Pool Origination

LTV Weighted Average Loan To Value at deal origination (%).

FICO Weighted FICO score at deal origination (%).

Top Originators Indicator variables or Top Mortgage originators during the sample time period including Countrywide Bank, WellsFargo, IndyMac, BoA, Citibank, Greenpoint Bank (CapOne), Option One Mortgage, and Residential Funding (GMAC)

Vintage Year Vintage origination year (2001 to 2010)

Loan Type Loan collateral type: Heloc, High LTV, Home Equity/Closed End 2nds, Subprime, Alt-A, FHA-VA, Jumbo, Prime Con-orming, Scratch and Dent, Subprime 2nds, Option Arm

Macroeconomic series

Disposable Income Income: Per capital disposable income, (Constant$, SAAR, annual growth rate)

Home Prices Median Sales Price Existing Single-Family Homes, (Ths. $, SA, annual growth rate)

Change in HP since 0 Dierence in Median Sales Price since Deal Origination (%)

Negative Equity Dummy 1 i change in HP since 0 is negative; 0 otherwise

Unemployment Rate Household survey: Unemployment rate, (%, SA)

Avg. Hourly Earnings Avg. Hrly. Earnings: Total Private, ($ Per Hrs., SA, annual growth rate)

GDP NIPA: Gross domestic product, (Bil. 2000 $, SAAR, annual growth rate)

Personal Bankruptcies Bankruptcies: Personal, Total, (# 3-Month Ending, SAAR, annual growth rate)

REFI Volume MBA: Percent REFI Volume, (%, annual growth rate)

Fed Funds Interest Rates: Federal Funds Rate, (%,P.A.)

Debt Service Burden Debt Service Burden: Total, (% o Disposable Personal Income)

Existing Home Sales Existing Home Sales: Single-Family, (Mil., SAAR, annual growth rate)Mort. REFI Originations Mortgage Originations: Renances, (Mil. $, SAAR, annual growth rate)


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Appendix B: Summary o U.S. Economics Scenarios (July 2010)

Scenario Real GDP Median Home Price Fed Funds Target Unemployment

S1

Stronger Recovery

in 2010

Real growth o 3.9%

in 2010, 4.6% in 2011

Expected to rise 2.9% in

2010 and 2.0% in 2011

The unds rate is

expected to end 2010 at1.6% and 2011 at 2.5%

Peaks at 10.1%

in Q4 2009 andends 2010 at 8.3%

BL Baseline, CurrentReal growth o 2.9%in 2010, 3.6% in 2011

Peak-to-trough declineo 26%, turnaround inmid 2011

The unds rate isexpected to end 2010 at0.2% and 2011 at 1.8%

Peaks at 10.0%in Q1 2011

S2Mild Second

RecessionReal growth o 2.0%in 2010, 1.3% in 2011

Peak-to-trough declineo 36%, turnaround atermid 2011


Peaks at 11.8%in Q2 2011

S3Deeper Second

RecessionReal growth o 1.6%in 2010, -0.7% in 2011

Peak-to-trough declineo 40%, turnaround atbeginning o 2012

The unds rate isexpected to remainbelow 1% until Q3 2012

Peaks at 14.0%inQ4 2011

S4Complete Collapse,

DepressionReal growth o 1.3%in 2010, -1.9% in 2011

Peak-to-trough decline

o 45%, turnaround inlate 2012

The unds rate is

expected to end 2010 at0.1% and 2011 at 0.2%

Peaks at 15.1%in mid 2012

S5Aborted Recovery,

Below-TrendLong-Term Growth

Real growth o 2.1%in 2010, 1.8% in 2011



Peaks at 11.4%in mid 2011

S6Fiscal Crisis, DollarCrashes, Infation

Real growth o 2.2%in 2010, 2.6% in 2011



Peaks at 13.0%in early 2013, ends2010 at 10.2%


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