[Goldman Sachs] Alt-A Market - An Introduction

8/6/2019 [Goldman Sachs] Alt-A Market - An Introduction

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Mortgage Strategies United States

2 Goldman Sachs

Overview

The Alt-A market emerged in 1996, as investors started to better understand thecredit characteristics of mortgagors and as originators began expanding the spectrumof credit they would lend to. Prime originators expanded their score cards toaccommodate lower FICO scores, while subprime originators expanded theirs toaccommodate higher ones.

As the market has evolved over time, different outlying characteristics began todefine the Alt-A universe, such as documentation levels, occupancy, and other risk-layering attributes. Today, the guidelines for what constitutes an Alt-A loan areconstantly changing and vary by issuer. Some originators view Alt-A loans as fallinginto one of two categories:

Prime Alt-A, which consist of mortgages to borrowers who are prime but whoare missing documentation (those who are self-employed, have no employerreferences, etc.), or to borrowers who will not occupy the subject property; or

Non-Prime Alt-A, which consist of mortgages to strong subprime borrowers.

Other participants in the market divide the categories even further. FitchRatingsdefines traditional Alt-A loans as those with agency-conforming insured balancesand a prime credit history, but with expanded guidelines for other categoriesincluding loan purpose, debt-to-income ratios, and loan-to-value ratios. FitchRatingsalso divides the Alt-A universe into Prime Alt-A, A-AltA, and Alt-B loans in anattempt to group together perceived and real distinctions in the credit risk of poolswithin each category. 1 Exhibit 1 summarizes the criteria for these three buckets.

Exhibit 1: FitchRatings Alt-A categories

Category Prime Alt-A A-Alt-A Alt-B

FICO 700 730 670 700 640 670LTV 60% - 70% 65% - 85% < 80%% Investor Property 10% - 30% < 30% 5% - 30%Stated/NoDocumentation

30% - 50% 50 75% < 75%

Typical Issuers RALI, BOA,Countrywide CB and

TI, Wells Fargo

Impac, CSFB First Franklin,Chase, Ameriquest,

New Century,Meritage

Other Second Liens,Prepayment Penalties

Source: FitchRatings, Mortgage Principal and Interest , May 2004.

1 FitchRatings, Mortgage Principal and Interest , Tug of War: New Dimensions of the Alternative AMarket. May 2004.


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United States Mortgage Strategies

Goldman Sachs 3

Countrywide has defined Alt-A in a different way. Deals issued off of the CWALT T1shelf include jumbo loans with low documentation or jumbo loans with fulldocumentation but lower-than-prime FICO scores (


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Goldman Sachs 5

Over time, the largest shift in Alt-A characteristics has been in loan size, percentageof ARM loans, and percentage of loans with a prepayment penalty. In 1996, only 1%of loans originated were ARMs; however, in 2004, almost 70% of loans originatedwere ARMs. It is interesting to note that both prime jumbo and subprime loans hadthe same directional change.

Exhibit 4: Shift in non-agency MBS characteristics by outstandings

1996 2004Alt-A Prime Subprime Alt-A Prime Subprime

Original Volume ($mn) 5,496 21,550 10,512 26,254 56,481 44,326WA FICO 706 713 646 709 735 632WA LTV 74 76 78 75 68 82ARM % 1% 12% 36% 69% 73% 72%% Investor Property 22% 0% 5% 20% 3% 5%% Limited or NoDocumentation

51% 19% 18% 61% 42% 35%

WAC 8.88 7.75 9.28 5.62 4.70 7.06Average Loan Size 133,858 283,505 66,440 225,334 425,787 149,248% Prepayment Penalty 0% 1% 13% 21% 8% 49%

% Primary MortgageInsurance

13% 17% 6% 14% 2% 19%

Sources: Loan Performance, Goldman, Sachs & Co.

Alt-A originations have seen a general shift over time in occupancy, FICO scores, anddocumentation. Exhibit 5 shows that credit scores have stayed relatively stagnant indeals defined as Alt-A, dipping slightly in 2000 but lately improving; meanwhile,occupancy and documentation levels have shifted. It is interesting to note that in2000-2002, originating Alt-A loans tended to have higher owner occupancy levelsand lower documentation levels.


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6 Goldman Sachs

Exhibit 5: FICO, occupancy, and documentation changes, 1997-2004

Alt-A FICO

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1997 1998 1999 2000 2001 2002 2003 2004

820+740 - 820660 - 740


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Goldman Sachs 7

Issuers and underwriters in the Alt-A market are constantly changing as well, partlyowing to the ongoing entrance of new players in the market. Exhibit 6 and Exhibit 7show the Alt-A issuers and underwriters with the highest dollar volume of Alt-Asecurities issued during the first three quarters of 2004.

Exhibit 6: Top Alt-A issuers in 2003 and 2004 (Jan-Sept); $, millions

Volume 2004Rank Issuer 2004 2003 Change Deals Market

Share1 Countrywide Financial $17,561.6 $10,758.9 63.2% 30 15.8%2 Impac 14,753.7 4,857.7 203.7 11 13.33 Bear Stearns 10,752.7 4,730.2 127.3 18 9.74 CS First Boston/ABSC 10,484.0 6,061.0 73.0 9 9.45 IndyMac 10,342.2 4,380.7 136.1 19 9.36 UBS Warburg 9,515.6 3,233.6 194.3 20 8.67 Lehman Brothers 6,234.8 5,740.0 8.6 19 5.68 GMAC-RFC 4,580.3 6,981.9 -34.4 17 4.19 American Home Mortgage 4,332.3 0.0 NA 4 3.9

10 Chevy Chase Bank 4,076.7 2,251.6 81.1 4 3.7

Top 10 Sub-Total 92,633.9 48,995.6 89.1 151 83.5Market Total $110,988.7 $55,953.0 98.4% 208 100.0%

Source: Inside MBS & ABS, November 12, 2004

Exhibit 7: Top Alt-A underwriters in 2003 and 2004 (Jan-Sept); $, millions

Volume 2004Rank Issuer 2004 2003 Change Deals Market

Share1 Bear Stearns $22,587.9 $11,673.6 93.5% 48 20.4%2 RBS Greenwich Capital

Markets

15,585.2 4,295.0 262.9 28 14.0

3 Countrywide SecuritiesCorp.

15,405.0 10,590.3 45.5 29 13.9

4 CS First Boston 14,813.6 8,605.5 72.1 17 13.35 UBS Warburg 11,651.6 5,386.7 116.3 29 10.56 Lehman Brothers 9,783.3 6,227.5 57.1 25 8.87 Banc of America

Securities3,796.5 4,156.2 -8.7 13 3.4

8 Goldman Sachs 3,602.0 35.5 NA 20 3.29 Deutsche Bank 3,111.8 1,132.7 174.7 11 2.8

10 Citigroup / Solomon 2,851.4 1,842.4 54.8 17 2.6Top 10 Sub-Total 103,188.3 53,945.4 91.3 237 2 93.0Market Total $110,988.7 $55,953.0 98.4% 208 100.0%

Source: Inside MBS & ABS, November 12, 2004

2 Includes co-mandates, hence some deals are double counted.


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8 Goldman Sachs

Why the Alt-A Market is attractive

GrowthThe dollar volume of issuance in the Alt-A market has increased dramatically, risingfrom $10.95 billion at the end of 1Q2002 to $47.96 billion at the end of 3Q2004 more

than a 300% increase.

Exhibit 8: Alt-A issuance 1Q2002-3Q2004 ($, billions)

-

10

20

30

40

50

60

1Q02 2Q02 3Q02 4Q02 1Q03 2Q03 3Q03 4Q03 1Q04 2Q04 3Q04

Source: Inside MBS & ABS, November 12, 2004.

This explosive growth is expected to continue. In order to maintain volume levels asrefinancing opportunities decline, prime originators will expand downward in thecredit spectrum while subprime originators will expand upward, meeting at the Alt-

A market. Alt-A originations amounted to an estimated $90 billion for the first half of 2004, an increase of 136.8% from the first half of 2003.

As the Alt-A market expands, top subprime originators are currently developing Alt-A programs and underwriting guidelines; however, to currently manage theirprograms, they have begun to carve out higher FICO originations and sell themseparately from their typical originations. Potential true Alt-A entrants includeAmeriquest, New Century, and Fremont.

Compared with prime outstandings, the number of Alt-A loans has continued togrow while prime originations have had dips in production.


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Goldman Sachs 9

Exhibit 9: Alt-A, jumbo prime, and subprime outstandings, 19972004 3 asestimated by Loan Performance ($, millions)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

1997 1998 1999 2000 2001 2002 2003 2004

Alt-A Subprime Jumbo

Sources: Loan Performance, Goldman, Sachs & Co .

ConvexityWhen they are in the money, Alt-A securities typically prepay more slowly than non-agency prime securities. For prime Alt-A securities, prepayments are usually slowin the first year and mimic prime prepayments in later years. For non-prime Alt-Asecurities, prepayments are typically similar to those of subprime initially, or slightlyfaster (since they do have better credit quality); then they speed up if their creditquality has improved and the borrower can refinance at a prime rate.

Although default levels for Alt-A securities are higher than those of jumbo MBS, theyare not nearly as high as default levels for MBS with subprime collateral. So, Alt-Asecurities have higher coupons to compensate for this risk, though it has proven to beminimal.

As a result, the Alt-A market enjoys lower prepayment risk than a prime pool, andlower default risk than a subprime pool.

The convexity of Alt-A securities is less negative than that of agency MBS.Specifically, when rates are low, fewer Alt-A borrowers refinance (because they havecredit, document, and other problems) this is also known as call protection. Whenrates are high, prepayments are higher for Alt-A securities than for agency MBS

owing to defaults and credit curing this is also called extension protection.

A sophisticated investor base

Now that Alt-A characteristics are becoming better understood, investors havegained a clearer idea of the risks they are taking, which means they have becomemore willing to pay for the higher coupon the Alt-A collateral produces.

3 It is estimated that Loan Performance captures 85% of outstanding non-agency MBS.


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10 Goldman Sachs

Historical performance of Alt-A securitiesIn practice, different risk factors are layered for Alt-A securities, so certain concernsare mitigated. But in this initial primer on the subject, we examine each different Alt-A characteristic on its own.

PrepaymentsThe behavior of prepayment curves for Alt-A securities by vintage is similar to whatwe would expect for prepayment curves of other mortgage products. To examinehistorical Alt-A prepayments, we first need to determine current coupons for fixedloans of various vintages. It was determined that the weighted average currentcoupon for fixed, 30-year Alt-A loans was 8.29% in 1999, 9.24% in 2000, 8.05%in 2001,and 7.37% in 2002. Using these weight averages, we determine current coupon buckets by vintage and gather prepayment data along these lines. Exhibit 10 showsthe results of this exercise. Historically, the vast majority of Alt-A loans have beenfixed, so we will examine only this subset in our analysis.

Exhibit 10: Alt-A prepayments by vintage

1999

0%10%20%30%40%50%60%70%80%90%

Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04

3 M o n

t h C P R

< 7.75% Bucket 7.75 - 8.75% Bucket> 8.75% Bucket Jumbo Prime Current CouponSubprime Current Coupon

2000

0%10%20%30%40%50%60%70%80%90%

Jan-00 Jan-01 Jan-02 Jan-03 Jan-04

3 M o n

t h C P R


2001

0%10%20%

30%40%50%60%70%80%90%

Jan-01 Jan-02 Jan-03 Jan-04

3 M o n

t h C P R


2002

0%10%20%

30%40%50%60%70%80%90%

Jan-02 Jan-03 Jan-04

3 M o n

t h C P R




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Goldman Sachs 11

Based on these different vintage and bucket combinations, we can examine thecharacteristics of these pools. As Exhibit 11 shows, FICO scores of Alt-A borrowershave increased slightly over time for current coupon and below-market coupon borrowers. The lower boundaries of FICO scores, which indicate lower borrowerquality, have stayed the same. In each vintage/WAC bucket scenario, the lowerWAC buckets have higher FICO scores, which would be expected since better

borrowers receive lower rates. LTVs have the same pattern, and also seem to havestayed relatively stagnant over time.

Exhibit 11: Alt-A characteristics by vintage and WAC bucket

OriginalInvestorBalance

OriginalNumberof Loans

AvgLoanSize

OriginalWACreditScore

OriginalWACombinedLTV

1999 < 7.75% Bucket 2,644,896,981 15,393 171,825 705 74

7.75 - 8.75% Bucket 5,915,758,953 39,612 149,343 697 77

> 8.75% Bucket 3,115,151,593 21,466 145,120 686 82

2000 < 8.75% Bucket 3,895,561,394 16,369 237,984 712 768.75 - 9.75% Bucket 5,657,511,655 30,852 183,376 699 81

> 9.75% Bucket 2,527,643,773 14,745 171,424 681 86

2001 < 7.5% Bucket 7,584,342,572 25,975 291,986 719 74

7.5 - 8.5% Bucket 10,285,046,960 41,732 246,455 702 79

> 8.5% Bucket 5,911,968,438 30,423 194,326 679 87

2002 < 6.75% Bucket 10,116,213,119 36,171 279,677 725 71

6.75 - 7.75% Bucket 14,819,499,105 64,798 228,703 711 78

> 7.75% Bucket 8,771,051,840 48,067 182,476 683 87



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12 Goldman Sachs

Exhibit 12 highlights Alt-A prepayment performance by loan size. As would beexpected, larger loans are more sensitive to changes in the interest rate environment:When rates drop, borrowers with high mortgage payments have an incentive torefinance leading to higher prepayments. When rates rise, it becomes moreexpensive to re-locate or buy a more expensive house, so people tend not to moveand prepayments decline.

Exhibit 12: Alt-A loan size prepayment speeds by vintage and WAC

1999 Vintage, 7.75 - 8.75% WAC Bucket

0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

0+ - 300k 300k+ - 600k 600k+ - 1M


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

0+ - 300k 300k+ - 600k 600k+ - 1M 1M+


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

0+ - 300k 300k+ - 600k 600k+ - 1M 1M+


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

0+ - 300k 300k+ - 600k 600k+ - 1M 1M+



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Goldman Sachs 13

FICO score is an indicator of credit. Exhibit 13 shows Alt-A prepayment performance by FICO score. In falling interest rate environments, higher FICO score loans havehistorically prepaid slightly faster. This could possibly reflect the greatersophistication of the borrowers and the options for refinancing that are mostavailable.

Exhibit 13: Alt-A FICO score prepayment speeds by vintage and WAC


0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

600 - 659 660 - 719 720 - 779


0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

600 - 659 660 - 719 720 - 779


0%

10%20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

600 - 659 660 - 719 720 - 779


0%

10%20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

600 - 659 660 - 719 720 - 779

Sources : Loan Performance, Goldman, Sachs & Co .


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14 Goldman Sachs

Exhibit 14 demonstrates Alt-A prepayment performance by LTV. As would beexpected, higher LTV loans have historically prepaid more slowly, as fewerrefinancing options are available to the borrowers. Also, the borrowers have lessequity in their homes, so they are less likely to move.

Exhibit 14: Alt-A LTV Prepayment speeds by vintage and WAC


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

90


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

90


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

90


0%

10%

20%

30%

40%

50%

60%

70%

80%


3 M o n

t h C P R

90



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Goldman Sachs 15

As mentioned in the introduction of this report, one typical characteristic of Alt-A borrowers is that they are taking out a mortgage on a property that they will use asan investment or a second home (such properties are commonly called non-owneroccupied). Deals made up of this type of collateral can offer investors a prime type borrower with a higher coupon (to adjust for the non-owner occupancy) and slowerprepayments (investors are less likely to engage in the hassle of refinancing and they

will not relocate). As Exhibit 15 shows, the vintage WAC buckets with the higherWACs have a higher percentage of non-owner occupied properties.

Exhibit 15: Alt-A occupancy type by vintage and WAC bucket

Owner Occupied Second HomeNon-OwnerOccupied

1999 < 7.75% Bucket 86.5% 2.7% 10.8%

7.75 - 8.75% Bucket 75.2% 2.8% 22.0%

> 8.75% Bucket 72.1% 2.6% 25.3%

2000 < 8.75% Bucket 90.6% 2.1% 7.3%

8.75 - 9.75% Bucket 79.9% 2.5% 2.5%> 9.75% Bucket 82.6% 2.2% 15.2%

2001 < 7.5% Bucket 94.1% 1.7% 4.2%

7.5 - 8.5% Bucket 89.0% 2.3% 8.7%

> 8.5% Bucket 85.2% 2.4% 12.4%

2002 < 6.75% Bucket 85.7% 2.0% 12.3%

6.75 - 7.75% Bucket 83.0% 2.2% 14.8%

> 7.75% Bucket 81.8% 2.5% 15.7%


Even when the incentive is there, non-owner occupied properties are less likely toexperience prepayment, partly owing to the inconvenience and time that refinancinginvolves. Also, investors in such properties do not have many alternatives forobtaining financing. In addition, the relocation component of this investor base isvery small, as the owners are not living on the subject property (or live there onlypart time).


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16 Goldman Sachs

Exhibit 16 looks at 12-month CPR (June 2003 June 2004) for deals that have a higherpercentage of properties that are used as investments (or investor ownedproperties, part of the non-owner occupied universe). Note that if we look at theinvestor owned universe and the total Alt-A universe in the loan performancedatabase, the investor owned properties deals have historically prepaid more slowly

Exhibit 16: Sample of Alt-A deals where investor owned properties make upmore than 33% of total

Deal

%InvestorOwned

12MonthCPR Original Balance WAC Age

BAMALT 2003-1 74.16% 29% 326,022,467 6.49 20BAMALT 2003-4 73.81% 17% 569,523,193 5.95 15BAMALT 2003-2 64.15% 27% 605,393,831 6.37 18BAMALT 2003-5 63.52% 14% 459,054,279 5.89 14BOA Funding-2 63.51% 38% 150,266,740 6.64 21BAMALT 2003-3 60.38% 21% 301,176,362 6.25 16

BAMALT 2003-6 58.63% 12% 504,754,939 5.92 13Master ALT 2003-01 43.72% 42% 328,533,613 7.20 21Master ALT 2003-02 38.19% 41% 580,836,253 6.92 19Countrywide2002AL8 37.31% 49% 502,262,670 7.41 27RFC 2002-QS11 36.26% 44% 240,263,928 7.27 24Countrywide2002AL15 33.09% 47% 460,582,846 7.66 23Total 53.84% 31% 5,028,671,121 6.63 19< 1/3% Investor OwnedAlt-A

16.07% 47% 266,139,498,601 7.08 21

Total Alt-A 16.77% 47% 271,168,169,722 7.07 21



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Goldman Sachs 17

Looking at the current coupon buckets by vintage, investor owned properties do prepay a bitmore slowly over time, but mimic the shape of the prepayment curve of owner-occupiedproperties. Below are graphs by vintage in current-coupon buckets of occupancy data.

Exhibit 17: Alt-A occupancy prepayment speeds by vintage and WAC


0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

Owner Investor


0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

Owner Investor


0%

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3 M o n

t h C P R

Owner Investor


0%

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3 M o n

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Owner Investor



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18 Goldman Sachs

Another Alt-A characteristic that contributes to prepayment behavior is thedocumentation of the loan. As would be expected, more than 50% of all loans in ourvintage WAC buckets have low or no documentation. As with non-owner occupiedproperties, this can be a way of achieving higher coupons (to adjust for a lack of documentation) and slower prepays from otherwise prime borrowers (who havefewer refinancing options). Exhibit 18 lists documentation by vintage and WAC. It

should be noted that lower WAC buckets have a higher percentage of fulldocumentation loans.

Exhibit 18: Alt-A documentation by vintage and WAC bucket

FullDocumentation

LowDocumentation

No Documentation

1999 < 7.75% Bucket 38.9% 58.5% 2.7%7.75 - 8.75% Bucket 40.1% 55.3% 4.6%

> 8.75% Bucket 38.1% 54.9% 7.0%2000 < 8.75% Bucket 44.6% 54.6% 0.9%

8.75 - 9.75% Bucket 34.2% 61.4% 4.4%

> 9.75% Bucket 24.7% 65.0% 10.3%2001 < 7.5% Bucket 42.7% 55.4% 1.9%

7.5 - 8.5% Bucket 34.0% 61.4% 4.6%> 8.5% Bucket 26.4% 58.9% 14.7%

2002 < 6.75% Bucket 40.6% 55.9% 3.5%6.75 - 7.75% Bucket 38.7% 56.5% 4.7%

> 7.75% Bucket 33.8% 51.6% 14.6%



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20 Goldman Sachs

As would also be expected, prepayment penalties are more likely on higher couponloans, because such loans involve lower quality borrowers. In addition, highercoupon loans are more likely to carry insurance. Over the last several years, the totalamount of loans with prepayment penalties has risen, while the amount of loanswith insurance has stayed about the same. This is a clear sign that originators haveexpanded down the credit spectrum.

Exhibit 20: Alt-A prepayment penalties and primary mortgage insurance byvintage and WAC buckets

Prepayment Penalty(% Yes)

Primary MortgageInsurance (% Yes)

1999 < 7.75% Bucket 7.6% 12.2%

7.75 - 8.75% Bucket 8.4% 24.0%

> 8.75% Bucket 14.2% 46.6%

2000 < 8.75% Bucket 16.8% 19.1%

8.75 - 9.75% Bucket 20.8% 38.7%

> 9.75% Bucket 37.4% 54.9%2001 < 7.5% Bucket 15.3% 13.7%

7.5 - 8.5% Bucket 16.6% 26.1%

> 8.5% Bucket 32.5% 45.9%

2002 < 6.75% Bucket 14.3% 10.4%

6.75 - 7.75% Bucket 15.3% 20.7%

> 7.75% Bucket 31.9% 38.5%



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Goldman Sachs 21

Exhibit 21 shows that loans with prepayment penalties have historically prepaidmore slowly, as expected. Investors are less likely to prepay their mortgage if theyhave to pay a penalty in order to do so.

Exhibit 21: Alt-A prepayment penalties speeds by Vintage and WAC buckets

1999 Vintage, 7.75 - 8.75% WAC BucketPrepayment Penalty

0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

PP No PP


0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

PP No PP


0%10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

PP No PP


0%10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

PP No PP

Sources : Loan Performance, Goldman, Sachs & Co.


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22 Goldman Sachs

Historically, PMI does not seem to be an indicator of prepayments. This measure becomes important when examining the credit of different Alt-A mortgages, andinsurance will provide benefits for high LTV defaulted loans.

Exhibit 22: Alt-A PMI speeds by vintage and WAC buckets

1999 Vintage, 7.75 - 8.75% WAC BucketPMI

0%

10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

PMI No PMI


0%

10%

20%

30%

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60%

70%


3 M o n

t h C P R

PMI No PMI


0%10%

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30%

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60%

70%


3 M o n

t h C P R

PMI No PMI


0%10%

20%

30%

40%

50%

60%

70%


3 M o n

t h C P R

PMI No PMI



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Goldman Sachs 23

A main driver of voluntary refinancing is the ability to do so. One way that this iscaptured is through a spread at origination, or SATO, analysis. This value for eachloan is calculated as the difference between the loan rate and the rate on a par-pricedFNMA 30-year mortgage for each period. The different SATO calculations are then bucketed to create prepayment history. While all CPR curves generally follow theinterest rate environment, the loans with SATO equal to 0 have the slowest prepays

and the loans with the largest spread have the fastest prepays.

Exhibit 23: Alt-A SATO analysis

0%

10%

20%

30%

40%

50%

60%

Feb-98 Feb-99 Feb-00 Feb-01 Feb-02 Feb-03 Feb-04

C P R

1.5%


When we compare these results with FNMA (prime) current coupon loans, weobserve a similar pattern, even though the FNMA collateral is all conforming. FICOscores have continued to rise over time, and LTV ranges have stayed relativelystagnant.

Exhibit 24: FNMA current coupon characteristics by vintage and WAC bucket

CouponAverageLoan Size

CreditScore LTV

OwnerOccupied

SecondHome

InvestorOwned

1999 6.0 110,547 718 75 96.0% 2.3% 1.7%6.5 101,774 709 76 93.1% 3.1% 3.8%7.0 98,246 698 78 89.5% 3.2% 7.3%

2000 7.0 102,857 697 79 94.6% 3.3% 2.1%7.5 94,806 690 80 91.0% 4.3% 4.7%8.0 84,183 682 80 84.5% 3.7% 11.7%

2001 6.0 131,642 716 75 95.7% 2.5% 1.8%6.5 120,138 703 77 91.5% 3.3% 5.1%7.0 104,058 688 79 79.5% 3.0% 17.6%

2002 5.5 161,368 728 71 95.7% 2.6% 1.7%6.0 142,312 715 74 90.5% 3.1% 6.4%6.5 127,269 701 77 87.2% 3.5% 9.3%



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24 Goldman Sachs

While historically Alt-A loans have mainly been fixed, a trend of originating ARMshas emerged. As we noted in the first section of this paper, the industry has gonefrom producing mostly fixed loans to producing close to 70% with some adjustablecomponent. Interestingly, the type of adjustable rate issued has shifted over time.More recently, 3/1, 3/6, 5/1, and 5/6 hybrids have been the products of choice.Appendix A lists characteristics of adjustable rate mortgages.

As would be expected, CPR performance is fairly constant over product classes.Shorter fixed-period hybrids have a slight tendency to prepay a bit faster thanhybrids with longer fixed periods.


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Goldman Sachs 25

Exhibit 25: Hybrid Alt-A prepayments (categories with over $1m outstanding)

OriginationYear

LoanType

Active InvestorBalance ($)

1 MonthCPR

3 MonthCPR

6 MonthCPR

12MonthCPR

1996 ARM (1/1) 5,302,474 33% 22% 22% N/A1997 ARM (1/1) 3,114,049 27% 29% 26% 12%

2/1 4,681,689 11% 20% 23% 0%5/1 5,283,782 40% 46% 42% 34%1998 ARM (1/1) 5,128,877 1% 25% 20% 19%

2/1 3,794,942 7% 15% 35% 39%5/1 17,706,619 56% 43% 40% 41%7/1 4,147,933 N/A 41% 69% 65%10/1 2,834,074 N/A 53% 32% 59%

1999 ARM (1/1) 4,444,638 98% 79% 55% 44%2/1 58,943,232 55% 50% 49% 47%3/1 10,102,595 29% 37% 36% 54%5/1 35,657,739 67% 56% 50% 54%7/1 11,047,072 72% 52% 40% 46%

2000 ARM (1/1) 11,874,321 50% 52% 39% 26%2/1 36,358,681 67% 54% 50% 67%3/1 18,579,518 18% 53% 53% 54%5/1 66,331,964 57% 52% 53% 61%7/1 8,194,898 41% 85% 72% 72%10/1 4,368,369 N/A 68% 73% 73%

2001 ARM (1/1) 262,535,666 46% 54% 49% 46%2/1 276,401,806 55% 59% 63% 64%3/1 136,235,058 51% 55% 58% 62%5/1 531,070,296 64% 63% 57% 61%7/1 94,185,919 50% 61% 58% 62%10/1 15,240,080 39% 48% 61% 60%

2002 ARM (1/1) 1,107,638,719 53% 48% 43% 41%2/1 664,948,783 69% 66% 61% 55%3/1 395,408,398 59% 59% 55% 55%5/1 1,984,656,536 52% 57% 54% 53%7/1 407,791,466 32% 39% 38% 40%10/1 55,033,790 N/A 20% 22% 22%

2003 ARM (1/1) 644,933,872 40% 39% 34% 33%2/1 3,936,889,848 43% 39% 32% 34%3/1 4,041,707,910 44% 40% 35% 40%5/1 10,961,841,645 37% 39% 36% 37%7/1 950,102,949 13% 24% 22% 23%

10/1 356,452,901 17% 23% 20% 20%2004 ARM (1/1) 211,045,918 29% 25% 17% N/A

2/1 3,296,176,567 17% 15% 9% N/A3/1 5,004,249,366 21% 19% 14% N/A5/1 7,408,806,855 15% 15% 36% N/A7/1 564,281,326 11% 7% N/A N/A10/1 269,346,267 8% 15% N/A N/A



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26 Goldman Sachs

Refinancing incentiveBelow we move into the credit aspect of the Alt-A universe and examinedelinquencies and defaults. But first lets look at S-curves, which relate refinancingincentive to prepayments. As would be expected, the S-curve for Alt-A collateral hasthe same general shape as that of jumbo and agency collateral, yet with less overallconvexity. With the same incentive, Alt-A borrowers are less likely to refinance a

reflection of the issues that did not allow them to apply as prime borrowersoriginally. In an environment without rate incentives, Alt-A borrowers are morelikely to prepay owing to defaults and credit curing.

Exhibit 26: MBS S-curves

0

10

20

3040

50

60

70

80

90

100

-2 -1 0 1 2 3

Incentive

C P R

Alt-A Jumbo Subprime

Sources: Loan Performance, Intex, Goldman, Sachs & Co.

Below are S-curves for the larger Alt-A issuers compared with the agency FNMA S-

curve.

Exhibit 27: Alt-A S-curves

-

10

20

30

40

50

60

(3.00) (2.00) (1.00) - 1.00 2.00 3.00 4.00

Incentive

1 M o n

t h C P R

CountryWide RAST RALI FNM

Sources: Intex, Goldman, Sachs & Co.


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Goldman Sachs 27

As with the historical CPR analysis, it is interesting to look at S-curves for differentAlt-A characteristics. As would be expected from the above, occupancy is adeterminate factor in the convexity story of Alt-A securities. Investor properties (i.e.,those purchased solely as an investment) are clearly less negatively convex thanowner-occupied properties, with properties purchased as second homes fallingsomewhere in the middle.

Exhibit 28: Alt-A S-curve by occupancy

0%

10%

20%

30%

40%

50%

60%

-2.0% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

Spread to FHLMC 30 Year

C P R

Owner Investor Second


In theory, loans with low or no documentation should have less negative convexitythan loans with full documentation owing to their inability to finance. However, aswe observed in the CPR analysis, the S-curves of loans with low versus fulldocumentation show little pickup for convexity. This is again due to the risk layering

aspect of higher requirements for low documentation loans.

Exhibit 29: Alt-A S-curve by documentation

0%

10%

20%

30%

40%

50%

60%

-2.0% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

Spread to FHLMC 30 Year

C P R

Full None Low



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28 Goldman Sachs

DelinquenciesAs we would expect, Alt-A delinquencies fall between those of non-agency primeand subprime loans. Exhibit 30 compares delinquencies for Alt-A securities versusthose of other non-agency MBS.

Exhibit 30: Non-agency delinquencies

30 - 59 Day Delinquency

0%

2%

4%

6%

8%

10%

0 6 12 18 24 30 36 42 48 54 60Age

% o

f B a l a n c e

Alt-A Subprime

60 - 89 Day Delinquency

0%

1%

2%

3%

4%

0 6 12 18 24 30 36 42 48 54 60Age

% o

f B a l a n c e

Prime

90 + Day Delinquency

0%2%4%6%8%

10%12%14%16%18%20%

0 6 12 18 24 30 36 42 48 54 60

Age

% o

f B a l a n c e

Bankruptcy

0%1%

2%3%4%5%6%7%8%

0 6 12 18 24 30 36 42 48 54 60

Age

% o

f B a l a n c e

Foreclosure

0%1%2%3%

4%5%6%7%8%

0 6 12 18 24 30 36 42 48 54 60

Age

% o f B a l a n c e

REO

0%

1%

1%

2%

2%

3%

3%

4%

0 6 12 18 24 30 36 42 48 54 60

Age

% o f B a l a n c e



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Goldman Sachs 29

To start our investigation, lets look at 60+ day delinquencies for all Alt-A WAC buckets. For the first three years, the 60+ day delinquencies are directly correlated tothe coupon on the loans. Higher coupons, which generally indicate poorer creditquality for similar vintages, have the highest delinquencies. After year 3, the 5.0-6.0%and < 5.0% coupon buckets continue to rise, but the delinquencies remain under7.0%.

Exhibit 31: Alt-A 60+ day delinquencies

0%

5%

10%

15%

20%

25%

0 6 12 18 24 30 36 42 48 54 60

Age

D e l

i n q u e n c y a s

% o

f M B A B a

l a n c e

0+ - 5.00% 5.00%+ - 6.00% 6.00%+ - 7.00%7.00%+ - 8.00% 8.00%+ - 9.00% 9.00%+ - 10.00%10.00%+ - 11.00%



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30 Goldman Sachs

Loan size, FICO, and LTV show historical delinquencies, as one would expect. Largerloans have high delinquencies, as do loans with lower FICO scores and higher LTVs.

Exhibit 32: Alt-A 60+ day delinquencies by loan size, FICO, and LTV

By Loan Size

0%2%4%6%8%

10%12%14%16%18%20%

0 6 12 18 24 30 36 42 48 54 60

Age

6 0 + D a y s

D e

l i n q u e n c y

R a t e

720

By LTV

0%

2%

4%

6%

8%

10%

12%

0 6 12 18 24 30 36 42 48 54 60

Age

6 0 +

D a y s

D e l

i n q u e n c y

R a t e

< 60 60+ - 70 70+ - 80 80+ - 90 > 90



31/46


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32 Goldman Sachs

Loans with PMI have historically had higher delinquencies. This might be due to thefact that PMI is required for lower quality borrowers where a higher LTV and otherimpairing characteristics are involved.

Exhibit 35: Alt-A 60+ day delinquencies by PMI

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0 6 12 18 24 30 36 42 48 54 60

Age

6 0 +

D a y s

D e

l i n q u e n c y

R a

t e

No PMI PMI


Cumulative defaultsAs we noted at the beginning of this paper, default levels for Alt-A collateral fall between those of prime and subprime. Cumulative defaults for Alt-A collateral as awhole generally fall under 2%, compared with subprime at closer to 11% and primeat under 0.25%.

Exhibit 36: Non-agency cumulative defaults

0%

2%

4%

6%

8%

10%

12%

0 6 12 18 24 30 36 42 48 54 60 66 72

Age

C u m u

l a t i v e

D e

f a u

l t s

Subprime Alt-A Jumbo



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Goldman Sachs 33

Defaults can change drastically by vintage. Economic conditions play an importantrole in the ongoing defaults of loans. As Exhibit 37 shows, loans originated in 2000have historically had the highest defaults, followed by loans originated in 2001.These loans were originated during a time when the economy was booming andmany people were making higher sums of money than usual. When the economyslumped, people who had been able to make large mortgage payments in the past

were less likely to be able to maintain their previous lifestyle.

Exhibit 37: Alt-A cumulative defaults by vintage

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Age

C u m u

l a t i v e

D e

f a u

l t s

1998 1999 2000 2001 2002 2003


While ARMs currently represent 70% of the origination market, this is a new trend;hence we do not have enough data to use in comparing fixed rate mortgages andARMs. Nonetheless, for the initial 18 months, there is no difference in the behavior of

these two types of loans.


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34 Goldman Sachs

Cumulative defaults based on loan size do not show significant differences. In thefirst four years, the higher the loan amount, the higher the defaults. However, afterthe four-year mark is reached, an inflection is observed and lower-balance loans havehigher cumulative defaults. This has do with the fact that borrowers with bad creditcan obtain only small loans; thus, over time, loans for borrowers in this category tendto have higher defaults.

Exhibit 38: Alt-A cumulative defaults by loan size

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60Age

C u m u

l a t i v e

D e

f a u

l t s

200k


As would be expected, loans with higher FICO scores have lower defaults, as theFICO score is a direct indicator of credit performance.

Exhibit 39: Alt-A cumulative defaults by FICO

0%

2%

4%

6%

8%

10%

12%

14%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Age

C u m u

l a t i v e

D e

f a u

l t s

700



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Goldman Sachs 35

We observe a similar pattern when we look at loan buckets of LTVs. The lower theLTV, the lower the cumulative default rate. Besides indicating that the borrowers areof higher quality, lower LTVs mean the borrowers have greater equity in theirhomes, and hence are less likely to default. It is interesting to note that loans withLTVs greater than 100 have a cumulative default rate of 8%, while loans with LTVs between 80 and 100 have cumulative defaults of just over 2%.

Exhibit 40: Alt-A cumulative defaults by LTV

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Age

C u m u

l a t i v e

D e

f a u

l t s

=100


Some Alt-A characteristics have interesting trends with respect to cumulativedefaults. As is true of prepayments and delinquencies, occupancy appears to be agood indicator of cumulative defaults. Owner occupied houses have the highest rateof default, followed by investment properties and then by properties that are secondhomes. This makes sense: Investors in real estate as well as those who can afford asecond home are likely to be in a more stable financial position than people who aretaking out a mortgage on their only home.

Exhibit 41: Alt-A cumulative defaults by occupancy

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

2.0%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Age

C u m u

l a t i v e

D e f a u

l t s

Non-Owner Owner 2nd Home



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36 Goldman Sachs

At first glance, documentation does not seem to be an indicator of defaults. However,as with prepayments, the risk of many loans with low documentation is mitigated bylow LTVs and higher FICO scores, hence limiting the poor performance of theseloans.

Exhibit 42: Alt-A cumulative defaults by documentation

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

2.0%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Age

C u m u

l a t i v e

D e

f a u

l t s

Full Low/reduced



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Goldman Sachs 37

SeveritySeverities are the next performance measure to examine in connection with defaults.Once a loan defaults, it no longer matters if it was originally classified as a subprime,Alt-A, or prime. Instead, the important characteristics are the LTV and size of theloan. When looking at different Alt-A characteristics in this space, LTV and loan sizeare still the drivers of the severity, but occupancy also seems to play a role. The

severity of loans of investor properties is almost double that of owner-occupiedproperties or second homes. In general, investor properties have lower LTVs andlower loan balances. The sharp increase in severities in investor properties reflectstwo characteristics. First, when such a loan defaults, the investor makes less of aneffort to preserve the property, which causes a decline in its value. Second,residential investment properties are typically in less desirable locations andexperience more-volatile market price movement.

Exhibit 43: Alt-A severities by characteristic

Vintage 1998 1999 2000 2001 2002 2003Severity 27.68% 37.55% 32.05% 22.51% 24.84% 23.16%

Loan Size =300kSeverity 42.83% 29.41% 23.09% 27.51%

LTV 100Severity 30.64% 25.38% 32.52%FICO 700Severity 36.38% 40.41% 31.07% 31.03% 28.75%OccupancyType

2ndHome Investor Owner

Severity 27.58% 40.54% 27.12% Doc Type Full Low NoDocSeverity 30.78% 28.13% 28.94%Purpose Cashout Purchase Rate/TermSeverity 30.04% 29.25% 29.21%



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38 Goldman Sachs

Modeling Alt-AIn order to model any new type of credit mortgage collateral, adjustments are madeto base prepayment and default curves. These changes are referred to as the relativestrength of different characteristics, and are developed through historicalinformation and the views of the Goldman Sachs mortgage strategy group.

Exhibit 44 provides relative strength factors for different Alt-A collateral using thefollowing prime base case: LTV between 70 and 80, loan size between $250,000 and$350,000, FICO greater than 739, full documentation, and owner occupied. Thecharacteristics that are the most determinant for prepayments (i.e., that have thegreatest effect on prepayments) are loan size and occupancy. Loan size causes thecurve to shift by a 50% slowdown for small loan balances and a 35% speed increasefor larger balances. Investor owned properties cause the curve to shift 30% slower.

Exhibit 44: Prepayment relative strength

Fraction of agency default curveCharacteristic 15yr 30yr720 < fico score


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Goldman Sachs 39

When looking at the relative strength from a default perspective, the mostdeterminant characteristics are LTV and documentation. In analyzing the cumulativedefault curve, we can obtain factors for similar characteristics. In this example, weuse a base cumulative default curve of 80 LTV, 710 FICO, $100,000 loan size, fulldocumentation, and owner occupied. Unlike in the case of prepayments, LTV has thegreatest fluctuation in relative strength, with defaults increasing as much as 500% for

high LTVs and defaults decreasing as much as 90% for low LTVs. Documentationalso has a large impact on defaults.

Exhibit 45: Default relative strength

Fraction of agency prepayment curveCharacteristic 30yrFICO = 640 3.194FICO = 680 2.013FICO = 750 0.662FICO = 780 0.436Loan Size = 50,000 1.914Loan Size = 70,000 1.273Loan Size = 180,000 0.908Loan Size = 290,000 1.594Loan Size = 400,000 2.346LTV = 55 0.071LTV = 65 0.309LTV = 75 0.869LTV = 90 1.499LTV = 100 4.582Low Documentation 1.806Non-Owner Occupied 1.018

Source: Goldman, Sachs & Co.

ConclusionWhile the analysis throughout this report would suggest that deals with a highconcentration of investor property have better convexity, lower delinquencies, andlower defaults, Alt-A collateral is complex, with many characteristics contributing tooverall performance. As the Alt-A market expands and this type of collateral is betterunderstood, participating in the market becomes more attractive. Thus we expect tosee new classifications of Alt-A characteristics. The next step in this analysis would be to examine how different risk factors are layered in Alt-A loans and securities..


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Appendix B: Top 50 Documentation Combinations

Rank Loan Size Top Level FICO LTV1 300k+ - 600k 720 - 779 70+ - 802 300k+ - 600k 660 - 719 70+ - 803 0+ - 300k 660 - 719 70+ - 80

4 0+ - 300k 600 - 659 70+ - 805 0+ - 300k 720 - 779 70+ - 806 300k+ - 600k 720 - 779 60+ - 707 0+ - 300k 600 - 659 80+ - 908 300k+ - 600k 600 - 659 70+ - 809 0+ - 300k 540 - 599 70+ - 80

10 300k+ - 600k 660 - 719 60+ - 7011 0+ - 300k 660 - 719 80+ - 9012 600k+ - 1M 720 - 779 70+ - 8013 600k+ - 1M 720 - 779 60+ - 7014 300k+ - 600k 780 - 839 70+ - 8015 300k+ - 600k 720 - 779 50+ - 6016 0+ - 300k 660 - 719 60+ - 7017 0+ - 300k 660 - 719 90+ - 10018 600k+ - 1M 660 - 719 70+ - 8019 600k+ - 1M 660 - 719 60+ - 7020 0+ - 300k 720 - 779 60+ - 7021 0+ - 300k 600 - 659 90+ - 10022 300k+ - 600k 660 - 719 80+ - 9023 0+ - 300k 600 - 659 60+ - 7024 300k+ - 600k 600 - 659 80+ - 9025 0+ - 300k 480 - 539 70+ - 8026 0+ - 300k 540 - 599 80+ - 90

27 600k+ - 1M 720 - 779 50+ - 6028 300k+ - 600k 720 - 779 40+ - 5029 0+ - 300k 720 - 779 90+ - 10030 300k+ - 600k 660 - 719 50+ - 6031 0+ - 300k 540 - 599 60+ - 7032 0+ - 300k 720 - 779 80+ - 9033 300k+ - 600k 780 - 839 60+ - 7034 1M+ 720 - 779 60+ - 7035 0+ - 300k 660 - 719 50+ - 6036 300k+ - 600k 600 - 659 60+ - 7037 300k+ - 600k 540 - 599 70+ - 8038 600k+ - 1M 720 - 779 40+ - 5039 0+ - 300k 780 - 839 70+ - 8040 0+ - 300k 720 - 779 50+ - 6041 600k+ - 1M 660 - 719 50+ - 6042 300k+ - 600k 720 - 779 80+ - 9043 300k+ - 600k 780 - 839 50+ - 6044 0+ - 300k 480 - 539 60+ - 7045 300k+ - 600k 660 - 719 90+ - 10046 1M+ 660 - 719 60+ - 70


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Appendix B: Top 50 Documentation Combinations

Rank Loan Size Top Level FICO LTV47 300k+ - 600k 660 - 719 40+ - 5048 600k+ - 1M 780 - 839 70+ - 8049 300k+ - 600k 720 - 779 30+ - 4050 0+ - 300k 600 - 659 50+ - 60


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Documents

[Goldman Sachs] Alt-A Market - An Introduction