From New Deal Institutions to Capital Markets: Commercial Consumer Risk Scores and the Making of Subprime Mortgage Finance

tags: Finance FICO Score

Notes

co-ordinate the economic agencies of ordinary U.S. homeowners with those of international capital investors.

NOTER_PAGE: (3 0.6060411311053984 . 0.29201331114808654)

U.S. mortgage finance has shifted from an industry driven by government sponsored enterprises (GSEs) and specialized deposit-funded lenders, to an industry fuelled in large part by high-risk investment capital.

NOTER_PAGE: (4 0.09545163356822549 . 0.2752902155887231)

consumer mortgages have become the asset class feeding some of the most popular debt securities

NOTER_PAGE: (4 0.20435618193465727 . 0.1318407960199005)

how mortgage credit became unsustainably amplified

NOTER_PAGE: (4 0.43113388853299167 . 0.2960199004975124)

Either way, these perspectives sidestep the pressing contemporary question of how a financial network for lending so freely has come into being.

NOTER_PAGE: (5 0.12491992312620115 . 0.1451077943615257)

understanding subprime lending is less about unravelling the motivations and psychologies that might lead to financial overextension, than it is about understanding the development of technical apparatuses that have supported the practical activities of a new cadre of financial agents

NOTER_PAGE: (5 0.766175528507367 . 0.3067993366500829)

money or credit flows transversally in one direction while what is known as ‘paper’ in the industry, or debt, flows in the other. This is an extraordinary problem of coordination

NOTER_PAGE: (6 0.5105701473414478 . 0.4386401326699834)

numerous agents must come to similar understandings of the value of the asset

NOTER_PAGE: (6 0.7130044843049327 . 0.24378109452736318)

suspect that the recent explosion of secondary subprime financial activity is the result of a process thorough which a novel chain of mortgage valuation has been put into place.

NOTER_PAGE: (6 0.8167841127482383 . 0.14096185737976782)

calculative activities are by nature disorderly – that is, that at the outset, there are as many potential solutions to a problem of valuation as there are participating agents.

NOTER_PAGE: (7 0.09929532351057015 . 0.291044776119403)

it is unlikely that agents working on a calculative problem independently, from different fields, would spontaneously come to the same evaluative results.

NOTER_PAGE: (7 0.2011531069827034 . 0.27114427860696516)

the empirical concern is to document the work that has been done to selectively reduce calculative multiplicity,

NOTER_PAGE: (7 0.30813581037796284 . 0.5232172470978441)

Freddie’s goal was simple and clear: it wanted to standardize underwriting practices in federally sanctioned, prime mortgage lending by introducing a consistent means of screening credit risk into its newly automated system.

NOTER_PAGE: (7 0.5048046124279307 . 0.14427860696517414)

how, in redefining the calculation of prime quality, commercial scores simultaneously provided an expression of non-prime quality

NOTER_PAGE: (7 0.6130685458039717 . 0.17412935323383086)

Under control-by-risk, managerial decision making was no longer confined to approving or withholding loans, but was extended to the exploitation of stabilized grades of credit quality accessed through scores to create multiple borrowing options tailored to accommodate varying levels of risk. This point is pivotal. It is through this calculative shift, enacted through FICO®, that the original GSE markets were circumvented by the development of a second, infrastructurally distinct circuit of high-risk mortgage investment known as the ‘subprime’.

NOTER_PAGE: (8 0.5579756566303651 . 0.5)

punctual empirical assessment of the odds that a consumer might default on a loan expressed as a probability.

NOTER_PAGE: (10 0.12556053811659193 . 0.36318407960199006)

scoring has come to loosely refer to any system that produces a rank ordering of a population of credit consumers

NOTER_PAGE: (10 0.2517616912235746 . 0.357379767827529)

also a thriving industry for ‘analytics’

NOTER_PAGE: (10 0.8161434977578474 . 0.18325041459369817)

These firms may have little or no ability to generate consumer data on their own, but each one possesses a delicate savoir-faire

NOTER_PAGE: (11 0.09737347853939782 . 0.30265339966832505)

exploit credit information and fix the results of their analysis into applications suited for business decision-making.

NOTER_PAGE: (11 0.17552850736707237 . 0.4253731343283582)

The co-ordinating effects of this widely circulating piece of ‘economic information’ (Callon, 2002) are significant: the overwhelming commercial success of this tool is arguably what has given recent U.S. consumer credit markets their coherence, confluence and vibrancy.

NOTER_PAGE: (13 0.14798206278026904 . 0.3333333333333333)

Credit scoring is a prime example of how numbers might matter to market activity not so much because of what they represent and whether they represent accurately, but because of what they enable agents to do

NOTER_PAGE: (14 0.48238308776425365 . 0.13681592039800997)

In the U.S., homeownership is not just a part of the ‘American Dream’; it is also actively facilitated by specialized state initiated institutions.

NOTER_PAGE: (15 0.344650864830237 . 0.13349917081260365)

federal government’s intention was that the GSEs would ‘attract private capital for public purpose’, serving as a kind of ‘institutional market maker’

NOTER_PAGE: (16 0.12299807815502882 . 0.39469320066334995)

The GSEs were not intended to make loans like banks. Rather, their purpose was to facilitate the movement of debts in one direction in order to generate renewed funds in the other, either by purchasing and holding, or packaging and selling, financial instruments called mortgage-backed securities

NOTER_PAGE: (16 0.2235746316463805 . 0.2230514096185738)

the default state of debts is inertial.

NOTER_PAGE: (16 0.40550928891736066 . 0.22885572139303484)

“in the days before securities, banks were essentially portfolio lenders; they held loans until they matured or were paid off”.

NOTER_PAGE: (16 0.48238308776425365 . 0.3308457711442786)

difficulties in comparing the riskiness of loans from different lenders”

NOTER_PAGE: (17 0.4561178731582319 . 0.18407960199004975)

The GSEs calculated a value of loans and loan pools, but their original methods were not quantitative. Instead, prior to the advent of scoring, their main strategy was to issue thick books of underwriting guidelines, stringently designed to screen for acceptable quality loans.

NOTER_PAGE: (18 0.1518257527226137 . 0.46434494195688225)

interpretation of the rules on the ground “differed from one company to the next”

NOTER_PAGE: (18 0.5861627162075592 . 0.2777777777777778)

Given the wide margins of uncertainty in the resulting grades, the GSEs rendered their debt products attractive by investing exclusively in ‘A’ quality loans

NOTER_PAGE: (18 0.6572709801409352 . 0.44527363184079605)

the way credit data was brought into the mortgage underwriting was through an ‘RMCR’ – a residential mortgage credit report.

NOTER_PAGE: (19 0.5176169122357462 . 0.2728026533996683)

Fair Isaac’s first instinct, therefore, was to try and partner with report merging firms to develop a scoring system for RMCR data.

NOTER_PAGE: (19 0.6406149903907751 . 0.24875621890547264)

The problem with scoring the RMCRs was that the reports were infamous for being inherently unreliable.

NOTER_PAGE: (19 0.7200512491992312 . 0.148424543946932)

process of merging reports provided commission motivated mortgage brokers with “the wiggle room, […] to manipulate the system to get a mortgage loan through”.

NOTER_PAGE: (19 0.8199871877001921 . 0.25787728026533996)

other “service [brokers] did was to ‘cleanse’ the credit report. They formatted it a certain way, and then if the mortgage worker said, ‘this information is wrong’ they would manually fix it on their merged credit report”.

NOTER_PAGE: (20 0.12491992312620115 . 0.37396351575456055)

GSEs were aware of these kinds of procedural loopholes which they tried to close by passing more and more supplementary rules. So as time went on, the mortgage underwriting guidelines became “so rigid that if you followed them by the letter no one would ever originate a loan”!

NOTER_PAGE: (20 0.1748878923766816 . 0.6558872305140963)

Numerous efforts were being made, in particular at Fannie Mae, to produce automated underwriting programs based on mentored artificial intelligence

NOTER_PAGE: (20 0.58680333119795 . 0.3482587064676617)

train a system to reproduce the credit decisions of a human underwriter

NOTER_PAGE: (21 0.09993593850096091 . 0.2935323383084577)

by mid-1990’s “mentored AI systems had largely lost out to or begun to progress to statistical

NOTER_PAGE: (21 0.17616912235746315 . 0.23051409618573798)

With their massive stores of historical mortgage data the GSEs were the only institutions in a position to envisage and implement such a project.

NOTER_PAGE: (21 0.40294682895579753 . 0.2935323383084577)

did Freddie decide to pursue statistical underwriting to the detriment of the traditional rule-based methods,

NOTER_PAGE: (21 0.4836643177450352 . 0.48507462686567165)

opted to insert consumer credit data, pre-digested in the form of numerical commercial bureau scores, into their nascent systems.

NOTER_PAGE: (21 0.5605381165919282 . 0.39469320066334995)

Freddie Mac’s primary objective was to include a reliable selection of consumer credit data into their automated systems in a form that could not be locally manipulated by the brokers.

NOTER_PAGE: (21 0.7117232543241512 . 0.3175787728026534)

these goals might have been equally achieved by employing electronically transferred raw credit data purchased from the bureaus, and dissolving them seamlessly into the proprietary algorithms the GSEs were assembling from scratch.

NOTER_PAGE: (22 0.14990390775144138 . 0.2570480928689884)

although ‘credit data’ was only one category of information included in mortgage scores, it was now reduced to a discreet factor whose composition could potentially become invariable between automated systems.

NOTER_PAGE: (22 0.22869955156950672 . 0.2164179104477612)

Freddie announced its decisions, including a third significant stipulation: that a FICO® score of 660 was the eyeball threshold for their definition of loans eligible for the prime investment. Within a month Fanny Mae swiftly followed suit

NOTER_PAGE: (22 0.5067264573991032 . 0.2263681592039801)

Fannie had no choice because they suddenly found themselves besieged by bad paper

NOTER_PAGE: (22 0.5855221012171684 . 0.7205638474295191)

The decision to use FICO® as well as GSE manner of interpreting them was materially hardwired into the system through the release of proprietary, agency designed, automated underwriting software

NOTER_PAGE: (23 0.09673286354900704 . 0.6948590381426203)

The FICO® feature of automated system design was politically useful when the software was showcased to legislators

NOTER_PAGE: (23 0.30493273542600896 . 0.14096185737976782)

FICO® scores conveniently substitute for the quality of ‘creditworthiness’ which government officials and the public had come to recognize as being an essential part of loan evaluation.

NOTER_PAGE: (23 0.4042280589365791 . 0.2603648424543947)

made an explicit equivalence between the use of FICO® scores and an evaluation of ‘creditworthiness’ even though the former is a shifting quality assigned statistically with respect to the aggregate and the latter has traditionally been considered a personal property of the individual

NOTER_PAGE: (23 0.4855861627162075 . 0.7056384742951908)

Through this analogy with known concepts (even though the commonalities were thin 36 ) FICO® helped circumvent some of the technical difficulties in explaining statistical underwriting to lay audiences.

NOTER_PAGE: (23 0.5855221012171684 . 0.5124378109452736)

Given the GSE mandate to help and not hinder homeownership, 660 was intended to be a soft minimum score and not a firm cutoff,

NOTER_PAGE: (24 0.12235746316463804 . 0.3996683250414594)

FICO® 660 rapidly became a free standing benchmark of prime investment grade status,

NOTER_PAGE: (24 0.27738629083920563 . 0.20149253731343283)

In the absence of automation and scores, the secondary market had learned to rely on indicators designed to describe the risk level of the aggregated pool,

NOTER_PAGE: (25 0.6860986547085202 . 0.2504145936981758)

securitizing bodies “weren’t used to looking at metrics that allowed you to drill so deeply into an individual consumer credit profile so effectively”.

NOTER_PAGE: (26 0.1748878923766816 . 0.3540630182421227)

Work had to be done to educate each of the securitization and ratings agencies ‘about how credit scores worked’.

NOTER_PAGE: (26 0.3292761050608584 . 0.1492537313432836)

the existence of separate, competing systems to carry out this work for non-GSE destined loans impeded horizontal market integration.

NOTER_PAGE: (27 0.6105060858424086 . 0.41873963515754564)

Controlling the problems that flourishing calculative diversity posed was S&P’s business.

NOTER_PAGE: (28 0.25240230621396537 . 0.14759535655058043)

With regards to solving the problem of horizontal coordination, however, these results were “then compared with those of other automated underwriting systems and discussed with the issuer” (Raiter et al., 1997, p 3-12). Acting to produce coordination in financial markets, S&P aligned the risk outcomes of various models, by imposing definitions or by modifying the factors they took into account.

NOTER_PAGE: (28 0.4811018577834721 . 0.3781094527363184)

This was not merely a suggestion. A key incentive to adopt FICO® the was that pools of loans tagged with an S&P validated ‘mortgage score’ could be more easily rated for securitization by S&P’s proprietary securities rating system.

NOTER_PAGE: (28 0.7117232543241512 . 0.2744610281923715)

validation and rating gave S&P the opportunity not only to push the FICO® scores, but to transmit the specific interpretations of them that it had absorbed from its earlier collaboration with the GSEs.

NOTER_PAGE: (29 0.3542600896860986 . 0.6907131011608624)

the government sanctioned prime and the private label subprime. What divides them are information systems, their regard for risk, and product development; what unites them is a common reliance and baseline interpretation of FICO® scores

NOTER_PAGE: (29 0.7911595131326072 . 0.13681592039800997)

The difficulty of precisely evaluating individual mortgage quality – that is, in stating credit risk as a firm expression transferrable across domains – is the reason why, for half a century, there was only weak investment activity outside of a slow and steady, federally chartered prime investment market.

NOTER_PAGE: (30 0.19730941704035873 . 0.1492537313432836)

It was on the authority of the institutions’ guidelines, their initiatives in interface design, as well as their dirty, hands-on involvement as a driver of RMBS production that the market was made.

NOTER_PAGE: (30 0.4324151185137732 . 0.42786069651741293)

establishing creditworthiness under traditional lending was subject to subtle negotiations in which numerous forms of justification could come into play (Wissler, 1989). What was considered ‘manipulation’ of the RMCR reports by brokers grew out of the permissiveness of this type of practice. Such activities were able to occur, because the definition of the creditworthiness, even when filtered through rules and guidelines, was being flexibly assembled in the moment of loan production rather than being taken from fixed criteria. It is precisely this aspect of traditional consumer lending that demanded the stabilizing force of the GSEs in quality assessment.

NOTER_PAGE: (30 0.7123638693145419 . 0.22802653399668327)

The introduction of a numerical scale of consumer credit quality into mortgage origination permitted calculative actions that were simply unanticipated from within the conventional frameworks of the GSEs.

NOTER_PAGE: (31 0.4298526585522101 . 0.15754560530679934)

a startling paradox: “Fannie and Freddie have become the opposite of what they were. They are now lenders to safe markets, while private institutions serve markets that were once liquidity-deprived”

NOTER_PAGE: (35 0.19602818705957717 . 0.31260364842454397)

mortgage risk is, in all actuality, widely dispersed among many investors. For the duration of the housing boom, it was investment capital that generously funded a proliferation of mortgage options, and attended to the very groups that are arguably most in need of ownership assistance according to the mandate of the GSEs.

NOTER_PAGE: (35 0.34785393978219087 . 0.31840796019900497)

In the last decade private capital has been tripping over itself – or so it appeared – to become a handmaiden to the American Dream. The subprime collapse has turned the tables back again,

NOTER_PAGE: (36 0.5643818065342728 . 0.14013266998341625)

GSEs need to maintain more conservative portfolios because they have a “statutory requrement to provide liquidity to the nation’s mortgage market”

NOTER_PAGE: (37 0.09865470852017937 . 0.19071310116086235)

blow of irony is that as the crisis began, the GSEs themselves were caught holding some $170 billion in private-label subprime securities

NOTER_PAGE: (37 0.14990390775144138 . 0.19734660033167498)

because the tool had inscribed within it the possibility of making financially meaningful risk management calculations, it enabled the rise of a new form of financial activity: credit control-by-risk.

NOTER_PAGE: (38 0.48046124279308133 . 0.1799336650082919)

It is the pioneering journey of FICO® scores throughout the industry that has integrated, assembled, and aligned different market agents. The integrity of the chain – which might have been truncated at any point along its length had an alternative solution or even another interpretation of these scores been adopted – is what has rendered these divers agents capable of engaging together in a distinctive and coherent, globe spanning circuit of productive subprime real estate finance.

NOTER_PAGE: (39 0.09993593850096091 . 0.4867330016583748)

From Fair Isaac’s point of view the impetus for selecting their product across the board is its scientific superiority within a competitive market for scores. Yet as we have seen, the constitution of this staying power is deeply entangled with the activities of government and ratings agencies whose endorsements, independent research initiatives, interpretations and automated systems

NOTER_PAGE: (39 0.8161434977578474 . 0.615257048092869)

Once this calculative tool was stabilized in and as an infrastructure it intensified and generated downstream complexity 60 as it made an alternative form of co-ordinated and coherent collective decision making possible.

NOTER_PAGE: (40 0.22998078155028825 . 0.4369817578772803)

at the outset, credit bureau scores were considered a sub-optimal, if not inappropriate tool for mortgage underwriting, by scoring experts at Fair Isaac. Nonetheless they were a convenient solution to the problem of controlling credit quality, one that was perhaps cheaper and faster to implement than doing R&D.

NOTER_PAGE: (41 0.7668161434977578 . 0.47512437810945274)

Worse is Better

The tensions that make democratic lending a puzzle in a regime of control-by-screening, seemed to dissolve away in a regime of control-by-risk. Yet what went overlooked was that the transition from low-risk exclusionary to high-risk inclusionary lending practices had transformed the very nature of the homeownership. It intensified competition and raised properties prices by equipping more home buyers across the nation with immediate purchasing capability. Moreover, faced with complex choice sets it demanded that everyday people exercise degrees of financial judgment that had heretofore not been required of them.

NOTER_PAGE: (43 0.27738629083920563 . 0.3018242122719735)

To belabour the point of underwriting error is to forget that the rationale of statistical automation was to minimize and overcome the virulence of precisely this kind of well-recognized ground level activity. A provocative hypothesis would be that such error could be expected to proceed unchecked and to increase exactly as it ostensibly did, once muted at the systemic level.

NOTER_PAGE: (43 0.6598334401024983 . 0.7213930348258707)

NOTER_PAGE: (44 0.09609224855861627 . 0.7346600331674958)

the explosion of the subprime was not caused by a sheer increase in lending volume stemming from irrational, fraudulent, or extra-governmental activity, but by the super-coordination of market actors’ decision-making around stabilized frames of risk provided by third party commercial consumer analytics companies.

NOTER_PAGE: (44 0.22928994082840237 . 0.2602870813397129)

Notes

co-ordinate the economic agencies of ordinary U.S. homeowners with those of international capital investors.

U.S. mortgage finance has shifted from an industry driven by government sponsored enterprises (GSEs) and specialized deposit-funded lenders, to an industry fuelled in large part by high-risk investment capital.

consumer mortgages have become the asset class feeding some of the most popular debt securities

how mortgage credit became unsustainably amplified

Either way, these perspectives sidestep the pressing contemporary question of how a financial network for lending so freely has come into being.

understanding subprime lending is less about unravelling the motivations and psychologies that might lead to financial overextension, than it is about understanding the development of technical apparatuses that have supported the practical activities of a new cadre of financial agents

money or credit flows transversally in one direction while what is known as ‘paper’ in the industry, or debt, flows in the other. This is an extraordinary problem of coordination

numerous agents must come to similar understandings of the value of the asset

suspect that the recent explosion of secondary subprime financial activity is the result of a process thorough which a novel chain of mortgage valuation has been put into place.

calculative activities are by nature disorderly – that is, that at the outset, there are as many potential solutions to a problem of valuation as there are participating agents.

it is unlikely that agents working on a calculative problem independently, from different fields, would spontaneously come to the same evaluative results.

the empirical concern is to document the work that has been done to selectively reduce calculative multiplicity,

Freddie’s goal was simple and clear: it wanted to standardize underwriting practices in federally sanctioned, prime mortgage lending by introducing a consistent means of screening credit risk into its newly automated system.

how, in redefining the calculation of prime quality, commercial scores simultaneously provided an expression of non-prime quality

punctual empirical assessment of the odds that a consumer might default on a loan expressed as a probability.

scoring has come to loosely refer to any system that produces a rank ordering of a population of credit consumers

also a thriving industry for ‘analytics’

These firms may have little or no ability to generate consumer data on their own, but each one possesses a delicate savoir-faire

exploit credit information and fix the results of their analysis into applications suited for business decision-making.

The co-ordinating effects of this widely circulating piece of ‘economic information’ (Callon, 2002) are significant: the overwhelming commercial success of this tool is arguably what has given recent U.S. consumer credit markets their coherence, confluence and vibrancy.

Credit scoring is a prime example of how numbers might matter to market activity not so much because of what they represent and whether they represent accurately, but because of what they enable agents to do

In the U.S., homeownership is not just a part of the ‘American Dream’; it is also actively facilitated by specialized state initiated institutions.

federal government’s intention was that the GSEs would ‘attract private capital for public purpose’, serving as a kind of ‘institutional market maker’

The GSEs were not intended to make loans like banks. Rather, their purpose was to facilitate the movement of debts in one direction in order to generate renewed funds in the other, either by purchasing and holding, or packaging and selling, financial instruments called mortgage-backed securities

the default state of debts is inertial.

“in the days before securities, banks were essentially portfolio lenders; they held loans until they matured or were paid off”.

difficulties in comparing the riskiness of loans from different lenders”

The GSEs calculated a value of loans and loan pools, but their original methods were not quantitative. Instead, prior to the advent of scoring, their main strategy was to issue thick books of underwriting guidelines, stringently designed to screen for acceptable quality loans.

interpretation of the rules on the ground “differed from one company to the next”

Given the wide margins of uncertainty in the resulting grades, the GSEs rendered their debt products attractive by investing exclusively in ‘A’ quality loans

the way credit data was brought into the mortgage underwriting was through an ‘RMCR’ – a residential mortgage credit report.

Fair Isaac’s first instinct, therefore, was to try and partner with report merging firms to develop a scoring system for RMCR data.

The problem with scoring the RMCRs was that the reports were infamous for being inherently unreliable.

process of merging reports provided commission motivated mortgage brokers with “the wiggle room, […] to manipulate the system to get a mortgage loan through”.

other “service [brokers] did was to ‘cleanse’ the credit report. They formatted it a certain way, and then if the mortgage worker said, ‘this information is wrong’ they would manually fix it on their merged credit report”.

GSEs were aware of these kinds of procedural loopholes which they tried to close by passing more and more supplementary rules. So as time went on, the mortgage underwriting guidelines became “so rigid that if you followed them by the letter no one would ever originate a loan”!

Numerous efforts were being made, in particular at Fannie Mae, to produce automated underwriting programs based on mentored artificial intelligence

train a system to reproduce the credit decisions of a human underwriter

by mid-1990’s “mentored AI systems had largely lost out to or begun to progress to statistical

With their massive stores of historical mortgage data the GSEs were the only institutions in a position to envisage and implement such a project.

did Freddie decide to pursue statistical underwriting to the detriment of the traditional rule-based methods,

opted to insert consumer credit data, pre-digested in the form of numerical commercial bureau scores, into their nascent systems.

Freddie Mac’s primary objective was to include a reliable selection of consumer credit data into their automated systems in a form that could not be locally manipulated by the brokers.

these goals might have been equally achieved by employing electronically transferred raw credit data purchased from the bureaus, and dissolving them seamlessly into the proprietary algorithms the GSEs were assembling from scratch.

although ‘credit data’ was only one category of information included in mortgage scores, it was now reduced to a discreet factor whose composition could potentially become invariable between automated systems.

Freddie announced its decisions, including a third significant stipulation: that a FICO® score of 660 was the eyeball threshold for their definition of loans eligible for the prime investment. Within a month Fanny Mae swiftly followed suit

Fannie had no choice because they suddenly found themselves besieged by bad paper

The decision to use FICO® as well as GSE manner of interpreting them was materially hardwired into the system through the release of proprietary, agency designed, automated underwriting software

The FICO® feature of automated system design was politically useful when the software was showcased to legislators

FICO® scores conveniently substitute for the quality of ‘creditworthiness’ which government officials and the public had come to recognize as being an essential part of loan evaluation.

made an explicit equivalence between the use of FICO® scores and an evaluation of ‘creditworthiness’ even though the former is a shifting quality assigned statistically with respect to the aggregate and the latter has traditionally been considered a personal property of the individual

Through this analogy with known concepts (even though the commonalities were thin 36 ) FICO® helped circumvent some of the technical difficulties in explaining statistical underwriting to lay audiences.

Given the GSE mandate to help and not hinder homeownership, 660 was intended to be a soft minimum score and not a firm cutoff,

FICO® 660 rapidly became a free standing benchmark of prime investment grade status,

In the absence of automation and scores, the secondary market had learned to rely on indicators designed to describe the risk level of the aggregated pool,

securitizing bodies “weren’t used to looking at metrics that allowed you to drill so deeply into an individual consumer credit profile so effectively”.

Work had to be done to educate each of the securitization and ratings agencies ‘about how credit scores worked’.

the existence of separate, competing systems to carry out this work for non-GSE destined loans impeded horizontal market integration.

Controlling the problems that flourishing calculative diversity posed was S&P’s business.

This was not merely a suggestion. A key incentive to adopt FICO® the was that pools of loans tagged with an S&P validated ‘mortgage score’ could be more easily rated for securitization by S&P’s proprietary securities rating system.

validation and rating gave S&P the opportunity not only to push the FICO® scores, but to transmit the specific interpretations of them that it had absorbed from its earlier collaboration with the GSEs.

the government sanctioned prime and the private label subprime. What divides them are information systems, their regard for risk, and product development; what unites them is a common reliance and baseline interpretation of FICO® scores

It was on the authority of the institutions’ guidelines, their initiatives in interface design, as well as their dirty, hands-on involvement as a driver of RMBS production that the market was made.

The introduction of a numerical scale of consumer credit quality into mortgage origination permitted calculative actions that were simply unanticipated from within the conventional frameworks of the GSEs.

a startling paradox: “Fannie and Freddie have become the opposite of what they were. They are now lenders to safe markets, while private institutions serve markets that were once liquidity-deprived”

In the last decade private capital has been tripping over itself – or so it appeared – to become a handmaiden to the American Dream. The subprime collapse has turned the tables back again,

GSEs need to maintain more conservative portfolios because they have a “statutory requrement to provide liquidity to the nation’s mortgage market”

blow of irony is that as the crisis began, the GSEs themselves were caught holding some $170 billion in private-label subprime securities

because the tool had inscribed within it the possibility of making financially meaningful risk management calculations, it enabled the rise of a new form of financial activity: credit control-by-risk.

Once this calculative tool was stabilized in and as an infrastructure it intensified and generated downstream complexity 60 as it made an alternative form of co-ordinated and coherent collective decision making possible.

This is the flaw of ‘error’ as a social scientific concept in situations that are in motion: it can only be fixed retrospectively and defined from an analytically external point of view.