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Discussion - Credit Boom and Lending Standard: Evidence...

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  • INTRODUCTIONCOMMENTS

    SUMMARY

    DiscussionCredit Boom and Lending Standard: Evidence from Subprime

    Mortgage Lending

    Satyajit Chatterjee

    Research DepartmentFederal Reserve Bank of Philadelphia

    September 2008

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    OBJECTIVES AND FINDINGS

    Consensus that the source of the current subprimemortgage crisis is increasingly lax lending standardsWhy did lending standards decline?

    Rapid expansion of credit caused lower lending standardsIncrease in the number of lenders caused lower lendingstandards

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    OBJECTIVES AND FINDINGS

    Consensus that the source of the current subprimemortgage crisis is increasingly lax lending standardsWhy did lending standards decline?

    Rapid expansion of credit caused lower lending standardsIncrease in the number of lenders caused lower lendingstandards

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    OBJECTIVES AND FINDINGS

    Consensus that the source of the current subprimemortgage crisis is increasingly lax lending standardsWhy did lending standards decline?

    Rapid expansion of credit caused lower lending standardsIncrease in the number of lenders caused lower lendingstandards

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    OBJECTIVES AND FINDINGS

    Consensus that the source of the current subprimemortgage crisis is increasingly lax lending standardsWhy did lending standards decline?

    Rapid expansion of credit caused lower lending standardsIncrease in the number of lenders caused lower lendingstandards

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    APPROACHKey Idea

    Use loan-level data (HMDA) for 2000 to estimate

    Dj k = X ′j β + �j k

    Identity of lender is proxy for prime or subprimeUse β̂ to predict denial rate of mortgage applications inyears 2001-2006 for different metropolitan areas (MSAs)Regress prediction error (actual - predicted) on log of thenumber of loan applicants in the MSA and other MSA-levelcontrolsLog of the number of applicants affects the prediction errornegatively – more applicants there are, the less choosylenders become

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    APPROACHKey Idea

    Use loan-level data (HMDA) for 2000 to estimate

    Dj k = X ′j β + �j k

    Identity of lender is proxy for prime or subprimeUse β̂ to predict denial rate of mortgage applications inyears 2001-2006 for different metropolitan areas (MSAs)Regress prediction error (actual - predicted) on log of thenumber of loan applicants in the MSA and other MSA-levelcontrolsLog of the number of applicants affects the prediction errornegatively – more applicants there are, the less choosylenders become

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    APPROACHKey Idea

    Use loan-level data (HMDA) for 2000 to estimate

    Dj k = X ′j β + �j k

    Identity of lender is proxy for prime or subprimeUse β̂ to predict denial rate of mortgage applications inyears 2001-2006 for different metropolitan areas (MSAs)Regress prediction error (actual - predicted) on log of thenumber of loan applicants in the MSA and other MSA-levelcontrolsLog of the number of applicants affects the prediction errornegatively – more applicants there are, the less choosylenders become

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    APPROACHKey Idea

    Use loan-level data (HMDA) for 2000 to estimate

    Dj k = X ′j β + �j k

    Identity of lender is proxy for prime or subprimeUse β̂ to predict denial rate of mortgage applications inyears 2001-2006 for different metropolitan areas (MSAs)Regress prediction error (actual - predicted) on log of thenumber of loan applicants in the MSA and other MSA-levelcontrolsLog of the number of applicants affects the prediction errornegatively – more applicants there are, the less choosylenders become

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    APPROACHKey Idea

    Use loan-level data (HMDA) for 2000 to estimate

    Dj k = X ′j β + �j k

    Identity of lender is proxy for prime or subprimeUse β̂ to predict denial rate of mortgage applications inyears 2001-2006 for different metropolitan areas (MSAs)Regress prediction error (actual - predicted) on log of thenumber of loan applicants in the MSA and other MSA-levelcontrolsLog of the number of applicants affects the prediction errornegatively – more applicants there are, the less choosylenders become

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSContribution

    Use of data reported under the Home Mortgage DisclosureAct for assessing lending standards in local housingmarkets (MSAs)Exploit variations across local housing markets to shedlight on determinants of lending standards – great idea!!

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSContribution

    Use of data reported under the Home Mortgage DisclosureAct for assessing lending standards in local housingmarkets (MSAs)Exploit variations across local housing markets to shedlight on determinants of lending standards – great idea!!

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSMain Concern

    Why did the number of applicants go up so much across allMSAs?This question is not directly addressed in the paperbecause the authors focus on differences across MSAsBut thinking about why the number of applicants went upsuggests that the authors are ignoring causal influencesrunning the other way – from lower denial rates to moreapplicants

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSMain Concern

    Why did the number of applicants go up so much across allMSAs?This question is not directly addressed in the paperbecause the authors focus on differences across MSAsBut thinking about why the number of applicants went upsuggests that the authors are ignoring causal influencesrunning the other way – from lower denial rates to moreapplicants

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSMain Concern

    Why did the number of applicants go up so much across allMSAs?This question is not directly addressed in the paperbecause the authors focus on differences across MSAsBut thinking about why the number of applicants went upsuggests that the authors are ignoring causal influencesrunning the other way – from lower denial rates to moreapplicants

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSAn Alternative Story

    The key event was the arrival of a cost-effective businessmodel for making higher-risk mortgages: “originate anddistribute”This meant lower denial rates on higher-risk loans – sothere were more buyers chasing homesThe resulting increase in house prices induced people toupdgrade or switch from renting to owning, whichgenerated more applicantions for mortgagesThis story explains how decline in denial rates could leadto an increase in applications and house pricesHowever, it is possile that the influx of these newapplicants may have led to further decline in lendingstandards thru channels suggested by the authors

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSAn Alternative Story

    The key event was the arrival of a cost-effective businessmodel for making higher-risk mortgages: “originate anddistribute”This meant lower denial rates on higher-risk loans – sothere were more buyers chasing homesThe resulting increase in house prices induced people toupdgrade or switch from renting to owning, whichgenerated more applicantions for mortgagesThis story explains how decline in denial rates could leadto an increase in applications and house pricesHowever, it is possile that the influx of these newapplicants may have led to further decline in lendingstandards thru channels suggested by the authors

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSAn Alternative Story

    The key event was the arrival of a cost-effective businessmodel for making higher-risk mortgages: “originate anddistribute”This meant lower denial rates on higher-risk loans – sothere were more buyers chasing homesThe resulting increase in house prices induced people toupdgrade or switch from renting to owning, whichgenerated more applicantions for mortgagesThis story explains how decline in denial rates could leadto an increase in applications and house pricesHowever, it is possile that the influx of these newapplicants may have led to further decline in lendingstandards thru channels suggested by the authors

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSAn Alternative Story

    The key event was the arrival of a cost-effective businessmodel for making higher-risk mortgages: “originate anddistribute”This meant lower denial rates on higher-risk loans – sothere were more buyers chasing homesThe resulting increase in house prices induced people toupdgrade or switch from renting to owning, whichgenerated more applicantions for mortgagesThis story explains how decline in denial rates could leadto an increase in applications and house pricesHowever, it is possile that the influx of these newapplicants may have led to further decline in lendingstandards thru channels suggested by the authors

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSAn Alternative Story

    The key event was the arrival of a cost-effective businessmodel for making higher-risk mortgages: “originate anddistribute”This meant lower denial rates on higher-risk loans – sothere were more buyers chasing homesThe resulting increase in house prices induced people toupdgrade or switch from renting to owning, whichgenerated more applicantions for mortgagesThis story explains how decline in denial rates could leadto an increase in applications and house pricesHowever, it is possile that the influx of these newapplicants may have led to further decline in lendingstandards thru channels suggested by the authors

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSEndogeneity and the IV Regression

    Is the number of applicants for prime mortgages a goodinstrument for the number of applicants for subprimemortgages?With decline in the subprime denial rate, homes would sellat a faster rate and if some fraction of departing ownersapply for prime mortgages then applications for primemortgages will go up as wellSo, in theory, there could be a causal link from subprimedenial rates to the number of prime mortgage applicationsSuggestion: How about using number of prime mortgageapplications in “far away” MSAs as the instrument?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSEndogeneity and the IV Regression

    Is the number of applicants for prime mortgages a goodinstrument for the number of applicants for subprimemortgages?With decline in the subprime denial rate, homes would sellat a faster rate and if some fraction of departing ownersapply for prime mortgages then applications for primemortgages will go up as wellSo, in theory, there could be a causal link from subprimedenial rates to the number of prime mortgage applicationsSuggestion: How about using number of prime mortgageapplications in “far away” MSAs as the instrument?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSEndogeneity and the IV Regression

    Is the number of applicants for prime mortgages a goodinstrument for the number of applicants for subprimemortgages?With decline in the subprime denial rate, homes would sellat a faster rate and if some fraction of departing ownersapply for prime mortgages then applications for primemortgages will go up as wellSo, in theory, there could be a causal link from subprimedenial rates to the number of prime mortgage applicationsSuggestion: How about using number of prime mortgageapplications in “far away” MSAs as the instrument?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSEndogeneity and the IV Regression

    Is the number of applicants for prime mortgages a goodinstrument for the number of applicants for subprimemortgages?With decline in the subprime denial rate, homes would sellat a faster rate and if some fraction of departing ownersapply for prime mortgages then applications for primemortgages will go up as wellSo, in theory, there could be a causal link from subprimedenial rates to the number of prime mortgage applicationsSuggestion: How about using number of prime mortgageapplications in “far away” MSAs as the instrument?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSCompetition and Lending Standards

    In the “originate and distribute model”, incumbentmortgage lenders earn money in origination feesAll else remaining the same, lower denial rates mean moreoriginations per lenderThe additional return could have attracted new entrantsSo, again, in theory there could be a link between a drop indenial rates and entry of new lenders

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSCompetition and Lending Standards

    In the “originate and distribute model”, incumbentmortgage lenders earn money in origination feesAll else remaining the same, lower denial rates mean moreoriginations per lenderThe additional return could have attracted new entrantsSo, again, in theory there could be a link between a drop indenial rates and entry of new lenders

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSCompetition and Lending Standards

    In the “originate and distribute model”, incumbentmortgage lenders earn money in origination feesAll else remaining the same, lower denial rates mean moreoriginations per lenderThe additional return could have attracted new entrantsSo, again, in theory there could be a link between a drop indenial rates and entry of new lenders

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    COMMENTSCompetition and Lending Standards

    In the “originate and distribute model”, incumbentmortgage lenders earn money in origination feesAll else remaining the same, lower denial rates mean moreoriginations per lenderThe additional return could have attracted new entrantsSo, again, in theory there could be a link between a drop indenial rates and entry of new lenders

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    SUMMARY

    The idea that expansion of credit leads to lax standards isplausibleRich data set, but the regressions suffer from potentialendogeneity issuesSimultaneous equation approach?Be more specific about the exact channel via whichexpansion affects standards?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    SUMMARY

    The idea that expansion of credit leads to lax standards isplausibleRich data set, but the regressions suffer from potentialendogeneity issuesSimultaneous equation approach?Be more specific about the exact channel via whichexpansion affects standards?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    SUMMARY

    The idea that expansion of credit leads to lax standards isplausibleRich data set, but the regressions suffer from potentialendogeneity issuesSimultaneous equation approach?Be more specific about the exact channel via whichexpansion affects standards?

    Satyajit Chatterjee Discussion

  • INTRODUCTIONCOMMENTS

    SUMMARY

    SUMMARY

    The idea that expansion of credit leads to lax standards isplausibleRich data set, but the regressions suffer from potentialendogeneity issuesSimultaneous equation approach?Be more specific about the exact channel via whichexpansion affects standards?

    Satyajit Chatterjee Discussion

    INTRODUCTIONCOMMENTSSUMMARY

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