Credit Growth and the Financial Crisis: A New Narrative Stefania - - PowerPoint PPT Presentation

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Credit Growth and the Financial Crisis: A New Narrative Stefania - - PowerPoint PPT Presentation

Aug 16, 2023 454 likes •1.13k views

Credit Growth and the Financial Crisis: A New Narrative Stefania Albanesi, University of Pittsburgh Giacomo De Giorgi, University of Geneva Jaromir Nosal, Boston College Fifth Conference on Household Finance and Consumption Banque du France,

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SLIDE 1

Credit Growth and the Financial Crisis: A New Narrative

Stefania Albanesi, University of Pittsburgh Giacomo De Giorgi, University of Geneva Jaromir Nosal, Boston College Fifth Conference on Household Finance and Consumption Banque du France, Paris December 14-15, 2017

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SLIDE 2

Introduction

  • Prevailing narrative about the financial crisis:

credit growth during boom concentrated in subprime segment defaults during financial crisis also concentrated in this segment → expansion of subprime credit leading cause for the crisis

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SLIDE 3

Introduction

  • Prevailing narrative about the financial crisis:

credit growth during boom concentrated in subprime segment defaults during financial crisis also concentrated in this segment → expansion of subprime credit leading cause for the crisis

  • Mechanism:

mortgage defaults → drop in house prices → contraction in credit for high MPC households → drop in consumption and employment (Lorenzoni & Guerreri 2015, Midrigan & Philippon 2016, Justiniano & al. 2016, Berger & al. 2015, Kaplan, Mittman &Violante 2017, Hedlund & Garriga 2016, etc.)

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SLIDE 4

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data

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SLIDE 5

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data Findings:

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SLIDE 6

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data Findings:

  • I. Credit growth during boom primarily for mid-high credit score borrowers

(consistent with Adelino, Shoar & Severino 2015, Ferreira & Guyourko 2015 and Foote, Loewenstein & Willen 2016 )

  • II. Larger rise in defaults for mid-high credit score borrowers during crisis
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SLIDE 7

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data Findings:

  • I. Credit growth during boom primarily for mid-high credit score borrowers

(consistent with Adelino, Shoar & Severino 2015, Ferreira & Guyourko 2015 and Foote, Loewenstein & Willen 2016 )

  • II. Larger rise in defaults for mid-high credit score borrowers during crisis
  • III. High credit score defaults driven by real estate investors
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SLIDE 8

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data Findings:

  • I. Credit growth during boom primarily for mid-high credit score borrowers

(consistent with Adelino, Shoar & Severino 2015, Ferreira & Guyourko 2015 and Foote, Loewenstein & Willen 2016 )

  • II. Larger rise in defaults for mid-high credit score borrowers during crisis
  • III. High credit score defaults driven by real estate investors

Lessons:

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SLIDE 9

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data Findings:

  • I. Credit growth during boom primarily for mid-high credit score borrowers

(consistent with Adelino, Shoar & Severino 2015, Ferreira & Guyourko 2015 and Foote, Loewenstein & Willen 2016 )

  • II. Larger rise in defaults for mid-high credit score borrowers during crisis
  • III. High credit score defaults driven by real estate investors

Lessons:

  • Reassessment of role of subprime credit
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SLIDE 10

Our Contribution

  • Study household debt and delinquency in 1999-2013:

based on large administrative panel of credit report data Findings:

  • I. Credit growth during boom primarily for mid-high credit score borrowers

(consistent with Adelino, Shoar & Severino 2015, Ferreira & Guyourko 2015 and Foote, Loewenstein & Willen 2016 )

  • II. Larger rise in defaults for mid-high credit score borrowers during crisis
  • III. High credit score defaults driven by real estate investors

Lessons:

  • Reassessment of role of subprime credit
  • Critical role of real estate investors in foreclosure crisis
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SLIDE 11

Data

  • FRBNY Consumer Credit Panel/Equifax Data

1% of all individuals with an Equifax credit report (2.5 mil borrowers per quarter) quarterly, 1999:Q1-2013:Q4

  • Information

all consumer debt except pay day loans delinquent behavior public record items credit score, age, ZIP code matched to payroll data for 2009

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SLIDE 12

Prevailing Narrative

  • Initial credit score used to assess borrower quality

(Mian&Sufi 2009 and 2017)

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SLIDE 13

Prevailing Narrative

  • Initial credit score used to assess borrower quality

(Mian&Sufi 2009 and 2017)

Individuals by Initial Credit Score

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Real per capita real mortgage balances, ratio to 2001Q3. (FRBNY CCP/Equifax Data.)

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SLIDE 14

Prevailing Narrative

  • Initial credit score used to assess borrower quality

(Mian&Sufi 2009 and 2017)

Individuals by Initial Credit Score

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Zip Codes by Initial Subprime Share

1 1.5 2 2.5

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Real per capita real mortgage balances, ratio to 2001Q3. (FRBNY CCP/Equifax Data.)

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SLIDE 15

Prevailing Narrative

  • Initial credit score used to assess borrower quality

(Mian&Sufi 2009 and 2017) → Stronger mortgage debt growth for subprime borrowers

Individuals by Initial Credit Score

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Zip Codes by Initial Subprime Share

1 1.5 2 2.5

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Real per capita real mortgage balances, ratio to 2001Q3. (FRBNY CCP/Equifax Data.)

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SLIDE 16

Problems with Initial Credit Score Ranking

  • Low credit score borrowers disproportionately young

Median Age Quartile 1: 39 Quartile 2: 44 Quartile 3: 48 Quartile 4: 58

0.005 0.01 0.015 0.02 0.025 0.03 0.035 20 25 30 35 40 45 50 55 60 65 70 75 80 85 Quartile 1 Quartile 2 Quartile 3 Quartile 4

Age distribution by credit score quartile, 2004-2012 average. (Experian Data.)

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SLIDE 17

Problems with Initial Credit Score Ranking

  • Low credit score borrowers disproportionately young
  • Young experience life cycle debt and credit score growth

Credit Score Debt

  • 20

20 40 60 80 100 120 140 160 21 26 31 36 41 46 51 56 61 66 71 76 81

  • $50,000
  • $40,000
  • $30,000
  • $20,000
  • $10,000

$0 $10,000 $20,000 $30,000 $40,000 21 26 31 36 41 46 51 56 61 66 71 76 81 Total Debt Balances Mortgage Balances

Estimated age effects. (FRBNY CCP/Equifax Data.)

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SLIDE 18

Problems with Initial Credit Score Ranking

  • Low credit score borrowers disproportionately young
  • Young experience life cycle debt and credit score growth

→ Initial credit score lower than at time of borrowing

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SLIDE 19

Problems with Initial Credit Score Ranking

  • Low credit score borrowers disproportionately young
  • Young experience life cycle debt and credit score growth

→ Initial credit score lower than at time of borrowing

  • Life cycle growth of credit scores and debt driven by income growth
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SLIDE 20

Life Cycle Credit Scores, Debt and Income

  • Credit score and debt growth for young in 1999 rise with 2009 income

25-34 year olds in 1999 by income quintile in 2009 Credit Score Mortgage Balances

15 30 45 60 75 15 30 45 60 75 Difference from 2001 2001 2003 2005 2007 2009

Quintile 1 (Lowest) Quintile 5 (Highest)

1 1.5 2 2.5 3 1 1.5 2 2.5 3 Ratio to 2001 2001 2003 2005 2007 2009

Quintile 1 (Lowest) Quintile 5 (Highest)

Difference with 2001 (credit score) and ratio to 2001 (mortgage balances). (FRBNY CCP/Equifax Data.)

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SLIDE 21

Life Cycle and Borrowing by Initial Credit Score

  • I. Removing differences in age distribution

Individuals by Initial Credit Score

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Age Distribution Set to Quartile 4

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Real per capita mortgage balances by 1999 Equifax Risk Score, ratio to 2001. (FRBNY CCP/Equifax Data.)

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SLIDE 22

Life Cycle and Borrowing by Initial Credit Score

  • I. Removing differences in age distribution

→ Differences in debt growth across initial credit scores attenuated

Per Capita 2001Q3-2007Q4 Real Mortgage Balance Growth Difference with Quartile 4 Explained by Age Distribution Quartile 1 Quartile 2 Quartile 3 25% 20% 14%

Borrowers ranked by 1999 Equifax Risk Score. (FRBNY CCP/Equifax Data.)

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SLIDE 23

Life Cycle and Borrowing by Initial Credit Score

  • II. Removing life cycle effects

Individuals by Initial Credit Score

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Life Cycle Effects Removed

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Real per capita mortgage balances by 1999 Equifax Risk Score, ratio to 2001. Life cycle effects removed by assigning to each 1999 age bin balances of borrowers in that age bin in current quarter. (FRBNY CCP/Equifax Data.)

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SLIDE 24

Life Cycle and Borrowing by Initial Credit Score

  • II. Removing life cycle effects

→ Differences in debt growth by initial credit score mostly eliminated

Individuals by Initial Credit Score

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Life Cycle Effects Removed

0.5 1 1.5 2 2.5 3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Real per capita mortgage balances by 1999 Equifax Risk Score, ratio to 2001. Life cycle effects removed by assigning to each 1999 age bin balances of borrowers in that age bin in current quarter. (FRBNY CCP/Equifax Data.)

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SLIDE 25

Credit Scores, Debt and Defaults

  • Alternative to initial credit score? recent credit score
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SLIDE 26

Credit Scores, Debt and Defaults

  • Alternative to initial credit score? recent credit score

→ Strongly positively related to income, given age

550 600 650 700 750 800 850 5,000 20,000 35,000 50,000 65,000 80,000 95,000 110,000 125,000 140,000 155,000 170,000 185,000 200,000 215,000 230,000 245,000 25 30 35 40 45 50 55 60 65 Predicted relation between credit score and total labor income by age in 2009. (FRBNY CCP/Equifax Data.)

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SLIDE 27

Debt and Defaults by Recent Credit Score

  • Analysis from lender’s perspective
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SLIDE 28

Debt and Defaults by Recent Credit Score

  • Analysis from lender’s perspective

Regression Specification Dependent variable: future change in balances (4-12 quarter ahead)

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SLIDE 29

Debt and Defaults by Recent Credit Score

  • Analysis from lender’s perspective

Regression Specification Dependent variable: future change in balances (4-12 quarter ahead) Explanatory variables: 1 quarter lagged credit score quartile

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SLIDE 30

Debt and Defaults by Recent Credit Score

  • Analysis from lender’s perspective

Regression Specification Dependent variable: future change in balances (4-12 quarter ahead) Explanatory variables: 1 quarter lagged credit score quartile lagged change in credit score (4-8 quarter change)

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SLIDE 31

Debt and Defaults by Recent Credit Score

  • Analysis from lender’s perspective

Regression Specification Dependent variable: future change in balances (4-12 quarter ahead) Explanatory variables: 1 quarter lagged credit score quartile lagged change in credit score (4-8 quarter change) time effects, age effects time and age effects interacted with 1 quarter lagged credit score

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SLIDE 32

Debt and Defaults by Recent Credit Score

  • Analysis from lender’s perspective

Regression Specification Dependent variable: future change in balances (4-12 quarter ahead) Explanatory variables: 1 quarter lagged credit score quartile lagged change in credit score (4-8 quarter change) time effects, age effects time and age effects interacted with 1 quarter lagged credit score

  • Findings:

Strongest growth in debt and defaults for mid-high credit score borrowers

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SLIDE 33

Debt by Recent Credit Score: Mortgage Balances

  • Growth strongest for quartiles 2-3 during boom

Predicted 8 quarter ahead change in mortgage balances

  • 10,000
  • 5,000

5,000 10,000 15,000 20,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Quartile 1 Quartile 2 Quartile 3 Quartile 4 Age adjusted, by 1Q lagged Equifax Risk Score quartile, USD. (FRBNY CCP/Equifax Data.)

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SLIDE 34

Debt by Recent Credit Score: Mortgage Balances

  • Sizable estimated age effects only for quartiles 2-4

Age effects for 8 quarter ahead change in mortgage balances

  • 5,000

5,000 10,000 15,000 20,000 25,000 20 25 30 35 40 45 50 55 60 65 70 75 80 Quartile 1 Quartile 2 Quartile 3 Quartile 4

By 1Q lagged Equifax Risk Score quartile, USD. (FRBNY CCP/Equifax Data.)

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SLIDE 35

Credit Growth by Credit Score: More Evidence

  • No growth in new originations for quartile 1

Fraction with New Originations

0.1 0.2 0.3

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

By 8Q lagged Equifax Risk Score quartile. Quartile cutoffs: 615, 720, 791, 840. (FRBNY CCP/Equifax Data.)

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SLIDE 36

Credit Growth by Credit Score: More Evidence

  • No growth in new originations for quartile 1
  • No growth in fraction with first mortgages for quartile 1

Fraction with First Mortgages 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 2001 2003 2005 2007 2009 2011 2013

Quartile 2 Quartile 3 Quartile 4 Quartile 1

By 8Q lagged Equifax Risk Score quartile. Quartile cutoffs: 615, 720, 791, 840. (FRBNY CCP/Equifax Data.)

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SLIDE 37

Defaults by Recent Credit Score: Balances

  • Delinquent mortgage balances grow most for quartiles 2-4 during crisis

Predicted 8 quarter ahead change in delinquent mortgage balances

  • 7,000
  • 5,000
  • 3,000
  • 1,000

1,000 3,000 5,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Quartile 1 Quartile 2 Quartile 3 Quartile 4 Age adjusted, 90+ day delinquent, by 1Q lagged Equifax Risk Score quartile, USD. (FRBNY CCP/Equifax Data.)

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SLIDE 38

Defaults by Recent Credit Score

  • Quartile 1 share of foreclosures drops during crisis

Fraction with Share of

.002 .004 .006 .002 .004 .006 Fraction (3QMA) 2001 2003 2005 2007 2009

Quartile 1 (Lowest) Quartile 2 Quartile 3 Quartile 4 (Highest)

.2 .4 .6 .8 .2 .4 .6 .8 Share (3QMA) 2001 2003 2005 2007 2009 2011 2013

Quartile 1 (Lowest) Quartile 2 Quartile 3 Quartile 4 (Highest)

Foreclosures in the last 4 quarters by 8 quarter lagged Equifax Risk Score quartile. (FRBNY CCP/Equifax Data)

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SLIDE 39

Explaining High Credit Score Defaults

  • Why did borrowers with ’good credit’ default during crisis?

Rise in investors → borrowers with 2 or more first mortgages

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SLIDE 40

Explaining High Credit Score Defaults

  • Why did borrowers with ’good credit’ default during crisis?

Rise in investors → borrowers with 2 or more first mortgages

Fraction of Investors Quartile 1 Quartile 2 Quartile 3 Quartile 4 2001Q3-2004Q3 mean 0.063 0.103 0.110 0.107 Investor Share of Mortgage Balances Quartile 1 Quartile 2 Quartile 3 Quartile 4 2001Q3-2004Q3 mean 0.123 0.196 0.212 0.226 By 8 quarter lagged Equifax Risk Score. (FRBNY CCP/Equifax Data.)

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SLIDE 41

Explaining High Credit Score Defaults

  • Why did borrowers with ’good credit’ default during crisis?

Rise in investors → borrowers with 2 or more first mortgages

Fraction of Investors Quartile 1 Quartile 2 Quartile 3 Quartile 4 2001Q3-2004Q3 mean 0.063 0.103 0.110 0.107 2007Q4 peak 0.082 0.156 0.162 0.142 Investor Share of Mortgage Balances Quartile 1 Quartile 2 Quartile 3 Quartile 4 2001Q3-2004Q3 mean 0.123 0.196 0.212 0.226 2007Q4 peak 0.183 0.333 0.350 0.317 By 8 quarter lagged Equifax Risk Score. (FRBNY CCP/Equifax Data.)

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SLIDE 42

High Credit Score Defaults: Role of Investors

  • Rise in foreclosure rate more pronounced for investors

Investors (2+) Non Investors (1)

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

Foreclosure rate by 8 quarter lagged Equifax Risk Score, 3QMA. (FRBNY CCP/Equifax Data.)

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SLIDE 43

High Credit Score Defaults: Role of Investors

  • Rise in foreclosure rate more pronounced for investors

→ Rise in investor share of defaults for high credit score borrowers

Investor Share of Foreclosures

0.1 0.2 0.3 0.4 0.5 0.6

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Quartile 1 Quartile 2 Quartile 3 Quartile 4

By quartile of the 8 quarter lagged Equifax Risk Score, 3QMA. (FRBNY CCP/Equifax Data.)

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SLIDE 44

Macroeconomic Implications

  • Aggregate consequences of growth in subprime lending

Mortgage defaults → drop in house prices → contraction in credit for high MPC households → drop in consumption and employment

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SLIDE 45

Macroeconomic Implications

  • Aggregate consequences of growth in subprime lending

Mortgage defaults → drop in house prices → contraction in credit for high MPC households → drop in consumption and employment

  • Causal link identified from geographical variation

(zip code, MSA, county, state) (Mian & Sufi 2014, Mian, Rao & Sufi 2013, Kehoe, Midrigan & Pastorino 2014, Mian, Sufi & Trebbi 2014, Midrigan & Philippon 2016, Justiniano, Primiceri & Tambalotti 2016, Guren, Nakamura, Steinsson 2017 etc )

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SLIDE 46

Macroeconomic Implications

  • Aggregate consequences of growth in subprime lending

Mortgage defaults → drop in house prices → contraction in credit for high MPC households → drop in consumption and employment

  • Causal link identified from geographical variation

(zip code, MSA, county, state) (Mian & Sufi 2014, Mian, Rao & Sufi 2013, Kehoe, Midrigan & Pastorino 2014, Mian, Sufi & Trebbi 2014, Midrigan & Philippon 2016, Justiniano, Primiceri & Tambalotti 2016, Guren, Nakamura, Steinsson 2017 etc ) → New findings challenge causal mechanism

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SLIDE 47

Growth in Mortgage Balances By Zip Code

  • Strongest growth for prime borrowers in all zip codes

Quartile 1 Quartile 2

0.75 1 1.25 1.5 1.75 2 2.25 2.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Above 660 Below 660 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Above 660 Below 660

Quartile 3 Quartile 4

0.75 1 1.25 1.5 1.75 2 2.25 2.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Above 660 Below 660 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Above 660 Below 660

Real per capita mortgage balance growth by fraction of subprime borrowers in 2001. Ratio to 2001. (FRBNY CCP/Equifax Data.)

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SLIDE 48

Zip Code Variation: Role of Age Distribution

  • Highest debt growth in high subprime zip codes for all borrowers
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SLIDE 49

Zip Code Variation: Role of Age Distribution

  • Highest debt growth in high subprime zip codes for all borrowers
  • More young borrowers in high subprime zip codes

Quartile 1 Quartile 2 Quartile 3 Quartile 4 2001 subprime share 19% 32% 44% 60% median age 50 49 48 46 Fraction in each age bin Quartile 1 Quartile 2 Quartile 3 Quartile 4 20-34 0.22 0.25 0.28 0.30 35-54 0.42 0.41 0.41 0.41 55-85 0.38 0.34 0.32 0.30

By fraction of subprime in 2001. 2001Q1-2013Q4 averages. (FRBNY CCP/Equifax Data.)

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SLIDE 50

Zip Code Variation: Role of Age Distribution

  • Highest debt growth in high subprime zip codes for all borrowers
  • More young borrowers in high subprime zip codes

→ Quartile 4-Quartile 1 difference mostly explained by age distribution

2001Q1-2007Q4 Real Per Capita Mortgage Balance Growth Difference relative to Quartile 1 explained by age distribution Quartile 2 Quartile 3 Quartile 4 44% 43% 84%

By fraction of subprime in 2001. (FRBNY/CCP Equifax Data.)

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SLIDE 51

Defaults By Zip Code

  • Level differences in foreclosure rates, similar rise during crisis

Foreclosure Rate

0.005 0.01 0.015 0.02 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Quartile 1 Quartile 2 Quartile 3 Quartile 4

By fraction of subprime in 2001. (FRBNY CCP/Equifax Data.)

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SLIDE 52

Defaults By Zip Code

  • Level differences in foreclosure rates, similar rise during crisis
  • Large rise in prime share of defaults in all zip codes during crisis

Prime Share of Foreclosures

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Quartile 1 Quartile 2 Quartile 3 Quartile 4

By fraction of subprime in 2001. (FRBNY CCP/Equifax Data.)

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SLIDE 53

Defaults By Zip Code

  • Level differences in foreclosure rates, similar rise during crisis
  • Large rise in prime share of defaults in all zip codes during crisis

→ Higher default rates for prime borrowers in high subprime zip codes Prime Share of Foreclosures

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Quartile 1 Quartile 2 Quartile 3 Quartile 4

By fraction of subprime in 2001. (FRBNY CCP/Equifax Data.)

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SLIDE 54

Defaults By Zip Code: Role of Investors

  • Larger rise in investors for prime borrowers, similar across zip codes
  • More subprime investors in low subprime zip codes

Prime Borrowers Subprime Borrowers

0.05 0.1 0.15 0.2 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Quartile 1 Quartile 2 Quartile 3 Quartile 4 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Quartile 1 Quartile 2 Quartile 3 Quartile 4

Fraction with 2+ first mortgages by fraction of subprime borrowers in 2001. Prime status based on 8Q lagged credit score. (FRBNY CCP/Equifax Data.)

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SLIDE 55

Defaults By Zip Code: Role of Investors

  • Stronger rise in balances and foreclosures for prime investors in high

subprime zip codes

Prime Borrowers 2001Q3-2007Q4 net mortgage balance growth

  • no. first mortgages

Quartile 1 Quartile 2 Quartile 3 Quartile 4 2 86% 85% 97% 104% 3 94% 104% 117% 118% 4+ 102% 122% 133% 125% 2005Q4-2007Q4 change in foreclosure rate

  • no. first mortgages

Quartile 1 Quartile 2 Quartile 3 Quartile 4 2 0.023 0.027 0.045 0.053 3 0.040 0.063 0.087 0.115 4+ 0.076 0.096 0.123 0.151

Zip code level investor activity for prime borrowers by fraction of subprime in 2001. (FRBNY CCP/Equifax Data.)

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SLIDE 56

Zip Code Variation: Role of Demographics

  • Why did high subprime zip codes experience more severe recession?
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SLIDE 57

Zip Code Variation: Role of Demographics

  • Why did high subprime zip codes experience more severe recession?

Young, low education, high minority share

Zip Code Level Indicators Quartile 1 Quartile 2 Quartile 3 Quartile 4 Associate+ degree (2012) 45% 31% 23% 17% Percent white 93% 90% 83% 63% Percent black 1.7% 3.6% 7.6% 24.6%

By fraction of subprime in 2001. PDI in 2012 USD. (FRBNY CCP/Equifax Data, IPUMS, IRS, ACS.)

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SLIDE 58

Zip Code Variation: Role of Demographics

  • Why did high subprime zip codes experience more severe recession?

Young, low education, high minority share High unemployment, low income, high inequality

Zip Code Level Indicators Quartile 1 Quartile 2 Quartile 3 Quartile 4 Average UR 2001-2007 4.94% 5.19% 5.38% 5.72% Average PDI 2001-2007 $41k $30k $26k $21k PDI Growth 2001-2007 25% 16% 10% 4%

Mean Income ≥ $200K Mean Income

(2006-11)

6.4 7.9 9.4 11.8

By fraction of subprime in 2001. PDI in 2012 USD. (FRBNY CCP/Equifax Data, IPUMS, IRS, ACS.)

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SLIDE 59

Zip Code Variation: Role of Demographics

  • Why did high subprime zip codes experience more severe recession?

Young, low education, high minority share High unemployment, low income, high inequality Higher population density, more pronounced housing cycle

Zip Code Level Indicators Quartile 1 Quartile 2 Quartile 3 Quartile 4 Pop per sq mile 1,214 1,380 1,386 2,322 HPI Growth 2001-2007 29% 37% 42% 47% HPI Growth 2007-2010

  • 21%
  • 30%
  • 27%
  • 36%

By fraction of subprime in 2001. PDI in 2012 USD. (FRBNY CCP/Equifax Data, IPUMS, IRS, ACS.)

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SLIDE 60

Zip Code Variation: Role of Demographics

  • Why did high subprime zip codes experience more severe recession?

Young, low education, high minority share High unemployment, low income, high inequality Higher population density, more pronounced housing cycle → Prevalence of business cycle sensitive, high MPC populations = ⇒ stronger impact of recession on employment and consumption

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SLIDE 61

Zip Code Variation: Role of Demographics

  • Why did high subprime zip codes experience more severe recession?

Young, low education, high minority share High unemployment, low income, high inequality Higher population density, more pronounced housing cycle → Prevalence of business cycle sensitive, high MPC populations = ⇒ stronger impact of recession on employment and consumption → Prevalence of urban areas = ⇒ accentuated house price cycle gentrification (Guerrieri et al. 2013) international capital inflows

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SLIDE 62

Conclusions

  • I. Reassessment of role of subprime credit
  • II. Important role of real estate investors for foreclosure crisis
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SLIDE 63

Conclusions

  • I. Reassessment of role of subprime credit
  • II. Important role of real estate investors for foreclosure crisis
  • drivers of investor activity?
  • alternative default risk indicators?
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SLIDE 64

Conclusions

  • I. Reassessment of role of subprime credit
  • II. Important role of real estate investors for foreclosure crisis
  • drivers of investor activity?
  • alternative default risk indicators?
  • III. Geographical variation
  • larger rise in debt and defaults for prime borrowers everywhere
  • more severe recession in high subprime areas linked to demographics
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SLIDE 65

Conclusions

  • I. Reassessment of role of subprime credit
  • II. Important role of real estate investors for foreclosure crisis
  • drivers of investor activity?
  • alternative default risk indicators?
  • III. Geographical variation
  • larger rise in debt and defaults for prime borrowers everywhere
  • more severe recession in high subprime areas linked to demographics

Why stronger housing cycle and investor activity in high subprime areas?

  • preference for urban locations
  • labor market factors

rise in initial local income (Ferreira and Gyourko 2012) concentration of growing industries (Liebersohn 2017)