Greg Kaplan Chicago - Princeton Universityviolante.mycpanel.princeton.edu › Slides ›...

��The Housing Boom and Bust:

Model Meets Evidence

Greg KaplanChicago

Kurt MitmanIIES - Stockholm

Gianluca ViolantePrinceton

��������� � � �������

The Questiony��

Year1995 2000 2005 2010 2015

Logs

(19

97:Q

1 =

0)

-0.2

-0.1

0

0.1

0.2

0.3

Relative House Price

Boom

Bust

The Questiony��

Year1995 2000 2005 2010 2015

Logs

(19

97:Q

1 =

0)

-0.2

-0.1

0

0.1

0.2

0.3

Relative House Price

Boom

Bust

• What caused the boom and bust in house prices?

Two Viewsy��

1. Credit view

• Availability of credit to marginal borrowers determines demand forhousing and house prices

• Financial deregulation and rise in securitization in early 2000s led to‘unsustainable’ lending to subprime low-income borrowers

Two Viewsy��

1. Credit view

• Availability of credit to marginal borrowers determines demand forhousing and house prices

• Financial deregulation and rise in securitization in early 2000s led to‘unsustainable’ lending to subprime low-income borrowers

2. Expectations view

• Waves of optimism and pessimism affect desire to borrow, housingdemand and house prices

• Middle- and high-income prime borrowers crucial to the story

Two Viewsy��

1. Credit view

• Availability of credit to marginal borrowers determines demand forhousing and house prices

• Financial deregulation and rise in securitization in early 2000s led to‘unsustainable’ lending to subprime low-income borrowers

2. Expectations view

• Waves of optimism and pessimism affect desire to borrow, housingdemand and house prices

• Middle- and high-income prime borrowers crucial to the story

� What do the microdata say?

Equilibrium Models of the Credit Viewy��

Favilukis-Ludvigson-van Nieuwerburgh (2015); Justiniano-Primiceri-Tambalotti (2015); Greenwald (2016)

• Successful in generating large house price movements

Equilibrium Models of the Credit Viewy��

Favilukis-Ludvigson-van Nieuwerburgh (2015); Justiniano-Primiceri-Tambalotti (2015); Greenwald (2016)

• Successful in generating large house price movements

• What does it take for looser credit to push up house prices?

1. Large effect of credit shocks on housing risk premium

2. Many households constrained in their housing consumption

Equilibrium Models of the Credit Viewy��

Favilukis-Ludvigson-van Nieuwerburgh (2015); Justiniano-Primiceri-Tambalotti (2015); Greenwald (2016)

• Successful in generating large house price movements

• What does it take for looser credit to push up house prices?

1. Large effect of credit shocks on housing risk premium

2. Many households constrained in their housing consumption

• Model features that deliver these outcomes:

1. Short-term debt & no default: housing is very risky

2. No rental market: many households that want to consumemore housing, but can’t

Our Papery��

• Equilibrium model with rental market and long-term mortgages

• Aggregate shocks: income, credit, and beliefs

• Parameterize to cross-sectional and life-cycle facts

• Compare to aggregate time-series on: house prices, rent-price ratio,home ownership, leverage, and foreclosures

• Decompose the role of each shock

• Compare with new micro evidence

• Study transmission of house prices to consumption

• Evaluate debt forgiveness policies

Model: Household and Financial Sectorsy��

• OLG with two phases in lifecycle: work and retirement

• CES utility over ND consumption (1− φ) and housing (φ)

• Idiosyncratic uninsurable earnings shocks y

• Saving in risk-free bonds, exogenous fixed interest rate

• Housing can be bought at ph (sold s.t. transaction cost) or rented at ρ

• Long-term mortgages (to be repaid before death), with cash-out refioption, defaultable, competitively priced by financial intermediaries

• At origination: max LTV and max PTI constraints (λm, λπ) andorigination costs (κm, ζm)

• HELOCs: one-period non defaultable debt (λb)

Model: Production and Governmenty��

Final good sector

• Linear technology in labor with productivity Z → w = Z

Construction sector

• Housing permits + labor → aggregate housing investments I(ph)

Rental sector

• Frictionless conversion of rental units into OO units and viceversa

• Zero-profit condition yields equilibrium rental rate ρ

Government

• Taxes workers (with mortgage interest deduction) and properties,sells land permits, and pays SS benefits to retirees

Lifecycle Profiles of Ownership and Leveragey��

30 40 50 60 70 80Age

0

0.2

0.4

0.6

0.8

1

Home Ownership - ModelHome Ownership - Data

30 40 50 60 70 80Age

0

0.2

0.4

0.6

0.8

1Leverage - ModelLeverage - Data

• Steep rise in home ownership from age 25 to 50

• Home ownership remains flat during retirement

• Sharp decline in leverage over the life cycle

Aggregate Shocksy��

• Aggregate labor income: Z

• Credit conditions: (i) mortgage origination cost (κm, ζm)

(ii) LTV and PTI limits (λm, λπ)

Aggregate Shocksy��

• Aggregate labor income: Z

• Credit conditions: (i) mortgage origination cost (κm, ζm)

(ii) LTV and PTI limits (λm, λπ)

• Beliefs / News about future housing demand

Three regimes for φ (share of housing services in u):

��� φL: low housing share and unlikely transition to φ H

��� φ∗L: low housing share and likely transition to φ H

��� φH: high housing share

Aggregate Shocksy��

• Aggregate labor income: Z

• Credit conditions: (i) mortgage origination cost (κm, ζm)

(ii) LTV and PTI limits (λm, λπ)

• Beliefs / News about future housing demand

Three regimes for φ (share of housing services in u):

��� φL: low housing share and unlikely transition to φ H

��� φ∗L: low housing share and likely transition to φ H

��� φH: high housing share

Boom-Bust: shift from (a) to (b), and back to (a)

Aggregate Shocksy��

• Aggregate labor income: Z

• Credit conditions: (i) mortgage origination cost (κm, ζm)

(ii) LTV and PTI limits (λm, λπ)

• Beliefs / News about future housing demand

Three regimes for φ (share of housing services in u):

��� φL: low housing share and unlikely transition to φ H

��� φ∗L: low housing share and likely transition to φ H

��� φH: high housing share

Boom-Bust: shift from (a) to (b), and back to (a)

• Calibration of news shock: use data on expectations... but residual

Household Expectations in the Modely��

2000 2005 2010 2015Year

0

0.5

1Probability of

H

Household Expectations in the Modely��

2000 2005 2010 2015Year

0

0.5

1Probability of

H

• For boom years, survey evidence in Case-Shiller-Thompson showsUS households expected house price to grow 5-10 pct per year

House Pricesy��

2000 2005 2010 2015Year

0.8

0.9

1

1.1

1.2

1.3

House Price

BenchmarkBeliefIncomeCreditData

House Pricesy��

2000 2005 2010 2015Year

0.8

0.9

1

1.1

1.2

1.3

House Price

BenchmarkBeliefIncomeCreditData

• Belief shock accounts for all boom-bust in house prices

House Pricesy��

2000 2005 2010 2015Year

0.8

0.9

1

1.1

1.2

1.3

House Price

BenchmarkBeliefIncomeCreditData

• Belief shock accounts for all boom-bust in house prices

• Households unconstrained with respect to housing consumption

Rent-Price Ratioy��

2000 2005 2010 2015Year

0.7

0.8

0.9

1

1.1

Rent-Price Ratio

BenchmarkBeliefIncomeCreditData

ρ = ψ + ph −(

1− δh − τh

1 + rb

)Eph

[p′h]

• Belief about future appreciation shared by investment company

Home Ownership Ratey��

2000 2005 2010 2015Year

0.95

1

1.05

1.1Home Ownership

BenchBeliefIncomeCreditData

• Cheap credit drives rise in home ownership

• Households constrained in tenure choice, not housing choice

Explaining the Effects of Credit Shocksy��

• Why looser/tighter credit does not affect housing demand?

� Defaultable long-term debt: housing risk premium is small

� Rental market: buyers are not constrained in housing choice

Explaining the Effects of Credit Shocksy��

• Why looser/tighter credit does not affect housing demand?

� Defaultable long-term debt: housing risk premium is small

� Rental market: buyers are not constrained in housing choice

• Why is rise in home ownership disconnected from house prices?

� Renters buy houses of similar size of those they rented

� It’s the current home owners who upsize and push up demand

Explaining the Effects of Credit Shocksy��

• Why looser/tighter credit does not affect housing demand?

� Defaultable long-term debt: housing risk premium is small

� Rental market: buyers are not constrained in housing choice

• Why is rise in home ownership disconnected from house prices?

� Renters buy houses of similar size of those they rented

� It’s the current home owners who upsize and push up demand

• If hh’s already consume optimal amount of housing, why buy more?

� Housing is both a consumption good and an asset

� Many households buy larger houses to realize expected capital gains

Leverage (debt/house value) y��

2000 2005 2010 2015Year

0.8

1

1.2

1.4

1.6

1.8Leverage

BenchmarkBeliefIncomeCreditData

• Credit loosening is crucial to maintain constant leverage pre-boom

Endogenous Credit Boom Through Beliefsy��

Endogenous Credit Boom Through Beliefsy��

Loan-to-Value Ratio0.4 0.5 0.6 0.7 0.8 0.9 1

Mor

tgag

e R

ate

0.04

0.06

0.08

0.1

0.12

0.14

0.16

shift in lender beliefs

• Lender’s optimistic beliefs → lower expected default rates → lowermortgage rates, especially for subprime borrowers

Foreclosure Rate y��

2000 2005 2010 2015Year

0

0.01

0.02

0.03

0.04Foreclosure rate

BenchmarkBeliefIncomeCreditData

• Interaction between optimistic belief and looser credit

Distribution of Debt and Foreclosuresy��

• Foote et al. (2016) and Adelino et al. (2016): Credit growth during boomuniform across income levels

0.1

.2.3

.4.5

Sha

re o

f Deb

t

1 2 3 4 5

Income Quintile of Household

Shares of Mortgage Debt

2001 2007

Distribution of Debt and Foreclosuresy��

• Foote et al. (2016) and Adelino et al. (2016): Credit growth during boomuniform across income levels

0.1

.2.3

.4.5

Sha

re o

f Deb

t

1 2 3 4 5

Income Quintile of Household

Shares of Mortgage Debt

2001 2007

• Albanesi et al. (2016): Foreclosure rise during bust disporportionately largefor prime borrowers

0.2

.4.6

.81

Sha

re

2006 2008 2010 2012 2014Year

FICO Q1 FICO Q2FICO Q3 FICO Q4

Shares of Foreclosures

Consumptiony��

2000 2005 2010 2015Year

0.95

1

1.05

1.1Consumption

BenchBeliefIncomeCreditData

• House prices explain 1/2 of boom and bust in C (rest is income)

Consumptiony��

2000 2005 2010 2015Year

0.95

1

1.05

1.1Consumption

BenchBeliefIncomeCreditData

−.3

−.2

−.1

0.1

Cha

nge

in L

og C

onsu

mpt

ion

0 .1 .2 .3 .4Housing Share of Total Wealth

Renters Owners

• House prices explain 1/2 of boom and bust in C

• It’s because of a wealth effect, i.e. through household balance sheet

Principal Reduction Programy��

• Forgive excess debt of all homeowners with LTV >95%

Principal Reduction Programy��

• Forgive excess debt of all homeowners with LTV >95%

2000 2005 2010 20150.8

1

1.2

House Price

2000 2005 2010 20150.95

1

1.05

1.1Consumption

2000 2005 2010 2015Year

0

0.01

0.02

0.03

Foreclosure rate

BenchPolicy

2000 2005 2010 2015Year

0.8

1

1.2

1.4

1.6

Leverage

Summary: What Did We Learn from the Model?y��

• Shift in expected house appreciation drives the boom-bust in ph

• Credit important for home ownership, leverage, and foreclosures

• Rental market + long-term mortgages are the key model features

• Micro evidence and aggregate time series agree

• Changes in ph transmit to C through wealth effect

• Principal reduction program would not have cushioned the bust

An Economy where Credit Matters for House Pricesy��

An Economy where Credit Matters for House Pricesy��

Short-term debt + No default + High risk-aversion + No rental market

An Economy where Credit Matters for House Pricesy��

Short-term debt + No default + High risk-aversion + No rental market

2000 2005 2010 2015Year

0.8

0.9

1

1.1

1.2

1.3

House Price

Alt CreditData

2000 2005 2010 2015Year

0.8

1

1.2

1.4

1.6

1.8Leverage

Alt CreditData

• Counterfactual surge in leverage during the boom

Alternative Views of Credit Relaxationy��

Alternative Views of Credit Relaxationy��

2000 2005 2010 2015Year

0.8

1

1.2

1.4House Price

BenchRfARMATMData

2000 2005 2010 2015Year

0.95

1

1.05

1.1Consumption

2000 2005 2010 2015Year

0.9

0.95

1

1.05

1.1Home Ownership

BenchRfARMATMData

2000 2005 2010 2015Year

0.6

0.8

1

1.2

1.4

1.6Leverage

BenchRfARMATMData

• Houses as ATMS: relax and tighten HELOC limit λb

• Adjustable rate mortgages: lower and raise amortization rate rm

• Fall in risk-free rate: reduce rb

Belief Shift or Preference Shift?y��

Belief Shift or Preference Shift?y��

2000 2005 2010 2015Year

0.8

0.9

1

1.1

1.2

1.3

House Price

BenchmarkPreferences OnlyData

2000 2005 2010 2015Year

0.9

0.95

1

1.05

1.1Consumption

• Preference shock generates similar rise in house prices

• But consumption falls

Beliefs about Demand or Supply?y��

• Shock to beliefs about future availability of land permits

Beliefs about Demand or Supply?y��

• Shock to beliefs about future availability of land permits

2000 2010Year

0.8

1

1.2

1.4House Price

2000 2010Year

0.95

1

1.05

1.1Consumption

2000 2010Year

0.4

0.6

0.8

1

1.2Rent Price Ratio

2000 2010Year

0.95

1

1.05

1.1Home Ownership

2000 2010Year

0.8

1

1.2

1.4

1.6Leverage

2000 2010Year

0

0.01

0.02

0.03

0.04

0.05Foreclosure

ModelData

• Actual reduction in land permits leads to fall in housing investment

Own Beliefs vs Other Beliefsy��

Own Beliefs vs Other Beliefsy��

2000 2005 2010 2015Year

0.8

1

1.2

1.4House Price

BenchmarkOther BeliefsOwn BeliefsData

• Strong interaction:

• Own beliefs raise future demand

• Others’ beliefs lead to optimistic house price growth which bringsdemand to present