Greg Kaplan Chicago - Princeton Universityviolante.mycpanel.princeton.edu › Slides › KMV_Edinburgh_fromeps.pdf · Final good sector • Linear technology ... FICO Q1 FICO Q2 FICO

��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



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



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




• 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




• 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)





• 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)










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


• 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


• 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


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





• 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





• 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


• 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


• 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


• 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


−.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��


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


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

Documents

Greg Kaplan Chicago - Princeton Universityviolante.mycpanel.princeton.edu › Slides › KMV_Edinburgh_fromeps.pdf · Final good sector • Linear technology ... FICO Q1 FICO Q2 FICO