The Housing Boom and Bust: Model Meets Evidence Greg Kaplan - - PowerPoint PPT Presentation

• The Housing Boom and Bust:

Model Meets Evidence

Greg Kaplan Chicago Kurt Mitman IIES - Stockholm Gianluca Violante Princeton

The Questiony

Year

1995 2000 2005 2010 2015

Logs (1997:Q1 = 0)

• 0.2
• 0.1

0.1 0.2 0.3

Relative House Price Boom Bust

The Questiony

Year

1995 2000 2005 2010 2015

Logs (1997:Q1 = 0)

• 0.2
• 0.1

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 for

housing 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 for

housing 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, housing

demand 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 for

housing 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, housing

demand 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 consume

more 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 refi
• ption, defaultable, competitively priced by financial intermediaries
• At origination: max LTV and max PTI constraints (λm, λπ) and
• rigination 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 80 Age 0.2 0.4 0.6 0.8 1

Home Ownership - Model Home Ownership - Data 30 40 50 60 70 80

Age

0.2 0.4 0.6 0.8 1 Leverage - Model Leverage - 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):

L: low housing share and likely transition to φ H

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):

L: low housing share and likely transition to φ H

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):

L: low housing share and likely transition to φ H

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 2015

Year

0.5 1 Probability of H

Household Expectations in the Modely

• 2000

2005 2010 2015

Year

0.5 1 Probability of H

• For boom years, survey evidence in Case-Shiller-Thompson shows

US households expected house price to grow 5-10 pct per year

House Pricesy

• 2000

2005 2010 2015

Year

0.8 0.9 1 1.1 1.2 1.3

House Price

Benchmark Belief Income Credit Data

House Pricesy

• 2000

2005 2010 2015

Year

0.8 0.9 1 1.1 1.2 1.3

House Price

Benchmark Belief Income Credit Data

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

House Pricesy

• 2000

2005 2010 2015

Year

0.8 0.9 1 1.1 1.2 1.3

House Price

Benchmark Belief Income Credit Data

• Belief shock accounts for all boom-bust in house prices
• Households unconstrained with respect to housing consumption

Rent-Price Ratioy

• 2000

2005 2010 2015

Year

0.7 0.8 0.9 1 1.1

Rent-Price Ratio

Benchmark Belief Income Credit Data

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 2015

Year

0.95 1 1.05 1.1

Home Ownership Bench Belief Income Credit Data

• 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 2015

Year

0.8 1 1.2 1.4 1.6 1.8

Leverage Benchmark Belief Income Credit Data

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

Endogenous Credit Boom Through Beliefsy

Endogenous Credit Boom Through Beliefsy

Loan-to-Value Ratio

0.4 0.5 0.6 0.7 0.8 0.9 1

Mortgage Rate

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 → lower

mortgage rates, especially for subprime borrowers

Foreclosure Rate y

2000 2005 2010 2015

Year

0.01 0.02 0.03 0.04

Foreclosure rate Benchmark Belief Income Credit Data

• Interaction between optimistic belief and looser credit

Distribution of Debt and Foreclosuresy

• Foote et al. (2016) and Adelino et al. (2016): Credit growth during boom

uniform across income levels

.1 .2 .3 .4 .5

Share of Debt

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 boom

uniform across income levels

.1 .2 .3 .4 .5

Share of Debt

1 2 3 4 5

Income Quintile of Household Shares of Mortgage Debt 2001 2007

• Albanesi et al. (2016): Foreclosure rise during bust disporportionately large

for prime borrowers

.2 .4 .6 .8 1

Share

2006 2008 2010 2012 2014 Year

FICO Q1 FICO Q2 FICO Q3 FICO Q4 Shares of Foreclosures

Consumptiony

• 2000

2005 2010 2015

Year

0.95 1 1.05 1.1

Consumption Bench Belief Income Credit Data

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

Consumptiony

• 2000

2005 2010 2015

Year

0.95 1 1.05 1.1

Consumption Bench Belief Income Credit Data

−.3 −.2 −.1 .1

Change in Log Consumption

.1 .2 .3 .4

Housing 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 2015 0.8 1 1.2 House Price 2000 2005 2010 2015 0.95 1 1.05 1.1 Consumption 2000 2005 2010 2015

Year

0.01 0.02 0.03 Foreclosure rate

Bench Policy

2000 2005 2010 2015

Year

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 2015

Year

0.8 0.9 1 1.1 1.2 1.3

House Price

Alt Credit Data

2000 2005 2010 2015

Year

0.8 1 1.2 1.4 1.6 1.8

Leverage Alt Credit Data

• Counterfactual surge in leverage during the boom

Alternative Views of Credit Relaxationy

Alternative Views of Credit Relaxationy

• 2000

2005 2010 2015

Year

0.8 1 1.2 1.4

House Price

Bench Rf ARM ATM Data

2000 2005 2010 2015

Year

0.95 1 1.05 1.1

Consumption

2000 2005 2010 2015

Year

0.9 0.95 1 1.05 1.1

Home Ownership

Bench Rf ARM ATM Data

2000 2005 2010 2015

Year

0.6 0.8 1 1.2 1.4 1.6

Leverage

Bench Rf ARM ATM Data

• 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 2015

Year

0.8 0.9 1 1.1 1.2 1.3

House Price

Benchmark Preferences Only Data

2000 2005 2010 2015

Year

0.9 0.95 1 1.05 1.1

Consumption

• 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 2010 Year 0.8 1 1.2 1.4

House Price

2000 2010 Year 0.95 1 1.05 1.1

Consumption

2000 2010 Year 0.4 0.6 0.8 1 1.2

Rent Price Ratio

2000 2010 Year 0.95 1 1.05 1.1

Home Ownership

2000 2010 Year 0.8 1 1.2 1.4 1.6

Leverage

2000 2010 Year 0.01 0.02 0.03 0.04 0.05

Foreclosure

Model Data

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

Own Beliefs vs Other Beliefsy

Own Beliefs vs Other Beliefsy

• 2000

2005 2010 2015

Year

0.8 1 1.2 1.4

House Price

Benchmark Other Beliefs Own Beliefs Data

• Strong interaction:
• Own beliefs raise future demand
• Others’ beliefs lead to optimistic house price growth which brings

demand to present