sign in sign up

<<

The Mortgage Credit Channel of Macroeconomic Transmission

The Mortgage Credit Channel of Macroeconomic Transmission

The Mortgage Credit Channel of Macroeconomic Transmission

Daniel L. Greenwald (MIT Sloan)

GCFP Annual Conference

September 29, 2016

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 1 / 19 Introduction

I Motivation: despite importance of mortgage markets, much to learn about core mechanisms connecting credit, house prices, economic activity.

I Main question: if and how mortgage credit issuance ampliﬁes and propagates fundamental shocks. - Mortgage credit channel of transmission.

I Approach: General equilibrium framework centered on two important but largely unstudied features of US mortgage markets: 1. Size of new loans limited by payment-to-income (PTI) constraint, alongside loan-to-value (LTV) constraint. Underwriting 2. Borrowers hold long-term, ﬁxed-rate loans and can choose to prepay existing loans and replace with new ones. Prepay Data

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 2 / 19 Main Findings

Main Finding #1: When calibrated to US mortgage microdata, novel features amplify transmission from interest rates into debt, house prices, economic activity.

I Initial source: PTI limits are highly sensitive to nominal interest rates. - Change by ∼ 10% in response to 1% change in nominal rates.

I Key propagation mechanism: changes in which constraint is binding for borrowers move house prices (constraint switching eﬀect). - Price-rent ratios rise up to 4% after persistent 1% fall in nominal rates. Main Finding #2: PTI liberalization appears essential to boom-bust.

I Changes in LTV standards alone insuﬃcient. PTI liberalization compelling theoretically and empirically.

I Quantitative impact: 38% of observed rise in price-rent ratios, 47% of the rise in debt-household income from PTI relaxation alone.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 3 / 19 Simple Example

I Consider homebuyer who wants large house, minimal down payment. Faces PTI limit of 28%, LTV limit of 80%.

100

80

60

40 Max PTI Price Down Payment 20

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 4 / 19 Simple Example

I At income of $50k per year, 28% PTI limit =⇒ max monthly payment of ∼ $1,200.

100

80

60

40 Max PTI Price Down Payment 20

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 4 / 19 Simple Example

I At 6% interest rate, $1,200 payment =⇒ maximum PTI loan size $160k. Plus 20% down payment =⇒ house price of $200k.

100

80

60

40 Max PTI Price Down Payment 20

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 4 / 19 Simple Example

I Kink in down payment at price $200k. Below this point size of loan limited by LTV, above by PTI. Kink likely optimum for homebuyers.

100

80

60

40 Down Payment Down Payment Max PTI Price 20

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 5 / 19 Simple Example

I Interest rates fall from 6% to 5%. Borrower’s max PTI now limits loan to $178k (rise of 11%). Kink price now $223k, housing demand increases.

100

80

60

40 Down Payment Down Payment Max PTI Price 20

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 6 / 19 Simple Example

I Increasing the maximum PTI ratio from 28% to 31% has a similar eﬀect to fall in rates, increases max loan size and corresponding price.

100

80

60

40 Down Payment Down Payment Max PTI Price 20

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 7 / 19 Simple Example

I In contrast, increasing maximum LTV ratio from 80% to 90% means that $160k loan associated with only $178k house. Housing demand falls.

100

80

60

40 Down Payment Down Payment Max PTI Price 20 Max PTI Loan

0 140 160 180 200 220 240 260 House Price

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 8 / 19 LTV and PTI in the Data

I LTV constraint: balance cannot exceed fraction of house value. - Key property: moves with house prices. - Clear inﬂuence on borrowers: large spikes at institutional limits.

0.6 0.10

0.5 0.08 0.4 0.06 0.3 0.04 0.2

0.1 0.02

0.0 0.00 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80

(a) CLTV Histogram: 2014 Q3 (b) PTI Histogram: 2014 Q3

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 9 / 19 LTV and PTI in the Data

I PTI constraint: payment cannot exceed fraction of income. - Key property: moves with interest rates (elasticity ' 10) - Data consistent with some PTI constrained + search frictions.

0.6 0.10

0.5 0.08 0.4 0.06 0.3 0.04 0.2

0.1 0.02

0.0 0.00 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80

(a) CLTV Histogram: 2014 Q3 (b) PTI Histogram: 2014 Q3

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 9 / 19 LTV and PTI in the Data

I PTI bunching larger in cash-out reﬁnances, where no housing search occurs. - But majority of borrowers probably not PTI constrained.

0.6 0.10

0.5 0.08 0.4 0.06 0.3 0.04 0.2

0.1 0.02

0.0 0.00 50 60 70 80 90 100 110 0 10 20 30 40 50 60 70 80

(a) CLTV Histogram: 2014 Q3 (b) PTI Histogram: 2014 Q3

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 10 / 19 Constraint Switching Eﬀect

I General model includes population heterogeneity. - Fraction of LTV-constrained borrowers (F ltv ) depends on macro state. - LTV-constrained value housing more, willing to pay premium.

I When rates fall, PTI limits loosen. - Borrowers switch from PTI-constrained to LTV-constrained, increasing F ltv . - House prices rise, also loosening LTV limits.

Interest PTI F ltv Rates Limits

LTV House Limits Prices

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 11 / 19 Comparison of Models

I Main Result #1: Strong transmission from interest rates into debt, house prices, economic activity.

I Experiment: consider economies that diﬀer by credit limit and compare response to interest rate movements: 1. LTV Economy: LTV constraint only. 2. PTI Economy: PTI constraint only. 3. Benchmark Economy: Both constraints, applied borrower by borrower.

I Computation: Linearize model to obtain impulse responses.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 12 / 19 Constraint Switching Eﬀect (Inﬂation Target Shock)

I Response to near-permanent -1% (annualized) fall in nominal rates.

IRF to Infl. Target IRF to Infl. Target IRF to Infl. Target 10 4 3 ) l

e 2 2 v LTV e

5 L PTI (

Debt

v Benchmark t l 0 F 1 Price-Rent Ratio

0 0 2 5 10 15 20 5 10 15 20 5 10 15 20 Quarters Quarters Quarters

Exog. Prepay Version TFP IRFs Credit Standards IRFs 43% PTI

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 13 / 19 Constraint Switching Eﬀect (Inﬂation Target Shock)

I Debt response of Benchmark Economy closer to PTI Economy even though most borrowers constrained by LTV (∼ 75% in steady state).

IRF to Infl. Target IRF to Infl. Target IRF to Infl. Target 10 4 3 ) l

e 2 2 v LTV e

5 L PTI (

Debt

v Benchmark t l 0 F 1 Price-Rent Ratio

0 0 2 5 10 15 20 5 10 15 20 5 10 15 20 Quarters Quarters Quarters

Exog. Prepay Version TFP IRFs Credit Standards IRFs 43% PTI

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 13 / 19 Credit Standards and the Boom-Bust

I Main Result #2: PTI liberalization essential to the boom-bust. - So far, have been treating maximum LTV and PTI ratios as ﬁxed, but credit standards can change. - Fannie/Freddie origination data: substantial increase in PTI ratios in boom.

I Experiment: unexpectedly change parameters, unexpectedly return to baseline 32Q later. 1. PTI Liberalization: max PTI ratio from 36% → 54%. 2. LTV Liberalization: max LTV ratio from 85% → 99%.

I Computation: nonlinear transition paths.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 14 / 19 Credit Standards and the Boom-Bust

I Fannie Mae data: PTI constraints appear to bind after bust but not during boom.

0.10 0.10

0.08 0.08

0.06 0.06

0.04 0.04

0.02 0.02

0.00 0.00 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80

(a) PTI Histogram: 2006 Q1 (b) PTI Histogram: 2014 Q3

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 15 / 19 Credit Standards and the Boom-Bust

I Cash-out reﬁ plots even more striking.

0.10 0.10

0.08 0.08

0.06 0.06

0.04 0.04

0.02 0.02

0.00 0.00 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80

(a) PTI Histogram: 2006 Q1 (b) PTI Histogram: 2014 Q3

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 16 / 19 Credit Standards and the Boom-Bust

I Main Result #2: PTI liberalization essential to the boom-bust. - So far, have been treating maximum LTV and PTI ratios as ﬁxed, but credit standards can change. - Fannie/Freddie origination data: substantial increase in PTI ratios in boom.

I Experiment: unexpectedly change parameters, unexpectedly return to baseline 32Q later. 1. PTI Liberalization: max PTI ratio from 36% → 54%. 2. LTV Liberalization: max LTV ratio from 85% → 99%.

I Computation: nonlinear transition paths.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 17 / 19 Credit Liberalization Experiment

I LTV Liberalization generates small rise in debt-to-household income (19%). House prices, price-rent ratios fall (-2%).

30 25 100 LTV Liberalized PTI Liberalized 20 20 90 ) l

15 e v e

10 L 80 (

Debt v t

10 l F

Price-Rent Ratio 0 70 5

10 0 60 0 20 40 0 20 40 0 20 40 Quarters Quarters Quarters

Data More Series LTV Intuition PTI Intuition Preference Shocks

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 18 / 19 Credit Liberalization Experiment

I PTI Liberalization generates large boom in house prices, price-rent ratios (38%), debt-household income (47%).

30 25 100 LTV Liberalized PTI Liberalized 20 20 90 ) l

15 e v e

10 L 80 (

Debt v t

10 l F

Price-Rent Ratio 0 70 5

10 0 60 0 20 40 0 20 40 0 20 40 Quarters Quarters Quarters

Data More Series LTV Intuition PTI Intuition Preference Shocks

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 18 / 19 Credit Liberalization Experiment

I Macroprudential policy: cap on PTI ratios more eﬀective at limiting boom-bust cycles.

30 25 100 LTV Liberalized PTI Liberalized 20 20 90 ) l

15 e v e

10 L 80 (

Debt v t

10 l F

Price-Rent Ratio 0 70 5

10 0 60 0 20 40 0 20 40 0 20 40 Quarters Quarters Quarters

Data More Series LTV Intuition PTI Intuition Preference Shocks

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 18 / 19 Conclusion

I Macro model with two novel features: - Payment-to-income constraint. - Endogenous prepayment of long-term debt.

I Novel transmission channel from interest rates into credit, house prices, economic activity. - Credit, house prices through constraint switching effect. - Amplification into output through endogenous prepayment (see paper). - Monetary policy more effective, but may pose tradeoff (see paper).

I PTI liberalization appears essential to boom-bust. - Cap on PTI ratios, not LTV ratios more eﬀective macroprudential policy.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 19 / 19 APPENDIX

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 1 / 61 Literature Review

I Empirical Work: Adelino, Schoar, Severino (2015a, 2015b), Aladangady (2014), Anderson, Campbell, Nielsen, Ramadorai (2014), Di Maggio, Kermani (2015), Keys, Pope, Pope (2014), Mian, Suﬁ (2008, 2014). Here: Income-based lending, behavioral prepayment in general equilibrium.

I Heterogeneous Agent Models: Campbell, Cocco (2015), Chatterjee, Eyigungor (2015), Chen, Michaux, Roussanov (2013), Corbae, Quintin (2013), Elenev, Landvoigt, Van Nieuwerburgh (2015), Gorea, Midrigan (2015), Guler (2014), Hedlund (2013), Garriga, Hedlund (2016), Kaplan, Mitman, Violante (2016), Kaplan, Violante (2014), Khandani, Lo, Merton (2013), Landvoigt (2015), Laufer (2013), Wong (2015). Here: Embed into monetary DSGE, transmission through PTI.

I Monetary DSGE Models: Eggertsson, Krugman (2012), Garriga, Kydland, Sustek (2015), Kiyotaki, Moore (1997), Iacoviello (2005), Iacoviello, Neri (2011), Liu, Wang, Zha (2013), Monacelli (2008). Here: Realistic mortgage structure, transmission through PTI.

I Credit Standards and the Boom-Bust: Campbell, Hercowitz (2005), Favilukis, Ludvigson, Van Nieuwerburgh (2015), Iacoviello, Pavan (2013), Kermani (2015), Justiniano, Primiceri, Tambalotti (2015). Here: PTI liberalization critical to boom-bust.

I Redistribution Channel: Auclert (2015), Calza, Monacelli, Stracca (2013), Rubio (2011). Here: Transmission through credit growth, not mortgage payments.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 2 / 61 Model Overview

I Borrowing= ⇒ impatient borrowers/patient savers.

- Permanent types with ﬁxed measure χj for j ∈ {b, s}. - Preferences: (n /χ )1+ϕ V = log(c /χ ) + ξ log(h /χ ) − η j,t j + β V j,t j,t j j,t j 1 + ϕ j Et j,t+1

I Mortgage debt= ⇒ durable housing. - Divisible, cannot change stock without prepaying mortgage. - Fixed housing stock, saver housing demand.

I Realistic mortgage contracts= ⇒ long-term ﬁxed-rate bonds

- Endogenous fraction ρt prepay each period, update balance and interest rate. ∗ I Movements in long rates= ⇒ Taylor rule, shock to inﬂation target πt . - Any shock to real rates or term premia should activate channel.

I Eﬀects on real economy= ⇒ labor supply, sticky prices, TFP shocks.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 3 / 61 Representative Borrower’s Problem

I State variables: average principal balance mt−1, mortgage payment payt−1, housing stock hb,t−1.

I Control variables: nondurable consumption cb,t , labor supply nb,t , ∗ ∗ prepayment rate ρt , size of new houses hb,t , size of new loans mt .

I Budget constraint:

∗ −1 −1 cb,t ≤ ρt mt − (1 − ν)πt mt−1 + wt nb,t − πt payt−1 | {z } new issuance h ∗ h ∗ − ρt pt hb,t − hb,t−1 − δpt hb,t−1 − Cost(ρt ) − Rebatet mt .

I Credit constraint: Z ∗ ltv pti mt ≤ min m¯ i,t , m¯ i,t dΓ(incomei ).

Agg. LOM Borr. Optimality Saver’s Problem Eqm. Defn. Monetary Policy Prod. Tech.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 4 / 61 Representative Borrower’s Housing Decision

I Housing optimality condition (unconstrained or no LTV):

h c n h o ub,t /ub,t + (1 − δ)Et Λb,t+1pt+1 ph = t 1

I Λb,t+1 is borrower stochastic discount factor, µt is multiplier on credit constraint.

I Ct (“collateral value”) is marginal value of relaxing constraint via extra $1 of house value:

ltv ltv Ct ≡ µt Ft θ

h I Long-term debt: pt includes value of collateralizing a new loan, but probability 1 − ρt+1 will not prepay.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 5 / 61 Representative Borrower’s Housing Decision

I Housing optimality condition (one-period debt, LTV only):

h c n h o u /u + (1 − δ) t Λb,t+1pt+1 h b,t b,t E pt = ltv 1−µt θ

I Λb,t+1 is borrower stochastic discount factor, µt is multiplier on credit constraint.

I Ct (“collateral value”) is marginal value of relaxing constraint via extra $1 of house value:

ltv ltv Ct ≡ µt Ft θ

h I Long-term debt: pt includes value of collateralizing a new loan, but probability 1 − ρt+1 will not prepay.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 5 / 61 Representative Borrower’s Housing Decision

I Housing optimality condition (one-period debt, LTV and PTI):

h c n h o u /u + (1 − δ) t Λb,t+1pt+1 h b,t b,t E pt = 1−Ct

I Λb,t+1 is borrower stochastic discount factor, µt is multiplier on credit constraint.

I Ct (“collateral value”) is marginal value of relaxing constraint via extra $1 of house value:

ltv ltv Ct ≡ µt Ft θ

h I Long-term debt: pt includes value of collateralizing a new loan, but probability 1 − ρt+1 will not prepay.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 5 / 61 Representative Borrower’s Housing Decision

I Housing optimality condition (Benchmark model):

h c n h h io u /u + (1 − δ) t Λb,t+1pt+1 1 − (1 − ρt+1)Ct+1 h b,t b,t E pt = 1−Ct

I Λb,t+1 is borrower stochastic discount factor, µt is multiplier on credit constraint.

I Ct (“collateral value”) is marginal value of relaxing constraint via extra $1 of house value:

ltv ltv Ct ≡ µt Ft θ

h I Long-term debt: pt includes value of collateralizing a new loan, but probability 1 − ρt+1 will not prepay.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 5 / 61 Demographics and Preferences

I Two types of inﬁnitely lived agents:

I Family of borrowers( b) with measure χb. I Family of savers( s) with measure χs = 1 − χb.

I Both types provide labor: nt = nb,t + ns,t .

I Complete set of contracts over consumption and housing services traded within each family, but not across families.

I Separable, expected utility preferences over consumption, housing services, and labor supply (for j ∈ {b, s}):

(n /χ )1+ϕ V = log(c /χ ) + ξ log(h /χ ) − η j,t j + β V j,t j,t j j,t j 1 + ϕ j Et j,t+1

I Borrowers are more impatient than savers: βb < βs . - Motivation to borrow.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 6 / 61 Asset Technology

Housing:

I Divisible, owned by both types, requires maintenance cost.

I Cannot change housing stock without prepaying mortgage.

I Fixed housing stock H¯ , saver demand H¯s . - Total collateral value, not price, crucial to constraints. - Price eﬀects are upper bound. One-Period Bonds

I Nominal risk-free bond in zero net supply with rate Rt .

I No short positions/borrowing in one-period bond =⇒ traded by savers only in equilibrium.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 7 / 61 Asset Technology

Mortgages:

I Only source of borrowing in the economy.

I Long-term nominal bonds with fixed interest rates. - See paper for adjustable-rate version. ∗ I Originated with principal balance mt , borrower repays fraction ν of principal each period. ∗ I Contract specifies fixed coupon rate qt (interest + principal), saver receives

k ∗ ∗ $(1 − ν) qt mt

at all t + k until prepayment.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 8 / 61 Demographics and Preferences

I Two types of inﬁnitely lived agents:

I Family of borrowers( b) with measure χb. I Family of savers( s) with measure χs = 1 − χb.

I Both types provide labor: nt = nb,t + ns,t .

I Complete set of contracts over consumption and housing services traded within each family, but not across families.

I Separable, expected utility preferences over consumption, housing services, and labor supply (for j ∈ {b, s}):

(n /χ )1+ϕ V = log(c /χ ) + ξ log(h /χ ) − η j,t j + β V j,t j,t j j,t j 1 + ϕ j Et j,t+1

I Borrowers are more impatient than savers: βb < βs . - Motivation to borrow.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 9 / 61 Asset Technology

Housing:

I Divisible, owned by both types, requires maintenance cost.

I Cannot change housing stock without prepaying mortgage.

I Fixed housing stock H¯ , saver demand H¯s . - Total collateral value, not price, crucial to constraints. - Price eﬀects are upper bound. One-Period Bonds

I Nominal risk-free bond in zero net supply with rate Rt .

I No short positions/borrowing in one-period bond =⇒ traded by savers only in equilibrium.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 10 / 61 Asset Technology

Mortgages:

I Only source of borrowing in the economy.

I Long-term nominal bonds with fixed interest rates. - See paper for adjustable-rate version. ∗ I Originated with principal balance mt , borrower repays fraction ν of principal each period. ∗ I Contract specifies fixed coupon rate qt (interest + principal), saver receives

k ∗ ∗ $(1 − ν) qt mt

at all t + k until prepayment.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 11 / 61 Idiosyncratic Heterogeneity

1. Income shocks: An endogenous fraction of borrowers (those with low enough income draws) are constrained by PTI, the rest by LTV. - Equivalent to any shock that creates dispersion in house value-to-income ratio. Details PTI by Income - Eﬀect: smooth out constraint, dampen mechanism. 2. Prepayment cost shocks: An endogenous fraction of borrowers (those with low enough costs) prepay their loans. - Simplifying assumption: borrower must choose whether to prepay based only on aggregate state. Details Redistribution Eﬀects - Can still respond to: average existing rate vs. new rate, total extractable equity, forward looking expectations.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 12 / 61 Monetary Policy

I Monetary policy follows a Taylor rule with time-varying inﬂation target.

log Rt = logπ ¯t + φr (log Rt−1 − logπ ¯t−1) h real i + (1 − φr ) log R¯ + ψπ(log πt − logπ ¯t )

for

ss logπ ¯t = (1 − φπ¯ ) log π + φπ¯ logπ ¯t−1 + επ,¯ t .

I Why consider near-permanent policy shocks? - ‘‘Level factor” shocks needed to move long-term nominal rates. - But movements in term premia would also be ampliﬁed. - With ARMs, ampliﬁcation of transitory monetary policy shocks.

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 13 / 61 Productive Technology

I Embed in simple New Keynesian environment (e.g., Gali (2008)).

I Intermediate goods producers operate the linear production function

yt (i) = at nt (i)

where at is productivity, and nt (i) are labor hours.

I TFP process at :

log at+1 = φa log at + εa,t+1.

I Monopolistic intermediate producers with Calvo price rigidity (can’t reset price with probability ζp). Back Details

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 14 / 61 Calibration (Quarterly)

Parameter Name Value Internal Target/Source Fraction of borrowers χ 0.35 N 2001 SCF h Borr. housing preference ξ 0.253Y p hb/wbnb = 8.68 (2001 SCF) St. Dev. Incomes σe 0.411 N Fannie Mae Loan-Level Data Issuance cost mean µκ 0.188 Y Avg. FRM prepayment 1994-2015 Issuance cost scale sκ 0.0330 Y Fannie Mae MBS Data Max PTI Ratio θpti 0.28N Max LTV Ratio θltv 0.85N Saver discount factor βs 0.993 Y Real rate = 3% Borrower discount factor βb 0.95 N Mortgage amortization ν 1/120 N 30-year duration Taylor rule (inflation) ψπ 1.5 N Taylor rule (output) ψy 0 N Taylor rule (smoothing) φr 0.89 N Campbell et al (2014) Trend infl (pers.) φπ¯ 0.994 N Garriga et al. (2013) TFP (pers.) φa 0.9641 N Garriga et al. (2013) PTI Ratio Offset τ 0.0375 / 4 N 200bp + Taxes, Ins.

Other Params. SCF Income Shocks Issuance Costs

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 15 / 61 Frontloading Eﬀect

I PTI constraints deliver transmission into credit, house prices, but need endogenous prepayment for transmission into output.

I Credit issuance can increase demand, but only aﬀects output through sticky prices if occurs in short run.

I Without endogenous prepayment, debt limits increase, but few borrowers take advantage right away =⇒ slow issuance.

I With endogenous prepayment, get issuance wave when rates fall, frontloaded spending aﬀects output.

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 16 / 61 Frontloading Eﬀect (TFP Shock)

I TFP shock lowers nominal rates (deﬂationary) and raises labor income =⇒ loosens PTI limits.

IRF to TFP IRF to TFP 1.5 0.3

1.0 0.2

0.5 0.1

Avg. Debt Limit 0.0 0.0 New Issuance (Level) 5 10 15 20 5 10 15 20 LTV (Exog Prepay) 1.5 Benchmark (Exog Prepay) Benchmark 1.0 0.5 0.5 Output

0.0

0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Quarters Quarters π∗ IRFs 43% PTI

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 17 / 61 Frontloading Eﬀect (TFP Shock)

I Eﬀects large: output response to 1% TFP shock increased by 46% (0.52 to 0.76) on impact.

IRF to TFP IRF to TFP 1.5 0.3

1.0 0.2

0.5 0.1

Avg. Debt Limit 0.0 0.0 New Issuance (Level) 5 10 15 20 5 10 15 20 LTV (Exog Prepay) 1.5 Benchmark (Exog Prepay) Benchmark 1.0 0.5 0.5 Output

0.0

0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Quarters Quarters π∗ IRFs 43% PTI

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 17 / 61 Inﬂation Stabilization (TFP Shock)

I Monetary policy experiment: how much does central bank need to move policy rate to fully stabilize inﬂation, πt =π ¯?

IRF to TFP IRF to TFP 0.00 1.0

0.01

t 0.5 R 0.02 Debt

0.03 0.0 5 10 15 20 5 10 15 20 LTV (Exog Prepay) 1.0 1.0 Benchmark

0.5 0.5 Output

0.0 0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 18 / 61 Inﬂation Stabilization (TFP Shock)

I Monetary policy “stronger” under Benchmark model: smaller movement in policy rate required to stabilize.

IRF to TFP IRF to TFP 0.00 1.0

0.01

t 0.5 R 0.02 Debt

0.03 0.0 5 10 15 20 5 10 15 20 LTV (Exog Prepay) 1.0 1.0 Benchmark

0.5 0.5 Output

0.0 0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 18 / 61 Inﬂation Stabilization (TFP Shock)

I But smaller movement in policy rate comes with larger movement in debt. Potential trade-oﬀ for policymakers.

IRF to TFP IRF to TFP 0.00 1.0

0.01

t 0.5 R 0.02 Debt

0.03 0.0 5 10 15 20 5 10 15 20 LTV (Exog Prepay) 1.0 1.0 Benchmark

0.5 0.5 Output

0.0 0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 18 / 61 Credit Liberalization Experiment

I Experiment liberalizing both LTV and PTI accounts for more than 92% of rise in debt-household income (but not much more of price-rent).

30 50

40 20 30

Debt 20 10 10 Price-Rent Ratio

0 0 0 20 40 0 20 40

95 25 Both Liberalized PTI Liberalized 90 20 ) l e

v 85 15 e L (

v 80 10 t l F 75 5 Prepay Rate (Level) 70 0 0 20 40 0 20 40 Quarters Quarters

Data More Series Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 19 / 61 Macroprudential Policy: Dodd-Frank Limit

ltv pti I Counterfactual with Dodd-Frank cap, (θ , θ ) → (0.99, 0.35) substantially dampens cycle, cuts price-rent ratio rise by 63%.

30 50

40 20 30

Debt 20 10 10 Price-Rent Ratio

0 0 0 20 40 0 20 40

85 25 Both Liberalized Dodd-Frank 20 80 ) l e

v 15 e

L 75 (

v 10 t l

F 70 5 Prepay Rate (Level) 65 0 0 20 40 0 20 40 Quarters Quarters Data More Series

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 20 / 61 Intensive Margin: Credit Constraints

I Actual 2015 underwriting standards from Fannie Mae (“DTI” = PTI).

Standard Eligibility Requirements - Manual Underwriting Excludes: Refi Plus, HomeStyle Renovation, and HomeReady

Maximum DTI ≤ 36% Maximum DTI ≤ 45% Transaction Number of Maximum Minimum Minimum Credit Score/LTV Credit Score/LTV Type Units LTV, CLTV, HCLTV Reserves Reserves Principal Residence FRM: 680 if > 75% FRM: 620 if ≤ 75% 700 if > 75% 0 0 ARM: 680 if > 75% 640 if ≤ 75% FRM: 95% 1 Unit ARM: 640 if ≤ 75% ARM: 90% FRM: 680 if > 75% 660 if > 75% 6 FRM: 620 if ≤ 75% 2 Purchase ARM: 680 if > 75% Limited Cash- 700 if > 75% Out Refinance 6 FRM: 85% 680 if > 75% 660 if ≤ 75% 2 Units 6 ARM: 75% 640 if ≤ 75% 680 if > 75% 12 640 if ≤ 75% FRM: 75% 680 6 3-4 Units 660 6 ARM: 65% 660 12 680 if > 75% 700 if > 75% 0 0 FRM: 80% 660 if ≤ 75% 680 if ≤ 75% 1 Unit ARM: 75% 660 if > 75% 680 if > 75% BackCash-Out to Intro Back to Credit Limits 6 2 Refinance 640 if ≤ 75% 660 if ≤ 75% Daniel L. Greenwald (MIT Sloan) FRM: 75%The Mortgage Credit Channel September700 29, 20166 21 / 61 2-4 Units 680 6 ARM: 65% 680 12 Second Home 700 if > 75% Purchase 2 FRM: 90% 680 if > 75% 660 if ≤ 75% Limited Cash- 1 Unit 2 ARM: 80% 640 if ≤ 75% 680 if > 75% Out Refinance 12 640 if ≤ 75% Cash-Out FRM: 75% 700 2 1 Unit 680 2 Refinance ARM: 65% 680 12 Investment Property 700 if > 75% 6 FRM: 85% 680 if > 75% 660 if ≤ 75% 1 Unit 6 ARM: 75% 640 if ≤ 75% 680 if > 75% Purchase 12 640 if ≤ 75% FRM: 75% 680 6 2-4 Units 660 6 ARM: 65% 660 12 FRM: 75% 680 6 1 Unit 660 6 ARM: 65% 660 12 Limited Cash- 700 6 Out Refinance FRM: 75% 2-4 Units 680 6 ARM: 65% 680 12 FRM: 75% 720 6 1 Unit 700 6 ARM: 65% 700 12 Cash-Out Refinance FRM: 70% 720 6 2-4 Units 700 6 ARM: 60% 700 12 NOTE: THERE MAY BE EXCEPTIONS TO THE ABOVE REQUIREMENTS FOR CERTAIN TRANSACTIONS. REFER TO THE NOTES SECTION ON PAGE 7 FOR THE EXCEPTIONS.

© 2015 Fannie Mae. Trademarks of Fannie Mae. September 29, 2015 This document is incorporated by reference into the Fannie Mae Selling Guide. 4 Loan Level Price Adjustments

I PTI not priced, strictly a limit.

Table 1: All Eligible Mortgages (excluding MCM) – LLPA by Credit Score/LTV Ratio

LTV Range Applicable for all mortgages with terms greater than 15 years Representative 60.01 – 70.01 – 75.01 – 80.01 – 85.01 – 90.01 – 95.01 – Credit Score < 60.00% SFC 70.00% 75.00% 80.00% 85.00% 90.00% 95.00% 97.00% ≥ 740 0.000% 0.250% 0.250% 0.500% 0.250% 0.250% 0.250% 0.750% N/A 720 – 739 0.000% 0.250% 0.500% 0.750% 0.500% 0.500% 0.500% 1.000% N/A 700 – 719 0.000% 0.500% 1.000% 1.250% 1.000% 1.000% 1.000% 1.500% N/A 680 – 699 0.000% 0.500% 1.250% 1.750% 1.500% 1.250% 1.250% 1.500% N/A 660 – 679 0.000% 1.000% 2.250% 2.750% 2.750% 2.250% 2.250% 2.250% N/A 640 – 659 0.500% 1.250% 2.750% 3.000% 3.250% 2.750% 2.750% 2.750% N/A 620 – 639 0.500% 1.500% 3.000% 3.000% 3.250% 3.250% 3.250% 3.500% N/A < 620 (1) 0.500% 1.500% 3.000% 3.000% 3.250% 3.250% 3.250% 3.750% N/A (1) A minimum required credit score of 620 applies to all mortgage loans delivered to Fannie Mae in accordance with the Selling Guide; exceptions to this requirement are limited to loans in which all borrowers have nontraditional credit.

Back to Intro Back to Credit Limits Table 2: All Eligible Mortgages (excluding MCM unless otherwise noted) – LLPA by Product Feature

LTV Range PRODUCT FEATURE 60.01 – 70.01 – 75.01 – 80.01 – 85.01 – 90.01 – 95.01 – < 60.00% SFC 70.00% 75.00% 80.00% 85.00% 90.00% 95.00% 97.00% ManufacturedDaniel L. Greenwald home (MIT0.500% Sloan) 0.500% 0.500%TheMortgage 0.500% Credit0.500% Channel 0.500% 0.500% SeptemberN/A 29, 2016235 22 / 61 Investment property 2.125% 2.125% 2.125% 3.375% 4.125% N/A N/A N/A N/A

Investment property – matured balloon mortgages 1.750% 236 (refinanced or modified) redelivered as FRM

© 2015 Fannie Mae. Trademarks of Fannie Mae. 2 09.29.2015 The Matrix supersedes any earlier dated version of the Matrix. Prepayment Rates

I Fraction prepaying small, but volatile and highly responsive to interest rate incentives.

0.8 2 Prepayment Rate Rate Incentive

0.6 1

0.4 0

0.2 -1

0 -2 1995 2000 2005 2010 2015 Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 23 / 61 Subprime PTIs

Reducing Foreclosures 103 I Plot from Foote, Gerardi, Willen (2009) shows subprime PTIs bunch at 50 and 55.

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 24 / 61

Fig. 1. Loan-specific characteristics in LPS sample for DTI, FICO, and LTV, each loan in the sample contributes a number of monthly observations to each of these two figures. Figure 2 shows that the distribution of price changes is skewed toward positive changes. In part, this reflects the large number of loans originated in the early years of the sample (2005 and 2006), when house prices were rising. In our empirical work, we allow positive price changes to have different effects than negative price changes.19 Finally, we also include a number of interactions among risk characteristics and macro variables. These interactions play an important role given the strong functional form assumption embedded in the propor- tional hazard model. Denote hðtjxjÞ as the hazard rate for either a

This content downloaded from 66.251.73.4 on Thu, 25 Apr 2013 13:13:10 PM All use subject to JSTOR Terms and Conditions LTV and PTI in the Data

I Individual borrower’s process: pti 1. Given income, interest rates, compute max loan sizem ¯ i,t . 2. Given max loan size, compute min house price associated with this h¯ pti ltv loan: pt hi,t =m ¯ i,t /θt .

3. Search for house such that hi,t ≤ h¯i,t . 4. Obtain largest possible loan given house value:

∗ ltv ltv h ltv h¯ pti mi,t =m ¯ i,t = θt pt hi,t < θt pt hi,t =m ¯ i,t .

I Result: LTV exactly at limit, PTI slightly below.

I Why asymmetry? Can choose house price, not income/rates.

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 25 / 61 Income Shocks

I Want heterogeneity so that endogenous fraction are constrained by PTI. iid I Idiosyncratic labor eﬃciency shocks ei,t ∼ Γe , so individual borrower’s income is incomei,t = wt nb,t ei,t .

I Shocks aﬀect only credit limits, not consumption or labor supply (due to insurance, timing). - Equivalent to any shock causing variation in house price/income ratios.

I PTI binds for ltv h θ pt ht ei,t ≤ e¯t ≡ pti ∗ . θ wt nb,t /(qt + τ)

I Fraction constrained by LTV:

ltv Ft = 1 − Γe (¯et ).

Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 26 / 61 PTI by Income

I PTI appear more binding for low income. High (low) income is top (bottom) quartile.

Fannie Mae: PTI Ratio, Low Income Buyers Fannie Mae: PTI Ratio, High Income Buyers 0.10 0.10

0.08 0.08

0.06 0.06

0.04 0.04

0.02 0.02

0.00 0.00 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 PTI Ratio (%) PTI Ratio (%) (a) PTIs: 2014 Q3 (Low Income) (b) PTIs: 2014 Q3 (High Income)

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 27 / 61 PTI by Income

I Very high PTIs for low-income borrowers at height of boom.

Fannie Mae: PTI Ratio, Low Income Buyers Fannie Mae: PTI Ratio, High Income Buyers 0.10 0.10

0.08 0.08

0.06 0.06

0.04 0.04

0.02 0.02

0.00 0.00 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 PTI Ratio (%) PTI Ratio (%) (a) PTIs: 2006 Q1 (Low Income) (b) PTIs: 2006 Q1 (High Income)

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 28 / 61 CLTV by Income

I In contrast, CLTVs look very similar across income groups during boom and bust.

Fannie Mae: CLTV Ratio, Low Income Buyers Fannie Mae: CLTV Ratio, High Income Buyers 0.6 0.6

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

0.0 0.0 50 60 70 80 90 100 110 50 60 70 80 90 100 110 CLTV Ratio (%) CLTV Ratio (%) (a) CLTVs: 2014 Q3 (Low Income) (b) CLTVs: 2014 Q3 (High Income)

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 29 / 61 CLTV by Income

I In contrast, CLTVs look very similar across income groups during boom and bust.

Fannie Mae: CLTV Ratio, Low Income Buyers Fannie Mae: CLTV Ratio, High Income Buyers 0.6 0.6

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1

0.0 0.0 50 60 70 80 90 100 110 50 60 70 80 90 100 110 CLTV Ratio (%) CLTV Ratio (%) (a) CLTVs: 2006 Q1 (Low Income) (b) CLTVs: 2006 Q1 (High Income)

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 30 / 61 Prepayment

I Prepayment: - Borrower pays remaining principal to lender, cancels future payments. - Borrower can immediately take out new loan, adjust housing holdings.

I Transaction cost shocks: ∗ iid - Borrower must pay cost κi,t mt , to obtain a new loan where κi,t ∼ Γκ.

- If κi,t ≤ κ¯i,t , then the borrower executes transaction, prepays.

I Timing within the period:

1. Borrowers choose labor supply nb,t , threshold transaction costκ ¯t , ∗ target house size ht (conditional on prepaying).

2. Borrowers draw κi,t , prepay if κi,t ≤ κ¯t . ∗ ltv pti 3. Borrowers draw ei,t , obtain new loan of size mi,t = min(m ¯ i,t , m¯ i,t ). 4. Insurance claims are paid out, equalizing consumption across borrowers.

Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 31 / 61 Credit or Redistribution?

I Prepayment has two eﬀects: - Allows borrower to obtain new debt (credit channel). - Changes payments on existing debt (redistribution channel).

I Unlike previous work (Rubio (2011), Calza et al. (2013), Auclert (2015)), this framework can generate large redistributions in ﬁxed-rate mortgage environment from prepayment.

I However, impact on aggregate demand is very small.

I Key is persistence of transfers. - Impatient borrower consumes out of current income, while patient saver consumes out of permanent income. - But with FRMs, prepayment leads to constant change in payments each month for decades. - Changes in current and permanent income nearly identical =⇒ oﬀsetting consumption responses.

Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 32 / 61 Aggregation

I Aggregate laws of motion:

∗ −1 mt = ρt mt + (1 − ρt )(1 − ν)πt mt−1 ∗ ∗ −1 payt = ρt qt mt + (1 − ρt )(1 − ν)πt payt−1 ∗ hb,t = ρt hb,t + (1 − ρt )hb,t−1.

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 33 / 61 Borrower Optimality

I Labor supply (nb,t ) condition: n ub,t wt = − c . ub,t

∗ I New loan size (mt ) condition: m ∗ pay 1 = Ωb,t + qt Ωb,t + µt

m pay where µt is multiplier, Ωb,t and Ωb,t are marginal continuation costs of extra unit of face value debt and promised payments:

m n $ h m io Ωb,t = Et Λb,t+1 (1 − ν)ρt+1 + (1 − ν)(1 − ρt+1)Ωb,t+1

pay n $ h pay io Ωb,t = Et Λb,t+1 1 + (1 − ν)(1 − ρt+1)Ωb,t+1

$ and Λb,t+1 is the nominal SDF.

Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 34 / 61 Borrower Optimality

I Prepayment optimality condition: ∗ −1 m ∗ −1 ρt = Γ (mt ) (1 − Ωb,t ) mt − (1 − ν)πt mt−1 | {z } new debt pay ∗ ∗ −1 − Ωb,t qt mt − (1 − ν)πt payt−1 | {z } new payments h ∗ − Ct pt hb,t − (1 − δ)hb,t−1 . | {z } cost of collateral m pay I Ωb,t and Ωb,t are the marginal costs of extra unit of principal balance and promised payment:

m n $ h m io Ωb,t = Et Λb,t+1 (1 − ν)ρt+1 + (1 − ν)(1 − ρt+1)Ωb,t+1

pay n $ h pay io Ωb,t = Et Λb,t+1 1 + (1 − ν)(1 − ρt+1)Ωb,t+1 .

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 35 / 61 Saver’s Problem

I Budget constraint: ∗ −1 −1 cs,t ≤ Πt + wt ns,t − ρt (mt − (1 − ν)πt mt−1) + πt payt−1 | {z } New Issuance h −1 − pt (hs,t − (1 − δ)hs,t−1) − Rt bt + bt−1.

I Optimality conditions:

h $ i (b) : 1 = Rt Et Λs,t+1 ∗ m pay ∗ (m ) : 1 = Ωs,t + Ωs,t qt m pay I Ωs,t and Ωs,t are the marginal beneﬁts of extra unit of principal balance and promised payment:

m n $ h m io Ωs,t = Et Λs,t+1 (1 − ν)ρt+1 + (1 − ν)(1 − ρt+1)Ωs,t+1

pay n $ h pay io Ωs,t = Et Λs,t+1 1 + (1 − ν)(1 − ρt+1)Ωs,t+1 .

Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 36 / 61 Equilibrium Deﬁnition

A competitive equilibrium in this model is deﬁned as a sequence of endogenous states (mt−1, qt−1, hb,t−1, hs,t−1), allocations (cj,t , nj,t , hj,t ), mortgage market ∗ h ∗ quantities (mt , ρt ), and prices (πt , wt , pt , Rt , qt ) such that: ∗ ∗ 1. Given prices, (cb,t , nb,t , hb,t , mt , ρt ) solve the borrower’s problem. ∗ 2. Given prices and borrower reﬁnancing behavior, (cs,t , ns,t , hs,t , mt ) solve the saver’s problem.

3. Given wages and consumer demand, πt is the outcome of the intermediate ﬁrm’s optimization problem.

4. Given inﬂation and output, Rt satisﬁes the monetary policy rule. 5. The resource, bond, and housing markets clear:

h yt = cb,t + cs,t + xt , bs,t = 0

ht = H¯ , hs,t = H¯s .

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 37 / 61 Calvo Pricing

Solution to intermediate ﬁrm’s problem:

λ Z λ−1 λ−1 at nt yt = yt (i) λ di = ∆t mc π λ N = y t + ζ Λ t+1 N t t mcss pEt s,t+1 πss t+1 π λ−1 D = y + ζ Λ t+1 D t t pEt s,t+1 πss t+1

Nt p˜t = Dt 1 1−λ λ−1 ss 1 − (1 − ζp)˜p πt = π ζp −λ ss λ ∆t = (1 − ζp)˜p + ζp(πt /π ) ∆t−1

where Nt and Dt are auxiliary variables,p ˜t is the ratio of the optimal price for resetting ﬁrms relative to the average price, and ∆t is price dispersion. Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 38 / 61 Calibration (Quarterly)

Parameter Name Value Internal Target/Source Steady state inflation πss 1.0075 N Avg. infl. = 3% Variety elasticity λ 6.0 N Inv. Frisch Elasticity ϕ 1.0 N Disutility of Labor η 7.935 Y n = 1/3 Price stickiness ζ 0.75 N Average productivity µa 1.098 Y y = 1 Trend infl (std.) σπ¯ 0.0015 N Garriga et al. (2013) TFP (std.) σa 0.0082 N Garriga et al. (2013)

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 39 / 61 Calibration: Fraction of Borrowers

I Calibrate borrower/saver division to match 2001 Survey of Consumer Finances (SCF).

I Borrowers in the model: have house and mortgage but no liquid assets, save in home equity. - Match to households in 2001 SCF with less than one month’s income in liquid assets (Kaplan and Violante (2014)) with a mortgage (24.3%). - Use housing preference ξ to match housing wealth / income for borrowers.

I Savers in the model: unconstrained agents with liquid assets. - Match to households in 2001 SCF with more than one month’s income in liquid assets (45.4%).

I Remove households with no liquid assets and no mortgage (mostly renters) who are not represented in the model and normalize: χb = 0.35.

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 40 / 61 Calibration: Income Shock Distribution

I Parameterize ei shocks to be lognormal, only need to calibrate σe .

2.0

1.5

1.0

0.5

0.0 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 log value - log income

Back Figure: House Price / Income Ratio: 2000 Q1

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 41 / 61 Calibration: Income Shock Distribution

I Choose σe to match cross-sectional dispersion of log valuei,t − log incomei,t in Fannie Mae loan-level origination data (average over 2000-2014). - This ratio determines which constraint is binding, given aggregates.

2.0

1.5

1.0

0.5

0.0 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 log value - log income

Back Figure: House Price / Income Ratio: 2000 Q1

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 41 / 61 Calibration: Issuance Costs

I Choose Γκ so that approx. annualized prepayment rate cprf t = 4ρt has a logistic functional form: 1 cprf t = . 1 + exp − κ−µκ sκ

I To calibrate sk , estimate prepayment regression

∗ logit(cpri,t ) = γ0,t + γ1(qt − q¯i,t−1) + ei,t using pool-level MBS data (Fannie Mae 30-Year FRMs, 1994-2015).

I Choose sκ so that model equation Ωpay −1 b,t ∗ (1 − ν)πt mt−1 logit(cprf t ) = γ0,t − qt − q¯t−1 ∗ sκ mt pay satisﬁes Ωb /sκ =γ ˆ1 in steady state.

Back Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 42 / 61 Calibration: Issuance Costs

I Given sκ can choose µκ to match average prepayment rates on the same MBS series.

8

7

6

5

4

Density 3

2

1

0 0.1 0.0 0.1 0.2 0.3 0.4 0.5

DanielBack L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 43 / 61 Calibration: Issuance Costs

I Resulting costs are high (threshold prepayer pays 13.1%, average prepayer pays 8.1%). Needed to match “inertial” behavior.

8

7

6

5

4

Density 3

2

1

0 0.1 0.0 0.1 0.2 0.3 0.4 0.5

DanielBack L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 43 / 61 Monetary Policy Shock (Trend Inﬂ.)

I Focus on mechanism: exogenous prepayment (ρt =ρ ¯).

IRF to Infl. Target IRF to Infl. Target

4 5

Debt 2 House Price

0 0 5 10 15 20 5 10 15 20 LTV (Exog) 0.0 PTI (Exog) Benchmark (Exog) ) ) l

2 l e e v v e e L L ( (

0.5

v 1 t ∗ l t q F

0 5 10 15 20 5 10 15 20 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 44 / 61 Constraint Switching Eﬀect (TFP Shock)

I TFP shock lowers nominal rates (deﬂationary) and raises labor income =⇒ loosens PTI limits.

IRF to TFP IRF to TFP

1.5 0.4

1.0 0.2 Debt 0.5 0.0 Price-Rent Ratio 0.0 5 10 15 20 5 10 15 20 LTV 0.00 PTI 0.4 Benchmark ) ) l l e e

v 0.05 v e e L L

( 0.2 (

v t ∗ l t q

F 0.10 0.0

5 10 15 20 5 10 15 20 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 45 / 61 Credit Liberalization: LTV

I For IRFs, assume log θt is AR(1) with persistence 0.9.

IRF to LTV Limit IRF to LTV Limit

10 5

5 Debt House Price

0 0 5 10 15 20 5 10 15 20 LTV 0 PTI Benchmark ) l e

v 0 e L (

5 v t l F Price-Rent Ratio

5 10 15 20 5 10 15 20 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 46 / 61 Credit Liberalization: LTV

I Loosening LTV (10%) causes decrease in collateral value, house prices and price-rent ratios fall in Benchmark model.

IRF to LTV Limit IRF to LTV Limit

10 5

5 Debt House Price

0 0 5 10 15 20 5 10 15 20 LTV 0 PTI Benchmark ) l e

v 0 e L (

5 v t l F Price-Rent Ratio

5 10 15 20 5 10 15 20 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 46 / 61 Credit Liberalization: PTI

I Loosening PTI (10%) causes increase in collateral value, house prices and price-rent ratios rise in Benchmark model.

IRF to PTI Limit IRF to PTI Limit 4

5 2 Debt House Price

0 0 5 10 15 20 5 10 15 20 LTV PTI 4 Benchmark ) l e v

e 1 L (

2 v t l F Price-Rent Ratio 0 0 5 10 15 20 5 10 15 20 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 47 / 61 Constraint Switching Eﬀect (Monetary Policy Shock)

pti I θ = 43% (Dodd-Frank): only 13% constrained by PTI.

IRF to Trend Infl. IRF to Trend Infl. 10 4

5 2 Debt

0 Price-Rent Ratio 0 5 10 15 20 5 10 15 20

2 LTV 0.0 PTI Benchmark ) ) l l e e v v e

1 e L L ( (

0.5

v t ∗ l t q F

0 5 10 15 20 5 10 15 20 Quarters Quarters Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 48 / 61 Frontloading Eﬀect (Monetary Policy Shock)

I Large response of output to -1% near-permanent monetary policy shock.

IRF to Infl. Target IRF to Infl. Target 2 6

4 1

2

Avg. Debt Limit 0 0 New Issuance (Level) 5 10 15 20 5 10 15 20 LTV (Exog Prepay) Benchmark (Exog Prepay) 1.5 Benchmark 5 1.0

Output 0.5

0

0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 49 / 61 Frontloading Eﬀect (TFP Shock)

pti I θ = 43% (Dodd-Frank): only 13% constrained by PTI.

IRF to TFP IRF to TFP

1 Debt 0.0

0 Price-Rent Ratio 5 10 15 20 5 10 15 20

0.00 ) ) l l e e

v 0.05 0.2 v e e L L ( (

v t ∗ l t

q 0.10 F 0.0

5 10 15 20 5 10 15 20

LTV PTI 1 Benchmark 0.5 Output

0

0.0 Prepay Rate (Level) 5 10 15 20 5 10 15 20 Quarters Quarters DanielBack L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 50 / 61 Credit Standards and the Boom-Bust

I Large rise in PTI ratios relative to CLTV ratios.

CLTV Ratio, 75th percentile PTI Ratio, 75th percentile 96 49

94 48

47 92 46 90 45 88 44 86 43 84 42

82 41

80 40 2001 2003 2005 2007 2009 2011 2013 2001 2003 2005 2007 2009 2011 2013

(a) CLTV: 75th Percentile (b) PTI: 75th Percentile

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 51 / 61 Credit Standards and the Boom-Bust

CLTV Ratio, 90th percentile PTI Ratio, 90th percentile 95 56

54 94

52 93

50

92 48

91 46

90 44 2001 2003 2005 2007 2009 2011 2013 2001 2003 2005 2007 2009 2011 2013

(a) CLTV: 90th Percentile (b) PTI: 90th Percentile

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 52 / 61 Credit Standards and the Boom-Bust

Fannie Mae 2007 Selling Guide

I Although we have established a benchmark qualifying debt-to-income ratio, we recognize that often there are legitimate reasons for exceeding this guideline. Therefore, a lender may use a ratio that is higher than our benchmark guideline, as long as its assessment of the comprehensive risk of the mortgage identiﬁes and documents factors that justify the higher ratio...Our benchmark debt-to-income ratio is 36 percent of the borrower’s monthly income. Fannie Mae 2009 Selling Guide

I For manually underwritten loans, Fannie Mae’s benchmark total debt-to-income ratio is 36% of the borrower’s stable monthly income. The benchmark can be exceeded up to a maximum of 45% with strong compensating factors... For loan casefiles underwritten through DU [Desktop Underwriter], DU determines the maximum allowable debt-to income ratio based on the overall risk assessment of the loan casefile. DU will apply a maximum allowable total expense ratio of 45%, with flexibilities offered up to 50% for certain loan casefiles with strong compensating factors.

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 53 / 61 Credit Standards and the Boom-Bust

“A New Method for Evaluating Your Debt” (Los Angeles Times: January 27, 2002)

I “In the 1970s and 1980s, a common rule of thumb was that your mortgage-related payments shouldn’t eat up more than 25% of your monthly household income. During the late 1980s and into the 1990s, that rule began to stretch into the 31% to 33% range and sometimes higher.”

I “In the 1990s, acceptable ratios began creeping above 40%. Late in the decade, even Freddie Mac conﬁrmed that it no longer had hard and fast rules on total monthly debt to monthly income ratios, and lenders reported selling loans to Freddie with debt-to-income ratios of 55% and higher.”

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 54 / 61 Boom-Bust Paths (Data)

I Percent deviations from price-rent trough (1997Q4).

50 60

40 50

40 30 30 20 20 10 10 Log Price-Rent (%)

0 0 Log Debt-Household Income (%) 10 10 1999 2001 2003 2005 2007 2009 2011 2013 2015 1999 2001 2003 2005 2007 2009 2011 2013 2015

(a) Log Price-Rent (b) Log Debt-Household Income

LTV/PTI Both Dodd-Frank

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 55 / 61 Credit Liberalization Experiment

25 6

20 4

15 2

10 0

Avg. Debt Limit 5 2

0 New Issuance (Level) 4 0 20 40 0 20 40

1.0 LTV Liberalized ) l PTI Liberalized e v e ) L l ( 0.5

0.0 e v R g e o L l (

− ∗ t

Y 0.0 q 0 1

R 0.2 g o l 0.5 0 20 40 0 20 40 Quarters Quarters

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 56 / 61 Credit Liberalization Experiment (Intuition)

I Changes to LTV standards cannot explain the boom-bust with PTI limits at traditional levels. - Direct eﬀect: PTI constraints limit debt boom. - GE eﬀect: constraint switching limits house price boom.

PTI F ltv Limits

Max LTV House LTV Limits Prices

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 57 / 61 Credit Liberalization Experiment (Intuition)

I Relaxation of PTI standards increases house prices, price-rent ratios through constraint switching eﬀect.

I High house prices relax LTV limits =⇒ large increase in debt.

Max PTI F ltv PTI Limits

LTV House Limits Prices

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 58 / 61 Macroprudential Policy: Preference Shocks

pti I Alternative experiment: leave PTI limits ﬁxed at θ = 0.28, instead increase housing preference ξ by 10%, return to baseline after 32Q.

5 25

0 20

5 15

10 Debt 10

15 5 Price-Rent Ratio

20 0 0 20 40 0 20 40

40 25 LTV Economy Benchmark Economy 30 20

20 15

10 10

Avg. Debt Limit 0 5 Prepay Rate (Level) 10 0 0 20 40 0 20 40 Back Quarters Quarters

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 59 / 61 Credit Liberalization Experiment

50 10

40 5 30

20 0

Avg. Debt Limit 10

0 New Issuance (Level) 5 0 20 40 0 20 40

1.0 Both Liberalized ) l PTI Liberalized e v

e 0.5 ) L l (

0.0 e v R g e

o 0.0 L l (

− ∗ t Y q 0

1 0.5

R 0.2 g o l 1.0 0 20 40 0 20 40 Quarters Quarters

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 60 / 61 Macroprudential Policy: Dodd-Frank Limit

50 10

40 5 30

20 0

Avg. Debt Limit 10

0 New Issuance (Level) 5 0 20 40 0 20 40

1.0 Both Liberalized ) l Dodd-Frank e v

e 0.5 ) L l (

0.0 e v R g e

o 0.0 L l (

− ∗ t Y q 0

1 0.5

R 0.2 g o l 1.0 0 20 40 0 20 40 Quarters Quarters

Back

Daniel L. Greenwald (MIT Sloan) The Mortgage Credit Channel September 29, 2016 61 / 61