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Housing
John Muellbauer,
Nuffield College,
Oxford.
Presentation for FMG’s
London Financial Regulation
Conference, July 2, 2009.
Presentation draws on
“House Prices and Credit Constraints:
Making Sense of the U.S. Experience”
John V. Duca* (Federal Reserve Bank of
Dallas, Southern Methodist University)
John Muellbauer (Oxford University),
Anthony Murphy (Oxford University).
(*usual Fed disclaimer applies)
US House Prices Fall Since 2006,
Reversing Some of Earlier Gains
2008:q4
+23%
+11%
+ 6%
Source: Freddie Mac, Bureau of Economic Analysis, Federal Reserve Board, and authors’ calculations.
Mortgage and
Housing Crisis
Lower Demand
for Housing
Less Home
Construction
Lower Capital of
Financial Firms
↓Home Prices &
Wealth, Slower
Consumption
↑ Counter-Party
Risk, Money &
Bond Mkts Hit
Slower
GDP Growth
Credit Standards
Tightened
on All Loans
RHS of J. Duca graphic: defaults, bad
loans & spreads feed back via banks
and credit markets
• Empirical literature on defaults necessarily
implies non-linear or asymmetric responses to
asset prices, e.g. Aron and Muellbauer (2009)
on UK mortgage defaults.
• Link defaults to asset base of banks, as in BOE
RAMSI.
• Bernanke (1983), Brunnermeier (2008), Von
Peter (2009) and BIS 2009 papers by Duffie,
Shin, Tirole, and Rajan all about aspects of this
amplification.
Late Mortgages More Prevalent,
Reaching New Records
Source: Mortgage Bankers Association.
LHS of pic: sources of
amplification/feed back via household
sector
• Extrapolative expectations or momentum
trading.
• Log-linear house price model implies a type of
‘frenzy’ effect.
• Downside risk or fear induced by defaults can
amplify downside.
• Linearity of budget constraint implies elasticity of
consumption w.r.t. asset prices is higher with
higher asset prices.
Housing Services Approach to
Modeling Home Prices
• D for housing services (≈hs≡housing stock))
log hst = a0 + a1 log yt + a2 log zt − a3 (log rhpt + log usert )
• Inverted demand => house price equation
log rhpt = [a0 + a1 log yt + a2 log zt − log hst ] / a3 − log usert
where housing stock, income, and housing variables are
per capita.
Add adjustment dynamics…..
‘Neoclassical’ demand for durables
theory defines ‘user cost’
• Real user cost is (user)(real house price index).
• ‘User’ is real interest rate + (rate of tax,
transactions cost, risk premium) – expected rate
of appreciation of real house prices, see
J.S.Cramer (1957), Irving Fisher(1930) .
Housing Price-Rent Approach to
Modeling Home Prices
• Arbitrage between owner and rental markets implies
price-rent ratio akin to P/E ratio
( Rent / HP )t = usert
Also obtains when agency costs make renting housing
services more expensive vs. owning
• Inverting and taking logs:
log( HP / Rent )t = − log usert
real user elasticity = -1; and ratio invariant to housing stock
and swings in income
Housing Price-Rent Approach to
Modeling Home Prices
• In Yong Kim’s (2007, USC manuscript) model
binding (max LTV) credit constraints on marginal
home buyer plus rental agency:
( HP / Rent )t = f (usert , ltv, ydeviations )
Where the magnitude of the negative user
elasticity can be smaller than 1.
Comparing the Two Approaches
• Relative advantages of housing services:
– Practical where rent markets regulated (UK)
– Does not ignore that income shocks can drive rents and
home unlike the price-to-rent or P/E approach
– Grounded more in consumer demand
• Relative advantages of P/E approach:
– Good where rent markets flexible (US)
– Does not require good housing stock data
– Rents capture many factors special to housing absent
from variables in canonical housing services approach
– Grounded more in finance and arbitrage
Financial Innovations Affecting US
Mortgage Markets
• US: rising home-ownership, higher home-price ratios,
and increased leverage (lower home owner equity stake)
• Yet, avg. LTV conventional prime loans flat. Why?
– Innovations ease LTV constraint for 1st time buyers
• Credit scoring sorts subprime & nonprime borrowers
• New financial products enable selling of nonprime mortgages
by offering new (later insufficient) credit protection
– Innovations lower mortgage costs and easing ability
to tap housing wealth for existing owners
• US changes more evolutionary, less abrupt than in UK
• Home equity lines of credit, 1986 Tax Act
• Lower fixed costs of mortgage refinancing, deeper MBS mkt
– Increases option value of fixed rate financing
– Makes cash-out mortgage refinancing cheaper
Average LTVs for First-Time HomeBuyers Using Non-Gov’t Mortgages
Height of
subprime boom
Last point: 2007:q2
before August ’07
financial crisis
Savings & Loan Crisis resolution
passed 1989:q3, LTVs 3-4 % pts
lower in 1989:q3 & 1989:q4
Source: American Housing Survey, calculations from Duca, Johnson, and Muellbauer (2008, in process).
Non-gov’t mortgages exclude FHA & VA insured mortgages and other gov’t insured or direct mortgage
loans that are generally limited in their individual size. 3-quarter moving average.
Amplification via expectations
• Much evidence for extrapolative element
in expectations, or momentum trading,
Piazzesi and Schneider (2008).
• Hence a sequence of positive shocks, e.g.
sub-prime explosion – fall in LTVs for first
time buyers and lower interest rates, can
cause appreciation, generating more
demand and further appreciation.
• Our ‘user’ incorporates 3-4 year memory
of US hp appreciation.
Error-Correction Results: LTV models
Outperform non-LTV Models of House
Price-to-Rent Ratio
Log user costt-1
No LTV
81:3-2001
LTV
81:3-2001
No LTV
81:3-2007:2
LTV
1981:3-2007:2
-0.1864**
(-4.19)
-0.1951**
(-7.95)
-0.2673**
(-11.22)
-0.1698**
(-21.97)
0.8873**
(4.71)
First-Time Buyer
LTVt-1
0.8086**
(6.19)
No
Yes*
Yes*
Yes**
ECt-1
-0.0816*
(-2.32)
-0.1802**
(-3.11)
-0.0304+
(-1.67)
-0.1985**
(-4.31)
Cut CapGainTaxt-1
raises home price
0.0064**
(3.02)
0.0044**
(2.94)
0.0057**
(3.11)
0.0069**
(4.31)
R2
S.E.
0.541
.00506
0.662
.00447
0.639
.00500
0.748
.00432
Cointegration,
unique vector?
Vectors allow trends in variables but not in the cointegrating relationship. Controls include 0-1 dummies for
monetary targeting regime and 1998 capital gains tax relief, depreciation rate on rental properties, and
consumer income/interest rate expectations. Statistics from Tables 3 and 4 in the paper.
Amplification via intrinsic non-linearity
• Log user cost amplifies falls at low levels,
i.e. when recent years’ HP appreciation
high relative to tax etc. adj. interest rate.
• Similar fit and speed of adj. with LINEAR
user cost and CUBIC in last year’s HP
appreciation – like Hendry (1994), M&M
(EJ 1997) ‘frenzy’ effect.
• Similar results for inverse demand function
approach.
A Hostage to Fortune – The Simulated path
of Future US House Prices
• If our model is correct, the US house price bust
may last to end 2010 (on Freddie Mac)!
• The simulated path of future real US house
prices, shown in next figure, assumes that:
– The US economy recovers slowly;
– Mortgage credit condition revert to their end
1999 value;
– (Most importantly) our model is correct.
(Incorporates rent equation with slow
feedback from house prices.)
Forecast simulation for log US HP
from HP/rent model
HP index is Freddie Mac repeat sales,
excl. refis
• Index is less volatile than Case-Shiller 10
or 20 cities index and lags behind.
• Forecast scenario is consistent with CaseShiller correction a little over half way
through.
• Fairly consistent with ‘futures’ on CaseShiller reaching bottom in mid-late-2010
Don’t forget impact of residential
construction on GDP
%, Res. Const.
Share of GDP
Figure 9: Residential Construction Share of GDP Drops
in the U.S. and Spain
8
7
Spain
6
Germany
5
EU 27
4
U.S.
3
UK
Japan
2
1
0
98
00
02
04
06
08
Concluding Comments
• Study addresses an important data issue.
• Findings: important to measure credit
constraints facing marginal 1st time buyers.
• Results: U.S. experienced a credit bubble
that fuelled a housing bubble.
• Model builds in major over-shooting
possibility of HP given lagged appreciation
in user cost term – driven by sequence of
+’ve shocks – interest rates, fin. innovation
Conclusions cont’d
• Also amplification from intrinsic nonlinearity.
• Implication: the unwinding depends partly
on credit conditions returning to norm, but
overshooting on downside too.
• Timing pattern of 1st time buyers disrupted,
wave of foreclosure sales may alter s-run
dynamics
UK contrast
• UK sub prime problem a lot less serious.
• Low levels of construction in last decade imply
smaller supply-demand imbalance.
• Current City boomlet is short-term positive.
• Fall in mortgage interest rates is sharper than in
US, given floating rate market.
• But bigger hp boom and worse credit supply
contraction.
• CML right to correct mortgage repossessions
forecast for 2009 from 75k to 45k.
• But my guess is worse to come in 2010.
UK policy measures
•
•
•
•
ISMI rules relaxation
Code of practice for mortgage lenders
Home owner mortgage support scheme
CLG mortgage rescue for the most
vulnerable households
• Bank rescues and guarantees
• QE