Transcript Housing and the Crisis

http://guessthecorrelation.com/
http://www.cc.com/video-clips/zjr96n/the-daily-show-with-jon-stewart-julian-castro
http://www.newyorkfed.org/home-price-index/
Leung (2004)
 Value of residential capital stock > business capital (Greenwood &
Hercowitz, 1991)
 Standard deviation of residential investment is 2 x bigger than non-
residential investment (Davis and Heathcote, 2001)
 Residential investment leads cycle, non-residential investment lags cycle
(Davis and Heathcote, 2001)
From Wachter, Cho, Tcha (2014)
From
Wachter et al
(2014)
Wealth of housing is about 50% of total wealth
Housing wealth is greater than GDP and it’s volatile
Housing wealth affects consumption, thus GDP and
employment
Housing investment is only about 5 to 6% of GDP
Housing price appreciation is only loosely linked to
consumer inflation
Housing investment leads non-housing investment
There’s been an upward trend in housing prices since 1960 !!
Leamer: “Housing is the single most
critical part of the US business cycle,
certainly in a predictive sense, and,
I believe, also in a causal sense. “
Source: Leamer,
Dallas Fed conference
Duca et al (2010)
Housing markets & the financial crisis of 2008: Lessons for the future
Sub-prime mortgage bubble
Bubbles in general:
 Innovation gives initial high returns
 Optimism about later returns brings over-investment in the new product,
asset P rises
 Need funding source (leverage, liquidity) to sustain build-up
 Greater asset P rises with belief in changed mkt structure (“this time is
different”)
 Bubble until something sparks a reassessment of P; optimism unwinds
Financial innovation
 MBS packaged by Fannie Mae & Freddie Mac (gov’t sponsored enterprises
or GSE) had low default risk, guaranteed by US gov’t
 Non-GSE packages (e.g. jumbo & subprime) became 40% of residential
mortgage originations in 2006
Cause of Optimism
 Data for predicting sub-prime default loss started only in 1998
 Pre-2006 estimates predicted low default rates if job growth continued, but
estimates omitted past PH and interest rates
 Higher PH let borrowers sell & pay off mortgage or borrow on past capital
gains
 Low rates: ARM did not require higher payments
Duca explains sub-prime bubble
 Low interest rates
 Mortgage credit standards eased
 Innovation => housing wealth more
liquid
 Fig 4: CA deficits => capital inflows
lowered interest rates
 Borrowing funded by inflows, MBS
issuance, & leverage
GSEs bought or guaranteed many private MBS
($253 bil private MBS in 2007)
SEC raised leverage ratios of large investment
banks’ brokerage units from 15:1 to 33:1 in 2004
Serially Correlated PH
 Large transactions costs (agent fees 5-6% of PH)
 Thin markets: heterogeneous, low turnover rate (5-6% in US)
 Expected home values unlikely to be accurate
 Inelastic short-term supply => demand rise leads to PH & construction
taking several years to adjust
 => PH serially correlated => past info on fundamentals forecasts future
excess returns
Impact of Housing Demand Boom - Bust
 Housing construction
 Consumer spending via housing collateral (wealth)
 Financial sector health => availability of credit (including international
links)
 On other nations via exports from housing sensitive economies
Housing Construction
 Nations with price-elastic HS : demand
brought large construction boom (US
large rise, UK & Germany smaller)
 HD shocks destabilize by inducing changes
in housing construction or PH
Effect of PH on Consumption
 Empirical estimates vary: 3 – 6% in USA to negative in Japan
 Theory (perfect capital markets): permanent higher real PH raises
consumption if first > second
 wealth (save less) & substitution (buy fewer houses, more other stuff) effects
 income effect of paying more for housing services
 If household faces binding credit constraint, higher real PH increases
collateral so can more easily borrow to consume
 Stronger impact in nations with financial liberalization
q Theory
(Sorensen & Whitta-Jacobsen)
 Firm has incentive to build houses when PH > (replacement cost)
 => with higher PH, greater housing investment
 But PH > (replacement cost) may remain for long time since houses take a
long time to build (WSJ: 6 months on average)
To Find Current Value of Firm
Dte1  Vt e1  Vt
 r 
Vt
Saver compares return to firm (E dividends + E capital gain) against that
of a bond return + risk premium
Arbitrage => returns equal
Vt e1  Dte1  Vt 1  r   
Dte1  Vt e1
Vt 
1 r  
Recalls Gordon model: firm’s current value is PV(CF) = PV( E dividend
+ E sale P).
q measures
firm’s expected value
 te1  I t  C I t   qt K t  I t 
Vt 
1 r  
Vt  qt K t
An identity since:
Vt e1  qte1 K t 1
(step equation one period ahead)
Vt e1  qt K t 1
(using qt to estimate qt+1)
Vt e1  qt K t  I t 
(using:
Dte1   te1  I t  C I t 
Future E (dividends) = profits – investment – adjustment costs
K t 1  K t  I t
qt 
market value of K
replacemen t cost of K
, no depreciation)
Assume adjustment costs convex
Vt 
D V
1 r  
e
t 1
e
t 1
Substituting Ve and De from above
 te1  I t  C I t   qt K t  I t 
Vt 
1 r  
Firm chooses I to max Value of Firm
Vt 

e
t 1
 I t  C I t   q t K t  I t 
1 r  
dVt  1  C ' I t   qt

0
dI t
1 r  
With quadratic
adjustment cost
=> 𝑞𝑡 = 1 + 𝐶′ 𝐼𝑡
Optimally, firm invests until acquisition + installation cost
of the next K unit = its market value
 te1  I t  a I t2  qt K t  I t 
2
Vt 
1 r  
dVt  1  aI t  qt

0
dI t
1 r  
It 
qt  1
a
q
Larger a adjustment cost
=> steeper slope so smaller I*
1
0
I*
q Theory & Housing
1. Housing Demand
 Total housing cost is user cost (r mortgage rate; δ maintenance) * H
housing stock
r    p H H
 Sally divides her income for housing or non-durable consumption C. Her
budget constraint:
Y  C  r    p H H
 Her utility function
U  H  C 1

0<η<1
U  H  Y  r    p H

H

1
Substitute C from budget constraint
Short-run Housing Demand

U  H   Y  r    p H H

1
H  1  Y  r    p H H 
1
FOC:
HD 
Y
p
H
r   

 H   1    Y  r    p H H
PH
HD
Higher income, higher demand
Higher P cuts demand
H0


r    p H
0
Short-run Eqbm
Assume Housing Supply Fixed) at H0
H0 
(28)
PH 
Y
P H r   
Y
H 0 r   
(Eqbm condition: HS = HD)
Construction
Long-run Eqbm
P  aW  bP Q
 Construction firm profits:   p H I H  PX
I
H
I
 p I
H
FOC
(21)
I
 P
 A
H
H
P I


A  A
H
p
H



P

I H  A
A 
 P

H
H
 AX

 AX

P is index of input costs
(W = wage, PQ = materials price)
where
0 < β < 1 (DRTS)
A: sector’s productive capacity
=>
X

IH
 
 A



1



1 


1 
Similar to q (if PH > cost index, firms
find it profitable to construct more houses)
Evolution of Housing Stock over time
IH




 A 
  Y
 A 



H
r


P 
t


 


  

PH



1 
Substitute PH from (28) into (21)
I
H

A  1 

Y



 A
 H t r    P 
If income rises, housing investment rises
If housing stock or user cost rises, housing investment falls