Post on 28-Mar-2015
National Instituteof Economic and Social Research
International developments in housing markets – lessons for Sweden
E Philip Davis
Introduction
• In this presentation we seek to give an overview of recent developments in housing markets for 12 OECD countries, via data and relevant research
• We begin by noting key structural differences, before looking at developments in the crisis
• We proceed to put these in a longer term context• And finally look at key implications of house prices
for investment, consumption, public finance and financial stability/financial regulation
Background – structural features
• Housing markets cannot be treated as homogeneous
• Population density is correlated with dwelling size and availability of land, although the latter is also affected by planning restrictions
• Dwelling size and inhabitants per dwelling is indicators of living standards in terms of housing
• Interest rate risk may affect both supply and demand for housing, and demand may also be affected by the prevalence of fixed rate loans
Structure of housing markets
Population density (2005)
Housing density (2001)
Dwelling size (2001)
(persons per sq km)
(per 1000 inhabitants)
average m2 per capita 1980s 1990s 2000-2006
Australia 2.6 405 81.0 4.33 0.88 0.33Canada 3.2 403 69.7 0.64 0.60 0.07France 108.4 490 43.9 0.63 0.12 0.02Germany 231.0 469 42.1 0.88 0.30 0.07Italy 194.5 368(b) 35.0(b) 1.99 1.09 0.02Netherlands 391.5 417 41.2 1.35 0.16 0.02Spain 85.8 510 47.6 1.71 0.68 0.08United Kingdom 246.3 431 36.4 1.08 0.55 0.09United States 30.8 428 70.8 0.91 1.12 0.11
long real interest rate volatility(a)
Personal sector borrowing cost vulnerability
Net
hs
Ger
man
y
Den
mar
k
Fra
nce U
S Sw
eden UK
Ital
y
Irel
and
Fin
land
Spa
in
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
1
Flo
atin
g ra
te d
ebts
as
a pr
opor
tion
of d
ispo
sabl
e in
com
es
Recent house price developments
• Boom-bust cycle in housing in a number of countries…
• US housing credit linked to global crisis directly via falling CDO prices (note principal agent problem in US mortgage securitisation)
• Since crisis, house price falls less marked than widely expected, recovery in some countries
• Evidence of further financial distress with high level of arrears and repossessions in countries such as the US
• Less so in those such as the UK as interest rates low and house price falls modest – and loans recourse based
Recent house price developmentsHouse prices since 2001
-20
-15
-10
-5
0
5
10
15
20
25
Quarters
Ann
ual p
erce
nt c
hang
e AUphi
CNphi
FRphi
GEphi
IRphi
ITphi
Recent house price developmentsHouse prices since 2001
-15
-10
-5
0
5
10
15
20
25
30
Quarters
An
nu
al p
erc
en
t ch
an
ge
JPphi
NLphi
SDphi
SPphi
UKphi
USphi
Latest “Economist” Data
Country Year on year change
Country Year on year change
Australia 18.4 Japan -3.4 Canada 4.5 Netherlands 4.2 France 6.0 Sweden 8.9
Germany 4.8 Spain -4.0 Ireland -17.0 UK 3.0
Italy -2.8 US -4.9 (FHFA) 3.6 (CSNI)
A longer term perspective• Boom in house prices following liberalisation in
the 1980s, often leading to banking crises…• Long term rise in real house prices (higher
income, shortage of land) – implicit intergenerational transfers (Weale 2007)
• Rise in debt-income ratios to households correlated with rise in house prices (house purchase but also equity extraction)
• But credit should not drive house prices in liberalised financial system (conventional determinants are income, interest rates, supply conditions, demographics, see e.g. Muellbauer and Murphy 1997))
House price inflation since 1980House price inflation
-20
-10
0
10
20
30
40
50
Years
An
nu
al p
erc
en
tag
e c
han
ge
AUhpinfl
CNhpinfl
FRhpinfl
GEhpinfl
IRhpinfl
IThpinfl
House price inflation since 1980House price inflation
-20
-10
0
10
20
30
40
50
Years
An
nu
al p
erc
en
t ch
an
ge
JPhpinfl
NLhpinfl
SDhpinfl
SPhpinfl
UKhpinfl
UShpinfl
Real house pricesReal house prices (1980=100)
0
50
100
150
200
250
300
350
Years
Ind
ex
AUrph
CNrph
FRrph
GErph
IRrph
ITrph
Real house pricesReal house prices (1980=100)
0
50
100
150
200
250
300
350
400
Years
Ind
ex
JPrph
NLrph
SDrph
SPrph
UKrph
USrph
Debt-income ratiosHousehold debt/income ratios
0
50
100
150
200
250
Years
Perc
en
t
AUdyr
CNdyr
FRdyr
GEdyr
IRdyr
ITdyr
Debt-income ratiosHousehold debt-income ratios
0
50
100
150
200
250
300
Years
Perc
en
t
JPdyr
NLdyr
SDdyr
SPdyr
UKdyr
USdyr
Housing and investment
• Housing investment typically a small proportion of the stock, given houses are long-lived assets
• Overall housing investment has tended to decline as a proportion of GDP in a number of countries, even before 2008/9 when sharp falls especially Spain and Ireland
• Key determinant Q ratio (house prices/housing investment deflator) (Jud and Winkler (2003), Berg and Berger (2005))
• Correlation of house price change to investment/GDP change in 2008/9 is 0.73
Housing investment/GDPHousing investment/GDP ratios
0
2
4
6
8
10
12
14
16
Years
Perc
en
t
AUiy
CNiy
FRiy
GEiy
IRiy
ITiy
Housing investment/GDPHousing investment/GDP ratios
0
2
4
6
8
10
12
Years
Perc
en
t
JPiy
NLiy
SDiy
SPiy
UKiy
USiy
Q ratios for housing in upturn
-6
-4
-2
0
2
4
6
8
10
Aus
tral
ia
Can
ada
Fra
nce
Ger
man
y
Net
herl
ands
Spa
in
UK
US
ave.
an
nu
al p
erce
nta
ge
chan
ge
1991-1995 1996-2000 2001-2006
House prices and consumption• Research on wealth effect shows strong link
from house prices/housing wealth to consumption (e.g. Barrell and Davis (2007), Case et al (2005)), although effect on non-homeowners should partly offset
• Simple cross section regression shows house prices discriminated the falls in consumption between 2008/3 and 2009/4 better than equity prices, although RPDI and lagged debt/income also relevant
• Collateral effect likely intensified by credit rationing, but banking crisis dummy not significant
Change in consumption 2009/4 over 2008/3
• Dependent Variable: DC• Method: Least Squares• Date: 10/12/10 Time: 13:37• Sample: 1 12• Included observations: 11••• Variable Coefficient Std. Error t-Statistic Prob. ••• DPH 0.258924 0.092604 2.796035 0.0267• DRPDI 0.490681 0.224692 2.183794 0.0653• LDY -0.011408 0.005645 -2.020971 0.0830• DEQP -0.026005 0.058024 -0.448169 0.6676••• R-squared 0.752215 Mean dependent var -0.942513• Adjusted R-squared 0.646022 S.D. dependent var 2.813637• S.E. of regression 1.674003 Akaike info criterion 4.143600• Sum squared resid 19.61600 Schwarz criterion 4.288289• Log likelihood -18.78980 Durbin-Watson stat 2.391862•••
•
Housing and fiscal position
• NIER (2010) decomposed deterioration in fiscal position into cycle, policy changes, bank support and residual
• Residual linked partly to revenue from financial sector but also housing market
• Falling house prices affect tax revenues directly (via stamp duties and profits of construction/realtor sector) and indirectly (via consumption taxes – to the extent consumption fell more than in a normal cycle)
House prices and deficits
Ireland
Italy
Canada
Spain
United Kingdom
Greece
Denmark
Finland Portugal
Belgium
Netherlands
Japan
France
Sweden Germany
US
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1
Residual category of the deficit (% of GDP)
Ho
use
pri
ce g
row
th 2
009
House prices and banking crises
• Barrell, Davis, Karim and Liadze (2010) in JBF and subsequent work were first to find role for bank capital and liquidity in OECD crisis models
• House price bubbles matter• Sustained deficits matter
• Using logit model together with a banking sector sub-model of NiGEM global macro model enabled assessment of overall costs and benefits of regulation in the UK – optimal level of tightening (Barrell et al (2009) FSA OP)
• Recent work looks at the split between on balance sheet and other revenues (OBS)
• Level of OBS does not matter as it varies across countries a lot• Faster growth of OBS activity boosts crisis probabilities
Calibrating macroprudential surveillance
• In “Calibrating macroprudential surveillance” we put in all ‘normal’ variables including lagged house price rises and test down with 14 OECD countries, 12 crisis and data for 1980 to 1997 (vastly shorter sample than earlier work)
• As in earlier work, found that “traditional” variables such as credit growth, output growth and M2/reserves less relevant to OECD – artefact of dominance of global samples by emerging markets
• A researcher undertaking this work in the late 1990s could have picked the same equation
Explaining OECD Financial Crises
• We explain crisis probabilities (logit) in OECD 1980-1997
Box 1: List of Variables (with variable key)
1. Real GDP Growth (%) (YG) 2. Real Interest Rate (%) (RIR) 3. Inflation (%) (INFL) 4. Fiscal Surplus/ GDP (%) (BB) 5. M2/ Foreign Exchange Reserves (%) (M2RES)
Variables used in previous studies:
Demirguc-Kunt and Detragiache (2005);
Davis and Karim (2008). 6. Real Domestic Credit Growth (%) (DCG) 7. Liquidity (%) (LIQ) 8. Leverage (%) (LEV)
Variables introduced in JoBF.
9. Real Property Price Growth (%) (RHPG) This paper 10 Current Balance as % GDP (CBR)
it
it
X'
X'
itite1
eXF1YobPr
Nested testing of the crisis model, 1980-1997
Step (1) (2) (3) (4) (5) (6) (7)
LEV(-1) -0.339 (1.7)
-0.339 (1.8)
-0.348 (1.9)
-0.347 (1.9)
-0.417 (2.9)
-0.345 (2.7)
-0.384 (3.2)
NLIQ(-1) -0.106 (1.8)
-0.106 (1.9)
-0.108 (2.0)
-0.113 (2.2)
-0.126 (2.7)
-0.104 (2.5)
-0.105 (2.6)
RHPG(-3) 0.091 (1.9)
0.091 (1.9)
0.089 (1.9)
0.095 (2.4)
0.09 (2.4)
0.086 (2.3)
0.081 (2.1)
CBR(-2) -0.434 (2.3)
-0.434 (2.3)
-0.441 (2.4)
-0.438 (2.4)
-0.418 (2.3)
-0.3 (1.9)
-0.333 (2.2)
DCG(-1) -0.101 (1.5)
-0.101 (1.6)
-0.1 (1.6)
-0.1 (1.5)
-0.108 (1.7)
-0.053 (1.0)
YG(-1)) 0.277 (1.5)
0.277 (1.5)
0.274 (1.4)
0.279 (1.5)
0.29 (1.5)
RIR(-1) -0.054 (0.3)
-0.055 (0.6)
-0.055 (0.6)
-0.06 (0.7)
BB(-1) 0.022 (0.2)
0.02 (0.2)
0.023 (0.2)
M2RES(-1) -1.51E-05
(0.2) -1.52E-05
(0.2)
INFL(-1) -0.0012
(0.1)
Model character• Up to four lags tried in house prices, credit
growth, current account and GDP growth– Cyclical variables drop out– Lending growth drops out
• Lending quality matters with house price growth and current balances as indicators
9 out of 12 crises calledAlmost half of false calls precede crises
Estimated Equation
Dep=0 Dep=1 Total P(Dep=1)<
0.057 143 3 146 P(Dep=1)>
0.057 55 9 64
Total 198 12 210
Correct 143 9 152
% Correct 72 75 72
% Incorrect 28 25 28
Using the model in macroprudential surveillance setting• Forecasts over 1998-2008, using actual
for RHS (bold exceeds sample mean)
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008BG 0.005 0.004 0.003 0.004 0.009 0.005 0.007 0.014 0.025 0.048 0.070CN 0.032 0.054 0.056 0.033 0.018 0.022 0.026 0.037 0.030 0.036 0.042DK 0.015 0.041 0.060 0.046 0.048 0.029 0.043 0.030 0.042 0.030 0.113FN 0.004 0.006 0.011 0.007 0.000 0.000 0.000 0.004 0.002 0.007 0.008FR 0.025 0.018 0.012 0.014 0.040 0.028 0.032 0.053 0.100 0.193 0.218GE 0.026 0.027 0.029 0.045 0.058 0.031 0.016 0.020 0.007 0.007 0.007IT 0.001 0.002 0.002 0.009 0.017 0.020 0.026 0.039 0.034 0.054 0.019JP 0.071 0.025 0.009 0.010 0.007 0.007 0.003 0.002 0.001 0.001 0.002NL 0.020 0.018 0.050 0.049 0.157 0.141 0.079 0.028 0.017 0.019 0.007NW 0.011 0.006 0.039 0.016 0.001 0.001 0.006 0.003 0.002 0.001 0.001SD 0.019 0.016 0.034 0.048 0.039 0.058 0.017 0.006 0.009 0.011 0.008SP 0.005 0.006 0.010 0.028 0.043 0.044 0.047 0.096 0.266 0.516 0.580UK 0.049 0.060 0.088 0.173 0.203 0.201 0.115 0.207 0.282 0.277 0.254US 0.025 0.032 0.044 0.074 0.081 0.067 0.103 0.064 0.075 0.097 0.125
Using the model in macroprudential policy setting • We can invert the probability model to calculate
the additional levels of liquidity and leverage required for the probability of a crisis to be 0.01 in each country and year– Re-estimate each year from 1997, predict one year– Raise capital and liquidity to get probability 0.01
• Capital and liquidity form the defences, while house prices and current balances are the problems we need to provision against, not cycles or credit.
• Separate result shows credit does not Granger cause OECD house prices either (except Belgium, Canada and Finland)
Country and aggregate targets• Country max
reduces probability to 0.01 in worst year
• The average of these could be used as a criterion
• Major cross country differences in warranted tightening
Column 1 2 3 4
Under or overshoot
Additions to country specific levels of
liquidity and leverage to reduce all prob. to 0.01 or
below*
(column 1 - 3.7)
(column 2 -4.59)
Top Panel lev+nliq lev alone lev and nliq lev
Belgium 2.11 2.56 -1.59 -2.03 Canada 3.31 4.15 -0.39 -0.44 Denmark 3.35 4.15 -0.35 -0.44 Finland 0.00 0.00 -3.70 -4.59 France 5.08 6.25 1.38 1.66 Germany 3.12 3.79 -0.58 -0.80 Italy 1.74 2.14 -1.96 -2.45 Japan 3.96 5.19 0.26 0.60 Neths 4.72 5.80 1.02 1.21 Norway 2.34 2.87 -1.36 -1.72 Sweden 2.38 2.90 -1.32 -1.69 Spain 9.32 11.48 5.62 6.89 UK 6.08 7.63 2.38 3.04 US 4.35 5.34 0.65 0.75
Mean (International Benchmark) 3.70 4.59
SD 2.24 2.77
Countercyclical provisioning
• Has to be calibrated on house prices and current account and not credit or output gap – example of 5% higher house price growth
1998 0.0 -3.2 0.0 -0.1 0.6 -2.5 0.0 0.8
1999 0.0 1.3 0.0 -1.7 1.0 0.2 0.0 1.52000 0.0 0.8 0.0 0.0 1.9 6.2 0.4 1.82001 0.0 1.7 0.0 3.8 3.5 8.9 1.5 4.22002 0.1 7.5 0.3 5.3 3.8 9.5 1.7 3.12003 0.0 6.2 0.4 4.7 3.8 13.7 1.2 4.0
2004 0.0 6.1 0.5 6.1 2.5 6.1 2.2 5.42005 0.7 7.2 2.1 12.5 3.9 14.3 1.1 4.92006 2.2 10.0 4.6 14.1 4.8 13.9 1.5 4.32007 3.7 13.4 6.8 13.4 4.7 10.1 2.1 6.72008 4.0 13.6 7.3 10.2 4.4 3.0 2.7 8.4
Regulatory adjus tment
Actual RHPG (-3)
Regulatory adjustment
France
Actual RHPG (-3)
Ac tual RHPG (-3)
Regulatory adjustment
Actual RHPG (-3)
Regulatory adjustment
USUKSpain
Decomposing changes in crisis probabilities
France
2004 0.006 na na na na na na na2005 0.046 0.007 0.005 0.004 0.006 0.036 0.017 0.0402006 0.131 0.011 0.030 0.023 0.006 0.044 0.030 0.0852007 0.106 0.016 -0.008 0.024 0.027 -0.126 -0.043 -0.025 2008 0.895 -0.000 0.013 0.001 0.001 0.775 -0.000 0.789
Sum of changes na 0.034 0.040 0.051 0.039 0.729 0.004 0.889
Probability NLIQ CBR DOFFTOONChange in probability
Contribution to change in probabilityAdj for
InteractionLEV RHPG
Spain
2004 0.035 na na na na na na na2005 0.067 0.022 -0.012 0.026 0.004 -0.009 -0.001 0.0322006 0.173 0.031 0.040 0.017 0.055 -0.007 0.030 0.1062007 0.398 0.065 0.044 -0.013 0.120 0.045 0.037 0.2252008 0.479 -0.021 0.016 -0.063 0.098 0.050 -0.001 0.081
Sum of changes na 0.097 0.089 -0.033 0.277 0.079 0.064 0.444
Change in probability
Contribution to change in probability
Probability NLIQ LEV RHPG CBR DOFFTOONAdj for
Interaction
Decomposing changes in crisis probabilities
UK
2004 0.116 na na na na na na na2005 0.241 0.002 0.010 0.099 -0.007 0.035 0.015 0.1252006 0.442 0.009 0.057 -0.008 0.031 0.127 0.016 0.2012007 0.292 -0.002 0.024 -0.066 0.026 -0.135 -0.003 -0.151 2008 0.253 0.010 0.026 -0.119 0.036 -0.007 -0.015 -0.038
Sum of changes na 0.020 0.118 -0.094 0.087 0.020 0.013 0.137
RHPG CBR
Contribution to change in probability
Probability NLIQ LEVAdj for
InteractionChange in probability
DOFFTOON
US
2004 0.074 na na na na na na na2005 0.045 -0.017 -0.015 -0.002 0.004 0.003 0.001 -0.029 2006 0.043 0.002 -0.000 -0.002 0.006 -0.009 -0.001 -0.002 2007 0.064 0.002 -0.008 0.011 0.008 0.010 0.002 0.0212008 0.087 0.004 0.004 0.010 0.002 0.004 0.002 0.023
Sum of changes na -0.008 -0.019 0.017 0.019 0.008 0.004 0.013
Probability NLIQ LEV RHPG CBRAdj for
InteractionChange in probability
Contribution to change in probability
DOFFTOON
Conclusion
• Caution needed in directly comparing housing markets due to structural differences
• Housing finance clearly at core of recent financial crisis – US housing loans packaged into CDOs and recent defaults following price falls
• Falls in house prices can be related inter alia to changes in consumption, housing investment and fiscal deficits since the crisis
• And clear relation of lagged house price increases to OECD banking crises – relevant to ongoing bank regulation reform also
• Key lesson for Sweden is to avoid boom-bust cycle in housing, given macroeconomic volatility and systemic financial risk it generates – and long term intergenerational implications
• Control could be via appropriate monetary and macroprudential policies (including control of LTVs) – possibly also planning regulations
• If using securitisation ensure system is transparent and incentive compatible
• Ensure banks have sufficient capital as well as countercyclical reserves based on trends in house prices (not credit per se)