23rd September 2014
COMPARING HOUSING BOOMS AND
MORTGAGE SUPPLY IN THE MAJOR
OECD COUNTRIES
Angus Armstrong and E Philip Davis1
NIESR and Brunel University
London
Abstract: The house price and lending boom of the 2000s is widely considered to be the main
cause of the financial crisis that began in 2007. However, looking to the past, we find a
similar boom in the late 1980s which did not lead directly to a global systemic banking crisis
– there were widespread banking difficulties in the early 1990s but these were linked mainly
to commercial property exposures. This raises the question whether the received wisdom is
incorrect, and other factors than the housing boom caused the crisis, while macroprudential
policy is overly targeted at the control of house prices and lending per se. Accordingly, in this
paper we compare and contrast the cycles in house prices over 1985-94 with 2002-11. There
are more similarities than contrasts between the booms. Stylised facts include a similar rise in
real house prices where booms took place, and a marked rise in the real mortgage stock along
with real incomes. The aftermath periods are also comparable in terms of house price
changes. Econometrically, determinants of house prices are similar in size and sign from the
1980s to date. There remain some contrasts. Leverage rose far more in the later episode and
did not contract in the aftermath. Serial correlation of house prices, suggestive of
extrapolative expectations, is greater in the recent period. The earlier boom period showed
differences with average house price behaviour which was not mirrored in the most recent
boom and inflation was higher. Despite the contrasts, on balance we reject the idea that the
recent boom was in some way unique and hence the key cause of the crisis. There is a need
for further research to capture structural and conjunctural factors underlying the recent crisis
which differ from the earlier boom and some suggestions are made.
Keywords, House price booms, mortgage stock, housing markets
JEL classification: C52, E58, G21
1
Emails, a.armstrong@niesr.ac.uk (Armstrong) and e_philip_davis@msn.com (Davis). We thank John
Muellbauer, anonymous referees and participants at the NIESR/ESRC “Future of housing finance” conference at
the Ban k of England for helpful comments. Errors remain our own responsibility.
2
Introduction
The house price and lending boom of the 2000s is widely considered to be not only a unique
event but also the main cause of the global financial crisis that began in 2007, leading in turn
to the biggest losses in financial wealth for generations (IMF (2008a), Kemme and Roy
(2012)). Typical of current thinking is a speech earlier this year by Min Zhu, Deputy
Managing Director of the IMF who said “housing is an essential sector of the economy but
also one that has been the source of vulnerabilities and crises” (my italics). However, looking
to the past, we find a similar global housing boom in the late 1980s which did not lead
directly to a global systemic banking crisis – there were widespread banking difficulties in the
early 1990s but these were linked mainly to commercial property exposures (Davis 1995).
This raises the question whether the received wisdom is incorrect, and other factors than the
housing boom caused the crisis, while macroprudential policy is wrongly targeted at the
control of house prices and lending per se.
Accordingly, in this paper we compare the cycles and assess the evolution in house price
determination in major OECD countries over the past decades to see whether the current cycle
is unique. A key point in this context is that housing differs from other markets in that
informational reasons, transaction costs, credit rationing and supply side factors help explain
serial correlation and mean reversion in house prices which may in turn differ across countries
and time but may also lead to common patterns in global markets (Capozza et al 2002).
In terms of a comparison, we may ask whether the booms were similar in key features apart
from rising house prices, or were there major contrasts? We explore these questions via a
statistical comparison of roughly-defined booms periods as well as the “aftermath” of the
booms.2 We go on to assess whether there have been changes in the relationship of house
prices to their determinants more generally in the two main housing cycles since liberalisation
which in most OECD countries happened in the 1980s.3 Furthermore, it is a stylised fact that
mortgage debt should not have a direct influence on house prices in a liberalised financial
market such as characterised both the recent boom periods (since mortgage debt is then
demand-determined). We examine econometrically whether this was the case for the booms in
question. Finally we consider other unique factors that may distinguish the recent boom better
than house price and lending dynamics per se.
The paper is structured as follows. In the first section, we compare housing booms and assess
in particular the changes in real house prices and their main determinants, notably real
personal disposable income (RPDI) and real housing debt in 15 major OECD countries. In the
second section we briefly introduce work underlying house price equations before providing a
specification for house price determination (similar to Davis, Fic and Karim (2011)) in the
third section and results in the fourth. In the fifth section we look specifically at results for the
impact of credit supply on house prices, which is omitted by most extant specifications and in
the sixth we look at potential structural and conjunctural factors that may distinguish the
booms. The final section concludes.
1
Comparing global housing cycles
We prefer this word since house prices rarely “crash” in the way that financial asset prices do, not least owing
to the dual use of houses for consumption of housing services as well as for investment.
3
For example in the US, portfolio restrictions on banks and non-banks, prohibitions on adjustable rate
mortgages, tax inducements to non-banks and deposit rate ceilings were all abolished in the early 1980s
(Hendershott 1994). In following years, securitisation began to be prominent as a source of mortgage finance
albeit not attaining the importance it did in the 2000s.
2
3
We have quarterly data on house prices and other relevant macroeconomic and financial
variables covering both boom periods for 15 OECD countries, drawn from the BIS database.
We define the booms roughly as five year periods from 1985q1-1989q4 and 2002q1-2006q4,
in line with Dokko et al (2011) of the Fed and incorporating the periods that Igan and
Loungini (2012) of the IMF show for country-by country specific data on house price cycles.4
We also define an “aftermath” period for each boom which is the following five years,
namely 1990q1-1994q4 and 2007q1 -2011q4. It is in these periods that output typically
remained subdued and banking crises took place in certain countries,5 and falls in house prices
tended to occur with tight credit markets.
Our analysis of the booms and aftermath begins with Table 1 below which shows the relevant
changes in real house prices over the periods together with real personal disposable income (a
key determinant of house prices), the stock of real household sector debt6, nominal house
prices and real gross financial wealth.
Table 1: Changes in house prices, income, debt and wealth during booms
Percentage
change
Real house
prices
RPDI
Real household
debt
Nominal
house
prices
Real gross
financial
wealth
1985q1
-89q4
2002q1
-06q4
1985q1
-89q4
2002q1
-06q4
1985q1
-89q4
2002q1
-06q4
1985
q189q4
2002
q106q4
1985
q189q4
2002
q106q4
United Kingdom
71
49
23
10
74
50
29
17
14
40
48
1
-2
18
5
18
-3
France
28
64
14
11
51
42
Canada
32
25
17
19
53
44
Italy
32
20
17
3
88
40
Spain
110
62
27
17
23
83
Austria
68
-5
21
13
16
26
Netherlands
24
11
16
-2
16
42
Belgium
32
41
17
3
21
29
Denmark
-8
56
5
10
21
44
Ireland
12
48
16
18
38
145
Finland
56
32
24
17
78
83
Sweden
35
44
10
12
35
45
Japan
27
-17
22
4
59
0
112
31
6
49
60
76
190
81
25
46
8
33
91
78
33
65
44
4
78
35
36
90
4
21
56
67
69
35
52
-20
61
31
37
65
27
50
95
35
46
56
22
76
57
94
80
17
33
9
26
17
10
41
27
19
1
58
48
42
52
16
United States
12
Mean
Mean (boom
countries)
35
30
18
10
42
48
61
42
55
28
40
39
18
11
47
59
0.74
0.41
0.14
0.58
72
0.95
53
0.99
60
0.47
28
0.49
0.79
0.42
0.06
0.30
0.97
0.97
0.49
0.35
Germany
Correlation
Correlation
(boom countries)
4
IMF (2008b) date the end of the 2000s cycle in line with us, suggesting a corresponding overvaluation in the
“boom countries” at the end of the upturn of over 10%, with the exceptions being Finland and Canada.
5
Barrell et al (2010) show that the three year lagged difference of house prices is an important predictor of
banking crises in OECD countries.
6
We do not have mortgage debt for all countries so use this variable for comparability purposes – and because it
shows the overall vulnerability of the household sector.
4
Notes: Source:BIS and OECD. Real house prices and real household sector liabilities are deflated by the
consumers’ expenditure deflator. Calculations for the “boom countries” excludes Germany, Austria, Denmark
and Japan; it includes only UK, US, France, Canada, Italy, Spain, the Netherlands, Belgium, Ireland, Finland and
Sweden.
The table shows, first, that not all countries participated in both the first and the second global
house price boom. Using a rough benchmark of 10% rise in real house prices to qualify a
boom, Japan and Austria only experienced significant rises in house prices in the earlier
period, while Denmark saw large rises only in the later period. Germany did not experience
sizeable rises in real house prices in either period. The countries that saw rises of 10% or
more in both booms are the UK, US, France, Canada, Italy, Spain, the Netherlands, Belgium,
Ireland, Finland and Sweden. The average rise in house prices across all 15 countries was
somewhat lower in the latest boom than in the earlier one but when calculated only for the
boom countries mentioned above, it is almost identical at around a 40% rise in real house
prices. So in this fundamental aspect the boom periods are similar. As regards the dispersion
of real house price changes, it was lower in the more recent boom suggesting a role for
international contagion (the standard deviation of price rises in the boom countries was 17%
in the later boom and 29% in the earlier boom). Agnello and Schuknecht (2011) suggest that
global liquidity could have played an important role in occurrence of simultaneous housing
booms in the 2000s.
Real personal disposable income was considerably more buoyant in the earlier boom period
than in the 2002-6 period. On average incomes rose 18% in the 1980s and only 10-11% in the
2000s. On the other hand, the rise in household debt was higher in the later period, especially
for those countries that experienced booms in both periods, where the rise in the later period
was 59% compared to 47% in the earlier boom. We decided in the light of this to calculate
correlation coefficients for overall changes in each variable with real house prices in the
different boom periods. There are marked differences in that the correlation of RPDI with real
house prices was much higher in the earlier period, especially when one calculates across the
countries experiencing two distinct booms. On the other hand, Table 1 shows that the
correlation with household debt was markedly higher in the later period. This gives a starting
indication of differences between the booms that are worthy of further investigation.
Nominal house prices rose more in the earlier boom, corresponding to higher inflation in the
1980s. This in turn had an impact on real mortgage debt, with a greater reduction in value of
nominal debt in the earlier period. Real financial wealth grew much more in the earlier period
despite the stock market crash of 1987, rising at rates in excess of real house prices whereas in
the later boom real house prices rose more than wealth. Of course the series are not directly
comparable as real gross financial wealth rises due to accumulation as well as asset price
rises.
Table 2: Changes in house prices, income, debt and wealth during the aftermath of
booms
Percentage
change
Real house prices
RPDI
1990q1
-94q4
2007q1
-11q4
1990q1
-94q4
2007q1
-11q4
1990q1
-94q4
2007q1
-11q4
-21
-14
12
3
10
-8
United States
-3
-24
12
6
19
-9
Germany
-2
11
5
25
-7
United Kingdom
na
Real household
debt
Nominal
house
prices
Real gross
financial
wealth
1990
q194q4
2007
q111q4
1990
q194q4
2007
q111q4
-5
11
16
0
-17
9
21
16
29
-6
-4
2
5
Ireland
0
14
-4
15
8
Finland
-42
0
-13
8
-21
20
Sweden
-26
7
11
8
-18
28
Japan
-9
-8
9
0
19
-2
1
-8
45
22
13
38
28
9
14
-32
-7
-2
Mean
Mean (boom
countries)
-6
-7
7
2
8
10
9
-2
10
0
-7
-6
6
3
8
13
0.46
0.29
0.62
0.67
10
0.93
-2
0.97
9
0.41
-1
0.22
0.46
0.38
0.78
0.88
0.86
0.82
0.19
0.61
France
-8
-1
7
3
-4
22
Canada
-18
2
-1
11
13
36
Italy
12
-6
-2
-6
32
10
Spain
-7
-23
10
-2
8
-1
Austria
-2
4
12
0
14
5
Netherlands
21
-9
8
0
22
18
Belgium
14
7
14
2
9
23
Denmark
0
-26
8
2
-19
12
na
Correlation
Correlation
(boom countries)
Notes: See Table 1
7
9
3
-16
15
-5
18
-18
-48
13
17
-13
17
18
15
19
21
14
-3
-1
18
-16
-22
12
3
16
-17
-14
5
9
-2
-4
6
-4
5
0
Table 2 shows comparable data and calculations for the post-boom “aftermath” period for
each boom. The average change in real house prices was comparable in the earlier
“aftermath” from 1990-1994 with the more recent period covering 2007-11, both being
around -6 to -7%, despite the differing levels of general inflation. This masks considerable
cross country variation, however, with for example the UK, Sweden and Finland, that
experienced banking crises in the early 1990s, showing larger falls in the earlier period, and
the US and Spain among others in the later period On average, changes in personal income
were larger in the earlier period, at around 7% compared to 2%. On the other hand, real
mortgage debt rose more in the aftermath of the 2002-6 boom, at 10% or more compared to
8%. Again, this was not true of all countries, with the UK and US both showing falls in real
household debt over the more recent period, as households sought to delever. The correlation
of RPDI changes with real house prices is again lower in the later period while that of
household debt with house prices is higher, and is very high for the boom countries (0.88).
Meanwhile, nominal house prices rose in the aftermath of the earlier boom (reflecting general
inflation) while they fell in the later one. Similarly to income, real gross financial wealth rose
in 1990-4 while it was flat in 2007-11, reflecting the global financial crisis, Canada being the
main exception.7
Table 3: Indicators of leverage in booms and aftermath
Debt/personal income
ratio –
7
Debt/house prices –
percentage change
Debt/personal
income ratio –
Debt/house
prices –
We focus on the first moment in our presentation. We may add that housing markets are typically characterised
by less volatility than equity, bond or foreign exchange markets, but liberalised credit markets do give scope for
housing to be treated as an asset rather than only a source of housing services. Given the greater likely weight of
such investment demand in a boom we could expect house prices to be more volatile in such periods. We
calculate (not shown in detail) that house price volatility was higher in the earlier boom than the later one. Also,
in the 1985-1994 decade, house price volatility up to 1989 was considerably higher than in 1990-4, on average,
whereas in the 2002-11 period there was a rise in volatility after the onset of the banking crisis, a pattern which
was particularly apparent in the boom countries.
6
change in percentage
points
1985q189q4
United Kingdom
2002q106q4
1985q189q4
change in
percentage
points
1990q 2007q
1194q4
11q4
2002q106q4
percentage
change
1990q
194q4
2007q
111q4
25
30
2
1
-1
-10
39
7
3
6
25
15
1
-3
22
20
-1
-5
17
-1
10
-8
28
-9
France
9
12
18
-13
-4
10
5
23
Canada
14
16
16
15
7
24
39
34
Italy
8
14
42
17
7
9
17
17
Spain
-3
35
-42
13
-2
2
17
29
Austria
-1
8
-31
34
0
2
16
1
Netherlands
1
43
-6
28
7
24
1
30
Belgium
3
11
-9
-8
-2
10
-5
15
Denmark
8
44
32
-8
-33
16
-19
52
Ireland
13
84
23
65
-2
22
15
100
Finland
17
26
14
39
-9
10
35
20
Sweden
9
21
0
1
-12
24
12
20
21
-5
25
21
9
-5
31
7
8
23
9
15
-2
8
17
24
9
27
8
16
-1
11
18
28
United States
Germany
Japan
Mean
mean (boom
countries)
Notes: See Table 1
As a factor possibly underlying these patterns, as well as being of wider relevance to
macroprudential policy, we examine the behaviour of two indicators of financial fragility,
namely the household debt/personal income ratio (which is of course mainly housing debt),
and the household debt/house price ratio, a rough measure of leverage in housing. Note
however that the equilibrium level of the debt/income ratio may be rising, as cross country
analysis suggests that the income elasticity of credit exceeds 1 (Badev et al 2014). The
authors also note the ratio is higher in countries with mortgage bonds as a primary funding
source. Table 3 shows the more recent boom period was characterised by greater rises
leverage on both measures (and also from a higher base). On average, the debt/income ratio
for households rose by around 25 percentage points over 2002-6 as compared to only 8-9% in
1985-89. Obviously underlying this is the greater relative buoyancy of incomes in the earlier
period as shown in Table 1. Meanwhile, the rise in debt deflated by house prices was also
much higher in the recent boom, being around 15% compared with 8-9%.
These patterns are of interest since the earlier boom is often characterised as an adjustment to
desired levels of leverage following liberalisation, when in fact rises were smaller than in
recent years. This is an indicator of greater fragility of households in the 2000s. All other
things were not of course equal in that interest rates were typically higher in the earlier period,
meaning that the rise in the interest burden was less in the later period than if the same rise in
debt had occurred in the earlier period. That said, the recent rise in debt and in leverage did
leave many households vulnerable to negative equity when nominal house prices fell.
As regards the comparable figures for the aftermath periods, households reduced their
debt/income ratios in 1990-4 but they rose over 2007-11, albeit not in the UK or US. The
7
debt/house price ratio rose in both post-boom periods, with house price rises being lower than
changes in household debt. The run-up is remarkably high on average at around 20% in both
cycles.
Concluding this section, we have seen a great deal of commonality between the booms and
their aftermath from 1985-94 and 2002-11, notably in real house price rises and in their main
determinants. There are also some contrasts. These relate especially to weaker growth in
incomes in both the boom and the aftermath in the later period, while on most measures, debt
and indebtedness rose to a greater extent, even though house price patterns in both boom and
aftermath were on average very comparable. Correlations of house prices with income seem
to be lower and those with household debt higher in the later period. We now go on to further
investigate of possible changes and similarities to house price determination over the different
cycles since liberalisation, which is detailed in the following sections.
2
Specifications for house price determination8
Having looked statistically at the cycles we now seek an econometric approach to house
prices to assess differences across cycles more systematically. Typical estimates for
determination of house prices are in error correction format. There is first a cointegrating
levels equation which forms an inverted demand function for housing but also includes a
supply effect such as the stock of housing which determines the long-run price of housing
(Meen (2002), Barrell and Kirby (2004 2011) Adams and Fuss (2012), Loungini and Igan
(2012), Muellbauer and Murphy (2006, 2008), Capozza et al. (2002)) . The second stage
estimation of the dynamics recognises that actual house prices deviate from their fundamental
values in the short-run and typically uses an error correction framework to allow for these
differences. This allows the examination of factors that drive house price dynamics. The two
stages may be combined, as in our work shown below, in a single stage error correction
estimation.
In this context, considering housing as an asset among others, Capozza et al (2002)
specifically focus on the properties of serial correlation and mean reversion of house prices in
such an error correction framework. Informational reasons, transaction costs, credit rationing
and supply side factors help explain serial correlation and mean reversion which may in turn
differ across countries and time. To test the above proposition, they augment the long-run
relationship with dynamic terms according to:
Pt   Pt 1   ( Pt  Pt 1 )   Pt*
*
(4)
where
 is the serial correlation coefficient
 is the mean reversion coefficient to the gap with the long run value P* determined by the
cointegrating equation and the adjustment to disequilibrium 0    1
 is the immediate partial adjustment to the long run value
In general as  increases, the amplitude and persistence of the cycle will increase whilst as
 increases the frequency and the amplitude of the cycle will increase. Note that this
structure implies that house prices do not follow a random walk unlike tradable financial
assets but rather are predictable. We incorporate this structure into our own work, with the
8
This section draws partly on earlier work for the Swedish Riksbank by Davis, Fic and Karim (2011).
8
partial adjustment to the long term value being incorporated by dynamic difference terms in
each non-stationary variable.
For our long run we follow in the approach in the literature of a log-linear transformation of
all the variables, a cointegrating relationship would be identified with those fundamentals that
possess a unit root (defining P*). Studies vary in terms of the members of the vector of
fundamentals for the inverted demand function. For example, in Capozza et al. (2002) the set
of long-run determinants includes population levels, real median income levels, the long-run
(5 year) population growth rate, real construction costs and the user cost of housing. In
Muellbauer and Murphy (2008) the vector of long-run variables includes real disposable (nonproperty) income, the sum of mortgage rates and stamp duty rates, the national credit
conditions index and a term which interacts the mortgage rate with the credit conditions
index. Barrell, Kirby and Whitworth (2011) include the real borrowing rate, the 3-month
nominal interest rate, the loan-to-income ratio, the loan-to-value ratio, per capita real
disposable income, the ratio of the number of households to the housing stock, and the
number of households.9 Adams and Fuss (2010) include economic activity, construction costs
and the long term interest rate. Loungini and Igan (2012) model real house price changes as a
function of changes in disposable income, working-age population, equity prices, credit, and
the level of short- and long-term interest rates. Our previous work (Davis et al 2011) in line
with but also broadening the literature, used real personal disposable income, the real long
rate, real household liabilities, real gross financial wealth, the unemployment rate, log real
housing stock and 20-39 as a share of population (the main house buying cohort).
As regards econometric approaches, the studies cited above among others specify dynamics
by using autoregressive distributed lag models in panel error correction form, with a one
period lag on the long run to control for endogeneity. The VAR (Hott and Monin, (2008),
Calza et al (2013)) and the SVAR (Tsatsaronis and Zhu, 2004) are also commonly used to
estimate dynamics since such studies can then focus on the interdependencies of house prices
and their determinants such as term spreads, house price inflation, GDP growth and the
growth rate of private sector credit,. Other approaches include the VECM (Kemme and Roy
(2012), Gattini and Hiebert (2010), Lindner (2014)) and spatio-temporal impulse responses to
gauge the degree to which shocks diffuse over time and space (Holly, Pesaran and Yamagata
(2010)) Some recent studies have looked at housing booms and busts as individual
observations and estimated determinants by probit (Agnello and Schuknecht (2011), Benetrix
et al (2012)..
Whereas many studies have focused on house price determination in an individual country
(such as Muellbauer and Murphy (1997, 2008) and Barrell et al (2011) for the UK and
Lindner (2014) for the US) a number of recent pooled or panel studies are also extant. Besides
our own work (Davis et al 2011) for 18 OECD countries, which was focused on the possible
use of macroprudential tools in housing, Capozza et al (2002) look at US Metropolitan areas,
Adams and Fuss (2010) apply panel cointegration to 15 countries using Dynamic Ordinary
Least Squares, while Igan and Loungini (2012) apply pooled OLS to 22 countries.
All of these approaches are fraught with identification problems, which make it difficult to
separate supply and demand factors, and exogenous and endogenous determinants of house
prices. All work on house prices faces this challenge and there is no definitive solution.
Concerning identification in error correction models,10 there are several hard to observe
9
Estimating solely for the UK, there is scope for a much wider range of variables than in panel studies such as
Adams and Fuss (2010), Loungini and Igan (2012) and our own work
10
We thank John Muellbauer for these insights.
9
variables in a house price model: the risk premium and expected appreciation. Identifying
these would be a problem inside or outside the single equation framework. So it will always
be hard to give strict structural interpretations to an error correction model in the absence of
very good survey data that tried to measure these concepts. However, it can still be argued
that on reasonably plausible assumptions, one can still identify structural parameters such as
the implied income elasticity of demand for housing and the implied price elasticity by
estimating an inverse demand model, as do the authors above. Muellbauer argues that if the
risk premium is determined by the same variables as house prices, then one can still identify
the income or price elasticity of demand. Meanwhile expected appreciation may be captured
by a lagged difference as in most extant work. We follow his approach in our work.
Meanwhile SVARs can impose appropriate identifying restrictions, while in VARs and
VECMs shocks can be identified using the Chelsi decomposition.
Some variables have typically been omitted from house price equations, although economic
reasons for their inclusion can be suggested. For example, unemployment may impact on
house prices via demand and also if it entails widespread defaults and consequent “fire sales”
but is typically not included in house price equations. Indeed in Andrews (2010) the
unemployment rate is used as part of the identification framework as a form of demand shock.
Financial liberalisation distinguishes periods when there is or is not credit rationing and is
also used by Andrews (ibid) as showing demand shocks. Banking crises give rise to
uncertainty and credit rationing that other variables may not adequately capture and is a third
form of demand shock. We add all three of these variables to our work.
Mortgage spreads (loan less deposit rates) are also typically not included in house price
equations, whereas these could be relevant to the impact of capital requirements on interest
rates, as in Barrell et al (2009) and Davis and Liadze (2012) and have important
consequences for household incomes as well as for house price dynamics.
Furthermore, although housing is part of the asset portfolio of the household sector, most
studies do not include household gross financial wealth, as a substitute asset, a rise in whose
value would lead to rising demand for housing for portfolio balance reasons. Another
portfolio effect could be included via the long term interest rate, which is both a proxy for the
user cost (especially influencing mortgage rates) but also the opportunity cost of investing in
housing when the bond yield changes (Adams and Fuss 2010).
3
Specification and data
In the light of the data and the above brief literature survey, we sought to estimate panel
equations for house prices in OECD countries. Given the extensive availability of crosscountry data from the BIS, UN and OECD databases,11 we have scope to investigate the
common patterns of property price movements, while at the same time controlling for
heterogeneity across time in housing dynamics as well as between countries. From an
econometric perspective, a panel approach gives more informative data, more variability, less
collinearity among variables, more degrees of freedom and more efficiency (Baltagi, 2005, p.
5). Following Capozza et al (2002) we allow for serial correlation and mean reversion as well
as sensible long run variables in an inverse demand function estimated as an error correction
model
The data sample we are able to use for most countries is back to the 1970s. We hence include
periods when there has been liberalisation as well as structural regulation in the housing
11
Note that the population data that we use are interpolated annual data from the UN Demographic database.
10
market. This can be justified by the need for cointegration equations to have as long a data
period as possible, but will also enable us to capture the differences in behaviour between
liberalised and non-liberalised periods as well as between the cycles incorporating boom
periods outlined in the tables of Section 1. We accordingly estimate for three sub-periods
namely the pre-liberalisation period before 1982, the first post liberalisation cycle over 19821997 and the second broad cycle over 1998-2013. Note that we use quarterly data for the
cross country panel work and focus on the boom countries, namely UK, US, France, Canada,
Italy, Spain, the Netherlands, Belgium, Ireland, Finland and Sweden.
Our variables are as follows: log real house prices, log real personal disposable income, the
real long rate, log real household liabilities, log real gross financial wealth, unemployment
rate, log real housing stock and 20-39 as a share of population (the main house buying cohort
– which in countries such as the UK is also strongly driven by immigration in recent years, in
turn affecting house prices). The Im-Pesaran-Shin panel unit root tests for the main variables
(not illustrated) show most variables, being trended, are I(1) thus justifying an error
correction model based approach to estimation, while the share of 20-39s is stationary (I(0)).
Changes in real house prices were regressed on contemporaneous changes in explanatory
variables, and lagged dependent and explanatory variables (both in levels) as well. This errorcorrection specification is able to deal with non-stationarity in the data (as mentioned above),
and at the same time distinguishing short- and long-run influences, and differences between
cycles. The significance of the coefficients for lagged non-stationary variables (in levels) and
their magnitude reveal the long-term relationship among those variables.
Our modelling started from the approach of Capozza et al (2002) set out above with variables
as in Davis, Fic and Karim (2011) with a basic set of variables including real house prices,
real personal disposable income and the long term real interest rate (proxying the user cost),
to which we add extra variables in difference and level; rate of unemployment, real gross
financial wealth (as a portfolio balance effect), housing stock (lag only), the population of 2039 as a proportion of the total (lag only) and dummies for financial crises and the onset of
liberalisation. We undertake panel regression that treats all countries as equally important,
while the fixed effects take account of heterogeneity, and we impose cross section weights.
The breakdown over sub periods offer deeper insights by allowing for richer heterogeneity,
e.g. distinctive economic determinants in each sub-sample (compared to the full sample
regression). The combination of the full period regression and the sub-sample panel
regressions reveal elements of both commonality and uniqueness in cycles in those countries.
To confirm the existence of the long-term relationship, we also implement the panel
cointegration test proposed by Kao (1999) among those variables with significant lagged level
terms in a simple levels equation (i.e. the first step of an Engle and Granger (1987) two-step
estimation).
4
Results
Further to the discussion above, we present the results for an extended equation including
house prices, RPDI and real long rates but also including the log of real gross financial
wealth, the unemployment rate, the log of the real housing stock and the 20-39 age group as a
share of the population (see Davis Fic and Karim 2011 for earlier estimates of such a wider
specification using annual data).
Table 4: Panel results for the log difference of house prices – boom countries
All
Pre 1982
1982-1997
1998-2013
11
0.001
(0.1)
-0.77**
(2.4)
0.25**
(2.1)
0.093
(1.2)
0.17**
(6.7)
-0.00011
(0.2)
0.25**
(3.3)
0.00099
(0.5)
0.15**
(4.0)
-0.00094
(1.0)
0.19**
(5.5)
-7.13E-05
(0.1)
0.56**
(28.1)
-0.0097**
(4.7)
-9.26E-05
(0.0)
-0.0008**
(4.0)
0.41**
(7.1)
-0.045**
(2.4)
0.078
(1.5)
-0.00071
(0.7)
0.53**
(15.7)
-0.034**
(5.5)
-0.0073
(0.5)
-0.00073*
(1.7)
0.54**
(16.6)
-0.013**
(3.0)
0.043**
(3.6)
-0.00062
(0.7)
0.032
(1.4)
-0.0054
(1.1)
-0.61*
(1.9)
0.027
(0.5)
0.12*
(1.8)
-0.037**
(2.7)
-0.081*
(1.8)
-0.031**
(3.1)
-0.0041**
(3.5)
-1.47E-05
(0.1)
-0.0077*
(1.7)
-0.00052
(0.3)
-0.0048**
(2.9)
-0.00074**
(2.3)
-0.0027**
(2.1)
-0.00054*
(1.8)
0.053**
(4.6)
0.052*
(1.6)
0.07**
(4.1)
0.038**
(2.5)
0.008**
(3.5)
-0.0032**
(2.7)
-0.00089
(1.1)
0.028**
(5.2)
-0.0034**
(2.2)
-0.014**
(2.2)
-0.0038**
(2.5)
Constant
Log
difference of
RPDI
Difference
real long rate
Log
difference of
house prices
(-1)
Log of house
prices (-1)
Log of
RPDI(-1)
Real long
rate (-1)
Population 2039 as share of
total (-1)
Log stock of
housing (-1)
Difference of
unemployment
rate
Unemployment
rate (-1)
Log difference
of real gross
financial
wealth
Log of real
gross financial
wealth (-1)
Dummy for
banking crises
Dummy for
financial
liberalisation
Countries
Obs
Adjusted R2
SE of
regression
Durbin
Watson
Kao
0.00026
(0.2)
11
1612
0.5
0.16
10
275
0.38
0.02
11
687
0.53
0.16
11
650
0.6
0.011
2.13
2.09
2.09
2.11
-1.58
-1.85
-2.37
-2.54
(0.06)*
(0.03)**
(0.01)**
(0.01)**
Notes: (-1) indicates a first lag. Boom countries for both recent cycles are the UK, US, France, Canada, Italy,
Spain, the Netherlands, Belgium, Ireland, Finland and Sweden. Estimated using fixed effects and cross-section
weights. Coefficients marked ** are significant at the 95% level and * are significant at the 90% level (t values are in
brackets under each coefficient).
We find a consistent short run income effect, albeit it is lower after liberalisation. On the other
hand, the short run effect of interest rates is insignificant. The serial correlation effect is very
strong (i.e. the lagged first difference of real house prices) and rising over the sample. As
noted above this implies a higher amplitude and persistence of the cycle and a growing role
for extrapolative expectations in most recent cycles. The lagged house price variable is
generally significant. The implied speed of adjustment to the long run is lower since
liberalisation, suggesting longer cycles. Adams and Fuss (2010) find a similar long
adjustment period of 14 years in a cross country panel on a recent sample. The long run
12
income effect is positive and significant but only in the most recent period. The long run
interest rate effect is significant at the 10% level in the 1982-97 period only.
For the population distribution, signs change between periods. The share of 25-39’s in the
total population who are the main house buyers may be overwhelmed by the ageing of the
large baby boom generation that has the resources to buy houses at any age. The long run
effect of the housing stock is significant post liberalisation with an expected negative sign
whereby a higher stock (indicating greater supply) leads to lower house prices. The change in
unemployment is generally significant, albeit lower post liberalisation. The long run effect of
unemployment is significant post liberalisation. The short run financial wealth effect is
generally significant and positive, suggesting a portfolio balance effect (higher financial
wealth is distributed to housing as an additional asset). On the other hand, whereas the long
run financial wealth effect is significant post liberalisation its sign changes (this may reflect
stock market patterns). The banking crisis dummy is consistently significant, while the
liberalisation dummy is not. The Kao (1999) tests show consistent cointegration in the first
stage levels variables. On balance we suggest that these results do not suggest radical
differences between the two cycles since liberalisation.
Table 5: Leveraged coefficients for 1982-1997 (in regression 1982-2013)
Log difference of RPDI
Difference real long rate
Log difference of house prices (-1)
Log of house prices (-1)
Log of RPDI(-1)
Real long rate (-1)
Population 20-39 as share of total (-1)
Log stock of housing (-1)
Difference of unemployment rate
Unemployment rate (-1)
Log difference of real gross financial wealth
Log of real gross financial wealth (-1)
Coefficient
0.022
0.0015
-0.029
0.0087
-0.0024
0.0011
-0.051
0.0031
-0.0048
0.00041
0.034
-0.00066
T-value
(0.4)
(0.8)
(0.6)
(2.3)**
(0.8)
(1.2)
(1.3)
(1.0)
(2.2)**
(1.1)
(1.4)
(0.3)
Notes: (-1) indicates a first lag. Boom countries for both recent cycles are the UK, US, France, Canada, Italy,
Spain, the Netherlands, Belgium, Ireland, Finland and Sweden. Estimated using fixed effects and cross-section
weights. Coefficients marked ** are significant at the 95% level and * are significant at the 90% level (t values are in
brackets under each coefficient). Coefficients shown in Table 4 are also included but not reported.
Complementing Table 4, in Table 5 we show leveraged coefficients for the earlier cycle 198297 in a regression for 1982-2013. This shows that the only significant differences are mean
reversion being lower in the 1997-2013 period, while the impact of unemployment was higher
in the 1980s. Serial correlation is the same. Overall, this is strong evidence that the cycles are
similar.
In a further exercise we looked at leveraged effects during the booms, testing whether there is
a differential effect of the determinants in such periods, as shown in Table 6.
Table 6: Panel results for the log difference of house prices – boom countries – leveraged
coefficient for booms
Estimation period, 1982q1 to 2013q4
Leveraged
coefficient for
period 1985q11989q4 and
Leveraged
coefficient
for period
1985q1-
Leveraged
coefficient
for period
2002q1-
13
2002q12006q4
Log difference of RPDI
Difference of real long
rate
Log difference of house
prices (-1)
Log of house prices (-1)
Log of RPDI(-1)
Real long rate (-1)
Population 20-39 as share
of total (-1)
Log stock of housing (-1)
Difference of
unemployment rate
Unemployment rate (-1)
Log difference of real
gross financial wealth
Log of real gross financial
wealth (-1)
0.10*
(1.9)
0.0036**
(2.4)
0.099**
(2.2)
0.0016
(0.6)
0.002
(0.8)
-0.00012
(0.3)
0.048*
(1.7)
-0.0025
(1.4)
0.0032
(1.0)
0.00033
(1.1)
0.014
(0.6)
-0.0018
(0.6)
1989q4
0.22**
(3.3)
0.0034**
(2.0)
0.076
(1.5)
0.0047
(1.5)
-0.00089
(0.2)
-0.00048
(0.6)
0.047
(1.1)
0.00068
(0.2)
-0.0016
(0.3)
0.00047
(1.3)
0.023
(0.9)
-0.0072*
(1.7)
2006q4
-0.083
(1.0)
0.00099
(0.3)
0.015
(0.2)
-0.015*
(1.9)
0.0041
(1.1)
-0.0023
(1.0)
0.064
(1.5)
-0.0036
(0.9)
0.0032
(0.7)
-0.00041
(0.5)
-0.0078
(0.2)
0.00091
(0.1)
Notes: (-1) indicates a first lag. Boom countries for both recent cycles are the UK, US, France, Canada, Italy,
Spain, the Netherlands, Belgium, Ireland, Finland and Sweden. Estimated using fixed effects and cross-section
weights. Coefficients marked ** are significant at the 95% level and * are significant at the 90% level (t values are in
brackets under each coefficient). Coefficients shown in Table 4 are also included but not reported.
Leveraged coefficients show a higher effect for the rise in RPDI and a lower (negative) effect
for RR. There is shown to be more serial correlation with a larger coefficient on the lagged
difference of house prices, consistent with the suggestion in Dokko et al (2011) and Shiller
(2007) that expectations of future house price growth among borrowers, lenders and investors
plays a key role in bubbles. The demographic effect of a higher number of 25-39 year olds has
a higher effect in booms also consistent with Muellbauer and Murphy (1997) on the 1980s
boom in the UK. In the extended equation, it is again in the 1985-89 case that there are larger
effects of rising income and lesser effects of rising interest rates. The earlier boom also saw a
lower long run effect of gross financial wealth and a higher effect of debt, suggesting
households were leveraging themselves into real assets and partly substituting out of financial
assets. The only difference for the later boom in the leveraged coefficients is in the long run
adjustment coefficient, with a significant negative sign suggestive of more rapid adjustment to
long run equilibrium. All of these leveraged results are of potential relevance for
macroprudential policy, suggesting normal house price behaviour in respect of determinants is
not always maintained in booms. On the other hand they should not be exaggerated, for the
most part the equations are stable.
5
House prices and mortgage supply
Mortgage market innovations that have greatly altered the terms and availability of credit
have emerged in OECD financial markets over the past 30 years (OECD, 2005). Financial
deregulation in the 1980s not only increased competition, it has also led to the creation of new
products such as buy to let mortgages, interest only loans and offset mortgages which allow
borrowers to offset their savings against the mortgage balance. Meanwhile, the widespread
development of the securitisation markets in the 2000s, following their earlier evolution in the
US (Hendershott 1994) eased access to mortgage credit further since it is no longer limited by
the capital of the originating institution.
14
As a result of such innovations, the availability of mortgage credit has risen dramatically in
Europe and the US. Miles and Pillonca (2008) note that although the mortgage debt to GDP
ratio varies across Europe (exceeding 70% in countries like the UK and Denmark), the stock
of mortgage debt has risen in all cases. Consequently house buyers have seen a relaxation in
their borrowing constraints and they contend that this has fed back positively to house prices.
Few house price models have taken these fundamental changes into account. Indeed, a key
question raised by financial liberalisation is whether the stock of mortgages is appropriately
included in house price equations. This was traditionally the case in pre liberalisation
estimates in countries such as the UK (e.g. Hendry 1984) but was judged by authors such as
Muellbauer and Murphy (1997) to be inappropriate in a post liberalisation sample, since the
stock of lending is endogenous to the determination of house prices. On the other hand, if
there remains a degree of rationing for some participants in the housing market, then the
mortgage stock could have a role to play, and all the more if macroprudential policies have an
effect of reintroducing forms of credit rationing.
An alternative way of considering this question is set out in Lindner (2014), who notes there
are two alternative views of the link from asset prices (such as those of housing) to credit. The
first is the Bernanke and Gertler (1989) and Kyotaki and Moore (1987) view that it is asset
prices that drive credit availability via changes in the net worth of borrowers that in turn eases
borrowing constraints in the presence of asymmetric information. This is consistent with the
exclusion of credit from house price equations. On the other hand, Allen and Gale (2000)
suggest that the availability of credit is the more exogenous factor, with the key influence
being risk shifting by lenders and borrowers in the presence of asymmetric information and
limited liability, with consequent moral hazard. These may in turn be facilitated by financial
deregulation. Lindner (2014) suggests that the net worth argument is most relevant to credit
availability in general whereas risk shifting is appropriate for the financing of a particular
asset such as housing by credit. Consistent with this, empirical studies using total credit (such
as Davis and Zhu 2011) tend to be more consistent with one-way causality from asset prices
to credit than those focused on housing (such as Gimeno and Martinez-Carrascal 2010) which
find two way causality. Lindner (2014) finds mortgage credit does drive house prices in the
US although there is also Granger causality in the other direction.
Calza et al (2013) show that the structure of housing finance has an impact on the
transmission of interest rates to both house prices and consumption. Igan and Loungini (2012)
find a significant effect of the difference of credit but add that due to potential endogeneity
they comment that “we refrain from interpreting the positive correlation between credit
growth and house price appreciation as causation and leave establishment of such a causal
link for further research” (ibid p16) We proxy credit to attempt to overcome this problem.
Meanwhile, Muellbauer and Murphy (2008) include a credit conditions index which they
introduce both alone and as an interaction term with the mortgage rate. The credit conditions
index is constructed using 10 consumer credit and mortgage market indicators as described in
Fernandez-Corugedo and Muellbauer (2006). It is included so as to capture shifts in the credit
supply function faced by households in the post-1980s era. The authors note that by omitting
this variable, previous house price models in the literature (which typically utilise pre-1980s
data) suffer from omitted variable bias. Meanwhile, Claessens et al (2011) contend that credit
spreads and credit conditions may be more relevant to macroeconomic trends than the volume
of credit.
15
In our work we use the simpler measure of the real stock of mortgages as a credit variable, to
provide some suggestive results on the potential effects of credit and liberalisation thereof in
the different booms.
Table 7: Panel results for the log difference of house prices – boom countries – adding
debt variables
All
Proxy for log
difference of real
household debt
Log of real
household debt(1)
Pre 1982
0.092**
(11.0)
-0.0022
(0.9)
1982-1997
0.11**
(4.9)
0.0082
(0.2)
1998-2013
0.07**
(5.5)
-0.0047
(0.8)
0.1**
(6.4)
-0.0046
(0.8)
1982-2013
0.088**
(9.1)
-0.004*
(1.7)
Notes: (-1) indicates a first lag. Boom countries for both recent cycles are the UK, US, France, Canada, Italy,
Spain, the Netherlands, Belgium, Ireland, Finland and Sweden. Estimated using fixed effects and cross-section
weights. Coefficients marked ** are significant at the 95% level and * are significant at the 90% level. (t values are in
brackets under each coefficient) Coefficients shown in Table 4 are also included but not reported.
We went on to test within the panel error correction framework by adding the level and
difference of the real mortgage debt stock to the extended equation. As regards mortgages, no
long run effect of the debt stock on house prices is detectable, even pre liberalisation; on the
other hand, the short run effect is consistently significant (proxied by lags to avoid
simultaneity). Credit is shown to have a short run but not a long run impact on house prices
during boom periods, justifying a focus of macroprudential policy on credit for this reason as
well as due to risk,but with no major distinction for the latest cycle.
Table 8: Panel results extended equation – boom countries – leveraged coefficient for
booms and aftermaths
Estimation period,
1982q1 to 2013q4
Log difference of real
liabilities (proxy)
Log real liabilities (1)
Log difference of real
liabilities (proxy)
Log real liabilities (1)
Leveraged
coefficient for
period 1985q11989q4 and
2002q12006q4
Leveraged
coefficient
for period
1985q11989q4
0.034*
(1.7)
0.00053**
(3.6)
0.028
(1.3)
0.00071**
(3.6)
Leveraged
coefficient for
periods
1990q11994q4 and
2007q12011q4
Leveraged
coefficient
for period
1990q11994q4
0.016
(0.7)
-9.8E-05
(0.7)
0.061*
(1.8)
0.00033*
(1.6)
Leveraged
coefficient
for period
2002q12006q4
-0.0014
(0.1)
-0.00012
(0.7)
Leveraged
coefficient
for period
2007q12011q4
0.045
(1.3)
-0.00016
(0.8)
Notes: (-1) indicates a first lag. Boom countries for both recent cycles are the UK, US, France, Canada, Italy,
Spain, the Netherlands, Belgium, Ireland, Finland and Sweden. Estimated using fixed effects and cross-section
weights. Coefficients marked ** are significant at the 95% level and * are significant at the 90% level (t values are in
brackets under each coefficient). Coefficients shown in Table 4 are also included but not reported.
16
Using leveraged coefficients, we see that both the difference and the level effect of credit is
significantly more positive in booms than in other periods while there is no corresponding
effect in the aftermath except in 1990-4when effects were again more sizeable. In other
words, both a rise in credit and a higher level have a significant effect on house prices. This is
consistent with the suggestion that financial liberalisation had a significant effect on the
booms, again offering grounds for caution in macroprudential policy. This effect was most
strongly present in the earlier boom and not in the recent one, suggesting that the recent boom
is not out of line with historical experience.
6
Potential underlying factors
In this paper we have focused on the actual differences between booms rather than underlying
determinants of the differences. We have seen that the differences both statistically and
econometrically are fairly minor, suggesting the housing cycle itself was not core to the recent
crisis. As we conclude, we note briefly some structural differences between the 1980s and
2000s, common to a number of countries, that could underlie the differences and warrant
further research, not least as background for macroprudential policy.
Levels of debt and the relation to inflation. The earlier boom began at a much lower level
of the debt/income ratio, and followed a period of credit rationing. Accordingly, the earlier
boom is commonly cited as an adjustment to desired levels of debt. In contrast, the later boom
followed a period of less restricted availability of debt. In this context, it is interesting that
debt/income rose more in the more recent boom, which may of course link partly to higher
inflation in the 1980s affecting real debt more than real income.
Interest rates and the impact of global liquidity. Although the equations take into account
the levels and changes in real long term interest rates, there may be further investigation
warranted in terms of short rates and the response of monetary policy to high levels of global
liquidity, which in turn implied common house price patterns across countries (Agnello and
Schuknecht 2011).
Patterns of securitisation. Whereas as noted by Hendershott (1994), securitisation in the US
began to have an impact in that country’s housing market in the 1980s, it is in the 2000s that
securitisation has had a much more global impact, as well as being higher risk as private
securitisations became more dominant. Higher risk lending may underlie the higher serial
correlation found in house prices.
Changing patterns of owner occupation. If owner occupation is itself changing then the
pattern of debt/income has a different implication from a constant level of owner occupation.
Patterns for the UK show a marked rise in the 1980s following the “right to buy” council
houses whereas in the US the main recent rise in owner occupation was over the period 1994200412 (see Ortalo Magne and Rady (1999) for an analysis of the UK in this context).
Population density. Miles (2012) develops a model of the housing market where the major
determinant of house price rises relative to incomes is the evolution of population density.
Rising population density together with buoyant population and incomes increasingly
generate price responses and diminishing rises in the stock of housing as supply is less elastic
12
UK owner occupation for example rose from 50% in 1971 to 69% in 1991, whereas it fell in the 2001-2011
period from 69% to 64%. US owner occupation was flat from 1985-1990 then rose from 64% to 69% in the
period 1994-2004 but then fell back to 65% in 2014.
17
in densely populated countries. The related patterns for the different credit booms across the
OECD countries warrant investigation.
Behaviour of banks while our regressions show similar responses of house prices to their
direct determinants, a key difference in the cycles was clearly the impact a given change in
house prices had on financial markets, ratings and the behaviour of banks. Banks in the 1980s
had already suffered the LDC debt crisis of 1982 so those affected would likely be more
cautious in mortgage lending. Also the global transmission of risk was much greater in the
2000s (e.g via mortgage backed securities) as was opacity of credit markets.
Conclusion
In this article, we have undertaken a statistical and econometric comparison of house price
and mortgage behaviour in the booms of the 1980s and the 2000s. There are more similarities
than contrasts between the booms. Stylised facts include a similar rise in real house prices
where booms took place, and a marked rise in the real mortgage stock along with real incomes
and financial wealth. The aftermath periods are also comparable in terms of house price
changes and related determinants. Econometrically, determinants of house prices are similar
in size and sign from the 1980s to date. There remain some contrasts. Leverage rose far more
in the later episode and did not contract in the aftermath. Serial correlation of house prices,
suggestive of extrapolative expectations, is greater in the recent period. The earlier boom
period showed differences with average house price behaviour which was not mirrored in the
most recent boom and inflation was higher.
Despite the contrasts, on balance we reject the idea that the recent boom was in some way
unique and hence the key cause of the crisis. This poses a challenge for the existing narrative
claiming the housing boom was the unique and key determinant of the crisis. We suggest that
other factors distinguishing the cycles that warrant further research include the initial level of
debt/income and the related impact of inflation, the impact of lower interest rates in the recent
boom and global contagion via liquidity in the recent episode; the ready availability of credit
from mortgage bond issuance. Also changing owner occupation rates and patterns of
population densities may have had a markedly different effect across the booms. And of
course the behaviour of banks and financial markets differed All of these factors may need to
be allowed for in macroprudential policies.
References
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