Comparing housing booms and mortgage supply in the major OECD countries.
Armstrong, Angus ; Davis, E. Philip
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" (our 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 asset 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 boom periods as well as the 'aftermath' of the
booms. (1) 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. (2) 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 fifteen 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.
I. Comparing global housing cycles
We have quarterly data on house prices and other relevant
macroeconomic and financial variables covering both boom periods for
fifteen 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 Foungini (2012) of the IMF show for country-by country
specific data on house price cycles. (3) We also define an
'aftermath' period for each boom which is the following five
years, namely 1990Q1-94Q4 and 2007Q1-11Q4. It is in these periods that
output typically remained subdued and banking crises took place in
certain countries, (4) and falls in house prices tended to occur with
tight credit markets.
Our analysis of the booms and aftermath begins with table 1, 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 debt, (5) nominal
house prices and real gross financial wealth.
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 per cent 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 per cent 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 fifteen 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
per cent 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 per cent in the later boom and 29 per cent 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 per cent in the 1980s and only 10-11 per cent 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 per cent compared to
47 per cent 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.
Meanwhile nominal house prices rose more in the earlier boom,
corresponding to higher inflation in the 1980s. This in turn had an
impact on real household 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 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
1990-94 with the more recent period covering 2007-11, both being around
-6 to -7 per cent, 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 per cent
compared to 2 per cent. On the other hand, real household debt rose more
in the aftermath of the 2002-6 boom, at 10 per cent or more compared to
8 per cent. 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 1990-94 while it was flat in 2007-11, reflecting the global
financial crisis, Canada being the main exception. (6)
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 one (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 in leverage on both measures (it was 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
per cent in 1985-9. 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 per cent compared with 8-9
per cent.
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 and
from a lower base. 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 1990-94 but they rose
over 2007-11, albeit not in the UK or US. The 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 per cent 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 house price determination over the different
cycles since liberalisation, to assess differences across cycles more
systematically, which is detailed in the following sections.
2. Specifications for house price determination (7)
Typical estimates for determination of house prices are in two
parts. 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, Kirby and Whitworth, 2011; Adams and Fuss,
2010; Igan and Loungini, 2012; Muellbauer and Murphy, 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:
[DELTA][P.sub.t] = [alpha][DELTA][P.sub.t-1] +
[beta]([P.sup.*.sub.t] - [P.sub.t-1]) + [gamma][DELTA][P.sup.*.sub.t]
(1)
where [alpha] is the serial correlation coefficient, [beta] is the
mean reversion coefficient to the gap with the long-run value [P.sup.*]
determined by the cointegrating equation and the adjustment to
disequilibrium 0<[beta]<1. [gamma] is the immediate partial
adjustment to the long-run value.
In general as [alpha] increases, the amplitude and persistence of
the cycle will increase whilst as [beta] 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 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, where a cointegrating
relationship would be identified with those fundamentals that possess a
unit root (defining [P.sup.*]). 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 (non-property) 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. (8) Adams and Fuss (2010) include economic activity,
construction costs and the long-term interest rate. Igan and Loungini
(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 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 eighteen 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 fifteen 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, (9) there are
several hard-to-observe variables in a house price model, notably 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. Notably, if the risk premium is determined by
the same variables as house prices, then one can still identify the
income or price elasticity. Meanwhile expected appreciation may be
captured by a lagged difference as in most extant work. We follow this
approach, due to John Muellbauer, in our work. (10) Meanwhile SVARs can
impose appropriate identifying restrictions, while in VARs and VECMs
shocks can be identified using the Choleski 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, although 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 cross-country 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 goes back to
the 1970s. We hence include periods when there has been liberalisation
as well as structural regulation in the housing 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-hberalised periods as well as
between the cycles incorporating boom periods outlined in the tables of
Section 1. We also estimate for three sub-periods, namely the
pre-liberalisation period before 1982, the first postliberalisation
cycle over 1982-97 and the second broad cycle over 1998-2013, and for
some estimates the full 1982-2013 period. 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 as defined above.
Our modelling started from the approach of Capozza et al. (2002)
set out above with variables as in Davis, Fic and Karim (2011).
Accordingly, 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). We also
include dummies for banking crises and financial liberalisation. (12)
The Im-Pesaran-Shin panel unit root tests for the main variables (not
illustrated) show most variables, being trended, are 1(1) thus
justifying an error correction model-based approach to estimation, while
the share of 20-39s is stationary (1(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 error-correction specification is able to deal with nonstationarity
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.
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 offers
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).
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) also find a long adjustment period of fourteen years in a
cross-country panel on a recent sample. The long-run income effect is
positive and significant but only in the most recent period. The
long-run interest rate effect is significant at the 10 per cent level in
the 1982-97 period only.
For the population distribution, signs change between periods. The
share of 25-39 year olds 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.
Complementing table 4, and confirming this insight, we estimated
leveraged coefficients for the earlier cycle 1982-97 in a regression for
1982-2013 (not shown in detail). This shows that the only significant
differences between cycles 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.
Leveraged coefficients show a higher effect for the rise in RPDI
and a lower (negative) effect for real long rates. There is shown to be
more serial correlation in booms 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 play a key role in
bubbles. The demographic effect of a higher number of 25-39 year olds
has a higher effect in booms, consistent with Muellbauer and Murphy
(1997) on the 1980s boom in the UK. In the extended equation, it is in
the 1985-9 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.
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 per cent 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.
In the wider literature, 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, "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, p. 16). 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 ten 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, including those
incorporating volumes of credit. Meanwhile, Claessens et al. (2011)
contend that credit spreads and credit conditions may be more relevant
to macroeconomic trends than the volume of credit.
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.
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 debt (table 6), 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, j ustifying 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.
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 (table 7) while there is no corresponding effect in the
aftermath except in 1990-94, when effects were again more sizeable. In
that period, 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 (table 1), 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.
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 1994-2004
(13) (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 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. Many banks in the 1980s had already
suffered the LDC debt crisis of 1982 so those banks 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
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.
Mean reversion of house prices is greater in the earlier 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.
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NOTES
(1) We prefer the word 'aftermath' 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.
(2) 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. See also OECD (2000) for a compendium of
liberalisation measures in the major countries studied here.
(3) IMF (2008b) dates 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 per cent, with the
exceptions being Finland and Canada.
(4) Barrell et al. (2010) show that the three-year lagged
difference of house prices is an important predictor of banking crises
in OECD countries.
(5) 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.
(6) 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 1985-94 decade, house price volatility up to 1989 was
considerably higher than in 1990-94, 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.
(7) This section draws partly on earlier work for the Swedish
Riksbank by Davis, Fic and Karim (2011).
(8) 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),
Igan and Loungini (2012) and our own work.
(9) We thank John Muellbauer for these insights.
(10) Note, however, that unlike Muellbauer and his recent coauthors
(such as in Aron et al., 2007), we are unable to derive credit
conditions indices for a range of countries, so in Section 5 we use
credit itself which is less satisfactory as a credit supply proxy.
(11) Note that the population data that we use are interpolated
annual data from the UN Demographic database.
(12) We used dates from OECD (2000) to fix the time of financial
liberalisation, banking crises are as in Barrell et al. (2009).
(13) UK owner occupation for example rose from 50 per cent in 1971
to 69 per cent in 1991, whereas it fell in the 2001-11 period from 69
per cent to 64 per cent. US owner occupation was flat from 1985-90 then
rose from 64 per cent to 69 per cent in the period 1994-2004 but then
fell back to 65 per cent in 2014.
Angus Armstrong * and E. Philip Davis **
* National Institute of Economic and Social Research. E-mail:
[email protected]. ** National Institute of Economic and Social
Research and Brunei University. E-mail:
[email protected]. We thank
John Muellbauer, anonymous referees and participants at the conference
for helpful comments. Errors remain our own responsibility.
Table 1. Changes in house prices, income and debt during booms
Percentage Real house RPDI
change prices
1985Q1 2002Q1 1985Q1 2002Q1
-89Q4 -06Q4 -89Q4 -06Q4
United Kingdom 71 49 23 10
United States 12 29 17 14
Germany 1 -2 18 5
France 28 64 14 11
Canada 32 25 17 19
Italy 32 20 17 3
Spain 110 62 27 17
Austria 68 -5 21 13
Netherlands 24 11 16 -2
Belgium 32 41 17 3
Denmark -8 56 5 10
Ireland 12 48 16 18
Finland 56 32 24 17
Sweden 35 44 10 12
Japan 27 -17 22 4
mean 35 30 18 10
mean (boom
countries) 40 39 18 11
correlation 0.74 0.41
correlation
(boom countries) 0.79 0.42
Percentage Real household Nominal house
change debt prices
1985Q1 2002Q1 1985Q1 2002Q1
-89Q4 -06Q4 -89Q4 -06Q4
United Kingdom 74 50 112 65
United States 40 48 31 44
Germany 18 -3 6 4
France 51 42 49 78
Canada 53 44 60 35
Italy 88 40 76 36
Spain 23 83 190 90
Austria 16 26 81 4
Netherlands 16 42 25 21
Belgium 21 29 46 56
Denmark 21 44 8 67
Ireland 38 145 33 69
Finland 78 83 91 35
Sweden 35 45 78 52
Japan 59 0 33 -20
mean 42 48 61 42
mean (boom
countries) 47 59 72 53
correlation 0.14 0.58 0.95 0.99
correlation
(boom countries) 0.06 0.30 0.97 0.97
Percentage Real gross
change financial wealth
1985Q1 2002Q1
-89Q4 -06Q4
United Kingdom 61 17
United States 31 33
Germany 37 9
France 65 26
Canada 27 17
Italy 50 10
Spain 95 41
Austria 35 27
Netherlands 46 19
Belgium 56 1
Denmark 22 58
Ireland 76 48
Finland 57 42
Sweden 94 52
Japan 80 16
mean 55 28
mean (boom
countries) 60 28
correlation 0.47 0.49
correlation
(boom countries) 0.49 0.35
Source: BIS and OECD.
Notes: Real house prices and real household sector liabilities are
deflated by the consumers' expenditure deflator. Calculations for
the 'boom countries' exclude Germany, Austria, Denmark and Japan. They
include only UK, US, France, Canada, Italy, Spain, the Netherlands,
Belgium, Ireland, Finland and Sweden.
Table 2. Changes in house prices, income, debt and wealth during the
aftermath of booms
Percentage Real house RPDI
change prices
1990Q1 2007Q1 1990Q1 2007Q1
-94Q4 -11Q4 -94Q4 -11Q4
United Kingdom -21 -14 12 3
United States -3 -24 12 6
Germany -2 na 11 5
France -8 -1 7 3
Canada -18 2 -1 11
Italy 12 -6 -2 -6
Spain -7 -23 10 -2
Austria -2 4 12 0
Netherlands 21 -9 8 0
Belgium 14 7 14 2
Denmark 0 -26 8 2
Ireland 0 na 14 -4
Finland -42 0 -13 8
Sweden -26 7 11 8
Japan -9 -8 9 0
mean -6 -7 7 2
mean (boom
countries) -7 -6 6 3
correlation 0.46 0.29
correlation (boom
countries) 0.46 0.38
Percentage Real household Nominal house
change debt prices
1990Q1 2007Q1 1990Q1 2007Q1
-94Q4 -11Q4 -94Q4 -1IQ4
United Kingdom 10 -8 -5 0
United States 19 -9 11 -17
Germany 25 -7 16 9
France -4 22 1 7
Canada 13 36 -8 9
Italy 32 10 45 3
Spain 8 -1 22 -16
Austria 14 5 13 15
Netherlands 22 18 38 -5
Belgium 9 23 28 18
Denmark -19 12 9 -18
Ireland 15 8 14 -48
Finland -21 20 -32 13
Sweden -18 28 -7 17
Japan 19 -2 -2 -13
mean 8 10 9 -2
mean (boom
countries) 8 13 10 -2
correlation 0.62 0.67 0.93 0.97
correlation (boom
countries) 0.78 0.88 0.86 0.82
Percentage Real gross
change financial wealth
1990Q1 2007Q1
-94Q4 -11Q4
United Kingdom 21 -6
United States 16 -4
Germany 29 2
France 17 3
Canada 18 16
Italy 15 -17
Spain 19 -14
Austria 21 5
Netherlands 14 9
Belgium -3 -2
Denmark -1 -4
Ireland 18 6
Finland -16 -4
Sweden -22 5
Japan 12 0
mean 10 0
mean (boom -1
countries) 9
correlation 0.41 0.22
correlation (boom
countries) 0.19 0.61
Source: NiGEM macroeconomic database.
Notes: See table I.
Table 3. Indicators of leverage in booms and aftermath
Debt/personal Debt/house prices
income ratio-- --percentage
change in change
percentage points
1985Q1 2002Q1 1985Q1 2002Q1
-89Q4 -06Q4 -89Q4 -06Q4
United Kingdom 25 30 2 1
United States 3 6 25 15
Germany -1 -5 17 -1
France 9 12 18 -13
Canada 14 16 16 15
Italy 8 14 42 17
Spain -3 35 -42 13
Austria -1 8 -31 34
Netherlands 1 43 -6 28
Belgium 3 11 -9 -8
Denmark 8 44 32 -8
Ireland 13 84 23 65
Finland 17 26 14 39
Sweden 9 21 0 1
Japan 21 -5 25 21
mean 8 23 9 15
mean (boom
countries) 9 27 8 16
Debt/personal Debt/house prices
income ratio-- --percentage
change in change
percentage points
1990Q1 2007Q1 1990Q1 2007Q1
-94Q4 -11Q4 -94Q4 -11Q4
United Kingdom -1 -10 39 7
United States 1 -3 22 20
Germany 10 -8 28 -9
France -4 10 5 23
Canada 7 24 39 34
Italy 7 9 17 17
Spain -2 2 17 29
Austria 0 2 16 1
Netherlands 7 24 1 30
Belgium -2 10 -5 15
Denmark -33 16 -19 52
Ireland -2 22 15 100
Finland -9 10 35 20
Sweden -12 24 12 20
Japan 9 -5 31 7
mean -2 8 17 24
mean (boom
countries) -1 11 18 28
Note: see table 1.
Table 4. Panel results for the log difference of house prices--boom
countries
All Pre-1982 1982-1997
Constant 0.001 (0.1) -0.77 ** (2.4)
Log difference of RPDI 0.17 ** (6.7) 0.25 ** (3.3)
Difference real long rate -0.00011 (0.2) 0.00099 (0.5)
Log difference of house 0.56 ** (28.1) 0.41 ** (7.1)
prices (-1)
Log of house prices (-1) -0.0097 ** (4.7) -0.045 ** (2.4)
Log of RPDI(-I) -9.26E-05 (0.0) 0.078 (1.5)
Real long rate (-1) -0.0008 ** (4.0) -0.00071 (0.7)
Population 20-39 as share 0.032 (1.4) -0.61 * (1.9)
of total (-1)
Log stock of housing (-1) -0.0054 (1.1) 0.027 (0.5)
Difference of unemployment -0.0041 ** (3.5) -0.0077 * (1.7)
rate
Unemployment rate (-1) -1.47E-05 (0.1) -0.00052 (0.3)
Log difference of real 0.053 ** (4.6) 0.052 * (1.6)
gross financial wealth
Log of real gross financial 0.008 ** (3.5) -0.00089 (1.1)
wealth (-1)
Dummy for banking crises -0.0032 ** (2.7)
Dummy for financial 0.00026 (0.2)
liberalisation
Countries 11 10
Obs 1612 275
Adjusted R2 0.5 .38
SE of regression 0.16 0.02
Durbin Watson 2.13 2.09
Kao -1.58 (0.06) * -1.85 (0.03) **
All 1998-2013
Constant 0.25 ** (2.1) 0.093 (1.2)
Log difference of RPDI 0.15 ** (4.0) 0.19 ** (5.5)
Difference real long rate -0.00094 (1.0) -7.13E-05 (0.1)
Log difference of house 0.53 ** (15.7) 0.54 ** (16.6)
prices (-1)
Log of house prices (-1) -0.034 ** (5.5) -0.013 ** (3.0)
Log of RPDI(-I) -0.0073 (0.5) 0.043 ** (3.6)
Real long rate (-1) -0.00073 * (1.7) -0.00062 (0.7)
Population 20-39 as share 0.12 * (1.8) -0.081 * (1.8)
of total (-1)
Log stock of housing (-1) -0.037 ** (2.7) -0.031 ** (3.1)
Difference of unemployment -0.0048 ** (2.9) -0.0027 ** (2.1)
rate
Unemployment rate (-1) -0.00074 ** (2.3) -0.00054 * (1.8)
Log difference of real 0.07 ** (4.1) 0.038 ** (2.5)
gross financial wealth
Log of real gross financial 0.028 ** (5.2) -0.014 ** (2.2)
wealth (-1)
Dummy for banking crises -0.0034 ** (2.2) -0.0038 ** (2.5)
Dummy for financial
liberalisation
Countries 11 11
Obs 687 650
Adjusted R2 0.53 0.6
SE of regression 0.16 0.011
Durbin Watson 2.09 2.11
Kao -2.37 (0.01) ** -2.54 (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).
Table 5. Panel results for the log difference of house prices--boom
countries--leveraged coefficient for booms
Estimation period, Leveraged Leveraged
1982Q1-2013Q4 coefficient for coefficient for
period period
1985Q1-1989Q4 and 1985Q1-1989Q4
2002Q1-2006Q4
Log difference of RPDI 0.10 * (1.9) 0.22 ** (3.3)
Difference of real long rate 0.0036 ** (2.4) 0.0034 ** (2.0)
Log difference of house 0.099 ** (2.2) 0.076 (1.5)
prices (-1)
Log of house prices (-1) 0.0016 (0.6) 0.0047 (1.5)
Log of RPDI(-I) 0.002 (0.8) -0.00089 (0.2)
Real long rate (-1) -0.00012 (0.3) -0.00048 (0.6)
Population 20-39 as share 0.048 * (1.7) 0.047 (1.1)
of total (-1)
Log stock of housing (-1) -0.0025 (1.4) 0.00068 (0.2)
Difference of unemployment 0.0032 (1.0) -0.0016 (0.3)
rate
Unemployment rate (-1) 0.00033 (1.1) 0.00047 (1.3)
Log difference of real 0.014 (0.6) 0.023 (0.9)
gross financial wealth
Log of real gross financial -0.0018 (0.6) -0.0072 * (1.7)
wealth (-1)
Estimation period, Leveraged
1982Q1-2013Q4 coefficient for
period
2002Q1-2006Q4
Log difference of RPDI -0.083 (1.0)
Difference of real long rate 0.00099 (0.3)
Log difference of house 0.015 (0.2)
prices (-1)
Log of house prices (-1) -0.015 * (1.9)
Log of RPDI(-I) 0.0041 (1.1)
Real long rate (-1) -0.0023 (1.0)
Population 20-39 as share 0.064 (1.5)
of total (-1)
Log stock of housing (-1) -0.0036 (0.9)
Difference of unemployment 0.0032 (0.7)
rate
Unemployment rate (-1) -0.00041 (0.5)
Log difference of real -0.0078 (0.2)
gross financial wealth
Log of real gross financial 0.00091 (0.1)
wealth (-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.
Table 6. Panel results for the log difference of house prices--boom
countries--adding debt variables
All Pre-1982
Proxy for log difference
of real household debt 0.092 ** (11.0) 0.11 ** (4.9)
Log of real household
debt(-1) -0.0022 (0.9) 0.0082 (0.2)
1982-1997 1998-2013
Proxy for log difference
of real household debt 0.07 ** (5.5) 0.1 ** (6.4)
Log of real household
debt(-1) -0.0047 (0.8) -0.0046 (0.8)
1982-2013
Proxy for log difference
of real household debt 0.088 ** (9.1)
Log of real household
debt(-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.
Table 7. Panel results extended equation--boom countries--leveraged
coefficients for booms and aftermaths
Estimation period, Leveraged Leveraged
1982Q1-2013Q4 coefficient for coefficient for
periods period
1985Q1-1989Q4 1985Q1-1989Q4
and 2002Q1-2006Q4
Log difference of real
liabilities (proxy) 0.034 * (1.7) 0.028 (1.3)
Log real liabilities (-1) 0.00053 ** (3.6) 0.00071 ** (3.6)
Estimation period, Leveraged Leveraged
1982Q1-2013Q4 coefficient for coefficient for
period periods
2002Q1-2006Q4 1990Q1-1994Q4
and 2007Q1-2011Q4
Log difference of real
liabilities (proxy) -0.0014 (0.1) 0.016 (0.7)
Log real liabilities (-1) -0.00012 (0.7) -9.8E-05 (0.7)
Estimation period, Leveraged Leveraged
1982Q1-2013Q4 coefficient for coefficient for
period period
1990Q1-1994Q4 2007Q1-2011Q4
Log difference of real
liabilities (proxy) 0.061 * (1.8) 0.045 (1.3)
Log real liabilities (-1) 0.00033 * (1.6) -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.
