Consumption and credit constraints: A model and evidence for Ireland
Petra Gerlach-Kristen and Rossana Merola* July 2014 Abstract Since the onset of the financial crisis, consumption has fallen in many economies. This paper presents a smallscale DSGE model with occasionally binding credit constraints. Indebted households start facing credit constraints when the value of their main asset, which we assume to be housing, declines. As a response, they stop smoothing consumption and deleverage. We show that even households that only expect to face a credit constraint in the future deleverage. Using the Irish Household Budget Survey, we do not reject the permanent income hypothesis for the average Irish households, suggesting that most Irish households have continued to smooth consumption during the crisis. However, if the analysis focuses on mortgage households in negative equity, we find evidence for a disruption in consumption smoothing during the crisis and the permanent income hypothesis is rejected. This effect suggests the presence of credit constraints for this population group. These results highlight the advantages of micro-data analysis in taking into account agents’ heterogeneity. Keywords: Occasionally binding credit constraint, housing collateral, mortgage markets, DSGE, Ireland. JEL Classification: E21, R31.
* The views presented in this paper are the authors’ and do not represent those of the Swiss National Bank. We thank Alan Barrett, Tim Callan, David Duffy, Stefan Gerlach, Conor O’Toole, Margarita Rubio and seminar participants at the Deutsche Bundesbank, the ESRI, the National University of Ireland Maynooth, the Nottingham University and the UECE Conference in Lisbon for useful comments and suggestions on an earlier version of this paper and Claire Burke and Brian O’Connell for help with the data. We are indebted to Matteo Iacoviello for providing insightful clarifications on the OccBin Toolkit. All remaining errors are of course our own. Corresponding address: Petra Gerlach-Kristen, Swiss National Bank, Monetary Policy Analysis. E-mail:
[email protected]; Rossana Merola, ESRI and Trinity College Dublin. E-mail:
[email protected];
[email protected] .
1
1. Introduction One indication of the severity of the financial crisis that began in 2007 is that consumption, which typically evolves smoothly over time, has fallen alongside income in many economies. For instance, real per capita consumption decreased from the pre-crisis peak by between 8.6% and 17.0% in the European periphery. 1 By contrast, there was a decrease in consumption by only 5.3% in the United Kingdom and by 2.5% in the United States (Figure 1). The fall in consumption in the European periphery is large also by historical standards. During the Great Depression, US real per capita consumption fell by 20.6%. 2 The literature has identified two potential explanations for the size of the recent decline. The first one is the permanent income hypothesis (hereafter, PIH). The second one is the existence of credit constraints. Figure 1: Real per-capita consumption expenditure (2000Q1 = 100)
145 135
Greece
125
Ireland
115
Italy
105
Portugal Spain
95
United Kingdom 2012Q4
2012Q1
2011Q2
2010Q3
2009Q4
2009Q1
2008Q2
2007Q3
2006Q4
2006Q1
2005Q2
2004Q3
2003Q4
2003Q1
2002Q2
2001Q3
2000Q4
2000Q1
85
United States
Note: Consumption has been normalised to 100 in 2000Q1. Data for Greece ends in 2011Q1, the other series end in 2013Q1. Consumption expenditure deflated with the CPI, from Eurostat, and corrected for population growth, from the United Nations.
1
The most recent data are from 2013Q1. The declines are -8.6% for Greece (available data end in 2011Q1), -8.7% for Italy, -12.0% for Spain, -13.2% for Portugal and -17.0% for Ireland. The data are from Eurostat and, for population figures, the United Nations. The graph presents consumption data since 2000. 2
This number is derived from real personal consumption expenditure from the FRED database, which shows a decline by 18.6% between 1929 and 1933, and the 1940 United States Census, which indicates an average population growth in the 1930s of 0.7% a year.
2
This paper assesses the importance of these two factors in explaining the drop in consumption during the crisis. We use a small-scale DSGE model as a theoretical framework, and examine household-level data from the Irish Households Budget Survey to assess whether the model predictions are compatible with the empirics. A key advantage of survey data is that they allow us to analyse the consumption behaviour of households which differ in economic and social dimensions (e.g. income, wealth, mortgage owned etc). This is important because, for instance, poorer agent are likely to react strongly to transitory income shocks due to credit constraints, which might also matter more for younger or unemployed households than for older and richer ones. This paper relates to a large literature that has used household level data sets to evaluate the PIH. Earlier works have used survey data to analyze the response in consumption of U.S. heterogeneous households to the 2001 Federal income tax rebates (e.g. Agarwal, Liu and Souleles, 2007; Johnson, Parker and Souleles, 2006), or to gauge the response of consumption to temporary/permanent income shocks in the U.S. during the 1980s (Soulelas, 2002; Blundell, Pistaferri and Preston, 2008 ), in Italy before the crisis (Krueger and Perri, 2010), in the U.K. from 1974 to 2002 (Attanasio and Pavoni, 2011), in Germany in 2002 to 2007 (Beznoska and Ochmann, 2012) or for cross-country comparison before the recession (Krueger et al, 2010). However, only few analyses use survey data extended beyond 2007 to analyse the implications of the recent financial crisis on consumption of heterogeneous households. Our paper contributes to the literature in this respect and represents a step forward, because the recent financial crisis features some peculiarities which might have implications for consumption different from those observed during previous crisis episodes captured in the pre-2008 surveys. Differently from the crises during the 1990s, the recent financial crisis has been associated with major changes in asset and collateral prices, i.e. housing. In this context, consumption of highly leveraged households – those that bought a house with a large mortgage at the height of the property boom and have seen the value of their housing asset decline – is likely to be severely affected. For instance, high-leverage households are likely to be deemed a large credit risk, and hence find access to credit particularly difficult.
3
To best of our knowledge, the only other studies going in this same direction and using survey data to test the PIH during the recent financial crisis are Jappelli and Pistaferri (2013) and Blundell, Pistaferri and SaportaEksten (2012). While they study respectively Italy and the U.S. we believe that analyzing Ireland, where house prices halved during the crisis, is worthwhile because in that environment the impact of dropping collateral values should be clearest. Our analysis builds up on the literature on both the PIH and credit constraints. The PIH (Friedman, 1957 and – in a context of rational expectations – Hall, 1978) states that consumers will change consumption plans only if changes in disposable income are seen as permanent. Therefore the PIH would explain the decrease in consumption during the financial crises with a fall in permanent income. The underlying assumptions of the PIH are: (i) rational agents and (ii) “perfect” capital markets that allow agents to borrow for consumption if income declines temporarily. However, several empirical studies (e.g. Flavin, 1981 and 1985; Hayashi, 1982; Campbell and Deaton, 1989; Campbell and Mankin, 1990; Attanasio and Pavoni, 2011) suggest that the PIH does not hold in the data. 3 This strand of the empirical literature reports that either consumption is more sensitive to transitory current income shocks than what the PIH would predict or that consumption responds too smoothly to permanent income changes.4 One common interpretation of a rejection of the PIH is that assumption (i) about rational forward-looking agents holds, but not assumption (ii) of “perfect” capital markets, providing support for the existence of credit constraints. Therefore, a second explanation for the sizeable drop in consumption during the crisis is that households have stopped smoothing consumption because they face credit constraints or expect to face them in the near
3
For an empirical analysis on Irish data over the period 1960-1991, see Roche (1995).
4
However, Mankiw and Shapiro (1985) criticized the validity of some of these procedures for testing the permanentincome hypothesis. For instance, they show that, if income has a unit root, conclusion in Flavin (1981) can be severely biased toward rejection. Nelson (1987) finds evidence supporting the PIH. More recently, using Krueger and Perri (2010) have reappraised the evidence on the permanent-income hypothesis, based on the Italian Survey of Household Income and Wealth (1987-2008) and the two most recent waves of the Panel Study of Income Dynamics (2004-2006).
4
future.5 The literature on credit constraints goes back to Leland (1968) and Tobin and Dolde (1971). The main hypothesis in this literature is that households that are deemed to represent a large credit risk from the point of view of a bank are unable to smooth consumption because they cannot access credit in periods when actual income is below permanent income. Looking at the macroeconomic series of consumption in the European periphery (Figure 1), it seems likely that both restricted access to borrowing and a drop in permanent income explain the decline in consumption during the crisis. In the middle of a financial crisis, incomes are lower than potential, even if the latter has been adjusted downwards. If the PIH holds, consumption therefore should decline only in line with potential, but not by as much as actual income. Indeed, we find that real disposable per capita income has decreased by more than consumption in the European crisis countries (by between 15.6% and 26.5%). Nevertheless, the decrease in consumption in the crisis has been large, both in an international and a historical comparison, and this suggests that credit constraints may matter as well. In this paper, we present a small-scale DSGE model that assumes that households can finance part of their consumption expenditure by borrowing against their main asset, housing. If house prices decline, their leverage ratio increases, and at one point banks refuse to finance further loans, so that households have to reduce consumption. We show that they also deleverage; this is to improve their future access to credit. Moreover, we demonstrate that the mere expectation of a house price decline results in reduced consumption, anticipatory saving and deleveraging. This pattern relates our paper to the literature on precautionary or buffer-stock savings. This strand of the literature posits that even if credit constraints are not currently binding, risk averse households try to avoid a situation of binding credit constraints in the future by building up savings beforehand (see e.g. Japelli, 1990; Deaton, 1991; Carroll, 1992; Carroll and Kimball, 2001; Carroll and Toche, 2011). The anticipatory savings that
5
To introduce liquidity constrained agents, some authors (Campbell and Mankin, 1990; Roche, 1995) assume that there are two types of consumers: a constant proportion of households is forward looking optimising households and consumes their permanent income while the remaining population consumes their current disposable income. Alternatively, Flavin (1985) proposes a specification of the consumption function that includes the unemployment rate, which is assumed to be a proxy for the proportion of liquidity constrained households.
5
result in our model differ from precautionary savings in that they are related to the almost certain fact that house price that start collapsing after the end of a property price bubble will continue to decrease for some time; the literature on precautionary savings typically assume more diffuse risks. We then go on to assess whether the prediction made by the model – that consumption is not smoothed and the PIH is thus rejected for highly leveraged households – is compatible with Irish household data collected during the financial crisis.6 This indeed is the case. It seems that the more leveraged a household, the less it smoothes consumption. We interpret this as evidence of credit constraints for highly leveraged households and as evidence of anticipatory savings for less leveraged mortgage risk-adverse households that expect further house price declines. The rest of the paper is structured as follows. Section 2 presents the model. Section 3 discusses the data and analyses which households are most likely to have high leverage ratios. Section 4 presents the consumption function estimates and tests the theory predictions. Section 5 concludes. Technical details about the data are presented in the Appendix.
2. A small-scale model This section presents a small-scale DSGE model to link households’ consumption and savings decisions to the value of their mortgage and their real estate wealth. We assume that households try to smooth consumption and offer housing as collateral for the necessary credit. However, banks do not lend once households become too leveraged. This is equivalent to saying that households can borrow up to a specified fraction of their real estate wealth. A drop in the house price decreases the value of housing wealth and hence reduces the capacity of households to use debt to finance the purchase of consumption goods. Therefore, house price fluctuations matter for consumption decisions.
6
See O’Connell et al. (2013) for a comparison of real per capita consumption before and during the financial crisis in the euro zone. Studies on Irish household consumption include Hogan and O’Sullivan (2007), Lydon and O’Hanlon (2012) and Gerlach-Kristen (2013 and forthcoming).
6
The model thus assumes that the credit constraint is only occasionally binding, depending on developments in the housing market.7 It should be noted that occasionally binding constraints represent a large step forward compared to the literature on credit constraints. There are two basic ways in the earlier literature to model credit constraints. First, it can be assumed that there are two types of households, one of which smoothes consumption, while the other always consumes all available income. The latter type of household has been interpreted as credit constrained (Erceg et al., 2005; Lalonde et al., 2007; Ratto et al., 2009; Kumhof et al., 2010; Cogan et al., 2010; Gomes et al., 2012).8 Second, some authors have assumed that there is an ever-binding credit constraint (Iacoviello, 2005; Iacoviello and Neri, 2010; Guerrieri and Lorenzoni, 2011; Lambertini et al., 2013). In this situation, households always borrow as much as the constraint allows, thus increasing consumption when the value of their collateral rises and decreasing consumption when the collateral value falls. Both these approaches have the disadvantage that they assume that there are households that never smooth consumption. This contradicts the PIH and seems implausible for most households but those in abject poverty. We overcome this shortcoming by modelling an occasionally binding constraint. The main impact of occasionally binding credit constraints is that households smooth consumption in good times (Jappelli and Pistaferri, 2010; Guerrieri and Iacoviello, 2013b). In bad times, they cut their consumption, since the credit constraint means they cannot smooth consumption any longer. 9 We show that they even reduce consumption before the constraint starts binding.
7
To solve the model, we employ a piecewise linear solution technique developed by Guerrieri and Iacoviello (2013a) and available online under https://www2.bc.edu/matteo-iacoviello. 8
This approach is also chosen by Mayer and Gareis (2013), who present a DSGE model for Ireland.
9
The idea that occasionally binding collateral constraints deliver asymmetries and non-linearities in the responses of macroeconomic variables to shocks has been developed also in Brzoza-Brzezina et al. (2012) using a penalty function approach, as well as in Mendoza (2010) using a solution algorithm developed in Arellano and Mendoza (2002) and Mendoza and Smith (2006). Justiniano et al. (2013) model the borrowing constraint so to reproduce the asymmetry of mortgage contract and the downward stickiness of mortgage debt observed in 2006-2007 US data. Benigno et al. (2009) analyse optimal monetary policy rules for both crisis period when the borrowing constraints bind and for “normal” periods when the borrowing constraint is slack. They also conclude that optimal policy is non-linear. For more details on methodological aspects and a comparison of alternative parameterised expectations algorithms, we refer the reader to Christiano and Fisher (2000).
7
Our model is a simplified version of the model developed by Mendoza (2010) open-economy set-up. Differently from Mendoza (2010), we use this model to analyse the dynamics of consumption and rather than sudden stops. Moreover, we define the collateral constraint on housing instead of physical capital. As in Mendoza (2010), the collateral constraint produces amplification and asymmetries in the response of macroeconomic variables to shocks. However, these dynamics hold under more general conditions than in Mendoza (2010), where the amplification channel and asymmetric response are driven by the presence of an external finance risk premium and the debt-deflation effect.
2.1 The model Households provide housing as collateral good in order to have access to credit. Households gain utility from consumption
and housing
and therefore they maximise their utility function:
(1)
where
is a parameter which measures habit in consumption, (
is a scale factor which guarantees that
the marginal utility of consumption in the steady-state is independent from the habit parameter, and the parameter
measures preference on housing.
Households maximise their utility subject to a budget constraint and a credit constraint: (2)
(3)
Equation (2) describes the budget constraint, where which households pay the gross interest rate rate
denotes income and
. The term
denotes the mortgage debt, on
denotes the house price, which depreciates at
. The budget constraint requires that households' consumption and purchase of additional housing
must be covered by resources, i.e. current income and the change in debt. Equation (3) describes the borrowing constraint, which imposes that households are allowed to borrow only up to a certain level. In particular, the leverage ratio at time ,
8
, may not exceed the maximum loan-to-
value ratio,
, beyond which banks reject loan applications. We assume that this maximum
is constant
over time. However, if it were to vary, this would impact on households’ consumption decisions. When house prices fall such that the current parameter capturing this cost,
exceeds
, the credit constraint becomes binding and the
10
, becomes positive. If the borrowing constraint is not binding, equation (3) is
replaced with equation (3a): (3a)
Households choose consumption, housing and debt, so that the following first order conditions hold: (4) (5) where
and
denote respectively the marginal utility of consumption and housing and are defined as:
(6)
and
(7)
Finally, total output is assumed to be fixed and normalised to one:
(8)
and the house price is assumed to be exogenous, following an autoregressive process with an IID-normal error term
:
10
In models assuming an ever-binding constraint, equation (3) holds with equality and equation (3a) is never invoked. We present an impulse response based on an ever-binding constraint model in Figure 3.
9
(9) It should be noted that we choose equations (8) and (9) to keep the model simple and our analysis tractable. A more elaborate model, which would also allow labour to respond to wealth shocks, would be desirable to analyse the implications of higher risk of unemployment for consumption and credit constraints, as well as the contribution of labour supply to the deleveraging process. Also, the question how house prices respond to other variables could be explored. For the purpose of the present paper, the simple model chosen here is sufficient to assess the main effect of credit constraints. One central feature of the Irish data we use to examine the predictions of the model is that property prices were in free fall when the household interviews underlying the data were conducted. Prices had declined by 35% between the end of 2006 and 2009/10, and households had begun expecting further declines. Indeed, the house price index bottomed out at half its original level only in 2013. To capture the expectation of falling future house prices, we consider an alternative formulation for house prices, which assumes that households correctly anticipate the house price decline. In this case, equation (9) changes to (9a) where
denotes a shock which is known with certainty to private agents in period
, but that will
materialise only one period ahead.
2.2 Calibration and impulse response functions For most of the parameters, the calibration is standard and close to Iacoviello and Guerrieri (2013b). We set
, so that the constraint binds in normal times and the leverage is at its upper bound. 11 This
means that in equilibrium, households maximise consumption by maximising borrowing. The consumption
11
We acknowledge that ideally, our model would assume that the borrowing constraint is not binding in the steady-state, since it would be more intuitive to assume that household are not constrained in “normal times”. However, this is a shortcoming common to the literature on occasionally binding constraints (besides Guerrieri and Iacoviello, 2013b, see also Brzoza-Brzezina et al. , 2012 and Benigno et al., 2009).
10
habit parameter
is set equal to 0.9, close to the upper bound of the interval of standard values [0.5, 0.9].12
We set the housing utility weight
equal to 0.12, as in Iacoviello and Neri (2010) and Lambertini et al. (2013).
We assume that the logarithm of the house price follows an AR(1) process, with high persistence ( Finally, in the baseline calibration, we set the maximum loan-to-value ratio
0.975).
, beyond which banks reject
loan applications, equal to 0.9. This value is consistent with the increasing proportion of loan-to-value ratios on mortgage loans between 2004 and 2008 (see Honohan, 2009; Kennedy and Calder, 2011). We also consider, as alternative, an
of 0.8. The calibration is summarised in Table 1. Table 1: Calibrated parameters Parameters Discount factor Housing depreciation Consumption habit Housing utility weight House price AR parameter Loan-to-value ratio
Values 0.965 0.010 0.900 0.120 0.975 0.900
We compute impulse response functions of consumption, debt and the leverage ratio to a 2% shock in the house price. We first consider an unexpected house price decline (Figure 2), then an anticipated decline (Figure 3) and finally an unanticipated house price increase (Figure 4). This analysis allows us to assess the importance of anticipatory savings, credit constraints and asymmetries in the response of consumption.
13
Figure 2 considers the effect of a 2% drop in house price. The line marked with triangles shows the responses for the baseline calibration (
0.9). The line marked with circles shows the response assuming that the
regulatory authority sets a more prudent regulatory loan-to-value ratio (
0.8). The responses are
reported as the percentage deviation from the steady state, except for the leverage ratio, which is reported in level. If house prices fall, households start consuming less and use their savings to deleverage. Generally, households
12
We have calibrated χ=0.9 at a quarterly frequency, so to match the coefficient on the 5 year-lagged consumption that we observe when we estimate the consumption function using Irish survey data in section 4. 13
All simulations are run in Dynare 4.3.3, using the OccBin Toolkit developed by Guerrieri and Iacoviello (2013a)
11
use as much credit as they can get to achieve the highest possible consumption level. However, with house prices falling, the credit constraint starts binding more severely, and households attempt to return to a situation in which they can access credit. To do so, they pay back part of their debt and consume less. Once house prices return to the steady state, households start increasing their debt and consumption levels again. 14 The adjustment path to a fall in house prices is consistent with the dynamics discussed in Mian and Sufi (2010a). They argue that household leverage affects consumption through two channels. First, highly leveraged households stop purchasing purchase goods that require additional debt burdens. Second, disruption in the supply of credit makes it difficult for consumers to purchase additional goods. Figure 2 also shows that the deleveraging and consumption responses are weaker when more prudent credit conditions are in place (i.e.
0.8 instead of 0.9). Figure 2: Negative house price shock House price
LTV=0.8
LTV=0.9 1
% deviation from s.s.
% deviation from s.s.
-0.5
-1
-1.5
-2
10
20
30
40
0
-1
-2
50
Consumption
10
30
40
50
40
50
Leverage ratio
10
1
0
0.9
Level
% deviation from s.s.
Debt
20
-10
-20
0.8
10
20
30
40
0.7
50
10
20
30
Note: Simulations in Figure 2 refer to the model with the occasionally binding constraint and show the response of macroeconomic variables to a 2% drop in house price. Variables are reported as deviations from the steady-state, except for the leverage which is reported in level. The line marked with triangles shows the responses for the baseline calibration of the loan-to-value ratio (LTV= 0.9). The line marked with circles shows the response for an alternative calibration (LTV=0.8).
This result is due to the fact that consumption is less dependent on credit if the maximum loan-to-value ratio is
14
It should be noted that the degree of deleveraging in our model is sizeable. This is due to the simple framework we choose, which does not include labour and therefore neglects that households can deleverage by working more and keeping consumption constant.
12
low. In terms of macroprudential policies, this finding supports the notion that a reduction of the regulatory loan-to-value ratio mitigates the consequences of financial disruptions such as credit constraints and hence protects households.15 This finding is also in line with empirical evidence suggesting that countries with high maximum loan-to-value ratios are those in which the demand for new borrowing is more sensitive to shocks (Almeida et al., 2006). In the recent financial crisis, house prices have fallen in many economies over an extended period of time, and households came to expect further declines. Figure 3 therefore reports the response of macroeconomic variables to an anticipated 2% drop in the house price. It shows that the mere expectation of a house price fall reduces consumption. As soon as households start expecting a house price decrease, they start consuming less and deleveraging. This anticipatory behaviour is more marked when the maximum loan-to-value ratio is low, which suggests that households are better able to protect themselves from the risk of falling asset prices if they are not heavily indebted. Comparing the simulations in Figures 2 and 3, we see that consumption responds less to an anticipated than to an unanticipated house price fall, while deleveraging is stronger. This reflects that households are able to smooth consumption somewhat when the credit constraint is not binding yet. Moreover, our theoretical result is also consistent with the empirical finding that more leveraged households decrease their consumption by more during recessions (Mian and Sufi, 2010b, and Mian, Rao and Sufi, forthcoming) 16 .
15
A recent and growing literature investigates the implications of using loan-to-value ratios to contain boom-bust cycles in credit and housing prices (e.g. Christensen and Meh, 2011; Angelini et al., 2011 Lambertini et al., 2013). 16
Using a county-level dataset, Mian and Sufi (2010b) and Mian, Rao and Sufi (forthcoming) find that in those U.S. counties that experienced a large increase in household leverage during the boom (2002-2005), consumption dropped more dramatically during the first phase of the recession (2007:Q3-2008:Q4) than in other counties.
13
Figure 3: Anticipated future drop in house prices LTV=0.8
LTV=0.9 0.2
0
% deviation from s.s.
% deviation from s.s.
House price
-0.5 -1 -1.5 -2
10
20
30
40
0.1 0 -0.1 -0.2 -0.3
50
Consumption
10
30
40
50
40
50
Leverage ratio 1
0 -20
Level
% deviation from s.s.
Debt
20
0.9 0.8
-40 -60
10
20
30
40
0.7
50
10
20
30
Note: Figure 3 shows the responses of macroeconomic variables in the model with an occasionally binding constraint to a 2% drop in the house price. The house price shock materialises at time t+1 and is fully anticipated by households at current time t. Variables are reported as deviations from the steady-state, except for the leverage which is reported in level. The line marked with triangles shows the responses for the baseline calibration of the loan-to-value ratio (LTV= 0.9). The line marked with circles shows the response for an alternative calibration (LTV=0.8).
Figure 4 turns to recovery, which the data analysed in this paper do not cover. We here assume an unexpected 2% increase in the housing price. As discussed at the beginning of this section, households start smoothing consumption when a positive shock occurs in the occasionally binding constraints model. To show how this assumption, which we deem realistic, matters, we plot in Figure 4 also the impulse responses for a model which assumes that the borrowing constraint is always binding (denoted as ever-binding constraint model, dashed green lines). The responses are based on the baseline calibration of the maximum loan-to-value ratio (
0.9).
An increase in house price relaxes the credit constraint and allows households to borrow more in order to finance consumption in the short run. Households therefore consume more. However, the response of consumption in the model with the occasionally binding constraint is only modest when compared with the model with the ever-binding constraint. The reason for this is that in the ever-binding constraint model, households always borrow as much as banks are willing to lend them and consume correspondingly. In the
14
model with the occasionally binding constraint, households smooth consumption, so that the initial response of consumption is smaller but consumption is raised for longer. 17 Figure 4: Positive house price shock Consumption
House price 2
% dev. from ss
% dev. from ss
2
1.5
1
1.5
Occasionally binding constraint
1 0.5 0
10
20
30
40
50
10
20
30
40
50
40
50
Ever-binding constraint
Debt
Leverage
15
0.94 0.92
Ever-binding constraint
Ever-binding constraint
0.9
10
Level
% dev. from ss
20
Occasionally binding constraint
5
0.88 0.86
0 10
20
30
40
50
Occasionally binding constraint
10
Time
20
30 Time
Note: In Figure 4, the simulations show the response of macroeconomic variables to a 2% increase in house price. Variables are reported as deviations from the steady-state, except for the leverage which is reported in level. The solid line denotes the response in the model with an occasionally binding constraint. The dashed line denotes the response in a model which assumes that the borrowing constraint is always binding. The loan-to-value ratio, LTV, is set equal to 0.9.
The asymmetric response of consumption generated by the occasionally binding nature of the credit constraint suggests that busts in housing market can have severe consequences for consumption. It also suggests that if house prices undershoot after the collapse of a property price bubble and then recover somewhat, the response in consumption will be comparatively small. Finally, if households react more to losses than to gains, then it is crucial to distinguish between periods of rising and falling house prices when forecasting consumption.18 To summarise the model, we find that a decline in house prices reduces consumption for mortgage households
17
The asymmetric response of consumption captured by the model is confirmed in the empirical literature. Engelhardt (1996) for the U.S. and Berben et al. (2006) for the Netherlands show that households tend to respond more to losses than to gains. 18
For instance, in a small open economy DSGE model with occasionally binding collateral constraints, Benigno et al. (2009) find that government should intervene aggressively by subsidising the consumption of non-tradable goods only in periods of stress, when the borrowing constraint is binding. In “normal” times, it is not optimal to intervene before the constraint actually binds, and therefore the optimal policy does not exhibit any pre-emptive motive.
15
with high leverage because they face credit constraints. Thus, consumption falls while income stays constant. This means that the PIH does not hold for highly leveraged households, and this is what we will test for in the micro data in Section 4. The model also predicts that households that expect to find themselves with too high a leverage in the future cut back in consumption already today, and we will also test for this effect.
3. The data The Irish Household Budget Survey provides detailed information on household composition, expenditure, labour situation and income. 19 There is also information on financial circumstances, though this is more limited (e.g. the number of loans is reported, but not their value). In the regressions below, we concentrate on the latest wave of HBS interviews, which covers 2009 and 2010 and for which 5889 households gave answers. The use of household-level data for policy analysis has recently gained a lot of attention. 20 In the aftermath of the global recession, survey data can prove very useful to take household heterogeneity better into account. For instance, survey data are essential to understand how specific groups of population react to shocks. From a financial stability perspective, policy makers are interested in knowing where the risk in concentrated. When monitoring the evolution of financial vulnerability ratios, aggregate macroeconomic series may hide potential risk of defaults and bank losses if there are pockets of households with unsustainable financial burdens which are not spotted at macroeconomic level.21
19
Income and consumption are reported for the household as a whole, not broken down by individual.
20
The ECB, in collaboration with the Eurosystem, has recently initiated the Household Finance and Consumption Survey (HFCS), which conducts a harmonized household survey across the euro area that is also compatible with the U.S. Federal Reserve’s Survey of Consumer Finances. The project incorporates surveys already existing at national level, e.g. the Italian Survey of Household Income and Wealth, the Spanish Survey of Household Finances and the Household Survey of De Nederlandsche Bank. For a detailed description of data and results on the first wave, see Eurosystem Household Finance and Consumption Network (2009, 2013). 21
Studies within the Research Task Force Transmission Channel discuss the advantages of using granular data in conducing macro stress tests. Stress tests using only aggregate data assume that financial institutions have the same risk exposure in the financial sector. Generally, granular data are more advantageous for any realistic model of feedback effects both within the financial sector and from the financial sector to the real economy. For references and further discussion, see BIS (2012).
16
Moreover, comparing waves in surveys helps to indentify changes in status of the population. For instance, recently Barakova et al. (2014) have used data from the National Longitudinal Survey of Youth to assess the impact of borrowing constraints and house price dynamics on the probability of homeownership during the U.S. housing market boom between 2003 and 2007. Finally, policy makers are interested in households’ heterogeneity to assess the effect of both universal and targeted policies (such as welfare, taxation and other fiscal policies) on different socio-economic groups. For instance, recent studies have used survey data to analyze how different types of consumers responded to the 2001 Federal income tax rebates under the Economic Growth and Tax Relief Reconciliation Act of 2001. Agarwal, Liu and Souleles (2007) use new panel dataset of credit card accounts to analyze and they find significant heterogeneity in the response to the rebate across different types of consumers, notably consumption rises most for liquidity-constrained households. Similar results are stated in Johnson, Parker and Souleles (2006) who use data from the Consumer Expenditure Survey and Blundell, Pistaferri and SaportaEksten (2012) who use data from the 1999-2009 Panel Study of Income and Dynamics. Both studies conclude that households with little liquid wealth and without high past income react particularly strongly to the economic stimulus of the rebate in 2001, which is consistent with liquidity constraints. Implicitly these works using household-level data to analyze the response of consumption to temporary or permanent shocks are testing the evidence of the PIH for sub-categories of households. Without the ambition to be exhaustive, other related works who use household-level data to test the PIH are Krueger and Perri (2010) who find evidence in favour of the PIH, using the Italian Survey of Household Income and Wealth (19872008) and the two most recent waves of the Panel Study of Income Dynamics (2004-2006); Attanasio and Pavoni (2011) who use cohort data from the UK Family Expenditure Survey to show that in a model with private information model consumption react too smoothly to permanent income changes.
3.1 Consumption, actual and permanent income
17
We define as consumption all expenditure items except mortgage and rent payments. 22, 23 Figure 5 shows weekly consumption and disposable income by age group for the last four waves of the HBS. The data are inflation-adjusted to the 2010 price level. The percentage indicated in each plot reflects how large the respective age group is relative to the whole population. Income measures disposable income, i.e. earnings from labour, property and other assets after taxes. The impact of the crisis is most clearly visible for young households (defined as having a household reference person under the age of 35). Both disposable income and consumption drop sharply between 2004/05 and 2009/10, by on average 25.2% and 41.3%, respectively. For households in the middle-age bracket (head between 35 and 54 years of age), income rose by 4.4% in real terms, while consumption declined by 14.2%. For older households, income rose by 62.5% and consumption by 49.8%. In interpreting these results, it is important to note that the plots in Figure 5 show households in the respective age group at the time of data collection. Thus, a household with a head aged 50 in the 1999/2000 survey contributes to the average shown for that period in the “middle-aged” plot. In the 2009/2010 survey, this household, the head now being aged 60, contributes to the “older” plot. Since education and thus income levels of those aged 60 in 2009/10 clearly exceeded those of 60-year olds in 1999/2000, the rise in income for older households is at least partly explained by more highly educated individuals aging. That said, Callan et al. (2013) examine the impact of the crisis on the Irish income distribution and find that older households have suffered least. Figure 5 suggests that the typical older household was saving in all four HBS waves. Young households began consuming less than their current income in the 2004/05 survey, and middle-aged households in 2009/10.24
22
The HBS reports expenditure, not consumption. This means that a household’s consumption jumps up if for instance a new car is bought. The consumption utility derived from the services of the car is not recorded in the data. 23
We also performed robustness checks that include housing expenditure in consumption, and the results are robust to this change in definition. 24
For the economy as a whole, the savings rate computed from gross national disposable income and personal savings before stock appreciation is 4.2% for 1995, -0.5% for the year 2000, 2.9% for 2005 and 3.8% for 2010.
18
Figure 5: Weekly real consumption and disposable income by age group Middle-aged households (35 to 54 years)
Young households (15 to 34 years)
Older households (55 years and up)
1,200
1,200
1,200
1,000
1,000
1,000
800
800
800
29.3%
43.1% 600
600
27.6% of the population
400 200
600
400
400 Weekly expenditure Disposable income
200
0
0 94/95
99/00
04/05
09/10
200 0
94/95
99/00
04/05
09/10
94/95
99/00
04/05
09/10
Note: Values in 2010 prices, age of the household reference person. Average income and consumption by group, taking into account the grossing factors capturing the representativeness of the individual households interviewed in the HBS. Percentage numbers indicate the size of a group in question relative to the full population.
The PIH states that consumption decisions as based on permanent, rather than current, income. Current income is related to permanent income, since actual fluctuates around permanent. In the consumption regressions below, we treat actual income as a poor measure of permanent and instrument it with other variables that are related to permanent income but do not impact on consumption. In particular, we use three instruments. The first is education, since more highly educated households tend to have higher permanent income. The second instrument is gender of the household head, since men tend to earn more than equally qualified women. The third measure is a proxy for permanent income derived using earlier HBS waves and a pseudo-panel approach. 25 In micro panel studies, permanent income is often proxied by the long-term average income of the individual household. We are not able to construct individual average income from the HBS, since the survey waves constitute a series of cross-sectional datasets rather than a panel. Nevertheless there is a way to make use of the time-series dimension in income. Similar to the work on pseudo-panels in Alessie et al. (1997), we create groups of households with shared characteristics and compute their average income over time. In particular, we create 27 groups that differ by age (young, middle-
25
J-tests for the exogeneity of these instruments with respect to consumption do not reject by a wide margin.
19
aged, older), by education level (low, middle, high) and by tenancy (renters, mortgage and outright owners). We thus obtain a measure of typical income for these 27 different population groups (Appendix A provides the details), and this is the third instrument we use on current income in the consumption regression.
3.2 Leverage Since our model predicts that the PIH does not hold for highly leveraged households, we need to construct a measure of leverage. Generally, the HBS gives little information on assets and liabilities. However, we do know whether a household has a mortgage and when it last moved place. This information, in combination with the house price index, allows us to construct a proxy for leverage. Figure 6 shows the Irish house price index, which is normalised to 100 in 2006Q4, since 1999. It suggests that a household that bought in 2006Q4, when the index peaks, and got a mortgage with an 85% loan-to-value ratio saw its leverage ratio rise above 85% in the following quarter. As house prices declined further, households that had bought in 2006Q3 and earlier became overly leveraged, too.
Figure 6: Irish house prices (2006Q4 = 100) 120 100 80 60
40 20 Q1 1999 Q3 1999 Q1 2000 Q3 2000 Q1 2001 Q3 2001 Q1 2002 Q3 2002 Q1 2003 Q3 2003 Q1 2004 Q3 2004 Q1 2005 Q3 2005 Q1 2006 Q3 2006 Q1 2007 Q3 2007 Q1 2008 Q3 2008 Q1 2009 Q3 2009 Q1 2010 Q3 2010
0
Note: Permanent tsb/ESRI house price index, normalised to 100 for 2006Q4. The index was discontinued in 2011, when the CSO began publishing its own index.
To compute household leverage in 2009/10, we make use of data published by Duffy and O’Hanlon (2013) and
20
of the average mortgage interest rate published by the Central Statistics Office. Duffy and O’Hanlon present data showing that in each year since 2005, the median mortgage was issued at a loan-to-value ratio of between 80% and 90%. 26 We therefore assume as loan-to-value ratio at origination a rate of 85%. Moreover, they show that the median mortgage had a maturity of between 26 and 30 years, and we therefore assume a maturity of 28 years. We furthermore assume that mortgage payments combine an interest element and an amortisation element. We assume a fixed-rate contract and use as interest rate the average mortgage rate at the time of origination. Based on these assumptions, we are able to compute the leverage at the time of the household survey by year of mortgage origination. Appendix B presents details. Roughly speaking, we find that mortgage households that purchased in 2004 or later had a leverage ratio that exceeded 85% at the time of their HBS interview. The leverage ratio is lower for mortgage households that moved earlier. Of course, our measure of leverage is just a proxy. One further caveat is that we implicitly assume that all house prices declined in line with the general house price index. In reality, there clearly is variation around this index. Nevertheless, our leverage ratio should roughly capture the financial situation Irish mortgage households were facing in 2009/10. In the consumption analysis below, we make use of these numbers in two ways. First, we use the leverage ratio per se. This has the advantage of allowing for a stronger response in consumption for the most highly indebted households. It has the disadvantage that we have to exclude mortgage households that purchased their house before 1999, since we do not have mortgage rate information prior to that year. Second, we construct a dummy for highly leveraged households, for which the model implies a binding credit constraint. In particular, we set this dummy to unity for all households with a leverage ratio above 84%. We also construct a dummy for households at risk of facing a credit constraint in the near future, defined as those households having a leverage ratio between 60% and 84%.
26
Kennedy and McIndoe Calder (2011) report a somewhat lower average loan-to-value ratio of between 50% and 80% for the years before the crisis.
21
Before turning to the question how credit constraints change consumption decisions in the Irish dataset, two further observations are in order. The first one concerns deleveraging. Many mortgage contracts allow an early repayment of the principal only at a penalty rate. Households in this situation may prefer to accumulate savings and thus reduce their net liabilities rather than paying back the principal and thus reducing their outstanding debt. The second observation is that credit constraints may have an effect even without households actually applying for credit. If households believe that they would be rejected, their consumption decisions will not be distinguishable from those of households that actually have restricted access to finance. 27
4. Consumption function estimates To assess whether the PIH is rejected for highly leveraged households, we turn to estimating consumption functions. If we find that highly leveraged households consume less than other comparable households, we interpret this as evidence of credit constraints and deleveraging. If we find that also households with a leverage ratio below normal loan-to-value ratios consume less than expected, we take this as evidence of anticipatory deleveraging that is caused by households’ concern that house price might fall further and a credit constraint therefore might start binding in the future. The standard consumption function assumes that consumption of household j depends on permanent income in a nonlinear fashion,
where
is a shift factor and β is the income elasticity of consumption, also called the marginal propensity to
consume out of income. The PIH states that
. When permanent income changes by one percentage
point, so does consumption.
27
The CSO (2013) reports that in 2011/12, a quarter of Irish households applied for bank credit, and that a quarter of these were rejected.
22
Taking logarithms and denoting
In our regressions, we let
etc, one obtains
depend on demographic characteristics of the household, tenancy status and self
employment. Moreover, we let
vary depending on whether a household is young (under 35 years of age),
unemployed or leveraged.28 The consumption equation hence is the following:
where the coefficients
,
and
measure the income elasticity of consumption for leveraged,
unemployed and young households. The coefficient consumption and the last term
measures the degree of habit persistence in
considers the effect of N control variables capturing
demographic characteristics of the household, tenancy status and self employment. Formally, our hypotheses are the following. First, the PIH should hold for most households, i.e.
.
Second, highly leveraged households face credit constraints, which makes them deleverage and cease smoothing consumption during the house price decline captured in our sample, i.e.
.
Third, households with lower leverage may also deleverage and cease smoothing consumption because they expect a further house price drop. Again, this implies households also may face credit constraints, so that again
. Fourth, unemployed and young and
.
We estimate the consumption function using GMM. Since the PIH states that permanent, rather than current, income drives consumption decisions, we treat current household income as an imperfect measure of permanent. To control for measurement error, we instrument current income with variables related to permanent income, but not to consumption (see also Gerlach-Kristen, forthcoming). In particular, and as discussed in Section 3, we use the education level and the gender of the household reference person as well as 28
We also tried interacting generally insignificant.
with demographic, tenancy and employment information, but these interactions were
23
the pseudo-panel estimate of permanent income derived in Appendix A. We also include the pseudo-panel estimate of consumption in the 2004/05 HBS as lagged dependent variable. Tables 2 and 3 present the estimates. In Table 2, we use the leverage ratio to capture households’ difficulties in accessing credit. This variable has the disadvantage that we exclude households that moved prior to 1999, since no information on the mortgage rate is available. In Table 3, we use instead two dummy variables for households with high leverage (over 85%) and medium leverage (between 60% and 85%), and thus are able to include data for mortgage households that moved in 1999 or before. The first column in Table 2 shows that we estimate a long-run income elasticity of consumption of .29 The hypothesis that this coefficient is equal to unity, as predicted by the PIH, is not rejected (p-value of 0.48). The average Irish household thus smoothed consumption during the financial crisis. However, we find that for households with high leverage, the PIH is rejected. These households thus appeared not to smooth consumption, and the effect is the stronger, the higher the leverage ratio. Thus, if an indebted household sees its income rise, it does not increase spending proportionally, but instead saves part of the additional income. This is compatible with deleveraging efforts of highly leveraged households and the existence of credit constraints. The bottom of the table shows p-values for a Wald test that the PIH holds for leveraged households (i.e. we test if the sum of the baseline
and the
estimated for leveraged households, divided by (1-γ) sums to
unity). The test is clearly rejected. Interestingly, being young does not seem to matter for consumption patterns, nor does unemployment. The analysis on the HBS data points out that the PIH is not rejected for the average Irish households suggesting that most Irish households have continued to smooth consumption in the crisis. However, if the analysis focuses on mortgage households in negative equity, results point out that they consume less than the average household, and the permanent income hypothesis is rejected for this population group. These results highlight
29
In all the specifications discussed below, we test the PIH on this long-run income elasticity rather than on the short-run elasticity β.
24
the advantages of micro-data analysis in taking into account agents’ heterogeneity.30 It should be noted that we estimate a significantly larger shift factor
for leveraged households than for the
baseline household. This suggests that consumption is reduced only from a certain income level onwards. In particular, leverage seems to depress consumption from a weekly income level of 492 euros onwards. Below that level, consumption expenditure appears to be larger than for households without mortgage debt.
30
In a column on VoxEu, Gerlach-Kristen, Merola and O’Toole (2013) present results on consumption decisions based on both micro and aggregate data. Analyses at the aggregate level using macroeconomic data find it difficult to identify the exact channel through which credit constraints impact consumption. In order to identify the extent and effects of credit constraints, it is crucial to take households heterogeneity into account. Therefore, micro studies are needed alongside macro analyses.
25
Table 2: Consumption function estimates using leverage ratio
Consumption
Durable consumption
Nondurable consumption
Constant
-0.782***
-2.381***
1.901***
Lagged consumption
0.125***
0.059*
0.055*
Age
-0.012
-0.002
-0.060***
Size
0.028
-0.035
0.091***
Children Rural
-0.026 0.0287
0.031 0.032*
-0.079*** -0.031*
Local authority housing
0.195***
0.167***
-0.180***
Outright owned
0.323***
0.268***
-0.371***
Mortgage owned
0.343***
0.286***
0.103**
Self employed
0.169***
0.168***
0.166***
-2.000
-0.728
-1.618
Unemployment Leverage
-3.361 1.990***
-3.193 2.422***
-1.347 0.576
Income
0.907***
1.201***
0.514***
0.310
0.105
0.254
0.569 -0.321***
0.547 -0.385***
0.246 -0.072
0.551
0.527
0.556
Young
Income*young Income*unemployment Income*leverage Adjusted R2
Test of permanent income hypothesis (p-values) Young Unemployment
0.332 0.135
0.341 0.068
0.422 0.615
Leverage
0.000
0.068
0.000
Note: GMM estimates, 4687 observations. Income instrumented with pseudo-panel permanent income, household reference person age and gender. Income and consumption in logs. */**/*** denotes significance at the 10/5/1 percent level.
26
Table 3: Consumption function estimates using leverage dummies Consumption
Durable consumption
Nondurable consumption
-0.643*** 0.116***
-2.150*** 0.058*
1.812*** 0.050
Age
-0.011
-0.005
-0.056***
Size
0.038**
-0.024
0.094***
Children Rural
-0.022 0.0326*
0.032* 0.032*
-0.068*** -0.026
Local authority housing
0.179***
0.152***
-0.187***
Outright owned
0.319***
0.273***
-0.381***
Mortgage owned
0.266***
0.212***
0.059**
Self employed
0.161***
0.164***
0.161***
Young
-1.955
-0.883
-1.329
Unemployment
-2.416
-2.881
0.392
High leverage Medium leverage
1.935*** 2.463***
2.022*** 3.055***
0.677 1.061
Income
0.891***
1.165***
0.530***
0.303
0.128
0.208
Income*unemployment Income*high leverage
0.411 -0.302***
0.492 -0.318***
-0.041 -0.080
Income*medium leverage
-0.367***
-0.452***
-0.141
0.576
0.546
0.561
Constant Lagged consumption
Income*young
2
Adjusted R Young
Test of permanent income hypothesis (p-values) 0.367 0.363
0.303
Unemployment
0.181
0.048
0.122
High leverage
0.000
0.226
0.000
Medium leverage
0.000
0.070
0.000
Note: GMM estimates, 5196 observations. Income instrumented with pseudo-panel permanent income, household reference person age and gender. Income and consumption are in logs. */**/*** denotes significance at the 10/5/1 percent level.
The first column in Table 3 shows the analysis using leverage dummies rather than levels. Here we find that highly leveraged households, defined as those with a leverage ratio exceeding 84%, appear not to smooth consumption. For households with a medium leverage ratio, defined as ranging between 60% and 84%, the PIH also is rejected, and the coefficient estimates are similar to those of the highly leveraged households. 31 This
31
A Wald test for the equality of the income elasticities of households with medium and high leverage does not reject (pvalue of 0.63).
27
supports the prediction that households that expect to face credit constraints in the future adjust consumption already today and try to deleverage. The remainders of Tables 2 and 3 split consumption into durable and non-durable consumption. 32 For the baseline household, the PIH is rejected in both cases: households tend to increase their spending on durable goods more than proportionally when their incomes rise (p-value for test that
of 0.00), while the
expenditure on non-durables increases less (p-value of 0.00). This suggests that richer households spend a smaller fraction of their income on basic goods, such as food and fuel, than poorer households. We find a similar pattern for households with leverage, although the rejection of the PIH is less clear for durable goods. This suggests that, when indebted households see their incomes rise, they do not increase spending on durable goods by as much as the baseline household33. Again, this suggests that they are saving part of their additional income, presumably to deleverage.
5. Conclusions Credit constraints are a major concern especially during financial crises. If households are unable to smooth consumption, the drop in aggregate spending can cause a vicious circle in terms of falling output, falling asset prices and rising credit constraints. The question when and how credit constraints emerge and spread is therefore crucial. In this paper, we present a small-scale DSGE model that links a household’s access to credit to the value of the collateral it has to offer. This collateral is housing. If house prices decline, the leverage ratio of a household, defined as the mortgage debt relative to its housing wealth, rises. If the leverage ratio exceeds the maximum
32
We define as durable consumption expenditures on what the HBS calls “durable household goods” and motor vehicles. Non-durables are all remaining expenditure items. 33
Similarly, Mian and Sufi (2010b) and Mian, Rao and Sufi (forthcoming) find that U.S. counties that experienced a large increase in household leverage during the boom (2002-2005) showed a sharp decline in durable consumption during the first phase of the recession (2007:Q3-2008:Q4).
28
loan-to-value ratio used by banks in accepting loan applications, the household becomes credit constrained, ceases to smooth consumption and uses the savings to deleverage. We show that this effect is present even if households only anticipate a decline of house prices in the future and currently are not facing a credit constraint yet. We then use an Irish dataset from 2009/10 to examine whether consumption smoothing is disrupted for highly leveraged households. We find this to be the case, and the deviation from the permanent income hypothesis to be the stronger, the higher the leverage ratio. Households with leverage close to but below the standard loan-to-value ratio of 85% also seem to smooth consumption less than normal households. This is rational if they expect a further house price decline. In sum, this paper provides evidence of credit constraints that arise from falling property prices. In terms of policy, the model predicts a smaller effect if the loan-to-value ratio used by banks is lower. This is an argument for a regulatory maximum LTV ratio.
29
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Appendix A: Computation of permanent income and lagged consumption Table A shows permanent real income proxies by household tenancy, education and age. We compute this measure as the average over time (i.e. over HBS waves) of the average income within each population group. Mortgage owners, more educated households and middle-aged households tend to have the highest permanent incomes. Table A: Proxies for permanent income by age, tenancy and education level
Education Young Middle aged Older
low 507 467 301
Renters middle 663 575 357
high 889 820 298
Mortgage owners low middle high 817 919 1098 827 996 1185 516 704 777
Outright owners low middle high 672 689 899 700 841 1100 397 509 485
Note: Permanent income, in 2010 euros, by population group, constructed as the average over time of the average group income in the 1994/95 to 2009/10 HBS waves. Young = household reference person between 15 and 34 years of age, middle aged = between 35 and 54 years, older = 55 years and up.
Table B presents the proxies for lagged overall consumption, durable and non-durable consumption by household tenancy, education and age. It can be seen that consumption in 2004/05, at the height of the boom, was much above the permanent income proxy. Durable consumption was much lower for renter households than for property owners. Table B: Proxies for lagged consumption by age, tenancy and education level Overall consumption Renters Mortgage owners Outright owners Education low middle high low middle high low middle high Young 791 986 1256 1424 1538 1989 1147 1275 1589 Middle aged 780 1090 2080 1502 1806 2114 1223 1529 1820 Older 341 357 878 804 1262 2920 567 792 1034 Durable consumption Young 34 52 82 130 126 180 88 93 174 Middle aged 56 87 111 103 147 170 78 119 151 Older 18 18 105 96 114 156 48 52 85 Non-durable consumption Young 739 927 1395 1314 1528 2100 942 1042 1382 Middle aged 643 1115 2080 1336 1691 2018 999 1297 1651 Older 301 368 1023 709 1157 3008 443 656 886 Note: Consumption from the 2004/05 HBS, in 2010 euros, by population group, constructed as the average group consumption. Young = household reference person between 15 and 34 years of age, middle aged = between 35 and 54 years, older = 55 years and up.
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Appendix B: Computation of household leverage ratios Here we discuss how we compute the proxy for the leverage ratio of mortgage households. There are four main assumptions underlying this measure. First, we assume a loan-to-value ratio of 85% at origination, so that the mortgage corresponds to 85% of the value of the property the household purchases. Thus,
. We assume for simplicity
that after the down-payment for the mortgage, the household does not have any assets but the house. Second, we assume that the house value moves in unison with the general house price index presented in Figure 5. We use the house price index in the quarter in which the interview was conducted and we denote it by
. For each household, we know how many years ago it last moved, though there is no information on
quarters. We therefore assume that the move was exactly the number of years ago the household indicates, with no additional quarters. This gives us the value at origination
.
Third, we assume that the mortgage contract is fixed rate and has a maturity
of 28 years.34 The monthly
payments made are constant over time and combine the interest payment, which declines as the remaining principal decreases, and an amortisation payment, which correspondingly rises over time. As there are no data available on mortgage rates at origination, we use the average mortgage rate in the quarter of the house purchase and denote it by
We denote as
. We calculate the monthly payment as
the sum of the monthly payments made since the origination of the
mortgage. Fourth, we assume that each household only has one mortgage, so that the number of real-estate properties
34
Kennedy and McIndoe Calder (2011) report that most Irish mortgages are flexible-rate contracts. Since the speed of amortisation primarily depends on maturity, our measure of outstanding debt at the time of the HBS interview should be roughly accurate nevertheless.
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per household is
.
Based on these assumptions, we compute the leverage ratio in the quarter of the HBS interview as
Table C presents the leverage ratios by interview quarter and year of house purchase. Table C: Proxies for leverage ratios used in Section 4 Interview quarter Purchase year 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999
2009Q3
2009Q4
2010Q1
2010Q2
2010Q3
85% 97% 106% 106% 89% 81% 66% 61% 56% 50% 39%
85% 102% 111% 116% 100% 89% 79% 68% 60% 57% 44%
85% 103% 114% 122% 109% 94% 85% 74% 63% 62% 48% 40%
85% 100% 112% 121% 116% 97% 89% 78% 67% 64% 52% 42%
85% 98% 112% 122% 120% 101% 93% 81% 70% 66% 55% 45%
Table D shows which households were particularly likely to be highly-leveraged. It can be seen that the younger the mortgage household, the more likely it is to be highly-leveraged. There is also weak evidence that more highly educated households, small households, those with few children, those in rural areas and those unemployed are more affected. Table D: Determinants of a high leverage ratio Constant Age Education Female Size Children Rural Self employed Pseudo-panel measure of permanent income Current income
1.685*** -0.081*** 0.016* -0.001 -0.014* -0.019* 0.023* 0.000 -0.077 0.008
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Unemployment Number of obs Adjusted R2
0.041* 1407 0.128
Note: OLS estimates, robust standard errors, sample of mortgage households that last moved after 1998. Permanent and current income in logs. */**/*** denotes significance at the 10/5/1 percent level.
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