A Structural Model of Macroprudential Policy: The Case of Ireland

Niall McInerney1

Economic and Social Research Institute, Dublin

January 2016

Abstract

We build a structural model of the Irish retail banking sector in which we estimate supply and

demand equations for mortgage and consumer credit, corporate lending and loans to the

construction sector. We find evidence that variations in non-interest rate related credit conditions

have a significant effect on credit demand in the Irish case. We then examine the sensitivity of

lending behaviour to changes in those variables that can be used as prudential instruments such as

loan-to-income, loan-to-value, loan-to-deposit and capital adequacy ratios. Finally, we run a number

of counterfactual scenarios in which these variables are held at their pre-credit boom levels and find

that changes in loan-to-income ratios were the most important factor driving credit growth in the

pre-crisis period. Our results provide some evidence that the effective management of credit

conditions should be an important feature of the Irish regulatory landscape.

JEL Classification: E5, 53, E51, E52, G21

Key words: banking, mortgage credit, macroprudential policy, house prices, simulation

1 Corresponding author: niall.mcinerney@esri.ie. I thank Alan Barrett, Adele Bergin, Niall Conroy, Dawn

Holland, Kieran McQuinn, Edgar Morgenroth, Luca Onorante and seminar participants at the ESRI and

Ce t al Ba k of I ela d fo o e ts a d suggestio s. I a pa ti ula ly g ateful to Ge a d O’Reilly fo insightful discussions and to Reamonn Lydon for advice on the data. All remaining errors are my own.

1. Introduction The recent financial crisis has highlighted the importance of developing models of financial

intermediation the can capture the importance of financial sector shocks to the real economy

(Blanchard et al, 2010). In particular, recent models have focused on the nature of credit supply

disruptions particularly those arising through the capital channel, and have began integrating new

policy instruments that specifically target the maintenance of financial stability (Gertler and Kiyotaki,

2010; Benes et al, 2014).

A model that can capture how banks intermediate the supply and demand for credit is particularly

important in the case of Ireland. Between 2003 and 2008 the balance sheet of the domestically-

relevant Irish banking sector tripled in size driven mainly by lending to Irish households and non-

financial corporations. By 2014, however, the total assets of this sector had fallen to almost half of

its pre-crisis level.

This boom and bust cycle in the lending behaviour of the banking sector has amplified cycles in the

real economy (Claessens et al, 2012). Ireland provides a particularly dramatic example of this. In the

decade prior to 2007, Ireland experienced the largest increase in real house prices across all OECD

economies. Since the onset of the financial and sovereign debt crises Ireland has endured the mirror

image of this period, with house prices recording the largest drop across all advanced countries.

However, any model that attempts to capture these macro-financial linkages is, at least in the Irish

context, incomplete if it ignores the significant structural change in the funding environment facing

banks over this period. Thus, looking at the liability side of the banking sector balance sheet is at

least as important as the assets side (Beck, 2014). Figure 1 shows the deposit claims on Irish banks

by nationality. The rising share of non-euro funding in the 2003-2008 period suggests that Irish

banks became increasingly reliant on wholesale rather retail funding to finance the growth in lending

(Coates and Everett, 2013).2 This source of funding then drained rapidly in the post-crisis period

which initiated the deleveraging.

Therefore, the complex interaction between innovations in the funding environment, changes

financial regulation and demand from the real sectors of the economy, requires the development of

models than can provide insight into and quantify the key channels though which the banking sector

operates in any economy.

In this paper, we outline a model of the Irish retail banking sector that can provide policy makers

with such a framework. Our model of the banking sector comprises supply and demand equations

for each type of lending: secured household credit (mortgages), unsecured household credit, loans

to non-financial corporations, and loans to construction firms. Following Davis and Liadze (2012)

credit demand is modelled in terms of the volume of lending and is assumed to be a function of

income or activity levels and the real cost of credit.

2 Irish banks also engaged in increased mortgage securitisation in the 2003-2008 period, primarily through the

issuance of covered bonds.

Our identifying assumption hinges on the assumption that loan quantities do not enter the supply

equations. This is consistent with our theoretical framework in which banks are monopolistically

competitive due, for example, to switching and monitoring costs, as well as informational

asymmetries that generate market power (Freixas and Rochet, 1997). This implies that banks set the

price of lending (the interest rate) as a markup over the cost of funding and also that banks can

accommodate any level of credit demand at this interest rate.3 Following Davis and Liadze (2012),

this markup is a function of cyclical and risk (balance sheet) factors and the regulatory environment.

The financial crisis also highlighted the importance of bank capital in the transmission of real and

financial shocks. In response to this, financial regulators through the Basel III accord have raised

levels of capital adequacy significantly in an effort to manage systemic risk. In effect, this has led to a

doubling of capital adequacy from the Basel II baseline of 4 percent of risk-weighted assets to

between 8 and 10 percent under Basel III (Basel Committee on Banking Supervision, 2011). In

response to these higher requirements and to an unstable funding environment, Tier 1 common

equity as a share of risk-weighted assets has risen by 4.5 percentage points for 102 of the largest

banks in the world, and by 2.7 percentage points for smaller banks (Cecchetti, 2014).

An important question facing policy makers across countries is to what extent is financial regulation

a free lunch . Although our modelling framework does not allow us to explicitly examine the

tradeoff between financial stability and the economic cost of financial regulation, it does allow us

the quantify the latter in terms of its effect on the price and volume of different types of credit. In

this respect, we can also identify the dimensions along which the Irish banking sector adjusts to

higher capital requirements in terms of the composition of its loan portfolio and the speed at which

this adjustment occurs. This is particularly important from an Irish regulatory perspective as the

cross-country diversity in how banks adjust to higher capital requirements does not permit

calibration based on experiences in other jurisdictions (Cohen and Scatigna, 2014).

As far as we are aware, we are the first study that examines the impact of different macroprudential

instruments in a structural econometric model. Although there have been several studies that have

used either reduced-form single-equations (Duca et al, 2011), vector autoregressions (Kuttner and

Shim, 2013), panel studies (IMF, 2011), they do not model explicitly the transmission mechanism

through which changes in these instruments affect both the intermediate and final target of

macroprudential policy.4

Therefore, a key contribution of this paper is that it identifies and quantifies the channels through

which sector-specific instruments such as the loan-to-income and loan-to-value ratios affect the

volume and cost of mortgage credit and their concomitant impact on the housing market. We also

show how instruments that affect the liability structure of this sector such as the loan-to-deposit

3 Clearly, this implies that the supply curve for each type of credit is perfectly elastic. See (Gerlach-Kristen and

McInerney (2014) for an alternative framework in which the price of credit rises with the quantity of credit

supplied due, for example, to implicit constraints on leverage. 4 One exception is Davis and Liadze (2012) who examine the impact of changes in capital requirements on the

price and quantity of credit in a structural model. However, this is the only prudential policy instrument that

they consider.

ratio, as well as the capital adequacy ratio discussed above, affect the lending behaviour of the

banking sector.

This paper is structure as follows. Section 2 discusses the relevant literature. Section 3 outlines our

empirical model and section 4 discusses the econometric results. Section 5 presents the models

response to policy shocks. Section 6 concludes.

2. Literature In an international context, there are relatively few studies that have examined the dynamics of

credit demand and supply from structural econometric perspective. The most similar study to ours is

that of Davis and Liadze (2012) who build a model of the banking sector in Germany, the United

Kingdom and the United Kingdom.

We follow their approach in terms of modelling credit demand in terms of the activity or income

levels and the cost of credit, and credit supply in terms of banks setting the interest rate as a markup

over funding costs. In addition, they allow shocks to capital to influence the markup. The key

difference between their methodology and our framework is that they not allow for changes in

prudential policy such as loan-to-income, loan-to-value and loan-to-deposit ratios to affect lending.

In addition, they do not model the interaction between mortgage credit, housing demand (house

prices) and housing supply which is of key interest from a macroprudential policymakers’ perspective.

Nobili and Zollino (2012) estimate a structural model of mortgage and housing markets in Italy and

identify similar drivers of supply and demand in each market to those found in our study and that of

Davis and Liadze (2012). However, as the focus of their study is not on the impact of

macroprudential policy, their framework does not allow for the relaxation or tightening of non-

interest rate related credit constraints by banks such as through variation in loan-to-income or loan-

to-value ratios, which is important in understanding lending behaviour of banks in the pre-crisis and

post-crisis period.

In an Irish context, Gerlach-Kristen and McInerney (2014) represents the closest study to our in

terms of motivation and modelling approach. The estimate a system of supply and demand

equations for the mortgage and housing markets and identify the key drivers in each market up until

2003, when the find that mortgage lending becomes explosive . They also relate mortgage credit

demand to activity levels and cost and supply to the cost of funding and to an indicator of

macroeconomic risk.

An important difference between their study and this paper is that we model credit dynamics in

both the housing bubble and bust periods and try to explicitly capture the drivers of these explosive

dynamics. For example, their framework is unable to capture the variation in credit conditions in the

pre- and post-crisis period. Importantly given the focus of this paper, Gerlach-Kristen and McInerney

(2014) do not consider the impact of macroprudential poli y o a ks’ le di g eha iou .

Our study also complements other single-equation studies that have examined the impact of

innovations in credit markets on house prices in Ireland. Addison-Smyth et al (2009) analyse the

fundamental drivers of the quantity of mortgage credit that was extended by the Irish banking

sector since the 1980s and show that changes in the composition of funding, represented by the

mortgage stock-to-deposit ratio can capture almost 30 percent of the increase in credit in the 1999-

2008 period. However, the authors do not try to disentangle the impact of supply and demand

factors in either the mortgage interest rate, which is taken as exogenous, and the volume of

mortgage credit.

Fitzpatrick and McQuinn (2007) also model a reduced-form relationship between mortgage credit

and house prices. They model the equilibrium level of credit as a function of house prices, disposable

income and mortgage interest rates in a single equation framework. This credit variable is also

included as a regressor in the house price equation and house prices are included in the credit

equation. Both variables are positive and significant indicating that there is a two-way interaction

between housing and credit markets in the long run.

This paper differs from Fitzpatrick and McQuinn (2007) in that we focus on disentangling the

individual impact of supply and demand factors in the determination of equilibrium credit volumes,

rather than including them together in a single-equation. In addition, we find that that the first-

difference rather than the level of house prices affects new mortgage lending and thus that there is

only a short-run impact of house prices and mortgage credit. However, the level of mortgage credit

enters the house price equation which suggests that the long-run relationship runs from innovations

in credit markets to house prices.

Our model also contributes to the literature on the effectiveness of prudential instruments. In this

respect, we are different to other studies which tend to examine the impact of changes in loan-to-

value or loan-to-income ratio in a single equation or reduced-form framework rather than in a

structural system of equations. For example, Crowe et al (2011) find in a panel of 21 countries that

LTV limits are positively correlated with house price growth with a 10 percentage point increase in

the LTV cap being associated with a 13 percent increase in nominal house prices.

Igan and Kang (2011) use macro and micro data from Korea to examine the impact of LTV and DTI

limits and find that a 10 percentage point reduction in the LTV leads to a 10 percentage point

reduction in the growth rate of house prices. Interestingly, they find that LTV restricitions have a

greater impact on house price appreciation than DTI restrictions, which, although have a negative

sign, are statistically insignificant. This contrasts with the cross-country panel evidence found by

Kuttner and Shim (2013) who find that credit restrictions that are tied to income are more effective

in constraining credit and house price growth and those that target leverage such as the LTV.

We also o t i ute to the lite atu e o the i pa t of ha ges i apital e ui e e ts o a ks’ lending and pricing behaviour. For example, Noss and Toffano (2014) find that an 15 basis point

increase in the aggregate capital ratio of UK banks would reduce lending by 1.4% after 16 quarters

while Nobili and Zollino (2012) find that a 1% increase in the risk-weighted capital ratio for Italian

banks leads to a 30 basis points increase in the mortgage interest rate.

In terms of microeconomic studies, Brun et al (2014) use loan level data for French banks to examine

the effects of bank capital requirements specifically on corporate lending and find that that a one

percentage point increase in capital requirements leads to a reduction in lending of approximately

10%.

Finally, Bridges et al (2014) find that banks in the United Kingdom take 3-4 years to rebuild capital

buffers above the regulatory minimum in response to a one percentage point permanent increase in

capital requirements. Interestingly given the focus of our study, they also find that banks respond to

increased capital requirements by reducing lending more to real estate sectors (households and

commercial) than other types of lending. As section 3 will show, we find that banks in Ireland tend to

adjust to higher capital requirements via unsecured lending to households and to non-financial

corporations.

3. A Model of the Irish Banking Sector Our model of the banking sector comprises a set of supply and demand equations for four types of

credit: mortgages, consumer credit, corporate lending, and loans to construction firms.

Mortgage Credit

Following Davis and Liadze (2012), the demand for new mortgage lending (MorVol) is modelled as an

increasing function of the change in disposable income per capita (Incomes) and real house prices

(HPrices), and a negative function of the real mortgage interest rate (MorRate). We use the change

in income and house prices rather than their levels given the differences in the order of integration

among the regressors.5 We assume that inflation expectations are extrapolative and therefore that

real interest rates are calculated as the current nominal rate minus the annualised lagged quarterly

change in the consumer expenditure deflator.6

We are interested in determining how credit conditions affect house prices and thus how

macroprudential policy could mitigate the impact of house prices on financial stability. Credit

conditions act as an accelerator in that they amplify the impact of income and collateral effects on

house prices (Almeida, Campello and Lu, 2006). For example IMF (2011) finds that high LTVs

strengthen the impact of real GDP growth on house price growth, with a coefficient that is half of

the direct effect of real GDP growth.

In the Irish case, however, Irish banks also vary credit conditions through the loan-to-income (or

alternatively, the debt-service-to-income ratio), which can also be targeted by macroprudential

policymakers (McCarthy and McQuinn, 2013). Our model of credit demand therefore incorporates

both the affordability (the loan-to-income) and collateral constraints (the loan-to-income ratio).

Our measures of the loan-to-value (LTV) and loan-to-income (LTI) ratios that are constructed using

data on new mortgage approvals from the Department of Environment, Community and Local

Government (DoECLG) and the Banking and Payments Federation Ireland (BPFI). We address

potential endogeneity concerns in using these constructed variables as exogenous changes in credit

conditions (or collateral and affordability constraints) by concentrating out the impact of house

5 The results of the unit root tests on each variable are available on request.

6 Our results are not sensitive to other specifications of adaptive expectations which incorporate information

from a longer time horizon.

price, income and interest rate expectations from each ratio (see Cameron et al, 2006; Duca et al,

2011).7

The demand for new mortgage lending therefore has the following form:

� =∝ + � �− + � � + � ∆ � + � ∆ � � �−+ � � + � � + � �

(1)

All variables, except for the mortgage interest rate, are in logs. We now turn to the supply of

mortgage credit.

Following Goggin et al (2012), the standard variable rate is modelled in an error correction

framework as a variable markup over the cost of funds which comprise the deposit rate (DepRate)

and the money market rate (MMRate). The latter is proxied by 3 month Euribor.8 The impact of

monetary policy is captured by Euribor variable and therefore we assume perfect pass-through from

ha ges i the ECB’s ai efi a i g ate to o ey a kets.

The markup is a function of cyclical, risk and policy variables. The riskiness of lending to the

households sector is should theoretically reflect the loss given default associated with lending as

well as the repayment capacity of households. The former can be captured by the undrawn equity

of households given by the ratio of housing wealth (HWealth) to mortgage debt (MorStock) while we

use the unemployment rate (URate) to capture the latter.9 The unemployment rate has also been

used in an Irish context to capture housing market sentiment (Kelly and McQuinn, 2014).

We assume that the Modigliani-Miller theorem does not hold, so that banks find it relatively costly

to raise capital. There are several ways in which banks can increase their capital adequacy ratio if

required by regulatory authorities. First, banks can increase the amount of earnings by raising

lending margins, reducing dividends or reducing operating costs. Second, banks can reduced risk-

weighted assets by reducing the overall volume of lending or alternatively, by changing the

composition of their lending portfolio and shifting away from riskier types of lending such as

consumer credit. Finally, banks may issue new equity. As the latter implies the dilution of existing

shareholders, we only consider the first two options in analysing how the bank sector responds to

higher capital requirements.

O e of the diffi ulties i odelli g the i pa t of apital e ui e e ts o a ks’ le di g eha iou is the s a ity of ti e se ies data o a ks’ holdi gs of egulatory capital. Moreover, banks may

choose to increase capital to achieve a desired or target level of capital that is only weakly related to

changes in regulations.

7 House price and income expectations are approximated by a 4-quarter moving average of lagged change in

each variable. Interest rate expectations are measured by the slope of the yield curve- the difference between

the yield on ten-year government bonds and 3-month notes. 8 See ECB (2009) for evidence that Irish banks used 3 month Euribor as the base rate off which the standard

variable rate was priced. 9 The o ept of u d a e uity has ee sho to e a sig ifi a t p edi to of o tgage a ea s Whitely

et al, 2004).

Following Berrospide and Edge (2010) and Davis and Liadze (2012), we assume that banks seek to

maintain a capital buffer over the regulatory minimum due, for example, to precautionary motives

o to edu e the isk ati g o the a k’s de t. We approximate this target level of capital as a

Hodrick-P es ott filte ed t e d ith λ= i the actual level of book equity.10

The level of regulatory capital is constructed using risk-weights for each type of lending under the

Basel I accord as this was the regulatory regime that was in place for much of our sample period.11

The desired buffer (BankCap) is therefore the difference between the target level and the mandated

level. This implies that there is a non-linear relationship between changes in capital requirements

and the supply (price) of credit. If buffers are low banks respond by raising interest rates on

particular types of lending by more that if buffers were closer to their desired level.12

We also allow macroprudential policy to enter the supply of credit through the constraints on the

composition of funding via the loan-to-deposit (LTD). Variation in the share of wholesale funding has

been shown to have been a significant driver of the supply of credit in the Irish case (Addison-Smyth

et al, 2009; Coates and Everett, 2013).

The long-run supply of mortgage lending has the following form:

� =∝ + � � ��− + � � + � �− + � �

+� � +� � + � �

(2)

(2)

The short-run dynamics of the mortgage rate as modelled in an error-correction framework.

Consumer Credit

The demand for (real) consumer credit is modelled in an error-correction framework as a function of

the interest on consumer loan (ConsRate), income and house prices. Real personal disposable

income is used to capture the repayment capacity of households (as in the mortgage demand

equation). Unfortunately in the Irish case, a sufficiently long time series is not available to measure

the net wealth, or specifically net financial wealth, of Irish households. Clearly this balance sheet

channel is important from a credit worthiness perspective in the extent to which households can

leverage their net worth (Nobili and Zollino, 2012).

However, as real assets such as housing tend to be the most important store of wealth for Irish

households we use the real house prices to capture variations in the value of collateral against which

they can borrow for consumption purposes.

The long-run demand for consumer credit therefore has the following form:

10

This ea s that e a e usi g total apital athe tha Tie E uity apital. The fo e ill i lude supplementary capital such as undisclosed reserves and subordinated debt. Berrospide and Edge (2010) argue

that banks adjusting to a target level are likely do so using such a broad measure of capital whereas Tier 1 risk-

adjusted capital is a regulatory concept for which the relevant target is the regulatory minimum. Clearly,

however, our target level does not account for the riskiness of a ks’ loa po tfolios. 11

The Basel 1 capital requirements were introduced in 1988. The capital charge on all loans was set at 8

percent. 12

See Merkl and Stolz (2006) for microeconomic evidence that this accurately characterises the behaviour of

German banks and Miani et al (2012) for Italian banks.

� � =∝ + � � + � � + � � � � + � �

(3)

As with the mortgage rate, the supply of consumer loans is modelled in terms of the interest rate on

consumer loans. As this type of lending is unsecured we considered a number of risk factors that

might distinguish this type of lending from mortgage credit such as income and interest rate

expectations, stock market volatility as an approximate measure of economic uncertainty, and the

spread between three-month Euribor and the three-month yield on treasury bills.

However, we found that the factors that determine the mortgage rate also appear to determine how

banks set interest rates on consumer loans. One reason for this may be that Irish households tend to

have a poorly diversified portfolio of assets, with housing generally representing the main storage of

wealth. Similarly, if the unemployment rate best captures the ability of a household to repay its

mortgage then it may also represent the best indicator of its ability to repay consumer loans.

Therefore, the interest rate on consumer loans has a similar functional form to that of the mortgage

rate. The only difference is that the household equity variable now represents the ratio of housing

wealth to total household debt (mortgages and consumer loans) instead of simply mortgage debt:

� =∝ + � � ��− + � � + � �− + � �

+� � +� � + � �

(4)

Corporate Credit

Given the importance of the construction sector to the Irish economy, we model non-real estate

corporate lending (CorpCredit) and loans to construction firms separately. Equation (5) shows that,

in the long run, the demand for bank credit by non-financial non-real estate firms is negatively

related to the cost of borrowing and positively related to real GDP (RGDP). The latter is used to

capture the real investment requirements of non-financial firms.13

� � =∝ + � � + � �� + � �

(5)

In terms of the supply of corporate lending, the interest rate on corporate credit is set as a markup

o e a ks’ fu di g osts equation (6)). As with the mortgage and consumer credit interest rates,

the markup depends on cyclical, risk and policy factors. The output gap (Gap) is used to capture

y li al a iatio s i fi s’ edit o thi ess, hile the o po ate i sol e y ate (Insolv) is used as

risk indicator specific to firms. The markup also depends on factors that we relate to prudential

policy such as the loan-to-deposit ratio and the capital adequacy ratio.

� =∝ + � � + � � + � �− + � �

+� � +� � + � �

(6)

The short-run dynamics of both corporate credit and the corporate rate are modelled in an error-

correction framework.

13

The share of non-real estate lending in GDP is relatively constant over the last three decades.

Demand for Construction Loans

We adopt a relatively simple specification for the demand for credit by construction firms due to the

lack of balance sheet or sector-specific data related to the banking sector. We relate credit demand

(per housing unit) to the cost of financing, as approximated by the corporate rate modelled above,

real GDP a d a easu e of To i ’s Q fo the o st u tio se to (Tobin). The latter is constructed as

the ratio of the market value of housing, given by the average house price, to the replacement cost

of housing, given by a measure of building costs.

The long-run demand for construction loans therefore has the following form:

� � � =∝ + � � + � �� + � � �+� �

(7)

As the interest rate that banks charge to construction loans is not available, we do not model the

supply of construction loans separately from that of non-financial firms. Although, banks may charge

a risk-premium over the normal corporate rate on such lending due to the volatility of the sector, a

quantification of this risk premium is not possible given the data constraints.

Housing

House Prices are modelled using the standard inverted housing demand function which is typically

found in the literature (Duca et al, 2011). Equation (8) shows that we relate real house prices to the

housing stock (proportional to the demand for housing services), the user cost of capital, the share

of 25-34 year olds in the population, personal disposable income and credit conditions.

The user cost of capital (User) is constructed as the difference between the mortgage rate and

expected house prices appreciation. House price expectations are approximated using a four-quarter

moving average of lagged annual house price inflation (Kelly and McQuinn, 2014). The share of 25-

34 year olds in the population (Pop2534) is used to capture the impact of demographics on housing

demand, which has been particularly important in the Irish. In this model credit conditions are

captured by new mortgages per capita (Mortgages).14

� � � =∝ + � � + � � + � � + � � �+� � � � + � �

(8)

On the supply side, housing completions (9) is modelled as an investment decision which depends on

the ost of edit a d a easu e of To i ’s Q fo the o st u tio se to . The use ost of edit is approximated by the interest rate on loans to non-financial corporations minus expected house

price appreciation.15

However, as this is not a sector-specific measure of credit availability to the

construction sector, we also include the change in the volume of construction loans per capita. The

latter can also be used to capture non-interest rate-related changes in credit conditions for

construction firms.

14

See Muellbauer and Murphy (1997) 15

We assume that households and construction firms share the same model for forecasting house price

growth.

� � =∝ + � � + � � � + � � � � �+� �

(9)

Finally, the housing stock (10) is modelled using the perpetual inventory method where the current

level of the housing stock depends on the depreciated level from the previous period and on new

housing completions. We assume a quarterly depreciation rate of 0.5 percent, which equates with

the 2 percent annual depreciation rate used in Bergin et al (2013).

� =∝ + 0.99 ∗ �− + � �

(10)

The next section presents the results from the joint estimation of these equations.

4. Results Table 1 presents the results from estimating the mortgage demand equations.

16 All variables are

statistically significant at the 1 percent confidence level. Mortgage demand is declining in the real

cost of borrowing and increasing in income and the change in house prices. The coefficient on

income implies that a 1 percent change in income leads to a 0.78 percent increase in the demand for

mortgages. The coefficient on the LTV and LTI are positive and are of similar magnitude. A 1 percent

increase in the LTV and LTI leads to a 0.74 percent and 0.86 percent increase in new mortgage

lending, respectively.

An Andrews-Ploberger structural break test finds evidence of two statistical breaks in the

relationship between mortgage lending and these variables since the outset of the crisis. Therefore,

the model allows for shifts in the constant at the beginning of the financial crisis and around the

sovereign debt crisis (2010Q3). Figure 2 shows the fit of the model when these breaks are included.

Table 2 shows the importance of funding costs in driving the standard variable mortgage rate. The

coefficient on the money market rate implies that 1 percentage point increase in Euribor leads to a

0.75 percentage point increase in the mortgage lending rate. 17

Similarly, a 1 percentage point

increase in the deposit rate increases the mortgage rate by 0.15 percentage points.

We also find evidence that potential prudential instruments such as the loan-to-deposit ratio and

capital adequacy have a significant effect on the pricing of new mortgage lending. In addition,

mortgage interest rates respond strongly to the unemployment but less so to our measure of

household equity. In the short-run, the mortgage rate is primarily driven by changes in the money

market rate. Figure 3 shows that this model can closely replicate the dynamics of the mortgage rate

over the sample period.

Table 3 presents the results of the consumer credit demand equation. The cost of consumer loans,

income and house prices are all important determinants of the demand for consumer loans.

Therefore, the significance of the house price coefficient indicates the presence of a small wealth

effect so that rising house prices may relax a collateral constraint attached to this form of unsecured

16

Although the equations are estimated jointly, we discuss them separately here for expository reasons. 17

The long-run coefficients in an error-correction model are calculated as the coefficient on the variable

divided by the coefficient on the lagged dependent variable. In the case of Euribor, this is 0.256/0.34

borrowing. The income coefficient suggest a long-run elasticity of approximately 0.65. Figure 4

shows that this specification broadly capture the trend increase in personal lending, although it

tends to underpredict the spike at the end of 2008.

Table 4 shows that the main drivers of the pricing of consumer loans are the same as those for

mortgages. However, the pass-through from funding costs appears to be much higher with a long-

run elasticity with respect to Euribor of 0.93. Similarly, banks tend to be raise consumer interest

rates by a greater margin relative to mortgage rates when unemployment increases and housing

equity falls. Further, there is evidence that the elasticity of interest rates with respect to changes in

capital adequacy ratios are significantly higher than for mortgage rates. Figure 5 shows that this

specification provides a good fit for the actual interest rate on consumer credit, although it does

tend to over-predict interest rates during financial and sovereign debt crises of the 2008-2010

period.

Table 5 shows that non-real estate corporate lending error corrects on the real corporate lending

rate and real GDP. The adjustment coefficient suggests that error-correction occurs at a relatively

slow pace- approximately 8 percent per quarter. The long-run elasticity with respect to GDP is

approximately unitary which explains the relatively constant share of corporate lending in GDP over

the sample period. Figure 6 illustrates how this relatively parsimonious specification can capture the

rapid growth and then decline in corporate lending in the pre- and post-crisis period.

On the supply side, Table 6 shows that the pricing of corporate lending follows the same pattern as

for the interest rates on mortgages and consumer credit. Error-correction of the corporate rate to its

long-run equilibrium level is relatively quick at 33 percent per quarter. Pass-though from changes in

funding costs is even higher for corporate loans than other loan types with a long-run elasticity with

respect to Euribor of 0.96. In addition, cyclical, risk factors as captured by the insolvency rate and

output gap are significant determinants of the markup on the funding costs. Further, coefficients on

the loan-to-value and capital adequacy ratios suggest that prudential policy that targeted these

variables could have an important impact on credit to corporate sector via the lending rate. Figure 7

shows the striking similarity between the actual level of the corporate lending rate and that

predicted by the model.

The final credit aggregate that we model is the demand for construction loans. As discussed below,

this source of credit is an important driver of housing completions. Table 8 shows that lending to

o st u tio fi s pe housi g u it is isi g i eal GDP pe apita a d To i ’s Q a d de li i g i the cost of this financing, as approximated by the corporate lending rate. Error-correction occurs at

approximately 13 percent per quarter. The long run elasticity with respect to GDP is 0.77. For

completeness, Figure 8 illustrates the fit of this relatively parsimonious specification.

Finally, Table 9 and Table 10 present the results of the house price and housing completions

equations, respectively. The most important driver of house prices is demographics, specifically the

share of 25-34 year olds in the population. The long run impact of a 1 percent increase in this ratio is

to increase house prices by over 2 percent. Income is also an important driver of house prices: a 1

percent increase in personal disposable income per capita generates a 1.3 percent increase in house

prices. Short-term house price dynamics are found to be a function of its own lags and the change in

unemployment.

The results for the completions model suggest that credit plays an important role in construction

activity, both through the traditional channel of the interest rate and also through credit conditions.

However, the most important driver of construction is the relative profitability of building more

housing units, as measured by the ratio of house prices to building costs. The fit of both the house

price and completions models are illustrated in Figure 9 and Figure 10, respectively.

We now show how our model can be of use to macroprudential policymakers in assessing the

probable effectiveness of different types of interventions in maintaining financial stability.

5. Simulations

5.1 Credit conditions

A key question in analysing the boom and bust period is what were the key drivers of the rapid

expansion in credit and then the sudden deleveraging that the crisis precipitated. Identifying these

drivers is clearly crucial from a macroprudential/financial regulation perspective.

McCarthy and McQuinn (2013) use loan level data from Irish banks to show that the change in non-

interest related credit conditions in terms of the income fraction represented by the average

mortgage, the loan-to-value ratio and the mortgage term to maturity can explain much of the

explosion in credit. They find that while all of the variables were highly correlated, it was the

increase in the income fraction that explains the majority of the variation in credit conditions.

We have developed a model above that allows us to go further than their paper as we can directly

examine how the changes in credit conditions affected mortgage lending and therefore house prices

and housing supply. Furthermore, as we have estimated a system of equations we can examine the

feedback from house prices in to mortgage lending.

Figure 11 shows how the endogenous variables in the model would have behaved had the different

indicators of credit conditions in our model remained at their 2003 Q1 level, the period after which

mortgage credit-house price relationship became statistically explosive (see Gerlach-Kristen and

McInerney, 2014). Figure 11 shows the percent deviation from a baseline of no change in credit

conditions under three scenarios: holding the LTV at its 2003 Q1 level, holding the LTI at its 2003 Q1

level, and holding both of these ratios at their 2003 Q1 level.

Clearly, changes in the loan-to-income ratio were the most important driver of credit conditions in

the 2003 to 2008 period. The increase in LTIs over the period meant that new mortgage lending was

almost 50 percent higher by 2008 than under the baseline case. This resulted in the total mortgage

stock being almost 25 percent higher and house prices approximately 8 percent higher. In our

model, the lower level of indebtedness would have allowed banks to reduce the mortgage rate by

about 10 basis points and the consumer credit by more than 30 basis points by 2008. Lower house

prices would also have meant that the housing stock 0.5 percent below the actual level by 2008.

Taking the change in the LTI and the LTV together allows us to quantify to total impact of the change

in credit conditions on both the banking and housing sectors. New mortgage lending would have

been almost 60 percent less than the pre-crisis peak resulting in a mortgage stock that was almost

30 percent lower. These credit dynamics imply that house prices would have been approximately ten

percent lower than at their peak. This reduction in the value of collateral would have constrained

households from borrowing for consumption purposes with the stock of this type of lending being

almost 3 percent lower than at the peak.

5.2 Loan-to-Deposit ratio

As shown in Figure 1 and outlined by Coates and Everett (2013) Irish banks were able to avail of

relatively cheap wholesale funding in the pre-crisis period that facilitated the rapid expansion of

their deposits. The Basel III accord suggests imposing certain restrictions on the proportions of

different types of funding that comprise the liability side of the balance sheet (BCBS, 2010).

Our model allows us to examine a counterfactual case in which the lending by Irish banks could only

be a particular multiple of their deposit base. Figure 12 outlines this scenario in which the loan-to-

deposit ratio is constrained to remain at its 2003 Q1 level until the end of 2008. Surprisingly, the

impact of this restriction is relatively muted. New mortgage lending and the mortgage stock would

have been 6 percent and 3 percent lower at the peak, respectively. The lower volume of credit

would have resulted in house prices being only 1 percent lower by 2008 with little change in the

housing stock.

Our model therefore suggests that restrictions such as the loan-to-deposit ratio are relatively less

important as a prudential instrument, at least from a historical Irish perspective.

Conclusion We build a structural model of the Irish banking sector in which we estimate supply and demand

equations for four types of credit: mortgages, consumer loans, corporate lending, and loans to

construction firms.

As the housing market has been a particularly important source of both financial and

macroeconomic instability, we focus on how the banking sector interacts with this sector. We show

how non-interest rate related credit conditions can be significant drivers of both the supply and

demand for housing.

We also illustrate the potential use of our model for macroprudential policy. Specifically, we show

how potential instruments such as loan-to-value, loan-to-income, loan-to-deposit and capital

adequacy can affect the supply and demand for credit. We consider two counterfactual scenarios

that highlight these mechanisms in the model and show that rising loan-to-income ratios were the

main channel through which banks were able to extend a greater volume of credit.

I futu e o k, e i te d to e doge ise a ks’ holdi gs of apital y elati g the to y li al a d risk factors such as mortgage arrears and corporate insolvencies, which can both also be

endogenised

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Figure 1: Deposit Claims on Irish Banks by nationality

Source: Central Bank of Ireland, Credit, Money and Banking statistics

Figure 2: Comparing Actual and Fitted New Mortgage Lending

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

90 92 94 96 98 00 02 04 06 08 10 12

Actual (€mn) Fitted

New Mortgage Lending

0

50

100

150

200

250

2003Q1 2005Q1 2007Q1 2009Q1 2011Q1 2013Q1

€Bn

Irish non-MFI Euro Area Rest of World

Figure 3: Comparing Actual and Fitted values of the Mortgage Interest rate

Figure 4: The empirical fit of the consumer credit demand equation (€mn)

0

5,000

10,000

15,000

20,000

25,000

30,000

90 92 94 96 98 00 02 04 06 08 10 12

Actual Fitted

Figure 5: Actual and fitted levels of the interest rate on consumer loans (%)

8

10

12

14

16

18

20

90 92 94 96 98 00 02 04 06 08 10 12

Actual Fitted

2

4

6

8

10

12

14

16

90 92 94 96 98 00 02 04 06 08 10 12

Actual Fitted

Mortgage Rate (%)

Figure 6: Actual and fitted levels of the stock of non- eal estate o po ate le di g €

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

90 92 94 96 98 00 02 04 06 08 10 12

Actual Fitted

Figure 7: Actual and fitted values of the corporate lending rate (%)

0

4

8

12

16

20

24

90 92 94 96 98 00 02 04 06 08 10 12

Actual Fitted

Figure 8: A tual a d fitted alues fo o st u tio loa s €

0

2,000

4,000

6,000

8,000

10,000

12,000

90 92 94 96 98 00 02 04 06 08 10 12

Actual Fitted

Figure 11: Response of Banking and Housing sector if Loan-to-Value and Loan-to-Income ratios held

at 2003 Q1 levels.

Note: figure shows percent deviation (percentage points for interest rates) from a baseline of no change in the LTV or LTI

between 2003 and 2008. LTV represents the scenario where the LTV is held at its 2003 Q1 level until 2008 Q4, while LTI

represents the scenario where the LTI is held fixed. LTV+LTV is the scenario in which both are held fixed.

-80

-60

-40

-20

0

t+4 t+8 t+12 t+16 t+20

New Mortgages

LTV LTI LTV+LTI-40

-30

-20

-10

0

t+4 t+8 t+12 t+16 t+20

Mortgage Stock

LTV LTI LTV+LTI

-0.2

-0.15

-0.1

-0.05

0

t+4 t+8 t+12 t+16 t+20

Mortgage Rate

LTV LTI LTV+LTI-0.4

-0.3

-0.2

-0.1

0

t+4 t+8 t+12 t+16 t+20

Consumer Credit Rate

LTV LTI LTv+LTI

-4

-2

0

t+4 t+8 t+12 t+16 t+20

Consumer Credit

LTV LTI LTV+LTI-15

-10

-5

0

t+4 t+8 t+12 t+16 t+20

House Prices

LTV LTI LTV+LTI

-10

-5

0

t+4 t+8 t+12 t+16 t+20Housing Completions

LTV LTI LTV+LTI -1

-0.5

0

t+4 t+8 t+12 t+16 t+20Housing Stock

LTV LTI LTV+LTI

Figure 12: Response of Banking and Housing sector if Loan-to-Deposit ratio held at 2003 Q1 levels.

Note: figure shows percent deviation (percentage points for interest rates) from a baseline of no change in the loan-to-deposit

(LTD) ratio between 2003 and 2008.

-2

-1.5

-1

-0.5

0

t+4 t+8 t+12 t+16 t+20

Mortgage Stock

-6

-4

-2

0

t+4 t+8 t+12 t+16 t+20

New Mortgages

0

0.2

0.4

0.6

t+4 t+8 t+12 t+16 t+20

Mortgage Rate

-0.8

-0.6

-0.4

-0.2

0

t+4 t+8 t+12 t+16 t+20

House Prices

-0.5

-0.4

-0.3

-0.2

-0.1

0

t+4 t+8 t+12 t+16 t+20

Housing Completions

-0.05

-0.04

-0.03

-0.02

-0.01

0

t+4 t+8 t+12 t+16 t+20

Housing Stock

-0.15

-0.1

-0.05

0

0.05

t+4 t+8 t+12 t+16 t+20

Consumer Credit Rate

-0.25

-0.2

-0.15

-0.1

-0.05

0

t+4 t+8 t+12 t+16 t+20

Consumer Credit

Table 1: Estimating the Demand for New Mortgage Lending

MorVolt

MorRatet -0.032***

LTVt 0.735***

LTIt 0.861***

Incomet 0.781***

MorVolt-1 0.345***

ΔH.P i est-1 0.726***

Break_FinCrisis -0.502***

Break_SovCrisis -0.283***

Constant 14.552***

Adj. R2 0.968

Sample 1990q1-2013q4

Note: *** indicates statistical significance at the 1 percent confidence level.

Table 2: Estimating the Mortgage Interest Rate (mortgage supply)

ΔMo .Ratet

Mor.Ratet-1 -0.341***

HHEquityt-1 -0.082**

URatet-1 0.033***

DepRatet-1 0.051***

Euribort-1 0.256***

LTDt-1 -0.238**

BankCapt 0.011**

ΔEu i o t 0.422***

ΔEu i o t-1 0.0483**

ΔDepRatet 0.129**

Sample 1990q1-2013q4

Adj. R2 0.889

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 3: The Mortgage Interest Rate (mortgage supply)

ΔCo sC edit

ConsCreditt-1 -0.109***

Incomet-1 0.081**

ConsRatet-1 -0.007***

HPricest-1 0.451**

Constant -1.424**

Adj R2 0.581

Sample 1990q1 - 2013q4

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 4: The Interest Rate on Consumer loans

ΔCo sRatet

ConsRatet-1 -0.337***

HHEquityt-1 -1.322**

URatet-1 0.211***

DepRatet-1 0.151***

Euribort-1 0.316***

LTDt-1 -0.448**

BankCapt 0.031**

ΔEu i o t 0.432***

ΔEu i o t-1 0.0483**

ΔDepRatet 0.129**

Sample 1990q1-2013q4

Adj. R2 0.989

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 5: The stock of Corporate Lending

ΔCo pC editt

CorpCreditt-1 -0.080***

CorpRate t-1 -0.002***

RGDP

Constant

0.095***

0.013***

Δ Co pC editt-1 0.017**

Sample 1990q1-2013q4

Adj. R2 0.934

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 6: The Interest Rate on non-real estate corporate loans

ΔCo pRatet

CorpRatet-1 -0.347***

Gapt-1 -5.521**

Insolvrt-1 1.881***

DepRatet-1 0.238***

Euribort-1 2.903***

LTDt-1 -1.114**

BankCapt 0.019*

ΔEu i o t 0.789***

ΔI sol t-1 0.097**

ΔDepRatet 0.129**

Sample 1990q1-2013q4

Adj. R2 0.989

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 8: Loans to the construction sector

ΔCo st u tio C edit

ConstructionCreditt-1 -0.129***

CorpRatet-1 -0.021**

RGDPt-1 5.942***

Tobint-1 6.251**

Constant 0.424**

Adj R2 0.899

Sample 1990q1 - 2013q4

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 9: Estimating Real National House Prices

ΔH. Prices

HPricest-1 -0.321***

H Stockt-1 -0.266**

Usert-1 -0.001**

Pop2534t-1 0.652***

Incomet-1 0.387***

Mortgagest 0.034***

ΔI o et 0.172***

ΔHPricest-1 0.450***

ΔHPricest-3 0.234**

ΔURatet -0.013***

Constant 0.001

Adj. R2 0.812

Sample 1990q1-2013q4

Note: ** and *** indicate statistical significance at the 5 and 1 percent levels, respectively.

Table 10: Estimating the supply of new housing units

Completionst

Completionst-1 0.879***

Tobint 0.118***

ΔCo st u tio Creditt 0.218***

CorpRatet -0.007**

Constant 0.340***

Adjusted R2 0.983

Sample 1990q1-2013q4

Note: *** indicates statistical significance at the 5 and 1 percent levels, respectively.