Credit conditions, macroprudential policy and house prices. No 36 / February 2017.
Date: 2017-02-16
Institutions: European Systemic Risk Board
Subjects: banking policy, central bank, credit policy, housing need, property market, public policy, real estate credit

Working Paper Series 

No 36 / February 2017 

Credit conditions,  
macroprudential policy 
and house prices 

by 
Robert Kelly 
Fergal McCann 
Conor O'Toole 

Abstract

We provide a micro-empirical link between the large literature on credit and house
prices and the burgeoning literature on macroprudential policy. Using loan-level data
on Irish mortgages originated between 2003 and 2010, we construct a measure of credit
availability which varies at the borrower level as a function of income, wealth, age, interest
rates and prevailing market conditions around Loan to Value ratios (LTV), Loan to Income
ratios (LTI) and monthly Debt Service Ratios (DSR). We deploy a property-level house
price model which shows that a ten per cent increase in credit available leads to an 1.5
per cent increase in the value of property purchased. Coeﬃcients from this model are
then used to ﬁt values under scenarios of macroprudential restrictions on LTV, LTI and
DSR on credit availability and house prices in Ireland for 2003 and 2006. Our results
suggest that macroprudential limits would have had substantial impacts on house prices,
and that both the level at which they are set and the timing of their introduction is a
crucial determinant of their impact on housing values.

Keywords: Mortgages, credit availability, macroprudential policy, house prices. JEL Clas-
siﬁcation: E58, G28, G21, R31.

1

1 Introduction

Given the key role of the housing market in credit and asset price cycles, as well as the link between

housing ﬁnance and the 2007-2009 global ﬁnancial crisis (Brunnermeier, 2009; Duca et al., 2010, 2011a),

regulatory authorities have recently put a greater focus on the use of macro-prudential instruments.

Such macro-prudential policy (MPP) measures are aimed at breaking the link between credit growth,

house prices and banking sector instability as well as building bank and borrower resilience to ﬁnancial

shocks (Kashyap et al., 2011; IMF, 2013). In particular, macro-prudential tools speciﬁcally targeting

the mortgage market have become extremely common especially given the role played by the mortgage

market in the recent global ﬁnancial crisis (Elul et al., 2010; Mian and Suﬁ, 2009). As part of their

overall ﬁnancial stability toolkit, many countries have imposed restrictions on the Loan to Value (LTV)

or Loan to Income (LTI) ratios of originated mortgages.1

While a large literature exists linking credit ﬂows and house prices,2 a relatively recent literature

has emerged attempting to measure the impact of speciﬁc MPP measures in the housing market, mainly

using cross-country or single country macroeconomic data (Kuttner and Shim, 2013; Vandenbussche

et al., 2015; Cerutti et al., 2015). The evidence suggests a tentative link between these MPP and house

prices with a more conclusive ﬁnding on the impact of MPP on credit growth. However, a limitation of

these papers is that the majority are at the aggregate level and rely on the use of average indicators in

their identiﬁcation strategy in cross country or single country models. In particular relating to LTI and

LTV limits in the mortgage market, this approach may miss the complexity through which these limits

impact borrower behaviour which can only be tested with adequate within-country borrower-speciﬁc

variation. One exception is Igan and Kang (2011) who show, using survey data, that households were

more likely to have dampened house price expectations and delayed house purchases in Korea after

the introduction of macroprudential limits on LTV and LTI.

Given this gap in the literature, we use loan-level data on roughly two-thirds of the Irish mortgage

market between 2003 and 2010 to provide a micro-empirical bridge between the literature on credit

and house prices and the nascent literature on the impacts of macroprudential policy. Our aims in

this paper are threefold. Firstly, we provide novel insights into the way in which credit conditions such

as the LTV, LTI and DSR act to determine the credit available for a heterogeneous distribution of

borrowers. We measure credit available in an innovative way at the loan level : for every individual

borrower there are three potential mortgages that a bank will supply as a function of the borrowers’

income, down-payment and age, combined with prevailing market limits on LTV, LTI and DSR. The

1See IMF (2013) for a review. The IMF Global Macroprudential Policy Database notes 46 countries with

LTV limits and 36 countries with DTI limits.

2Related literature includes Duca et al. (2011b); Anundsen and Jansen (2013); Fitzpatrick and Mcquinn

(2007); Lindner (2014); Gerlach and Peng (2005); Avouyi-Dovi et al. (2014).

Kelly, McCann, O’Toole

credit available to a borrower is then the minimum of the three mortgages calculated, consistent with

the way in which a bank would arrive at a credit allocation decision in practice. This measurement

of credit availability diﬀers greatly to aggregate measures used in the existing literature (Duca et al.,

2011b; Jansen and Krogh, 2011; Fernandez-Corugedo and Muellbauer, 2006) in terms of the granularity

of what is being measured and the way in which borrower heterogeneity is directly captured.

Secondly, we use a reduced-form, loan-level house price model to link our measure of credit supply

to house prices at the micro-level. This is the ﬁrst study of which we are aware to use micro-data to

link credit, macro-prudential instruments and house prices.

Thirdly, using the coeﬃcients of our loan-level house price model, we trace through the impact of

MPP regimes to house prices. Our set-up is extemely ﬂexible in that the user can set levels of LTV, LTI

and DSR at any level they wish, with the algorithm recalculating each borrower’s credit availability

as a function of three new mortgages. This diﬀers greatly from aggregate studies on macroprudential

housing instruments, which generally observe an average value for an instrument such as the LTV

across the entire market. The framework then allows a change to MPP limits on LTV, LTI and DSR

to impact house prices through two channels: directly through a reduction in credit available, and

indirectly through the role that reduced regional house prices have on the down-payment available to

previous-owners. We highlight two important factors that impact the magnitude of the eﬀect of a set

of MPP restrictions on house prices: (i) the stringency of the limits on LTV, LTI or DSR (ii) the point

in the housing cycle at which the MPP is implemented.

As well as the aforementioned literature estimating the impact of MPP in aggregate empirical

settings, our research is a complement to the burgeoning literature which tests the impact of macro-

prudential policy in an aggregate set-up through DSGE or structural econometric models (Akram,

2014; Bailliu et al., 2015; Clancy and Merola, 2014; Gerali et al., 2010; Kannan et al., 2012; Quint

and Rabanal, 2014). These papers mainly enter macro-prudential policy into their set-up through

aggregate credit growth, bank liabilities limits or the price of credit/external ﬁnancing premium (see

Brzoza-Brzezina and Kolasa (2013)). These models are not able to disentangle the impact of speciﬁc

macro-prudential limits such LTI and LTV on credit availability and in turn on house prices. By

estimating our model at the borrower level we can go beyond the aforementioned research and can

trace through the impact of each of these limits simultaneously.

Where such macro-economic models have channeled speciﬁc LTV limits (Brzoza-Brzezina, 2014;

Brzoza-Brzezina et al., 2015; Rubio and Carrasco-Gallego, 2014), these are entered as the average LTV.

Given that the average of observed LTV is in fact the intersection of both the supply of credit and

the borrowers’ demand for leverage, such measures do not cleanly identify pure credit supply impacts.

Additionally where LTI, LTV or DSR limits are in fact simultaneously imposed by macro-prudential

authorities these macro-studies cannot speciﬁcally test which measure has the greater impact on credit

2

Kelly, McCann, O’Toole

and house prices. Our measure, by focusing on credit availability and allowing each borrower to be

limited by one channel, can provide additional insight to existing research.

Our research is also linked to two further literatures. First, our work complements studies at

the aggregate level which have studied the link between MPP and ﬁnancial outcomes such as credit

growth, mortgage delinquency, leverage and indebtedness, without looking speciﬁcally at the impact

on house prices (Wong et al., 2011; Claessens et al., 2013; International Monetary Fund, 2011; Cerutti

et al., 2015; Jacome and Mitra, 2015). Second, we build on recent studies that exploit sub-national

data and provide evidence of a causal eﬀect of liberalised credit on house prices (Favara and Imbs,

2015; Di Maggio and Kermani, 2014; Adelino et al., 2012; Labonne and Welter-Nicol, 2015; Anenberg

et al., 2015) by providing a loan-level estimate of the credit-house price elasticity.

A number of ﬁndings emerge. Our estimates suggest that during the Irish housing boom, the

majority (70-80 per cent) of borrowers had their credit available determined along the loan-to-income

channel, with 20-30 per cent being cut oﬀ by monthly debt service ratio (DSR) limits, and only a

small amount being cut oﬀ by LTV limits. When these measures are combined into our indicator of

credit availability and this is included in a log-log house price speciﬁcation, the elasticity of house

prices with respect to credit availability is estimated in our baseline model at 15 per cent, which is

extremely close to estimates from Favara and Imbs (2015), who provide the only published estimate of

a cleanly-identiﬁed causal eﬀect of credit supply on house prices. Across a range of robustness tests,

we estimate the elasticity to always be within a range of 12 to 27 per cent. Our micro-level house price

model also estimates that a ten per cent increase in income increases house prices by between 2.1 and

2.6 per cent while a ten per cent increase in wealth increases house prices by 2.4-2.5 per cent.

To trace the impact of macro-prudential policy on house prices, we impose two separate regimes

which are drawn from the range of international parameterisations of such measures.3 We then apply

these regimes at periods of tighter and looser market conditions for general credit conditions and

estimate the new ﬁtted house price distribution under these scenarios. Given that our micro-data

framework cannot incorporate general equilibrium housing market eﬀects such as changes to housing

supply, impacts on the rental market and the entry of Buy-to-Let investors, we restrict our scenario

re-ﬁtting of house prices to the short-run. We do so by starting with observed average house prices in

2003 and 2006, and estimating the cumulative one-year eﬀect of the introduction of two sample MPP

regimes. The model estimates that the switching on of Regime 1 (75 LTV, 4 LTI and 37 DSR) in 2003

would have led to a fall in average house prices from observed levels of 6.4 per cent after four quarters.

During the high-growth period of 2006, this MPP regime would have led to one-year falls of 12.1 per

cent. Our less restrictive MPP regime, Regime 2 (85 LTV, 4.5 LTI and 45 DSR) is estimated to have

a one-year eﬀect of just over 1 per cent when switched on in 2003, with this eﬀect rising to 8.7 per

3See Jacome and Mitra (2015) for an overview.

3

Kelly, McCann, O’Toole

cent when switched on in 2006. We also present sensitivities across values of LTI and LTV to provide

further insight into the impact on house prices of diﬀerent policy calibrations.

From a policy perspective, our results provide insight for regulators attempting to understand the

calibration of MPP measures in the mortgage market. First, the speciﬁc parameterisation as well as

the instrument matters. We ﬁnd that LTI is in general the more binding tool relative to LTV and DSR.

However, this balance reverses as tighter caps on LTV and DSR are introduced. Second, the diﬀerential

between current market credit conditions and the stringency of proposed measures is important. Our

results indicate that MPP measures have a smaller impact when market credit conditions are tighter.

This suggest policymakers such take into account the point in the credit cycle when calibrating such

instruments.

The paper is structured as follows: Section 2 discusses data sources and measurement of credit

supply; Section 4 describes the impact of macroprudential policy on borrowers’ available credit supply;

Section 3 presents our house price model, Section4 describes the introduction of MPP regimes and

re-ﬁtting of the short-run house price distribution; Section 5 concludes.

2 Data

The data used come from the Central Bank of Ireland’s Loan Level Data (LLD). These ﬁles contain

information on all loans issued by Irish banks participating in the 2011 Financial Measures Programme

(FMP). In the case of the Irish residential mortgage market, these four lenders account for roughly

two thirds of the total market, making this a particularly rich source of data.4 The data have been

explained in detail by Kennedy and McIndoe-Calder (2012).

Information is provided on inter alia

current mortgage balance, bank, current interest rate, origination and current interest rate type,

origination and maturity dates, original installment, current loan to value ratio (LTV), and First Time

Buyer status (FTB). Certain characteristics of the borrower are also reported in the data, such as

marital status, geographic location, employment group, income and whether the application is joint

or single assessment. Crucially from the point of view of this study, the valuation of the property at

loan origination is included which provides a borrower-speciﬁc house purchase price. These variables

are all collected at the mortgage origination date and these data can be combined with the originating

mortgage criteria to provide a dataset linking indicators of LTV, LTI and Debt-Service-Ratio (DSR)

to house prices.

Given data availability, our sample runs from January 2003 to December 2010. A number of

4The four lenders are Allied Irish Banks, Bank of Ireland, the Educational Building Society (EBS) and
Permanent TSB (PTSB). EBS has subsequently been merged into the Allied Irish Banks group as part of the
FMP programme. PTSB was previously known as Irish Life and Permanent.

4

Kelly, McCann, O’Toole

inclusion criteria are applied to the data. Firstly, only primary dwelling (often referred to as owner-

occupied) mortgages are considered. Investor or Buy-to-Let (BTL) mortgages are omitted from the

data set, as the originating credit decision of the lender, as well as the type of macro-prudential limit

that a regulator would set, are distinct across the PDH and BTL segments of the mortgage market.5

We also restrict loans to those that were for the purpose of home purchase, removing those used for

equity release or for reﬁnancing. This restricts the sample to mortgages underlying transactions of

properties between two parties. Finally we control for outliers by removing the 99th percentile in our

key variables of interest.

The ﬁnal baseline regression sample consists of 188,405 property purchases. Figure 1 plots the

number of property purchases per quarter in the data. The data are highly seasonal, with a visible dip

in transaction numbers in the ﬁrst quarter of each year. Consistent with aggregate data on the Irish

mortgage market, activity is at its most intense in our data set in 2005, 2006, and 2007.

Figure 1: Number of transactions per quarter, regression sample 2003-2010

Source: Central Bank of Ireland Loan Level Data

2.1 Construction of measure of available credit

Credit conditions have previously been measured at the aggregate level in studies such as Fernandez-

Corugedo and Muellbauer (2006) which uses aggregate proportions of high-LTV and high-LTI loans,

5Internationally there is a developing trend towards the diﬀerential treatment of buy-to-let investors from
a macro-prudential perspective. In January, 2015 Ireland announced a diﬀerential LTV ceiling for buy-to-lets
while New Zealand has also brought in investor-speciﬁc limitations. See national Central Bank websites for
more detail.

5

02,0004,0006,0008,00010,000Number of quarterly mortgage transactions2003   2004   2005   2006   2007   2008   2009   2010   Kelly, McCann, O’Toole

the average ﬁrst time buyer LTV index for the USA of Duca et al. (2011b), and the Norwegian

index of Jansen and Krogh (2011). McCarthy and McQuinn (2013) provide an alternative for Ireland

by calculating the term, LTV and DSR chosen by borrowers at origination and showing that these

levels are heterogeneous across borrower groups. Their study also backs out an aggregate index from

regressions using micro-data.

Our approach departs from the extant literature: rather than constructing an index based on

aggregate averages or observed drawdown values, we calculate the maximum amount of credit that

each borrower could access when applying for a loan (credit available, CA). Our measurement has

two key characteristics: (i) it diﬀers for every borrower as a function both of borrower characteristics

and prevailing bank lending conditions (ii) it is a function of originating supply-side credit conditions

that can be limited by macroprudential policy, namely the LTI, LTV and DSR. These features make

our measure of CA well-suited to incorporating the impact of macroprudential limits. The approach

is similar in spirit to that of Anenberg et al. (2015) who apply a frontier estimation approach used fre-

quently in the industrial organizational literature to estimate borrower-speciﬁc loan amounts available,

but who do not incorporate credit conditions around LTV, LTI or DSR into their analysis.

We construct CA so that it can directly incorporate three distinct types of credit condition by

allowing all borrowers to have three potential loans. We reiterate that it is crucial to distinguish these

loans, which are measures of the supply of credit available to each borrower, from the observed loan

amount that was actually borrowed, as the latter is a function of both supply and demand for credit.

The three loans associated with each borrower are outlined in turn:

LoanLT V =

Deposit
100 − LT VM ax

− Deposit

(1)

LoanLT V is the loan that is consistent with the deposit or down-payment available to the borrower,

combined with the maximum LTV that banks are oﬀering on the market at the time of mortgage

origination. This measure of credit supply, LT VM ax, is calculated as the 98th percentile of observed

originating LTVs in the market in a given quarter. The evolution of this metric from 2003q1 to 2010q4

is depicted in Figure 2. The data suggest that down-payment constraints were essentially eliminated

during most of the sample period in Ireland, with the advent of 100 per cent LTV mortgages. Under

such a regime, there is an inﬁnite amount of credit available to borrowers along this channel.

LoanLT I = Income∗LT IM ax

(2)

LoanLT I is the loan that is consistent with the borrower’s income at origination, combined with

the maximum LTI that banks were oﬀering in the market at that point in time. We use total household

gross income, as this is the measure that is used by Irish banks when calculating the LTI. Figure 3

6

Figure 2: 98th percentile LTVs, observed quarterly mortgage data 2003-2010

Kelly, McCann, O’Toole

Source: Central Bank of Ireland Loan Level Data, authors’ calculations

highlights clearly how credit conditions loosened along this channel between 2003 and 2007, with the

available LTI limit going from just over 5 to 6.5. By end-2010, this measure had fallen to circa 5.5.

Figure 3: 98th percentile LTIs, observed quarterly mortgage data 2003-2010

Source: Central Bank of Ireland Loan Level Data, authors’ calculations

LoanDSR =

RepayM axit(1 − (1 + r))T ERMi
r

(3)

7

94969810098th percentile Loan to Value2003q12005q12007q12009q12011q155.566.598th percentile Loan to Income2003q12005q12007q12009q12011q1Kelly, McCann, O’Toole

LoanDSR is the loan consistent with the maximum monthly repayment that a bank will sanction

at time t. This repayment, RepayM axit is the product of borrower i’s net monthly income and the

monthly maximum DSR being oﬀered by banks at time t. The available DSR, calculated as the 98th

percentile of observed originating DSRs in the data, is pictured in Figure 5. The stark loosening of

credit conditions along this channel is clearly visible in the picture, with our measure of DSR availability

rising from 35 per cent of net monthly income to 50 per cent by late 2008. By 2010, the ratio had

returned to 2003 levels of 30 to 35 per cent, with part of the sharp decline being driven by falls in the

ECB main reﬁnancing operation rate during late 2008 and early 2009.

Along with RepayM axit, the calculation of LoanDSR requires information on the interest rate

being charged at time t and the maximum mortgage term that can be attained by borrower i. Equation

3 is a modiﬁcation of a standard annuity formula to transform a monthly repayment into a loan balance

amount. r is the interest rate on the chosen mortgage contract at origination.6 T ERMi is calculated

as the diﬀerence between the age of the borrower and the maximum age at which banks are willing to

allow customers make their ﬁnal mortgage payment, AgeF inal. We input the quarterly 98th percentile

of AgeF inal in the calculation of T ERMi. In the 2003 to 2010 period, the 98th percentile of AgeF inal

remains between 70 and 75 years, with an increasing trend between 2003 and 2007 as would be expected

during a period of loosening credit conditions.

CA = M in(LoanLT V , LoanLT I , LoanDSR)

(4)

Armed with three potential loans per borrower, each of which is a function both of a key borrower

characteristic and a prevailing supply-side credit condition, CA is calculated simply as the minimum of

the three loan sizes. As an illustration, if a borrower has a deposit of e5,000 and the prevailing available

Loan to Value ratio is 90 per cent, that borrower will not get a mortgage larger than e45,000 (i.e. the

deposit of e5,000 comprises 10 per cent of the purchase of a property worth e50,000), regardless of

income, age, the prevailing interest rate, or credit conditions along the LTI and DSR channels.

Figure 6 plots the share of all mortgages originated in a given quarter that have CA determined by

each of the three credit conditions LTV, LTI and DSR. The ﬁrst important feature to note is that, in

a period where the available LTV has reached 100 per cent, no borrower is deemed to be cut oﬀ by the

LTV channel, as in theory all borrowers can access a loan amount of inﬁnity along this channel. The

salience of the LTI channel is clear from this graph: up to 2007, 70 to 80 per cent of borrowers had a

6Measured for standard variable rate (SVR) mortgages using the advertised new lending rates for SVR
mortgages by bank and quarter provided by the Central Bank of Ireland Statistics division. For tracker loans,
we calculate a margin above the ECB rate using the diﬀerential between the interest rate observed in the data
and the ECB rate at December 2013, and then assuming this margin is constant through time to apply it to
the ECB rate at the point of mortgage origination. For the small number of ﬁxed rate loans in the data, we
make the simplifying assumption that the originating interest rate equaled the SVR rate.

8

Figure 4: 98th percentile implied age at mortgage maturity, observed quarterly mortgage data
2003-2010

Kelly, McCann, O’Toole

Source: Central Bank of Ireland Loan Level Data, authors’ calculations

Figure 5: 98th percentile DSRs, observed quarterly mortgage data 2003-2010

Source: Central Bank of Ireland Loan Level Data, authors’ calculations

CA that was determined on the LTI channel. Borrowers who have their CA determined by the DSR

are generally borrowers who apply for mortgages at an older age, which leaves them with a shorter

maximum available mortgage term, meaning that for any given monthly payment amount, they can

access a smaller total loan amount. Consistent with the prevalence of 100 per cent LTV mortgages,

9

707274767898th percentile Age at Last Payment2003q12005q12007q12009q12011q1.35.4.45.5.5598th percentile Debt Service Ratio2003q12005q12007q12009q12011q1Kelly, McCann, O’Toole

for the majority of our sample period, there are no borrowers restricted along the LTV channel.

Figure 6: Which prevailing market condition determines credit availability? Quarterly market
shares 2003 to 2010.

Source: Central Bank of Ireland Loan Level Data, authors’ calculations

One innovation of our research is an attempt to ensure that CA is constructed using the levels of

LTV, LTI and DSR that were made available by lenders, i.e. CA represents supply-side availability

of credit. We argue that by taking the 98th percentile of observed originating LTV, LTI or DSR in

the market in a given quarter, we are able to proxy the credit available to any borrower, should they

have wished to access such a large volume of credit.7 Taking the right tail of the observed distribution

of LTI, LTV or DSR is a valid proxy for the amount available to any borrower if one accepts the

assumption that in every time period there is a tail of borrowers with an insatiable appetite for credit,

who will always take a higher LTV, LTI or DSR if a bank is willing to oﬀer it. If this group of borrowers

exists, the right tail of observed LTV, LTI or DSR values represents an approximation of the point at

which banks’ credit supply restricts the amount of credit available. A very similar line of thought is

invoked by Anenberg et al. (2015) in estimating a “loan frontier” which approximates the supply-side

availability of credit using US mortgage data. The existence of borrowers who will go to the maximum

amount available is conﬁrmed in the Canadian mortgage market by Allen et al. (2015) who show that,

upon the move from 10 per cent to 5 per cent minimum downpayments in 2003, up to one third of

aﬀected mortgage borrowers moved to this new minimum downpayment amount, with a subsequent

7We take the 98th percentile rather than the maximum or 99th percentile of observed LTV, LTI and DSR as
our measure of credit supply in order to create a smoother series which is less prone to data errors, and outlier
credit allocations which may have been sanctioned by a local loan oﬃcer due to exceptional circumstances such
as a parental guarantee or high observed family wealth.

10

0.2.4.6.812003   2004   2005   2006   2007   2008   2009   2010   Imposing market conditions: what determines available credit?LTVLTIDSRKelly, McCann, O’Toole

increase in the average household debt to equity ratio from 8 to 14.

The imposition of a market-wide 98th percentile LTV, LTI or DSR in the calculation of CA for

all borrowers may seem crude, given that lender may impose diﬀerential lending standards in diﬀerent

market segments. The experience in Ireland suggests that this is less of a concern for this study than

those in other jurisdications: anecdotal evidence in Ireland suggests that lenders oﬀer mortgages as

standard products, whereby once any borrower is deemed credit-worthy, they can access loans at the

same level of LTV, LTI or DSR. As a robustness check to our empirical house price model, we allow

98th percentile values to be calculated within speciﬁc subgroups of the market, to allow for the fact that

lenders may diﬀerentiate the level of LTV, LTI or DSR at which they lend as a function of borrower

characteristics.

3 House price regression model

3.1 Empirical approach

A key aim of our research is to estimate the link between credit availability and house prices which

will allow us to evaluate the impact of macro-prudential limits in the mortgage market. By running

a regression model at the property level where CA is shown to have an eﬀect on house prices, we can

then trace a link through from a change in macroprudential policy rules to a new calculation of CA for

every mortgage borrower to estimates of ﬁtted house prices under the policy regime. Our construction

of CA as incorporating LTI, LTV and DSR means that macroprudential rules can be easily mapped to

borrower-speciﬁc credit shocks. To test the link between credit and house prices, we specify a reduced

form model of house prices at the loan level using the following:

HPit = f (CAit, Incomeit, W ealthit, Bankit, P ropertyT ypeit, λRT , Dit)

(5)

Where the dependent variable HPit is the euro price of the house sold, CA is our measure of credit

availability, Income is measured as the total income of borrowers ﬁlling in the mortgage application,

W ealth is proxied by the deposit posted by the borrower, Bank is a dummy for the four banks in our

sample and P ropertyT ype takes eight values for apartment, small houses (semi-detached or terraced),

large detached houses, and unclassiﬁed houses in both Dublin and outside Dublin. Dit are additional

demographic characteristics that we include as controls and are deﬁned in each regression speciﬁcation.

λRT is a vector of 256 region∗time control variables. These dummy variables will capture all

local macroeconomic dynamics that would be expected to impact the housing market in a given

quarter, including any feedback between house price developments and banks’ lending standards. We

do not believe it plausible that the price paid by an individual house purchaser should impact banks’

11

Kelly, McCann, O’Toole

lending standards vis-‘’a-vis that particular homeowner, once we are controlling for aggregate lending

standards. Therefore, we argue that the λRT purge our loan-level estimates of the impact of credit

availability on house purchase prices from the reverse causality concerns that typically arise when

estimating the eﬀect of credit on house prices using aggregate data.

In our data, we do not observe the total wealth of the borrower so instead must rely on the posted

value of the deposit as a proxy. Of concern is that down-payments are at the discretion of the borrower.

A borrower may have wealth available of e100,000, but may have a preference for lower leverage or

for continuation of savings, and therefore holds back a signiﬁcant portion of her wealth and posts a
deposit of e50,000. Our model above would in this case underestimate both W ealth and LoanLT V ,
given that it is calculated based on the value of e50,000 and the prevailing LTV limits. We make

an eﬀort to adjust the deposit amount to account for the fact that borrowers vary in their risk and

leverage appetite, and post down-payments which are some fraction of the true wealth available for

house purchase. Our approach is to take groups of households that are unique in their First Time Buyer

status (two categories), income quintile within originating year (5 categories), group of originating year

(3 categories), marital status (3 categories) and Dublin status (2 categories). Within these distinct

groups, we take the average ratio of deposit to income ( D

I ). W ealth is then calculated as

W ealthit = M ax(Income∗
it

D
I

, Depositit)

(6)

Table 1 provides summary statistics for the above explanatory variables. The average house price

in our sample is circa e314,000 with a standard deviation of e127,000. Average credit availability in

our data is e367,000. As expected, this is higher than the average loan size in the data and reﬂects the

value of credit the bank would have been willing to extend given the borrowers’ characteristics. Mean

income is approximately e61,000 while average posted deposit (our wealth proxy) is e133,000. On the

composition of borrowers in our sample, average borrower age is 35, 44 per cent are ﬁrst time buyers

and 84 per cent are employee’s with the remainder self-employed. Of the properties purchased in our

data, 30 per cent are detached houses outside Dublin, while 26 per cent are semi-detached or terraced

houses outside Dublin. In total, Dublin transactions account for 25.36 per cent of the purchases in the

data set.

12

Table 1: Summary statistics

Variable

Mean
314,148
367,837
61,307
111,745
133,199
35.037

Obs
188,405
188,405
188,405
188,405
188,405
188,405

House Price
CA
Income
Deposit
Wealth for Down-Payment
Borrower Age (years)
Sample shares for categorical variables
Bank 1
Bank 2
Bank 3
Bank 4
Non-Dublin Apartment
Non-Dublin House
Non-Dublin Other
Non-Dublin Terrace/Semi
Dublin Apartment
Dublin House
Dublin Other
Dublin Terrace/Semi
Second or Subsequent Buyer
First Time Buyer
Employed, Salary
Self Employed
Married
Separated/Divorced
Single

188,405
188,405
188,405
188,405
188,405
188,405
188,405
188,405
188,405
188,405
188,405
188,405
171,971
171,971
161,346
161,346
185,442
185,442
185,442

17%
26%
24%
34%
2.09%
29.52%
16.69%
26.33%
4.55%
1.36%
7.6%
11.85%
55.1%
44.9%
84.8%
15.2%
44.6%
4.7%
50.7%

Kelly, McCann, O’Toole

St. Dev.
127,130
154,996
26,985
106,469
97,899
7.971

13

Kelly, McCann, O’Toole

3.2 Estimation results

Results from our baseline model are reported in Table 2. The coeﬃcient on CA is extremely stable

across columns (1) and (2), where ﬁrstly the down-payment posted is used to proxy wealth, and

secondly our adjusted measure is used. A one-euro increase in CA is associated with an increase in the

value of house purchased of 20.5 to 21.8 cents. Older borrowers purchase less expensive houses, ceteris

paribus. One additional year in age is associated with a fall in house value of e850-1,070. Our controls

for property types have important explanatory power. Relative to Dublin detached houses, a non-

Dublin apartment sells on average for more than e100,000 less. The smallest coeﬃcient, in absolute

value terms, is for Dublin semi-detached and terraced houses, suggesting that this is the second most

valuable market segment in Ireland during the sample period.

Column (3) of Table 2 runs a log-log speciﬁcation where CA, income and wealth are all entered

in logarithmic form. The coeﬃcients are thus interpretable as elasticities, with a 10 per cent increase

in CA estimated to lead to a 1.5 per cent increase in HP . This elasticity estimate is strikingly close

to those estimated in Favara and Imbs (2015), who provide estimates of the elasticity of house prices

to credit supply of between .12 and .16. Our estimates of the elasticity of HP to income and wealth

are .21 and .24, respectively.

In Table 3, we examine the robustness of our ﬁndings on CA, W ealth and income to the inclusion

of additional control variables at the loan and borrower level. In each model (1) to (4), the sample

size is smaller than that in model (1) due to missing values for some of the additional explanatory

variables. We introduce a dummy variable for First Time Buyers, self-employed borrowers and a three-

category marital status variable (single, married or separated/divorced) in combinations. Across all

four models, the range of estimates for CA is between .2 and .27, which does represent an increase

from the baseline model. The eﬀects of income and wealth are in a very similar range in Table 3 to

those estimated in Table 2.

Finally we test the robustness of our model to the way in which CA is calculated. In the baseline

model, CA is estimated using the 98th percentile LTV, LTI and DSR among observed mortgages in

any given quarter. However, it may be that lenders systematically make credit available using diﬀering

degrees of risk aversion in diﬀerent market segments. To directly test whether such behaviour has an

impact on our results, we re-calculate CA by allowing 98th percentile values to be calculated both

within-quarter and within-group. We deﬁne four separate robustness tests based on four groups:

1. Bank (four groups)

2. First Time Buyer status (2 groups)

3. Dublin versus rest of country (2 groups)

14

Table 2: Baseline model 2003 - 2010; OLS.

Level
0.205∗∗∗
(0.00692)
0.772∗∗∗
(0.0400)
0.727∗∗∗
(0.00197)
-882.5∗∗∗
(22.19)

Level
0.218∗∗∗
(0.00732)
0.575∗∗∗
(0.0423)

-1070.6∗∗∗
(23.88)
0.761∗∗∗
(0.00221)

Credit Available

Income

Deposit

Borrower Age

Wealth for Down-Payment

Log Credit Available

Log Income

Log Wealth for Down-Payment

Reference category: Dublin Houses
Non-Dublin Apartment

Non-Dublin House

Non-Dublin Other

Non-Dublin Terrace/Semi

Dublin Apartment

Dublin Other

Dublin Terrace/Semi

N
R2
4 Bank dummies?
256 Region∗Time dummies?

-107636.5∗∗∗
(4793.4)
-90242.7∗∗∗
(4710.7)
-80508.9∗∗∗
(5109.9)
-102326.7∗∗∗
(4718.4)
-48827.4∗∗∗
(1966.5)
-34237.3∗∗∗
(2726.5)
-36789.2∗∗∗
(1885.8)
188,405
0.753
YES
YES

-116527.6∗∗∗
(5241.9)
-91174.4∗∗∗
(5151.1)
-82804.3∗∗∗
(5563.8)
-109876.6∗∗∗
(5159.6)
-50206.0∗∗∗
(2048.8)
-32924.8∗∗∗
(2853.4)
-38972.1∗∗∗
(1946.3)
188,405
0.725
YES
YES

Kelly, McCann, O’Toole

Log

-0.00357∗∗∗
(0.0000901)

0.155∗∗∗
(0.00956)
0.215∗∗∗
(0.00922)
0.246∗∗∗
(0.000851)

-0.511∗∗∗
(0.0229)
-0.382∗∗∗
(0.0226)
-0.384∗∗∗
(0.0235)
-0.481∗∗∗
(0.0226)
-0.138∗∗∗
(0.00505)
-0.0780∗∗∗
(0.00783)
-0.0964∗∗∗
(0.00460)
188,405
0.666
YES
YES

15

Kelly, McCann, O’Toole

Table 3: Robustness: iterative addition of explanatory variables

Log Credit Available

Log Income

Log Wealth for Down-Payment

Borrower Age

Reference: Previous-owners
FTB

Reference: Salaried
Self-Employed

Reference: Married
Separated/Divorced

Single

N
R2

(1)
0.2029∗∗∗
(0.0111)
0.1865∗∗∗
(0.0107)
0.2587∗∗∗
(0.0009)
-0.0023∗∗∗
(0.0001)

(2)
0.2366∗∗∗
(0.0113)
0.1590∗∗∗
(0.0109)
0.2879∗∗∗
(0.0011)
-0.0022∗∗∗
(0.0001)

(3)
0.2566∗∗∗
(0.0101)
0.1461∗∗∗
(0.0097)
0.3007∗∗∗
(0.0010)
-0.0010∗∗∗
(0.0001)

(4)
0.2723∗∗∗
(0.0114)
0.1378∗∗∗
(0.0110)
0.3006∗∗∗
(0.0011)
-0.0009∗∗∗
(0.0001)

0.0913∗∗∗
(0.0016)

0.0807∗∗∗
(0.0015)

0.0840∗∗∗
(0.0016)

0.0242∗∗∗
(0.0020)

0.0245∗∗∗
(0.0020)

-0.0161∗∗∗
(0.0029)
0.0648∗∗∗
(0.0013)
171971
0.673

-0.0070∗∗
(0.0027)
0.0641∗∗∗
(0.0013)
185442
0.672

161346
0.673

0.0230∗∗∗
(0.0020)

-0.0065∗∗
(0.0029)
0.0650∗∗∗
(0.0014)
160115
0.678

Dependent variable in all speciﬁcations is the natural log of property purchase price.
8 hedonic property type, 256 Region∗ Quarter, 4 bank dummies included in all models

Robust standard errors in parentheses
∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01

16

Kelly, McCann, O’Toole

Table 4: Robustness: Credit Conditions varying by sub-group

Credit condition varies by:
Log CA

Log Income

Log Wealth for Down-Payment

Borrower Age

Observations
R2

(1)
Bank
0.124∗∗∗
(0.00741)
0.246∗∗∗
(0.00714)
0.246∗∗∗
(0.000850)
-0.00376∗∗∗
(0.0000841)
188405
0.666

(2)
Dublin
0.157∗∗∗
(0.00942)
0.214∗∗∗
(0.00905)
0.246∗∗∗
(0.000851)
-0.00352∗∗∗
(0.0000907)
188405
0.666

(3)
FTB
0.238∗∗∗
(0.00926)
0.138∗∗∗
(0.00882)
0.247∗∗∗
(0.000849)
-0.00308∗∗∗
(0.0000894)
188405
0.666

(4)
Property Type
0.175∗∗∗
(0.00799)
0.197∗∗∗
(0.00771)
0.246∗∗∗
(0.000851)
-0.00347∗∗∗
(0.0000853)
188405
0.666

Dependent variable in all speciﬁcations is the natural log of property purchase price.
256 Region∗ Time dummies, 8 hedonic property dummies and 4 bank dummies included in all models.

Standard errors in parentheses
∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01

4. Property type (4 groups)

In each case (1) to (4), the 98th percentile of LTV, LTI and DSR is calculated, within-group, within-

quarter. The values for each within-group credit condition are reported in the Appendix in Figures

A.1, A.2 and A.3. Some intuitive diﬀerences are revealed, for example the looser credit conditions

among First-Time Buyers and within the Dublin housing market. For reasons of conﬁdentiality, we

cannot report LTV, LTI And DSR levels for each individual bank.

Table 4 reports the regression results for each re-run of the log-log speciﬁcation of Column (3)

of Table 2. Our estimates suggest that the aggregation level along which CA is calculated does not

dramatically change the picture: our elasticity estimates are between .12 and .17 for three of the

tests, and rise to .23 when CA is calculated while treating First-Time Buyers (FTB) and previous-

owners as separate market segments. The higher coeﬃcient when FTB credit conditions are calculated

diﬀerentially, combined with more liberalised credit conditions in the FTB market in Figure A.2

suggests that credit is more elastically passed through to house prices when it is made available to

an FTB than a previous owner. This ﬁnding is intuitive as FTBs are, in the main, the marginal

borrowers and are most sensitive to credit availability and down payment constraints in their house

purchase activity (Engelhardt, 1994; Engelhardt and Mayer, 1998; Barakova et al., 2014). For the

remainder of the paper, the coeﬃcient estimates of the baseline model (2) of Table 2 will be imposed

when assessing the role of macroprudential policy on the housing market.

Table 5 repeats the baseline model of Table 2, calculating all credit conditions at the 99th, rather

17

Kelly, McCann, O’Toole

Table 5: Repeat of Table 2, using 99th percentile credit conditions

(1)
Level
0.204∗∗∗
(0.00576)
0.784∗∗∗
(0.0337)
0.726∗∗∗
(0.00197)
-637.6∗∗∗
(24.59)

(2)
Level
0.220∗∗∗
(0.00608)
0.571∗∗∗
(0.0356)

-801.2∗∗∗
(26.38)
0.760∗∗∗
(0.00222)

Credit Available

Income

Deposit

Borrower Age

Wealth for Down-Payment

Log Credit Available

Log Income

Log Wealth for Down-Payment

Observations
R2
4 Bank dummies?
256 RegionTime dummies?
8 property type dummies?

188144
0.754
YES
YES
YES

188144
0.725
YES
YES
YES

Robust standard errors in parentheses
∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01

(3)
Log

-0.00291∗∗∗
(0.000100)

0.178∗∗∗
(0.00820)
0.204∗∗∗
(0.00747)
0.245∗∗∗
(0.000852)
188144
0.666
YES
YES
YES

than 98th percentile. This speciﬁcation, accounting for more extreme values of the observed LTV, LTI

and DSR distributions should be expected to increase the estimated impact of CA on house prices.

The points estimates across each model for CA, income and wealth are all extremely stable relative

to those in Table 2. Our key result, the elasticity of house prices to CA in Column (3), increases

marginally from 15.5 per cent in the baseline model to 17.8 per cent when the 99th percentile of LTV,

LTI and DSR are used to calculate CA.

4 Measuring the impact of macroprudential policies

Having established a relationship between credit availability and house prices, we can now test how

the introduction of macroprudential policies can impact house prices through the credit channel. In

this section, we outline scenarios for limits to LTI, LTV ratios and DSR informed by international

experience and trace the impact of these through to house prices.

18

Kelly, McCann, O’Toole

Table 6: Macroprudential limits imposed under two sample regimes

Regime 1 Regime 2
85
4.5
45%

75
4
37%

LTV
LTI
DSR

4.1 Macroprudential policy scenarios

Our methodology allows the imposition of any combination of LTV, LTI and DSR limits that the

researcher wishes to test. For brevity, we impose two sample regimes which are described in Table

6. Regime 1 is the more restrictive regime, where macroprudential limits are set at the high range of

values reported across six economies by Jacome and Mitra (2015). Regime 2 imposes less restrictive

limits. We refrain from imposing a regime that combines the most restrictive limits reported in Jacome

and Mitra (2015) as we deem it implausible for political reasons that such a combination would ever

be imposed without a long lead-in period. In both regimes, LTV, LTI and DSR values are signiﬁcantly

below those that were available in Ireland during the sample period, as reported in Figures 2, 3 and 5.

In Figure 7, we illustrate the impact of the two scenario macroprudential regimes on the way in

which CA is determined for Irish borrowers between 2003 and 2010. The ﬁrst key take-away message

from Figure 7 is that, across both regimes, income-related restrictions appear to have far more of an

impact than down-payment constraints. In Figure 7a, the LTV limit of 80 per cent is the determining

factor for less than 20 per cent of borrowers across the 2003-2010 period. In the less restrictive regime

in Figure 7b, the LTV limit of 90 has even less of an eﬀect in determining CA.

The diﬀering importance of the DSR limits across time is driven by either (i) changes in the

prevailing interest rate or (ii) changes in the age proﬁle of borrowers, or (iii) diﬀering bank policies on

the maximum borrower age at maturity. The timing of the increase in importance of the DSR between

2006 and 2008, when mortgage interest rates rose by 200 basis points over a two year period suggests

that the interest rate channel is the more important of the above factors.

4.2 Re-ﬁtting the house price series

We utilise the coeﬃcient estimates of Column (2) of Table 2 to re-ﬁt house price values under the

proposed MPP regimes. The way in which CA is constructed in our framework allows for an extremely

granular pass-through from a policy shift to CA. For every individual household obtaining a mortgage,

a restriction on LTV, LTI or DSR will lead to a new calculation of the three loan amounts available,

and a new estimate of CA. This new value of CA can then be used to ﬁt a new distribution of

house prices in period t. The change in average house prices under the scenario impacts the size of

deposit available to non-FTB borrowers (who are assumed to post down-payments as a function of

19

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Kelly, McCann, O’Toole

20

0.2.4.6.812003   2004   2005   2006   2007   2008   2009   2010   Which channel determines available credit?LTVLTIDSR0.2.4.6.812003   2004   2005   2006   2007   2008   2009   2010   Which channel determines available credit?LTVLTIDSRKelly, McCann, O’Toole

their housing equity), which means that CA eﬀects next-period house prices through a second channel,

βW ealth,t.

We are acutely aware of the limitations of such an approach, in that the counterfactual house price

calculation using the coeﬃcients of a reduced-form OLS regression merely ﬁts a new house price to

each borrower who purchased a property at a given point in time. There are a number of important

long-run features that cannot be incorporated into our framework: (i) the exclusion of borrowers from

the housing market, as we must assume that all borrowers simply access a cheaper house or the same

house at a cheaper price, (ii) delayed house purchase due to the need for larger down-payments, (iii)

a supply response to reductions in house prices (iv) increases in rents as a result of reduced access

to owner-occupied housing, and the subsequent entry of Buy-to-Let investors in response to increased

rental yields (v) general equilibrium eﬀects such as the feedback loop between housing activity and

incomes. For these reasons, we do not begin with the introduction of an MPP regime in period 1, and

allow the model to estimate a counterfactual house price series for all periods. Rather, we restrict our

exercise to the short run, where we assume that the above factors (i) to (v) may not have begun to

take hold and inﬂuence house prices.

The most tractable way to estimate short-run eﬀects in our framework is to iteratively cycle through

the time periods in the data, starting with observed house prices in period t, switching on the MPP

regime at t and allowing our model to estimate the cumulative impact of the regime on house prices up

to period (t + 4) (our data are quarterly). This allows us to estimate one-year eﬀects of MPP regimes

on house prices. We believe that one year is a reasonable approximation of the “short run”, beyond

which readers may worry that general equilibrium eﬀects such as those mentioned in the previous

paragraph become particularly relevant. The methodology is outlined more formally as follows:

In period t, ﬁtted house prices are calculated as in equation 7, where the coeﬃcients of our baseline

model are applied to observed data on our non-varying explanatory variables Xt (income, property
types, borrower age, region∗time dummies), credit available CAt and W ealtht.

ˆHP t = XitβX + W ealthitβW ealth + CAitβCA

(7)

The model then dynamically cycles through periods t+1 to t+4, retaining all coeﬃcient estimates

β and values of the non-varying Xit from the baseline model as ﬁxed. The updating procedure for

W ealthit and CAit at period t+1 is as follows: the average of the ﬁtted house prices from the previous
period ¯HP t is updated, and its ratio to house prices in the observed data is calculated. Our estimate

of the wealth available for down-payments is then updated to reﬂect the diﬀerential between house

prices at point t in the scenario, and average house prices in the data at period t (Equation 9). This

adjustment allows borrowers in our counterfactual scenario to be impacted by housing equity changes

21

Kelly, McCann, O’Toole

due to MPP when placing a deposit with their house purchase. Such an interaction between housing

collateral and house purchase price allows the logic of collateral channel models in the spirit of Kiyotaki

and Moore (1997) to be incorporated into the model.

Given that our wealth estimate W ealtht+1 is updated to reﬂect model-derived housing values

from period t, CAt+1 must now be updated to reﬂect these new deposit values. This involves a

new calculation of LoanLT V , the loan commensurate with the borrower’s wealth and prevailing LTVs

oﬀered in the market at period t+1 (Equation 10). Once a new CAt+1 is calculated (Equation 11),

the model is now in a position to ﬁt values for the house price distribution of borrowers in period t+1,
ˆHP t+1 (Equation 12). The average of these new ﬁtted values becomes ¯HP t+1, which is then inputted

as the lagged house price value for a full replication of the above steps at period t+2. This dynamic

updating process continues until period t+4, giving an estimate of the average house price one year

after the introduction of an MPP regime in Equation 18.

¯HP t = mean( ˆHP t)

W ealtht+1 =

W ealtht+1,data

∗

¯HP t
¯HP tdata
W ealtht+1
100 − LT VM ax

LoanLT V t+1 =

− W ealtht+1

CAt+1 = M in(LoanLT V,t+1, LoanLT I , LoanDSR)

ˆHP M P,t+1 = Xt+1βX + W ealtht+1βW ealth + CAt+1βCA

...

¯HP t+3 = mean( ˆHP M P,t+3)
∗

W ealtht+3 =

W ealtht+3,data

¯HP t+3
¯HP t+3,data
W ealtht+4
100 − LT VM ax,t+4

LoanLT V,t+4 =

− W ealtht+4

CAt+4 = M in(LoanLT V,t+4, LoanLT I , LoanDSR)

ˆHP M P,t+4 = Xt+4βX + W ealtht+4βW ealth + CAt+4βCA

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

As well as our main objective of testing how restrictions across LTV, LTI and DSR impact house

prices, we can also test a secondary hypothesis relating to the timing of the measures being introduced.

What matters for the impact of such policies is how they deviate from the current credit conditions

in the market. If MPP restricted levels are similar to current credit conditions, less of an impact on

the housing market will be expected. For example, turning strict macroprudential loan restrictions on

in a period of very loose market credit conditions will potentially have a larger impact on credit ﬂows

22

Kelly, McCann, O’Toole

and subsequently house prices than in a period of tighter credit conditions.

To illustrate how such diﬀerential impacts, we “turn on” our macroprudential scenarios at two

points in our data: 1) where market credit conditions were tighter (earlier in the Irish credit cycle)

and 2) where credit conditions were looser (at the peak of the Irish credit boom). In practical terms,

we turn on the two scenarios outlined in Table 6 in each quarter for the years 2003 (tighter market

conditions) and 2006 (looser market conditions), iterate the one year impact for each quarter and take

the mean across the four quarters. This provides an estimate of the impact on house prices of turning

on the measures at any time during the years 2003 and 2006. The results are presented in table 7.

Table 7: Impact of macroprudential limits on house prices

Regime 1: Tighter MPP
(75 LTV, 4 LTI, 37% DSR)

Regime 2: Looser MPP
(85 LTV, 4.5 LTI, 45% DSR)

Timing
Tighter Market Credit (2003):
Looser Market Credit (2006):

-6.4%
-12.1%

House Price Changes

-1.2%
-8.7%

Intuitively, the one-year impact on house prices is largest in the more restrictive MPP regime. The

model estimates that the switching on of Regime 1 (75 LTV, 4 LTI and 37 DSR) in 2003 would have

led to a fall in average house prices from observed levels of 6.4 per cent after four quarters. During the

high-growth period of 2006, this MPP regime would have led to one-year falls of 12.1 per cent. Our

less restrictive MPP regime, Regime 2 (85 LTV, 4.5 LTI and 45 DSR) is estimated to have a one-year

eﬀect of 1.2 per cent when switched on in 2003, with this eﬀect rising to 8.7 per cent when switched

on in 2006.

Table 7 conﬁrms that the timing of MPP matters hugely. For a given combination of LTV and

LTI, there are much larger eﬀects felt as the cycle moves towards its peak. This suggests that it may

be beneﬁcial to introduce MPP regimes away from the peak of the housing cycle, to avoid large shocks

to asset markets. Once introduced in a period of weak house price growth, an MPP regime may act

to bolster borrower and lender resilience against future shocks, without dramatic impacts on housing

values.

The magnitude impacts of varying combinations of LTV and LTI restrictions are presented through

a heatmap. For brevity, we also present the eﬀects for turning the model on in two speciﬁc quarters (Q1

2003 and Q3 2006). Figure 8 presents results from thirty-six versions of our model, where combinations

of six distinct LTV and LTI limits are imposed at 2006q3 and 2003q1,8 with the DSR set at the market

limit as per Figure 5. The left-hand panel shows for Q3 2006, as expected, that larger house price falls

are found at stricter joint values of LTV and LTI, with the house price impact at an LTV of 70 and

8This is the period with the largest amount of originated mortgages.

23

Kelly, McCann, O’Toole

LTI of 2.8 being over 20 per cent, while the impact at an LTV of 95 and an LTI of 4.5 being under

10 per cent. Of note is the symmetry of the eﬀects: movements from the top-right (loose conditions)

towards the bottom-left (tight conditions) appear to have similar impacts regardless of whether LTV

or LTI is relatively tighter. The only noticeable asymmetry is at extreme values: when LTI is ﬁxed

at 2.8, loosening LTV limits from 75 through to 95 has close to no impact on house prices, suggesting

that an LTI limit of 2.8 acts as an extremely hard limit. On the other hand, when LTV is set at 70, a

loosening of LTI from 2.8 to 4.5 does lead to some easing in the impact of the policy regime on house

prices.

The right-hand panel of Figure 8 shows house price changes from an MPP regime switched on in

2003q1. As would be expected given that this period is much earlier in the Irish housing boom period,

the magnitude of house price impacts for a given LTV-LTI combination is much smaller. However,

the substantive patterns in the data remain the same when viewed from this diﬀerent starting point.

An intuitive take-away from these images is that the expected impact of MPP regimes on the housing

market varies as a function of the point in the housing cycle in which they are implemented.

24

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3.03.54.04.5708090Loan−to−Value LimitLoan−to−Income Limit05101520Percentage One−Year House Price FallOne−year house price falls from 2006q3 as a function of LTV and LTI rules3.03.54.04.5708090Loan−to−Value LimitLoan−to−Income Limit05101520Percentage One−Year House Price FallOne−year house price falls from 2003q1 as a function of LTV and LTI rulesKelly, McCann, O’Toole

5 Conclusion

The link between macroprudential policy, credit allocation and the housing market is one of crucial

importance to economic and ﬁnancial stability. In this paper we present a novel measure of credit

available at the loan level, where we model the way in which credit supply can bind along three

channels: the Loan to Value ratio (LTV), the Loan to Income ratio (LTI) and the Debt Service Ratio

(DSR). Using prevailing market conditions on the level of originating LTV, LTI and DSR available in

a given quarter, we construct three possible loan amounts for each borrower, commensurate with their

income, deposit and age. The credit available to a borrower is calculated as the smallest of these three

loans.

We show initially that in Ireland between 2003 and 2010, the majority of borrowers had their credit

availability determined by the prevailing LTI ratio, with extremely small numbers of borrowers being

cut oﬀ along the LTV channel due to the existence of 100 per cent LTV mortgages.

Using our constructed measure of credit availability, we run a property-level house price model

on 190,000 mortgaged house purchases, where the transaction price is a function of credit, incomes,

deposits, a dummy variable for the lending institution, hedonic property characteristics as well as a

range of national and regional macroeconomic factors. In our baseline scenario, the elasticity of house

prices with respect to credit availability is found to be approximately 0.15, which is extremely close

to estimates from Favara and Imbs (2015). Our micro-level house price model also estimates that a

ten per cent increase in income increases house prices by between 2.1 and 2.6 per cent while a ten per

cent increase in wealth increases house prices by 2.4-2.5 per cent.

Our methodology allows us to directly model the link between MPP and each borrower’s credit

availability in a granular fashion. MPP drives house price changes in two ways: ﬁrstly through the

direct impact of instantaneously lower credit volumes; secondly through the collateral channel, by

scaling down the down-payment available to borrowers via weakened housing equity. We ﬁt values

using our regression coeﬃcients and restricted credit for four quarters after the introduction of the

MPP regime, due to the fact that our exercise cannot account for general equilibrium relationships

between lower house prices and housing supply, rents, Buy to Let purchases or construction sector and

related incomes.

Our ﬁndings imply an important role for MPP in cooling a rapidly-growing housing market. Further

they suggest that the levels at which LTV, LTI and DSR limits are set are crucially important in

determining the impact on prices. We show that the introduction of an LTV-LTI combination of 70

and 2.8 leads to a house price fall two and a half times as large as a combination of 95 and 4.5.

Further, we highlight the importance of the timing of the introduction of an MPP regime: estimates

of the impact on house prices are orders of magnitude larger for regimes switched on in 2006, at the

26

Kelly, McCann, O’Toole

top of the Irish housing cycle, versus 2003.

We believe that our paper is the ﬁrst to treat the relationship between MPP, mortgage credit and

house prices at such a granular level of detail, and the ﬁrst to quantitatively estimate the impact of

MPP scenarios on house price paths. Further research is warranted on the general equilibrium eﬀects

that follow on from the introduction of an MPP, particularly relating to borrower exclusion, housing

supply and the rental market.

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6 Appendix

30

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9294969810010298th percentile Loan to Value2003q12005q12007q12009q12011q1Non-DublinDublin55.566.577.598th percentile Loan to Income2003q12005q12007q12009q12011q1Non-DublinDublin.35.4.45.5.55.698th percentile DSR2003q12005q12007q12009q12011q1Non-DublinDublins
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9294969810098th percentile Loan to Value2003q12005q12007q12009q12011q1Previous OwnerFTB55.566.5798th percentile Loan to Income2003q12005q12007q12009q12011q1Previous-OwnerFTB.35.4.45.5.5598th percentile DSR2003q12005q12007q12009q12011q1Previous OwnerFTBe
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909510010511011598th percentile Loan to Value2003q12005q12007q12009q12011q1ApartmentHouseOtherSemi D / Terrace4681098th percentile Loan to Income2003q12005q12007q12009q12011q1ApartmentHouseOtherSemi D / Terrace.35.4.45.5.55.698th percentile DSR2003q12005q12007q12009q12011q1ApartmentHouseOtherSemi D / TerraceImprint and acknowlegements 

We thank, in alphabetical order, Minhea Constantinescu, Christian Danne, Edward Ga_ney, John Krainer, Terry 
O'Malley, Stefano Neri and Christian Schnittker for helpful discussions, along with seminar participants at the 
Central Bank of Ireland, participants at the ESRB workshop on "The dual relationship between the financial 
sector and the real economy: lessons for macroprudential oversight", participants at the Central Bank of Ireland 
workshop "Banking, credit and macroprudential policy: what can we learn from micro data?", and the European 
Banking Authority workshop on "Financial regulation and the real economy: a micro-prudential perspective". 
The views presented in this paper are those of the authors alone and do not represent the official views of the 
Central Bank of Ireland or the European System of Central Banks. Any remaining errors are our own. 

Robert Kelly 
Financial Stability Division, Central Bank of Ireland; email: robert.kelly@centralbank.ie 

Fergal McCann 
Financial Stability Division, Central Bank of Ireland; email: fergal.mccann@centralbank.ie 

Conor O'Toole 
Financial Stability Division, Central Bank of Ireland; email: conor.otoole@centralbank.ie 

© European Systemic Risk Board, 2017 

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Note: 
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affiliated. 

ISSN 
ISBN 
DOI 
EU catalogue No 

2467-0677 (online) 
978-92-95081-95-6 (online) 
10.2849/477164 (online) 
DT-AD-17-003-EN-N (online)