Description
The purpose of this paper is to investigate the impact of the Goods and Services Tax (GST)
on mortgage pricing and to measure the GST shifting ratio of Australian credit unions.
Accounting Research Journal
The impact of the GST on mortgage pricing of Australian credit unions: An empirical
analysis
Benjamin Liu Allen Huang Brett Freudenberg
Article information:
To cite this document:
Benjamin Liu Allen Huang Brett Freudenberg , (2014),"The impact of the GST on mortgage pricing of
Australian credit unions", Accounting Research J ournal, Vol. 27 Iss 1 pp. 37 - 51
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http://dx.doi.org/10.1108/ARJ -08-2013-0059
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Low Sui Pheng, Carol P.W. Loi, (1994),"Implementation of the Goods and Services Tax (GST) in
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dx.doi.org/10.1108/09588689410078593
J ohn Breen, Sue Bergin-Seers, Ian Roberts, Robert Sims, (2002),"The Impact of the Introduction of
the GST on Small Business In Australia", Asian Review of Accounting, Vol. 10 Iss 1 pp. 89-104 http://
dx.doi.org/10.1108/eb060751
J ohn Passant, (2014),"The Minerals Resource Rent Tax: The Australian Labor Party and the continuity of
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The impact of the GST on
mortgage pricing of Australian
credit unions
An empirical analysis
Benjamin Liu, Allen Huang and Brett Freudenberg
Department of Accounting, Finance and Economics, Griffth University,
Brisbane, Australia
Abstract
Purpose – The purpose of this paper is to investigate the impact of the Goods and Services Tax (GST)
on mortgage pricing and to measure the GST shifting ratio of Australian credit unions.
Design/methodology/approach – Using the proprietary data from79 credit unions in Australia, we
perform multivariate regression analysis on the effect of the GST on mortgage effective yield spreads
and interest margins, respectively. We also introduce a model that is used to measure the GST shifting
ratio.
Findings – We document that the introduction of the GST in July 2000 led to the substantial rise
in mortgage costs charged by credit unions in the post-GST periods. Overall, the GST alone
contributed to the increase of effective yield spreads and interest margin by 65.3 and 70.1 basis
points, respectively. As measured by the GST-shifting ratio, credit unions passed more than twice
of the GST rate. This suggests GST over-shifting, and it is generally consistent with tax
over-shifting literature.
Originality/value – This is the frst time the GSTshifting ratio has been robustly measured with the
use of multivariate models on mortgage costs.
Keywords Credit unions, GST shifting, Mortgage pricing
Paper type Research paper
1. Introduction
In Australia, the rise of mortgage cost and the decline in housing affordability has raised
concern among consumers, banking regulators and the media. The Australian
mortgage sector is largely comprised of three segments: the four major banks; mortgage
corporations and cooperative lenders. While the Australian lending sector is highly
regulated, there are concerns about high concentrations and the pricing behaviour
(Swan, 2010)[1].
This is of potential concern, as tax incidence research from the USA demonstrates
that “tax over-shifting” can occur when imperfect competition markets exist (Young and
Bielinksa-Kwapisz, 2002; Besley and Rosen, 1994).
One potential tax cost in Australia is the introduction fromJuly 2000 of the goods and
services tax (GST). While fnancial supplies are largely not subject to GST, there is a
restriction on the ability to claim the GST input tax credits back on acquisitions, thus
The current issue and full text archive of this journal is available at
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Impact of the
GST
37
Accounting Research Journal
Vol. 27 No. 1, 2014
pp. 37-51
©Emerald Group Publishing Limited
1030-9616
DOI 10.1108/ARJ-08-2013-0059
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increasing the cost. This is in addition to compliance costs that can be associated with
obligations associated with the tax.
After over a decade of the implementation of the new tax system, there is a
consensus that it is time for a review of the system to examine, among other issues,
what lessons we have learnt from the introduction of GST and what reform still
needs to take place (McCarthy, 2011; Peacock, 2011). With respect to the GST
treatment of fnancial services, in particular, there is wide recognition that the
current approach has problems and is only a “second-best” one (Evans, 2011;
McCarthy, 2011; Stitt, 2011). In fact the Henry Tax Review noted that ‘input
taxation of fnancial services under the GST is ineffcient, reduces competition and
harms Australia’s position as a regional fnancial services centre’ (Australia, 2009,
p. D4-1).
Recently, researchers have started investigating the GST impact on mortgage
costs in Australia. Huang and Liu (2012) tested mortgage yield spreads of banks in
response to the introduction of the GST and Huang and Liu (2013) tested mortgage
yield spreads of mortgage corporations. These results would tend to indicate these
institutions have engaged in tax over-shifting with the introduction of the GST in
Australia. However, is the experience of credit unions different, especially given that
they are cooperative lenders and non-proft-maximising organizations, with their
clientele being members, concentrated in specifc industries? Preliminary research
by Liu and Huang (2012), using univariate models, examined the GST impact on the
yield spreads of credit unions which found a signifcant increase. This study
extends Liu and Huang’s (2012) study by examining Australia’s credit unions using
a unique model to measure the GSTshifting ratio more preciously; with multivariate
tests being conducted to analyse yield spreads, as well as interest margins.
In particular, by analysing monthly mortgage data of 79 Australian credit unions
over 36 consecutive months straddling the introduction of the GST (January 1999 to
December 2001) two research questions are addressed in respect of Australian credit
unions:
• has the introduction of the GST impacted on the mortgage pricing?
• to what extent did the GSTcontribute to mortgage costs in respect of yield spreads
and interest margins?
The results would tend to indicate that there was a tax over-shifting by Australian credit
unions, with greater effective yield spreads and effective interest margins. These results
would question to what extent the Australian fnancial market is really competitive,
resulting in greater costs to consumers.
The rest of the paper is structured as follows: Section 2 reviews the relevant
literature; Section 3 describes the data and methodology, while Section 4 presents and
discusses the results. Conclusions are drawn in Section 5.
2. Literature review
2.1 Tax incidence theory
Research from the USA into the effects of taxes on consumer prices dates back several
decades. Brown (1939), Due (1942), Rolph (1952), Musgrave (1959) and Bishop (1968)
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developed a tax incidence theory for studying the general sales taxes on particular
goods. Empirical studies using the tax incidence theory document the fact that sales tax
is fully refected in prices borne by consumers. From the perspective of frms, this is
known as tax shifting because the frms do not alter their profts. Some studies indicate
that, when imperfect competition markets exist, consumer prices may rise more than the
amount of retail taxes levied, suggesting tax over-shifting (Haig and Shoup, 1934; Sidhu,
1971; Besley and Rosen, 1994; Young and Bielinksa-Kwapisz, 2002). In contrast, Poterba
(1996) found that for the post-war period, retail prices rose by approximately the same
amount as the sales tax. However, these studies are exclusively limited to the US
consumer goods industries and consider a retail tax as opposed to a value-added tax
(VAT) that is not in use in the USA.
To broaden the research using the tax incidence theory, it was important to
consider mortgage lenders’ pricing in response to the GST using Australian credit
union data.
2.1.1 GST in Australia. The GST commenced in Australia on 1 July 2000, which
replaced a range of indirect taxes including the Wholesale Sales Tax (WST). The
GST applies at a rate of 10 per cent and is broadly similar to the VAT operating in
several other countries. The GST applies to most forms of economic activities, such
as the supplies of goods and services, requiring the supplier of a taxable supply to
remit the GST collected. However, GST-registered enterprises are broadly able to
claim a refund (input tax credit) of the GST in their acquisitions. In this way, each
enterprise remits just the GST on the value added to its particular product or
service. However, as private consumers are not able to claim such a GST input tax
credit, the end cost of GST is passed on to them (Maclntyre, 2001; Bolton and
Dollery, 2005).
The treatment of fnancial supplies, however, differs from other types of supplies.
First, fnancial supplies (such as taking securities and deposits, issuing loans and
charging interest on loans) are not subject to GST. In other words, no GSTis paid on the
fnancial supplies that fnancial institutions acquire, nor is GST liable on fnancial
supplies made by fnancial institutions. Second, no GST input tax credit is available for
acquisitions of taxable supplies that relate to making fnancial supplies. In this way,
fnancial institutions are effectively treated as the end-consumer. This is somewhat
altered by the allowance of a reduced GSTinput credit for a number of acquisitions that
fnancial institutions have outsourced in recent years (such as processing account
information) (s 70-5 of the GST Act)[2].
This means that the GST on acquisitions are largely absorbed by fnancial
institutions and then allocated in their fnancial products (e.g. loans) to their customers
through increasing the overall charges (Maclntyre, 2001). Although this pricing could be
offset to some extent by the removal of WST on some of the goods purchased by
fnancial institutions, such as computers, how and to what extent the Australian
fnancial institutions have passed the cost of GST to their mortgage customers remains
largely unknown.
It is argued that this is a signifcant and interesting research question. In
Australia, mortgage is the single largest asset of banks and many non-bank
fnancial institutions and the single largest liability of households. The effciency of
the mortgage fnance system plays an important role, as an ineffcient one renders
39
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the economy vulnerable to crisis (Ebrahim and Mathur, 2007). According to a recent
survey (Demographia, 2014), Australian housing affordability is the lowest among
the surveyed countries (including Australia, Canada, Ireland, New Zealand, the UK
and the USA). The period observing sharp declines in housing affordability in
Australia corresponds with the implementation of the GST system. An
understanding of precisely what factors have contributed to the rise in mortgage
costs and how the GST has affected the mortgage costs is, therefore, a signifcant
issue and an important consideration for banking regulators and policymakers to
reach appropriate policy decisions.
Since the introduction of the GSTin Australia, studies have examined two aspects of
its effects on consumer prices, being the general price level of goods and services and the
estimate of compliance costs – each of which is discussed below.
2.2 The GST effects on price levels
The infationary effect of the GST has raised concerns for governments. The consensus
of government surveys is that the GST’s effect on the goods and services included in the
consumer price index (CPI) would be a one-off price perturbation in the quarter of the
introduction of the GST, with the magnitude of the effect varying within a small range.
Part of the reason for this lower price effect relates to the removal of the WST which
could apply at rates higher than GST, and it was non-refundable for most enterprises.
Due to these reasons, the Commonwealth Treasury estimated that the 10 per cent GST
could increase the overall CPI by just 2.75 per cent in July 2000 (Commonwealth
Treasury, 2000, p. 11).
Academic researchers have also endeavoured to study the effect of the GST on price
levels. Before the GST, Johnson et al. (1999) and Warren et al. (1999) thoroughly
evaluated the revenue, effciency and equity effects of the government’s tax package.
Warren et al. (1999) also predicted the possible effect of the GST on infation and
estimate, under different assumptions, that this effect was likely to be between 0.8 and
3.6 per cent in July 2000.
Valadkhani and Layton (2004) examined the magnitude and duration of the GST
effect on the overall rate of infation. Using intervention analysis, Valadkhani and
Layton (2004) found that the GST effect on infation was only temporary (in the
third-quarter of 2000) and the size of the effect was 2.8 per cent. In another study,
(Valadkhani, 2005) using the same methodology, investigates the price changes of goods
and services in the four quarters before, and four quarters after, the third quarter of 2000.
Valadkhani found that the overall effect was a one-off lift in infation of approximately
3 per cent in the third quarter of 2000, with prices not increasing signifcantly before or
after this quarter.
2.3 The tax compliance costs
Other studies have focused on estimating the compliance costs of the GST. According to
Tran-Nam (1999, 2000) and others (Sandford et al., 1989), the introduction of a new tax
(such as the GST) gives rise to two new types of costs: the implementation compliance
costs and the recurrent compliance costs. The implementation compliance costs are the
costs incurred in complying with the GSTwhen it was implemented and include mainly
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the administrative costs and the compliance costs of the implementation of the GST. The
compliance costs of implementing the GST refer to the resources expended by the
economy to comply with the GST.
In terms of empirical studies on VAT, the international evidence suggests that, in
most countries, the introduction of a VAT results in signifcantly higher compliance
costs for taxpayers than other forms of taxation (Vaillancourt, 1987; Pope, 2001) and
that VAT compliance costs are disproportionately higher for small businesses than
large businesses (Cnossen, 1994; Rametse and Pope, 2002; Coolidge, 2012). Glover
and Tran-Nam (2005) have estimated both the net transitional cost and the recurrent
GST compliance for small businesses.
The estimated implementation costs of the GST reviewed above are based on
different assumptions, using different survey techniques with varying sample sizes
and lumping the GST-related and normal upgrading computer and system costs
together. According to Tran-Nam (2000), these estimates are either baseless or
diffcult to justify.
In summary, the existing research all suggests that the GST effect on prices and
the GST compliance costs are substantial, which inevitably increases costs to both
businesses and consumers (Huang and Liu, 2012). However, the research that has
been conducted to date, except for Huang and Liu’s (2012) on bank mortgage costs,
remains at the level of estimating either the general price effect or the overall
compliance costs in the society. It is argued that more research is needed to enhance
our understanding of the GST effects on mortgage costs charged by lenders in
Australia, especially given concerns about competition in the fnance sector.
3. Data and methodology
3.1. Data
The mortgage data for analysis is extracted from Cannex’s monthly survey of
Australian lenders, which includes monthly information on mortgage interest rates,
mortgage fees and charges, credit criteria and other data of all credit unions
operating in the country. Other data (i.e. 90-day bank bill rates, Treasury bond and
corporate bond yields and three-month term deposit interest rates of banks) are
collected from the statistics of the Reserve Bank of Australia (RBA). The period
selected for the analysis covers 36 consecutive months from January 1999 to December
2001 (that is, 18 months before and 18 months after the GSTcame into effect on 1 July 2000).
The selection of the period is mainly determined by the key research questions this paper
addresses, that is, the impact of the introduction of the GST on mortgage costs. Hence,
inclusion of both pre- and post-GST periods in the analysis would allow the authors to
compare the mortgage costs before and after the implementation of the GST, although it
would include some of the implementation costs of starting to comply with GSTas well
as ongoing costs.
Seventy-nine credit unions, operating in the mortgage market during the period, are
included in the analysis, which are listed in the appendix. The data selection results in
2,805 monthly observations. To make comparisons between the pre- and post-GST
periods more valid, only the standard products of residential mortgages, that is, the
owner-occupied home loans with adjustable interest rates (with which about 80 per cent
41
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of Australian home loans are originated), are examined. Loans for other purposes are
excluded from the analysis.
3.2 Variable and model specifcations
We use the annualised average percentage rates (AAPR) which include the nominal
interest rate and all other fees and charges levied on mortgages, that is, the effective
rates. The AAPR adopted in this study is computed using the standard calculations
required under the Australian Uniform Consumer Credit Code and is considered to be a
benchmark for comparing mortgage products in Australia. When comparing mortgage
costs, however, we use mortgage yield spreads and interest margins, rather than the
interest rates. Effective yield spreads are the differences between AAPR rates and
90-day bank bill rates.
To perform robustness tests, we also use the effective interest margin (differences
between the AAPR rates and the three-month term deposit interest rates) to more
precisely refect the degree of GST cost shifting.
The use of yield spreads or interest margins is a standard approach in measuring
mortgage costs, as it helps overcome the impact of infation and adjustments of
monetary policy over time on the interest rates (Liu and Skully, 2005, 2008).
We followmark-up theory for pricing banking products (Rousseas, 1985). According
to the literature, mortgages are regarded as special kinds of products of lenders
(Ambrose et al., 2004; Liu and Skully, 2005, 2008). For simplicity, mortgage unit price
(the interest rate, P
i
) of lender i at time t can be unit funding cost (C
f
)?risk (C
r
) ?unit
operating cost (C
o
) ?proft margin (P
m
). Considering credit unions as nonproft seeking
lenders, proft margin (P
m
) is assumed to be zero, we derive unit price:
P
i
?C
f
?C
r
?C
o
(1)
Pre GST period GST Post GST period
?t ?3 ?2 ?1 0 +1 +2 + 3 +t
P
i
= C
f
+ C
r
+ C
o
P
i
= C
f
+ C
r
+ C
o
+ C
g
Rearranging equation (1), we derive: for the pre-GST period, P
i
?C
f
?C
r
?C
o
, and for
the post-GSTperiod, P
i
?C
f
?C
r
?C
o
?C
g
, while C
g
stands for the GSTcost included
in mortgages. Thus, for an adjustable rate mortgage, P
i
, C
f
is either the yield spread
(YSP) or interest margin (MGN) where C
f
is the unit funding cost. C
o
and C
r
are further
assumed to remain constant for the two short periods.
Because there is no GSTcharge on mortgages and funding of the lender, C
g
(the GST
cost) for a lender is resulted from a 10 per cent GST levied on lender operating items,
such as rentals and electricity. Therefore, in theory, C
g
should be around 10 per cent of
C
o
. If C
g
/C
o
?10 per cent, we can say there is GST over-shifting; if C
g
/C
o
?10 per cent,
there is GST under-shifting. In this paper, C
g
/C
o
is defned as GST shifting ratio.
In a competitive funding market, all credit unions are assumed to have the same
funding cost. As explained above, the 90-day bank bill rate and three-month term
deposit interest rates of banks act as proxies for funding costs. Other variables used in
our analysis include the introduction of the GSTas a dummy variable, the credit criteria
variables (that is, loan to value ratios and maximum loan amounts), a market default
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risk variable and seasonal effect variables. Defnitions of all variables and their
predicted signs, in detail, are provided in Table I.
To answer the research questions, we develop multivariate models (pooled
cross-sectional regression) to examine the GSTeffects on mortgage costs using monthly
data. The regression analysis estimates the effective yield spreads (EYSP) in regression
equation (2).
Table I.
Defnition and predicted
signs of variables
Equations
Panel A: dependent variables
EYSP standard adjustable interest rates plus sum of all
other fees and charges on mortgage over 90-day bill
rates ? ?
EMGN standard adjustable interest rates plus sum of all
other fees and charges on mortgage over 90-day
deposit rates ? ?
Panel B: independent variables
GST ?dummy variable, pre-July 2000 ?0; otherwise ?1 ?
1
(?) ?
1
(?)
MAXNO ?no maximum loans required by the lender ?
2
(?) ?
2
(?)
MAXLOAN200 ?maximum loans ?A$1,000,000 ?
3
(?) ?
3
(?)
MAXLOAN50 ?maximum loans ?A$500,000 ?
5
(?) ?
5
(?)
MAXLTV90 ?90 per cent ?maximum loan to value ratio ?85
per cent ?
7
(?) ?
7
(?)
MAXLTV85 ?maximum loan to value ratio ?85 per cent ?
8
(?) ?
8
(?)
MDFT(-1) ?lag of yield differences between AAA- and A-
rated corporate bonds with a two-four year
maturity constant maturity, proxying the market
default risk ?
9
(?) ?
9
(?)
Month 1 ?January each year from 1999 to 2001 ?
10
(?) ?
10
(?)
Month 2 ?February each year from 1999 to 2001 ?
11
(?) ?
11
(?)
Month 3 ?March each year from 1999 to 2001 ?
12
(?) ?
12
(?)
Month 4 ?April each year from 1999 to 2001 ?
13
(?) ?
13
(?)
Month 5 ?May each year from 1999 to 2001 ?
14
(?) ?
14
(?)
Month 6 ?June each year from 1999 to 2001 ?
15
(?) ?
15
(?)
Month 7 ?July each year from 1999 to 2001 ?
16
(?) ?
16
(?)
Month 9 ?September each year from 1999 to 2001 ?
18
(?) ?
18
(?)
Month 10 ?October each year from 1999 to 2001 ?
19
(?) ?
19
(?)
Month 11 ?November each year from 1999 to 2001 ?
20
(?) ?
20
(?)
Month 12 ?December each year from 1999 to 2001 ?
21
(?) ?
21
(?)
Quarter 1 ?The frst quarter each year from 1999 to 2001 ?
1
(?) ?
1
(?)
Quarter 2 ?The second quarter each year from 1999 to 2001 ?
2
(?) ?
2
(?)
Quarter 4 ?The fourth quarter each from 1999 to 2001 ?
4
(?) ?
4
(?)
Panel C: control variables
MAXLOAN100 ?A$1,000,000 ?maximum loans ?A$500,000 ?
4
?
4
MAXLTV95 ?maximum loan to value ratio ?90 per cent ?
6
?
6
Month 8 ?August each year from 1999 to 2001 ?
18
?
18
Quarter 3 ?The third quarter each year from 1999 to 2001 ?
3
?
3
43
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EYSP
i
?
?
?
0
??
1
GSTi??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(?1)i??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
(2)
In equation (2), the dependent variable is the EYSP of credit unions. The independent
variables are the GST, lending criteria, market default risk and seasonal effects. As of 1
July 2000, when the GST was implemented, a difference on mortgage costs between the
pre and post-GST periods was expected. In the regression model, therefore, a dummy
variable for the GST is considered, with the pre-GST date with a value of 0 and
otherwise 1 (Table I). In equation (2), ?
0
is constant consisting of credit risk (C
r
) and
operating cost (C
o
). If ?
1
/?
0
?10 per cent, this shows GSTover-shifting in the context of
yield spreads as C
g
/C
o
?C
g
/(C
r
?C
o
).
With respect to the credit criteria, prior studies (Hendershott and Shilling, 1989;
Ambrose et al., 2004) have used loan-to-value (LTV) ratios and loan size to test models
for credit risk of mortgages. Asimilar approach is adopted in this study. LTVratios are
divided into three categories (i.e. maximumLTV?90 per cent; 90 per cent ?maximum
LTV?85 per cent; and maximumLTV?85 per cent) and loan size are divided into four
groups (Table I), following Liu and Skully (2005). As loans with higher LTV ratios are
considered to have higher credit risk (Ambrose et al., 2004; Liu and Skully, 2005), a
positive relationship is expected between the yield spreads and LTVratios. Similarly, as
larger loans are considered to have higher risk, a positive relationship is also expected
between the yield spreads and the loan size.
In addition, the market default premium is proxied by the yield differences between
AAA- and A-rated corporate bonds with a constant maturity between 2 and 4 years,
which has also been used by prior studies (Ambrose et al., 2004; Liu and Skully, 2005). A
positive relation is expected between the market default premium (a lag) and the
mortgage yield spreads. In the model, individual months or quarters are included to
detect seasonal effects, which are similarly used in prior studies (Ambrose et al., 2004).
To perform robustness checks, we further develop a multivariate model to test
mortgage interest margins. As one may argue that not all credit unions use 90-day bank
bills as a funding channel and a funding cost benchmark, we use three-month deposit
interest rates as a proxy for a funding cost benchmark for credit unions. In the following
model (equations 3), the dependent variable is the effective mortgage interest margin
(EMGN). The independent variables are the same as those in equation (2). The ?
0
in
equation (3) is constant consisting of credit risk (C
r
) and operating cost (C
o
). If ?
1
/?
0
?10
per cent, this shows GST over-shifting in the context of interest margins as C
g
/C
o
?C
g
/
(C
r
?C
o
).
EMGN
i
?
?
?
0
??
1
GST
i
??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(-1)
i
??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
(3)
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4. Empirical results
4.1 Results on mortgage yield spreads
As shown in Panels Aand Bof Table II, the GSTincreased the effective yield spreads by
65.3 (with quarterly effects) and 65.1 (with monthly effects) basis points (both signifcant
Table II.
Regression estimate on
EYSP
Panel A Panel B
EYSP EYSP
Explanatory variables Coeffcient t-statistic VIF Coeffcient t-statistic VIF
Intercept 2.039 47.667*** ? 1.998 43.138*** ?
GST 0.653 38.917*** 1.199 0.651 39.664*** 1.204
MAXNO 0.072 3.618*** 1.690 0.071 3.657*** 1.690
MAXLOAN200 0.150 3.775*** 1.174 0.152 3.921*** 1.175
MAXLOAN50 0.102 4.527*** 1.645 0.101 4.591*** 1.645
MAXLTV90 0.018 1.086 1.095 0.018 1.117 1.095
MAXLTV80 0.052 2.141** 1.084 0.052 2.201** 1.084
MDFT(-1) ?0.022 ?23.041*** 1.119 ?0.023 ?24.246*** 1.140
Month Effects
Month 1 ? ? ? 0.565 14.032*** 1.596
Month 2 ? ? ? 0.673 16.907*** 1.590
Month 3 ? ? ? 0.441 12.237*** 1.854
Month 4 ? ? ? 0.489 13.593*** 1.860
Month 5 ? ? ? 0.194 5.388*** 1.859
Month 6 ? ? ? 0.226 6.284*** 1.858
Month 7 ? ? ? 0.106 2.979*** 1.825
Month 9 ? ? ? 0.118 3.335*** 1.827
Month 10 ? ? ? 0.185 5.215*** 1.853
Month 11 ? ? ? 0.131 3.703*** 1.871
Month 12 ? ? ? 0.083 2.345** 1.843
Quarter Effects
Quarter 1 0.468 20.375*** 1.469 ? ? ?
Quarter 2 0.230 10.567*** 1.567 ? ? ?
Quarter 4 0.056 2.670*** 1.500 ? ? ?
No. of observations 2540 2540
Adjusted R
2
0.431 0.459
F-statistic 193.17 120.576
p-value 0.000 0.000
Notes: In this table, we report the cross-sectional regression results of EYSP in relation to the GST
impact; the regression equation is:
EYSP
i
?
?
?
0
??
1
GSTi??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(-1)i??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
See Table I for a defnition of each of these variables; *, **and ***denote the 10 per cent, 5 per cent and
1 per cent levels of signifcance respectively; VIF is the variance infating factor to detect the
multicollinearity problem among independent variables
45
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at p ? 0.01), indicating that the GST contributed signifcantly to the rise in mortgage
costs in the post GST periods. As we discussed, if ?
1
/?
0
?10 per cent, this suggests the
GST over-shifting. In Panels A and B, ?
1
/?
0
is 32.0 per cent (?0.653/2.039) and 32.6 per
cent (?0.651/1.998), respectively.
These increases are much higher than those of banks (Huang and Liu, 2012),
generally consistent with prior research in relation to the impact of the GST on price
levels (Johnson et al., 1999; Tran-Nam, 2000, 2001). As we discussed, if ?
1
/?
0
? 10 per
cent, this shows the GSTover-shiftinginthe context of yieldspreads as C
g
/C
o
?C
g
/(C
r
?C
o
).
This may relate to tax compliance literature that has found that smaller businesses can
have greater regressive compliance costs compared to larger operations (Evans et al.,
1997).
The loan size, as a measure of credit risk, is found to have a positive relation to the
spreads, as predicted in Table I. When MAXLOAN100 (A$1,000,000 ? maximum
loans ? A$500,000) is controlled, larger loans MAXLOAN200 (maximum loans
?A$1,000,000) and MAXNO (no maximum loans required by the lender), have
positive coeffcients, indicating higher yield spreads. However, smaller loans
MAXLOAN50 (maximum loans ? A$500,000) also have higher yield spreads,
contrary to the predicted sign. This result is perhaps due to the fact that credit
unions may not have the economies of scale in making smaller loans cheaper (see Liu
and Skully, 2005). The results for LTV ratios show that, when the MAXLTV 95
group (maximum LTV ?90 per cent) is controlled, MAXLTV 80 (maximum LTV ?
85 per cent) have higher effective yield spreads (with positive coeffcients), again
contrary to the predicted sign. This means the mutual lenders do not consider the
loan to value ratio (maximum LTV) as a credit risk in pricing a loan due to the fact
that if the LTV ratio is ? 80 per cent, the borrower is required to buy mortgage
insurance. Therefore, the lender is not exposed to higher credit risk associated with
maximum LTV ratios loans.
In Table II, the market default risk variable (MDFT-1) is signifcant to the yield
spreads, which is again consistent with our prediction and previous literature (e.g.
Hendershott and Shilling, 1989). With respect to seasonal effects, when the third
quarter (Qut 3) is controlled, the other three quarters are signifcantly positive,
indicating the third quarter is the cheapest season.
In relation to model ft in Table II, the overall models achieve signifcant
regression results with F-statistics (p ?0.001). Adjusted R
2
slightly improves from
0.431 with quarterly effects to 0.459 with monthly effects, indicating the model with
monthly effects is a better one.
4.2 Robustness tests on mortgage interest margins
Table III shows, after other variables are controlled, the GSTalone led to an increase
in the margins by 70.1 (with quarterly effects) and 70.3 (with monthly effects) basis
points (both signifcant at p ? 0.01). The GST shifting ratio (?
1
/?
0
) is equal to 22.5
per cent (? 0.701/3.112) and 22.9 per cent (? 0.703/3.057), respectively, suggesting
GST over-shifting in the context of interest margins.
The fndings on effective interest margins are more robust and meticulous. As credit
unions in Australia are nonproft-seeking frms, one may question why credit unions
over-shift the GST costs not only to borrowers (the members) but also to depositors (the
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members) as the margins come frombothsides of lendingandfunding. Perhaps the reasons
for such GST costs charged in mortgages may be the result of the allocation of their
compliance costs for the implementation of the GST, as they operate on a smaller scale
compared to banks, as well as the GSTlevied on operational items.
Table III.
Regression estimate on
effective interest margins
Panel C Panel D
EMGN EMGN
Explanatory Variables Coeffcient t-statistic VIF Coeffcient t-statistic VIF
Intercept 3.112 82.446*** ? 3.057 73.945*** ?
GST 0.701 47.337*** 1.199 0.703 48.036*** 1.204
MAXNO 0.065 3.735*** 1.690 0.065 3.752*** 1.690
MAXLOAN200 0.141 4.006*** 1.174 0.140 4.039*** 1.175
MAXLOAN50 0.100 5.040*** 1.645 0.099 5.066*** 1.645
MAXLTV90 0.025 1.668* 1.095 0.025 1.712* 1.095
MAXLTV80 0.057 2.643*** 1.084 0.056 2.656*** 1.084
MDFT(-1) ?0.004 ?4.654*** 1.119 ?0.004 ?5.113*** 1.140
Month Effects
Month 1 ? ? ? 0.295 8.208*** 1.596
Month 2 ? ? ? 0.236 6.639*** 1.590
Month 3 ? ? ? 0.269 8.367*** 1.854
Month 4 ? ? ? 0.255 7.935*** 1.860
Month 5 ? ? ? 0.147 4.592*** 1.859
Month 6 ? ? ? 0.147 4.598*** 1.858
Month 7 ? ? ? 0.040 1.268 1.825
Month 9 ? ? ? 0.165 5.232*** 1.827
Month 10 ? ? ? 0.162 5.121*** 1.853
Month 11 ? ? ? 0.166 5.249*** 1.871
Month 12 ? ? ? 0.013 0.402 1.843
Quarter Effects
Quarter 1 0.197 9.718*** 1.469 ? ? ?
Quarter 2 0.114 5.951*** 1.567 ? ? ?
Quarter 4 0.044 2.372** 1.500 ? ? ?
No. of observations 2540 2540
Adjusted R-square 0.490 0.504
F-statistic 245.285 144.413
p-value 0.000 0.000
Notes: In this table, we report the cross-sectional regression results of EMGN in relation to the GST
impact; the regression equation is:
EMGN
i
?
?
?
0
??
1
GST
i
??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(-1)
i
??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
See Table I for a defnition of each of these variables; *, **and ***denote the 10, 5 and 1 per cent
levels of signifcance, respectively; VIF is the variance infating factor to detect the multicollinearity
problem among independent variables
47
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5. Conclusion
The key fndings of this study are that the introduction of the GSTin July 2000 led to the
substantial rise in mortgage costs charged by credit unions in the post-GST periods.
Overall, the GSTalone contributed to the increase of effective yield spreads by 65.3/65.1
basis points and of effective interest margins by 70.1/70.3 basis points. Furthermore,
measured by the GST-shifting ratio, credit unions passed more than twice of the GST
amount they paid to their mortgage holders, suggesting GST over-shifting.
The mortgage burdens on Australian borrowers have risen substantially and
housing affordability has declined sharply in the past decade. The fndings of this study
may have some practical implications for regulators and policymakers in the banking
industry in their contemplation of the proper regulations and policies to control and curb
mortgage costs – especially the need to provide for greater competition in the sector. For
the borrowers, the evidence suggests that lenders have passed the GST costs and more
to them each year.
Notes
1. Opposition bill to take on banks a “thought bubble” in the Australian, 21 November, 2010,
www.theaustralian.com.au/national-affairs/people-in-politics/opposition-bill-to-take-on-
banks-a-thought-bubble-treasurer-wayne-swan-says/story-fn5nzhg1-122595783352
2. The reason for this unique treatment of fnancial institutions is said to be the diffculty in
quantifying the fnancial service charge included in moneys earned by fnancial institution.
As Cnossen (2000) explains, conventional wisdom holds that fnancial services cannot be
included in the GST base calculated on the tax credit method because the intermediation
charge that should be taxed cannot be separated from the pure interest rate, premium or rate
of return that should not be taxed. Therefore, it is considered unavoidable that fnancial
services should be exempted from GST (Evans, 2011).
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Australia: a case study approach”, Australian Tax Forum, Vol. 17 No. 4, pp. 499-536.
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About the authors
Benjamin Liu is a Lecturer of Finance at Griffth University. Benjamin was awarded a PhD in
Finance at Monash University in 2006. Before starting his academic career, he had worked for the
industry of fnance and investments for over 10 years. His expertise demonstrates that he has
made 30 refereed publications (26 refereed international journal articles) and won 5 competitive
research grants and awards, and under his supervision, in the last seven years, as a qualifed
principal supervisor, four research students successfully completed their PhD in the area of
Banking, Finance and Investments at Griffth University. Benjamin’s research interests include
mortgage fnance and securitisation, corporate fnance, corporate governance, IPOs, applied asset
pricing, banking and international fnancial markets. Benjamin is a course convenor at both
undergraduate and postgraduate levels as well as Honours. The courses he convened include
Corporate Finance, Investments, Advanced Financial Modelling, Corporate Financial Risk
Management, Multinational Finance and Special Topics in Finance Honours. More details about
Benjamin can be found at www.griffth.edu.au/business-government/griffth-business-school/
departments/department-accounting-fnance-economics/staff/dr-benjamin-liu Benjamin Liu is
the corresponding author and can be contacted at: [email protected]
Allen Huang is a Senior Lecturer in Accounting at Griffth University. Allen has taught at the
undergraduate and postgraduate levels and has supervised Honours, Master and PhD theses. He
has published a research monograph and book chapters. He has also published numerous
research articles in refereed journals in several countries including the UK, the USA, Continental
Europe, Australia, Canada and China.
Brett Freudenberg is an Associate Professor – Taxation at Griffth University (Australia).
Brett’s tax expertise is evidenced by a Fulbright scholarship (2006), as well as over 20 refereed tax
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and Islamic Finance) and the taxation of business forms. In 2009, Brett was invited to present his
PhDresearch fndings to the Australian Treasury as part of the Henry Tax Review. Also his PhD
was awarded the CCH-ATTA Doctoral Prize which saw it published as a book in 2011: Tax
Flow-Through Companies. Brett’s effectiveness as a teacher has been recognised through fve
national awards (including the award of two Australian Learning and Teaching Council citations:
2008 and 2011). He is also a meticulous and rigorous scholar in learning and teaching with over 15
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51
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doc_415757711.pdf
The purpose of this paper is to investigate the impact of the Goods and Services Tax (GST)
on mortgage pricing and to measure the GST shifting ratio of Australian credit unions.
Accounting Research Journal
The impact of the GST on mortgage pricing of Australian credit unions: An empirical
analysis
Benjamin Liu Allen Huang Brett Freudenberg
Article information:
To cite this document:
Benjamin Liu Allen Huang Brett Freudenberg , (2014),"The impact of the GST on mortgage pricing of
Australian credit unions", Accounting Research J ournal, Vol. 27 Iss 1 pp. 37 - 51
Permanent link to this document:
http://dx.doi.org/10.1108/ARJ -08-2013-0059
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Users who downloaded this article also downloaded:
Low Sui Pheng, Carol P.W. Loi, (1994),"Implementation of the Goods and Services Tax (GST) in
the Singapore Construction Industry", J ournal of Property Finance, Vol. 5 Iss 3 pp. 41-58 http://
dx.doi.org/10.1108/09588689410078593
J ohn Breen, Sue Bergin-Seers, Ian Roberts, Robert Sims, (2002),"The Impact of the Introduction of
the GST on Small Business In Australia", Asian Review of Accounting, Vol. 10 Iss 1 pp. 89-104 http://
dx.doi.org/10.1108/eb060751
J ohn Passant, (2014),"The Minerals Resource Rent Tax: The Australian Labor Party and the continuity of
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The impact of the GST on
mortgage pricing of Australian
credit unions
An empirical analysis
Benjamin Liu, Allen Huang and Brett Freudenberg
Department of Accounting, Finance and Economics, Griffth University,
Brisbane, Australia
Abstract
Purpose – The purpose of this paper is to investigate the impact of the Goods and Services Tax (GST)
on mortgage pricing and to measure the GST shifting ratio of Australian credit unions.
Design/methodology/approach – Using the proprietary data from79 credit unions in Australia, we
perform multivariate regression analysis on the effect of the GST on mortgage effective yield spreads
and interest margins, respectively. We also introduce a model that is used to measure the GST shifting
ratio.
Findings – We document that the introduction of the GST in July 2000 led to the substantial rise
in mortgage costs charged by credit unions in the post-GST periods. Overall, the GST alone
contributed to the increase of effective yield spreads and interest margin by 65.3 and 70.1 basis
points, respectively. As measured by the GST-shifting ratio, credit unions passed more than twice
of the GST rate. This suggests GST over-shifting, and it is generally consistent with tax
over-shifting literature.
Originality/value – This is the frst time the GSTshifting ratio has been robustly measured with the
use of multivariate models on mortgage costs.
Keywords Credit unions, GST shifting, Mortgage pricing
Paper type Research paper
1. Introduction
In Australia, the rise of mortgage cost and the decline in housing affordability has raised
concern among consumers, banking regulators and the media. The Australian
mortgage sector is largely comprised of three segments: the four major banks; mortgage
corporations and cooperative lenders. While the Australian lending sector is highly
regulated, there are concerns about high concentrations and the pricing behaviour
(Swan, 2010)[1].
This is of potential concern, as tax incidence research from the USA demonstrates
that “tax over-shifting” can occur when imperfect competition markets exist (Young and
Bielinksa-Kwapisz, 2002; Besley and Rosen, 1994).
One potential tax cost in Australia is the introduction fromJuly 2000 of the goods and
services tax (GST). While fnancial supplies are largely not subject to GST, there is a
restriction on the ability to claim the GST input tax credits back on acquisitions, thus
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
Impact of the
GST
37
Accounting Research Journal
Vol. 27 No. 1, 2014
pp. 37-51
©Emerald Group Publishing Limited
1030-9616
DOI 10.1108/ARJ-08-2013-0059
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increasing the cost. This is in addition to compliance costs that can be associated with
obligations associated with the tax.
After over a decade of the implementation of the new tax system, there is a
consensus that it is time for a review of the system to examine, among other issues,
what lessons we have learnt from the introduction of GST and what reform still
needs to take place (McCarthy, 2011; Peacock, 2011). With respect to the GST
treatment of fnancial services, in particular, there is wide recognition that the
current approach has problems and is only a “second-best” one (Evans, 2011;
McCarthy, 2011; Stitt, 2011). In fact the Henry Tax Review noted that ‘input
taxation of fnancial services under the GST is ineffcient, reduces competition and
harms Australia’s position as a regional fnancial services centre’ (Australia, 2009,
p. D4-1).
Recently, researchers have started investigating the GST impact on mortgage
costs in Australia. Huang and Liu (2012) tested mortgage yield spreads of banks in
response to the introduction of the GST and Huang and Liu (2013) tested mortgage
yield spreads of mortgage corporations. These results would tend to indicate these
institutions have engaged in tax over-shifting with the introduction of the GST in
Australia. However, is the experience of credit unions different, especially given that
they are cooperative lenders and non-proft-maximising organizations, with their
clientele being members, concentrated in specifc industries? Preliminary research
by Liu and Huang (2012), using univariate models, examined the GST impact on the
yield spreads of credit unions which found a signifcant increase. This study
extends Liu and Huang’s (2012) study by examining Australia’s credit unions using
a unique model to measure the GSTshifting ratio more preciously; with multivariate
tests being conducted to analyse yield spreads, as well as interest margins.
In particular, by analysing monthly mortgage data of 79 Australian credit unions
over 36 consecutive months straddling the introduction of the GST (January 1999 to
December 2001) two research questions are addressed in respect of Australian credit
unions:
• has the introduction of the GST impacted on the mortgage pricing?
• to what extent did the GSTcontribute to mortgage costs in respect of yield spreads
and interest margins?
The results would tend to indicate that there was a tax over-shifting by Australian credit
unions, with greater effective yield spreads and effective interest margins. These results
would question to what extent the Australian fnancial market is really competitive,
resulting in greater costs to consumers.
The rest of the paper is structured as follows: Section 2 reviews the relevant
literature; Section 3 describes the data and methodology, while Section 4 presents and
discusses the results. Conclusions are drawn in Section 5.
2. Literature review
2.1 Tax incidence theory
Research from the USA into the effects of taxes on consumer prices dates back several
decades. Brown (1939), Due (1942), Rolph (1952), Musgrave (1959) and Bishop (1968)
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developed a tax incidence theory for studying the general sales taxes on particular
goods. Empirical studies using the tax incidence theory document the fact that sales tax
is fully refected in prices borne by consumers. From the perspective of frms, this is
known as tax shifting because the frms do not alter their profts. Some studies indicate
that, when imperfect competition markets exist, consumer prices may rise more than the
amount of retail taxes levied, suggesting tax over-shifting (Haig and Shoup, 1934; Sidhu,
1971; Besley and Rosen, 1994; Young and Bielinksa-Kwapisz, 2002). In contrast, Poterba
(1996) found that for the post-war period, retail prices rose by approximately the same
amount as the sales tax. However, these studies are exclusively limited to the US
consumer goods industries and consider a retail tax as opposed to a value-added tax
(VAT) that is not in use in the USA.
To broaden the research using the tax incidence theory, it was important to
consider mortgage lenders’ pricing in response to the GST using Australian credit
union data.
2.1.1 GST in Australia. The GST commenced in Australia on 1 July 2000, which
replaced a range of indirect taxes including the Wholesale Sales Tax (WST). The
GST applies at a rate of 10 per cent and is broadly similar to the VAT operating in
several other countries. The GST applies to most forms of economic activities, such
as the supplies of goods and services, requiring the supplier of a taxable supply to
remit the GST collected. However, GST-registered enterprises are broadly able to
claim a refund (input tax credit) of the GST in their acquisitions. In this way, each
enterprise remits just the GST on the value added to its particular product or
service. However, as private consumers are not able to claim such a GST input tax
credit, the end cost of GST is passed on to them (Maclntyre, 2001; Bolton and
Dollery, 2005).
The treatment of fnancial supplies, however, differs from other types of supplies.
First, fnancial supplies (such as taking securities and deposits, issuing loans and
charging interest on loans) are not subject to GST. In other words, no GSTis paid on the
fnancial supplies that fnancial institutions acquire, nor is GST liable on fnancial
supplies made by fnancial institutions. Second, no GST input tax credit is available for
acquisitions of taxable supplies that relate to making fnancial supplies. In this way,
fnancial institutions are effectively treated as the end-consumer. This is somewhat
altered by the allowance of a reduced GSTinput credit for a number of acquisitions that
fnancial institutions have outsourced in recent years (such as processing account
information) (s 70-5 of the GST Act)[2].
This means that the GST on acquisitions are largely absorbed by fnancial
institutions and then allocated in their fnancial products (e.g. loans) to their customers
through increasing the overall charges (Maclntyre, 2001). Although this pricing could be
offset to some extent by the removal of WST on some of the goods purchased by
fnancial institutions, such as computers, how and to what extent the Australian
fnancial institutions have passed the cost of GST to their mortgage customers remains
largely unknown.
It is argued that this is a signifcant and interesting research question. In
Australia, mortgage is the single largest asset of banks and many non-bank
fnancial institutions and the single largest liability of households. The effciency of
the mortgage fnance system plays an important role, as an ineffcient one renders
39
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the economy vulnerable to crisis (Ebrahim and Mathur, 2007). According to a recent
survey (Demographia, 2014), Australian housing affordability is the lowest among
the surveyed countries (including Australia, Canada, Ireland, New Zealand, the UK
and the USA). The period observing sharp declines in housing affordability in
Australia corresponds with the implementation of the GST system. An
understanding of precisely what factors have contributed to the rise in mortgage
costs and how the GST has affected the mortgage costs is, therefore, a signifcant
issue and an important consideration for banking regulators and policymakers to
reach appropriate policy decisions.
Since the introduction of the GSTin Australia, studies have examined two aspects of
its effects on consumer prices, being the general price level of goods and services and the
estimate of compliance costs – each of which is discussed below.
2.2 The GST effects on price levels
The infationary effect of the GST has raised concerns for governments. The consensus
of government surveys is that the GST’s effect on the goods and services included in the
consumer price index (CPI) would be a one-off price perturbation in the quarter of the
introduction of the GST, with the magnitude of the effect varying within a small range.
Part of the reason for this lower price effect relates to the removal of the WST which
could apply at rates higher than GST, and it was non-refundable for most enterprises.
Due to these reasons, the Commonwealth Treasury estimated that the 10 per cent GST
could increase the overall CPI by just 2.75 per cent in July 2000 (Commonwealth
Treasury, 2000, p. 11).
Academic researchers have also endeavoured to study the effect of the GST on price
levels. Before the GST, Johnson et al. (1999) and Warren et al. (1999) thoroughly
evaluated the revenue, effciency and equity effects of the government’s tax package.
Warren et al. (1999) also predicted the possible effect of the GST on infation and
estimate, under different assumptions, that this effect was likely to be between 0.8 and
3.6 per cent in July 2000.
Valadkhani and Layton (2004) examined the magnitude and duration of the GST
effect on the overall rate of infation. Using intervention analysis, Valadkhani and
Layton (2004) found that the GST effect on infation was only temporary (in the
third-quarter of 2000) and the size of the effect was 2.8 per cent. In another study,
(Valadkhani, 2005) using the same methodology, investigates the price changes of goods
and services in the four quarters before, and four quarters after, the third quarter of 2000.
Valadkhani found that the overall effect was a one-off lift in infation of approximately
3 per cent in the third quarter of 2000, with prices not increasing signifcantly before or
after this quarter.
2.3 The tax compliance costs
Other studies have focused on estimating the compliance costs of the GST. According to
Tran-Nam (1999, 2000) and others (Sandford et al., 1989), the introduction of a new tax
(such as the GST) gives rise to two new types of costs: the implementation compliance
costs and the recurrent compliance costs. The implementation compliance costs are the
costs incurred in complying with the GSTwhen it was implemented and include mainly
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the administrative costs and the compliance costs of the implementation of the GST. The
compliance costs of implementing the GST refer to the resources expended by the
economy to comply with the GST.
In terms of empirical studies on VAT, the international evidence suggests that, in
most countries, the introduction of a VAT results in signifcantly higher compliance
costs for taxpayers than other forms of taxation (Vaillancourt, 1987; Pope, 2001) and
that VAT compliance costs are disproportionately higher for small businesses than
large businesses (Cnossen, 1994; Rametse and Pope, 2002; Coolidge, 2012). Glover
and Tran-Nam (2005) have estimated both the net transitional cost and the recurrent
GST compliance for small businesses.
The estimated implementation costs of the GST reviewed above are based on
different assumptions, using different survey techniques with varying sample sizes
and lumping the GST-related and normal upgrading computer and system costs
together. According to Tran-Nam (2000), these estimates are either baseless or
diffcult to justify.
In summary, the existing research all suggests that the GST effect on prices and
the GST compliance costs are substantial, which inevitably increases costs to both
businesses and consumers (Huang and Liu, 2012). However, the research that has
been conducted to date, except for Huang and Liu’s (2012) on bank mortgage costs,
remains at the level of estimating either the general price effect or the overall
compliance costs in the society. It is argued that more research is needed to enhance
our understanding of the GST effects on mortgage costs charged by lenders in
Australia, especially given concerns about competition in the fnance sector.
3. Data and methodology
3.1. Data
The mortgage data for analysis is extracted from Cannex’s monthly survey of
Australian lenders, which includes monthly information on mortgage interest rates,
mortgage fees and charges, credit criteria and other data of all credit unions
operating in the country. Other data (i.e. 90-day bank bill rates, Treasury bond and
corporate bond yields and three-month term deposit interest rates of banks) are
collected from the statistics of the Reserve Bank of Australia (RBA). The period
selected for the analysis covers 36 consecutive months from January 1999 to December
2001 (that is, 18 months before and 18 months after the GSTcame into effect on 1 July 2000).
The selection of the period is mainly determined by the key research questions this paper
addresses, that is, the impact of the introduction of the GST on mortgage costs. Hence,
inclusion of both pre- and post-GST periods in the analysis would allow the authors to
compare the mortgage costs before and after the implementation of the GST, although it
would include some of the implementation costs of starting to comply with GSTas well
as ongoing costs.
Seventy-nine credit unions, operating in the mortgage market during the period, are
included in the analysis, which are listed in the appendix. The data selection results in
2,805 monthly observations. To make comparisons between the pre- and post-GST
periods more valid, only the standard products of residential mortgages, that is, the
owner-occupied home loans with adjustable interest rates (with which about 80 per cent
41
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of Australian home loans are originated), are examined. Loans for other purposes are
excluded from the analysis.
3.2 Variable and model specifcations
We use the annualised average percentage rates (AAPR) which include the nominal
interest rate and all other fees and charges levied on mortgages, that is, the effective
rates. The AAPR adopted in this study is computed using the standard calculations
required under the Australian Uniform Consumer Credit Code and is considered to be a
benchmark for comparing mortgage products in Australia. When comparing mortgage
costs, however, we use mortgage yield spreads and interest margins, rather than the
interest rates. Effective yield spreads are the differences between AAPR rates and
90-day bank bill rates.
To perform robustness tests, we also use the effective interest margin (differences
between the AAPR rates and the three-month term deposit interest rates) to more
precisely refect the degree of GST cost shifting.
The use of yield spreads or interest margins is a standard approach in measuring
mortgage costs, as it helps overcome the impact of infation and adjustments of
monetary policy over time on the interest rates (Liu and Skully, 2005, 2008).
We followmark-up theory for pricing banking products (Rousseas, 1985). According
to the literature, mortgages are regarded as special kinds of products of lenders
(Ambrose et al., 2004; Liu and Skully, 2005, 2008). For simplicity, mortgage unit price
(the interest rate, P
i
) of lender i at time t can be unit funding cost (C
f
)?risk (C
r
) ?unit
operating cost (C
o
) ?proft margin (P
m
). Considering credit unions as nonproft seeking
lenders, proft margin (P
m
) is assumed to be zero, we derive unit price:
P
i
?C
f
?C
r
?C
o
(1)
Pre GST period GST Post GST period
?t ?3 ?2 ?1 0 +1 +2 + 3 +t
P
i
= C
f
+ C
r
+ C
o
P
i
= C
f
+ C
r
+ C
o
+ C
g
Rearranging equation (1), we derive: for the pre-GST period, P
i
?C
f
?C
r
?C
o
, and for
the post-GSTperiod, P
i
?C
f
?C
r
?C
o
?C
g
, while C
g
stands for the GSTcost included
in mortgages. Thus, for an adjustable rate mortgage, P
i
, C
f
is either the yield spread
(YSP) or interest margin (MGN) where C
f
is the unit funding cost. C
o
and C
r
are further
assumed to remain constant for the two short periods.
Because there is no GSTcharge on mortgages and funding of the lender, C
g
(the GST
cost) for a lender is resulted from a 10 per cent GST levied on lender operating items,
such as rentals and electricity. Therefore, in theory, C
g
should be around 10 per cent of
C
o
. If C
g
/C
o
?10 per cent, we can say there is GST over-shifting; if C
g
/C
o
?10 per cent,
there is GST under-shifting. In this paper, C
g
/C
o
is defned as GST shifting ratio.
In a competitive funding market, all credit unions are assumed to have the same
funding cost. As explained above, the 90-day bank bill rate and three-month term
deposit interest rates of banks act as proxies for funding costs. Other variables used in
our analysis include the introduction of the GSTas a dummy variable, the credit criteria
variables (that is, loan to value ratios and maximum loan amounts), a market default
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risk variable and seasonal effect variables. Defnitions of all variables and their
predicted signs, in detail, are provided in Table I.
To answer the research questions, we develop multivariate models (pooled
cross-sectional regression) to examine the GSTeffects on mortgage costs using monthly
data. The regression analysis estimates the effective yield spreads (EYSP) in regression
equation (2).
Table I.
Defnition and predicted
signs of variables
Equations
Panel A: dependent variables
EYSP standard adjustable interest rates plus sum of all
other fees and charges on mortgage over 90-day bill
rates ? ?
EMGN standard adjustable interest rates plus sum of all
other fees and charges on mortgage over 90-day
deposit rates ? ?
Panel B: independent variables
GST ?dummy variable, pre-July 2000 ?0; otherwise ?1 ?
1
(?) ?
1
(?)
MAXNO ?no maximum loans required by the lender ?
2
(?) ?
2
(?)
MAXLOAN200 ?maximum loans ?A$1,000,000 ?
3
(?) ?
3
(?)
MAXLOAN50 ?maximum loans ?A$500,000 ?
5
(?) ?
5
(?)
MAXLTV90 ?90 per cent ?maximum loan to value ratio ?85
per cent ?
7
(?) ?
7
(?)
MAXLTV85 ?maximum loan to value ratio ?85 per cent ?
8
(?) ?
8
(?)
MDFT(-1) ?lag of yield differences between AAA- and A-
rated corporate bonds with a two-four year
maturity constant maturity, proxying the market
default risk ?
9
(?) ?
9
(?)
Month 1 ?January each year from 1999 to 2001 ?
10
(?) ?
10
(?)
Month 2 ?February each year from 1999 to 2001 ?
11
(?) ?
11
(?)
Month 3 ?March each year from 1999 to 2001 ?
12
(?) ?
12
(?)
Month 4 ?April each year from 1999 to 2001 ?
13
(?) ?
13
(?)
Month 5 ?May each year from 1999 to 2001 ?
14
(?) ?
14
(?)
Month 6 ?June each year from 1999 to 2001 ?
15
(?) ?
15
(?)
Month 7 ?July each year from 1999 to 2001 ?
16
(?) ?
16
(?)
Month 9 ?September each year from 1999 to 2001 ?
18
(?) ?
18
(?)
Month 10 ?October each year from 1999 to 2001 ?
19
(?) ?
19
(?)
Month 11 ?November each year from 1999 to 2001 ?
20
(?) ?
20
(?)
Month 12 ?December each year from 1999 to 2001 ?
21
(?) ?
21
(?)
Quarter 1 ?The frst quarter each year from 1999 to 2001 ?
1
(?) ?
1
(?)
Quarter 2 ?The second quarter each year from 1999 to 2001 ?
2
(?) ?
2
(?)
Quarter 4 ?The fourth quarter each from 1999 to 2001 ?
4
(?) ?
4
(?)
Panel C: control variables
MAXLOAN100 ?A$1,000,000 ?maximum loans ?A$500,000 ?
4
?
4
MAXLTV95 ?maximum loan to value ratio ?90 per cent ?
6
?
6
Month 8 ?August each year from 1999 to 2001 ?
18
?
18
Quarter 3 ?The third quarter each year from 1999 to 2001 ?
3
?
3
43
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EYSP
i
?
?
?
0
??
1
GSTi??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(?1)i??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
(2)
In equation (2), the dependent variable is the EYSP of credit unions. The independent
variables are the GST, lending criteria, market default risk and seasonal effects. As of 1
July 2000, when the GST was implemented, a difference on mortgage costs between the
pre and post-GST periods was expected. In the regression model, therefore, a dummy
variable for the GST is considered, with the pre-GST date with a value of 0 and
otherwise 1 (Table I). In equation (2), ?
0
is constant consisting of credit risk (C
r
) and
operating cost (C
o
). If ?
1
/?
0
?10 per cent, this shows GSTover-shifting in the context of
yield spreads as C
g
/C
o
?C
g
/(C
r
?C
o
).
With respect to the credit criteria, prior studies (Hendershott and Shilling, 1989;
Ambrose et al., 2004) have used loan-to-value (LTV) ratios and loan size to test models
for credit risk of mortgages. Asimilar approach is adopted in this study. LTVratios are
divided into three categories (i.e. maximumLTV?90 per cent; 90 per cent ?maximum
LTV?85 per cent; and maximumLTV?85 per cent) and loan size are divided into four
groups (Table I), following Liu and Skully (2005). As loans with higher LTV ratios are
considered to have higher credit risk (Ambrose et al., 2004; Liu and Skully, 2005), a
positive relationship is expected between the yield spreads and LTVratios. Similarly, as
larger loans are considered to have higher risk, a positive relationship is also expected
between the yield spreads and the loan size.
In addition, the market default premium is proxied by the yield differences between
AAA- and A-rated corporate bonds with a constant maturity between 2 and 4 years,
which has also been used by prior studies (Ambrose et al., 2004; Liu and Skully, 2005). A
positive relation is expected between the market default premium (a lag) and the
mortgage yield spreads. In the model, individual months or quarters are included to
detect seasonal effects, which are similarly used in prior studies (Ambrose et al., 2004).
To perform robustness checks, we further develop a multivariate model to test
mortgage interest margins. As one may argue that not all credit unions use 90-day bank
bills as a funding channel and a funding cost benchmark, we use three-month deposit
interest rates as a proxy for a funding cost benchmark for credit unions. In the following
model (equations 3), the dependent variable is the effective mortgage interest margin
(EMGN). The independent variables are the same as those in equation (2). The ?
0
in
equation (3) is constant consisting of credit risk (C
r
) and operating cost (C
o
). If ?
1
/?
0
?10
per cent, this shows GST over-shifting in the context of interest margins as C
g
/C
o
?C
g
/
(C
r
?C
o
).
EMGN
i
?
?
?
0
??
1
GST
i
??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(-1)
i
??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
(3)
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4. Empirical results
4.1 Results on mortgage yield spreads
As shown in Panels Aand Bof Table II, the GSTincreased the effective yield spreads by
65.3 (with quarterly effects) and 65.1 (with monthly effects) basis points (both signifcant
Table II.
Regression estimate on
EYSP
Panel A Panel B
EYSP EYSP
Explanatory variables Coeffcient t-statistic VIF Coeffcient t-statistic VIF
Intercept 2.039 47.667*** ? 1.998 43.138*** ?
GST 0.653 38.917*** 1.199 0.651 39.664*** 1.204
MAXNO 0.072 3.618*** 1.690 0.071 3.657*** 1.690
MAXLOAN200 0.150 3.775*** 1.174 0.152 3.921*** 1.175
MAXLOAN50 0.102 4.527*** 1.645 0.101 4.591*** 1.645
MAXLTV90 0.018 1.086 1.095 0.018 1.117 1.095
MAXLTV80 0.052 2.141** 1.084 0.052 2.201** 1.084
MDFT(-1) ?0.022 ?23.041*** 1.119 ?0.023 ?24.246*** 1.140
Month Effects
Month 1 ? ? ? 0.565 14.032*** 1.596
Month 2 ? ? ? 0.673 16.907*** 1.590
Month 3 ? ? ? 0.441 12.237*** 1.854
Month 4 ? ? ? 0.489 13.593*** 1.860
Month 5 ? ? ? 0.194 5.388*** 1.859
Month 6 ? ? ? 0.226 6.284*** 1.858
Month 7 ? ? ? 0.106 2.979*** 1.825
Month 9 ? ? ? 0.118 3.335*** 1.827
Month 10 ? ? ? 0.185 5.215*** 1.853
Month 11 ? ? ? 0.131 3.703*** 1.871
Month 12 ? ? ? 0.083 2.345** 1.843
Quarter Effects
Quarter 1 0.468 20.375*** 1.469 ? ? ?
Quarter 2 0.230 10.567*** 1.567 ? ? ?
Quarter 4 0.056 2.670*** 1.500 ? ? ?
No. of observations 2540 2540
Adjusted R
2
0.431 0.459
F-statistic 193.17 120.576
p-value 0.000 0.000
Notes: In this table, we report the cross-sectional regression results of EYSP in relation to the GST
impact; the regression equation is:
EYSP
i
?
?
?
0
??
1
GSTi??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(-1)i??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
See Table I for a defnition of each of these variables; *, **and ***denote the 10 per cent, 5 per cent and
1 per cent levels of signifcance respectively; VIF is the variance infating factor to detect the
multicollinearity problem among independent variables
45
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at p ? 0.01), indicating that the GST contributed signifcantly to the rise in mortgage
costs in the post GST periods. As we discussed, if ?
1
/?
0
?10 per cent, this suggests the
GST over-shifting. In Panels A and B, ?
1
/?
0
is 32.0 per cent (?0.653/2.039) and 32.6 per
cent (?0.651/1.998), respectively.
These increases are much higher than those of banks (Huang and Liu, 2012),
generally consistent with prior research in relation to the impact of the GST on price
levels (Johnson et al., 1999; Tran-Nam, 2000, 2001). As we discussed, if ?
1
/?
0
? 10 per
cent, this shows the GSTover-shiftinginthe context of yieldspreads as C
g
/C
o
?C
g
/(C
r
?C
o
).
This may relate to tax compliance literature that has found that smaller businesses can
have greater regressive compliance costs compared to larger operations (Evans et al.,
1997).
The loan size, as a measure of credit risk, is found to have a positive relation to the
spreads, as predicted in Table I. When MAXLOAN100 (A$1,000,000 ? maximum
loans ? A$500,000) is controlled, larger loans MAXLOAN200 (maximum loans
?A$1,000,000) and MAXNO (no maximum loans required by the lender), have
positive coeffcients, indicating higher yield spreads. However, smaller loans
MAXLOAN50 (maximum loans ? A$500,000) also have higher yield spreads,
contrary to the predicted sign. This result is perhaps due to the fact that credit
unions may not have the economies of scale in making smaller loans cheaper (see Liu
and Skully, 2005). The results for LTV ratios show that, when the MAXLTV 95
group (maximum LTV ?90 per cent) is controlled, MAXLTV 80 (maximum LTV ?
85 per cent) have higher effective yield spreads (with positive coeffcients), again
contrary to the predicted sign. This means the mutual lenders do not consider the
loan to value ratio (maximum LTV) as a credit risk in pricing a loan due to the fact
that if the LTV ratio is ? 80 per cent, the borrower is required to buy mortgage
insurance. Therefore, the lender is not exposed to higher credit risk associated with
maximum LTV ratios loans.
In Table II, the market default risk variable (MDFT-1) is signifcant to the yield
spreads, which is again consistent with our prediction and previous literature (e.g.
Hendershott and Shilling, 1989). With respect to seasonal effects, when the third
quarter (Qut 3) is controlled, the other three quarters are signifcantly positive,
indicating the third quarter is the cheapest season.
In relation to model ft in Table II, the overall models achieve signifcant
regression results with F-statistics (p ?0.001). Adjusted R
2
slightly improves from
0.431 with quarterly effects to 0.459 with monthly effects, indicating the model with
monthly effects is a better one.
4.2 Robustness tests on mortgage interest margins
Table III shows, after other variables are controlled, the GSTalone led to an increase
in the margins by 70.1 (with quarterly effects) and 70.3 (with monthly effects) basis
points (both signifcant at p ? 0.01). The GST shifting ratio (?
1
/?
0
) is equal to 22.5
per cent (? 0.701/3.112) and 22.9 per cent (? 0.703/3.057), respectively, suggesting
GST over-shifting in the context of interest margins.
The fndings on effective interest margins are more robust and meticulous. As credit
unions in Australia are nonproft-seeking frms, one may question why credit unions
over-shift the GST costs not only to borrowers (the members) but also to depositors (the
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members) as the margins come frombothsides of lendingandfunding. Perhaps the reasons
for such GST costs charged in mortgages may be the result of the allocation of their
compliance costs for the implementation of the GST, as they operate on a smaller scale
compared to banks, as well as the GSTlevied on operational items.
Table III.
Regression estimate on
effective interest margins
Panel C Panel D
EMGN EMGN
Explanatory Variables Coeffcient t-statistic VIF Coeffcient t-statistic VIF
Intercept 3.112 82.446*** ? 3.057 73.945*** ?
GST 0.701 47.337*** 1.199 0.703 48.036*** 1.204
MAXNO 0.065 3.735*** 1.690 0.065 3.752*** 1.690
MAXLOAN200 0.141 4.006*** 1.174 0.140 4.039*** 1.175
MAXLOAN50 0.100 5.040*** 1.645 0.099 5.066*** 1.645
MAXLTV90 0.025 1.668* 1.095 0.025 1.712* 1.095
MAXLTV80 0.057 2.643*** 1.084 0.056 2.656*** 1.084
MDFT(-1) ?0.004 ?4.654*** 1.119 ?0.004 ?5.113*** 1.140
Month Effects
Month 1 ? ? ? 0.295 8.208*** 1.596
Month 2 ? ? ? 0.236 6.639*** 1.590
Month 3 ? ? ? 0.269 8.367*** 1.854
Month 4 ? ? ? 0.255 7.935*** 1.860
Month 5 ? ? ? 0.147 4.592*** 1.859
Month 6 ? ? ? 0.147 4.598*** 1.858
Month 7 ? ? ? 0.040 1.268 1.825
Month 9 ? ? ? 0.165 5.232*** 1.827
Month 10 ? ? ? 0.162 5.121*** 1.853
Month 11 ? ? ? 0.166 5.249*** 1.871
Month 12 ? ? ? 0.013 0.402 1.843
Quarter Effects
Quarter 1 0.197 9.718*** 1.469 ? ? ?
Quarter 2 0.114 5.951*** 1.567 ? ? ?
Quarter 4 0.044 2.372** 1.500 ? ? ?
No. of observations 2540 2540
Adjusted R-square 0.490 0.504
F-statistic 245.285 144.413
p-value 0.000 0.000
Notes: In this table, we report the cross-sectional regression results of EMGN in relation to the GST
impact; the regression equation is:
EMGN
i
?
?
?
0
??
1
GST
i
??
2
MAXNO
i
??
3
MAXLOAN200
i
??
4
MAXLOAN100
i
??
5
MAXLOAN50
i
??
6
MAXLTV95
i
??
7
MAXLTV90
i
??
8
MAXLTV85
i
??
9
MDFT(-1)
i
??
j ?
MON
ji
or ??
j ?
QUT
ji
?e
i
See Table I for a defnition of each of these variables; *, **and ***denote the 10, 5 and 1 per cent
levels of signifcance, respectively; VIF is the variance infating factor to detect the multicollinearity
problem among independent variables
47
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:
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9
2
4
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a
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2
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(
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5. Conclusion
The key fndings of this study are that the introduction of the GSTin July 2000 led to the
substantial rise in mortgage costs charged by credit unions in the post-GST periods.
Overall, the GSTalone contributed to the increase of effective yield spreads by 65.3/65.1
basis points and of effective interest margins by 70.1/70.3 basis points. Furthermore,
measured by the GST-shifting ratio, credit unions passed more than twice of the GST
amount they paid to their mortgage holders, suggesting GST over-shifting.
The mortgage burdens on Australian borrowers have risen substantially and
housing affordability has declined sharply in the past decade. The fndings of this study
may have some practical implications for regulators and policymakers in the banking
industry in their contemplation of the proper regulations and policies to control and curb
mortgage costs – especially the need to provide for greater competition in the sector. For
the borrowers, the evidence suggests that lenders have passed the GST costs and more
to them each year.
Notes
1. Opposition bill to take on banks a “thought bubble” in the Australian, 21 November, 2010,
www.theaustralian.com.au/national-affairs/people-in-politics/opposition-bill-to-take-on-
banks-a-thought-bubble-treasurer-wayne-swan-says/story-fn5nzhg1-122595783352
2. The reason for this unique treatment of fnancial institutions is said to be the diffculty in
quantifying the fnancial service charge included in moneys earned by fnancial institution.
As Cnossen (2000) explains, conventional wisdom holds that fnancial services cannot be
included in the GST base calculated on the tax credit method because the intermediation
charge that should be taxed cannot be separated from the pure interest rate, premium or rate
of return that should not be taxed. Therefore, it is considered unavoidable that fnancial
services should be exempted from GST (Evans, 2011).
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a-thought-bubble-treasurer-wayne-swan-says/story-fn5nzhg1-1225957833524
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Further reading
Tran-Nam, B. and Glover, J. (2002), “Estimating the transitional compliance costs of the GST in
Australia: a case study approach”, Australian Tax Forum, Vol. 17 No. 4, pp. 499-536.
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About the authors
Benjamin Liu is a Lecturer of Finance at Griffth University. Benjamin was awarded a PhD in
Finance at Monash University in 2006. Before starting his academic career, he had worked for the
industry of fnance and investments for over 10 years. His expertise demonstrates that he has
made 30 refereed publications (26 refereed international journal articles) and won 5 competitive
research grants and awards, and under his supervision, in the last seven years, as a qualifed
principal supervisor, four research students successfully completed their PhD in the area of
Banking, Finance and Investments at Griffth University. Benjamin’s research interests include
mortgage fnance and securitisation, corporate fnance, corporate governance, IPOs, applied asset
pricing, banking and international fnancial markets. Benjamin is a course convenor at both
undergraduate and postgraduate levels as well as Honours. The courses he convened include
Corporate Finance, Investments, Advanced Financial Modelling, Corporate Financial Risk
Management, Multinational Finance and Special Topics in Finance Honours. More details about
Benjamin can be found at www.griffth.edu.au/business-government/griffth-business-school/
departments/department-accounting-fnance-economics/staff/dr-benjamin-liu Benjamin Liu is
the corresponding author and can be contacted at: [email protected]
Allen Huang is a Senior Lecturer in Accounting at Griffth University. Allen has taught at the
undergraduate and postgraduate levels and has supervised Honours, Master and PhD theses. He
has published a research monograph and book chapters. He has also published numerous
research articles in refereed journals in several countries including the UK, the USA, Continental
Europe, Australia, Canada and China.
Brett Freudenberg is an Associate Professor – Taxation at Griffth University (Australia).
Brett’s tax expertise is evidenced by a Fulbright scholarship (2006), as well as over 20 refereed tax
publications involving international comparative law(tax transparent companies, arts tax reform
and Islamic Finance) and the taxation of business forms. In 2009, Brett was invited to present his
PhDresearch fndings to the Australian Treasury as part of the Henry Tax Review. Also his PhD
was awarded the CCH-ATTA Doctoral Prize which saw it published as a book in 2011: Tax
Flow-Through Companies. Brett’s effectiveness as a teacher has been recognised through fve
national awards (including the award of two Australian Learning and Teaching Council citations:
2008 and 2011). He is also a meticulous and rigorous scholar in learning and teaching with over 15
refereed publications in the areas of work-integrated learning, self-effcacy, generic capabilities
and information literacy.
A full list of Associate Professor Freudenberg’s research can be viewed at: http://ssrn.com/
author?498263
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
51
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