Housing Price and Interest Rate Hike: A Tale of Five Cities in Australia
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Journal of
Risk and Financial
Management
Article
Housing Price and Interest Rate Hike: A Tale of Five Cities
in Australia
Fennee Chong
Department of Accounting and Finance, Charles Darwin University, Casuarina, NT 0810, Australia;
fen.chong@cdu.edu.au
Abstract: Australian housing prices are reported to be overvalued and unaffordable for the past
two decades. Many researchers and practitioners have attributed the persistent growth in housing
prices to the prolonged period of low borrowing costs. However, due to inflationary pressure, the
Central Bank has raised its cash rate consecutively in recent months. This paper aims to examine
whether interest rate rises affect housing price in different parts of Australia. Evidence generated
from the analysis reported bipolar results between the large and smaller cities, whereby housing
prices in Sydney and Melbourne show a significant negative relationship with interest rate changes
while Brisbane and the Gold Coast and Perth and Adelaide, respectively, are showing negative but
insignificant results during the study period. Short-run trend projections on housing prices indicate
that Sydney, Melbourne, Brisbane and the Gold Coast are on a downward trend while Adelaide and
Perth will maintain its current momentum before plateauing out later next year. Likewise, control
variables, such as oil prices, inflation rate and stock market performance, are found to be related
to housing prices in larger cities only. These findings have implications on housing policy, house
purchase decisions and investment portfolio management strategy.
Keywords: housing price; interest rate; oil price; stock market
1. Introduction
Citation: Chong, Fennee. 2023.
According to the OECD Report (2004) and Cox (2021) in Demographia International
Housing Price and Interest Rate Hike:
Housing Affordability 2021, Australian housing prices are overvalued and unaffordable.
A Tale of Five Cities in Australia.
Journal of Risk and Financial
These reports indicated a 51.8 percent and 40 percent overvaluation in 2004 and 2021,
Management 16: 61. https://doi.org/
respectively. The ratios of house prices to incomes and rents are also at the highest quantile
10.3390/jrfm16020061 of the OECD countries since 2003. In terms of affordability, Cox (2021) indicated that the
median multiple of national house prices to household income was 6.4 times in Australia
Academic Editor: Rafael
compared to the US’s 3.7 and UK’s 4.6 times in 2016. In the third quarter of 2021, the
González-Val
national house price to household income ratio deteriorated to 12.1 times to become
Received: 15 December 2022 severely unaffordable comparing to 5.1 times in UK and 5.0 times in the US (Cox 2022).
Revised: 15 January 2023 In Sydney, it was 15.3 times, Melbourne 9.7 times, Adelaide 8 times, Perth 7.1 times and
Accepted: 16 January 2023 Brisbane 7.4 times, respectively. Generally, all five Australian major cities are in the severely
Published: 18 January 2023 unaffordable category. Sydney has become the second least affordable metropolitan in
the world after Hong Kong. This overvaluation and severe unaffordability concur with
increases in debt-to-income ratio, place Australia’s housing at the top quantile of the
OECD countries. A recent study on the Australian housing market by Cho et al. (2021)
Copyright: © 2023 by the author. asserts that the steadily increasing housing prices have attributed to a steady decline of
Licensee MDPI, Basel, Switzerland.
home ownership. Additionally, they also found that the fundamental housing prices of all
This article is an open access article
Australian cities are significantly overvalued. Melbourne and Sydney had their housing
distributed under the terms and
prices overvalued for the last two decades. The authors content that the near zero interest
conditions of the Creative Commons
rate was one of the variables that affects the fundamental prices of the housing in Australia.
Attribution (CC BY) license (https://
Figure 1 presents the development of Australia housing prices from 1996 to 2021. As
creativecommons.org/licenses/by/
4.0/).
can be seen from this figure, housing prices have surpassed the performance of GDP per
J. Risk Financial Manag. 2023, 16, 61. https://doi.org/10.3390/jrfm16020061 https://www.mdpi.com/journal/jrfmJ. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 2 of 13
J. Risk Financial Manag. 2023, 16, 61 2 of 13
Figure 1 presents the development of Australia housing prices from 1996 to 2021. As
can be seen from this figure, housing prices have surpassed the performance of GDP per
capital in the past
capital pastfewfewyears.
years.Since
SincethetheGlobal
Global Financial
FinancialCrisis (GFC)
Crisis (GFC)in 2008, researchers
in 2008, researchersand
industry
and analysts
industry have been
analysts have predicting an imminent
been predicting housinghousing
an imminent market meltdown. Nonethe-
market meltdown.
less, in the absence
Nonetheless, in the of a prominent
absence trigger, the
of a prominent housing
trigger, the market
housingcrash
marketpredictions remains a
crash predictions
common subject of talk until recently. In the past decade, despite being
remains a common subject of talk until recently. In the past decade, despite being over- overvalued and
coupled with high debt-to-income ratio, mortgage stress remains insignificant
valued and coupled with high debt-to-income ratio, mortgage stress remains insignificant due to record
lowto
due interest
recordrates. An easing
low interest rates.ofAn
monetary
easing ofpolicy offerspolicy
monetary liquidity
offerstoliquidity
the market whilst
to the mar-a
contracting
ket whilst a monetary
contractingpolicy works
monetary in theworks
policy opposite direction.
in the opposite The historical
direction. Thelow interest
historical
rate interest
low was onerate
of the keyone
was factors that
of the keydrove housing
factors prices housing
that drove to greaterprices
heightsto (Cho
greateret al. 2021).
heights
However, the recent surge in inflation rate has prompted the Reserve
(Cho et al. 2021). However, the recent surge in inflation rate has prompted the Reserve Bank of Australia to
increase its cash rate each month since May 2022. These moves drive mortgage
Bank of Australia to increase its cash rate each month since May 2022. These moves drive rates higher.
The current
mortgage high
rates inflation
higher. Theand growing
current highmortgage
inflationrates
and environment
growing mortgage has put homeowners
rates environ-
and investors into a new and uncertain situation. With that in
ment has put homeowners and investors into a new and uncertain situation. Withmind, the main objectives of
that in
this study are:
mind, the main objectives of this study are:
(i) To
(i) Toassess
assesswhether
whetherthe
theincrease
increasein
inmortgage
mortgagerate,
rate,as
asaaresult
resultof
of the
the increases
increases in
in cash
cash
rate implemented by the Central Bank, has a significant impact on the
rate implemented by the Central Bank, has a significant impact on the persistently persistently
risinghousing
rising housingprices
pricesininthe
thedifferent
differentcities
citiesin
inAustralia.
Australia.
(ii) To examine whether the increase in mortgage rates affects housing prices in different
(ii) To examine whether the increase in mortgage rates affects housing prices in different
cities differently.
cities differently.
(iii) To examine whether other factors, such as oil price, which influences inflation and eco-
(iii) To examine whether other factors, such as oil price, which influences inflation and
nomic growth and stock prices which represent an alternative investment, influence
economic growth and stock prices which represent an alternative investment, influ-
housing prices.
ence housing prices.
Figure
Figure1.1.Development
Developmentofof
Australia Housing
Australia Price,
Housing 1996–2021.
Price, Source:
1996–2021. Source: https://www.oecd.org/
https://www.oecd.org/housing/data/affordable-housing-database/housing-market.htm; accessed
housing/data/affordable-housing-database/housing-market.htm; accessed on 3 September 2022.
on 3 September 2022
The remaining of the article is structured as follows: Sections 2 and 3 explores the back-
Theand
ground remaining
related of the article
literature is structured
while as follows: Sections
data and methodology 2 and 3 explores
will be discussed in Sectionthe4,
background
findings andand related literature
discussions while
in Sections data
5 and and methodology
6 presents will be discussed in Sec-
the conclusion.
tion 4, findings and discussions in Sections 5 and 6 presents the conclusion.
2. Background
2. Background
Information gathered from OECD and Demographia indicates that Australia’s housing
market has beengathered
Information overheated fromand overpriced
OECD the past twenty
and Demographia years.
indicates thatStrong demand
Australia’s hous-on
housing was not only generated by those who are buying a home but also
ing market has been overheated and overpriced the past twenty years. Strong demand on from investors.
Furthermore,
housing was notinvestors’ willingness
only generated to pay
by those beyond
who the fundamental
are buying a home butvalue can be
also from another
investors.
factor that has driven housing prices high. Bubbles are usually formed when
Furthermore, investors’ willingness to pay beyond the fundamental value can be another asset prices
are rapidly increase beyond fundamental values and become unsustainable.
factor that has driven housing prices high. Bubbles are usually formed when asset prices The rise in
property prices is driven by the market’s expectations or average beliefs about future
price trends (Keynes 1936; Dovman et al. 2012). Information made available, particularlyare rapidly increase beyond fundamental values and become unsustainable. The rise in
property prices is driven by the market’s expectations or average beliefs about future price
J. Risk Financial Manag. 16, 61
2023,(Keynes
trends 1936; Dovman et al. 2012). Information made available, particularly mac- 3 of 13
roeconomic variables and policy changes related to the housing industry can influence
market expectations as well.
Researchers and practitioners
macroeconomic positand
variables that factors
policy that could
changes relatedattribute to a property
to the housing industry can influence
crash are recession,
market a expectations
surge in interest rates or oversupply. By definition, when a crash
as well.
happen, housing value would fall
Researchers and at practitioners
least 20 percent market
posit that wide.
factorsInthata recession, higher to a property
could attribute
unemploymentcrash rate leads to high default
are recession, a surgerates and thisrates
in interest debtorservicing
oversupply.stressBycauses hous- when a crash
definition,
ing market plunges,
happen, similar
housing to the
valueonewould
that happened
fall at leastin20the previous
percent GFC.
market The In
wide. unem-
a recession, higher
ployment rate at the time of this study is low at 3.4%; therefore, it is unlikely that Australiacauses housing
unemployment rate leads to high default rates and this debt servicing stress
is going into a market
recession plunges,
soon. similar
Housing to oversupply
the one that couldhappened in the previous
be another factor but GFC.
the The
con-unemployment
stant insufficientratesupply
at the time of this
of land andstudy is low
the lack of at 3.4%; therefore,
builders it is unlikely
in Australia have ruledthatoutAustralia
this is going into
possibility. a recession soon. Housing oversupply could be another factor but the constant insufficient
supply
In Australia, of land and
the mortgage therefers
rate lack ofto builders
the 3-year in bank
Australia haverate
lending ruled
for out
housethisbuy-
possibility.
In Australia, the mortgage rate refers to the
ers. As can be seen from the graph (Figure 2), mortgage rates have been on a downward 3-year bank lending rate for house buyers.
As can be seen from the graph (Figure 2), mortgage
trend from its highest at 17 percent in 1990 to below 6 percent from 2013 until mid-2022. rates have been on a downward trend
from its highest at 17 percent in 1990 to below
The lowest mortgage rate was 2.07 percent per annum offered by Bankwest in November 6 percent from 2013 until mid-2022. The
2020. In 2016, lowest
the cash mortgage rate was 2.07
rate in Australia waspercent
reduced pertoannum offered
1.5 percent by maintained
and Bankwest inat November 2020.
In 2016, the
historical low between 0.10cash rate in
percent to Australia
1.5 percent was
fromreduced
August to 2016
1.5 percent and2022.
until July maintained
This at historical
is beneficial tolow betweenborrowers
mortgage 0.10 percent as to
it 1.5
keepspercent from August
the borrowing 2016
cost until
low. July 2022.
However, This is beneficial
this
to mortgage borrowers as it keeps the borrowing
more conducive borrowing environment has resulted in a steady growth of the aggregate cost low. However, this more conducive
borrowing environment has resulted in a steady
borrowing from AUD 64.9 billion to 1413.6 billion for owner occupied home and an in- growth of the aggregate borrowing from
AUD 64.9 billion to 1413.6 billion for owner occupied home
crease from 10.5 billion to 696.9 billion for investor housing from 1990 to 2022, respectively and an increase from 10.5 billion
(see Figure 3). to 696.9 billion for investor housing from 1990 to 2022, respectively (see Figure 3).
Figure 2. Mortgage rates (1990–2022). (Data Source: Reserve Bank of Australia, accessed on 3 Sep-
Figure 2. Mortgage rates (1990–2022). (Data Source: Reserve Bank of Australia, accessed on 3
tember 2022). Note: Dashed line indicates that mortgage rate is on a downward trend.
September 2022). Note: Dashed line indicates that mortgage rate is on a downward trend.J. Risk2023,
Financial Manag. Financial
16, xManag. 16, 61
2023,REVIEW
FOR PEER 4 of 13 4 of 13
k Financial Manag. 2023, 16, x FOR PEER REVIEW 4 of 13
Figure
Figure3.3.Aggregate borrowing
Figure
Aggregate (in
(inlog)
3. Aggregate
borrowing log)(1990–2022).
borrowing (in log) (1990–2022).
(1990–2022).
Nevertheless, the
theCentral
Centralbank
Nevertheless,Nevertheless, the had
bank hadreversed
Central bank from
reversed had
from itsitsexpansionary
reversed monetary
monetarypolicy
from its expansionary
expansionary monetary policy
policy
due to the escalating
due to inflation
the happening
escalating inflationworldwide,
happening including
worldwide, Australia (see
including
due to the escalating inflation happening worldwide, including Australia (see Figure 4). Figure 4).
Australia (see Figure 4).
InInresponding
responding to
Intothe sudden
responding surge
the suddentosurge of the
the sudden inflation rate,
surge of the
of the inflation the Reserve
rate,inflation Bank
rate, Bank
the Reserve of Australia
the Reserve has
Bank has
of Australia of Australia has
increased cash rate for
increased 25 basis
cash point
rate for in
25 May
basis2022,
point followed
in May by another
2022, four
followed by 50 basis
another point
increased cash rate for 25 basis point in May 2022, followed by another four 50 basis point50 basis point
four
ininJune,
June,July,
July,August
in June,and
August andSeptember
July, August
Septemberand2022.
September 2022.
2022.
Figure
Figure4.4.Changes ininCPI
Figure
Changes (Quarterly,
Changes
4.CPI (Quarterly,2003–2022).
in CPI (Quarterly,(Data
2003–2022). (Datasource:
2003–2022). https://www.abs.gov.au;
(Data
source: accessed
source: https://www.abs.gov.au;
https://www.abs.gov.au; accessed accessed on
onon3 3September
September2022).
2022).
3 September 2022).
Ambrose
Ambroseetetal.al.
(2013),
(2013),inintheir study
studyon Amsterdam’s housing prices,
prices,provided evi-
dence that an Ambrose
overheated et their
al. (2013),
housing market
on
in Amsterdam’s
their
does notstudy onhousing
Amsterdam’s
necessarily readjust or
provided
housing
end with
evi- provided
prices,
a
dence that an overheated
evidence that housing market
an overheated does
housing not necessarily
marketpolicy.
does not readjust or
necessarily end
readjust aor end with a
with
crash. A soft landing
crash. A soft crash.
landing is possible with a fitting government It would be interesting
A is possible
soft landing with a fittingwith
is possible government policy. It would
a fitting government be interesting
policy. It would be interesting
totosee
see how the Australian housing market flares in a high inflation and risinginterest
how the Australian housing market flares in a high inflation and rising interestrate
rate
environment.
environment.J. Risk Financial Manag. 2023, 16, 61 5 of 13
to see how the Australian housing market flares in a high inflation and rising interest
rate environment.
3. Literature Review
Australia’s total housing value was estimated at AUD10.1 trillion in March 2022
(Australia Bureau of Statistics 2022), more than its total GDP at AUD 14,147 million as
at June 2022. This informs the significance of the housing market in determining the
financial stability of the economy. Numerous researchers have documented the importance
of influencing housing prices through different government measures to maintain a healthy
economy. Monetary policies and fiscal policies are the popular channels used to achieve
this objective. Generally, it is accepted that a tightening monetary policy would help to
curb an overheating housing market to prevent a housing crush (Williams 2016; Torre
2016). This is supported by Xu and Tang’s (2014) study which found that interest rate is
negatively related to housing price in the UK 1971–2012. Chong and Liew (2020) studied
the New Zealand housing market from 2009 and 2019 assert that mortgage rate is negatively
related to housing prices, both in the short and long run. Studies by Upadhyaya et al.
(2017) confirm the influential power of interest rates on housing price in the U.S whilst
Zhang et al. (2018) report that, under a high money supply setting, there is a tendency
for investors to bid, offer and trade significantly higher for houses than in a low money
supply environment. Cho et al. (2021) studied the housing affordability in Australia and
postulate that the deteriorating housing affordability and the deviation of housing prices
from fundamental value in the past two decades are driven by low interest rates. As
housing supply can be inelastic in the short to medium terms, lower interest rates lead to
higher housing prices due to a higher demand.
Conversely, some studies contend that changes in monetary policy were not trans-
mitted to housing prices. Dreger and Wolters (2011) posit that the association between
expansionary monetary policy and housing prices are not significant while Cohen and
Karpaviciute (2017) posit that interest rates are not a determinant of housing prices in
Lithuania for 2005–2015. Tsai’s (2013) study on the UK market asserts that housing price
increases under an expansionary monetary policy but market failed to react to a contract-
ing monetary policy. A recent study by Yin et al. (2020) examines the impact of money
supply (M2) and interest rate on housing prices in China. They contend that money supply
affects housing prices positively in the medium run. On the other hand, monetary policy
transmission via interest rate is not effective in China’s housing market.
In addition to interest rates, many researchers argue that housing prices and stock
prices are interrelated as they are investment alternatives. An increase in stock prices
might motivates investors to rebalance their portfolio by holding more houses while
an increase in housing price will encourage investments in stocks (Bayer et al. 2013).
Upadhyaya et al. (2017) contend that there is a bilateral relationship between stock prices
and housing prices. Nonetheless, an earlier study by Fereidouni (2010) posits that there is
no significant relationship between stock market performance and housing prices.
Oil price is another macroeconomic variable that could explain house price movements.
In economies that rely on oil revenue (oil exporting countries), an increase in oil price will
inject more investment into the housing market and lead to an increase in housing prices
(Fereidouni 2010). Grossman et al. (2019) studied the effect of oil price shocks on housing
prices in Texas (a major oil production centre) and contend that an oil price shock has the
highest pass-through among oil-dependent cities. Australia is an oil importing country.
An increase in oil prices will trigger higher logistic cost and lead to increases in the price
of products and services. A high CPI induces rate hikes. Under this scenario, family
expenditure increases, savings are reduced, and this can put pressure on the housing
industry. If the growth in salary is slower than inflation, consumers will suffer from a loss
in purchasing power. This could impact housing prices negatively. However, there are
opposite forces at work as well. As oil prices are determined by the supply and demand
forces, the easing of COVID-19 led to higher mobility and, therefore, higher demand. TheJ. Risk Financial Manag. 2023, 16, 61 6 of 13
Ukraine war also caused oil supply disruption shock. All these factors instigated higher
oil price which were passed on to industry and consumers. In the context of housing
industry, a higher oil price leads to higher building material costs which will bring about
higher housing prices. Nonetheless, if wages are not keeping up, housing affordability
will deteriorate and this would affect housing prices negatively. Hence, based on the
discussions above, the impact of oil prices on housing price is mixed and inconclusive.
4. Methodology
In this study, intraday data spanning from September 2021 to September 2022 on
housing index are sourced from CoreLogic, Australia. The data covers the five cities
Sydney, Melbourne, Brisbane and Gold Coast, Adelaide and Perth. Data for mortgage rates,
crude oil WTI and ASX 200 index were collected from Yahoo Finance, Reserve Bank of
Australia and Australia Bureau of Statistics (2022). This data range was chosen as more
dynamics in borrowing rates were exhibited during this period. Prior to this study period,
mortgage rates had been unchanged for many years at an historical low (see Figure 2).
Based on the background information of the Australian housing market and the
literature review discussed earlier, the research questions of this study are:
(i) Do increases in mortgage rate as a result of the increases in cash rate implemented
by the Central Bank have significant impact on housing prices in different cities in
Australia?
(ii) Do increases in mortgage rates affect housing price in different cities differently?
(iii) Are there any associations between factors, such as oil price and stock market perfor-
mance, and housing prices?
As the time series data for housing indexes are not normally distributed after log
transformation, nonparametric statistical tests including Spearman’s correlation and Boot-
strap regression are used to analyse and provide answers for the above questions. These
statistical tests are discussed below.
4.1. Spearman’s Rank Correlation
Spearman correlation coefficient is a nonparametric rank statistic designed to measure
the strength of association between two variables. This testing statistic assesses the extent
of an arbitrary monotonic function which depicts the relationship between two variables
with no further assumption being made about the frequency distribution (Zhang 2021).
As the dataset for this study meets all the key assumptions required for the Spearman
correlation test, it was used to gauge the association between variables in this study. The
Spearman correlation coefficient can be defined as follows:
6∑ d2i
ρ = 1−
n( n2 − 1)
ρ = Spearman’s rank correlation coefficient;
di = difference between the two ranks of each observation;
n = number of observations.
Whereby, ρ equals to +1 implies a perfect correlation between ranks and −1 indicates
perfect negative correlation between ranks, while 0 means no correlation between ranks.
4.2. Bootstrap Regression
Bootstrapping regression model is another nonparametric approach used in this study
as it does not require distributional assumptions. According to Cline (2019), bootstrapping
analysis is asymptotically consistent and more accurate than using the standard intervals
obtained using sample variance and the assumption of normality. Applying this analysisJ. Risk Financial Manag. 2023, 16, 61 7 of 13
method, the daily data collected from September 2021 to September 2022 were resampled
to
k Financial Manag. 2023, 16, x FOR PEER REVIEW perform the regression models stated below with 2000 bootstrap samples.
7 of 13
HPI (Y) = α + βX1 + βX2 + βX3 + ε
where Y = Sydney
where or
Y=Melbourne,
Sydney or Brisbane andBrisbane
Melbourne, Gold Coast,
and Perth, Adelaide
Gold Coast, housing
Perth, price
Adelaide housing price
index index
X1 = MortgageX1 rate;
= Mortgage rate;
X2 = WTI Crude
X2 =Oil
WTIIndex;
Crude Oil Index;
X3 = ASX 200Xindex performance;
3 = ASX 200 index performance;
ε = Error term.
ε = Error term.
5. Analysis and Findings
5. Analysis and Findings
5.1.Statistics
5.1. Descriptive Descriptive Statistics
Line graphs Line
of thegraphs
housingof the
valuehousing
index value
for theindex for thecities
five major five under
major cities
studyunder
and thestudy and the
national aggregate are depicted in Figure 5. From the figure,
national aggregate are depicted in Figure 5. From the figure, Sydney is outperforming allSydney is outperforming all
other cities and the national aggregate. It shows a decline after
other cities and the national aggregate. It shows a decline after reaching its peak at 215 on reaching its peak at 215
on May 2022.
May 2022. Melbourne beingMelbourne
the secondbeinglargestthe second
city has thelargest
second city has the
highest secondvalue
housing highest housing
after Sydneyvalue after Sydney
and displays and displays
a similar a similar
trend to that trend to
of Sydney that of
during Sydney
the study during
period. the
Ad-study period.
elaide ranked Adelaide
third in ranked
terms ofthird in terms
housing priceofand
housing price
displays and displays
strong strong
growth with nogrowth
sign ofwith no sign
decline duringof decline
the study during
period.theBrisbane
study period.
and Gold Brisbane and Gold
Coast ranked Coast
fourth in ranked
the chartfourth
with in the chart
a steep growth until mid-August 2022. On the other hand, Perth’s housing was rather was rather
with a steep growth until mid-August 2022. On the other hand, Perth’s housing
stable andthe
stable and represents represents the mostofaffordable
most affordable of thecities
the five major five major
under cities
study.under study.
Housing Value Index
(Sept 2021–Sept 2022)
220
200
Housing Value Index
180
160
140
120
100
80
Date
HVI Sydney HVI Melbourne HVI Brisbane & Gold Coast
HVI Adelaide HVI Perth Aggregate
Figure 5. Housing Value
Figure Index for
5. Housing the Index
Value national
foraggregate, Sydney,
the national Melbourne,
aggregate, Sydney,Brisbane and Brisbane
Melbourne, Gold and Gold
Coast, Adelaide and Perth.
Coast, Adelaide and Perth.
5.2. Spearman’s Rho Correlation Analysis
Results of the correlation analysis performed using Spearman’s rho rank correlation
are as shown in Table 1 below.J. Risk Financial Manag. 2023, 16, 61 8 of 13
5.2. Spearman’s Rho Correlation Analysis
Results of the correlation analysis performed using Spearman’s rho rank correlation
are as shown in Table 1 below.
Table 1. Spearman’s rho correlation matrix.
HVI
HVI HVI Mel- Brisbane HVI HVI WTI Mortgage ASX
CPI
Sydney bourne Gold Adelaide Perth Crude Rate 200
Coast
Correlation −0.330
HVI 1.000 0.985 ** −0.015 −0.014 −0.021 0.123 * −0.701 ** 0.429 **
Coefficient **
Sydney
Sig. (2-tailed)J. Risk Financial Manag. 2023, 16, 61 9 of 13
of housing (Fereidouni 2010; Grossman et al. 2019). Second, it increases logistic cost which
escalates building material and labour costs. Both forces are contradictory as the first one
leads to more supply than demand, hence, a decrease in housing price whilst the latter push
up housing prices due to higher construction cost. Spearman’s rho correlation analysis
indicates a mild significant positive impact of WTI crude on Sydney’s housing price. Other
cities however are displaying insignificant relationship with WTI crude movements.
The literature generally indicates a negative relationship between the performance of
the share market and interest rates (Bayer et al. 2013). A higher interest rate discourages
investment and promotes savings. In addition, as investment in housing represents a
substitution of share investment, a negative relationship is expected. Nonetheless, only
Brisbane and Gold Coast, Perth and Adelaide are displaying the expected sign in the
Spearman’s rho test analysis but insignificant. On the contrary, for the more affluent
cities, such as Sydney and Melbourne, stock market performance is positively related to
housing price movement. Possible explanations for this phenomenon are (i) investors
are more affluent in these two cities and, therefore, are financially capable of forming a
more diversified investment portfolio, (ii) investors from these two larger cities have better
financial literacy and hence, value portfolio diversification more and do not view property
and stock investment as substitutes. Bootstrap regression analysis was conducted on the
variables under study and the findings are discussed in the next section.
5.3. Bootstrap Regression Analysis and Results
As can been seen from Table 2, regressions are performed on five models to gauge the
relations of housing performance across five cities with independent variables including
mortgage rates, WTI crude index and ASX200 index. Results of R2 obtained from the
analyses indicate that independent variables included in the model are able to explain vari-
ations of the housing performance in Sydney by 84.4 percent and Melbourne 87.5 percent,
whereas the other three cities are in the range of 0.1 to 5 percent. The findings in Panel 1
(Sydney) and 2 (Melbourne) reveal that there is a significant negative relationship between
mortgage rate and housing performance of Sydney and Melbourne. These findings are
consistent with Chong (2020), Xu and Tang (2014) and Chong and Liew 2020). However,
housing prices in smaller cities including Perth, Adelaide and Brisbane and Gold Coast are
unaffected by interest rate during the study period. Indeed, larger cities, such as Sydney
and Melbourne, display different characteristics comparing to other cities in Australia
(Cho et al. 2021).
These results have policy implications indicating that tightening the monetary policy
through rising interest rates to ease inflationary pressure will produce different results
for different city’s housing prices. For Sydney and Melbourne, both with extremely high-
medium housing price at $1,601,467 and $1,101,612, respectively, as of January 2022, house
owners are required to take on a much higher loan-to-income ratio comparing to those
in Brisbane, Adelaide and Perth with medium housing prices of $792,065, $731,547 and
$612,348, respectively (Lutton 2022). Under a low wage growth and inflationary environ-
ment, borrowing capability and repayment capability deteriorates, therefore, a housing
correction is inevitable for cities with high housing prices. Under this scenario, home-
owners would also sell their houses in Melbourne and Sydney and emigrate to more
affordable cities to ease the burden of increasing mortgage rate. The availability of flexible
work arrangements, such as working from home after the pandemic, have also provided
opportunity for homeowners to move to cities with better housing affordability.J. Risk Financial Manag. 2023, 16, 61 10 of 13
Table 2. Results of Bootstrap regression.
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 10 of 13
Variables B p-Value
Panel 1: HVI Sydney
Mortgage rate −7.276 0.001
WTI crude −0.030 0.539
WTI crude 0.102 0.001
ASX200
ASX200 index
index 0.052 −0.001 0.0500.803
Panel 2: HVI Melbourne
Theserate
Mortgage results have policy implications indicating that tightening
−3.827 0.001the monetary policy
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 10 of 13
through
WTI cruderising interest rates to ease inflationary
0.046 pressure will produce
0.001different results for
different city’s housing prices. For Sydney
ASX200 index and Melbourne, both with
0.002 0.073extremely high-me-
dium
Panel
WTI
housing price at
3: HVI Brisbane
crude and$1,601,467
Gold Coast and−0.030
$1,101,612, respectively,0.539
as of January 2022, house
owners
Mortgage
ASX200 are
index required
rate to take on a much higher
−0.023
−0.001 loan-to-income0.803 ratio comparing to those in
0.770
crude −
Brisbane, Adelaide and Perth with medium housing prices of $792,065, $731,547 and
WTI 0.339 0.601
ASX200
Theseindex
$612,348, results have policy
respectively implications
(Lutton 2022). Under −0.001
indicating that
a lowtightening
wagethe monetary
growth 0.726
and policy
inflationary environ-
through rising interest rates to ease inflationary pressure will produce different results for
ment,
Panel 4:borrowing
HVI Perthcapability and repayment capability deteriorates, therefore, a housing
different city’s housing prices. For Sydney and Melbourne, both with extremely high-me-
Mortgage rateinevitable for cities with high
correction 0.009 0.800scenario, homeown-
dium housingisprice housing as
at $1,601,467 and $1,101,612, respectively, prices. Under
of January this
2022, house
WTI
ers
owners
crude
would also sell
are required to their
take onhouses
a much in Melbourne
higher
− 0.001
and Sydney
loan-to-income
0.686
and emigrate
ratio comparing to those into more affordable
ASX200 Adelaide
Brisbane, index 0.001prices of $792,065, $731,547 0.721
cities to ease theand Perth of
burden with medium housing
increasing mortgage rate. The availabilityand of flexible work ar-
$612,348,
Panel 5:respectively
rangements, such (Lutton
HVI Adelaide 2022). Under a low wage growth and inflationary environ-
as working from home after the pandemic, have also provided oppor-
ment, borrowingrate capability and repayment capability deteriorates, therefore, a housing
Mortgage
tunity foris homeowners
correction towith
inevitable for cities movehigh cities −
tohousing 0.220
with
prices.better housing
Under this
0.863
affordability.
scenario, homeown-
WTI crude −0.030 0.539
ers would also sell their houses in Melbourne and Sydney and emigrate to more affordable
ASX200 index −0.001 0.803
5.4.
citiesTrend
to easeAnalysis
the burden of increasing mortgage rate. The availability of flexible work ar-
rangements, such as working from home after the pandemic, have also provided oppor-
tunity After completing the regression tests, using the data collected, trend
for homeowners analyses were
5.4. Trend Analysis to move to cities with better housing affordability.
performed on the five major cities. A one year projection was conducted, and the results
After
5.4. Trend
are as completing
Analysis
shown in Figuresthe regression
6–10. From thetests, using
figures the data
below, we can collected,
see thattrend analyses
Sydney, were
Melbourne,
performed on the fiveregression
After completing major cities. usingA one theyear
data projection was conducted, and the results
Brisbane and Goldthe Coast are ontests,the downward collected,
trend, trend
while analyses
Perth andwere
Adelaide are trend-
are as shown
performed infive
on the Figures 6–10.AFrom
major cities. theprojection
one year figures was
below, we canand
conducted, seethethat Sydney, Melbourne,
results
ing upwards before slowing down in the second half of next year
are as shown
Brisbane andin Figures 6–10. From
Gold Coast are onthethe
figures below, wetrend,
downward can see while
that Sydney,
PerthMelbourne,
and Adelaide are trending
Brisbane and Gold Coast are on the downward trend, while Perth and Adelaide are trend-
upwards before slowing down in the second half of next year
ing upwards before slowing down in the second half of next year
Sydney
(Trend analysis with one year projection)
Sydney
(Trend analysis with one year projection)
300
300
200
200
100
100 y = -0.0004x2 + 35.637x - 794600
y = -0.0004x2 + 35.637x - 794600
R² = 0.9866
00 R² = 0.9866
Figure 6. Trend analysis and one year prediction on housing prices for Sydney.
Figure
Figure 6.6. Trend
Trendanalysis
analysisand
andoneoneyear
yearprediction
predictionon onhousing
housingprices
pricesfor
forSydney.
Sydney.
Melbourne
(Trend analysis with one year projection
Melbourne
200 (Trend analysis with one year projection
150
200
100
150
50
y = -0.0002x2 + 17.577x - 391796
0
100
R² = 0.9755
50
y = -0.0002x2 + 17.577x - 391796
0 R² = 0.9755
Figure 7. Trend analysis and one year prediction on housing prices for Melbourne.
Figure 7.
Figure 7. Trend
Trendanalysis
analysisand
andone
oneyear
yearprediction
predictionon
onhousing
housingprices
pricesfor
forMelbourne.
Melbourne.J.J.Risk
RiskFinancial
FinancialManag. 2023,16,
Manag.2023, 16,61
x FOR PEER REVIEW 11 of 13
11 13
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 11 of 13
J. Risk Financial Manag. 2023, 16, x FOR PEER REVIEW 11 of 13
Brisbane
Brisbane and
and Gold
Gold Coast
Coast
Brisbane
(Trend andone
analysis with Gold
yearCoast
projection)
(Trend analysis with one year projection)
(Trend analysis with one year projection)
200
200
200
150
150
150
100
100
100
50
50 y = -0.0003x22 + 28.544x - 638552
50 y = -0.0003x + 28.544x - 638552
R²2 =+ 0.9791
y = -0.0003x 28.544x - 638552
0
0 R² = 0.9791
R² = 0.9791
0
Figure
Figure 8. Trend
8. Trend
Figure 8. analysis
Trend analysis and
analysis and one
and one year
one year prediction
year prediction on
predictionon housing
onhousing prices
housingprices for
pricesfor Brisbane
forBrisbane and
Brisbaneand Gold
andGold Coast.
GoldCoast.
Coast.
Figure 8. Trend analysis and one year prediction on housing prices for Brisbane and Gold Coast.
Perth
Perth
Perth
(Trend analysis with one year projection)
(Trend analysis with one year projection)
(Trend analysis with one year projection)
125
125
125
120
120
120
115
115
115
110
110
110
105 y = 0.000009-06x22 - 0.7904x + 17338
105 y = 0.000009-06x - 0.7904x + 17338
105
100 R² = 0.9468
y = 0.000009-06x 2 - 0.7904x + 17338
R² = 0.9468
100 R² = 0.9468
100
95
95
95
Figure 9.
9. Trend
Figure 9. analysis
analysis and
Trend analysis one
one year
and one prediction
prediction on
year prediction housing
housing prices
onhousing for
for Perth.
pricesfor Perth.
Figure 9. Trend
Figure Trend analysis and
and one year
year predictionon
on housingprices
prices forPerth.
Perth.
Adelaide
Adelaide
(Trend analysisAdelaide
with one year projection)
(Trend analysis with one year projection)
(Trend analysis with one year projection)
200
200
200
150
150
150
y = -0.0001x22 + 12.139x - 272779
100
100 y = -0.0001x + 12.139x - 272779
R²2 =+ 0.9942
y = -0.0001x 12.139x - 272779
100 R² = 0.9942
R² = 0.9942
50
50
50
0
0
0
Figure
Figure 10.
10. Trend
Trend analysis
analysis and
and one
one year
year prediction
prediction on
on housing
housing prices
prices for
for Adelaide.
Adelaide.
Figure 10.
Figure 10. Trend
Trend analysis
analysis and
and one
one year
year prediction
predictionon
onhousing
housingprices
pricesfor
forAdelaide.
Adelaide.
6.
6. Conclusions
Conclusions
6.
6. Conclusions
Conclusions
The
The aim ofofthis
thispaper
paperis to examine whether consecutive monthly mortgage rate hikes
in The aim
The
Australia
of
aimfrom
of this
this paper
paper
May
is
until
to
is is examine
to to examine
examine
September
whether
whether
whether
2022 have
consecutive
consecutive
consecutive
affected
monthly
monthly
monthly
housing
mortgage
mortgage
mortgage
prices in its
rate hikes
rate
ratemajor
five hikes
in Australia
hikes in
in Australia from
Australia
from MayMay
from until
May September
until
untilindicated 2022
September
September 2022 have
2022 affected
have
have affected housing
affected
housing prices
housing in its
prices
pricesinverse five
in
in its five major
its five
major
cities.
cities. Correlation
Correlation analyses
analyses indicated that
that mortgage rate has a significant relation-
major
cities.
ship
cities.
with
Correlation
Correlation
housing analyses analyses
performanceindicated
for that mortgage
indicated
citiesmortgage
with
rate
that mortgage has
rate medium
higher
aa significant
hasrate significant
housing
inverse
has a significant
inverse
prices,
relation-
inverse
relation-
such as
ship
ship with
relationship housing
with
with housing performance
housing
performance for
performance
for cities
for with
cities
cities with higher
with medium
higher
higher medium medium housing
housing
housing prices,
prices,
prices, such
suchsuchas
as
Melbourne
as Melbourne
Melbourne and
andandSydney. Positive
Sydney.Positive
Sydney. significant
Positivesignificant relationships
significantrelationships
relationshipsare are also
are also reported between
also reported between
between WTI
WTI
WTI
Melbourne
crude, and
inflation Sydney.
rate Positive
with mortgage significant
mortgage raterate and relationships
Sydney housing
housingare also reported
prices. Bootstrapbetween
regressionWTI
crude,
crude, inflation
inflation rate
rate with and Sydney prices. Bootstrap regression
crude,
results inflation
using rate with
independentwith mortgage
mortgage
variables
rate
rate and
and Sydney
including Sydney
mortgage
housing
housing
rate,
prices.
prices.
WTI
Bootstrap
Bootstrap
crude and
regression
regression
ASX200 per-
results
results using independentvariables
independent variablesincluding
includingmortgage
mortgage rate,
rate, WTI WTI
crudecrude
and and
ASX200ASX200 per-
results
formance using
wereindependent
also variables
performed. including
Findings mortgage
indicate that rate,
the WTI
proposed crude and
models ASX200
are ableper-
to
performance
formance werewere also
also performed.
performed. Findings
Findings indicate
indicate that
that the
the proposed
proposed models
models are able to
to
formance
explain were
variations alsoof performed.
the housing Findings
performance indicate
by that
84.4 the
percent proposed
and 87.5 models
percent are
for able
Sydney to
explain
explain variations
variations of
of the
the housing
housing performance
performance by
by 84.4
84.4 percent
percent and
and 87.5
87.5 percent
percent for
for Sydney
Sydney
explain
and variations
Melbourne, of the housing
respectively but performance
are by
insignificant 84.4 percent and 87.5 percent for Sydney
and
and Melbourne,
Melbourne, respectively
respectively but
butare
areinsignificant
insignificant forfor
thethe
for smaller
smaller
the cities.
cities. AsAs 49.86
49.86 percent
percent of
and
of Melbourne,
the total number respectively
of dwellings but in
areAustralia
insignificant
are in the smaller
forSydney smaller
and
cities.
cities. As
As 49.86
Melbourne, 49.86
this
percent
percent
finding
the
of total
the number
total number of dwellings
of dwellings in Australia
in are in
Australia Sydney
are in and and
Sydney Melbourne, this finding
Melbourne, this will
finding
of the total number of dwellings in Australia are in Sydney and Melbourne, this findingJ. Risk Financial Manag. 2023, 16, 61 12 of 13
contribute heavily to the overall national housing performance following the mortgage rate
hikes. Based on the analysis discussed above, this study concludes that: (1) An increase
in mortgage rate has a significant negative impact on the housing prices in Sydney and
Melbourne. Other cities’ housing prices are negatively associated with interest rates, but
were insignificant during the study period, (2) The increases in mortgage rate have affected
housing prices in different cities differently and (3) The influence of oil price and stock price
on housing prices are found to be insignificant for all cities except Sydney and Melbourne.
As the evidence reported bipolar results between the large cites comparing to their smaller
counterparts and low explanatory power for models of smaller cities, more factors need to
be taken into consideration when analysing housing prices of the smaller cities. Among
others, the availability of flexible work arrangement which motivates homeowners with
higher debt-to-income ratio to liquidate properties situated in cities with higher housing
prices to avoid rising mortgage payment and emigrate to a more affordable city are worth
looking into. In addition, borrowing power, repayment capability and the level of financial
literacy of existing and potential homeowners between different cities could also offer some
explanations to housing price performance in Australia.
Funding: This research received no external funding.
Data Availability Statement: Not applicable.
Conflicts of Interest: The author declares no conflict of interest.
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