BANKWEST CURTIN ECONOMICS CENTRE THE NEXUS BETWEEN...

Kelly Burns, Steven Rowley, George B. Tawadros and Ankita Mishra

BCEC Research Report No. 10/18

BANKWEST CURTIN ECONOMICS CENTRE

March 2018

THE NEXUS BETWEEN EQUITY MARKETS & HOUSING PRICES IN AUSTRALIA

About the Centre

The Bankwest Curtin Economics Centre is an independent economic and social research organisation located within the Curtin Business School at Curtin University. The Centre was established in 2012 through the generous support of Bankwest, a division of the Commonwealth Bank of Australia. The Centre’s core mission is to deliver high quality, accessible research that enhances our understanding of key economic and social issues that contribute to the wellbeing of West Australian families, businesses and communities.

The Bankwest Curtin Economics Centre is the first research organisation of its kind in WA, and draws great strength and credibility from its partnership with Bankwest, Curtin University and the Western Australian government. The Centre brings a unique philosophy to research on the major economic issues facing the State.

By bringing together experts from the research, policy and business communities at all stages of the process - from framing and conceptualising research questions, through the conduct of research, to the communication and implementation of research findings - we ensure that our research is relevant, fit for purpose, and makes a genuine difference to the lives of Australians, both in WA and nationally.

The Centre is able to capitalise on Curtin University’s reputation for excellence in economic modelling, forecasting, public policy research, trade and industrial economics and spatial sciences. Centre researchers have specific expertise in economic forecasting, quantitative modelling, micro-data analysis and economic and social policy evaluation. The Centre also derives great value from its close association with experts from the corporate, business, public and not-for-profit sectors.

Contents


i

List of figuresList of tablesExecutive summaryKey findings

iiiiiivv

IntroductionThe dynamic relationship between house and equity prices

vivii

Patterns of house, equity and energy prices 2000 to 2014Equity and energy pricesNational house pricesNational house rentsRegional Western Australia

12369

The nexus between national house, equity and energy pricesData and methodologyKey findings for equity pricesKey findings for energy prices

13141619

The nexus between capital city house, equity and energy pricesKey findings for house prices and equity pricesKey findings for house prices and energy pricesKey findings for house rents and equity pricesKey findings for house rents and energy prices

2122263033

The Nexus between WA regional house, equity and energy pricesKey findings for house prices and equity pricesKey findings for house prices and energy pricesThe magnitude of spill over effects between key regions of WA

35363941

Discussion and conclusion 45

AppendixData sourcesMethodology

495051

References 53

List of figures

ii

Figure 1 Equity and energy prices, 2000 to 2014 2

Figure 2 Median house prices, Australia and capital cities, 2000 to 2014 4

Figure 3 Median house rents, Australia and capital cities, 2000 to 2014 6

Figure 4 Proportion of persons employed in “Mining” in selected capital cities, 2001 to 2014

8

Figure 5 Key regions of WA 9

Figure 6 Median house prices, WA regional areas, 2000 to 2014 10

Figure 7 Response of house prices and rents to shocks to equity prices (Australia) 18

Figure 8 Response of house prices and rents to shocks to energy prices (Australia) 20

Figure 9 Response of house prices to shocks to equity prices (Brisbane, Adelaide, Darwin)

24

Figure 10 Response of house prices to shocks to equity prices (Melbourne, Sydney, Canberra, Perth)

25

Figure 11 Response of house prices to shocks to energy prices (Melbourne, Brisbane, Hobart)

28

Figure 12 Response of house prices to shocks to energy prices (Sydney, Canberra, Darwin, Adelaide)

29

Figure 13 Response of house prices to shocks to energy prices (Perth) 29

Figure 14 Response of house rents to shocks to equity prices (Brisbane, Perth, Darwin)

31

Figure 15 Response of house rents to shocks to equity prices (Sydney) 32

Figure 16 Response of house rents to shocks to equity prices (Melbourne, Adelaide, Canberra, Hobart)

32

Figure 17 Response of house rents to shocks to energy prices (Adelaide, Perth, Hobart)

34

Figure 18 Response of house rents to shocks to energy prices (Sydney, Darwin) 34

Figure 19 Response of house rents to shocks to energy prices (Adelaide, Perth, Hobart) 34

Figure 20 Response of house prices to shocks to equity prices (WA regional) 38

Figure 21 Response of house prices to shocks to equity prices (South West) 38

Figure 22 Response of house prices to shocks to energy prices (WA regional) 40

Figure 23 Response of house prices to shocks to energy prices (Kimberley, South West) 40

Figure 24 Positive spill over effects from shocks to Pilbara house prices 41

Figure 25 Negative spill over effects from shocks to Pilbara house prices 41

Figure 26 Positive spill over effects from shocks to Kimberley house prices 42

Figure 27 Negative spill over effects from shocks to Kimberley house prices 42

Figure 28 Spill over effects from shocks to Perth house prices 43

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List of tables

Table 1 Comparison of advantages and disadvantages of alternative investment options

vii

Table 2 Summary statistics, equity and energy prices, 2000 to 2014 2

Table 3 Summary statistics, house prices, Australia and capital cities, 2000 to 2014

3

Table 4 Correlation between house, equity and energy prices, 2000 to 2014 5

Table 5 Correlation between house, equity and energy prices, 2010 to 2014 5

Table 6 Summary statistics, house rents, Australia and capital cities, 2000 to 2014

6

Table 7 Correlation between house rents, and equity and energy prices, 2000 to 2014

7


7

Table 9 Summary statistics, house prices, WA regional areas, 2000 to 2014 11

Table 10 Correlation between house, equity and energy prices - WA regional areas, 2000 to 2014

11

Table 11 Correlation between house, equity and energy prices - WA regional areas, 2010 to 2014

12

Table 12 Estimation results - house prices, house rents and equity prices - Australia

17

Table 13 Estimation results - house prices, house rents and energy prices - Australia

19

Table 14 Estimation results - house and equity prices - capital cities 23

Table 15 Estimation results - house and energy prices - capital cities 27

Table 16 Estimation results - house rents and equity prices - capital cities 30

Table 17 Estimation results - house rents and energy prices - capital cities 33

Table 18 Estimation results - house and equity prices - WA regional 37

Table 19 Estimation results - house and energy prices - WA regional 39

Executive summary

iv

This report examines the impact of equity and energy prices on house prices across Australian capital cities and regional Western Australia (WA), over the period 2000 to 2014. Energy prices, for the purposes of this study, refers to a broad range of consumable fuels (including oil, gas and coal).

In recent times, there has been significant debate around, and research into, the drivers of Australian house prices. The role played by equity and energy prices, if any, has been raised but not explored in depth. The findings presented in this report shed light on the interrelationship between energy, equity and house prices to help inform policy debates.

This research employs a dynamic model to investigate the short and long run impact of equity and energy prices, as well as shocks to these prices and spill over effects on house prices and rents. This is the first study to consider this dynamic relationship for key regional areas of WA, many of which are reliant on resource industry employment and investment, and therefore makes a significant contribution to the existing body of knowledge.

As policy makers grapple with housing affordability issues across Australia, this report provides valuable insights into the impact of movements in equity and energy prices on house prices and rents. By conducting this research at the capital city and WA key regional area level, the report investigates how the impact of energy and equity prices varies according to the region’s level of resource intensity. This is important from a policy perspective, particularly in constrained regional markets, permitting predictions of potential demand shocks which require a supply response.

Key findings

v

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Patterns of housing, equity and energy prices 2000 to 2014• Trends in house, energy and equity

prices varied dramatically pre and post the Global Financial Crisis.

• Energy prices grew at more than twice the rate of equity prices in the lead up to the Global Financial Crisis. Since then, growth in equity and energy prices has slowed markedly.

• House price growth slowed markedly across all Australian capital cities over the period 2010-2014.

• House price growth has varied significantly across capital cities in the last 5 years.

• House prices are correlated with energy and equity prices, showing a strong and positive association from 2000 to 2014.

• House prices were more strongly correlated to energy prices from 2000 to 2007, and more closely related to equity prices from 2010 to 2014.

• House prices in Western Australia show the strongest correlation with energy prices, consistent with the state being the mining and resource hub of Australia.

The nexus between national housing, equity and energy prices• House prices in Australia are related

to macroeconomic factors and equity prices over the long run.

• At a national level, shocks to equity prices can drive house prices down, and house rents up, over the longer term.

• Shocks to energy prices can drive house prices down over the longer term, but have little impact on house rents.

The nexus between capital city housing, equity and energy prices• Equity and energy prices, along

with key macroeconomic indicators (income, inflation, interest rates) are useful leading indicators of house prices across many capital cities.

• The direction and magnitude of any impact that energy price shocks have on house prices and rents differs immensely depending on a region’s level of resource intensity.

• Energy prices can have positive income effects on house prices in resource intensive regions, and negative income effects on house prices in non-resource intensive regions.

• Evidence suggests that equity prices have short-run effects on house rents for Sydney and Perth only; whereas energy prices have short-run effects on house rents in Perth and Hobart.

The nexus between WA regional housing, equity and energy prices• Energy prices are a leading indicator of

changes in house prices over the short-run in most regions of WA.

• Energy price shocks can have positive and immediate impacts on house prices across most WA regional areas.

• There is no evidence that equity prices impact house prices at the WA regional level in the short or long run.

• Shocks to house prices in one region of WA can spill over into other regional housing markets, with positive spill over effects occurring in those regions that are contiguous.

vi

Introduction

Housing affordability is an increasingly important socio-economic issue that is attracting the attention of policymakers at all levels of government across Australia (see, for example, Cassells et al., 2014, Duncan et al 2016). Australia is widely considered to be experiencing a housing affordability crisis and Australian housing is considered by some, although the methodology is questionable, to be extremely unaffordable compared to other cities internationally (Angel, 2015). Declining housing affordability in Australia has led to significant debate about the underlying drivers of this trend. Although housing affordability is a broad term encompassing various factors (Leishman and Rowley, 2012), house prices and rents are traditionally the major focus when discussing affordability in Australia and are thus used as a proxy for affordability in this report.

There are several well established drivers of housing markets: prices, population, income, inflation, consumer confidence, employment and interest rates. These factors directly impact the supply and demand of housing in Australia. Several other factors have been observed as possible drivers of Australia’s housing market, including energy prices and equity prices. It has been widely suggested that the recent boom in commodity prices may have played a role in driving house prices and rents in Australia.

There are a variety of transmission mechanisms through which equity prices can influence house prices. For instance, there is a direct transmission mechanism between house prices and equity prices influencing changes in consumption and investment decisions through income and wealth effects. The income effect occurs because rising equity prices increase investment income and provides opportunities to invest in other assets, such as housing. The wealth effect occurs because higher equity prices increase the value of an investment portfolio, and portfolio rebalancing often leads to greater investment in housing. House prices can also be affected indirectly by equity prices through changes in interest rates as a result of monetary policy intervention by the Reserve Bank of Australia to stimulate or restrain economic growth. Changes in the relative price of equities and housing can also give rise to a substitution effect towards the asset class offering higher returns, and thereby influence the level of housing demand.1

On the consumption side, energy prices constitute expenditure for households and therefore any change in these prices will directly impact a current, or future, household’s level of disposable income. Changes in disposable income impact housing investment decisions, for both owner occupiers and investors. Accordingly, a rise in energy prices is expected to have a negative impact on housing demand and therefore prices (ceteris paribus). On the investment side, households in resource intensive regions are likely to experience an increase in employment and income as a result of a booming local economy tied to the energy market. In this case, rising energy prices can in fact lead to a rise in income and thus have a positive impact on housing demand.

Policy makers have been grappling with how to address rising house prices and rents across Australia, but lack an informed understanding of the complex interrelationship between house prices, equity prices, energy prices and resource intensity. Housing policy tends to be capital city focused ignoring the different drivers of rural and regional markets (Beer et al., 2011) many of which are particularly reliant on energy related industries. We therefore model the relationship between house prices against relevant macroeconomic variables as well as the equity price index and energy price index, for national, capital city and, importantly, WA regional areas.

1 In this study, we employ the energy price index to directly capture the impact of the strength of the energy sector on investment, employment and housing demand. This index is also used as a proxy for changes in household energy consumption costs by assuming there is a link between movements in these measures. Please refer to the Data and Methodology section for further information.

The dynamic relationship between house and equity prices

Housing and equities are considered investment alternatives in financial economics. Indeed, both houses and equities tend to be important investments in people’s portfolios. Many scholars and practitioners have argued that investment in housing leads to diversification benefits for investors because of its low correlation with several commonly used equity price indices. Table 1 summarises some of the advantages and disadvantages that can motivate a person to invest in housing and/or equities.

Table 1 Comparison of advantages and disadvantages of alternative investment options

Capital gains

Returns Tax benefits

Capital gains

Returns Tax benefits

Investment house

Yes Capital gain, Rental return

Negative gearing; 50% capital gains tax discount

High Management fees; maintenance costs

Large

Owner-occupied house

Yes Capital gain No capital gains tax

Low Maintenance costs; running costs

Large

Equities Yes Capital gain, dividends

Negative gearing

Medium Management / broker fees

Small

Piazzesi et al. (2007) and Kapopoulos and Siokis (2005) put forward several explanations to describe the dynamic relationship between house prices and equity prices. The first explanation is that of a wealth effect, which states that as equity prices rise, investors with unanticipated increases in wealth will increase their demand for housing. As such, the equity market will lead the housing market. This operates through two mechanisms because housing is both a consumption good and an investment asset. The first mechanism suggests that unanticipated increases in equity prices will lead to an increase in aggregate consumption. The second operates through portfolio adjustment. That is, as equity prices increase, households will rebalance their portfolios by selling equities and purchasing other assets, such as a house (Markowitz, 1952).

Second, equity prices may have an effect on house prices through the income channel. For instance, Green (2002) highlights the fact that equity prices reflect the profitability of firms and profit-based remuneration of employees, such as wages and bonuses. In this instance, an increase in equity prices will lead to an increase in the demand for housing as both a consumption and an investment good, which will lead to higher house prices.

The third explanation given to describe the dynamic relationship between house prices and equity prices is what is known as the credit-price effect. This effect suggests that because real estate serves as collateral, credit constrained firms are able to borrow more freely for investments. The credit-price effect postulates that the housing market will lead the equity market because firms holding commercial and residential properties will have large unrealised gains that will lead investors to bid up the equity value of the firm. However, because firms demand more commercial and

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viii

residential real estate to undertake more expanded investment projects, the price of commercial and residential properties will also increase, suggesting an increase in both property and equity prices, with persistent feedback effects.

A fourth explanation given is that of composition risk, which relates changes in consumption expenditure to asset prices. The decisions made to consume and save depend not only on the size of future consumption, but also on the composition of that consumption and that between housing and other consumption. Since investors expect higher future consumption during expansionary periods, they sell equities during a recession in the current time period to increase current consumption, which drives current equity prices down. As such, this inter-temporal substitution drives the price of equities down during recessionary periods. Piazzesi et al. (2007) develop a model where the concern by investors about composition risk suggests that the size of this inter-temporal effect will depend on the share of housing in consumption. Their model suggests that recessionary periods will be more severe when the equity of housing consumption is low.

Finally, the dynamic adjustment of housing prices to shocks in economic fundamentals is likely to create a series of lead and lag relationships between house price and equity price movements. Since house prices are slower than equity prices to adjust to shocks, the lead and lag relations identified by Granger causality can be attributed to the slow adjustment of the house market. Clayton (1996) and Himmelberg et al. (2005) show that while economic fundamentals are important factors that cause movements in house prices, house prices themselves tend to react slowly to shocks in the fundamentals.

Patternsof house, equity and energy prices 2000 to 2014

Equity and energy prices

Over the last 15 years, house2, equity and energy prices have exhibited some interesting trends and patterns. House prices have exhibited a range of similarities and differences across Australian capital cities. In this section, we shed light on recent trends in these markets, and how these trends varied prior to the Global Finance Crisis (GFC hereafter) and more recently from 2010 to 2014.

As Figure 1 shows, up until 2004 equity and energy prices remained relatively stable with a slight steady upward trend. At this point, prices began to diverge from one another and growth in energy prices largely outstripped that of equity prices. Despite this divergence, they continue to exhibit some similarities. Overall, energy prices exhibit greater volatility than equity prices.

Over the period 2000 to 2014, equity prices grew by 3.9 per cent per annum on average, whereas energy prices grew at well over twice the rate (10.3% per annum, as seen in Table 2). However, the largest growth in these prices occurred prior to the GFC where equity and energy prices grew by 8.8 and 21.5 per cent per annum on average, respectively. Both energy and equity prices exhibited greater volatility, and a sharp decline, following the onset of the 2007 GFC. This is reflected in the average annual growth rates over the past five years, which have markedly slowed to 1.4 and -3.8 per cent for equity and energy prices, respectively.

Table 2 Summary statistics, equity and energy prices, 2000 to 2014

Average annual growth rate %

2000-2014 2000-2007 2010-2014

Equity prices 3.9 8.8 1.4

Energy prices 10.3 21.5 -3.8

Source: Standard & Poors; Author calculations.

Figure 1 Equity and energy prices, 2000 to 2014

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Equity Energy

2000Q2 2002Q2 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2

Source: Standard & Poors.

2

2 For the purposes of this report ‘house’ refers to separate houses with the remaining dwelling types classified as other residential. Separate houses dominate dwelling types in Western Australia (80 per cent). For further details refer to page 13.

National house prices

3

3


House prices grew on average by 7.2 per cent per annum across Australia from 2000 to 2014, as seen in Table 3. The largest growth in house prices occurred in Perth (8.7% per annum), Adelaide (8.2% per annum) and Brisbane (8.0% per annum). Median house prices more than tripled in these capital cities over the 15 year period. Darwin and Perth also exhibited the highest volatility in house prices over this period. Sydney maintained the highest median house price ($562,456), followed by Melbourne ($442,386). These are the only capital cities to exceed the national average median house price of $437,239.

Overall, house prices in most capital cities grew at a similar rate from 2000 to 2014 (ranging from 6.8 per cent in Melbourne to 8.7 per cent in Perth per annum, on average). However, there were marked differences in the lead up to the GFC (2000 to 2007) where house price growth ranged from 6.7 per cent in Melbourne to 16.8 per cent in Perth. Growth in house prices in Perth eclipsed all other capital cities from 2000 to 2014, especially during 2000 to 2007, although the trend has been downward since 2014.

As Figure 2 shows, real house prices exhibited some convergence in late 2008 and early 2009, at the same time the GFC was coming to an end. Significant growth occurred prior to the GFC (2000-2007), and growth rates have declined across all capital cities since this time (Table 3). From 2010 to 2014, the average annual rate of growth of median house prices fell in every capital city and across Australia (except for Sydney). Growth in house prices across capital cities has shown a degree of convergence in the last 5 years, now ranging from -1.1 in Hobart to 7.7 in Sydney.

Table 3 Summary statistics, house prices, Australia and capital cities, 2000 to 2014


Average 2000-2014 2000-2007 2010-2014

Australia $437,239 7.2 9.1 4.5

Sydney $562,456 6.9 7.2 7.7

Melbourne $442,386 6.8 6.7 5.0

Brisbane $347,018 8.0 12.3 1.3

Adelaide $306,811 8.1 12.0 1.0

Perth $371,532 8.7 16.8 1.6

Canberra $399,047 7.6 12.1 2.2

Hobart $281,161 7.1 12.6 -1.1

Darwin $395,092 7.9 10.3 1.4

Source: REIA, Author calculations.

4

Figure 2 Median house prices, Australia and capital cities, 2000 to 2014

1,0000,000

900,000

800,000

700,000

600,000

500,000

400,000

300,000

200,000

100,000

0

$

2000Q2 2002Q2 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2

Australia Sydney Melbourne Brisbane Adelaide Perth Canberra Hobart Darwin

Source: REIA.

Correlation is a measure of the strength and direction of the association between two variables. The strength of the association is indicated by the value of the correlation coefficient (ranging between 0 and 1) and the sign of the correlation coefficient indicates whether the two variables move in the same (positive) or opposite (negative) direction. Correlation does not necessarily imply causality. Table 4 shows the correlation between house prices, energy prices and equity prices (both adjusted for inflation) over the period 2000 to 2014. As can be seen in Table 4, energy prices have a positive correlation with house prices across all capital cities. The strength of this relationship is highest in Perth and lowest in Sydney, consistent with the city’s level of resource intensity and proximity to resource intensive regions.

Interestingly, house prices exhibit a stronger association with energy rather than equity prices across all capital cities (excluding Sydney). Despite this, house prices across most capital cities (except for Melbourne and Sydney) similarly exhibit a positive correlation with equity prices. In the case of Sydney, and to a lesser extent Melbourne, higher equity prices are associated with lower house prices (and vice versa). Traditionally, it has been assumed housing has a weak relationship with equity prices hence the role of both in a balanced investment portfolio.

However, the story looks vastly difference when we consider the more recent period 2010 to 2014 (Table 5). Over this period, house prices were more closely tied to equity prices, as opposed to energy prices, across all capital cities. While equity prices continue to exhibit a positive correlation to house prices across Australian capital cities, the correlation to energy prices has changed markedly. We now find a much weaker - but still positive - association between energy prices and house prices in Adelaide, Hobart, Canberra, Brisbane and Melbourne. In contrast, this period is characterised by a negative and relatively weaker correlation between energy and house prices at the national level as well as in Perth, Darwin and Sydney. Overall, these results show that there is a relationship between house, energy and equity prices, and that this relationship has undergone some interesting changes in recent times and across different regions of Australia.

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Table 4 Correlation between house, equity and energy prices, 2000 to 2014

Energy Equities

Australia 0.59 0.12

Perth 0.91 0.42

Adelaide 0.84 0.18

Hobart 0.83 0.28

Brisbane 0.82 0.23

Darwin 0.80 0.11

Canberra 0.76 0.19

Melbourne 0.50 -0.01

Sydney 0.01 -0.06

Source: Author calculations.

Table 5 Correlation between house, equity and energy prices, 2010 to 2014

Energy Equities

Australia -0.03 0.86

Adelaide 0.67 0.70

Hobart 0.64 0.32

Canberra 0.49 0.39

Brisbane 0.44 0.76

Melbourne 0.05 0.80

Perth -0.03 0.86

Darwin -0.31 0.62

Sydney -0.31 0.71


National house rents

6

House rents grew by 5.1 per cent per annum on average across Australia from 2000 to 2014. House rents were highest in Darwin, followed by Canberra and Sydney. Perth and Darwin exhibited the strongest growth in house rents over this period, increasing by 7.1 and 6.7 per cent per annum on average, respectively. Growth in rents was higher prior to the GFC in all capital cities, most notably in Hobart (8.0%), Perth (7.7%) and Canberra (7.7%).

More recently, in the period 2010 to 2014, growth in house rents has slowed across all Australian capital cities (with the exception of Sydney). In fact, house rents in Canberra have showed no growth over this period whatsoever. Sydney and Perth reported the highest growth in house rents over this period at 3.4 per cent growth per annum on average.

Table 6 Summary statistics, house rents, Australia and capital cities, 2000 to 2014


Average $ p/wk 2000-2014 2000-2007 2010-2014

Australia 297 5.1 4.6 2.4

Sydney 323 4.7 2.7 3.4

Melbourne 272 4.5 3.9 1.5

Brisbane 288 5.4 7.1 1.1

Adelaide 256 5.0 6.5 1.9

Perth 303 7.1 7.7 3.4

Canberra 354 5.3 7.7 0.0

Hobart 264 5.7 8.0 1.2

Darwin 420 6.7 3.8 5.1

Source: REIA, ABS; Author calculations.

Overall, house rents have been relatively stable compared to house prices. The main exception is Darwin, where house rents spiked upwards and continue to outstrip all other capital cities since the onset of the GFC. As is the case with house prices, median rents were most volatile in Darwin and Perth over this period.

Figure 3 Median house rents, Australia and capital cities, 2000 to 2014

800

700

600

500

400

300

200

100

0

2000Q2 2002Q2 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2

Australia Sydney Melbourne Brisbane Adelaide Perth Canberra Hobart Darwin

Source: REIA; ABS; Author calculations.

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7


As Table 7 and Table 8 show, energy prices have consistently exhibited a strong and positive correlation with house rents across all Australian capital cities over the 2000 to 2014 period, even after the GFC. Interestingly, house rents in several capital cities (Sydney, Melbourne, Canberra and Darwin) - as well as the national average - had a weak but negative association with equity prices over the period 2000 to 2014. However, in the past five years (2010 to 2014), house rents have become increasingly related to equity prices in all capital cities. In addition, house rents now show a positive correlation with equity prices across Australian capital cities (except for Canberra).


Energy Equities

Australia 0.65 -0.12

Hobart 0.87 0.18

Brisbane 0.78 0.06

Adelaide 0.78 0.05

Perth 0.74 0.04

Canberra 0.74 -0.05

Darwin 0.70 -0.01

Melbourne 0.57 -0.23

Sydney 0.41 -0.29



Energy Equities

Australia 0.76 0.42

Brisbane 0.85 0.56

Hobart 0.83 0.14

Adelaide 0.83 0.08

Canberra 0.82 -0.70

Perth 0.82 0.14

Darwin 0.80 0.48

Melbourne 0.68 0.65

Sydney 0.59 0.39


8

The impact of a region’s level of resource intensity on house prices and rents is further illuminated in Figure 4, showing the proportion of person employed in the mining industry across selected capital cities. As alluded to earlier, the impact of energy prices on house prices and rents is likely to be vastly different depending on the regions level of resource intensity. One way to measure the importance of resources to the local economy is the proportion of persons employed in the mining industry. As would be expected, Perth maintains the highest proportion of persons employed in mining, with an average of 6 per cent of all persons employed over the study period. The proportion of people employed in Perth’s mining sector reached as high as one in ten in early 2012. The Northern Territory reported the second highest proportion of persons employed in the mining industry over this period.

Figure 4 Proportion of persons employed in “Mining” in selected capital cities, 2001 to 2014

12

10

8

6

4

2

0

2001-02 2003-02 2005-02 2007-02 2009-02 2011-02 2013-02

Sydney Melbourne Brisbane Adelaide Perth Northern Territory

Per c

ent

Source: ABS; Author calculations.

Regional Western Australia

In this report, we analyse house prices across key regions of WA, as depicted in Figure 5.

Figure 5 Key regions of WA

Source: Government of Western Australia.

As Figure 6 shows, house prices across regional WA and Perth were relatively similar up until 2007. This is the same point in time when house prices and rents began to diverge across capital cities, consistent with the commodity price boom and then the onset of the GFC. At this point, energy prices soared and house prices in the Pilbara and Kimberley (and to a lesser extent, Perth) outstripped all other regions. These regions continued to record the highest house prices across WA until the 2013 downturn in prices commenced in the Pilbara. House prices in the Pilbara, Kimberley and Perth regions exhibited the most volatility over this period.

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Figure 6 Median house prices, WA regional areas, 2000 to 2014

1,0000,000

900,000

800,000

700,000

600,000

500,000

400,000

300,000

200,000

100,000

0

2000Q2 2002Q2 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2

Perth Gascoyne Goldfields Great Southern Kimberley Mid West Peel Pilbara South West Wheatbelt

Source: REIWA; REIA.

Interestingly, from 2000 to 2014, house prices grew the most in the Mid West and Gascoyne regions, with average annual growth rates of 9.6 and 9.0 per cent, per annum, respectively. Growth in median house prices across all regions of WA outstripped that of all other capital cities across Australia, indicating that an important driver of house prices during the GFC and commodity price boom is a region’s level of resource intensity. Constrained market size in these regions is also likely to have played an important role. Limited land supply, with a number of legal and physical impediments, in a geographically constrained market means prices and rents react very quickly, and strongly, to demand shifts (McKenzie and Rowley 2015).

House price growth across WA soared up until the onset of the GFC, and reached as high as 20.5 and 19.2 per cent per annum on average in the Peel and Gascoyne regions, respectively. In the last five years, however, house price growth has been negative in six of the ten WA regional areas. The largest decline in house prices occurred in the Pilbara, with an average fall of 7.2 per cent per annum from 2010 to 2014, and these falls have continued. This reversal in house price growth across WA in recent times corresponds with the decline in commodity prices, contraction of the mining sector and improved housing supply.

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Table 9 Summary statistics, house prices, WA regional areas, 2000 to 2014


WA regions Average 2000-2014 2000-2007 2010-2014

Perth $371,532 8.7 16.8 1.1

Gascoyne $303,335 9.0 19.2 -1.1

Goldfields $238,692 6.7 8.9 0.2

Great Southern $273,686 6.9 13.8 -0.8

Kimberley $440,524 6.8 9.0 -2.8

Mid West $258,919 9.6 14.9 -2.6

Peel $317,809 8.3 20.5 0.2

Pilbara $482,664 7.8 16.5 -7.2

South West $302,276 7.6 16.4 0.7

Wheatbelt $160,542 8.3 13.0 -0.4

Source: REIWIA, ABS; Author calculations.

As Table 10 identifies, the correlation between energy prices and house prices across all WA regional areas was strong and positive over the period 2000 to 2014. The strength of this association is also higher than for any other capital city across Australia, again providing some evidence that the level of a region’s resource intensity plays a role in driving house price growth. Consistent with the capital city level results, we continue to find a positive association between house and equity prices across all WA regional areas over this period.

Table 11 shows that the strength of the association between house and energy prices has weakened across all WA regional areas in recent times consistent with the fall in commodity prices. In addition, the direction of this association has reversed in the Pilbara, Perth and Gascoyne regions. In fact, house prices across WA regional areas now have a stronger association to equity, as opposed to energy, prices. All WA regional areas continue to show a positive correlation between equity and house prices, except the Pilbara and Kimberley regions where prices have fallen significantly.

Table 10 Correlation between house, equity and energy prices – WA regional areas, 2000 to 2014

Energy Equities

Perth 0.91 0.42

Great Southern 0.94 0.50

South West 0.92 0.54

Mid West 0.91 0.29

Kimberley 0.88 0.32

Wheatbelt 0.88 0.22

Gascoyne 0.87 0.33

Peel 0.86 0.59

Goldfields 0.85 0.14

Pilbara 0.84 0.11


11

11


Table 11 Correlation between house, equity and energy prices – WA regional areas, 2010 to 2014

Energy Equities

Perth -0.03 0.86

Great Southern 0.69 0.36

Mid West 0.38 0.10

Goldfields 0.36 0.21

Peel 0.31 0.62

Kimberley 0.29 -0.27

Wheatbelt 0.29 0.08

South West 0.19 0.76

Gascoyne -0.01 0.02

Pilbara -0.03 -0.71


12

The nexusbetween national house, equity and energy prices

Data and methodology

14

3 While the use of a VECM is a popular approach in the literature, there are a number of other empirical approaches that can also be employed. For instance, Chen (2001), Green (2002), Sutton (2002), Kakes and Ven Den End (2004), and Sim and Chang (2006), use vector autoregression (VAR) modeling and the concept of Granger causality to analyse the causal interactions between house prices and stock prices. Ibrahim (2010) and Lean and Smyth (2012) use the VECM approach, while Fry et al. (2010) use a structural vector autoregression (SVAR) to assess whether Australian house prices have been overvalued for the period 2002-2008. Finally, Yuksel (2016) employs a threshold cointegration technique that allows for threshold adjustment in the short run between house prices and its determinants, while maintaining linearity in the long run.

4 Business in the energy sector is defined by either of the following activities: the construction or provision of oil rigs, drilling equipment and other energy related service and equipment, including seismic data collection; or, companies engaged in the exploration, production, marketing, refining and/or transportation of oil and gas products, coal and other consumable fuels.

This study examines the short and long run dynamics between house prices, equity prices and energy prices within the confines of the structure of the Australian economy. House prices are determined by two key macroeconomic variables (real income and the inflation rate), as well as two financial variables (nominal interest rate and real equity prices or real energy prices). Real income is always included in any house equation, as its importance in determining the level of housing demand and supply in the economy is well established. The price level is included to address the assertion that investment in real property can hedge against inflation. Interest rates are included to capture mortgage costs. The interest rate also captures the substitution between investing in interest earning assets and housing. Data are sourced from the Australian Bureau of Statistics (ABS), Real Estate Institute of Australia (REIA), Real Estate Institute of Western Australia (REIWA) and Thomson Reuters DataStream. Further details about the data sources and methodology are provided in the Appendix.

There are several studies documenting the dynamic relationship between equity prices and house prices. These studies demonstrate that equity prices can have both short and long run impacts on house prices, and that there can be direct or indirect transmission mechanisms through which these impacts can occur. Accordingly, we estimate a Vector Error Correction Model (VECM) for each housing market of interest (Australia, capital cities and WA regional areas). This approach enables us to examine the relationship between house, equity and energy prices in the short as well as in the long run. Another benefit of this approach is that we are able to examine the impact of shocks to equity and energy prices on house prices directly at the national, capital city and WA regional level.3

A limitation to the macroeconomic approach adopted in this report is that we are unable to distinguish between investors and owner occupiers, and how credit, income, wealth and substitution effects differ between these cohorts. This report serves as an excellent platform for future investigations into these issues. It should also be noted that the focus of this report is on house prices and rents, rather than other dwellings. There are several reasons for this approach. First, the findings of this investigation provide less conclusive results for other dwelling prices and rents. This is most likely attributable to differences in the motivations of owner occupiers and investors. Second, by focusing on house prices rather than other dwelling prices, the investigation is comparable with international literature. Third, the data for houses is the only constant definition available from the various data-sets employed in this research.

There are also some important caveats to the findings presented in this report. First, the measure of energy prices, as used here, is S&P/ASX 200 Energy Price index. This index consists of stock prices of all the companies out of 200 companies listed on Australian Securities Exchange, whose business is in the ‘energy’ sector.4

The potential limitation of using this index is that this index is constituted from the stock prices of the energy companies rather than the retail price of energy. We do acknowledge that housing consumers looking to buy or rent houses are more affected by the running costs of housing, which includes the price of energy, rather than the stock prices of energy companies. However, there is a strong link between retail price of energy and energy stock prices.

Second, the impulse response functions trace the temporal responses of one variable to a one unit shock in another variable, holding all other variables constant. While it is well known that the impulse response functions are sensitive to the ordering of variables under a Cholesky decomposition, we use what are known as generalised impulse response functions. Generalized impulse response functions fully incorporate the historical patterns of correlations among different shocks, making the impulse response functions unique, and therefore invariant to the ordering of the variables. Thus, given the likelihood that the money market and the stock market are contemporaneously correlated, generalised impulse response functions are used in this study.

Third, the estimation results should be interpreted with care. An estimated coefficient indicates how the dependant variable (house prices) varies when that explanatory variable changes by one unit, holding all other variables in the model constant. However, there are a range of immeasurable factors that impact house prices that simply cannot be included in the model (such as consumer tastes and preferences) and therefore cannot be held constant by the model, and this may impact the results. In addition, it is important to remember that alternative sample periods, model specifications and estimation methodologies may generate a different set of results.

Fourth, due to the dynamic nature of the Australian housing market, a change in one variable is unlikely to occur without a simultaneous change in another explanatory variable(s). While some of these factors push prices in one direction, there are always other factors at work influencing house prices in the same or opposite. In reality, one could not expect to observe the exact change estimated by the model because other variables that influence house prices are also changing simultaneously. The estimation results should not be used to “predict” how a change in one variable will cause housing prices to change. Rather, they should be used to shed light on what variables can influence house prices and the nature of any relationship (strength, direction and temporal characteristics).

15

15


Key findings for equity prices

16

At the national level, the results of this study find that house prices are influenced by key macroeconomic variables and equity prices. In terms of short-run effects, the results shown in Table 12 show that changes in Australia’s inflation rate are a leading indicator of house prices across Australia. Lagged real income also affects Australia’s median house prices. Similarly, the rate of interest prevailing in the previous first and second quarters also has an impact on Australia’s median house prices. However, equity prices have no impact in the short-run. In the long run, we find a relationship between equity prices and other macroeconomics determinants that helps explain house prices in Australia.

Table 12 also reports the results for house rents. Unlike the results for house prices, these results show that there is no long-run relationship between house rents, equity prices and other macroeconomic variables. In Australia, house rents are influenced by real incomes and interest rates in the short-run, however, equity prices have no impact.

17

17


Table 12 Estimation results - house prices,house rents and equity prices - Australia

Prices Rents

Dependant variable ΔHP1t‑1 ΔHP2

t

Intercept 0.04**(0.02)

0.00(0.01)

ΔHPit‑1 0.33**

(0.16)0.08

(0.14)

ΔHPit‑2 0.18

(0.17)0.34**(0.13)

ΔHPit‑3 0.17

(0.15)0.02

(0.13)

ΔHPit‑4 0.13

(0.12)

ΔCPIt‑1 -1.38**(0.72)

0.35(0.46)

ΔCPIt‑2 -0.01(0.68)

-0.16(0.46)

ΔCPIt‑3 -0.93(0.75)

-0.56(0.35)

ΔCPIt‑4 0.17(0.59)

ΔGDPt‑1 -1.21***(0.45)

0.39***(0.14)

ΔGDPt‑2 -0.96**(0.40)

0.26**(0.12)

ΔGDPt‑3 -0.92**(0.35)

0.01(0.08)

ΔGDPt‑4 -0.40(0.32)

ΔINTt‑1 -0.02**(0.01)

-0.01(0.00)

ΔINTt‑2 0.02*(0.01)

-0.01**(0.01)

ΔINTt‑3 0.01(0.01)

0.01**(0.01)

ΔINTt‑4 0..00(0.01)

ΔSPIt‑1 0.00(0.05)

0.01(0.03)

ΔSPIt‑2 0.02(0.05)

-0.02(0.03)

ΔSPIt‑3 -0.06(0.05)

0.04(0.03)

ΔSPIt‑4 -0.02(0.05)

ECTt‑1 -0.16***(0.06)

-0.04(0.04)

Note: Standard errors are reported in parenthesis. An *, **, and ***, represents statistical significance at the 10, 5 and 1% levels, respectively. Please note that i takes the values of 1 and 2 for prices and rents, respectively.


The Impulse Response Function (IRF) based on the estimated error correction models are shown in Figure 7. The IRF shows the impact of a positive shock from one variable on another variable over a period of time (holding all other variables constant). From these functions, we are able to assess the direction, magnitude and persistence of the response of housing prices to the stock and energy price index. In this case, we examine the impact of a shock to equity prices on house prices and rents over a period of 3 years (i.e. 12 quarters). As Figure 7 shows, Australian house prices initially have a positive response to shocks emanating from the equity market, but that effect dampens out quickly. After a period of one year, we find an inverse association between equity and house prices. The reason is that higher equity prices motivate people to invest in equities rather than housing, reducing demand for housing and therefore prices.

On the other hand, house rents alternate between being positive and negative in their response to shocks from the equity market for the first eight quarters, after which they become consistently positive. This result lends support to suggestions that shocks to equity prices drives a switch in consumption from ownership to renting and, driven by investors, a fall in the supply of rental housing. The combination causes an increase in rents over the longer term.

These results again support the hypothesis that people elect to rent rather than invest in, or consume, housing when returns from the equity market increase. The inverse response of house prices and rents to shocks from the equity market clearly demonstrates how movements in the equity market can incentivise people to switch between the two asset classes.

Figure 7 Response of house prices and rents to shocks to equity prices (Australia)

1 2 3 4 5 6 7 8 9 10 11 12

0.004

0.002

0

-0.002

-0.004

-0.006

-0.008

-0.01

-0.012

-0.014

-0.016

Perc

enta

ge c

hang

e

Quarters

Prices Rents


18

Key findings for energy prices

Table 13 presents the estimation results for energy prices and other macroeconomics determinants to help explain house prices and rents in Australia. The results show there is no long-run relationship between house prices and energy prices at the national level. In the short-run, we find evidence that house prices are impacted by interest rates.

For the equation describing house rents, we find that lagged real income, interest rates and inflation all have an effect in determining house rents. Changes in these macroeconomic variables therefore provide an indication of future changes in house prices and rents. We find no evidence of long run effects running from energy prices to house rents.

Table 13 Estimation results - house prices, house rents and energy prices – Australia

Prices Rents

Dependant variable ΔHP1t‑1 ΔHP2

t

Intercept 0.03(0.03)

0.00(0.01)

ΔHPit‑1 0.49***

(0.17)0.04

(0.14)

ΔHPit‑2 0.16

(0.19)0.34**(0.14)

ΔHPit‑3 0.02

(0.17)0.02

(0.13)

ΔHPit‑4 0.07

(0.13)

ΔCPIt‑1 -1.53(0.97)

0.46**(0.48)

ΔCPIt‑2 -0.00(0.95)

-0.21(0.47)

ΔCPIt‑3 -1.30(0.95)

-0.45(0.36)

ΔCPIt‑4 0.44(0.86)

ΔGDPt‑1 -0.32(0.33)

0.36(0.14)

ΔGDPt‑2 -0.25(0.35)

0.23*(0.12)

ΔGDPt‑3 -0.44(0.36)

0.00(0.08)

ΔGDPt‑4 -0.12(0.39)

ΔINTt‑1 -0.03***(0.01)

0.00(0.00)

ΔINTt‑2 0.02*(0.01)

-0.02**(0.01)

ΔINTt‑3 -0.00(0.01)

0.01***(0.01)

ΔINTt‑4 -0.01(0.01)

ΔEPIt‑1 -0.00(0.04)

-0.01(0.02)

ΔEPIt‑2 -0.03(0.04)

0.01(0.02)

ΔEPIt‑3 0.03(0.04)

0.00(0.02)

ΔEPIt‑4 -0.02(0.04)

ECTt‑1 -0.04(0.05)

-0.02(0.05)

Note: Standard errors are reported in parenthesis. An *, **, and ***, represents statistical significance at the 10, 5 and 1% levels, respectively. Please note that i takes the values of 1 and 2 for prices and rents, respectively.


19

19


The response of house prices and house rents to a shock emanating from energy prices are shown in Figure 8. For house rents, the response appears to be persistently negative, but very small in magnitude and dampens out within a few years. The response of house prices, on the other hand, becomes consistently negative after one year. These results lend support to a negative income effect such that higher energy prices (and thus cost of living) reduce one’s capacity to invest in housing and thus reduces demand for, and therefore the price of, housing. Rising energy costs reduce a household’s capacity to save for a deposit and then meet mortgage payments making it more difficult to enter home ownership thus reducing demand. Overall, we find that house rents show little response to energy price shocks, whereas house prices exhibit greater sensitivity.

Figure 8 Response of house prices and rents to shocks to energy prices (Australia)

0.004

0.002

0

-0.002

-0.004

-0.006

-0.008

-0.01

-0.012

-0.014

-0.016

Perc

enta

ge c

hang

e

Quarters

Prices Rents

1 2 3 4 5 6 7 8 9 10 11 12


20

The nexusbetween capital city house, equity and energy prices

Key findings for house prices and equity prices

The estimation results by capital city reveal some interesting differences about the drivers of house prices across Australia (Table 14). In the short-run, evidence suggests that equity prices can have positive wealth effects on house prices in Brisbane, Adelaide, and Canberra. In the long-run, we also find that equity prices and other macroeconomic determinants can help explain the price of houses in Sydney, Melbourne, Brisbane, Canberra, and Darwin. The speed of adjustment to long-run equilibrium varies, with Melbourne being the fastest, and Sydney and Perth being the slowest. Inflation is found to be a leading indicator of house prices across most capital cities (Melbourne, Brisbane, Adelaide, Perth, Hobart and Darwin). Similarly, interest rates prevailing in the previous quarter can signal a change to house prices in Brisbane, Adelaide, Perth and Darwin.

22

Table 14 Estimation results – house and equity prices – capital cities

Sydney Melbourne Brisbane Adelaide Perth Canberra Hobart Darwin

Intercept -0.01(0.02)

0.15(0.03)

0.06*(0.03)

0.04(0.03)

0.04(0.04)

0.11**(0.05)

0.01(0.06)

0.08(0.05)

ΔHPt‑1 0.42**(0.18)

0.15(0.13)

0.57***(0.15)

0.24(0.15)

0.49**(0.18)

0.25(0.17)

0.14(0.18)

0.47**(0.21)

ΔHPt‑2 0.03(0.19)

0.25(0.14)

0.47***(0.17)

0.41**(0.16)

0.29(0.18)

0.16(0.17)

0.56***(0.16)

0.13(0.71)

ΔHPt‑3 0.16(0.17)

0.09(0.14)

-0.17(0.16)

0.21(0.16)

0.14(0.17)

0.33*(0.18)

0.17(0.18)

0.09(0.18)

ΔHPt‑4 0.12(0.14)

-0.05(0.12)

-0.05(0.12)

-0.23(0.15)

0.06(0.18)

0.04(0.17)

-0.20(0.16)

0.04(0.18)

ΔCPIt‑1 0.67(0.82)

-4.93(1.30)

-2.56***(0.88)

-2.11*(1.90)

0.14(1.59)

-1.62(1.77)

1.88(2.22)

-3.45(2.12)

ΔCPIt‑2 0.65(0.78)

-3.29(1.23)

-1.68*(0.97)

-1.85(1.15)

-2.53(1.59)

-1.92(1.69)

-3.96*(2.19)

-3.40*(1.90)

ΔCPIt‑3 0.90(0.80)

-7.04***(1.36)

1.23(1.10)

-0.48(1.18)

-0.01(1.60)

-1.16(1.85)

1.02(2.37)

-0.83(2.05)

ΔCPIt‑4 0.89(0.69)

-2.11(1.08)

-1.24*(0.72)

-2.20**(0.95)

-2.41**(1.18)

-1.28(1.43)

-1.37(1.86)

-2.52*(1.44)

ΔGDPt‑1 -0.45(0.35)

-0.45(0.47)

-0.80(0.50)

0.39(0.45)

0.02(0.51)

-2.58**(1.07)

0.88(0.76)

0.65(0.62)

ΔGDPt‑2 -0.31(0.30)

-0.77(0.44)

-0.97**(0.47)

0.38(0.47)

-0.27(0.57)

-2.22**(1.00)

0.42(0.80)

-0.02(0.64)

ΔGDPt‑3 -0.17(0.28)

-1.11(0.45)

-0.92*(0.46)

0.14(0.47)

-0.47(0.60)

-2.20**(0.91)

0.04(0.83)

-0.40(0.68)

ΔGDPt‑4 0.24(0.30)

-0.89(0.49)

-0.53(0.46)

0.29(0.51)

-0.47(0.68)

-1.82**(0.87)

0.36(0.93)

-0.92(0.82)

ΔINTt‑1 -0.00(0.01)

-0.02(0.04)

-0.03***(0.01)

-0.03**(0.01)

-0.05***(0.02)

-0.02(0.02)

-0.03(0.02)

-0.04**(0.02)

ΔINTt‑2 -0.01(0.01)

0.02(0.02)

0.01(0.01)

0.01(0.01)

-0.00(0.02)

0.03(0.02)

-0.01(0.02)

0.01(0.02)

ΔINTt‑3 0.01(0.01)

0.00(0.02)

0.01(0.01)

-0.01(0.01)

0.02(0.02)

0.00(0.02)

0.02(0.02)

-0.03*(0.02)

ΔINTt‑4 0.00(0.01)

-0.01(0.01)

0.00(0.01)

-0.00(0.01)

-0.01(0.01)

0.01(0.02)

-0.02(0.02)

0.00(0.02)

ΔSPIt‑1 0.03(0.05)

-0.14(0.08)

-0.02(0.05)

0.06(0.06)

-0.01(0.09)

-0.03(0.17)

-0.12(0.13)

0.02(0.10)

ΔSPIt‑2 -0.02(0.05)

-0.06(0.08)

0.09*(0.05)

-0.11(0.06)

-0.03(0.08)

0.23**(0.11)

-0.07(0.13)

-0.03(0.09)

ΔSPIt‑3 -0.07(0.05)

-0.08(0.08)

-0.04(0.05)

-0.03(0.06)

0.04(0.08)

-0.19(0.12)

-0.04(0.12)

0.10(0.09)

ΔSPIt‑4 -0.04(0.06)

0.24(0.07)

0.00(0.05)

0.12*(0.06)

-0.01(0.08)

0.01(0.13)

0.11(0.13)

0.12(0.11)

ECTt‑1 -0.06**(0.03)

-0.33***(0.09)

-0.09**(0.04)

-0.07(1.60)

-0.06(0.06)

-0.23**(0.10)

-0.01(0.05)

-0.15*(0.08)

Note: Standard errors are reported in parenthesis. An *, **, and ***, represents statistical significance at the 10, 5 and 1% levels, respectively.Source: Author calculations.

23

23


As Figure 9 and Figure 10 show, the response of house prices to a positive shock emanating from the equity market varies across capital cities. Overall, we find that house prices consistently respond positively to such shocks in Brisbane, Adelaide and Darwin. On the other hand, house prices in Hobart respond negatively to such shocks.

House prices in Melbourne, Sydney, Canberra and Perth respond both negatively and positively to equity market shocks. House prices in Sydney exhibit the largest response to shocks in the equity market, consistent with it being the financial capital of Australia. Although Sydney house prices initially exhibit a positive response to shocks emanating from the equity market, this effect dampens out after one year, and subsequently becomes negative. Similarly, we find that the house prices in Adelaide, Perth and Darwin all have an immediate positive response to shocks emanating from equity prices. On the other hand, the house prices of Melbourne and Hobart have an immediate negative response to equity shocks. The immediate impact is similar in magnitude for both cities.

Over the longer run, shocks to equity prices have a positive impact on house prices in Canberra and Perth, and a negative impact in Melbourne and Sydney. This suggests that over the longer run, the wealth and income effects arising from positive shocks to the equity market drive people out of the housing investment market in Melbourne and Sydney, and incentivise people to enter the housing market in Canberra and Perth (where a house is, on average, more affordable).

The key finding from the analysis is that capital cities respond differently to shocks in equity prices, there is no uniform impact. The different nature of housing markets including the varying levels of investor activity influence outcomes.

Figure 9 Response of house prices to shocks to equity prices (Brisbane, Adelaide, Darwin)

0.03

0.025

0.02

0.015

0.01

0.005

0

-0.005

Perc

enta

ge c

hang

e

Quarters

Brisbane Adelaide Darwin

1 2 3 4 5 6 7 8 9 10 11 12


24

25

25


Figure 10 Response of house prices to shocks to equity prices (Melbourne, Sydney, Canberra, Perth)

1 2 3 4 5 6 7 8 9 10 11 12

0.015

0.01

0.005

0

-0.005

-0.01

-0.015

-0.02

-0.025

Perc

enta

ge c

hang

e

Quarters

Melbourne Sydney Canberra Perth


Key findings for house prices and energy prices

26

The estimation results examining the relationship between house and energy prices across Australian capital cities are presented in Table 15. The results show that energy prices can be a leading indicator of house prices over the short-run only in Sydney and Darwin. There are also some important differences in the short-run impacts in these capital cities. The results show that energy prices can have a negative short-run impact on house prices in Sydney, which prevails in the second and fourth quarters. In Darwin, however, we find energy prices have a positive effect on house prices in the short-run. Only in these two capital cities do we find a long run interrelationship between macroeconomic variables, energy prices and house prices.

Table 15 Estimation results – house and energy prices – capital cities



0.07*(0.04)

0.03(0.03)

0.03(0.04)

0.03(0.03)

0.06(0.07)

-0.04(0.06)

0.09**(0.04)

ΔHPt‑1 0.43**(0.17)

0.05(0.18)

0.58***(0.16)

0.29*(0.16)

0.44**(0.18)

0.10(0.18)

0.07(0.19)

0.18(0.17)

ΔHPt‑2 -0.15(0.19)

0.29(0.17)

0.57***(0.18)

0.38**(0.17)

0.27(0.19)

0.27(0.17)

0.58***(0.17)

-0.21(0.16)

ΔHPt‑3 0.22(0.16)

-0.12(0.18)

-0.28(0.17)

0.19(0.16)

0.20(0.19)

0.12(0.20)

0.17(0.18)

-0.05(0.16)

ΔHPt‑4 0.05(0.13)

-0.10(0.15)

-0.14(0.14)

-0.24(0.15)

-0.01(0.20)

-0.02(0.18)

-0.21(0.16)

-0.20(0.15)

ΔCPIt‑1 1.13(0.85)

-1.75(1.80)

-2.43***(1.15)

-1.70(1.27)

0.72(1.23)

-0.97(2.63)

3.68(2.52)

-1.53(1.32)

ΔCPIt‑2 1.13(0.82)

-0.90(1.70)

-1.18(1.23)

-1.56(1.30)

-1.96(1.17)

-1.71(2.50)

-1.79(2.52)

-2.56*(1.30)

ΔCPIt‑3 1.40(0.87)

-5.16***(1.76)

1.45(1.38)

-0.13(1.38)

0.35(1.37)

-1.24(2.73)

1.90(2.61)

-0.49(1.55)

ΔCPIt‑4 1.23(0.73)

0.42(1.41)

-0.95(0.91)

-2.17**(1.07)

-2.15**(1.01)

0.23(2.08)

-0.75(2.02)

-2.44*(1.15)

ΔGDPt‑1 -0.23(0.28)

-0.68(0.68)

-0.03(0.43)

0.60(0.44)

-0.42(0.88)

-0.92(0.86)

0.99(0.73)

-1.03(0.75)

ΔGDPt‑2 -0.10(0.27)

-0.66(0.59)

-0.30(0.44)

0.61(0.47)

-0.48(0.76)

-0.80(0.91)

0.65(0.75)

-1.11(0.72)

ΔGDPt‑3 -0.02(0.28)

-0.71(0.56)

-0.34(0.47)

0.30(0.49)

-0.47(0.65)

-1.12(0.93)

0.23(0.79)

-0.91(0.68)

ΔGDPt‑4 0.32(0.30)

-0.10(0.60)

0.05(0.51)

0.42(0.53)

-0.24(0.56)

-1.04(1.01)

0.68(0.86)

-0.80(0.64)

ΔINTt‑1 -0.01(0.01)

-0.01(0.02)

-0.03**(0.01)

-0.03**(0.01)

-0.05***(0.02)

-0.02(0.02)

-0.03(0.02)

-0.02(0.02)

ΔINTt‑2 -0.01(0.01)

0.01(0.02)

0.01(0.01)

0.01(0.01)

-0.01(0.02)

0.02(0.03)

0.00(0.02)

0.00(0.02)

ΔINTt‑3 0.01(0.01)

0.00(0.02)

0.00(0.01)

-0.01(0.01)

0.02(0.02)

-0.01(0.02)

0.02(0.02)

-0.02(0.02)

ΔINTt‑4 -0.00(0.01)

-0.02(0.02)

0.00(0.01)

-0.01(0.01)

-0.01(0.01)

0.01(0.02)

-0.01(0.02)

-0.01(0.02)

ΔEPIt‑1 -0.02(0.04)

-0.11(0.08)

-0.02(0.04)

0.03(0.05)

-0.25(0.06)

-0.03(0.09)

-0.08(0.09)

-0.03(0.06)

ΔEPIt‑2 -0.08**(0.03)

-0.08(0.08)

-0.00(0.04)

-0.05(0.05)

0.02(0.06)

0.04(0.09)

-0.17(0.09)

0.04(0.06)

ΔEPIt‑3 -0.06(0.034)

0.01(0.07)

0.03(0.04)

-0.01(0.05)

0.01(0.06)

0.01(0.09)

-0.06(0.10)

0.04(0.06)

ΔEPIt‑4 -0.07*(0.04)

0.08(0.06)

0.02(0.04)

0.07(0.05)

0.05(0.06)

-0.05(0.09)

0.04(0.09)

0.13**(0.060)

ECTt‑1 0.02**(0.01)

-0.00(0.11)

-0.03(0.05)

-0.05(0.05)

-0.08(0.09)

-0.08(0.10)

0.04(0.08)

-0.03***(0.01)


27

27


An examination of how house prices respond to energy price shocks across Australian capital cities highlights how house prices in resource intensive regions respond differently to changes in energy prices. As Figure 11 shows, a positive shock to energy prices is likely to translate into a negative shock to house prices in Melbourne, Brisbane and Hobart. This is consistent with energy prices having a negative income effect in regions with low resource intensity.

In Figure 11 and Figure 12 we observe that the immediate response of house prices to energy price shocks can vary in the first year across Sydney, Darwin, Canberra and Adelaide. After this period, house prices consistently exhibit a negative response to energy price shocks in these capital cities, consistent with other capital cities across Australia (except Perth). This is consistent with a negative income effect, namely that higher energy prices reduce income, and thus demand for housing (as both a consumption and investment good). An expansion of the energy sector has no impact on employment, for example, and therefore demand for housing.

On the other hand, in Perth we find that positive shocks to energy prices are likely to have a positive and sustained impact on house prices (see Figure 13). The estimated magnitude of the impact of energy prices on house prices in Perth also outstrips that of all other capital cities (except Sydney). Given that Perth is Australia’s mining capital, this result is not surprising and demonstrates that energy prices are likely to have a positive income effect in resource intensive regions.

Overall, these findings demonstrate how differently the Perth housing market reacts to changes in energy prices compared to all other capital cities. The positive impact of the shock is driven by an increase in demand for housing stemming from the growth in economic activity arising from the energy sector.

Figure 11 Response of house prices to shocks to energy prices (Melbourne, Brisbane, Hobart)

1 2 3 4 5 6 7 8 9 10 11 120

-0.005

-0.01

-0.015

-0.02

-0.025

-0.03

-0.035

Perc

enta

ge c

hang

e

Quarters

Melbourne Brisbane Hobart


28

Figure 12 Response of house prices to shocks to energy prices (Sydney, Canberra, Darwin, Adelaide)

1 2 3 4 5 6 7 8 9 10 11 12

0.005

0

-0.005

-0.01

-0.015

-0.02

-0.025

-0.03

Perc

enta

ge c

hang

e

Quarters

Sydney Canberra Darwin Adelaide


Figure 13 Response of house prices to shocks to energy prices (Perth)

1 2 3 4 5 6 7 8 9 10 11 12

0.03

0.025

0.02

0.015

0.01

0.005

0

-0.005

Perc

enta

ge c

hang

e

Quarters

Perth


29

29


Key findings for house rents and equity prices

30

In contrast to the empirical results for house prices and equity prices, we find less conclusive results for house rents. We find a long-run relationship between equity prices, macroeconomic conditions and house rents for Melbourne, Brisbane, and Perth. The fastest speed of adjustment to long-run equilibrium occurs in Brisbane, while the slowest occurs in Perth. For Perth, we find that equity prices can have positive short-run impacts on house rents. In contrast, equity prices initially can have negative short-run impacts on house rents in Sydney, and the direction of this effect is reversed in the third quarter.

Table 16 Estimation results – house rents and equity prices – capital cities



0.00(0.01)

-0.01(0.01)

0.01(0.01)

0.00(0.01)

-0.01(0.01)

0.00(0.12)

0.03(0.02)

ΔHPt‑1 0.12(0.15)

-0.29*(0.16)

-0.35**(0.14)

-0.08(0.14)

0.03(0.15)

-0.32**(0.15)

-0.45**(0.18)

-0.05(0.15)

ΔHPt‑2 0.17(0.14)

0.19(0.16)

-0.01(0.15)

-0.05(0.11)

0.32**(0.14)

0.21(0.16)

0.04(0.17)

0.26*(0.15)

ΔHPt‑3 -0.08(0.14)

0.26*(0.14)

0.02(0.14)

-0.13(0.10)

0.07(0.15)

0.15(0.16)

0.13(0.16)

0.32**(0.15)

ΔCPIt‑1 0.77(0.67)

1.07(0.89)

0.49(0.99)

-1.30(0.84)

-1.35(1.02)

-0.55(0.82)

-0.75(1.21)

-1.40(1.98)

ΔCPIt‑2 -0.17(0.64)

-0.81(0.89)

-0.96(0.92)

1.07(0.81)

1.45(0.98)

0.29(0.83)

-0.28(1.15)

-4.19**(1.90)

ΔCPIt‑3 -0.85*(0.50)

-1.62**(0.65)

0.33(0.77)

-0.81(0.79)

-0.31(0.76)

0.78(0.69)

0.66(0.93)

0.40(1.45)

ΔGDPt‑1 0.14(0.13)

0.90***(0.27)

0.74***(0.17)

0.19(0.25)

0.60**(0.26)

0.60***(0.15)

0.59**(0.27)

0.61(0.58)

ΔGDPt‑2 0.02(0.11)

0.93***(0.22)

0.55***(0.16)

0.11(0.19)

0.50**(0.21)

0.52***(0.15)

0.62**(0.26)

0.31(0.44)

ΔGDPt‑3 -0.06(0.08)

0.37**(0.15)

0.39***(0.11)

-0.04(0.11)

0.16(0.13)

0.12(0.12)

-0.09(0.18)

0.34(0.27)

ΔINTt‑1 0.01(0.01)

-0.01(0.01)

-0.02*(0.01)

0.01(0.01)

-0.02*(0.01)

-0.02*(0.01)

-0.01(0.01)

-0.03(0.02)

ΔINTt‑2 -0.02***(0.01)

-0.01(0.01)

-0.02*(0.01)

-0.03***(0.01)

0.00(0.01)

-0.01(0.01)

0.00(0.01)

0.00(0.02)

ΔINTt‑3 0.01(0.01)

0.00(0.01)

0.01(0.01)

0.02**(0.01)

0.00(0.01)

0.00(0.01)

0.01(0.01)

0.02(0.02)

ΔSPIt‑1 -0.07*(0.04)

0.04(0.06)

0.01(0.06)

0.02(0.05)

0.13*(0.06)

0.06(0.05)

0.06(0.08)

-0.01(0.13)

ΔSPIt‑2 -0.02(0.04)

-0.06(0.06)

0.06(0.06)

-0.08(0.05)

0.03(0.07)

0.01(0.05)

-0.08(0.08)

0.06(0.12)

ΔSPIt‑3 0.07*(0.04)

0.06(0.06)

-0.01(0.06)

0.08(0.05)

-0.08(0.06)

0.00(0.05)

0.00(0.08)

0.02(0.12)

ECTt‑1 0.00(0.02)

-0.15*(0.08)

-0.16**(0.07)

-0.07(0.09)

-0.09**(0.04)

-0.08(0.06)

0.01(0.07)

-0.05(0.04)


31

31


An analysis of the impact of positive shocks emanating from the equity market on house rents across capital cities is provided in Figure 14 to Figure 16. The results indicate that house rents respond positively to equity price shocks in Perth, Darwin and Brisbane. This is consistent with positive wealth effects, but reveal that the wealth effect drives an increase in rental demand rather than motivating people to invest in housing which could be due to increased population growth and employment activity. In Adelaide, Canberra and Hobart, we find that equity price shocks can generate positive impacts on house rents initially and over the longer run.

In contrast, evidence suggests that house rents respond negatively to positive equity price shocks in Sydney. After two years, the impact on house rents in Melbourne is similarly negative. These results are indicative of positive wealth effects driving investors and consumers towards housing rather than equities. These differences across capital cities are likely to reflect differences in financial sophistication, housing affordability and portfolio composition.

Figure 14 Response of house rents to shocks to equity prices (Brisbane, Perth, Darwin)

1 2 3 4 5 6 7 8 9 10 11 12

0.035

0.03

0.025

0.02

0.15

0.01

0.005

0

Perc

enta

ge c

hang

e

Quarters

Brisbane Perth Darwin


32

Figure 15 Response of house rents to shocks to equity prices (Sydney)

1 2 3 4 5 6 7 8 9 10 11 120

-0.001

-0.002

-0.003

-0.004

-0.005

-0.006

Perc

enta

ge c

hang

e

Quarters

Sydney


Figure 16 Response of house rents to shocks to equity prices (Melbourne, Adelaide, Canberra, Hobart)

1 2 3 4 5 6 7 8 9 10 11 12

0.0175

0.0125

0.0075

0.0025

-0.0025

-0.0075

-0.0125

-0.0175

Perc

enta

ge c

hang

e

Quarters

Melbourne Adelaide Canberra Hobart


Key findings for house rents and energy prices

33

33


Over the long-run, we find evidence that indicates house rents are related to macroeconomic fundamentals and energy prices in Melbourne and Brisbane only, with the effects being similar in magnitude. The results suggest that energy prices have short-run dynamic impacts only in three capital cities. For Perth and Hobart, we find that energy prices can have a positive short-run effect on house rents. This provides further evidence of a positive income effect in capital cities with higher levels of resource intensity. On the other hand, energy prices can have a short-run negative effect on house rents for Sydney, which prevails in the first quarter. Given Sydney’s relatively lower level of resource intensity, it is not surprising that we find that energy prices have a negative income effect in this capital city (reflecting higher living costs and a commensurate reduction in disposable income).

Table 17 Estimation results – house rents and energy prices – capital cities



-0.01(0.01)

0.00(0.01)

0.01(0.01)

0.00(0.01)

-0.01(0.01)

0.01(0.02)

0.03(0.02)

ΔHPt‑1 0.14(0.15)

-0.35**(0.15)

-0.32**(0.14)

-0.19(0.14)

0.08(0.16)

-0.34**(0.16)

-0.41**(0.17)

-0.05(0.16)

ΔHPt‑2 0.16(0.14)

0.17(0.16)

0.04(0.15)

-0.13(0.12)

0.21(0.16)

0.26(0.17)

0.00(0.17)

0.23(0.17)

ΔHPt‑3 -0.09(0.14)

0.27*(0.14)

0.05(0.14)

-0.19*(0.11)

0.05(0.17)

0.16(0.17)

0.10(0.16)

0.28*(0.17)

ΔCPIt‑1 1.05(0.65)

1.12(0.94)

0.73(1.02)

-1.31(0.85)

-1.40(1.18)

-0.33(0.83)

-1.55(1.25)

-0.23(2.10)

ΔCPIt‑2 -0.12(0.66)

-1.01(0.93)

-1.14(0.96)

0.69(0.86)

1.56(1.11)

0.34(0.85)

-0.60(1.21)

-3.95*(1.99)

ΔCPIt‑3 -0.88*(0.50)

-1.38**(0.68)

0.07(0.78)

-0.49(0.81)

-0.42(0.87)

1.07(0.68)

0.22(0.99)

0.76(1.46)

ΔGDPt‑1 0.16**(0.07)

0.98***(0.29)

0.59***(0.14)

0.18(0.18)

0.30(0.35)

0.52***(0.13)

0.72***(0.22)

0.05(0.73)

ΔGDPt‑2 -0.01(0.09)

0.98***(0.24)

0.46***(0.14)

0.06(0.15)

0.36(0.26)

0.47***(0.14)

0.72***(0.23)

-0.07(0.52)

ΔGDPt‑3 -0.10(0.07)

0.43***(0.15)

0.33***(0.11)

-0.05(0.10)

0.12(0.15)

0.10(0.11)

0.00(0.17)

0.15(0.31)

ΔINTt‑1 0.01(0.01)

-0.01(0.01)

-0.01(0.01)

0.01(0.01)

-0.02(0.01)

-0.01(0.01)

-0.02(0.01)

-0.01(0.02)

ΔINTt‑2 -0.02**(0.01)

-0.02*(0.01)

-0.02*(0.01)

-0.04***(0.01)

0.00(0.01)

-0.01(0.01)

0.00(0.01)

0.00(0.02)

ΔINTt‑3 0.01(0.01)

0.01(0.01)

0.00(0.01)

0.03***(0.01)

0.00(0.01)

0.00(0.01)

0.00(0.01)

0.02(0.02)

ΔEPIt‑1 -0.05*(0.03)

0.00(0.04)

-0.02(0.04)

0.01(0.04)

0.13**(0.05)

0.00(0.04)

0.09*(0.05)

-0.09(0.09)

ΔEPIt‑2 -0.02(0.03)

0.03(0.04)

0.00(0.04)

0.04(0.04)

0.03(0.06)

0.01(0.03)

-0.02(0.05)

0.00(0.09)

ΔEPIt‑3 0.02(0.03)

0.00(0.04)

0.03(0.04)

0.04(0.04)

0.01(0.05)

-0.01(0.01)

0.05(0.02)

-0.02(0.08)

ECTt‑1 0.00(0.01)

-0.13*(0.08)

-0.15**(0.07)

-0.01(0.02)

0.01(0.02)

-0.02(0.03)

-0.05(0.09)

-0.01(0.03)

Note: Standard errors are reported in parenthesis. An *, **, and ***, represents statistical significance at the 10, 5 and 1% levels, respectively.


As Figure 17 shows, house rents in Perth, Hobart and Adelaide are likely to respond positively to energy price shocks. As would be expected, house prices in Perth respond most to energy price shocks, providing further evidence that the magnitude of the impact of energy prices on house rents differs depending on the region’s level of resource intensity. However, house rents are likely to respond negatively to energy price shocks in Sydney and Darwin. House rents show mixed responses initially in Melbourne, Brisbane and Canberra, followed by sustained positive effects after 2 years.

34


1 2 3 4 5 6 7 8 9 10 11 12

0.018

0.016

0.014

0.012

0.010

0.008

0.006

0.004

0.002

0

-0.002

Perc

enta

ge c

hang

e

Quarters

Adelaide Perth Hobart


Figure 18 Response of house rents to shocks to energy prices (Sydney, Darwin)

1 2 3 4 5 6 7 8 9 10 11 120

-0.002

-0.004

-0.006

-0.008

-0.010

-0.012

-0.014

-0.016

-0.018

-0.020

Perc

enta

ge c

hang

e

Quarters

Sydney Darwin



1 2 3 4 5 6 7 8 9 10 11 12

0.004

0.003

0.002

0.001

0

-0.001

-0.002

-0.003

Perc

enta

ge c

hang

e

Quarters

Melbourne Brisbane Canberra


The nexusbetween WA regional house, equity and energy prices

Key findings for house prices and equity prices

The key regions of WA are highly differentiated in terms of resource intensity, geography and socio- economic characteristics. The purpose of this section of the report is to shed light on why house prices in some regions of WA have grown faster than others, and what role energy and equity prices have played in driving recent trends in house prices across WA.

Overall, the results show no evidence that equity prices have any sort of short or long-run effect on house prices at the regional level across WA. We find that key macroeconomic determinants (inflation, GDP and interest rates) are far more important in determining house prices in the short-run. Specifically, a rise in inflation can drive down house prices in the short-run in Kimberley, South West and Wheatbelt. Similarly, a rise in interest rates can quickly translate into reduced house prices across WA and, most notably, the Wheatbelt. House prices in the Goldfields and Mid West exhibit sensitivity to changes in economic growth, consistent with the income effect.

36

Table 18 Estimation results - house and equity prices - WA regional

GAS GOLD GSOUTH KIM MIDW PEEL PILB STHW WHEAT


-0.04(0.07)

-0.05(0.10)

0.14(0.15)

-0.06(0.12)

0.06(0.06)

-0.06(0.12)

-0.05(0.06)

0.26**(0.11)

ΔHPt‑1 -0.59**(0.26)

0.07(0.17)

-0.08(0.20)

-0.25(0.19)

-0.41**(0.15)

0.29(0.18)

-0.04(0.17)

0.32(0.19)

-0.03(0.16)

ΔHPt‑2 -0.42(0.27)

0.03(0.15)

0.25(0.17)

-0.23(0.18)

-0.06(0.16)

0.49***(0.18)

0.03(0.18)

0.22(0.18)

-0.15(0.16)

ΔHPt‑3 0.21(0.20)

-0.05(0.16)

0.15(0.20)

-0.23(0.17)

0.28(0.17)

0.09(0.18)

0.18(0.17)

0.36*(0.19)

0.22(0.18)

ΔHPt‑4 0.05(0.19)

0.09(0.16))

0.08(0.17)

-0.16(0.16)

0.05(0.16)

0.17(0.19)

-0.01(0.16)

0.05(0.17)

0.35**(0.17)

ΔCPIt‑1 -4.24(7.48)

2.92(3.17)

2.34(3.68)

-2.71(5.77)

-1.16(4.44)

-2.02(2.14)

5.43(5.26)

-1.46(2.28)

-10.28**(4.36)

ΔCPIt‑2 5.34(7.70)

-4.40(2.82)

-1.94(3.49)

-14.56**(5.85)

-5.25(3.97)

-0.55(2.14)

-2.07(5.01)

-4.43*(2.33)

-8.63*(4.41)

ΔCPIt‑3 9.06(7.79)

2.65(2.97)

5.24(3.51)

1.95(5.95)

7.35(4.84)

-1.05(2.18)

1.95(4.88)

-0.18(2.33)

-9.18**(4.26)

ΔCPIt‑4 0.78(6.26)

-0.21(2.15)

-1.99(2.56)

-6.22(4.59)

1.54(3.18)

-2.25(1.70)

-0.59(3.72)

-2.79(1.80)

-2.80(3.30)

ΔGDPt‑1 0.78(6.26)

-0.21(2.15)

-1.99(2.56)

-6.22(4.59)

1.54(3.18)

-2.25(1.70)

-0.59(3.72)

-2.79(1.80)

-2.80(3.30)

ΔGDPt‑2 3.44(2.36)

2.28**(0.89)

1.29(0.99)

2.33(1.89)

2.83**(1.24)

-0.36(0.84)

1.96(1.78)

0.97(0.67)

0.61(1.35)

ΔGDPt‑3 3.39(2.58)

1.59*(0.87)

1.27(1.07)

1.08(1.93)

2.11(1.34)

-0.61(0.87)

1.71(1.54)

0.55(0.73)

-0.82(1.31)

ΔGDPt‑4 3.19(2.72)

0.64(0.91)

1.27(1.19)

0.17(1.99)

1.56(1.42)

-1.06(0.95)

0.89(1.48)

0.07(0.79)

-2.17(1.36)

ΔINTt‑1 3.78(3.15)

0.42(1.06)

0.99(1.43)

-1.41(2.28)

0.96(1.61)

-1.06(0.95)

0.14(1.70)

-0.01(0.92)

-3.43**(1.61)

ΔINTt‑2 0.10(0.08)

-0.02(0.03)

0.01(0.03)

-0.05(0.06)

-0.08**(0.04)

-0.06**(0.02)

-0.03(0.05)

-0.01(0.02)

-0.07(0.04)

ΔINTt‑3 -0.09(0.08)

-0.08***(0.03)

-0.04(0.03)

-0.06(0.05)

-0.01(0.04)

-0.02(0.02)

-0.05(0.04)

-0.04*(0.02)

0.01(0.04)

ΔINTt‑4 0.01(0.08)

0.07**(0.03)

0.04(0.03)

0.03(0.06)

-0.01(0.04)

0.02(0.02)

0.01(0.05)

0.03(0.02)

0.01(0.04)

ΔSPIt‑1 -0.03(0.06)

-0.05**(0.03)

0.01(0.03)

-0.06(0.05)

-0.02(0.03)

-0.02(0.02)

0.06(0.04)

-0.01(0.02)

-0.05(0.03)

ΔSPIt‑2 0.58(0.39)

0.12(0.15)

0.16(0.18)

0.36(0.31)

0.36(0.22)

0.18(0.12)

0.35(0.23)

-0.13(0.12)

0.17(0.22)

ΔSPIt‑3 -0.25(0.38)

0.04(0.14)

-0.07(0.17)

0.08(0.29)

0.08(0.20)

-0.11(0.12)

0.16(0.24)

-0.03(0.11)

-0.12(0.22)

ΔSPIt‑4 -0.23(0.38)

0.03(0.14)

0.12(0.16)

-0.11(0.29)

0.17(0.20)

0.06(0.11)

0.28(0.25)

-0.03(0.11)

-0.07(0.21)

ECTt‑1 -0.17(0.44)

0.11(0.16)

-0.23(0.18)

0.17(0.31)

0.10(0.23)

0.06(0.12)

-0.35(0.28)

-0.02(0.12)

0.12(0.22)


37

37


In almost all the key regions of WA, house prices respond similarly to a positive equity price shock. As Figure 20 shows, a positive shock to equity prices translates into a positive response in house prices in all WA regional areas. This is consistent with positive wealth effects motivating people to enter the housing market, causing a rise in house prices. The one exception is the South West, where evidence suggests that a positive shock to equity prices impacts house prices negatively initially, but then positively after approximately 2 years. Shocks to equity prices are found to have had the largest impact in the Pilbara, WA’s most resource intensive region.

Figure 20 Response of house prices to shocks to equity prices (WA regional)

1 2 3 4 5 6 7 8 9 10 11 12

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0

Perc

enta

ge c

hang

e

Quarters

Gascoyne Goldfields Great Southern Kimberley Mid West Peel Pilbara Wheatbelt


Figure 21 Response of house prices to shocks to equity prices (South West)

1 2 3 4 5 6 7 8 9 10 11 12

0.01

0.008

0.006

0.004

0.002

0

-0.002

-0.004

-0.006

-0.008

Perc

enta

ge c

hang

e

Quarters

South West


38

Key findings for house prices and energy prices

The estimation results presented in Table 19 show that energy prices can influence house prices in the short-run across five of the nine WA regional areas. Evidence indicates there are positive short-run impacts in Gascoyne, Great Southern, Pilbara and Wheatbelt. The magnitude of the short-run impacts is largest in Gascoyne in the first quarter. These results indicate the presence of a positive income effect being transmitted from energy prices to house prices in highly resource intensive regions.

However, house prices in the Mid West are likely to respond negatively in the short-run to changes in energy prices. Although this is the only WA region where we find this, the Mid West is not considered to have a strong resource sector and prices in this region are mainly driven by those in the Geraldton area. We find no evidence that energy prices impact house prices in the short-run in the Goldfields, Kimberley, Peel and South West regions. There are only two WA regional areas where we find that house prices are influenced over the long-run by key macroeconomic variables and energy prices. These are Gascoyne and in the Midwest, and the speed of adjustment is highest in Gascoyne.

Table 19 Estimation results - house and energy prices - WA regional

GAS GOLD GSOUTH KIM MIDW PEEL PILB STHW WHEAT


0.00(0.03)

0.01(0.03)

0.03(0.05)

0.17***(0.03)

0.00(0.02)

-0.04(0.05)

0.03(0.02)

0.04(0.04)

ΔHPt‑1 -0.37**(0.18)

-0.08(0.15)

-0.22(0.16)

-0.25(0.16)

-0.32**(0.12)

0.16(0.15)

-0.07(0.17)

0.18(0.18)

-0.23(0.16)

ΔHPt‑2 -0.27(0.17)

0.15(0.14)

0.23(0.16)

-0.09(0.16)

-0.26**(0.12)

0.44***(0.14)

-0.01(0.17)

0.13(0.16)

-0.39**(0.16)

ΔHPt‑3 -0.21(0.14)

0.02(0.15)

0.03(0.15)

-0.14(0.16)

0.00(0.11)

0.09(0.16)

0.05(0.14)

0.14(0.15)

0.09(0.16)

ΔCPIt‑1 -10.23**(5.02)

1.95(2.27)

-0.92(2.30)

1.27(4.45)

-2.88(2.38)

-1.92(1.68)

4.86(3.70)

-1.51(1.63)

-3.03(3.23)

ΔCPIt‑2 0.58(5.08)

-2.20(2.18)

-4.46*(2.21)

-9.14**(4.15)

-7.00***(2.36)

1.35(1.66)

-5.05(3.43)

-3.46**(1.51)

-1.46(3.15)

ΔCPIt‑3 2.07(4.12)

-0.96(1.61)

2.10(1.74)

1.92(3.38)

-4.10**(1.65)

-0.99(1.31)

2.59(2.82)

0.19(1.30)

-0.42(2.41)

ΔGDPt‑1 -0.79(0.53)

1.19**(0.49)

0.86***(0.28)

1.00*(0.51)

-2.81***(0.70)

0.55***(0.18)

1.27**(0.59)

0.76**(0.31)

0.63(0.70)

ΔGDPt‑2 -0.32(0.58)

1.11**(0.43)

0.72**(0.31)

0.96*(0.54)

-1.63***(0.49)

0.47**(0.23)

0.95*(0.50)

0.42(0.29)

0.33(0.60)

ΔGDPt‑3 -0.35(0.47)

0.12(0.30)

0.42(0.25)

0.66(0.42)

-0.78**(0.31)

-0.21(0.19)

0.57*(0.34)

-0.11(0.20)

0.15(0.40)

ΔINTt‑1 0.08(0.05)

0.01(0.02)

0.01(0.03)

0.04(0.04)

0.01(0.02)

-0.03(0.02)

-0.04(0.03)

0.00(0.02)

-0.05(0.03)

ΔINTt‑2 -0.07(0.06)

-0.07**(0.03)

-0.04(0.03)

-0.07(0.05)

0.01(0.03)

-0.03(0.02)

-0.05(0.04)

-0.04**(0.02)

0.00(0.04)

ΔINTt‑3 -0.05(0.05)

0.05**(0.02)

0.02(0.03)

-0.02(0.21)

-0.02(0.02)

0.03(0.02)

0.01(0.04)

0.02(0.02)

0.03(0.03)

ΔEPIt‑1 0.43*(0.21)

0.07(0.09)

0.14(0.11)

-0.04(0.21)

-0.51***(0.15)

0.11(0.07)

0.32**(0.14)

0.05(0.08)

0.33**(0.15)

ΔEPIt‑2 -0.01(0.22)

0.05(0.09)

0.10(0.11)

-0.09(0.19)

-0.29**(0.13)

0.04(0.07)

0.16(0.16)

0.11(0.08)

0.09(0.15)

ΔEPIt‑3 0.17(0.22)

0.05(0.09)

0.19*(0.10)

0.18(0.18)

0.02(0.11)

0.06(0.07)

0.28*(0.16)

0.06(0.08)

0.02(0.14)

ECTt‑1 -0.31*(0.17)

-0.02(0.04)

0.00(0.02)

0.19*(0.10)

-0.80***(0.13)

-0.01(0.03)

0.02(0.02)

0.02(0.02)

0.00(0.00)

Note: Standard errors are reported in parenthesis. An *, **, and ***, represents statistical significance at the 10, 5 and 1% levels, respectively.


39

39


Positive shocks to energy prices can lead to higher house prices across most WA regional areas (Figure 22). Energy prices are more closely related to house prices in highly resource intensive regions, such as the Pilbara. Again, we find strong and positive impacts being transmitted from energy prices to house prices in the most resource intensive regions of WA. The findings indicate that the magnitude of the response of house prices to shocks from energy prices is lowest in the Goldfields and the Wheatbelt, which is not surprising as these regions are neither resource (with the exception of gold) nor energy rich. The results suggest that house prices in the South West (Figure 23) initially respond positively to energy price shocks, however, this response becomes negative after approximately 2 years. House prices in the Kimberley region are likely to exhibit volatility and respond both positively and negatively to energy price shocks, but these effects diminish after around 3 years.

Figure 22 Response of house prices to shocks to energy prices (WA regional)

1 2 3 4 5 6 7 8 9 10 11 12

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0

Perc

enta

ge c

hang

e

Quarters

Gascoyne Goldfields Great Southern Mid West Peel Pilbara Wheatbelt


Figure 23 Response of house prices to shocks to energy prices (Kimberley, South West)

1 2 3 4 5 6 7 8 9 10 11 12

0.01

0.005

0

-0.005

-0.01

-0.015

-0.02

Perc

enta

ge c

hang

e

Quarters

Kimberley South West


40

The magnitude of spill over effects between key regions of WA

A significant contribution of this research is an examination of whether house price shocks in one region of WA spill over into surrounding regions. Spill over effects indicate that house prices in one region influence house prices in another region, normally due to geographical proximity. Evidence of spill over effects indicates the presence of an indirect transmission mechanism arising in one region can influence house prices in another region. Of particular interest is whether rising house prices in the Pilbara and Kimberley (the key mining regions of WA), as well as Perth, drive house prices in other WA regional areas. As discussed earlier, there is some evidence that house prices in these regions are the most responsive to energy price shocks. Figure 24 and Figure 25 show the impact of a positive shock to house prices in the Pilbara on house prices in other WA regions. Positive shocks to house prices in the Pilbara are likely to have positive spill over effects on house prices in the Mid West, which adjoins the Pilbara region (see Figure 5). On the other hand, we find evidence of negative spill over effects in the Wheatbelt, South West, Perth, Peel, Great Southern and Gascoyne. This may be the result of housing investment being redirected to regions where house prices are showing the strongest growth.

Figure 24 Positive spill over effects from shocks to Pilbara house prices

1 2 3 4 5 6 7 8 9 10 11 12

0.016

0.014

0.012

0.01

0.008

0.006

0.004

0.002

0

Perc

enta

ge c

hang

e

Quarters

Goldfields Kimberley Mid West


Figure 25 Negative spill over effects from shocks to Pilbara house prices

1 2 3 4 5 6 7 8 9 10 11 120

-0.002

-0.004

-0.006

-0.008

-0.01

-0.012

-0.014

-0.016

Perc

enta

ge c

hang

e

Quarters

Gascoyne Great Southern Peel Perth South West Wheatbelt


41

41


42

We now turn to the spill over effects arising from the Kimberley house market (Figure 26 and Figure 27). House prices in the Mid West and Pilbara regions can respond positively to a rise in house prices in the Kimberley region. The close geographic proximity of these regions reveals a direct transmission mechanism that may drive house prices down in the central part of WA. In all other regions we mostly find that house prices are likely to respond negatively to a surge in house prices.

Figure 26 Positive spill over effects from shocks to Kimberley house prices

1 2 3 4 5 6 7 8 9 10 11 12

0.03

0.025

0.02

0.015

0.01

0.005

0

Perc

enta

ge c

hang

e

Quarters

Mid West Pilbara


Figure 27 Negative spill over effects from shocks to Kimberley house prices

1 2 3 4 5 6 7 8 9 10 11 12

0.01

0.005

0

-0.005

-0.01

-0.015

-0.02

-0.025

Perc

enta

ge c

hang

e

Quarters

Gascoyne Goldfields Great Southern Peel Perth South West Wheatbelt


As Figure 28 shows, positive shocks to house prices in Perth can push up house prices in all other regions of WA. The only exception is the Kimberley, where there are likely to be small but temporary negative impacts on house prices, but these impacts disappear after one year. The findings indicate that house prices in the Pilbara respond most to house price shocks in Perth. These results demonstrate that a rise in Perth house prices can be a leading indicator of rising house prices across all regions of WA.

Figure 28 Spill over effects from shocks to Perth house prices

1 2 3 4 5 6 7 8 9 10 11 12

0.05

0.04

0.03

0.02

0.01

0

-0.01

Perc

enta

ge c

hang

e

Quarters

Gascoyne Goldfields Great Southern Kimberley Mid West Peel Pilbara South West Wheatbelt


43

43


Discussionand conclusion

This report provides a comprehensive investigation into how movements in equity markets and energy prices can influence house prices, and how this differs across Australian capital cities, and at the sub-market level within Western Australia. The main findings of this report can be summarised as follows. First, there is a strong association between movements in equity and energy prices, and house prices, across Australia. Second, the level of a region’s resource intensity plays an important role in driving house prices. Most importantly, the direction and magnitude of the income effect in resource intensive regions, especially WA, is vastly different to other parts of Australia. Third, equity prices and energy prices can have a range of short and long term impacts on house prices and rents across Australia. In some capital cities, movements in equity and energy prices can be used as a leading indicator of changes in house prices. Finally, regional house prices may cause spill over effects, meaning that regions that are not resource intensive but are geographically proximate to a resource intensive region, can similarly expect positive income effects arising from energy prices to be transmitted to the local housing market.

Movements in equity prices are closely related to changes in house prices across Australia. This is indicative of income and wealth effects arising from changes in portfolio valuation, employment and costs of living that are directly and indirectly transmitted to the price of housing across Australia. The magnitude and direction of this association varies depending on the state of the global and domestic economy, as well as the mining and resource industry. The impact of the GFC and commodity prices boom is reflected in the vastly different growth rates and trends in these prices before and after these economic events.

Shocks to energy prices can cause Australian house prices to fall over the longer term, consistent with an increase to the costs of living. In essence, an unanticipated rise in energy prices corresponds to a negative income effect and thus, a fall in house prices. However, at the capital city level, we find that Perth is a notable outlier. In Perth, energy price shocks can result in large and sustained increases in house prices. The reason is that Perth maintains the highest level of persons employed in the mining sector. Therefore, a rise in energy prices transmits a positive income effect (through higher wages, salaries, bonuses, and so on) that may lead to higher house prices in this capital city. At the regional level of WA, energy price shocks can similarly transmit positive effects on house prices via the income channel. This effect is greatest in the Pilbara, one of the most resource intensive regions of WA. Overall, we conclude that energy prices can be a leading indicator of house prices across Australia, but the magnitude and direction of the impact depends on the regions level of resource intensity.

Shocks to equity prices can lead to a reduction in Australian house prices, and an increase in house rents, over the longer term. This is consistent with an unanticipated rise in the equity market providing an incentive for people to invest in equities rather than housing. However, at the capital city level, we find this result only holds for Melbourne and Sydney. For the remaining capital cities, shocks to equity markets can cause a rise in house prices over the longer term, reflecting positive wealth effects being transmitted from the equity to the housing market. We find similar evidence across all regions of WA.

46

Discussion and conclusion

47

47


Evidence of spill over effects across WA regions indicates that house price movements in one region may signal a change in house prices in geographically proximate regions. This means that a shock to energy prices that drives up house prices in a resource intensive region can transmit positive impacts to house prices in non-resource intensive regions that are geographically proximate. The spill over effect is an indirect mechanism through which house prices across WA can be influenced by energy prices, regardless of the region’s level of resource intensity.

In summary, our findings help shed light on the complex interrelationship between equity, energy and house prices across Australia. The results highlight the importance of a region’s level of resource intensity in describing how energy and equity prices can transmit impacts to the price of housing across Australia. While energy and equity prices, as well as a range of key macroeconomic indicators, can broadly be used as leading indicators of movement’s in house prices, the magnitude and direction of these impacts varies across capital cities. Furthermore, widespread evidence of spill over effects should be taken into account by regional policy makers when analysing local housing markets.

From a policy perspective, the findings are particularly important for regional housing markets. Often neglected when it comes to housing policy and forward planning relating to land release and housing supply, understanding how energy prices, in particular, impact on regional housing markets is an important finding. Sustained growth in energy prices is a key, early indicator of potential demand pressures within constrained, resource intensive housing markets. In order to avoid a repeat of the housing market failures in the resource intensive regions that have occurred in the past, an understanding of market drivers allows government to anticipate and plan supply responses to meet demand pressures.

Additionally, understanding the spill over effects from resource intensive regions and from Perth itself is very important for government and the housing industry to ensure demand is anticipated and adequate housing developed to meet supply in response to early market signals.

Appendix

Data sources

50

5 The index covers approximately 80% of Australian equity market capitalization.6 This is referred to as the “energy price” throughout this study.

Median house and rental prices are sourced from the Real Estate Institute of Australia (REIA) at the capital city level and the Real Estate Institute of Western Australia (REIWA) for WA regional data. The remaining macroeconomic and financial variables are sourced from the Thomson Reuters DataStream database. The real equity price and real energy price variables are measured by the nominal Australian S&P/ASX 200 equity price index5 and S&P/ASX 200 energy price index,6 respectively. All nominal variables are deflated by the Australian Implicit Price Deflator Index (IPDI). The variable used for real income is the measure of nominal GDP, deflated by the Australian IPDI, while the overall price level is measured by the consumer price index (CPI). The interest rate is measured by the money market 3-month bank accepted Treasury bill rate.

We opt to use national measures of CPI and GDP for several reasons. First, as investors diversify their property portfolios across different regions of Australia, focusing on capital city measures may introduce bias into the results because it may overlook the drivers of house prices outside the immediate geographical region. This approach ensures we are able to accurately capture the broader wealth and income impacts. Despite this, we acknowledge that this approach may underestimate the local macroeconomic drivers of house prices to a small extent. Second, the purpose of this study is to investigate house prices within the confines of the Australian economy. Third, this approach could not be used at the regional WA level due to data unavailability.

Methodology

51

51

7 These results are available from the author upon request.


The static baseline model specifications employed are as follows:

HPit = α + β1CPIt + β2GDPt + β3INTt + β4SPIt + ε1t (1)

HPit = α + β1CPIt + β2GDPt + β3INTt + β4EPIt + ε2t (2)

where: HP is log of median Australian house prices (measured in $’000 for Australia and capital cities) or median house rents ($ per week),

CPI is the log of consumer price index;

GDP is log of gross domestic product, seasonally adjusted;

INT is the 90 day Australian dealer bill middle rate,

SPI is the log of Standard & Poors ASX 200 equity price index,

EPI is the log of Standard & Poors ASX 200 Energy price index, and

i takes the values of 1 to 9 for the national/capital city level models (where 1 = Australia, 2 = Sydney, 3 = Melbourne, 4 = Brisbane, 5 = Adelaide, 6 = Perth, 7 = Canberra, 8 = Hobart, 9 = Darwin), and values of 1 to 10 for the WA regional level models (where 1 = Perth, 2 = Gascoyne, 3 = Goldfields-Esperance, 4 = Great Southern, 5 = Kimberley, 6 = Mid West, 7 = Peel, 8 = Pilbara, 9 = South West, 10 = Wheatbelt).

We employ a range of standard unit root tests and find that the data are non-stationary in levels and stationary in first differences.7 We estimate this model (equations 1 and 2) as a Vector Error Correction Model (VECM). We adopt the VECM approach for several reasons. First, this approach allows for the likely bi-directional interrelationship between the variables. Second, after testing a variety of dynamic model specifications, we find that the results of the VECM estimation are robust. Third, this methodology allows us to extract the impulse response functions which are useful for the type of analysis presented in this report. Impulse response functions are also used to ascertain whether, and to what extent, there are spill over effects arising between house prices across key regions of WA. We employ the LM test to determine the appropriate lag structure in each case.

References

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Corresponding Author DetailsDr Kelly BurnsCurtin University78 Murray Street, Perth, WA 6000 Phone: +61 8 9266 4235Fax: +61 8 9266 3368Email: [email protected]

Other AuthorsAssociate Professor Steven RowleyDirector - Australian House and Urban Research Institute - Curtin Research CentreChair - House Industry Forecasting Group Curtin University

Associate Professor George B. TawadrosRoyal Melbourne Institute of Technology

Dr Ankita MishraRoyal Melbourne Institute of Technology

It can be cited as: Burns, K., Rowley, S., Tawadros, G. and Mishra, A. (2018) The nexus between equity markets and housing prices in Australia, Bankwest Curtin Economics Centre, Perth

Copyright© Bankwest Curtin Economics Centre, March 2018ISBN: 978-1-925083-79-8

Acknowledgement and DisclaimerThe authors thank Vanessa Juliana, Research Assistant, for her assistance in producing this report. The research reported in this publication is funded by the Bankwest Curtin Economics Centre under a projected entitled ‘The nexus between equity prices and house prices in Australia’. While every effort has been made to ensure the accuracy of this document, the uncertain nature of economic data, forecasting and analysis means that the centre, Curtin University and/or Bankwest are unable to make any warranties in relation to the information contained herein. Any person who relies on the information contained in this document does so at their own risk. The centre, Curtin University, Bankwest, and/ or their employees and agents disclaim liability for any loss or damage, which may arise as a consequence of any person relying on the information contained in this document. Except where liability under any statute cannot be excluded, the centre, Curtin University, Bankwest and/or their advisors, employees and officers do not accept any liability (whether under contract, tort or otherwise) for any resulting loss or damage suffered by the reader or by any other person.The views in this publication are those of the authors and do not represent the views of Curtin University and/or Bankwest or any of their affiliates. This publication is provided as general information only and does not consider anyone’s specific objectives, situation or needs. Neither the authors nor the centre accept any duty of care or liability to anyone regarding this publication or any loss suffered in connection with the use of this publication or any of its content.

© Curtin University of Technology 2018CRICOS Provider Code 00301JADV100006

Bankwest Curtin Economics Centre (BCEC)Level 4, Building 408 Curtin UniversityGPO Box U1987 Perth WA 6845Tel: +61 8 9266 2873 Email: [email protected]

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