DEPARTMENT OF ECONOMICS

ISSN 1441-5429

DISCUSSION PAPER 49/15

Self-Exciting Effects of House Prices on Unit Prices

in Australian Capital Cities

Abbas Valadkhani* and Russell Smyth

+

Abstract: This paper examines the long- and short-run relationship between Australian house and unit

prices across all capital cities over the period December 1995 to June 2015. We find that

house and unit prices are cointegrated and, based on the results of Granger causality and

generalised impulse responses, that house prices significantly influence unit prices across all

cities. However, bi-directional causality (responses) exists only for major capital cities with

the exception of Brisbane. We also, for the first time, apply self-excited threshold models to

explore the complex interplay between house and unit prices in Australia. We find that when

the market for units is self-excited, or bullish, the positive effects of house prices on unit

prices are noticeably larger than otherwise. There is a varying degree of herd mentality in the

Australian property market with Sydney and Darwin being the most and least “excitable”

capital cities, respectively.

Keywords: Australia, cointegration, house prices, unit prices, self-exciting threshold model

* Swinburne Business School, Swinburne University of Technology, Victoria 3122 Australia.

+ Department of Economics, Monash Business School, Monash University, Clayton, Victoria 3800 Australia.

© 2015 Abbas Valadkhani and Russell Smyth

All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the prior

written permission of the author.

monash.edu/ business-economics

ABN 12 377 614 012 CRICOS Provider No. 00008C

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1. Introduction

1.1. Background

Australian housing prices have experienced strong growth since the middle of the 1990s. Unlike

many countries, the upward trajectory in Australian housing prices has proven to be remarkably

resilient in the aftermath of the Global Financial Crisis (GFC). According to Worthington (2012,

p.235), “housing affordability in Australia has worsened significantly in the past quarter century,

including in both urban and regional areas, and is now among the world's most unaffordable”.

Based on the latest Annual International Housing Affordability Survey, housing in each of the

five major metropolitan areas (Sydney, Melbourne, Brisbane, Adelaide and Perth) is considered

“severely unaffordable”, with the two largest cities (Sydney and Melbourne) rated the third and

sixth least affordable city in the world respectively (Demographia, 2015).

This strong growth has fuelled debate about the existence of a housing bubble in Australian

capital cities. The Prime Minister, Tony Abbott, and Treasurer, Joe Hockey, have rejected the

notion that Australia has a housing bubble (Aston, 2015). However, when appearing before a

parliamentary inquiry into home ownership in June 2015, Treasury Secretary, John Fraser, stated

that Sydney and parts of Melbourne were “unequivocally in a housing bubble” (Janda, 2015). In a

submission to the same parliamentary inquiry, David and Soos (2015) claimed that Australia is in

the middle of the “largest housing bubble on record” and that when it bursts it will be a

“bloodbath” with Melbourne at its epicentre. Reinforcing these concerns, Costello et al. (2011)

found that there have been periods of sustained deviations of house prices from values warranted

by income in each of the capital cities with the largest deviations occurring in Sydney and

Melbourne, particularly since the beginning of the millennium.

A feature of the Australian housing market is the growth in inner city units in many capital cities

and much of the concern about a housing bubble has focused on oversupply of inner city units, in

particular in Melbourne and Sydney (Aston, 2015). The strong growth in housing prices, coupled

with fears of a bubble, suggests that there is a need to better understand the house-unit price

nexus in Australia. The market for inner city units has been fuelled by several factors. These

include tax incentives to encourage investment in housing, low interest rates, land use regulation

at the state and local level that has reduced affordable land for building, strong demand from

international students and strong investor interest from Asia.

1.2. Houses and units as submarkets

The growth in inner city units in many capital cities in Australia has given rise to the emergence

of housing submarkets consisting of (mainly inner city) units and detached (mainly suburban)

housing. These submarkets occur when there are alternative dwelling types that are connected by

a chain of substitution (Morrison & McMurray, 1999). According to Leishman et al (2013, p.

1201): “Housing sub-markets arise as a result of the co-existence of a high degree of

heterogeneity of preferences in relation to house types, sizes and locations on the demand side of

the market and an extremely variegated and indivisible stock of properties on the supply side.”

2

Inner city units and detached suburban housing respond to heterogeneous preferences along a

range of dimensions. The first aspect is size. In Melbourne, for instance, most units are small (70

square metres or less). It is not possible to construct and sell family-friendly units (90 square

metres or more) for less than AUD 700,000. Detached houses can be purchased for less than two

bedroom units in the inner city (Birrell & Healy, 2013). Running parallel to the growth in inner

city units, there has been sizeable growth in housing estates in the outer suburbs of many capital

cities. Many of the houses built in these estates resemble the typical McMansion, popularised in

the literature on American suburban home ownership (Nasar et al., 2007).

A second aspect is heterogeneous preferences in terms of how close one wants to live to their

workplace and the number of hours one spends at work. Morrison and McMurray (1999) found

that an important market for inner city apartments in Wellington were high-income singles, who

worked long hours in the city. In Australia there has been a gentrification of the inner city in

which middle and high income white collar workers have moved in, forcing lower income

individuals into public housing on the city fringes (Wulff & Lebo, 2009).

A third aspect is preferences for a house and garden setting. The Great Australian Dream was

always couched in terms of a detached house on a quarter acre block. While this is still

undoubtedly the dream for many, perhaps reflecting longer working hours, others are shunning

this idea in favour of inner-city living in smaller residences. For these people, being close to good

restaurants, major sporting events and cultural events that are less readily available in the suburbs

are major attractions.

1.3. House and unit submarkets in the long run

While units and detached housing are submarkets, we expect these markets to be connected in the

long run. One reason is that from the viewpoint of the potential investor, units and detached

housing represent substitutable investments. When Granger (1986, p.213) first developed the

concept of cointegration, one of the examples of variables that are potentially bound together in

the long run that he gave was “market prices of substitute commodities”. If housing markets are

efficient, arbitrage should take place to eliminate price differences across submarkets.

A second reason is that property markets, by their nature, are highly co-dependent. Housing can

represent both a consumption and investment good (see e.g. Piazzesi et al., 2007). As an

investment good, households can borrow against their equity in the home and invest in a second

property. This is particularly prevalent in a home ownership society such as Australia. When the

price of the family home, which is often a detached house in the suburbs, increases this raises the

capacity of the homeowner to borrow against their equity to purchase an investment property,

which will often be a unit. This, in turn, pushes up the price of units.

A third reason is by way of analogy to the suggestion that regional house prices might converge

due to migration flows between regions (Meen, 1999). The same reasoning can be applied to the

demographic-based flows between inner city units and detached housing. One major category of

those occupying inner city units is single young professionals. When they get married and have

children, many prefer a house and garden in the suburbs. Moving in the opposite direction, a

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second major category of those occupying inner city units are older empty nesters who have

downsized when their children leave home (Yardney, 2015). If these flows are large enough, in

the long run price differences between detached housing and units will be arbitraged away.

1.4 The current study

In this paper, we examine the relationship between house and unit prices in the eight capital cities

of Australia over the period December 1995 to June 2015. We find that house and unit prices in

each capital city enjoy a high degree of correlation up until the GFC, after which the relationship

becomes less stable and noisy. However, despite irregular narrowing and widening of the gap,

house and unit prices have remained cointegrated. The results of the Granger causality and

generalised impulse responses indicate that short-run changes in house prices indiscriminately

affect unit prices in all capital cities, whereas reverse causality exists in Adelaide, Melbourne,

Sydney and, to a lesser degree, Perth. Hence, with the exception of Brisbane, bi-directional

causality (responses) exists only for the larger, and more populous, capital cities. Based on the

estimated self-exciting threshold models, we find that after a certain price growth threshold, an

increase in house prices exerts a significantly larger positive impact on unit prices.

The findings are important for several reasons. First, analysing behaviour of prices in housing

submarkets in Australia is important as it is currently in an extended boom and there are fears of a

housing bubble. This is in the context of the boom-bust cycle in housing prices that has been

experienced in many countries around the world (Balcilar et al., 2012; Canarella et al., 2012).

Australian household debt is currently 125% of GDP, which is higher than other comparable

developed economies and higher than the United States before the GFC (McKenna, 2015).

Analysis of the time series properties of housing prices assists in understanding whether Australia

is heading in the same direction as the United States and, if so, what can be done to avoid it.

Second, related to the first point, the results provide a better understanding of the market

dynamics underpinning house prices and the mechanism through which shocks are transmitted

between submarkets. Third, the results have important practical implications for different groups

of people. Better understanding of how unit and house prices are related assists urban planners to

develop a more effective housing development strategy. This is particularly important in

Australia given concerns about land use regulation in the outer suburban areas and the number,

and size, of inner city units being built in the most populous cities of Melbourne and Sydney (see

eg. Birrell & Healy, 2013). This study also has important and timely implications for market risk

management and portfolio decision-making, as it provides some insight into the degree of

substitutability and co-movement between houses and units as alternative types of investments.

We make the following contributions. This is the first study to test for a long-run relationship,

and explore the market dynamics, between different types of housing markets. The fall in house

prices is considered a major cause of the GFC and has led to heightened interest in the

relationship between house prices across regions and in comparison to prices of other assets such

as stocks. We extend this literature to examine the relationship between subclasses of housing.

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Our second contribution is that we not only consider the relationship between submarkets, but

regional variation between submarkets and we do so for Australia, which represents an interesting

contrast to the much more studied United Kingdom and United States. In particular, the increased

spatial dimension provides an interesting context in which to examine the relationship between

house and unit prices. As Akimov et al (2015) noted, while Australia’s population is just 36% of

the United Kingdom and 7% of the United States, its geographic size is similar to the latter. The

Australian population is geographically diverse with a high concentration in a few major cities

along the south eastern seaboard. As a consequence, it might be argued that each of the capital

cities is more likely to represent separate markets in which house and unit prices in those cities

are related, particularly in the more isolated capital cities.

Third, we use the patented CoreLogic Index, which is a new and important source of Australian

housing price data. This index has been specifically developed as a reference asset for the

settlement of exchange traded property index contracts. The accuracy and robust characteristics

of this index make it preferable to other indices such as the Australian Bureau of Statistics’

(ABS) quarterly median house prices that have typically been employed in previous time series

studies of Australian house prices. Importantly, the CoreLogic Index data is compiled at higher

frequency time series (monthly) than alternative series.

Fourth, to the best of our knowledge, this is the first application of a self-exciting threshold model

in the context of the Australian housing market. This allows us to explore a number of important

features of time series data, which cannot adequately be captured by conventional linear models

such as time-irreversibility, limit cycles, jump phenomena and occasional bursts of outlying

observations. Given the likely presence of herd mentality in the housing market, the use of self-

exciting threshold models enables us to observe how the market responds when prices are on

upward versus downward trajectories. We find that when unit prices are on the rise (i.e. the

market is self-excited or bullish) house prices exert significantly greater positive impacts on unit

prices, suggesting that individuals are highly likely to follow the buying behaviour of others.

2. Literature review

Most of the time series literature on housing prices has focused on whether there is regional

convergence in house prices (Balcilar et al., 2013; Blanco et al., 2015; Canarella et al., 2012;

Gupta et al., 2015; Lean & Smyth, 2013). Relatively few studies have examined links between

house prices and other variables. Most have focused on the relationship between house prices and

market fundamentals (Oikarinen, 2014). Other studies have focused on the relationship between

house and other asset prices, such as stocks (He, 2000) or house prices and macroeconomic

variables (Kemme & Roy, 2012). Only a limited number of recent studies have used self-exciting

threshold models to examine bullish or bearish behaviour of housing prices in the United

Kingdom and the United States (Barari, et al. 2014; Park & Hong, 2011; Walther, 2011).

Among existing Australian studies, most of the literature has concentrated on determinants of

house prices (Abelson et al., 2013). Some of the Australian literature mirrors that for other

countries. There are studies of regional house price convergence (Ma & Liu, 2014) and the

5

relationship between house prices and market fundamentals (Costello et al., 2011). Other studies

examine issues such as common cycles in house prices (Akimov et al., 2015).

To summarize, while there is a large literature on whether regional housing submarkets converge,

there are no studies examining whether housing submarkets defined by housing type converge.

There are no studies applying self-exciting threshold models to Australian house prices or outside

the UK and US. We address this issue by examining the market dynamics of Australian housing

and unit prices.

3. Methodology

We examine time series properties of the data by using the Augmented Dickey and Fuller (ADF)

and the additive outlier unit root tests (Vogelsang & Perron, 1998). We then proceed to apply the

Johansen (1995) test to examine cointegration between each pair. We investigate the robustness

of the cointegration results using the Hansen (1992) Lc statistic. We conduct a conventional

Granger causality test between monthly returns of house and unit prices. The optimal lag length is

chosen using the Schwarz information criterion (SIC). In order to better understand the future

dynamic behaviour of unit prices to shocks affecting house prices, we also use generalised

impulse response functions proposed by Koop et al. (1996).

To address endogeneity caused by long-run covariation between the residuals and the right hand

side variable, we use Fully Modified Ordinary Least Squares (FMOLS) (Phillips, 1995). With

this approach the initial OLS estimates of the symmetric and one-sided long-run covariance

matrix of the residuals are modified using a semi-parametric correction. The FMOLS estimators

are asymptotically unbiased with fully efficient mixture normal asymptotics. Once cointegration

is established, the long-run relationship between house and unit prices can be written as follows:

0 1 2t t t tU T H e (1)

Where:

UPt=monthly unit prices at time t,

HPt=monthly house prices at time t,

Ut=Ln(UPt),

Ht=Ln(HPt),

Tt=time trend,

et=random residuals at time t,

Ln=natural logarithm, and

s =long-run coefficients to be estimated.

The above equation assumes that the cointegrating vector is normalised in terms of unit prices.

Given the causality test results, such an assumption is not too far-fetched. The time trend variable

is kept in the regression as long as it is statistically significant. In order to avoid the loss of

important information in level data, we incorporate the resulting lagged residual from the

6

cointegrating equation as an error correction (et-1=ECt-1) mechanism into a short-run dynamic

model. However, a self-exciting model can represent a number of important features of time

series data, which cannot adequately be captured by conventional Gaussian linear models (Tsay,

1989). The parameters of self-exciting models enjoy a higher degree of flexibility through regime

switching behaviour. To address this problem we adopt the following two-regime self-exciting

threshold model of order k with the threshold parameter :

1 1 1 1 1

0 1

2 2 2 2 1

0 1

1

1

k k

t i t i i t i t t d

i i

k k

i t i i t i t t d t

i i

U H U EC U

H U EC U

(2)

Where 1(·) is the indicator function, which equals one if the condition in the parentheses is met

and zero otherwise, d is known as the length of the delay, and t is the stochastic residual series

distributed independently and identically with zero mean and finite non-zero variance 2 . Unlike

a conventional short-run dynamic model, at any point in time the intercept, the slope and error

correction coefficients can all switch from regime 1 (1 , 1i , 1i and

1 ) to regime 2 (2 , 2i ,

2i and 2 ) depending on the value taken by the ‘threshold variable’ or t dU . Equation (2) can

also be written in a compact form:

2

1

1 0 1

1 ( , )k k

t j t d j ji t i ji t i j t t

j i i

U U H U EC

(3)

If the threshold variable was a variable other than the lagged dependent variable, we would have

a conventional threshold model. Moreover, if all ji coefficients were set equal to zero, equation

3 would be reduced to a self-exciting threshold autoregression (SETAR) model. In order to

estimate the “nuisance parameters” (γ, d), it is a standard practice to conduct a two-dimensional

grid search for γ and d. For each pair (γ, d) in the grid, the indicator function is first defined and

equation (2) is estimated. Then, based on a given trimming region, a lower bound γl and an upper

bound γu are defined for the threshold parameter. Following Andrews (1993), the trimming

percentage is assumed to be 15. Within the specified range for γ, the aim is to minimise the

following residual sum of squares (RSS) with respect to the four sets of parameters:

2

2

1

1 1 0 1

( , , , ) 1 ,T k k

ji ji j t d j t d j ji t i ji t i j t

t j i i

S U U H U EC

(4)

We add a small increment such as 0.0001 to the lower bound (i.e. γl +0.0001) and re-estimate

RSS. In the next iteration we consider γl +0.0002 and record RSS. This iterative process

continues until we reach the upper bound γu. We then select an optimum value of the threshold,

which yields the lowest residual sum of squares. Put simply:

ˆ arg min RSS( )

[ , ]l u

(5)

7

Knowing d a priori does not affect the asymptotic properties of the estimators (Chan, 1993).

After determining (γ, d), the sample is divided into two sub-samples and a conventional

estimation method can then be applied to each sub-sample. The threshold model (equation 3) is

tested against a standard non-threshold linear model using the Bai and Perron (2003) test. The

number of regimes can be decided based on theory or preliminary examination of the data.

4. Data

Monthly house and unit prices from December 1995 to June 2015 for Australia’s eight capital

cities (Adelaide, Brisbane, Canberra, Darwin, Hobart, Melbourne, Perth and Sydney) are sourced

from CoreLogic RP Data. The series are compiled based on the hedonic imputation methodology,

which is recognised as being robust at varying levels of disaggregation both across time and

space (Goh et al, 2012). This method utilises information from transacting properties and their

attributes for the general stock of housing at any given location and time interval to impute a

value for properties having a certain set of characteristics. The sample may include both sold and

unsold properties. By considering a wide range of property attributes, this approach reduces index

bias that exists in other house price indicators for Australia such as median or repeat sale indices.

Moreover, unlike the ABS or Real Estate Institute of Australia (REIA) price indices, which are

only available at quarterly frequency, CoreLogic RP Data are available monthly.

5. Results

5.1. Preliminary statistics

Summary statistics of house and unit prices are shown in Table 1. The highest average house and

unit prices are observed in Sydney (AUD539,000, AUD403,000) and the lowest in Hobart

(AUD246,000, AUD214,000) with Perth displaying the greatest volatility for both house

(AUD188,000=standard deviation) and unit (AUD141,000) prices. All 16 house and unit price

series resemble a platykurtic distribution as Kurtosis is less than three. Except house prices in

Melbourne, all 15 other price series are negatively skewed, suggesting that the median is greater

than the mean. Therefore, the Jarque-Bera normality hypothesis is rejected for all series at the 1%

level of significance, except for prices in Sydney, which are rejected at 10%.

Figure 1 presents the scatterplot of monthly house and unit prices during the sample period. There

is a strong positive relationship between house and unit prices with some noise occurring after the

GFC. The pairwise correlation coefficients are very high, ranging between a minimum of 0.983

(Hobart) and maximum of 0.998 (Perth). The Kernel density distributions of house and unit

prices are displayed on the horizontal and vertical axes of the individual graphs, respectively, and

the red curve shows the fitted Kernel values. Consistent with the results of the Jarque-Bera

normality test, Figure 1 reveals that the density distributions do not look anything like a normal

distribution. In six out of eight capital cities (Adelaide, Brisbane, Canberra, Darwin, Hobart and

Perth) we observe a “double hump distribution” at the left and right sides of the mean. However,

it seems that Melbourne and Sydney have different distributions compared to each other and also

the other capital cities. For Melbourne the Kernel distribution, particularly for house prices,

appears to follow a uniform distribution and for Sydney the observations are more clustered

around the mean, exhibiting some resemblance to a normal distribution. This is consistent with

8

the results in Table 1 because for Sydney the skewness statistics for house (-0.079) and unit (-

0.052) prices are close to zero and the corresponding Kurtosis indices (2.338; 2.245) are

reasonably close to 3.0. Hence, the normality hypothesis is rejected only at the 10% level.

Table 1. Summary statistics of the monthly data (1995M12-2015M06)

City No.

obs.

Mean

($000)

Max.

($000)

Min.

($000)

Std. Dev.

$000 Skewness Kurtosis

Jarque-

Bera χ2

Adelaide

Houses 235 307 472 125 126 -0.216 1.421 26.24*

Units 235 244 369 114 94 -0.166 1.392 26.40*

Brisbane

Houses 235 340 521 140 144 -0.238 1.380 27.90*

Units 235 283 418 151 97 -0.089 1.314 28.15*

Canberra

Houses 235 414 642 159 172 -0.276 1.524 24.33*

Units 235 301 444 135 110 -0.354 1.521 26.31*

Darwin

Houses 195 366 578 183 144 -0.017 1.352 22.08*

Units 195 298 473 141 111 -0.097 1.394 21.26*

Hobart

Houses 235 246 371 105 104 -0.340 1.306 32.65*

Units 214 214 318 73 83 -0.526 1.636 26.48*

Melbourne

Houses 235 422 760 147 185 0.064 1.688 17.02*

Units 235 319 510 129 122 -0.059 1.700 16.68*

Perth

Houses 235 394 644 152 188 -0.068 1.226 31.00*

Units 235 319 510 140 141 -0.060 1.246 30.26*

Sydney

Houses 235 539 972 221 180 -0.079 2.338 4.54

Units 235 403 651 198 113 -0.052 2.245 5.69

Note: * Significant at 5% or better.

9

Figure 1. Scatterplot of monthly house and unit prices in Australian capital cities.

Note: (a) The solid red curve shows the fitted Kernel values. (b) Kernel density distributions

of house and unit prices are displayed on the horizontal and vertical axes, respectively.

100

150

200

250

300

350

400U

nit

pri

ces

($0

00

)

120 160 200 240 280 320 360 400 440 480

House prices ($000)

Main

ly p

ost

GFC

(20

08

M0

7-2

015

M0

6)

Adelaide: r=0.996

100

150

200

250

300

350

400

450

Un

it p

rice

s ($

00

0)

100 200 300 400 500 600

House prices ($000)

Main

ly p

ost

-GF

C (

201

0M

04

-20

15

M06

)

Brisbane: r=0.992

100

150

200

250

300

350

400

450

Un

it p

rice

s ($

00

0)

100 200 300 400 500 600 700

House prices ($000)

Ma

inly

post

GFC

(20

08

M0

5-2

015

M0

6)

Canberra: r=0.997

120

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480

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it p

rice

s ($

00

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House prices ($000)

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inly

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C (

200

8M

02-2

01

5M

06

)

Darwin: r=0.990

50

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Un

it p

rice

s ($

00

0)

100 150 200 250 300 350 400

House prices ($000)

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(20

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5M

06

)

Hobart: r=0.983

100

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600

Un

it p

rice

s ($

00

0)

100 200 300 400 500 600 700 800

House prices ($000)

Ma

inly

po

st G

FC

(200

8M

3-2

015

M0

6)

Melbourne: r=0.996

120

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480

520

Un

it p

rice

s ($

00

0)

100 200 300 400 500 600 700

House prices ($000)

Main

ly p

ost

GF

C (

200

8M

04

-20

15M

06

)

Perth: r=0.998

100

200

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600

700

Un

it p

rice

s ($

00

0)

200 300 400 500 600 700 800 900 1,000

House prices ($000)

Ma

inly

duri

ng G

FC

(20

07M

01

-20

12

M12

)

Ma

inly

du

rin

g G

FC

(20

07

M01

-20

12

M1

2)

Sydney: r=0.995

10

Figure 2. Monthly house and unit prices in Australian capital cities.

Note: Green dotted lines show the narrowing of the cointegrated pairs over time.

100

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500

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m2

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Adelaide

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m2

20

02

m1

0

20

03

m6

20

04

m2

20

04

m1

0

20

05

m6

20

06

m2

20

06

m1

0

20

07

m6

20

08

m2

20

08

m1

0

20

09

m6

20

10

m2

20

10

m1

0

20

11

m6

20

12

m2

20

12

m1

0

20

13

m6

20

14

m2

20

14

m1

0

20

15

m6

Canberra

100

200

300

400

500

600

19

96

m2

19

96

m1

0

19

97

m6

19

98

m2

19

98

m1

0

19

99

m6

20

00

m2

20

00

m1

0

20

01

m6

20

02

m2

20

02

m1

0

20

03

m6

20

04

m2

20

04

m1

0

20

05

m6

20

06

m2

20

06

m1

0

20

07

m6

20

08

m2

20

08

m1

0

20

09

m6

20

10

m2

20

10

m1

0

20

11

m6

20

12

m2

20

12

m1

0

20

13

m6

20

14

m2

20

14

m1

0

20

15

m6

Darwin

50

100

150

200

250

300

350

400

19

96

m2

19

96

m10

19

97

m6

19

98

m2

19

98

m10

19

99

m6

20

00

m2

20

00

m10

20

01

m6

20

02

m2

20

02

m10

20

03

m6

20

04

m2

20

04

m10

20

05

m6

20

06

m2

20

06

m10

20

07

m6

20

08

m2

20

08

m10

20

09

m6

20

10

m2

20

10

m10

20

11

m6

20

12

m2

20

12

m10

20

13

m6

20

14

m2

20

14

m10

20

15

m6

Hobart

100

200

300

400

500

600

700

800

19

96

m2

19

96

m10

19

97

m6

19

98

m2

19

98

m10

19

99

m6

20

00

m2

20

00

m10

20

01

m6

20

02

m2

20

02

m10

20

03

m6

20

04

m2

20

04

m10

20

05

m6

20

06

m2

20

06

m10

20

07

m6

20

08

m2

20

08

m10

20

09

m6

20

10

m2

20

10

m10

20

11

m6

20

12

m2

20

12

m10

20

13

m6

20

14

m2

20

14

m10

20

15

m6

Melbourne

100

200

300

400

500

600

700

19

96

m2

19

96

m1

0

19

97

m6

19

98

m2

19

98

m1

0

19

99

m6

20

00

m2

20

00

m1

0

20

01

m6

20

02

m2

20

02

m1

0

20

03

m6

20

04

m2

20

04

m1

0

20

05

m6

20

06

m2

20

06

m1

0

20

07

m6

20

08

m2

20

08

m1

0

20

09

m6

20

10

m2

20

10

m1

0

20

11

m6

20

12

m2

20

12

m1

0

20

13

m6

20

14

m2

20

14

m1

0

20

15

m6

Perth

100

200

300

400

500

600

700

800

900

1,000

19

96

m2

19

96

m1

0

19

97

m6

19

98

m2

19

98

m1

0

19

99

m6

20

00

m2

20

00

m1

0

20

01

m6

20

02

m2

20

02

m1

0

20

03

m6

20

04

m2

20

04

m1

0

20

05

m6

20

06

m2

20

06

m1

0

20

07

m6

20

08

m2

20

08

m1

0

20

09

m6

20

10

m2

20

10

m1

0

20

11

m6

20

12

m2

20

12

m1

0

20

13

m6

20

14

m2

20

14

m1

0

20

15

m6

House prices ($000) Unit prices ($000)

Sydney

11

In order to better understand the relationship between monthly house and unit prices, Figure 2

presents the individual time series plots of house and unit prices for each city separately. House

and unit prices exhibit a strong degree of co-movement without any sign of collapsing over time.

It is important to recognise that house and unit prices tend to deviate from each other quite often.

However, as can be seen by the green dotted lines in Figure 2, every now and then the inflated

gap is narrowed (adjusted). In all cities, except for Canberra, the narrowing takes place after a

sustained period of widening. This suggests that most homebuyers may eventually consider

houses and units to be close substitutes, even if they initially regard them differently.

5.2. Unit root and cointegration test results

The ADF test results indicated that the logarithm of house prices in Brisbane, Darwin, Hobart,

Melbourne and Perth are I(1). The logarithm of unit prices in Adelaide, Brisbane, Darwin and

Hobart are stationary after first differencing. According to the ADF test, house prices in Brisbane,

Darwin, Hobart, Melbourne and Perth are also I(1). However, all 16 monthly return series

become I(0) when we apply the additive outlier test with one breakpoint in the trend function.1

Table 2. Johansen and Hansen cointegration tests

No. of vectors

Johansen test Hansen test

Eigenvalue Max-eigen

statistic

5% critical

value

Trace

statistic

5% critical

value

Lc statistic p-value

Adelaide

None 0.14 36.47* 15.89 40.41* 20.26

At most 1 0.02 3.93 9.16 3.93 9.16 0.038 > 0.20

Brisbane

None 0.08 18.28* 14.26 20.82* 15.49

At most 1 0.01 2.55 3.84 2.55 3.84 0.075 > 0.20

Canberra

None 0.06 14.24* 11.22 14.32* 12.32

At most 1 0 0.08 4.13 0.08 4.13 0.103 > 0.20

Darwin

None 0.09 18.13* 15.89 25.89* 20.26

At most 1 0.04 7.75 9.16 7.75 9.16 0.086 > 0.20

Hobart

None 0.07 16.11* 15.89 23.07* 20.26

At most 1 0.03 6.96 9.16 6.96 9.16 0.113 > 0.20

Melbourne

None 0.13 32.14* 15.89 34.14* 20.26

At most 1 0.01 2 9.16 2 9.16 0.083 > 0.20

Perth

None 0.06 15.19 15.89 22.45* 20.26

At most 1 0.03 7.25 9.16 7.25 9.16 0.109 > 0.20

Sydney

None 0.14 35.45* 15.89 38.87* 20.26

At most 1 0.01 3.42 9.16 3.42 9.16 0.343 0.19

Note: * Significant at 5% or better.

Table 2 shows the results of the Johansen (1995) and Hansen (1992) cointegration tests between

house and unit prices. The Johansen trace test indicates that the null of no cointegration is

rejected at the 5% level of significance for all eight cities. These results are consistent with the

Max-eigen test, whereby the null is rejected for all cities except Perth. As a robustness check, we

1 Due to lack of space these results are not reported but they are available upon request.

12

also present the Hansen (1992) cointegration test in Table 2. It suggests that a long-run

relationship between Ht and Ut does exist and is not subject to significant instability in all eight

capital cities.

5.3. Causality and generalised impulse responses

The causality test results between ΔUt= ΔLn(UPt) and ΔHt= ΔLn(HPt) are presented in Table 3.

The Granger causality test reveals that short-run changes in house prices can influence unit prices

in all capital cities. However, unit price changes affect house prices only in Adelaide, Melbourne,

Perth and Sydney. Therefore, with the exception of Brisbane, bi-directional causality exists only

for the larger and more populous capital cities. In smaller capital cities such as Canberra, Darwin

and Hobart house prices can influence unit prices, but not the other way around.

Table 3. Granger causality test between house and unit prices

City

Null hypothesis

Optimal

lag length

Schwarz

information

criterion

ΔUt does not Granger cause

ΔHt

ΔHt does not Granger

cause ΔUt

F statistic p-value

F statistic p-value

Adelaide 8.53 < 0.01

6.36 < 0.01 3 -12.27

Brisbane 1.31 0.27

11.34 < 0.01 3 -12.79

Canberra 1.26 0.29

24.41 < 0.01 2 -12.20

Darwin 0.76 0.38

10.04 < 0.01 1 -9.73

Hobart 0.26 0.61

4.57 0.03 1 -9.35

Melbourne 21.27 < 0.01

8.36 < 0.01 1 -12.77

Perth 2.90 0.04

22.27 < 0.01 3 -12.53

Sydney 5.56 0.02

19.04 < 0.01 1 -13.43

Figure 3 shows the generalised impulse responses of ΔUt to a one standard deviation shock

imposed on the corresponding innovations of ΔHt using the estimated VAR model. The results

for the impulse responses are fairly consistent with the Granger causality test results in that unit

prices react to changes in house prices in almost all capital cities, particularly in Adelaide,

Canberra, Darwin, Hobart, Melbourne and Sydney. In Darwin, Hobart, Melbourne and Sydney

the dynamic responses die off after approximately five months, whereas in the other capital cities

these responses are smaller albeit more persistent.

13

Figure 3. Generalised impulse responses of unit [ΔUt] to house [ΔHt] prices.

Note: Responses are based on one standard deviation imposed on the resulting innovations.

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Adelaide

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Brisbane

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Canberra

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Darwin

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Hobart

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Melbourne

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Perth

.000

.002

.004

.006

.008

.010

1 2 3 4 5 6 7 8 9 10

Monthly growth rate ± 2 S.E.

Sydney

14

5.4 Estimated self-exciting threshold models

The estimated self-exciting threshold models for all eight capital cities are shown in Table 4. For

comparative purposes, if 1ˆ

i or 1ˆ

i in regime 1 are statistically significant, the corresponding

coefficients for regime 2 (i.e. 2ˆ

i or 2ˆ

i ) are also reported irrespective of whether they are

significant or not. Out of 16 error correction coefficients ( 1i and 2i ), nine are statistically

significant at the 10% level or better with the expected negative sign. Despite the fact that the

dependent variable is in logarithmic differences (i.e. monthly returns), the eight estimated

equations presented in Table 4 perform well in terms of goodness-of-fit statistics, particularly for

Sydney ( 2R =0.554), Melbourne ( 2

R =0.508) and Perth ( 2R =0.411). The residual-based

diagnostic tests in Table 4 also indicate that there is no sign of serial correlation. The coefficient

sum of the lagged dependent variables in both regimes, i.e. 1i and

2i , are well below

unity.

Using a conventional 15% trimming region, the estimated threshold parameter ( ̂ ) is positive for

seven out of eight capital cities: Adelaide (0.0154), Brisbane (0.0047), Canberra (0.0062),

Darwin (0.0240), Hobart (-0.0211), Melbourne (0.0107), Perth (0.0010) and Sydney (0.0000). To

illustrate what the monthly growth figures in parenthesis imply, consider Adelaide as an example.

In Adelaide when unit prices are on an upward trajectory [regime 2 with a return exceeding

1.54% in the previous month or ΔLn(UPt-1) ≥ 0.0154], a 1% increase in house prices has a total

effect ( 2ˆ

i ) of 1.89% on unit prices. However, when the market is not “excited” and the

previous month’s growth rate is below 1.54%, the above total effect ( 1ˆ

i =0.56%) in regime 1

is less than one-third of that of regime 2. Therefore, rising house prices has a three-fold greater

positive influence on unit prices when the Adelaide property market for units is “excited” (i.e.

experiencing monthly growth in excess of 1.54%).

With a threshold of zero ( ̂ =0.00) the Sydney market for units is the most “excitable” capital city

(see Table 4). When unit prices experience negative growth, a 1% rise in house prices boosts unit

prices by 0.418%, but when unit prices experience positive monthly growth (however small), this

same rise leads to a 0.54% increase. The threshold parameter (1.07%) for Melbourne is more than

that of Sydney (0.00%), suggesting that Melbourne requires greater growth in unit prices to get

excited. However, both 1ˆ

i =0.842 and 2ˆ

i =0.978 for Melbourne are greater than those of

Sydney.

15

Table 4. Estimated self-exciting threshold models

Variable

Adelaide Brisbane Canberra Darwin

ΔUt-1 < 0.0154 (n=183)

ΔUt-1 < 0.0047 (n=109)

ΔUt-1 < 0.0062 (n=119)

ΔUt-1 < 0.0240 (n=157)

Coefficient p-value

Coefficient p-value

Coefficient p-value

Coefficient p-value

Intercept 0.001 0.249

0.003 0.043

0.000 0.80

0.003 0.16

ΔHt 0.488 0.000

0.275 0.046

0.257 0.04

0.069 0.42

ΔHt-1 0.127 0.216

0.316 0.01

0.167 0.07

ΔHt-3 -0.058 0.565

Sum 0.557

0.275

0.573

0.236

ΔUt-1 -0.198 0.020

-0.235 0.032

-0.191 0.02

0.091 0.33

ΔUt-2

-0.028 0.69

ΔUt-3 -0.277 0.000

-0.068 0.433

0.212 0.00

ΔUt-4

0.100 0.15

ΔUt-5

-0.119 0.09

ΔUt-6

0.222 0.00

ECt-1 -0.065 0.013

-0.077 0.012

-0.098 0.03

-0.073 0.00

ΔUt-1≥ 0.0154 (n=47)

ΔUt-1≥0.0047 (n=121)

ΔUt-1≥ 0.0062 (n=111)

ΔUt-1≥ 0.0240 (n=30)

Intercept -0.003 0.637

0.006 0.007

0.002 0.15

0.010 0.35

ΔHt 0.980 0.000

0.443 0.000

0.515 0.00

0.592 0.00

ΔHt-1 0.425 0.004

0.369 0.00

0.329 0.09

ΔHt-3 0.488 0.002

Sum 1.894

0.443

0.884

0.921

ΔUt-1 -0.254 0.239

-0.473 0.001

-0.079 0.37

-0.764 0.00

ΔUt-2

-0.503 0.01

ΔUt-3 -0.321 0.009

0.353 0.000

-0.098 0.53

ΔUt-4

0.969 0.00

ΔUt-5

0.836 0.00

ΔUt-6

0.076 0.71

ECt-1 -0.103 0.137

-0.037 0.091

-0.177 0.00

-0.056 0.43 2

R 0.386

0.308

0.383

0.328

Overall F test 12.08

12.32

16.79

5.78

DW 1.97

2.02

1.96

2.03

Schwarz criterion -5.871

-6.129

-5.997

-4.735

BGSC(a) LM test F(2,212)=0.110 0.90

F(2,218)=0.365 0.69

F(2,218)=1.308 0.27

F(2,165)=1.922 0.15

Bai-Perron scaled F

test(b) 40.37 0.00 19.84 0.00 39.55 0.00 51.05 0.00

Notes: (a) BGSC=Breusch-Godfrey Serial Correlation. (b) The Bai-Perron test (2003) for zero versus one threshold.

16

Table 4. Estimated self-exciting threshold models (continued)

Variable

Hobart Melbourne Perth Sydney

ΔUt-1 < -0.0211 (n=37) ΔUt-1 < 0.0107 (n=155) ΔUt-1 < 0.0010 (n=67) ΔUt-1 < 0.0000 (n=61)

Coefficient p-value

Coefficient p-value

Coefficient p-value

Coefficient p-value

Intercept -0.026 0.03 0.001 0.27 0.001 0.71 -0.002 0.03

ΔHt 0.187 0.69 0.669 0.00 0.465 0.00 0.405 0.00

ΔHt-1 0.593 0.04 0.245 0.00 -0.185 0.17 0.055 0.60

ΔHt-2 -0.072 0.25 -0.344 0.00

ΔHt-3 -0.170 0.67 0.288 0.04 0.302 0.00

Sum 0.609 0.841 0.567 0.419

ΔUt-1 -0.694 0.01 -0.312 0.00 -0.287 0.02

ΔUt-2 0.518 0.00 -0.270 0.01 -0.579 0.00

ΔUt-3 -0.019 0.79 0.518 0.00

ΔUt-4 0.229 0.02

ECt-1 -0.139 0.02 -0.029 0.22 -0.161 0.00 -0.036 0.44

ΔUt-1≥ -0.0211 (n=173) ΔUt-1≥ 0.0107 (n=75) ΔUt-1≥ 0.00101 (n=162) ΔUt-1≥ 0.0000 (n=169)

Intercept 0.001 0.62 -0.003 0.32 -0.001 0.31 0.002 0.00

ΔHt 0.520 0.00 0.665 0.00 -0.028 0.79 0.365 0.00

ΔHt-1 0.036 0.84 -0.043 0.72 0.504 0.00 0.175 0.00

ΔHt-2 0.356 0.00 -0.033 0.55

ΔHt-3 0.294 0.05 0.254 0.06 0.033 0.58

Sum 0.849 0.978 0.730 0.540

ΔUt-1 -0.012 0.91 0.072 0.66 -0.032 0.76

ΔUt-2 -0.034 0.66 0.190 0.02 -0.077 0.31

ΔUt-3 -0.052 0.52 -0.014 0.86

ΔUt-4 0.005 0.95

ECt-1 -0.056 0.04 -0.002 0.94 -0.033 0.30 0.006 0.81 2

R 0.193 0.508 0.411 0.554

Overall F test 4.85 19.15 11.59 19.97

DW 1.99 1.95 2.00 2.01

Schwarz criterion -3.930 -6.565 -6.028 -7.210

BGSC(a) LM test F(2,194)=1.520 0.22 F(2,214)=0.161 0.85 F(2,211)=1.778 0.17 F(2,212)=0.239 0.79

Bai-Perron scaled F

test(b) 25.41 0.05 23.58 0.05 39.72 0.00 52.07 0.00

Notes: (a) BGSC=Breusch-Godfrey Serial Correlation. (b) The Bai-Perron test (2003) for zero versus one threshold.

17

Hobart is the only capital city for which the threshold parameter is negative (-0.0211). This

implies that 82% of times during the adjusted sample period (173 out of 210 months), unit prices

respond to house prices according to the short-run responses in regime 2. Under these

circumstances, within the first three months a 1% increase in Hobart house prices pushes unit

prices up by 2ˆ

i =0.85%. This effect is limited to 0.61% when the previously monthly return is

below -2.1%. In a sense one may argue that unit prices in Hobart are easily excitable by a higher

chance of switching to regime 2. Having said that, as can be seen from Figure 2, both house and

unit prices have been quite stagnant since the 2008 GFC, making Hobart very different from

Sydney in terms of the likelihood of market excitability.

With the highest threshold parameter (0.0240), Darwin is the least excitable city. When the

lagged monthly growth rate of unit prices is below 2.4%, a 1% increase in house prices results in

a meagre rise (0.234%) in unit prices. Only when unit prices in Darwin enjoy extremely buoyant

market conditions, do house prices exert a greater degree of influence (regime 2 in lieu of regime

1 with 2ˆ

i =0.921). This only occurred 16% of the time (30 out of 187 months). At the bottom

of Table 4 we have also shown the results of the Bai-Perron (2003) test, which compares zero

threshold (one-regime model) with one threshold (two-regime model). Since the null hypothesis

is rejected at the 5% level for all eight capital cities, the varying threshold effects are statistically

justifiable.

6. Discussion and conclusion

We have examined the dynamic interaction between house prices and unit prices in Australian

capital cities. A feature of our analysis is that we employ a high quality monthly dataset, for

which, on the tenth of each month, price data becomes available for the previous month. In

advocating the use of monthly house price data in the United States context, Park and Hong

(2012, p. 16) suggest: “Prompt and accurate projection of the US housing market trends should

be carried out not only to prevent a recurrence of the recent GFC, but also to minimize the risk of

executing quick judgment and corresponding errors”. The timely availability of data afforded by

the CoreLogic index means that analysis, such as ours, can provide early detection of any

abnormal behaviour in the relationship between unit prices and house prices. This is a significant

advantage of our dataset over using ABS or REIA quarterly house and unit price indices.

We find that house and unit prices are cointegrated. There are at least three reasons for this

finding. First, from the perspective of the potential investor, houses and units are substitutable

investments. Second, negative gearing encourages individuals to borrow against the equity in

their home to buy an investment property, which is often a unit. According to the Australian Tax

Office, in 2010, 10% of Australian taxpayers were negatively geared landlords (Colebatch, 2010).

When the price of the family home rises, this increases demand for units, pushing up their price

as well. Third, the long run relationship between house and unit prices is reinforced by

demographic-based flows between those purchasing houses and units as owner-occupiers.

Based on the results of Granger causality and generalised impulse responses, house prices

significantly influence unit prices across all cities. However, there is bi-directional causality

18

between unit and house prices only in three of the major capital cities; namely, Melbourne, Perth

and Sydney. This result provides some support for the findings in other studies of Australian

housing markets employing different methodologies that the Melbourne and Sydney markets are

different from the other capital cities (see eg. Akimov. et al., 2015). According to Figure 2, in

recent times unit prices are falling (Perth) or stagnant (Melbourne). Therefore, this indicates that

the boom in house prices in these two cities may come to an end, particularly in Melbourne, in

which there is an oversupply of units (Birrell & Healy 2013). This result is consistent with the

sentiment expressed by David and Soos (2015) in the introduction that when the housing bubble

bursts, its epicentre will be in Melbourne. This result is also consistent with the view expressed

by 2002 Nobel Prize winner in Economics, Vernon Smith, who, on a visit to Australia in July

2015, expressed the view that Melbourne and Sydney house prices have grown too fast and that

the housing bubble centred on these two major capital cities is threatening to burst (Ryan, 2015).

We also, for the first time, have applied self-exciting threshold models to examine the

relationship between house and unit prices in Australia. The advantage of employing self-exciting

models is that one can explore the non-linear dynamics between the series, which is not possible

with conventional linear models. In particular, self-exciting models allow us to examine whether

there is evidence of a herd mentality in Australian metropolitan property markets. The latter is

important given a widely publicised report by global fund managers PIMCO that suggests low

interest rates and rising house prices in Australia are driving a herd mentality (Ryan, 2015a).

Our main finding from the self-exciting models is that when the market for units is self-excited,

or bullish, the positive effects of house prices on unit prices are markedly larger than would

otherwise be the case. We find evidence of varying degrees of herd mentality in the Australian

property market with Sydney and Darwin being the most and least “excitable” capital cities,

respectively. The finding for Sydney is consistent with a commonly accepted view, evident in the

PIMCO report (Ryan, 2015a) that Sydney property prices exhibit irrational exuberance. For

example, in a speech in April 2015, Reserve Bank of Australia Governor, Glen Stephens,

described Sydney property prices as “rather exuberant” (Greber, 2015). One area for future

research would be to apply self-exciting models to the house price to income ratio to ascertain if

there have been bubbles over regimes (Walther, 2011). Another would be to apply self-exciting

models to forecast housing price dynamics (Park & Hong, 2012). Studies of this nature would

complement our major findings.

19

References

Abelson, P., Joyeux, R., and Mahuteau, S. (2013) Modelling house prices across Sydney,

Australian Economic Review, 46(3), pp. 269-285.

Akimov, A., Stevenson, S. and Young, J. (2015) Sychronisation and commonalities in

metropolitan housing market cycles, Urban Studies, 52(9), pp. 1665-1682.

Andrews, D. (1993) Tests for parameter instability and structural change with unknown change

point, Econometrica, 61, pp.821–856.

Aston, H. (2015) Australian housing market facing “bloodbath” collapse: economists. Sydney

Morning Herald, June 22.

Bai, J. and Perron, P. (2003) Computation and analysis of multiple structural change models,

Journal of Applied Econometrics, 18(1), pp.1–22.

Balcilar, M., Beyene, A., Gupta, R. and Seleteng, M. (2013) Ripple effects in South African

house prices, Urban Studies, 50, pp. 876-894.

Barari, M., Sarkar, N., Kundu, S., and Chowdhury, K. B. (2014) Forecasting house prices in the

United States with multiple structural breaks, International Econometric Review, 6(1), pp.1-

23.

Birrell, B. and Healy, E. (2013) Melbourne’s high rise apartment boom, Centre for Population

and Urban Research, Monash University, Research Report, September.

Blanco, F., Martin, V. and Vazquez, G. (2015) Regional house price convergence in Spain during

the housing boom, Urban Studies (in press).

Canarella, G., Miller, S. and Pollard, S. (2012) Unit roots and structural change: An application to

US house price indices, Urban Studies, 49(4), pp. 757-776.

Chan, K.S., (1993) Consistency and limiting distribution of the least squares estimator of a

threshold model. Annals of Statistics, 21 (1), pp.520–533.

Colebatch, T. (2010) Caught in the cogs of the tax regime, The Age, March 30.

Costello, G., Fraser, P. and Groenewold, N. (2011) House prices, non-fundamental components

and interstate spillovers: The Australian experience, Journal of Banking & Finance, 35, pp.

653-669.

David, L. and Soos, P. (2015) The great Australian household debt trap: Why housing prices have

increased, Submission to the House of Representatives Standing Committee on Economics

2015 Inquiry into Home Ownership.

Demographia (2015) International housing affordability survey, 11th

edn.

http://www.demographia.com/dhi.pdf (last accessed August 11, 2015).

Goh, Y.M., Costello, G., and Schwann, G. (2012) The accuracy and robustness of real estate

price index methods, Housing Studies, 27(5), pp. 643-666.

Granger, C.W.J. (1986) Developments in the study of cointegrated economic variables, Oxford

Bulletin of Economics and Statistics, 48(3), pp. 213-228.

Greber, J. (2015) RBA weighs household debt as critical factor in interest rate cut call, Australian

Financial Review, April 21.

20

Gupta, R., Andre, C. and Gil-Alana, L. (2015) Comovement in Euro area housing prices: A

fractional cointegration approach, Urban Studies (in press).

Hansen, B.E. (1992) Tests for parameter instability in regressions with I(1) processes, Journal of

Business and Economic Statistics, 10, pp. 321-335.

He, L.T. (2000) Causal relationships between apartment REIT stock returns and unsecuritized

residential real estate, Journal of Real Estate Portfolio Management, 6(4), pp. 365-372.

Janda, M. (2015) Housing market puts Australia at risk of becoming nation of “imprisoned tenant

serfs”, Liberal MP John Alexander says, ABC News, June 27

http://www.abc.net.au/news/2015-06-26/australia-risks-becoming-nation-of-tenant-serfs-

liberal-mp/6575656 (last accessed, August 4 2015).

Johansen, S. (1995) Likelihood-based Inference in Cointegrated Vector Autoregressive Models,

Oxford: Oxford University Press.

Kemme, D.M. and Roy, S. (2013) Did the recent housing boom signal the Global Financial

Crisis? Southern Economic Journal, 78(3), pp. 999-1018.

Koop, G., Pesaran, M.H. and Potter, S. (1996) Impulse response in nonlinear multivariate models,

Journal of Econometrics, 74, pp. 119–147.

Lean, H.H. and Smyth, R. (2013) Regional house prices and the ripple effect in Malaysia, Urban

Studies, 50(5), pp. 895-922.

Leishman, C., Costello, G., Rowley, S and Watkins, C. (2013) The predictive performance of

multilevel models of housing sub-markets: A comparative analysis, Urban Studies, 50(6),

pp. 1201-1220.

Ma, L. and Liu, C. (2014) A spatial decomposition approach for investigating house price

convergences, Australasian Journal of Regional Studies, 20(3), pp. 487-511.

McKenna, G. (2015) New record: The rise and rise of Australian household debt, Business

Insider, February 23, http://www.businessinsider.com.au/new-record-the-rise-and-rise-of-

australian-household-debt-2015-2 (last accessed August 13, 2015).

Meen, G. (1999) Regional house prices and the ripple effect: A new interpretation, Housing

Studies, 14, pp. 733-753.

Morrison, P.S. and McMurray, S. (1999) The inner city apartment versus the suburb: Housing

sub-markets in a New Zealand city, Urban Studies, 36(2), pp. 377-397.

Nasar, J.L., Evans-Cowley, J.S. and Mantero, V. (2007) McMansions: The extent and regulation

of super-sized houses, Journal of Urban Design, 12(3), pp. 339-358.

Oikarinen, E. (2014) Is urban land price adjustment more sluggish than housing price

adjustment? Empirical evidence, Urban Studies, 51(8), pp.1686-1706.

Park, J., and Hong, T. (2012) Trends and prospects of the US housing market using the Markov

switching model, Journal of Urban Planning and Development, 138(1), pp. 10–17.

Piazzesi, M., Schneider, M. and Tuzel, S. (2007) Housing, consumption and asset pricing,

Journal of Financial Economics, 83, pp. 531-569.

Phillips, P.C. (1995) Fully modified least squares and vector autoregression, Econometrica,

63(5), pp.1023-1078.

21

Ryan, P. (2015) Housing bubble could burn investors, warns Nobel Prize-winning economist,

ABC News online, July 31, http://www.abc.net.au/news/2015-07-30/australian-housing-

bubble-nobel-prize-winning-economist/6659014 (last accessed August 18, 2015).

Ryan, L. (2015a) A model of Australian household leverage. PIMCO Quantitative Research,

July.

Tsay, R.S. (1989) Testing and modeling threshold autoregressive processes, Journal of the

American Statistical Association, 84, pp.231–240.

Vogelsang, T.J., and Perron, P. (1998), Additional test for unit root allowing for a break in the

trend function at an unknown time, International Economic Review, 39, pp. 1073–1100.

Walther, A. (2011). An episodic time series model of bubbles and crashes. In European

Economic Association & Econometric Society Conference 25 - 29 August 2011, Oslo (pp.

25-29).

Worthington, A. C. (2012). The quarter century record on housing affordability, affordability

drivers, and government policy responses in Australia, International Journal of Housing

Markets and Analysis, 5(3), pp. 235-252.

Wulff, M. and Lobo, M. (2009) The new gentrifiers: The role of households and migration in

reshaping Melbourne’s core and inner suburbs, Urban Policy and Research, 27(3), pp. 315-

331.

Yardney, M. (2015) Is it time to get nervous about the inner city apartment glut? Smart Money,

March 26, http://www.smartcompany.com.au/finance/investment/46234-is-it-time-to-get-

nervous-about-the-inner-city-apartment-glut.html (last accessed August 13, 2015)