An Econometric Analysis of SEQ Dwelling Pricesimages.brisbanetimes.com.au/file/2016/01/06/7123280... · · 2016-01-06... Gold Coast, Ipswich, ... real loan rate, ... Literature

An Econometric Analysis of SEQ

Dwelling Prices

Local Government Association of Queensland

Final Draft v1.3

December 2015

An Econometric Analysis of SEQ Dwelling Prices Final Draft v1.3

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Document Control

Job ID: 18115BNE

Job Name: An Econometric Analysis of SEQ Dwelling Prices

Client: Local Government Association of Queensland

Client Contact: Greg Hallam

Project Manager: Simon Smith

Email: [email protected]

Telephone: 0419 664 774

Document Name: Determinants of SEQ Dwelling Prices 2015 FINAL DRAFT v1.3.docx

Last Saved: 23/12/2015 2:16 PM

Version Date Reviewed Approved

Draft v1.0 13/11/2015

Draft v1.1 with PJC feedback 24/11/2015

Final Draft v1.2 14/12/2015 SS SS

Final Draft v1.3 23/12/2015 SS SS

Disclaimer:

Whilst all care and diligence have been exercised in the preparation of this report, AEC Group Pty Ltd does not warrant the accuracy of the information contained within and accepts no liability for any loss or damage that may be suffered as a result of reliance on this information, whether or not there has been any error, omission or negligence on the part of AEC Group Pty Ltd or their employees. Any forecasts or projections used in the analysis can be affected by a number of unforeseen variables, and as such no warranty is given that a particular set of results will in fact be achieved.


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Key Findings

This is an economic study to examine the factors influencing:

House, unit and land prices (Demand).

Dwelling completions, lot registrations, building approvals (Supply).

in South East Queensland (SEQ) and selected local government areas which included:

Brisbane, Gold Coast, Ipswich, Moreton Bay, Logan and Sunshine Coast.

Price data was supplied by Corelogic RP Data from March 1991 to March 2015.

The economic variables tested for explanatory power were: All Ordinaries index, loan rate, gross disposable income, exchange rate, unemployment rate, consumer price index (housing), housing stock.

Demand Modelling

The following demand models were constructed to model prices:

House Prices: total, 1-2, 3, 4, 5 bedrooms per sqm by SEQ + 5 LGAs (30)

Unit Prices: total, 1, 2, 3 bedroom per sqm by SEQ + 5 LGAs (24)

Land Prices (total, per sqm) by SEQ + 5 LGAs (12)

Findings of the SEQ demand models were:

House and land prices were found to be impacted by:

o All Ordinaries index (negatively).

o Loan rate (negatively).

o Unemployment (negatively).

Unit prices were found to be impacted by:

o All Ordinaries index (negatively).

o Loan rate (negatively).

o Gross disposable income (positively)

o Exchange rate (positively)

o Unemployment rate (negatively)

o Consumer price index (negatively).

For the disaggregated demand models (number of bedrooms per sqm) the findings were:

Results are relatively consistent across house sizes for the responses to the All Ordinaries Index, real loan rate, real gross disposable income per capita and the unemployment rate.

Results are different for different unit sizes.

For land prices per sqm there is no effect from the All Ordinaries index, real loan rate of housing stock. There are significant positive effects from real gross disposable income per capita, exchange rate and consumer price index and significant negative effects from unemployment.

For selected LGAs results shows an expected degree of difference in some aspects. For houses of different sizes the responses across sizes and LGAs are uniform to

movements in the All Ordinaries index and unemployment. For other variables the responses vary.

Supply Models

The following supply models were constructed to see if the supply measures were influenced by changes in prices and other variables:

Dwelling Completions: houses total, 1-2, 3, 4, 5 bedrooms per sqm, units total, 1, 2, 3 bedrooms, land total, land per sqm by SEQ + 5 LGAs (54).


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Lot Registrations: houses total, 1-2, 3, 4, 5 bedrooms per sqm, units total, 1, 2, 3

bedrooms, land total, land per sqm by SEQ + 5 LGAs (54).

Building Approvals: houses total, 1-2, 3, 4, 5 bedrooms per sqm, units total, 1, 2, 3 bedrooms, land total, land per sqm by SEQ + 5 LGAs (54).

Panel model of all LGAs combined.

Findings of the supply models were:

Supply responses from a change in prices show consistent results whether the growth is in houses, units or land prices.

Supply is responsive independently of how it is measured (registrations, approvals or completions).

The responses are different when studying supply responses for different sizes of

houses or units or land per sqm.

Supply response to growth in house prices varies across the sizes of houses and the

LGAs.

Supply responds strongly to changes in land prices in Brisbane, Moreton Bay and Sunshine Coast but not for other LGAs.

Lot registrations respond significantly to growth in unit prices for the Gold Coast.

The supply responses from the panel model shows both lot registrations and building approvals respond to changes in house prices but not from land prices.

Summary

Results were consistent with AEC (2010).

Demand is influenced by economic factors differently in disaggregated markets.

Supply responds to price changes but differently in disaggregated markets.

Economic factors that are significant at an aggregate level will have different impacts

at a disaggregated level therefore individual markets should be examined separately.


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Executive Summary

There are many aspects of the housing market that continue to received widespread attention including booming markets, affordability, availability of finance for investors and levels of foreign investment. Most of this debate has been at a national or capital city level with minimal investigation of regional markets or investigation of unique market

characteristics. For example, there has been no empirical investigation of the supply side in terms of residential lot production.

The Local Government Association of Queensland Inc. (LGAQ) commissioned AEC to undertaken an analysis of the demand and supply factors impacting real median prices of houses, units and land in South East Queensland (SEQ). Demand factors considered include macroeconomic, housing related and demographic factors, whilst supply factors include an estimate of the housing stock in SEQ, the supply of residential lots at various stages of

production and building costs.

AEC was also asked to see if there was any explanatory evidence of changes to the trunk

infrastructure charging regime impacting median prices. Unfortunately, this aspect could not be modelled due to an inability to differentiate “new” versus “established” dwelling sales with any confidence and an absence of any consistent time series of average infrastructure charges.

In an extension of the SEQ modelling AEC has also modelled selected local government area (LGA) markets individually to determine if there is homogeneity in explanatory variables in these smaller markets outside the capital city.

The study is a repeat of that undertaken by AEC in 2010. To the authors’ knowledge this was the first time such a study had been attempted for a regional area and for the supply side. The findings of AEC (2010) was that the SEQ housing market does not behave similarly to the national market demonstrated by the evidence over 20 years in SEQ that

downward pressure on prices was not as responsive to positive increases in supply. For the supply of residential land it was also clear that supply responds to increases in prices therefore other mechanisms may be required to enhance the supply of residential lots, which were clearly needed at the time to be higher than they were to have a greater

dampening influence on prices. AEC (2010) therefore highlighted the importance of analysing SEQ data instead of relying on estimates based on nationwide aggregate data for policy and planning purposes in relation to the housing market. Repeating the study in

2015 means an additional five years of quarterly data which also allows modelling of the supply side.

AEC approached Corelogic RP Data to supply the necessary median price information and in addition to monthly number of sales and median prices for houses, units and land were also able to supply a more diversified data set including:

1-2, 3, 4 and 5 bedroom houses on a square metre (sqm) basis.

1, 2 and 3 bedroom units on a sqm basis.

Land prices on a sqm basis.

Literature Review

A fresh review of the literature uncovered some twelve academic papers of relevance since 2010. A number of papers are applications to different countries and cities (including Australia) of the same basic methodology used in AEC (2010), namely, dynamic time series models such as Vector Autoregression (VAR), Autoregressive Distributed Lag models

(ARDL) and Error Correction Models (ECMs) (Afonso and Sousa, 2011; Costello et al, 2011; Fry et al, 2011; Wadud et al, 2012; Wilson et al, 2011). None of these really provided any additional insight into the modelling approach. There are also some panel studies, i.e. cross-sections over time (Adams and Füss, 2010; Agnello and Schuknecht, 2011) and a couple of studies that model the supply side (Gitelman and Otto, 2012; McLaughlin, 2011). It is the later supply side model by McLaughlin (2011) that is adopted here.


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Dwelling Prices & Lot Stock

Figure E.1 presents a plot of quarterly real median houses, units and land prices for SEQ since 1991. Three significantly different periods of change in the trends of all three categories have been identified. In the first period before 2001 real median prices for

houses were between $200,000 and $280,000 with an average quarterly growth rate of 0.7%. Real median unit process were between $200,000 and $300,000 with an average quarterly growth rate of 0.8%. Real median land prices were in the region of $85,000 to $140,000 with an average quarterly growth rate of 1.2%.

Prices then increased dramatically in the second period, between 2001 to 2004, with real median house prices growing on average every quarter by 3.4%; units by 2.3%; and land by 3.2%.

In the period 2005 to 2015, real median price growth has stabilised again at a new and higher level. Since 2005 real median house prices were around $450,000 with close to zero average quarterly growth; units were around $380,000 with growth of -0.4%; and land was in the neighbourhood of $220,000 with average quarterly growth of -0.2%.

Figure E.1 Real Median Dwelling Prices ($2011-12), South East Queensland

Source: Corelogic RP Data, AEC

Changes in the housing stock, in particular houses, depend on the supply of land for residential use. At any one time there exists a stock of uncompleted residential lots in SEQ (Figure E.2). These are lots with a reconfiguring a lot (RAL) development permit approval

but they have not yet proceeded to survey plan (operational works) endorsement. The stock of uncompleted residential lots is added to through approval of RALs and is decreased by lot endorsement (council approval of operational works to create lots) and lots lapsed (lots approved by the council but not yet developed or endorsed by the council within a prescribed period).

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

Mar

-91

Mar

-92

Mar

-93

Mar

-94

Mar

-95

Mar

-96

Mar

-97

Mar

-98

Mar

-99

Mar

-00

Mar

-01

Mar

-02

Mar

-03

Mar

-04

Mar

-05

Mar

-06

Mar

-07

Mar

-08

Mar

-09

Mar

-10

Mar

-11

Mar

-12

Mar

-13

Mar

-14

Mar

-15

Houses Units Land


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Figure E.2 Median Land Prices ($2011-12) & Stock of Lot Approvals, South East Queensland

Source: QT, AEC

The stock of undeveloped lots reached a low of 24,598 in Mar-02 and has grown since to reach 62,383 in Dec-14. The nadir in the stock of lot approvals is led by the increase in real median land prices and lot stock has increased following the rise in median prices. Stock levels, however, appear to have kept growing even though the real median land

prices appear to be in a long term decline.

Modelling

Four types of modelling were undertaken. The first three of these were a repeat of those used in AEC (2010) being:

A Karantonis & Ge (2007) model to consider the relationship, based on Granger non-causality testing, between real prices and dwelling completions when controlling for a number of macroeconomic variables.

A long-run demand model based on Abelson et al (2005) to determine the influence of a range of explanatory variables on median prices.

A short-run asymmetric version of Abelson et al (2005) to study responses during “boom” times.

The McLaughlin (2011) supply model to determine the influencing factors on supply represented as dwelling completion, total lot registrations, or building approvals.

Granger non-causality Testing

Two versions of the Granger non-causality testing were modelled, one when income is measured by gross disposable income per capita and second when income is measured by real gross state product per capita. The results in both cases show feedback between real

prices and dwelling completion. Overall, there is evidence to indicate prices and dwelling completions are endogenously determined as there is Granger causality in both directions. There is weaker evidence (at the 10% level) of feedback from the loan rate (R) to dwelling

completions which is consistent for house, units and land prices.

The long-run price models were firstly run over the same time period as in AEC (2010) to determine consistency. Even though some explanatory time series had been revised (notably gross disposable income per capita, SEQ population and housing stock) the signs and significance of the coefficients were proven to be extremely robust.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

$0

$50,000

$100,000

$150,000

$200,000

$250,000

$300,000

Mar

-91

May

-92

Jul-

93

Sep

-94

No

v-9

5

Jan

-97

Mar

-98

May

-99

Jul-

00

Sep

-01

No

v-0

2

Jan

-04

Mar

-05

May

-06

Jul-

07

Sep

-08

No

v-0

9

Jan

-11

Mar

-12

May

-13

Jul-

14

Land Lot Stock (RHS)


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Table 4.1 Long-run Model Estimation Comparison, House Prices, South East Queensland

Model (expected signs in parentheses)

AEC (2010) New data, Same period

Constant 7.399** 16.638**

Log Real All Ordinaries index (-) -0.247** -0.217**

Real loan rate (-) -0.069** -0.033**

Log Real gross disposable income pc (+) 0.098 -0.797

Log Trade weighted exchange rate (-) 0.233 0.035

Log Unemployment rate (-) -0.443** -0.793**

Log Consumer price index (+) 0.800* 1.087**

Log Housing stock pc (-) -2.392** -2.196**

R2 0.995 0.994

Note: Standard errors are the Newey-West HAC standard errors computed with 3 lags. pc = per capita. ** significant at the 5% level; * significant at the 10% level.

Source: AEC

Demand Modelling

The modelling was undertaken in a form that reports elasticities. That is the model results estimate by what percentage real median prices change given a 1% change in the

explanatory variable. Long-run modelling of the new time series resulted in the following:

For real median house prices:

o A 1% increase in the All Ordinaries Index will lead to a decrease in real median house prices of 0.37% (up from 0.25% in AEC (2010)).

o A 1% increase in the real loan rate leads to an expected decrease in real median house prices in the order of 0.025% (down from 0.07% in AEC (2010)).

o A 1% increase in unemployment leads to an expected decrease of 0.71% in real

median house prices (up from 0.44% in AEC (2010)).

For real median unit prices all explanatory variables were significant:

o A 1% increase in the All Ordinaries Index leads to an expected decrease in real median unit prices of 0.19%.

o A 1% increase in the real loan rate leads to a 0.02% decrease in real median unit prices.

o A 1% increase in real gross disposable income per capita leads to a 0.8% increase

in real median unit prices.

o A 1% increase in the exchange rate leads to a 0.41% increase in real median unit prices.

o A 1% increase in unemployment rate leads to a decrease in 0.45% in real median unit prices.

o A 1% increase in the consumer price index leads to a 0.59% decrease in real

median unit prices.

For real median land prices:

o A 1% increase in the All Ordinaries Index will lead to a decrease in real median land prices of 0.23%

o A 1% increase in loan rate leads to an expected decrease in real median land prices in the order of 0.022%.

o A 1% increase in unemployment leads to an expected decrease of 0.60% in real

median land prices.

For the disaggregated modelling (size by sqm):

The model results are relatively consistent across house sizes for the responses to the All Ordinaries Index, real loan rate, real gross disposable income per capita and the unemployment rate. However, the exchange rate is only significant for houses of 3 bedrooms (positive effect of 0.32% from a 1% increase in exchange rate). The


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unemployment rate is consistently significant and negative of the order of 0.6 – 0.7%.

Consumer price index and housing stock per capita are not significant.

The effects on different size units are more heterogeneous. Real gross disposable income per capita and the exchange rate are significant across all sizes. The real gross

disposable income per capita effects are larger for the 1 and 3 bedroom real median unit prices. It is weaker in significance and size for the 2 bedroom real median unit prices. The real loan rate shows a significant and negative effect across 1 and 2 bedroom units. The unemployment rate has a negative effect which is significant for 2 and 3 bedroom real median unit prices. The consumer price index has a negative effect significant for 1 and 3 bedroom real median unit prices. The housing stock per capita is only significant for 3 bedroom real median unit prices and it is positive as already

reported for real median unit prices.

For land prices per sqm there is no significant effect from the All Ordinaries Index, the real loan rate or housing stock per capita. There are significant positive effects from real gross disposable income per capita, exchange rate and consumer price index and significant negative effects from unemployment.

Short-run modelling over the same time period as AEC (2010) using the new data no longer

shows a significant response for real median house prices. However, the new data over the full time period shows a significant response for real median unit and land prices. The modelling shows that real median prices of units adjust back to trend at a rate of 10.4% and those for land at a rate of around 7% per quarter both over boom and non-boom times.

The demand modelling for selected LGAs shows an expected degree of heterogeneity in some aspects. For houses of different sizes the responses across sizes and LGAs is uniform

to movements in the All Ordinaries index and unemployment. Both are strong indicators of macroeconomic conditions and have a uniformly negative effect on houses prices. For other variables the responses vary. For example:

Increases in real gross disposable income per capita show a strong response for Logan and Ipswich real median houses prices.

The real loan rate is significant and has a negative impact in Brisbane and the Gold

Coast.

Housing stock per capita has a significant and negative impact on Brisbane and Ipswich prices.

The median price of units of all sizes in Brisbane are responsive to the real loan rate, gross disposable income per capita, unemployment and the All Ordinaries index.

For the Gold Coast the All Ordinaries Index, real disposable income per capita and unemployment rate seem to be significant determinants of real median unit prices (for

all sizes).

On the Sunshine Coast the real loan rate is significant for units and land.

Supply Modelling

As mentioned three supply models were estimated using dwelling completions per capita, lot registrations per capita and building approvals per capita1. These models were

estimated to assess their responses to change in real prices of houses (median and by number of bedrooms per sqm), units (median and by number of bedrooms per sqm), and

land (per sqm). Models for SEQ and separately for Brisbane, Gold Coast, Ipswich, Moreton Bay, Logan and sunshine Coast, and a panel model for the twelve LGAs were estimated.

Supply responses from change in prices measured as change in real median prices show consistent results whether the growth is in houses, units or land prices. Supply is responsive independently of how it is measured (registrations, approvals or completions). The responses are heterogeneous when studying supply responses for different sizes of houses or units or land per sqm.

1 The building approvals models’ results must be taken carefully as the data are only available over a 32 quarter

period (2006-2015).


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The supply modelling for selected LGAs shows heterogenous results. When the models

were to obtain responses to growth in real land prices (median or per sqm), Brisbane, Moreton Bay and Sunshine Coast show a strong supply response by lot registrations. Other LGAs show no supply response. The supply responses to growth in real median houses

prices varies across the sizes of houses and the LGAs. However, except for Logan, all other LGAs modelled had some supply response of lot registration and building approvals. A supply response to growth in real median unit prices is significant for the Gold Coast total lot registrations and for building approvals on the Sunshine Coast.

The supply responses from a model for all LGAs estimated as a panel shows both lot registrations and building approvals respond to changes in real median house prices. No significant responses on lot registration are found from real median or per sqm land prices.

Summary

The study confirms the influences on real median dwelling prices from AEC (2010) and has provided some insight into the supply response to increases in prices. The study also undertook modelling at the LGA level and found similar responses in supply to real median

price changes as in SEQ.


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Glossary

Cointegration When two or more non-stationary variables form a stable long-term relationship

Dependent variable

A variable that is being explained by other variables.

Dwellings Both houses and units. Interchangeable with housing.

Econometrics Econometrics combines economic theory with statistics to analyse and test economic relationships.

Elasticity The ratio of the percent change in the dependent variable due to the percent change in an explanatory variable.

Error correction model

An error correction model is a dynamic system with the characteristic that any deviation of the current state from its long-

run relationship will be fed into its short-run dynamics.

Explanatory variable

A variable that is used to explain another variable.

Housing Both houses and units. Interchangeable with dwellings.

Granger non-causality test

A technique designed to indicate if one variable can predict the future movement of another variable.

Median A value found by arranging all the observations from lowest value to highest value and picking the middle one.

Real Prices adjusted for the effect of inflation and centred on a point in time, e.g. 2012-13 prices.

Stationary A time series without trend or uneven fluctuations. Deviations due to shocks correct back to a long-term mean value. Non-stationary time series require different econometric modelling from

stationary time series.

sqm Square metre

Time series Observations of a variable over time.

Vector Autoregressive Model

A dynamic system of equations where all variables are allowed to influence the (future) response of all other variables in the system


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Table of Contents

DOCUMENT CONTROL .......................................................................................... I

KEY FINDINGS .................................................................................................... I

EXECUTIVE SUMMARY ....................................................................................... IV

GLOSSARY .......................................................................................................... X

TABLE OF CONTENTS......................................................................................... XI

1. INTRODUCTION .......................................................................................... 1

1.1 GUIDE TO THE STUDY ......................................................................................... 1

1.2 ACKNOWLEDGEMENTS ......................................................................................... 1

2. LITERATURE REVIEW .................................................................................. 3

2.1 DYNAMIC TIME SERIES MODELS ............................................................................. 3

2.2 PANEL MODELS ................................................................................................ 3

2.3 SUPPLY SIDE MODELS ........................................................................................ 4

3. IDENTIFICATION & COLLATION OF DATA ................................................... 5

3.1 GEOGRAPHY .................................................................................................... 5

3.2 DATA SET ...................................................................................................... 6

3.3 DWELLING PRICES ............................................................................................. 6

3.4 DEMAND FACTORS ............................................................................................. 9

3.5 SUPPLY FACTORS ............................................................................................ 13

3.6 MAJOR DATA DIFFERENCES FROM 2010 .................................................................. 14

3.7 OTHER ELEMENTS ........................................................................................... 16

4. MODELLING DWELLING PRICES ................................................................ 18

4.1 SELECTION & SPECIFICATION OF ECONOMETRIC MODELS .............................................. 18

4.2 GRANGER NON-CAUSALITY TESTING ...................................................................... 20

4.3 LONG & SHORT RUN PRICE MODELS ...................................................................... 22

4.4 SUPPLY SIDE MODELS ...................................................................................... 32

5. SUMMARY & DISCUSSION ......................................................................... 38

REFERENCES ..................................................................................................... 40

APPENDIX A: LITERATURE REVIEW .................................................................. 42

APPENDIX B: LGA DWELLING PRICE GRAPHS ................................................... 47

APPENDIX C: DATA CONSTRUCTION ................................................................. 53

APPENDIX D: LOT PRODUCTION PROCESS ........................................................ 55

APPENDIX E: LGA SUPPLY SIDE MODEL ESTIMATIONS ..................................... 56

APPENDIX F: MODELLING USING ALTERATIVE QUARTERLY PRICE DATA SERIES . ................................................................................................................ 63


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1. Introduction

In 2010 the Local Government Association of Queensland Inc. (LGAQ) engaged AEC to undertake an econometric analysis of the determinants of south east Queensland (SEQ) housing prices (AEC, 2010). The motivation for that research arose from the significant growth in house prices that had been experienced in the prior decade and a need to

understand the determinants of that growth to inform housing policy and decision making. A literature review at the time also failed to uncover any significant work on house prices undertaken outside capital cities.

The findings of AEC (2010) was that the SEQ housing market does not behave similar to the national average and therefore highlights the importance of analysing SEQ data instead of relying on estimates based on nationwide aggregate data for policy and planning purposes in relation to the housing market. This was demonstrated by the evidence over

20 years in SEQ that downward pressure on prices was not as responsive to positive increases in supply. For the supply of residential land it was also clear that supply responds

to increases in prices therefore other mechanisms may be required to enhance the supply of residential lots, which were clearly needed at the time to be higher than they were to have a greater dampening influence on prices.

Five years later LGAQ has once again engaged AEC to revisit the 2010 study and in addition

to determine if changes to the trunk infrastructure charging regime has had any influence on prices. As expected this has enabled new literature to be reviewed and importantly for modelling purposes a further five years of quarterly data resulting in longer time series. Also, a more disaggregated price data set has been modelled comprising:

1-2, 3, 4 and 5 bedroom houses on a square metre (sqm) basis.

1, 2 and 3 bedroom units on a sqm basis.

Land prices on a sqm basis.

Furthermore, selected local government areas (LGA) have been modelled to see if there is any variation in determinants compared to the SEQ LGA. Other policy variables have also

been explored such as the 2011 Brisbane River flood. With a longer time series supply modelling is also now possible.

1.1 Guide to the Study

Since it has been five years since AEC (2010) the first task of this study was to examine additional literature on dwelling prices published since that time. Section 2 examines this literature from a modelling perspective dividing it into dynamic time series models, panel models and supply side models.

The next task was to update all the explanatory variables from AEC (2010) as well as obtain fresh median house price data from Corelogic RP Data. The new data is described and

analysed in Section 3 grouped by dwelling prices, demand factors, supply factors. Major differences in the data or their construction are also described along with other factors that were research or taken into consideration such as the 2011 Brisbane floods and changes to trunk infrastructure charges regime.

Econometric models that are consistent with AEC (2010) and new models for the supply side are described in Section 4. Also included in this section are the approaches to the econometric modelling including results from Granger non-causality testing and the results

from the long-run price, short-rum price and supply side econometric models.

Finally, Section 5 summarises and discusses the findings of the research and suggests future areas for investigation.

1.2 Acknowledgements

This paper has been funded by LGAQ and has been produced through an independent joint effort between AEC and the University of Queensland, School of Economics. Specifically, the main contributors are:

Simon Smith, Director and Senior Consultant, AEC.


2

o Project management, data acquisition and report authoring.

Dr. Alicia Rambaldi, Associate Professor, School of Economics, The University of Queensland.

o Lead researcher and report authoring.

Dr K. Renuka Ganegodage, Research Fellow, School of Economics, The University of Queensland, Project Role: Research Assistant.

o Literature review search, computation of supply side models.

Dr. Peter Crossman, Research Fellow, AEC.

o Data and modelling advice, peer review.

Acknowledgement also goes to Corelogic RP Data who supplied the median house, unit and land price data used in this report. All other data sources are attributed to their respective

sources.


3

2. Literature Review

A review of relevant Australian literature in AEC (2010) was carried out to obtain an understanding of previous dwelling price modelling work in particular to identify suitable models and possible explanatory factors. A further literature review was undertaken for the current study since AEC (2010) to obtain a more recent understanding of new work

and modelling approaches in the area. The literature examined for this study can be divided into a number of sub-topics, namely:

Dynamic time series models.

Panel models.

Supply side models.

Each of these are discussed below with further details of the studies contained in Appendix A.

2.1 Dynamic Time Series Models

A number of papers have appeared in the literature (Afonso and Sousa, 2011; Costello et al, 2011; Fry et al, 2011; Hatzvi and Otto, 2008, Otto, 2007, Wadud et al,2012; Wilson et al, 2011) that seek to explain using time series models various phenomena associated with

house prices. All studies use similar explanatory variables to AEC (2010).

Afonso and Sousa, (2011) investigate links between fiscal policy shocks and asset markets in several countries including housing markets.

Costello et al (2011) Use Australian capital city data from 1984Q3–2008Q2, to construct time series of house prices depicting what aggregate house prices should be given expectations of future real disposable income. The find evidence of sustained deviations of house prices from values warranted by income for all state capitals.

Fry et al (2011) construct a structural vector autoregression model to identify overvaluation in house prices in Australia from 2002 to 2008. The results show strong evidence of

overvaluation in real house prices, reaching a peak of just over 15% by the end of 2003. They suggest that housing demand shocks and macroeconomic shocks drive the overvaluation and that monetary policy is not an important contributor of overvaluations.

A study by Hatzvi and Otto (2008) attempt to use asset pricing theory to explain residential

property prices across LGAs in Sydney. They found that price : rent ratios reflect changing expectations about future discount factors although not all variations in property prices can be explained by rents or discount factors and they concludes that there may be speculation at play.

Otto (2007) looks to explain real house prices in Australia’s capital cities using standard economic factors. A common factor is that the size of the mortgage rate is found to be most significant and volatile determinant in all eight cities. Other economic factors are less

systematic. For most Australian cities Otto (2007) finds economic factors are found to explain around 40% to 60% of the variation in the growth rate of house prices.

The role of monetary policy and the housing market is the main focus Wadud et al (2012).

Their results show that a contractionary monetary policy significantly reduces housing activity but does not exert any significant negative effect on the real house prices.

Finally, Wilson et al (2011) examines housing sub-markets in Aberdeen, Scotland, to identify sub-markets that may be price leaders and relates their performance to potential

economic factors. The study lays the basis for research aimed at identifying whether different housing markets respond to the same or to different economic stimuli. This paper is a potential sign post for further research in SEQ that may be of interest to policy makers, developers and financial institutions.

2.2 Panel Models

Panel models are where data is pooled (e.g. cross sectional over time) to give more data points for modelling. Adams and Füss (2010) use panel data from 15 countries over a


4

period of 30 years. Their results indicate that house prices increase in the long-run by 0.6%

in response to a 1% increase in economic activity while construction costs and the long-term interest rate show average long-term effects of approximately 0.6% and -0.3%, respectively. Variables used are macroeconomic in nature but they also conpile a

construction cost index.

Agnello and Schuknecht (2011) examine the characteristics and determinants of booms and busts in the property market in 18 countries between 1980 and 2007. They find that domestic credit and interest rates have significant influence on booms and busts occurring. They also found that deregulation of financial markets has magnified the impact of the domestic financial sector on the occurrence of booms. Similar macroeconomic measures for AEC (2010) are used with the addition of real domestic credit measures and

international liquidity.

2.3 Supply Side Models

Two papers were found that examine the supply side. Gitelman and Otto (2012) estimates

the supply elasticity for residential property in Sydney and generally found supply was

inelastic to price increases (less than unity response). They also tested the impact of the time taken by council to decide on development applications and not surprisingly found it to have a negative effect on supply. Explanatory variables with the exception of approval times were similar to those in AEC (2010).

McLaughlin (2011) examined the relationship between house price change, metropolitan growth policies, and new housing supply in Australia’s five major capital cities. Their thesis

is that tighter regulations reduces the elasticity of supply response. In contrast to Gitleman and Otto (2012) they find supply elastic to increases in price. They also found that different types of growth policies have differing level of negative impacts on supply. Aside from the policy variable the explanatory variables are also similar to those in AEC (2010).


5

3. Identification & Collation of Data

The starting point for the identification and collation of data was to extend that collected for AEC (2010). In addition a more disaggregated set of median dwelling price data was obtained from Corelogic RP Data. The data collected is explored in this section prior to econometric modelling.

3.1 Geography

The geographical area covered by this study is that of South East Queensland (SEQ). SEQ encompasses the twelve local government areas of:

Brisbane City.

Gold Coast City.

Ipswich City.

Lockyer Valley.

Logan City.

Moreton Bay.

Noosa Shire.

Redland City.

Scenic Rim.

Somerset.

Sunshine Coast.

Toowoomba.

Figure 3.1 Local Government Areas Comprising South East Queensland

Source: SEQ Council of Mayors


6

3.2 Data Set

The data sets, units, source, time period and transformations obtained and used in the study are summarised in the table below and expanded upon thereafter. Other potential factors were covered in AEC (2010) with the below reflecting available and suitable data for modelling.

Table 3.1 Data Collected, Constructed and Transformed

Code Description Units Source Time Period

Dependant variable

MHP Median house prices (SEQ and LGAs) (a)

Also by 1-2, 3, 4 & 5 bedrooms and sqm $ CRP, Authors Q: Mar-91:Jun-15

MUP Median unit prices (SEQ and LGAs) (a)

Also by 1, 2, 3 bedrooms and sqm $ CRP, Authors Q: Mar-91:Jun-15

MLP Median land prices (SEQ and LGAs) (a)

Also by sqm $ CRP, Authors Q: Mar-91: Jun-15

Macroeconomic variables

GSP Gross state product $M OESR Q: Mar-91:Mar-15

EMP Employment (Qld) ‘000s ABS 6291 Q: Mar-91:June-15

UEMP Unemployment rate (Qld) Rate ABS 6291 Q: Mar-91:June-15

ER Trade Weighted Index Index RBA Q: Mar-91:June-15

CPI Consumer Price Index (Brisbane all groups) Index ABS 6401 Q: Mar-91:June-15

CPIH Consumer Price Index (Brisbane housing) Index ABS 6401 Q: Mar-91:June-15

Housing related variables

Y Gross disposable income (Qld) (a) $M ABS 5220, Authors Q:Mar-91:Mar-15

AF Housing finance commitments to individuals $M RBA Q: Mar-91:Jun-15

R Bank standard variable loan rate Rate RBA Q: Mar-91:Jun-15

AO All Ordinaries share price index Index ABS 1350 Q: Mar-91:Jun-15

Demographic variables

POP Population (Qld) Number ABS 3201 Q:Mar-91-Dec-14

POP (i) Population of SEQ and LGAs (a) Number ABS 3218. Authors Q: Mar-91:Jun-14

NM Net migration (Qld) Number ABS 3412 A:90-91:07-14

Housing stock variables

SEQHS Housing stock (SEQ)(a) Number ABS Census. Authors Q: Mar-91:June-15

BA Building approvals residential (SEQ, LGAs) Number ABS 8731 Q: Sep-06:June-15

COMM Dwelling unit commencements (Qld) Number ABS 8752 Q: Mar-91:June-15

COMP Dwelling unit completions (Qld) Number ABS 8752 Q: Mar-91:June-15

LR Lot registrations (SEQ, LGAs) Number OESR Q:Mar-95:Dec-14

Cost of building variables

PP Producer price index (Qld) Index ABS 6427 Q: Mar-91:June-15

LC Average weekly earnings – construction (Qld) $ ABS 6302 Q: Sep-94: June-15

Notes: (a) The construction of these variables is described in Appendix C.

ABS = Australian Bureau of Statistics, CRP = Corelogic RP Data, RBA = Reserve Bank of Australia. Source: AEC

3.3 Dwelling Prices

As mentioned earlier an expanded dwelling price data set was obtained from Corelogic RP Data for the current study which included for the 12 SEQ LGAs:

Number and median sales prices for houses, units and land.

Number and median sales prices for 1-2, 3, 4 and 5 bedroom houses by sqm.

Number and median sales prices for 1, 2 and 3 bedroom units by sqm.


7

3.3.1 Median Dwelling Prices

Figure 3.2 show the real median prices for houses, units and land in SEQ over the sample period March 1991 to June 2015 in 2011-12 prices.

Three significantly different periods of change in the trends of all three categories have

been identified. In the first period before 2001 real median prices for houses were between $200,000 and $280,000 with an average quarterly growth rate of 0.7%. Real median unit process were between $200,000 and $300,000 with an average quarterly growth rate of 0.8%. Real median land prices were in the region of $85,000 to $140,000 with an average quarterly growth rate of 1.2%.

Prices then increased dramatically in the second period, between 2001 to 2004, with real median house prices growing on average every quarter by 3.4%; units by 2.3%; and land

by 3.2%.

In the period 2005 to 2015, real median price growth has stabilised again at a new and higher level. Since 2005 real median house prices were around $450,000 with close to zero average quarterly growth; units were around $380,000 with growth of -0.4%; and land

was in the neighbourhood of $220,000 with average quarterly growth of -0.2%.

As in AEC (2010) the measure of real median prices is taken as year to the end of the

quarter in the modelling as described in Appendix C. This means of calculation was selected so as to smooth the price data. It does, however, produce a forward phase shift. To test the significance of the phase shift, models using the unmodified quarterly data have been estimated in addition to those in section 4.3 (see Appendix F).

Figure 3.2 Real Median Dwelling Prices ($2011-12), South East Queensland


Graphs of the median prices for each SEQ LGA are given in Appendix B.

3.3.2 Dwelling Size

Table 3.2 and Table 3.3 present the distribution of shares of transactions across house

and units sizes, which will provide some perspective on the results.

Table 3.2 Share of Houses by Number of Bedrooms, South East Queensland & Selected LGAs

Period 1-2 Bedrooms 3 Bedrooms 4 Bedrooms 5 Bedrooms Total

SEQ

1990-1999 6.9% 55.0% 30.4% 7.7% 100.0%

2000-2009 5.4% 47.5% 38.7% 8.4% 100.0%

2010-2015 4.9% 42.1% 42.9% 10.2% 100.0%

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8

Period 1-2 Bedrooms 3 Bedrooms 4 Bedrooms 5 Bedrooms Total

Brisbane

1990-1999 9.9% 50.4% 29.6% 10.1% 100.0%

2000-2009 7.5% 46.3% 35.6% 10.6% 100.0%

2010-2015 7.1% 43.7% 37.0% 12.2% 100.0%

Gold Coast

1990-1999 3.7% 47.6% 38.9% 9.8% 100.0%

2000-2009 2.7% 39.5% 47.0% 10.8% 100.0%

2010-2015 2.5% 34.5% 49.9% 13.2% 100.0%

Ipswich

1990-1999 8.4% 68.3% 20.8% 2.6% 100.0%

2000-2009 7.1% 58.2% 30.8% 4.0% 100.0%

2010-2015 5.6% 48.3% 40.9% 5.2% 100.0%

Logan

1990-1999 1.2% 67.0% 26.8% 5.1% 100.0%

2000-2009 1.4% 57.2% 35.5% 5.9% 100.0%

2010-2015 0.8% 48.3% 42.9% 7.9% 100.0%

Moreton Bay

1990-1999 7.1% 60.7% 27.2% 5.1% 100.0%

2000-2009 4.9% 49.0% 39.8% 6.3% 100.0%

2010-2015 4.1% 41.0% 47.2% 7.7% 100.0%

Sunshine Coast

1990-1999 7.0% 54.6% 31.3% 7.1% 100.0%

2000-2009 4.8% 43.8% 43.2% 8.2% 100.0%

2010-2015 4.2% 39.1% 47.5% 9.3% 100.0%


It is clear that the trend in the SEQ is towards larger size houses. While a 3 bedroom was a dominant size in the 1990s, the 4 (and 5 to some extent) bedrooms houses are clearly

on the increase. One to two bedrooms houses account for a very small share of the market.

In the case of units, it is clear that the trend is also towards larger units. The two bedroom unit was clearly dominant in the 90s; however, the trend is towards a higher share of the

three bedroom unit although 1 bedrooms are also increasing in popularity as it is evident for Brisbane where in the last five years their share is 18% of the market up from 12% in the 90s.

Table 3.3 Units by Number of Bedrooms, South East Queensland & Selected LGAs

Period 1 Bedrooms 2 Bedrooms 3 Bedrooms Total

SEQ

1990-1999 8.7% 59.7% 31.6% 100.0%

2000-2009 11.5% 49.9% 38.7% 100.0%

2010-2015 12.8% 46.3% 40.9% 100.0%

Brisbane

1990-1999 12.4% 57.8% 29.7% 100.0%

2000-2009 15.4% 50.8% 33.8% 100.0%

2010-2015 17.7% 47.4% 35.0% 100.0%

Gold Coast

1990-1999 9.2% 56.7% 34.1% 100.0%

2000-2009 11.9% 47.2% 40.9% 100.0%

2010-2015 12.1% 47.3% 40.6% 100.0%



9

3.4 Demand Factors

Demand factors that could have an influence on, or determine dwelling prices include macroeconomic, housing related and demographic factors.

3.4.1 Macroeconomic Variables

Macroeconomic variables are those measures that describe the overall economic environment. Various macroeconomic variables are used as indicators for the overall growth or health of the economy. They include:

Gross state product (GSP). Gross state product is a measure of a state’s overall

economic output. It is the market value of all final goods and services produced within a state boundary in a year. Generally if GSP is increasing then so is consumer confidence and may have a positive influence on dwelling prices.

Figure 3.3 Real Median Dwelling Prices ($2011-12), South East Queensland & Real GSP

Source: Corelogic RP Data, QG, AEC

Employment (EMP). Both the level and growth of employment in an economy may be determinants of dwelling prices. Confidence levels may be high if employment is close to full employment (i.e. close to the available labour force) or if employment is growing at a steady pace.

Unemployment rate (UEMP). The unemployment rate is the percentage of the available labour force that is unemployed. A high unemployment rate or one which is deteriorating, will tend to dampen consumer confidence and borrowing and purchasing intentions and is therefore likely to have a negative correlation with dwelling prices.

$0

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Houses Units Land Real GSP ($M) RHS


10

Figure 3.4 Real Median Dwelling Prices ($2011-12), South East Queensland &

Unemployment Rate

Source: Corelogic RP Data, ABS, AEC

Exchange rate (ER). The exchange rate can affect dwelling prices in two ways. Firstly,

it will cause the cost of imported items used in house construction to vary – a depreciation of the Australian dollar will cause the prices of imported materials used in dwelling construction to increase. Secondly, a low or depreciating Australian dollar (in terms of overseas currencies) can influence overseas investor demand for dwellings as the more favourable conversion rates for their overseas sources of funds lead to a larger local budget for investment in Australian dwellings. These two factors are likely

to exert upward pressure on dwelling prices locally. This implies a negative correlation between the exchange rate and dwelling prices.

Figure 3.5 Real Median Dwelling Prices ($2011-12), South East Queensland & Exchange Rate

Source: Corelogic RP Data, RBA, AEC

Consumer price index (Brisbane all groups) (CPI). Consumer prices or the rate at which they are changing can influence a number of variables associated with dwelling prices

including the cost of materials and labour. As well, it includes the well understood positive correlation and role of dwelling asset prices as a common hedge against general price inflation, as measured by the consumer price index.

0.0%

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Houses Units Land Unemployment Rate (RHS)"

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Houses Units Land Trade Weighted Index (RHS)


11

Figure 3.6 Real Median Dwelling Prices ($2011-12), South East Queensland & CPI

Source: Corelogic RP Data, ABS, AEC

Consumer price index (Brisbane housing) (CPIH). The housing CPI, which covers increases in rents, new dwelling purchases, rates and charges and utilities indicates the general price changes in obtaining and operating a dwelling.

3.4.2 Housing Related Variables

Housing related variables are those that impact on the demand for housing. They include:

Gross disposable income (Y). The amount of income available for consumption and saving. This will include expenditures on dwelling investments including making interest and principal repayments on borrowing used to purchase or invest in a dwelling. There

is expected to be a positive correlation between dwelling prices and gross disposable income.

Figure 3.7 Real Median Dwelling Prices ($2011-12), South East Queensland & Gross Disposable Income per capita

Source: Corelogic RP Data, QG, ABS, AEC

Housing finance commitments to individuals (AF). The availability and ease of obtaining finance for dwellings, specifically the deposit requirements and the percentage of household income required by institutions to service loans, is reflected through the number of finance commitments.

0.0

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12

Interest rate (R). Effectively the price of borrowing to invest in a dwelling. In relation

to housing the appropriate variable is the mortgage rate. When interest rates are high, repayments can also be high relative to wages reducing demand for housing and vice versa. A high interest rate may also offer an alternative investment to dwellings. It is

expected that interest rates will be negatively correlated with dwelling prices.

Figure 3.8 Real Median Dwelling Prices ($2011-12), South East Queensland & Real Loan Rate


All ordinaries share price index (AO). Shares provide alternative investments to property and there may be investment demand substitution when returns in cash and

shares are higher than property. It is expected therefore that stock market prices will be negatively correlated with dwelling prices.

Figure 3.9 Real Median Dwelling Prices ($2011-12), South East Queensland & S&P ASX 200


3.4.3 Demographic Variables

Demographic variables are those that relate to the characteristics of those demanding housing. They include:

0

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Population (POP). Population growth is an indicator of housing demand. Population

growth occurs either through natural (excess births over deaths) means or through net migration.

Net migration (NM). Net migration is a component of population growth which

potentially has greater variability over time than natural population growth and therefore more influence on housing demand.

3.5 Supply Factors

Supply factors that could have an influence on, or determine housing prices include housing

stock and cost of building new dwellings.

3.5.1 Housing Stock Variables

Housing stock variables represent the majority of the supply side of housing. Factors include:

Housing stock (SEQHS). Housing stock is the overall level of housing stock available. The housing stock is added to overtime through the building of new dwellings and is reduced by the removal of dwellings (e.g. demolition) over time. An excess of housing

stock over demand should exert a downward pressure on housing prices and vice versa.

Building approvals (BA). The number of residential dwelling approvals as an indication of dwelling additions to the dwelling stock.

Dwelling commencements (COMM). Dwelling commencements are new dwellings starting construction which when completed will add to the dwelling stock.

Dwelling completions (COMP). Dwelling completions are the number of new dwellings

that have finished building and add to the housing stock.

Residential lot registration (LR). Residential lot production is the number of residential lots that are produced from broad hectare land. The production of insufficient lots may cause a reduction in commencements which in turn may lead to a shortage in new dwellings. Similarly the production of excess lots may lead to a surplus of new

dwellings. The production of a residential lot requires a number of approval steps. Appendix D contains an explanation of the lot production process. Depending on the

time taken for the lot production process there may be lengthy delays in lot production responding to new housing demand causing a short term increase in dwelling prices.

At any one time there exists a stock of uncompleted residential lots in SEQ (Figure 3.10). These are lots with a reconfiguring a lot (RAL) development permit approval but they have not yet proceeded to survey plan (operational works) endorsement. The stock of uncompleted residential lots is added to through approval of RALs and is decreased by lot endorsement (council approval of operational works to create lots)

and lots lapsed (lots approved by the council but not yet developed or endorsed by the council within a prescribed period).


14

Figure 3.10 Median Land Prices ($2011-12) & Stock of Lot Approvals, South East

Queensland

Source: QT, AEC

The stock of undeveloped lots reached a low of 24,598 in Mar-02 and has grown since to

reach 62,383 in Dec-14. The nadir in the stock of lot approvals is led by the increase in real median land prices and lot stock has increased following the rise in median prices. Stock levels however appear to have kept growing even though the real median land prices appear to be in a long term decline.

3.5.2 Cost of Building Variables

There are many costs that enter into the production of new housing. They include:

Producer price index (PP). Producer prices relate to the rate at which the costs of producing materials used in manufacturing and building are changing.

Average weekly earnings – construction (LC). The construction of dwellings requires many building trades. If there are labour shortages specific to the skills required for dwelling construction labour costs may rise resulting in higher housing costs.

3.6 Major Data Differences from 2010

In addition to the general extension of data sets to March or June quarter 2015 (where available) and the usual expected revisions within historical data, it is worth commenting on some of the key explanatory data sets and those that have been significantly revised or constructed in a different fashion to AEC (2010). These include population, housing stock and gross disposable income. The mechanics of construction are included in Appendix C.

3.6.1 Population

Figure 3.11 shows the population of SEQ for the study period comparing the series from AEC (2010) to the current series used. The population series for the LGAs and SEQ are from ABS 3218 from June 2001 onwards. The series for the earlier part of the study period were constructed by using the SEQ proportion of the Queensland population in the June Quarter of 2001. Similarly, the proportion the SEQ population for each LGA in the June Quarter of 2001 was used to distribute the SEQ population to each area.

0

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Land Lot Stock (RHS)


15

Figure 3.11 Population, South East Queensland, 2015 v 2010 study

Source: ABS, AEC

3.6.2 Housing Stock

In AEC (2010) a measure of housing stock was constructed. At the time the data available did not cover all LGAs in the SEQ. In this report a more complete dataset was available to construct the housing stock variable for the modelling. Figure 3.12 shows the constructed

housing stock per capita for the sample period. The stock shows an increase until around 2002 and since then it has remained relatively stable with a slight downward trend.

Figure 3.12 Housing Stock per capita, South East Queensland, 2015 v 2010 study

Source: ABS, AEC

3.6.3 Gross disposable Income

In AEC (2010) gross disposable income was obtained from ABS 5220, and it was expressed in real terms and per capita using population figures. In this study the data have been constructed using quarterly Australian gross disposable income per capita (from ABS 5206

National Accounts and ABS 3101 Australian Demographic Statistics). The quarterly annual share from the Australian data has been used to allocate the annual Queensland Gross to

1,800,000

2,000,000

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Mar

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2015 2010

0.2

0.25

0.3

0.35

0.4

0.45

Mar

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Mar

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Mar

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-14

2015 2010


16

quarters. Since the state accounts only went to 2013-14 the last three quarters have been

pushed forward using the Australian series growth rates. Figure 3.13 shows the two series.

Figure 3.13 Real Gross Disposable Income per capita, South East Queensland, 2015 v 2010 study

Source: ABS, AEC

3.7 Other Elements

3.7.1 2011 Brisbane Floods

Dwellings in low lying areas of Brisbane and in some other LGAs were inundated by severe flooding in January 2011. A dummy variable has been included to account for this event where there may have been immediate and longer lasting impacts on dwelling prices. The variable D11 is defined as 1 for the time periods 2011Q1-2012Q4.

3.7.2 Trunk Infrastucture Charges

AEC was asked to examine if changes to the trunk infrastructure charges regime over time have had any explanatory power on dwelling prices.

On 1 July 2012 the Queensland Government (2012) introduced the State Planning Regulatory Provision (adopted charges) which set maximum trunk infrastructure charges for new residential dwellings as follows:

$28,000 per 3 or more bedroom dwelling.

$20,000 per 1 or 2 bedroom dwelling.

Prior to 1 July 2012 the setting of infrastructure charges was left to individual local

governments and was broadly based on their Priority Infrastructure Plan (PIP) or adopted infrastructure charges schedule (ICS).

To test if trunk infrastructure charges have any explanatory power on dwelling prices requires the following:

Disaggregation of house prices as “new” versus “established” sales since the main effect of trunk infrastructure charges would be on the price of new stock coming onto the market.

A consistent measure of the average trunk infrastructure charge levied per new dwelling sale.

$5,000

$6,000

$7,000

$8,000

$9,000

$10,000

$11,000

$12,000M

ar-9

1

Ap

r-9

2

May

-93

Jun

-94

Jul-

95

Au

g-9

6

Sep

-97

Oct

-98

No

v-9

9

Dec

-00

Jan

-02

Feb

-03

Mar

-04

Ap

r-0

5

May

-06

Jun

-07

Jul-

08

Au

g-0

9

Sep

-10

Oct

-11

No

v-1

2

Dec

-13

2015 2010


17

Unfortunately Corelogic RP Data was unable to differentiate “new” versus “established”

dwelling sales with any confidence and a consistent time series of average infrastructure charges does not exist. Therefore this examination could not be undertaken.


18

4. Modelling Dwelling Prices

This section presents the modelling of the real house, unit and land price data for SEQ. A range of time series econometrics techniques are used in the analysis. Granger non-causality tests are carried out as robustness tests to establish the major determinants of movements in median house, units and land prices in SEQ over the sample period. Based

on the literature review modelling was carried out using three models. Two were used in AEC (2010) (Karantonis and Ge (2007) and Abelson et al (2005)), and the last is the supply model by McLaughlin (2011). The longer time series available on lot registration now allows for this supply modelling which was unable to be considered in AEC (2010).

4.1 Selection & Specification of Econometric Models

4.1.1 Unit Root Tests

The data series were checked for unit roots, however, as the data series are extensions of those in AEC (2010) the results are not included in this report as they were not materially different from AEC (2010).

4.1.2 Granger Non-Causality Testing

The Karantonis and Ge (2007) (KG) model considers the relationship between real prices

and dwelling completions when controlling for a number of macroeconomic variables (real loan rate, gross disposable income, unemployment and net migration). The model is a vector autoregression (VAR) system

𝑋𝑡 = 𝛤0 + ∑ 𝛤1𝑋𝑡−𝑗𝑝𝑗=1 + 𝜖𝑡 (1)

Where,

𝑋𝑡 = [𝐿𝑜𝑔(𝑅𝑒𝑎𝑙 𝑀𝑒𝑑𝑖𝑎𝑛 𝑃𝑟𝑖𝑐𝑒), log(𝐷𝑤𝑒𝑙𝑙𝑖𝑛𝑔 𝐶𝑜𝑚𝑝𝑙𝑒𝑡𝑖𝑜𝑛𝑠) , 𝑅𝑒𝑎𝑙 𝐿𝑜𝑎𝑛 𝑅𝑎𝑡𝑒,

𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑑𝑖𝑠𝑝𝑜𝑠𝑎𝑏𝑙𝑒 𝐼𝑛𝑐𝑜𝑚𝑒, 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑅𝑎𝑡𝑒, 𝑁𝑒𝑡 𝑀𝑖𝑔𝑟𝑎𝑡𝑖𝑜𝑛 𝑡𝑜 𝑄𝐿𝐷]

is the vector of variables in the model and p is the lag length.

Model (1) was estimated to conduct Granger non-causality tests for integrated and

cointegrated systems (MWALD of Toda and Yamamoto (1995)) to study whether different variables can predict the behaviour of others. The tests concentrated on the price and dwelling completions equations to determine whether other variables can predict prices and completions when all variables are treated as an endogenous system.

4.1.3 Demand Models

The Abelson et al (2005) model has two parts, a long-run model and a short-run

asymmetric model to study responses during “boom” times.

4.1.3.1 Long-Run Model

The long-run model is of the form:

log(𝑃𝑡) = 𝛼0 + 𝜃𝑥𝑡 + ∑ 𝛿𝑗∆𝑥𝑡−𝑗 + 𝑣𝑡𝑘𝑗=−𝑘 (2)

Where,

log(𝑃𝑡) = Log Real Prices

𝑥𝑡 =

[

log(𝑅𝑒𝑎𝑙 𝐴𝑙𝑙 𝑂𝑟𝑑𝑖𝑛𝑎𝑟𝑖𝑒𝑠 𝑖𝑛𝑑𝑒𝑥𝑡)

𝑅𝑒𝑎𝑙 𝑙𝑜𝑎𝑛 𝑟𝑎𝑡𝑒𝑡

og

log(𝑇𝑟𝑎𝑑𝑒 𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑒𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑟𝑎𝑡𝑒𝑡)

log(𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑒𝑡)

log(𝐶𝑃𝐼𝑡)

log(𝐻𝑜𝑢𝑠𝑖𝑛𝑔 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑡)

]

𝑣𝑡 is a random error term


19

∆𝑥𝑡−𝑗 are the k leads and lags of the first difference of 𝑥𝑡

k=2 following the choice made by Abelson et al (2005).

The expected signs in the long-run model are as follows:

Positive signs or correlations between real prices and real income per capita (as real incomes rise, dwellings become more affordable and prices are bid up) and also the consumer price index (as general inflation rises, the prices of dwelling assets will rise, especially as a real asset hedge).

Negative correlations between real prices and the real All Ordinaries index (stock market prices are expected to have a competitive or substation role compared with dwellings as an asset), the real loan rate (as costs of borrowing and financing dwellings

increases, there will downward pressure on prices through subdued demand), the trade weighted exchange rate (as the AUD depreciates, overseas investors in Australian real estate have an income boost and may add to competitive price pressures in the dwelling market), the unemployment rate (this is a general proxy for economic conditions as a rise in the unemployment rate increases uncertainty and has a restraining effect on

dwelling prices) and the housing stock per capita (as the demand for dwellings, or per

capita stock of dwellings, is expected to fall as dwelling prices increases).

4.1.3.2 Short-Run Model

The short-run model is in the form:

∆ log(𝑃𝑡) = 𝑏0 + 𝛼1𝐼𝑡−1(log(𝑃𝑡−1) − 𝜃𝑥𝑡−1) + 𝛼2(1 − 𝐼𝑡−1)(log(𝑃𝑡−1) − 𝜃𝑥𝑡−1)

+∑ 𝑏𝑗∆𝑧𝑡−𝑗 + 𝜖𝑡𝑘𝑗=1 (3)

where,

∆ log(𝑃𝑡) is the first difference of the logarithm of real medium prices (variously of

houses, units and land) in period t i.e. log(Pt) minus log(Pt-1).

𝐼𝑡 is the Heaviside indicator function which defines “boom” observations as

observations for which the real price growth over the past year has been over 2%

𝜃is the estimated DOLS cointegrating vector estimated from the long-run equation

∆𝑧𝑡−𝑗 are the k lags of the first differences of the variables in 𝑥𝑡 and the first difference

of the logarithms of prices.

4.1.4 Supply Side Models

The McLaughlin (2011) housing supply model explains the level of housing supply by changes in related prices, the cost of funds, construction costs and policy variables if appropriate. The formal equation specification is given by:

log(𝐷𝑤𝑒𝑙𝑙𝑖𝑛𝑔 𝐴𝑝𝑝𝑟𝑜𝑣𝑎𝑙𝑠𝑖𝑡) = 𝛼𝑖 + 𝛽1𝛥𝑃𝑖,𝑡 + 𝛽2𝐿𝑜𝑎𝑛 𝑅𝑎𝑡𝑒𝑖𝑡

+𝛽3 log(𝑅𝑒𝑎𝑙 𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡𝑖𝑡) + 𝛾𝑃𝑜𝑙𝑖𝑐𝑦 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑒𝑡 (4)

where

𝑖 is a geographic entity (e.g. an LGA) and 𝑡 is time (quarter).

The dependent variable is the logarithm of housing approvals, and the control variables include changes in housing prices, the real loan rate, and the logorithm of real construction costs, as well as appropriate policy control variables if available.

Other controls are also possible. The model can be estimated for a panel or for individual cross-sections.

Expected signs are positive for the change in price variables, as increases in price stimulates additional supply, and negative for the loan rate and real construction costs, as

higher costs associated with acquisition and construction will act to dampen supply.


20

4.1.5 Variables Used in the Models

Table 4.1 and Table 4.2 list the variables that are included in the models discussed above.

Table 4.1 Variables Used in the KG (2007) and Abelson et al (2005) Models

Variables Description Computation

RMHP(a) Real median house prices (MHP/CPIH)*100

RMUP(a) Real median units prices (MUP/CPIH)*100

RMLP(a) Real median land prices (MLP/CPIH)*100

R Real loan rate (R/CPI)*100

RAO Real All Ordinaries index (AO/CPI)*100

RYPC Real gross disposable income per capita (Y/CPI*POP)*100

RGSPP Real Gross State Product per capita (GSP/CPI*POP)*100

ER Trade weighted exchange rate ER

CPI Consumer price index CPI

UEMP Unemployment Rate UEMP

NM Net migration to Queensland

COMP Dwellings completions

HSPC Housing stock per capita SEQ housing stock/SEQ population

Note: (a) Real GSP per capita also tested. Source: AEC

Table 4.2 Variables Used in the McLaughlin (2011) Model

Variable Description Computation

BAPC (i) Building approvals per capita (i) BA/POP (i)

LRPC (i) Lot registrations per capita (i) LR/POP (i)

DPt-1 (i) Change in Real Median House Prices (i) 𝛥𝑃𝑖,𝑡−1 =ln(Pi,t-1)- ln(Pi,t-2)

DPUt-1 (i) Change in Real Median Unit Prices (i) 𝛥𝑃𝑈𝑖,𝑡−1 =ln(Pit)- ln(Pi,t-1)

DPLt-1 (i) Change in Real Median Land Prices (i) 𝛥𝑃𝐿𝑖,𝑡−1 =ln(Pi,t-1)- ln(Pi,t-2)

R Real loan rate (R/CPIH)*100

PP Real producer price index (PP/CPI)*100

Dummy for Quarters Q2, Q3, Q4 =1 if quarter is 2,3,4

Source: AEC

4.2 Granger Non-Causality Testing

This section presents the empirical results from the extensive Granger non-causality testing conducted using model (1) in a VAR system (VAR(3)). These tests are a technique used for determining whether one time series is useful in forecasting another. That is, if a variable is found to Granger-cause another, this indicates that past information on the first variable has significantly affected the time path of the second.

As explained above in section 4.1.1, following Karantonis and Ge (2007), this model

considers the relationship between real prices and dwelling completions, when controlling for a number of macroeconomic variables in an endogenous system. The tables below

present the results for SEQ over the sample period Mar-91 to Mar-15 for houses, units and land prices respectively.

The null hypothesis in the Granger non-causality test is that the variable listed on the rows of the tables below does not Granger-cause the variable listed on the columns of the table

i.e. the relevant Γ coefficients of model (1) are not significant different to zero, and so have no material predictive effect. Rejection of this null is presented at the usual levels of statistical significance, 1%, 5%, 10%.

Granger non-causality is a statement about prediction, that is, if the null hypothesis is rejected (so the result can be labelled as a level of statistical significance), we conclude that past information from the (row) variable, for example gross disposable income per capita, contains information that can improve the prediction of column variable, for

example real median house price. For example, in the case of Table 4.3, gross disposable income does granger-cause, or predict, house prices (at the 5% level of significance).


21

Two versions of the model reflecting different choices of measurement for the concept of

“income” are tested: M1 is when income is measured by real gross disposable income per capita (RYPC), M2 is when income is measured by real GSP per capita (RGSPPC).

Table 4.3 Granger Non-Causality Testing, Houses, South East Queensland

Real Median House Prices Dwelling Completions

M1: Income measure is real gross disposable income per capita

Real Median House Prices na XXX

Dwelling completions XXX na

Real gross disposable income pc XXX XXX

Real loan rate Not Significant Not Significant

Unemployment rate Not Significant Not Significant

Net migration Not Significant Not Significant

M2: Income measure is real Gross State Product per capita

Real Median House Prices na XXX


Real Gross State Product per capita XXX Not Significant

Real loan rate Not Significant XX



Note: XX: Significant at the 10% level; XXX: Significant at the 5% level. na is not applicable. Source: AEC

Table 4.4 Granger Non-Causality Testing, Units, South East Queensland

Real Median Unit Prices Dwelling Completions


Real Median Unit Prices na XXX

Dwelling Completions XXX na




Net Migration Not Significant Not Significant


Real Median Unit Prices na XXX

Dwelling Completions XXX na

Real Gross State Product per capita XXX Not Significant

Real loan rate Not Significant XX


Net Migration Not Significant Not Significant


Table 4.5 Granger Non-Causality Testing, Land, South East Queensland

Real Median Land Prices Dwelling Completions


Real Median Land Prices na XXX







Real Median Land Prices na XXX

Dwelling completions XX na

Real Gross State Product per capita XXX XX


22

Real Median Land Prices Dwelling Completions

Real loan rate Not Significant XXX




The causality modelling results for the three categories of houses, units and land, for both models of income, are broadly very similar. The results show:

1. There is consistent positive feedback between prices and dwelling completions i.e. there is evidence to indicate prices and dwelling completions are endogenously determined, as there is Granger causality in both directions (refer the first two rows of each table).

2. Income does granger-cause prices for all price categories, i.e. houses, units and land, for both models of income.

3. Income only partially granger-causes dwelling completions. It is significant for the gross disposable income model in all categories, but is non-significant in the gross state

product income model for houses and units (and of weaker significance for land).

4. The loan rate does not granger-cause prices for houses, units or land in either income

model. Nevertheless, the loan rate does granger-cause dwelling completions for all price categories, i.e. houses, units and land, although in the case of the gross state product income model only.

5. The other macro-economic variables, i.e. the unemployment rate and net migration, are uniformly non-significant, indicating that they appear from this evidence to be independent of prices and dwelling completions.

These are sound results for the next stage of modelling, although there is evidence that

the influence of some macro-economic variables, apart from income and cost of capital, may be marginal at best.

4.3 Long & Short Run Price Models

The long-run and short-run asymmetric models of Abelson et al (2005) have been re-

estimated using the new dataset over the AEC (2010) sample period (to check for consistency), as well as over the extended period covering 1991:Q1-2015:Q2.

4.3.1 Long-run Model Comparison with AEC (2010)

Table 4.6 presents a comparison of the model estimates of the Abelson et al (2005) long-run model that were obtained in AEC (2010), and a replication for the same period using the new dataset which, as indicated in Section 3, is based on a number of data revisions that have taken place.

Table 4.6 Long-run Model Comparison, Real Median House Prices, South East Queensland


AEC (2010) New data, Same period

Constant 7.399** 16.638**


Real loan rate (-) -0.069** -0.033**

Log Real gross disposable income pc (+) 0.098 -0.797



Log Consumer price index (+) 0.800* 1.087**

Log Housing stock pc (-) -2.392** -2.196**

R2 0.995 0.994

Note: Standard errors are the Newey-West HAC standard errors computed with 3 lags. pc = per capita. ** significant at the 5%

level. * significant at the 10% level. Source: AEC


23

Due to data revisions (discussed in detail in Section 3), the measures of State real gross

disposable income, SEQ population and SEQ housing stock differ from those used in AEC (2010).

Population enters the construction of real gross disposable income per capita and housing

stock per capita. However, the estimated significance of these two variables is similar in each of these models - real gross disposable income per capita remains not significant, whereas the significance of the housing stock per capita remains strongly significant.

Broadly, for variables found to be significant, the estimated coefficients continued to have the same signs as those predicted by Abelson et al (2005) in AEC (2010), and the previously non-significant variables did not become significant.

The responses of house prices to changes in the real All Ordinaries index and the

unemployment rate are significant and of the expected sign (negative) using either vintage of the data over the older sample (1991:Q1-2010:Q1) period.

The coefficient of the trade weighted exchange rate is not significant in either data set.

The consumer price index’s significance has improved with the updated data and remains correctly signed (positive).

As mentioned, housing stock per capita retains its strongly significant negative effect on

median house prices, whereas the coefficient for real gross disposable income per capita is still not significantly different from zero for the updated data.

Overall, these results are not unexpected for an unchanged model specification with data revisions over an identical sample period, indicating the reasonable robustness of the model itself.

4.3.2 Long-run Models for SEQ

Table 4.7 to Table 4.9 below contain the modelling results of the long-run part of the

demand model specification, following Abelson et al (2005) and AEC (2010), outlined in section 4.1.3 above.

Table 4.7 below presents the modelling for the period 1991:Q1-2015:Q2 for the SEQ for

real median house, unit and land prices. As in AEC (2010) the measure of real median prices is taken as year to the end of the quarter in the modelling as described in Appendix C. This means of calculation was selected so as to smooth the price data. It does, however, produce a forward phase shift. To test the significance of the phase shift, models using the

unmodified quarterly data have been estimated in Appendix F.

The only significant variables in the houses and land price equations are the financial variables (real All Ordinaries index and real loan rate) and the unemployment rate. The signs of these coefficients are as expected, all three being negatively correlated with prices.

In the equation for real median unit prices, all variables are significant, although three of these are wrongly signed (the trade weighted exchange rate, the consumer price index and

the housing stock per capita).

For real median house prices, the extension of the sample period from that of the 2010 study has resulted in changes for several variables in the long-run model:

The response of real median house prices to the real All Ordinaries index is still negative

but it is stronger. A 1% increase in the real All Ordinaries index will lead to an inelastic decrease in real median house prices of 0.37% (compared with a decrease of 0.25% estimated in AEC (2010)).

A 1% increase in the real loan rate (e.g. from 5% to 6%) leads to an expected decrease in real median house prices in the order of 2.6% (compared with an expected decrease of 6.9% in AEC (2010)). This may reflect the effect of the continuance of generally lower interest rates in the economy.

A 1 percentage point increase in the unemployment rate (noting this is a 1% change in the unemployment rate e.g. a change in the rate from 5% to 5.05%, and not a one percentage point change to 6%) leads to an expected decrease of 0.71% in real median

house prices (compared with an expected decrease of 0.44% in AEC (2010)). This would imply that, ceteris paribus, a 10% rise of the unemployment rate to a rate of


24

5.5% (which would be a considerable shock) would lead to a 7.1% decline in real

median house prices.

The coefficient for housing stock per capita is still not significant for house prices.

The results for real median land prices are similar to those of house prices, in that the

estimated coefficients for real gross disposable income per capita, the trade weighted exchange rate, the consumer price index and housing stock per capita are not significant.

However, in the case of unit prices, the estimated coefficients for all variables are significant:

A 1% increase in the real All Ordinaries Index leads to a highly inelastic decrease in real median unit prices of 0.19%.

A 1 percentage point increase in the real loan rate leads to a 2.00% decrease in real

median unit prices.

A 1% increase in real gross disposable income per capita leads to an inelastic 0.80% increase in real median unit prices.

A 1% increase in the trade weighted exchange rate leads to an inelastic 0.41% increase in real median unit prices. However, this is a positive relationship, contrary to the expected negative sign.

A 1% increase in the unemployment rate leads to an inelastic decrease in 0.45% in real median unit prices.

A 1% increase in the consumer price index leads to an inelastic 0.59% decrease in real median unit prices. This is also a case of the result being an unexpected sign, with an increase in general inflation thought to be correlated with an appreciation of dwelling and land prices as a hedge mechanism.

The only elastic response, with a significant estimated coefficient, for real median land

prices, is in the case of housing stock per capita. The model suggests that a 1% increase in the housing stock per capita leads to an elastic increase of 1.39% in the real median unit prices. However, the sign (being positive) is incorrect when compared with expectations of a negative correlation between an increase in the housing stock per capita

and unit prices. The positive implies that, for units, an increase in the housing stock per capita drives upwards movements in unit prices. In a demand model, the relationship should be inverse between quantity and price, leading to the suggestion that this may in

fact be representing a supply adjustment.

Table 4.7 Long-run Model for Real Median House, Unit and Land Prices, South East Queensland


Real Median House Prices

Real Median Unit Prices

Real Median Land Prices

Constant 11.311** 10.298** 8.295**

Log Real All Ordinaries index (-) -0.372** -0.189** -0.226**

Real loan rate (-) (a) -0.026** -0.020** -0.022**

Log Real gross disposable income pc (+) 0.614 0.795** 0.841

Log Trade weighted exchange rate (-) 0.191 0.415** ! 0.242

Log Unemployment rate (-) -0.715** -0.447** -0.597**

Log Consumer price index (+) -0.164 -0.593** ! -0.206

Log Housing stock pc (-) -0.533 1.390** ! 0.797

R2 0.989 0.989 0.983

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) This is a semi-logarithmic term, so the

coefficient is not an elasticity. pc = per capita. ** Significant at the 5% level. * Significant at the 10% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC

These results may be further explored by considering the effect of different sizes of dwelling for houses and units, proxied by various numbers of bedrooms. Standardisation for different sizes of dwelling, and also land, is achieved by conducting the price modelling on a per square metre basis. Table 4.8 shows the model results for five sizes of house, standardised by floor area.


25

Table 4.8 Long-run Model for House Size (real median price in sqm), South East Queensland


1-2 Bedrooms

3 Bedrooms 4 Bedrooms 5 Bedrooms

Constant 3.534 1.115 2.719 2.581

Log Real All Ordinaries index (-) -0.422** -0.395** -0.350** -0.336**

Real loan rate (-) (a) -0.025** -0.013 -0.020** -0.026**

Log Real gross disposable income pc (+) 0.900* 1.036** 0.979* 0.935*

Log Trade weighted exchange rate (-) 0.158 0.323* 0.208 0.205

Log Unemployment rate (-) -0.708** -0.626** -0.620** -0.650**

Log Consumer price index (+) -0.330 -0.215 -0.432 -0.380

Log Housing stock pc (-) -0.432 0.137 -0.029 -0.447

R2 0.983 0.987 0.983 0.989

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) This is a semi-logarithmic term, so the coefficient is not an elasticity. pc = per capita. ** Significant at the 5% level. * Significant at the 10% level. ! indicates a significant

coefficient, but with an incorrect sign. Source: AEC

The model results show consistently significant coefficients and correct signs across the

various house sizes for the real All Ordinaries index, the loan rate, real gross disposable income per capita (albeit with slightly weaker significance levels) and the unemployment rate. The elasticities and the change response (for the semi-logarithmic real loan rate) are very similar in size to the aggregate results for houses in table 4.8 above, indicating that, for these significant variables, standardisation has not made a material difference to the results.

There are some anomalies e.g. the case of 3 bedroom houses is not significant for the real loan rate, unlike the other sizes, which are all strongly significant in this case.

However, the trade weighted exchange rate is only just significant for houses of 3 bedrooms, indicating a positively inelastic effect of 0.32% from a 1% increase in the exchange rate, despite the expected sign being correctly negative. It is interesting that this variable was not significant for the two larger sizes, given the common perception of an influx of overseas buyers pushing up high end housing prices, although these results are

of course over the full sample period of a quarter of a century, and the perception may have formed in recent years only.

As was the case above for non-standardised house prices, the consumer price index and the housing stock per capita are not significant.

The effects on different size units are more heterogeneous. These results (and for land prices) are shown in Table 4.9 below.

Table 4.9 Long-run Model for Unit Size & Land (real median price in sqm), South East Queensland


1 Bedroom 2 Bedroom 3 Bedroom Land

Constant 2.409 8.234** 0.425 -9.157*

Log Real All Ordinaries index (-) -0.195 -0.371** -0.145** 0.090

Real loan rate (-) (a) -0.059** -0.023** -0.009 -0.007

Log Real gross disposable income pc (+) 1.481** 0.508* 1.394** 1.033**

Log Trade weighted exchange rate (-) 0.834** ! 0.308** ! 0.242** ! 0.356** !

Log Unemployment rate (-) -0.144 -0.682** -0.215** -0.209**

Log Consumer price index (+) -2.078** ! -0.180 -0.769** ! 0.993**

Log Housing stock pc (-) -0.917 0.590 1.068** ! 0.941

R2 0.879 0.987 0.971 0.992



For these unit price models, it may be noted that the results for the smaller sizes of unit

are weaker than for the larger (i.e. 3 bedrooms), and the larger size’s results reflects the results for the aggregate, but unstandardized, unit prices in table 4.7 above. In this case, only the real loan rate has failed to be significant. It is noticeable that while the housing


26

stock per capita is not only highly significant, it is also of the wrong sign, repeating the

suspected supply result which may have been captured in the aggregate unit price model.

The trade weighted exchange rate is uniformly significant but incorrectly signed for all sizes of unit. (The expected sign is negative, which implies that as the Australian dollar

depreciates, overseas investors may see local opportunities for real estate, particularly unit, investment, which may form upwards pressure on prices in SEQ.)

Real gross disposable income per capita is significant across all sizes. These effects are larger and are elastic responses for the 1 and 3 bedroom prices, although weak in significance and size for the 2 bedroom unit prices, indicating an inelastic response from income to prices in the case of this likely most popular unit dwelling.

The real loan rate shows a significant and negative effect across 1 and 2 bedroom units. A

1 percentage point rise in the real loan rate would see an expected fall in prices of 5.9% in 1 bedroom unit prices, and only 2.3% for 2 bedroom unit prices.

The unemployment rate has a significant, inelastic and negative effect for 2 and 3 bedrooms unit prices. The 1 bedroom unit case does not appear to be influenced by general

economic conditions, possibly because risks are outweighed by basic needs for the lowest quality segment of the market. The elasticity is absolutely higher for the popular 2 bedroom

case, as would be expected, with the step-up to 2 bedrooms likely being a risky discretionary purchase for lower end purchasers, easing off in size of effect for the 3 bedroom case.

The consumer price index has a significant coefficient in the cases of for 1 and 3 bedrooms real median unit prices, but has the wrong (negative) sign in both cases, indicating that as general inflation increases, these unit prices will tend to decline. A conjecture is that for the low end market of 1 bedroom units, where there is a highly elastic response (a 1% rise

in the consumer price index would lead to a 2.1% decline in 1 bedroom unit prices), a rise in general inflation may disproportionally negatively impact on household budgets and so slow the demand for and prices of low end units.

The housing stock per capita coefficient is significant only in the case of 3 bedroom units, and has an incorrect expected sign. This point has been discussed above for the aggregate model of real median unit prices.

For land prices per sqm, there is no significant effect from the “financial” variables, the real

All Ordinaries Index and the real loan rate, nor from the housing stock per capita.

However, there are significant coefficients for real gross disposable income (about unitary elasticity) the unemployment rate (inelastic) and the consumer price index (about unitary elasticity). Broadly the model suggests that land prices are determined in a steady way by several broad macroeconomic conditions: incomes, labour market and general prices.

4.3.3 Short-run Models for SEQ

The short-run model (Asymmetric Error Correction Model) for houses, units and land real median prices for the SEQ are presented in Table 4.10 and Table 4.11.

Table 4.10 presents the comparative results between the estimates obtained in AEC (2010) and those obtained with the current sample period for house prices. The estimates based on the new data are not significant, while they were in AEC (2010).

The short-run model in AEC (2010) found that over the period 1991-2010, real median prices of houses, land and units responded asymmetrically during boom times (defined as

an increase in real prices higher than 2% over the previous year). Using the updated data sets, we no longer find that houses prices show a significant asymmetric response.

Table 4.10 Short-Run Asymmetric Error Correction Model Comparison, Real Median House Prices, South East Queensland

Asymetric Effect AEC (2010) New Data

α1 -0.116** -0.036

α2 -0.115** -0.036

R2 0.721 0.708

Note: ** significant at the 5% level. * significant at the 10% level. Source: AEC


27

Table 4.11 provides the estimates of the adjustment parameters for unit and land prices

over the sample period under study which are statistically significant. However, the adjustment rates are numerically very close between boom and non-boom times. Real median prices of units for SEQ adjust back to trend at a rate of 10.4% and those for land

at a rate of around 7% per quarter both over boom and non-boom times.

Table 4.11 Short-Run Asymmetric Error Correction Model, Real Median Unit & Land Prices, South East Queensland

Asymetric Effect Units Land

α1 -0.104* -0.071**

α2 -0.104* -0.072**

R2 0.638 0.565

Note: ** significant at the 5% level. * significant at the 10% level. Source: AEC

4.3.4 Long-run Models for Selected LGAs

This section presents long-run model estimates for selected LGAs within SEQ. These models are standardised for area and are separated modelled by size of house and unit (proxied by number of bedrooms).

House prices, for the categories of 1-2, 3, 4 and 5 bedrooms, are modelled for five selected larger LGAs: Brisbane, Gold Coast, Ipswich, Logan and Sunshine Coast (Tables 4.12 to

4.16)

The house price results show generally consistent significant (and correct sign) results for the real All Ordinaries index and the unemployment rate for all five of these LGAs.

The real loan rate was not significant for any size of bedroom in Logan or Sunshine Coast. However, this coefficient was significant and correctly signed for all sizes in Brisbane (a 1 percentage point rise in the real loan rate leads to a 3.3% fall in 1-2 bedroom house prices in Brisbane). For the Gold Coast, all sizes were significant except for 3 bedrooms, with the

largest effects being for 1-2 and 5 bedrooms. In Ipswich the size of the significant and correctly signed coefficient for real loan rate was very high – a 1 percentage point rise in

the real loan rate leads to a 10.6% fall in 5 bedroom house prices.

Real gross disposable income was not significant for the Gold Coast or Sunshine Coast for any size of house, but was significant for Brisbane (4 bedrooms only, slightly inelastic), Ipswich (all sizes except 5 bedroom, and all highly elastic e.g. a 1% rise in income leads

to a 2% rise in 4 bedroom house prices in Ipswich) and Logan (all sizes but 1-2 bedrooms, all highly elastic).

The unemployment rate was generally significant and of correct sign in all LGAs and general declined in terms of size of elasticity as the size of houses increased. (An exception is for 5 bedroom houses in Ipswich which rose sharply to a highly elastic 1.5% response, compared with unitary elasticity for 1-2 bedroom house prices and inelastic responses for the 3 bedroom and 4 bedroom sizes.) In Logan, the elasticity for 1-2 bedroom houses was

very highly elastic – 2.5% rise in prices for a 1% fall in the unemployment rate (e.g. from 5% to a rate of 4.95%), and then falling away to an inelastic response for all other sizes.

The housing stock per capita variable was significant (and correctly signed) for 1-2

bedroom (slightly elastic) and 5 bedroom (slightly inelastic) sizes in Brisbane. The coefficient for the 5 bedroom size in Ipswich for housing stock per capita is also very high, with an elasticity of 10.6, indicating that a rise in the housing stock per capita in SEQ leads to a 10.6% fall in Ipswich 5 bedroom house prices. Housing stock for 1-2 bedroom houses

on the Sunshine Coast was significant but of the incorrect sign.

The trade weighted exchange rate variable did contribute successfully to these models. Although the coefficients for various sizes in different LGAs were significant, the signs were incorrect in all instances.


28

Table 4.12 Long-run Model for House Size (real median price in sqm), Brisbane


1-2 Bedrooms


Constant 7.330* 5.241 2.412 3.496


Real loan rate (-) (a) -0.033** -0.026** -0.026** -0.028**

Log Real gross disposable income pc (+) 0.504 0.580 0.913** 0.643

Log Trade weighted exchange rate (-) 0.029 0.227 0.180 0.165


Log Consumer price index (+) -0.027 0.048 -0.212 0.037

Log Housing stock pc (-) -1.300** -0.786 -0.576 -0.882**

R2 0.985 0.987 0.983 0.992



Table 4.13 Long-run Model for House Size (real median price in sqm), Gold Coast


1-2 Bedrooms


Constant 10.824** 10.328** 7.627* 8.676*


Real loan rate (-) (a) -0.040** -0.011 -0.020* -0.023**

Log Real gross disposable income pc (+) 0.667 0.193 0.629 0.601

Log Trade weighted exchange rate (-) 0.281 0.271 0.200 0.253


Log Consumer price index (+) -0.911* ! 0.076 -0.538 -0.540

Log Housing stock pc (-) 0.021 1.187** ! 0.282 0.358

R2 0.983 0.981 0.975 0.980


coefficient is not an elasticity. pc = per capita. ** Significant at the 5% level. * Significant at the 10% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

Table 4.14 Long-run Model for House Size (real median price in sqm) Ipswich


1-2 Bedrooms

3 Bedrooms 4 Bedrooms 5 Bedrooms(b)

Constant -4.364 -7.227* -9.105* -7.440

Log Real All Ordinaries index (-) -0.680** -0.511** -0.357** -0.425

Real loan rate (-) (a) 0.026 0.009 0.004 -0.106**

Log Real gross disposable income pc (+) 1.520* 1.991** 2.038** 1.784

Log Trade weighted exchange rate (-) -0.019 0.430** ! 0.139 -0.817


Log Consumer price index (+) 0.652 -0.346 -0.082 -0.592

Log Housing stock pc (-) -0.698 -0.221 -0.445 -10.604**

R2 0.975 0.987 0.983 0.958




29

Table 4.15 Long-run Model for House Size (real median price in sqm), Logan


1-2 Bedrooms


Constant 10.635 -5.110 -4.990 -6.052

Log Real All Ordinaries index (-) -1.118** -0.492** -0.429** -0.379*

Real loan rate (-) (a) -0.025 0.004 -0.001 -0.013

Log Real gross disposable income pc (+) 2.800 1.767** 1.721** 2.167**

Log Trade weighted exchange rate (-) -1.596 0.299* ! 0.299* ! 0.008

Log Unemployment rate (-) -2.461** -0.720* -0.641** -0.661**

Log Consumer price index (+) -2.581 -0.290 -0.385 -1.109** !

Log Housing stock pc (-) -1.122 -0.357 -0.577 -1.498

R2 0.827 0.987 0.986 0.989



Table 4.16 Long-run Model for House Size (real median price in sqm), Sunshine Coast


1-2 Bedrooms


Constant 8.471* 7.557 10.782** 6.509*


Real loan rate (-) (a) -0.006 -0.019 -0.020 -0.006

Log Real gross disposable income pc (+) 0.978 0.615 0.242 0.658

Log Trade weighted exchange rate (-) 0.238 0.340* ! 0.234 0.271


Log Consumer price index (+) -0.591 -0.228 0.024 0.298

Log Housing stock pc (-) 1.704** ! 0.779 0.598 1.858

R2 0.96 0.979 0.975 0.980



Source: AEC

Units (by size of unit) and land prices, standardised for area, have been modelled for three LGAs in SEQ – Brisbane (Table 4.17), Gold Coast (Table 4.18), Sunshine Coast (Table 4.19) and land prices, standardised, have been modelled for Ipswich and Logan LGAs (Table 4.20).

For units of different sizes (1, 2, 3 bedrooms), the number of transactions for many LGAs were small and thus there was no reliable price data to conduct any separate formal modelling except for the largest LGAs of Brisbane and the Gold Coast.For Brisbane, the “financial” variables were broadly significant and of correct sign, as well as the unemployment rate (inelastic responses) and real gross disposable income for the larger unit sizes (inelastic for 2 bedroom and elastic for 3 bedroom). There is a sizable response in 1 bedroom unit prices to the real loan rate, with a 1 percentage point increase in the

real loan rate leading to a fall of 10.5% in prices for that size. The 2 and 3 bedroom responses were 2% and 1.4% respectively. While there were significant coefficients for the

consumer price index and the housing stock per capita, both were incorrectly signed,

For the Gold Coast unit prices, the results were mixed – of the twenty-one coefficients (not including the intercept terms) fourteen were significant, but of these, only seven were correctly signed. The coefficients for all unit sizes for the real All Ordinaries index were significant and correctly signed, and coefficients for real gross disposable income (elastic

for both 1 and 3 bedroom sizes) and the unemployment rate (unitary elasticity for 2 bedroom and highly inelastic for 3 bedroom) were significant and correctly signed.

Brisbane land prices per sqm respond significantly (and correctly signed) to changes in the real loan rate (reasonable response of a 2.5% rise in land prices to a 1 percentage point fall in the real loan rate), the unemployment rate (inelastic) and the consumer price index (unitary elasticity).


30

For the Gold Coast, land prices per sqm respond significantly to the “financial” variables of

the real All Ordinaries index (inelastic) and the real loan rate (adjustment of 3.8% rise in land prices to a 1% fall in the real loan rate). Real gross disposable income and the unemployment rate are also significant and correctly signed (both are inelastic responses).

The response of real unit prices of 2 and 3 bedrooms on the Sunshine Coast are consistently significant to movements in the All Ordinaries index (inelastic). The price decrease is estimated at around 4% as result of a 1% increase in the index. This response is closest to that of the Gold Coast 2 bedroom units. Similar to the case of houses, the unemployment rate has a consistent and significant effect on prices of 0.86% and 1% for 2 and 3 bedroom units, respectively. The real loan rate has a small but significant effect on 2 bedroom units (0.04% decrease). The effect is twice as large as that found for 2 bedroom units in

Brisbane. Similar to the Gold Coast, housing stock is significant but with an incorrect positive sign, although this is only found in 3 bedroom units. Real land prices per sqm in the Sunshine Coast respond significantly to changes in the All Ordinaries index, the real loan rate and the unemployment rate. In all cases these are negative. The response to a 1% increase in the All Ordinaries index is 0.54%; it is 0.04% to a 1% increase in the real loan rate and 1% to an increase of 1% in the unemployment rate. These results are closer

to those found for the Gold Coast.

Logan land prices per sqm could not be modelled successfully, with no variable coefficients significant (except for the intercept term, indicating no variation and explanatory power at all). The number of transactions reported for the area shows a large decrease in the middle of the sample which does not seem to be consistent with the patterns of most other LGAs.

For Ipswich, land prices per sqm are explained by the unemployment rate and the consumer price index. Coefficients for both of these variables were significant and correctly

signed, with about unitary elasticity for the unemployment rate and the response of the consumer price index being highly elastic. It would seem that land prices in Ipswich are sensitive to both aggregate macroeconomic activity and inflation.

Table 4.17 Long-run Model for different sizes of Units & Land (real median price in sqm), Brisbane


1 Bedroom 2 Bedrooms 3 Bedrooms Land

Constant 18.022* 3.872** -1.578 -3.767

Log Real All Ordinaries index (-) 0.332 -0.274** -0.154** -0.093

Real loan rate (-) (a) -0.105** -0.020** -0.014** -0.025*

Log Real gross disposable income pc (+) -1.001 0.767** 1.619** 0.550

Log Trade weighted exchange rate (-) 1.258* ! 0.150 0.126 0.291


Log Consumer price index (+) 0.807 -0.028 -0.959** ! 1.019*

Log Housing stock pc (-) 10.981** ! -0.337 -0.361 -1.144

R2 0.912 0.990 0.984 0.988



Source: AEC


31

Table 4.18 Long-run Model for different sizes of Units & Land (real median price in sqm),

Gold Coast


1 Bedroom 2 Bedrooms 3 Bedrooms Land

Constant 9.469* 12.796** 0.264 2.008

Log Real All Ordinaries index (-) -0.327* -0.511** -0.181** -0.368**

Real loan rate (-) (a) -0.001 -0.010 -0.009 -0.038**

Log Real gross disposable income pc (+) 1.756** 0.351 1.703** 0.763*

Log Trade weighted exchange rate (-) 1.478** ! 0.266 0.355** ! 0.624** !


Log Consumer price index (+) -1.866** ! -0.350 -1.277** ! -0.100

Log Housing stock pc (-) 12.358** ! 1.266* ! 1.735** ! 0.803

R2 0.930 0.973 0.969 0.986



Source: AEC

Table 4.19 Long-run Model for different sizes of Units & Land (real median price in sqm), Sunshine Coast


2 Bedrooms 3 Bedrooms Land

Constant 6.496 17.731** 1.756


Real loan rate (-) (a) -0.036** -0.005 -0.040**

Log Real gross disposable income pc (+) 1.263 -0.399 0.556

Log Trade weighted exchange rate (-) 0.481 -0.506 0.284


Log Consumer price index (+) -1.403* ! -1.263 0.934

Log Housing stock pc (-) 0.246 4.629** ! -0.130

R2 0.950 0.942 0.989



Source: AEC

Table 4.20 Long-run Model for different sizes of Land (real median price in sqm), Ipswich & Logan


Ipswich Logan (b)

Constant -11.556** -36.569**

Log Real All Ordinaries index (-) -0.135 0.774

Real loan rate (-) (a) 0.028 0.074

Log Real gross disposable income pc (+) 0.832 2.582

Log Trade weighted exchange rate (-) -0.284 0.620

Log Unemployment rate (-) -0.924** 0.371

Log Consumer price index (+) 2.683** 1.370

Log Housing stock pc (-) -1.278 -2.083

R2 0.985 0.914



Source: AEC


32

4.4 Supply Side Models

Three supply models (3) were estimated in a variety of settings.

M1: Dwelling completions per capita.

M2: Lot registrations per capita.

M3: Building approvals per capita.

The M3 models’ results must be taken carefully as the data are only available over a 32 quarter period (2006-2015). These models were estimated to assess their responses to growth in prices of houses (median and by number of bedrooms per sqm), units (median

and by sqm), and land (per sqm). Models for SEQ and separately for Brisbane, Gold Coast, Ipswich, Moreton Bay and Logan, and a panel model for the twelve LGAs are presented.

4.4.1 Supply Side Models for SEQ

Table 4.21 and Table 4.22 show the supply responses to growth in median houses and

land prices respectively.

Supply curves for all three of these model variants (dwelling completions, lot registrations and building approvals) are upward sloping as the coefficients for the change in prices term

have significant and positive (correct sign) effects. The price change variable has a dominating signalling effect on each variant of supply.

The producer price index (PP) is used as a proxy for construction costs. While significant for dwelling completions and lot registrations, the estimated coefficients have the wrong sign, indicating that (improbably) quantity supplied increases as costs rise.

The real loan rate is significant only for lot registrations, but is positive, also indicating an

improbable result of increases in interest rates leading to a supply stimulus.

There appears to be a strong seasonal feature in building completions, which are all significant and higher in the 2nd, 3rd and 4th quarter of the year. This is expected as the first quarter covers most of the summer months, reflecting climatic conditions and leave traditions.

Similar results applied when real median land prices were substituted for real median house prices in the change of prices term.

Table 4.21 Supply Model (change in real median house prices), South East Queensland


Dwelling Completions per capita (a)

Lot Registrations per capita (a)

Building Approvals

per capita (a, c)

Constant -25.445** -19.021** -7.289

Change in real median house prices (ln,Δ1) (+) 2.961** 4.667* 10.003**

Real loan rate (-) 0.009 0.047** ! 0.032

Producer prices (ln) (-) 4.117** ! 2.642** ! 0.146

D11 (b) -0.102 -0.246** na

Quarter 2 0.126** 0.069 0.119

Quarter 3 0.147** 0.054 0.154*

Quarter 4 0.246** 0.238** 0.260**

R2 0.755 0.695 0.654

Sample Size 94 78 32

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) In logarithms. (b) =1 if 2011q1-2012q4. (c) = D11 not included. na = not applicable. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a

significant coefficient, but with an incorrect sign. Source: AEC


33

Table 4.22 Supply Model (change in real median land prices), South East Queensland




Building Approvals

per capita (a, c)

Constant -23.415** -6.807** 1.166

Change in real median land prices (ln,Δ1) (+) 2.025** 3.826** 1.536

Real loan rate (-) 0.011 0.051** ! 0.081** !

Producer prices (ln) (-) 3.677** ! 2.011 -1.763

D11 (b) -0.154** -0.367** na

Quarter 2 0.129** 0.072 0.129**

Quarter 3 0.146** 0.049 0.121**

Quarter 4 0.238** 0.227** 0.141**

R2 0.739 0.651 0.375


Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) In logarithms. (b) =1 if 2011q1-

2012q4. (c) = D11 not included. na = not applicable. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

For SEQ the supply measures are not responsive to changes in real median land prices per sqm; that is, the supply curve is flat, except for weak significance in the case of dwelling completions. All other significant coefficients in the models are of the wrong sign. For example, instead of shifting the supply curve downwards for an increase in the real loan

rate, the positive coefficient shifts it upwards, implying a fall in quantity supplied for a given price level.

Table 4.23 provides the supply responses to standardised real median land prices, i.e. adjusted by area in square metres, for the three supply measures.

Table 4.23 Supply Model (change in real median land prices by sqm), South East Queensland




Building Approvals

per capita (a, c)

Constant -21.611** -11.570 5.152

Change in real median land prices (ln,Δ1) (+) 1.716* 1.293 3.012

Real loan rate (-) 0.017* ! 0.054** ! 0.089** !

Producer prices (ln) (-) 3.271** ! 1.027 -2.646

D11 (b) -0.156** -0.379** na

Quarter 2 0.137** 0.077 0.124**

Quarter 3 0.162** 0.058 0.108**

Quarter 4 0.243** 0.223** 0.161**

R2 0.738 0.638 0.390




Source: AEC

To examine the sensitivity of estimates of responses in lot registrations per capita across houses and units of different sizes, Table 4.24 and Table 4.25 provide modelling of total lot registrations per capita for Houses of 1-2, 3, 4, and 5 bedrooms, and Units of 1, 2, or 3 bedrooms to growth in real prices per sqm for SEQ.

For house prices, the lot registrations supply curve is upward sloping when looking at the response from changes in standardised prices of houses, for all sizes except 5 bedrooms where it is flat.

For unit prices, the only significant (and correct sign) coefficient is in the case of 3 bedrooms, with lot registration supply unexplained in the cases of the other two sizes.


34

Table 4.24 Supply Model for Total Lot Registrations per capita (a) (change in real median

house prices sqm by size), South East Queensland


1-2 bedrooms

3 bedrooms 4 bedrooms 5 bedrooms

Constant -15.810** -17.130** -16.847** -17.529**

Change in real median house per m2 prices (ln,Δ1) (+)

3.253** 5.051** 4.976** 4.246

Real loan rate (-) 0.050** ! 0.050** ! 0.048** ! 0.044** !

Producer prices (ln) (-) 1.944 2.225** ! 2.167** ! 2.324* !

D11b -0.297** -0.238** -0.254** -0.297**

Quarter 2 0.082 0.086 0.085 0.088

Quarter 3 0.061 0.062 0.060 0.072

Quarter 4 0.223** 0.258** 0.255** 0.253**

R2 0.682 0.712 0.700 0.691

Sample Size 78 78 78 78

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b =1 if 2011q1-2012q4. na = not applicable. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an

incorrect sign. Source: AEC

Table 4.25 Supply Model for Total Lot Registrations per capita (a) (change in real median unit prices sqm by size), South East Queensland


1 bedroom 2 bedrooms 3 bedrooms

Constant -9.235** -15.304** -9.888

Change in real median unit per m2 prices (ln,Δ1) (+)

0.438 2.389 3.673**

Real loan rate (-) 0.065** ! 0.049** ! 0.054** !

Producer prices (ln) (-) 0.498 1.840 0.660

D11b -0.387** -0.329** -0.346**

Quarter 2 0.126** 0.083 0.069

Quarter 3 0.070 0.048 0.052

Quarter 4 0.224** 0.219** 0.227**

R2 0.632 0.652 0.688


Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b =1 if 2011q1-2012q4. na

= not applicable. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC

4.4.2 Supply Side Models for Selected LGAs

Table 4.26 and Table 4.27 provide the estimated supply response curves for lot registrations and building approvals for growth in real median house prices (RMHP) across Brisbane, Gold Coast, Ipswich, Moreton Bay and Logan.

For lot registrations, Brisbane, Ipswich and Sunshine Coast have upward sloping supply

curves as growth in prices have significant and positive effects with Ipswich the strongest and Sunshine coast 40% higher than Brisbane. Moreton Bay and Logan have no significant coefficients and so the model has no explanatory power for those LGAs. The real loan rate is significant for Brisbane, Gold Coast and Sunshine Coast, but incorrectly signed. However, the real loan rate in the case of Ipswich is not only significant but is correctly signed, indicating that a 1% increase in the interest rate leads to a 10.2% decline in lot

registrations per capita in Ipswich.


35


house prices), Selected LGAs


Brisbane Gold Coast

Ipswich Moreton Bay

Logan Sunshine Coast

Constant -14.598* -20.500 -33.004 -14.485 -21.781 -16.307*

Change in real median house prices (ln,Δ1) (+)

3.907** 6.187 9.330** 3.613 1.160 7.043**

Real loan rate (-) 0.079** ! 0.086** ! -0.102** -0.002 0.015 0.048* !

Producer prices (ln) (-) 1.581 2.929 5.929 1.805 3.208 2.104

D11b -0.106 -0.403 -0.060 -0.184 -0.041 -0.644**

Quarter 2 0.076 0.088 0.300** -0.028 -0.040 0.102

Quarter 3 0.032 0.051 -0.005 0.073 0.180 -0.044

Quarter 4 0.224** 0.333** 0.415** 0.248** 0.044 0.213**

R2 0.656 0.635 0.291 0.222 0.161 0.597

Sample Size 78 78 78 78 78 78

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b =1 if 2011q1-2012q4. Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

For building approvals, all LGAs except for Logan show an upwards sloping supply curve. In the case of Brisbane, the coefficient for producer prices is significant and correctly signed indicating that an increase in producer prices will shift the supply curve upwards (or to the left), signalling a reduction in quantity supplied for a constant price level. The elasticity is

highly elastic, indicating a 9.6% fall in supply for a 1% increase in producer prices. Real loan rates are only found to be significant for the Gold and sunshine Coast but are of the incorrect sign.

Table 4.27 Supply Model for Total Building Approvals per capita (a) (change in real median house prices), Selected LGAs


Brisbane Gold Coast

Ipswich Moreton Bay


Constant 35.884** -33.222 -35.907** -28.689** -12.675 -26.706

Change in real median house prices (ln,Δ1) (+)

9.565** 9.133* 10.292** 12.948** 2.136 11.610**

Real loan rate (-) -0.003 0.107* 0.034 0.009 0.063 0.068**

Producer prices (ln) (-) -9.201** 5.656 6.425** ! 4.878* ! 1.198 4.322

D11b

Quarter 2 0.248** 0.091 0.072 -0.009 0.162** 0.133**

Quarter 3 0.279** -0.041 0.254** 0.164 0.057 0.157**

Quarter 4 0.424** 0.144 0.168* 0.178 0.158 0.212**

R2 0.689 0.474 0.733 0.575 0.240 0.570

Sample Size 32 32 32 32 32 32

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b D11 is not included in this model. * Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect

sign. Source: AEC

Table 4.28 and Table 4.29 provide the estimated curves for lot registrations and building approvals for growth in real median land prices across the selected LGAs. The only upward sloping supply curves are those for lot registrations in Brisbane, Moreton Bay (albeit of weak significance) and Sunshine Coast (strong significance). For other cases there are no significant supply responses to changes in real median land prices.Ipswich, for lot registrations, has a significant influence from the real loan rate, and Brisbane, for building approvals, has a significant influence from producer prices. Both of these effects replicate

the results above for house prices.


36


land prices), Selected LGAs


Brisbane Gold Coast

Ipswich Moreton Bay


Constant -9.195 -13.329 -19.918 -11.224 -20.563 -10.117

Change in real median land prices (ln,Δ1) (+)

1.936* 3.504 3.941 2.783* 0.833 5.090**

Real loan rate (-) 0.084** ! 0.092** ! -0.098* -0.001 0.016 0.049

Producer prices (ln) (-) 0.411 1.371 3.106 1.104 2.947 0.771

D11b -0.193* -0.532** -0.309 -0.256** -0.064 -0.751**

Quarter 2 0.078 0.111 0.323* -0.025 -0.040 0.104

Quarter 3 0.025 0.062 0.023 0.072 0.165 -0.056

Quarter 4 0.203** 0.317** 0.369** 0.232** 0.048 0.174

R2 0.644 0.615 0.193 0.227 0.167 0.610

Sample Size 78 78 78 78 78 78

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b =1 if 2011q1-2012q4. * Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

Table 4.29 Supply Model for Total Building Approvals per capita (a) (change in real median land prices), Selected LGAs


Brisbane Gold Coast

Ipswich Moreton Bay


Constant 43.693** -20.504 -30.969* -29.156 -8.917 -18.587

Change in real median land prices (ln,Δ1) (+)

1.543 1.788 -0.493 -0.627 1.891 1.300**

Real loan rate (-) 0.045* ! 0.150** ! 0.106** ! 0.063 0.066** ! 0.107**

Producer prices (ln) (-) -10.968** 2.824 5.236 4.893 0.382 2.494

D11b

Quarter 2 0.254* 0.114 0.084 0.015 0.158** 0.163**

Quarter 3 0.235* -0.056 0.218** 0.122 0.047 0.118**

Quarter 4 0.306** 0.055 0.009 -0.031 0.148** 0.057

R2 0.512 0.402 0.628 0.361 0.240 0.434

Sample Size 32 32 32 32 32 32

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b D11 is not included in the model. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect

sign. Source: AEC

Table 4.30 replicates the model above in Table 4.28 for lot registrations with an alteration

in the control variable for prices, which are now changes in standardised land prices per sqm across the selected LGAs. The Brisbane, Moreton Bay and Sunshine Coast supply curves are similarly responsive to changes in land prices as the former model, but with increased significance, and the real loan rate continues to exert an influence in Ipswich.


37


land prices per sqm), Selected LGAs


Brisbane Gold Coast

Ipswich Moreton Bay


Constant -6.858 -10.264 -22.212 -9.750 -20.486 -8.262

Change in real median land prices per m2 (ln,Δ1) (+)

2.789** 3.026 1.780 2.493** 0.192 3.438**

Real loan rate (-) 0.090** ! 0.093** ! -0.100* 0.006 0.017 0.060**!

Producer prices (ln) (-) -0.113 0.705 3.612 0.764 2.929 0.346

D11b -0.205** -0.628** -0.355 -0.269** -0.072 -0.781**

Quarter 2 0.084 0.111 0.310* -0.015 -0.038 0.098

Quarter 3 0.041 0.081 0.007 0.086 0.170 -0.058

Quarter 4 0.214** 0.322** 0.359* 0.247** 0.046 0.176*

R2 0.670 0.614 0.175 0.235 0.159 0.552

Sample Size 78 78 78 78 78 78

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. a In logarithms. b =1 if 2011q1-2012q4. Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

Further supply side modelling results for individual LGAs are contained in Appendix E.

Tables E.1a-f present the estimated supply curves for lot registrations for changes in house prices by size, for the selected LGAs. The Brisbane supply curves are upward sloping for all size houses. Three and four bedroom houses supply curves for the Gold Coast are

upward sloping as are those for the Sunshine Coast. All house sizes except five bedrooms have upward sloping curves in Ipswich, with significant negative effects from the real loan rate for all but 5 bedroom houses. All sizes of house except for 1-2 bedrooms have upward sloping curves in Moreton Bay. The supply curves are flat for Logan for all house sizes. The size of the response for the 1-2 and 5 bedrooms in the Sunshine Coast is similar to that of Brisbane.

Tables E2.a-f present the estimated supply curves for building approvals for changes in house prices by size, for the selected LGAs. Overall the evidence is that supply is upward

sloping. The slope is positive and significant for Brisbane for 3 and 4 bedroom houses, for Moreton Bay for 4 and 5 bedroom houses, for the Gold Coast for 3 bedroom houses, for the Sunshine Coast for 3 and 4 bedroom houses and Logan for 5 bedrooms houses. For Brisbane this is the model where the shift in the supply curve is upwards (or to the left) with increases in producer prices. These results must be interpreted with care as there are

only 32 quarters of data.

Tables E.3a-c present the estimated supply curves for lot registrations for changes in unit prices for different sized units for Brisbane, Gold Coast and Sunshine Coast (as data were not sufficiently reliable to model other LGAs separately). Only the Gold Coast shows upward sloping curves for the 2 and 3 bedrooms market. The supply curve is flat for Brisbane ans sunshine Coast.

Table E.4 provides results for six supply curves estimated using panel data i.e. the

combination of cross sectional geographical data with the quarterly sample time series. This was done for two supply quantity measures, lot registrations and building approvals, using the twelve LGAs in a panel model. The model was replicated for three price measures:

real median house prices, real median land prices and standardised real median land prices per sqm, making six models in all.

In this procedure, all time periods and LGAs are modelled jointly. This provides the richness

of the variation across cross-sections and time periods and provides more degrees of freedom for statistical estimation. The estimates for individual variables can be compared and contrasted with those obtained using SEQ aggregate values.

The supply responses are significant when the models are estimated as responses to changes in real median house prices. Land price changes (whether standardised or not) did not lead to a significant response in building approvals. Lot registrations supply is upward sloping for changes in real medial land prices.


38

5. Summary & Discussion

This study is a repeat of AEC (2010) but extends that study in that there is a disaggregation of dwelling price data by size (bedrooms) and sqm. The data series is now sufficiently long that supply side modelling can also be undertaken.

Compared to AEC (2010) there are only minor changes from the estimates obtained in the

first report. It is clear that there is no longer pressure arising from the combination of low stocks and population growth (measured by net migration) and in fact stocks may be in over supply hence supressing prices. The revised measure of housing stock per capita based on better data on stock and population for SEQ used in this study shows an increase in the stock of dwelling units per capita until around 2002 and steady levels since.

The demand modelling for SEQ shows the markets for houses, units and land are heterogeneous across sizes of houses and units. Aggregate (i.e. unstandardized by sqm)

prices for houses, units and land are well determined by financial variables (the stock market and interest rates) and macroeconomic conditions (the unemployment rate).

However standardised (by sqm) land prices provides evidence that only gross disposable income, inflation and unemployment rates are important determinants of land prices. However, the significance of the variables is not as clear when the model is run using real median house and unit prices. Real median house and unit prices combine the trends from

all sizes of housing and might be too coarse to elucidate the effects. The determinants of real median houses prices are consistent across sizes of dwellings when looking at the aggregate SEQ. However, the effects are much more heterogeneous for different size units.

The supply modelling for SEQ used three measures, dwelling completions, lot registrations and building approvals. The data for dwelling completions and lot registrations were available for a longer period than that for building approvals. Supply responses from changes in prices, measured as changes in real median prices, show consistent results

whether the change is for house, unit or land prices. The quantity of supply is responsive independently of how it is measured (registrations, approvals or completions). The responses are heterogeneous when studying supply responses for different sizes of houses or units or land per sqm.

The demand modelling for selected LGAs shows an expected degree of heterogeneity in some aspects. For houses of different sizes the responses across LGAs is uniform to movements in the stock market and unemployment. Both are strong indicators of

macroeconomic conditions and have a uniformly negative effect on houses prices. For other variables the responses vary. For example, increases in real gross disposable income per capita show a strong response for Logan and Ipswich median houses prices. The real loan rate is significant and has a negative impact in Brisbane and the Gold Coast. Housing stock per capita has a significant and negative impact on Brisbane and Ipswich prices The real median price of units of all sizes in Brisbane are responsive to the real loan rate, real gross

disposable income per capita, unemployment and the stock market. For the Gold Coast, real loan rate, real disposable income and the unemployment rate were generally significant determinants of real median unit prices (for all sizes). Whereas on the Sunshine Coast the All ordinaries index, real loan rate and the unemployment rate are significant.

The supply modelling for selected LGAs shows heterogeneous results. For the quantity measure of lot registrations, real median land prices, whether unstandardized or

standardised by sqm, indicated a strong supply response for Brisbane, Moreton Bay and

Sunshine Coast. This model for other LGAs showed no supply response. The supply responses to growth in median houses prices varies across the sizes of houses and the LGAs. However, except for Logan, all other LGAs modelled had some supply response of lot registration and building approvals. A supply response to growth in real median unit prices is significant for the Gold Coast lot registrations.

The supply responses from a model for all LGAs estimated as a panel shows both lot registrations and building approvals respond to growth in real median house prices. No

significant responses on lot registration are found from changes in unstandardized or standardised per sqm land prices.

The study confirms the influences on median dwelling prices from AEC (2010) and has provided insight into the supply response to increases in prices e.g. this study also allowed


39

modelling at the LGA level which found similar responses in supply to price changes across

SEQ.


40

References

Abelson, P., Joyeux, R., Milunovich, G. and Chung, D. (2005), Explaining House Prices in Australia: 1970 to 2003, Economic Record, 81, S1–8.

Adams, Z. and R. Füss (2010), Macroeconomic determinants of international housing markets, Journal of Housing Economics, vol. 19 pp. 38-50.

AEC (2010), An Econometric Analysis of SEQ Housing Prices. Report prepared for the Local government Association of Queensland, Brisbane.

Afonso, A.and R. M. Sousa (2011), What are the effects of fiscal policy on asset markets?, Economic Modelling, Economic Modelling,vol. 28 pp. 1871–1890.

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Hamnett S. and Kellett, J. (2007), Onward, outward, upward? A review of contemporary Australian metropolitan growth policies. Third State of Australian Cities Conference, Adelaide, February 18-20.


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Hatzvi, E. and G. Otto, (2008), Prices, rents and rational speculative bubbles in the

Sydney housing market, The Economic Record, vol. 84, pp. 405-420.

Huang, M. (2012), Forecasts and implications of the current housing crisis: switching regimes in a threshold framework, Applied Economics Letters, vol. 19, pp. 557-

568.

Karantonis, A. & Ge, X. J., (2007), An Empirical Study of the Determinants of Sydney's Dwelling Price. Pacific Rim Property Research Journal, 13 (4), 493-509.

McLaughlin, R. B. (2011), Metropolitan growth policies and new housing supply: Evidence from Australia’s capital cities, Australasian Journal of Regional Studies, vol. 17. Pp. 60-80.

Otto, G. (2007), The Growth of House Prices in Australian Capital Cities: What do

Economic Fundamentals Explain? The Australian Economic Review, vol. 40, pp. 225–38.

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Department of State Development, Infrastructure and Planning, July 2012, Brisbane. Accessed from http://www.dilgp.qld.gov.au/resources/laws/state-planning-regulatory-provision/sprp-ict.pdf, 21 September 2015.

Queensland Government (2015a), Population Estimates, Age and Sex indicators by local government area (LGA), Queensland, 30 June 2014, customised report from the Australian Bureau of Statistics. Accessed from http://www.qgso.qld.gov.au/subjects/demography/population-estimates/index.php, 21 September 2015.

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Queensland Treasury. Accessed from http://www.qgso.qld.gov.au/subjects/industry-development/development-activity/reports/res-land-dwelling-activity-lga/index.php, 21 September 2015.

Note: Same reports as above were accessed for the other eleven LGAs.

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state-accounts-tables/index.php, 21 September 2015.

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Wadud, I.K.M., Bashar, O.H.M.N., and H. J. Ali Ahmed (2012) Monetary policy and the

housing market in Australia, Journal of Policy Modeling, vol. 34, pp. 849-863.

Wilson, P., White, M., Dunse, N., Cheong C. and R. Zurbruegg, (2011), Modelling Price Movements in Housing Micro Markets: Identifying Long-term Components in Local Housing Market Dynamics, Urban Studies, vol. 48pp. 1853–1874.

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42

Appendix A: Literature Review

Dynamic Time Series Models

Title What are the effects of fiscal policy on asset markets?

Author António Afonso, Ricardo M. Sousa

Date 2011

Source Economic Modelling 28 (2011) 1871–1890

Abstract We investigate the link between fiscal policy shocks and asset markets. Our results show that spending shocks have: a positive and persistent effect on GDP in the U.S. and in the U.K., while for Germany and Italy, such impact is temporary; a positive and persistent effect on housing prices; a negative effect on stock prices; and mixed effects on the price level. A VAR counter-factual exercise suggests that fiscal shocks play a minor role in the asset markets of the U.S. and Germany, and substantially increase the variability of housing and stock prices in the U.K., while government revenue shocks have increased volatility in Italy.

Variables Government spending Unemployment rate Stock market index Long term interest rate Government revenue Debt average cost of financial debt

Title House prices, non-fundamental components and interstate spillovers: The Australian experience

Author Greg Costello, Patricia Fraser, Nicolaas Groenewold

Date 2011

Source Journal of Banking & Finance 35 (2011) 653–669

Abstract Using Australian capital city data from 1984Q3–2008Q2, this paper utilizes a dynamic present value model within a VAR framework to construct time series of house prices depicting what aggregate house prices should be given expectations of future real disposable income – the ‘‘fundamental price” – and continues by comparing capital city fundamental prices with actual prices. The extent to which revealed capital city ‘‘non-fundamental” components spillover from state to state, as well as their long-term impact is also investigated. Results provide evidence of periods of sustained deviations of house prices from values warranted by income for all state capitals with the greatest deviations arising in the NSW market and starting around 2000. In general NSW is relatively more susceptible to spillovers transmitted from other states while ACT and WA are most isolated from the rest of the country.

Variables Real house prices (REIA) Real gross disposable income (ABS) Domestic final demand (ABS) State final demand (ABS) CPI (ABS) Risk-free rate of return


43

Title Overvaluation in Australian Housing and Equity Markets: Wealth Effects or Monetary Policy?

Author Renee A. Fry, Vance L. Martin, Nicholas Voukelatos

Date 2010

Source The Economic Record, Vol. 86, No. 275, December, 2010, 465–485

Abstract A structural vector autoregression model is used to identify overvaluation in house prices in Australia from 2002 to 2008. An important feature is the development of a housing sector where long-run restrictions are derived from theory to identify housing demand and supply shocks. The results show strong evidence of overvaluation in real house prices, reaching a peak of just over 15% by the end of 2003. Factors driving overvaluation are housing demand shocks before 2006 and post-2006 macroeconomic shocks. Wealth effects from equity markets are also important. The results suggest that monetary policy is not an important contributor to overvaluation of house prices.

Variables GDP current prices, millions, s.a. (RBA) GDP CVM, millions, s.a. (RBA) CPI all groups (RBA) Materials used in house production index (RBA) Money market 90-day bank-accepted bills (RBA) Share price index Australia, S&P/ASX 200 (RBA) Share price indices USA, S&P500 (dX database) Exchange rate, USD per AUD (RBA) Median house price index (REIA)

Title Prices, Rents and Rational Speculative Bubbles in the Sydney Housing Market

Author Eden Hatzvi and Glenn Otto

Date 2008

Source The Economic Record, Vol. 84, No. 267, December, 2008, 405–420

Abstract We examine whether asset pricing theory can explain residential property prices. Using quarterly data for Local Government Areas in Sydney from 1991 to 2006, we find little evidence that variations in price : rent ratios anticipate future real rent growth. Instead changes in price : rent ratios apparently reflect changing expectations about future discount factors. Some important geographical differences in the behaviour of property prices across metropolitan Sydney are identified. A significant proportion of the variation in property prices in outer western regions of Sydney is not explained by either rents or discount factors; pointing to a possible role for a speculative bubble.

Variables Median house prices (NSW Department of Housing) Median weekly rents (NSW Department of Housing) 10-year government bond rate (RBA) S&P/ASX 200 accumulation index (RBA) CPI (ABS)

Title Forecasts and implications of the current housing crisis: switching regimes in a threshold framework

Author MeiChi Huang

Date 2012

Source Applied Economics Letters, 2012, 19, 557-568

Abstract This article aims to explore the implications of the recent housing crisis from a forward-looking perspective in the form of the two-regime switching phenomena under a Threshold Autoregressive (TAR) model. Three categories of thresholds, which are housing prices, housing volumes and price-to-income ratios, are adopted in an attempt to examine the US state level housing market cycles in multiple dimensions. In general, while lagged differenced thresholds fail to capture the recent housing boom-and-bust regime switching, moving-average thresholds succeed in signalling a housing crisis in many states. The regime-switching autoregressive structures show that housing price cycle has remarkable series dependence, and the housing volume cycle tends to show a mean-reversion pattern. Most importantly, only four states, Arizona, California, Florida and Nevada, display the bust regime of housing price growth during 2006 to 2010. The price-over-income also indicates that less than five states have significant two switching regimes. Otherwise, most states exhibit low-growth regime of housing permits, implying a nationwide housing volume cycle during the current housing crisis. The forecasting system provides a helpful guideline for policy-making of the government as well as household decisions of consumption and investment.

Variables Housing price index Housing volumes Price income ratio


44

Title The Growth of House Prices in Australian Capital Cities: What Do Economic Fundamentals Explain?

Author Glenn Otto

Date 2007

Source The Australian Economic Review, vol. 40, no. 3, pp. 225–38

Abstract This paper examines the ability of standard economic factors to explain the growth of real house prices in Australia’s capital cities. Dynamic models are estimated for each city with the objective of identifying the major drivers of house price growth rates. The variable mortgage rate is found to be an important influence on growth rates in all eight capital cities. However, the size of the mortgage rate effect can differ substantially between cities. For example a 25 basis point rise in the mortgage rate reduces the long-run quarterly growth rate of real house prices by about 1% in Sydney compared with only 0.4% in Adelaide. The effects of other economic variables are less systematic, significantly affecting the growth rate of capital gains in some cities but not in others. Nevertheless, for most Australian cities economic factors are found to explain around 40% to 60% of the variation in the growth rate of house prices.

Variables House price index (ABS) CPI (ABS) State final demand (ABS) Unemployment rate (ABS) Population (ABS) Mortgage rate (RBA) Dwelling approvals (ABS) S&P/ASX 200 (RBA) Rents (REIA)

Title Monetary policy and the housing market in Australia

Author I.K.M. Mokhtarul Wadud, Omar H.M.N. Bashar, Huson Joher Ali Ahmed

Date 2012

Source Journal of Policy Modeling 34 (2012) 849–863

Abstract This paper models the role of monetary policy in the Australian housing market using structural vector autoregression model. Our results show that a contractionary monetary policy significantly reduces housing activity but does not exert any significant negative effect on the real house prices. The housing output and real house prices also respond significantly to shocks stemming from housing supply, housing demand and a number of other variables. The findings further suggest that monetary policy rule in Australia takes into account the changes in house price along with the usual targets of inflation and output gap. On the backdrop of the observed high house prices and increased affordability problem, the findings of this paper are expected to shed some lights on the current policy environment pertaining to the Australian housing sector.

Variables Housing approvals (ABS) Housing material costs (ABS) House price index (ABS) Gross domestic product (ABS) Short-term nominal interest rate (RBA) CPI (ABS) Foreign interest rate (US Federal Reserve Bank) Net government spending for housing (RBA) Exchange rate (RBA)

Title Modelling Price Movements in Housing Micro Markets: Identifying Long-term Components in Local Housing Market Dynamics

Author Patrick Wilson, Michael White, Neil Dunse, Chee Cheong and Ralf Zurbruegg

Date 2011

Source Urban Studies 48(9) 1853–1874, July 2011

Abstract This study identifies housing sub-markets (micro markets) that may be price leaders within local housing market areas and relates their performance to potentially relevant economic factors. This can be important as it provides both policy-makers and market players with a useful barometer regarding housing market behaviour. The study uses cointegration analysis to find those markets that interact with each other over the long run and isolates housing sub-markets that are not influenced by housing market behaviour elsewhere in the local urban economy. The study lays the basis for research aimed at identifying whether different housing markets respond to the same or to


45

different economic stimuli. The research is likely to be of interest to policy-makers (such as town planners) as well as to developers and lending institutions.

Variables House price indices Oil price Interest rate Inflationary expectations Securitised property

Panel Models

Title Macroeconomic determinants of international housing markets

Author Zeno Adams and Roland Füss

Date 2010

Source Journal of Housing Economics 19 (2010) 38–50

Abstract This paper examines the long-term impact and short-term dynamics of macroeconomic variables on international housing prices. Since adequate housing market data are generally not available and usually of low frequency we apply a panel cointegration analysis consisting of 15 countries over a period of 30 years. Pooling the observations allows us to overcome the data restrictions which researchers face when testing long-term relationships among single real estate time series. This study does not only confirm results from previous studies, but also allows for a comparison of single country estimations in an integrated equilibrium framework. The empirical results indicate house prices to increase in the long-run by 0.6% in response to a 1% increase in economic activity while construction costs and the long-term interest rate show average long-term effects of approximately 0.6% and -0.3%, respectively. Contrary to current literature our estimates suggest only about 16% adjustment per year. Thus the time to full recovery may be much slower than previously stated, so that deviations from the long-term equilibrium result in a dynamic adjustment process that may take up to 14 years.

Variables Real economic activity-created by the first principal component of the matrix consisting of: o real money supply o real consumption o real industrial production o real GDP o employment

Long-term interest rate Construction cost index-includes range of cost items, including:

o costs of transportation of building materials o labour costs o interest on loans

Title Booms and busts in housing markets: Determinants and implications

Author Luca Agnello, Ludger Schuknecht

Date 2011

Source Journal of Housing Economics 20 (2011) 171–190

Abstract This study looks at the characteristics and determinants of booms and busts in housing prices for a sample of eighteen industrialised countries over the period 1980–2007. From an historical perspective, we find that recent housing booms have been amongst the longest in the past four decades. Estimates of a Multinomial Probit model suggest that domestic credit and interest rates have a significant influence on the probability of booms and busts occurring. Moreover, international liquidity plays a significant role for the occurrence of housing booms and—in conjunction with banking crises—for busts. We also find that the deregulation of financial markets has strongly magnified the impact of the domestic financial sector on the occurrence of booms.

Variables Real housing price index Real GDP per capita Short-term interest rates Real domestic credit Working age population International liquidity


46

Supply Side Models

Title Supply Elasticity Estimates for the Sydney Housing Market

Author Emily Gitelman and Glenn Otto

Date 2012

Source The Australian Economic Review, vol. 45, no. 2, pp. 176–90

Abstract This article presents estimates of the supply elasticity for residential property in metropolitan Sydney over the period 1991–2006. Our results suggest that supply is inelastic—less than unity—for all types of housing, although the supply elasticity is relatively larger for strata properties (apartments and flats) than for nonstrata properties (separate and semi-detached houses, terraces and townhouses).We also find evidence of a significant fall in supply elasticity between 1991–1996 and 2001–2006. When the median time taken by a local council to decide on a development application is included in the supply curve, it is found to have a negative effect on the supply of residential property. However, split-sample estimates indicate this effect is largely confined to the 1991–1996 period.

Variables Housing supply (ABS census) Median property prices (Department of Housing) Income (ABS census) Population (ABS census) Nominal interest rate (RBA) Real interest rate (RBA) CPI (ABS) Development application approval times (NSW Division of Local Government)

Title Metropolitan Growth Policies and New Housing Supply: Evidence from Australia’s Capital Cities

Author Ralph B. McLaughlin

Date 2011

Source Australasian Journal of Regional Studies, Vol. 17, No. 1, 2011

Abstract This paper empirically examines the relationship between house price change, metropolitan growth policies, and new housing supply in Australias five major capital cities. Our hypothesis suggests capital cities with tighter regulations on new development will have fewer housing starts and price elasticities than those in less regulated markets. The empirical procedure used in this paper utilises the Urban Growth Model of Housing Supply developed in Mayer and Somerville (2000a and 2000b) and employed in Zabel and Patterson (2006) by using quarterly data on housing approvals and house prices from 1996-2010. Data on metropolitan growth policies in Australia is borrowed from Hamnett and Kellett (2007). Preliminary findings indicate that new housing supply in Australian capital cities is elastic to housing price changes, as a 1% increase in prices leads to an approximately 4-6% increase in housing approvals over five quarters. While this indicates a properly functioning housing market, the estimated elasticity is about a third of other developed countries, such as the United States. Furthermore, the use of established growth policies, such as urban growth boundaries and urban consolidation, appears to have a greater impact on new housing approvals than adoption of new-style growth policies, such as development corporations and infrastructure levies. However, both types of policies decrease new housing supply.

Variables House price index (Residex) Housing approvals (ABS) Construction cost index (ABS) Cash rate (RBA) Metropolitan growth policy strength (Hamnet & Kellet, 2007)


47

Appendix B: LGA Dwelling Price Graphs

The figures below show the real median prices for houses, units and land in SEQ LGAs over the sample period March 1991 to June 2015 in 2011-12 prices. Due to confidentiality reasons and/or a lack of transactions in some periods the data for smaller LGAs is erratic especially for units.

Figure B.1 Real Median Dwelling Prices ($2011-12), Brisbane


Figure B.2 Real Median Dwelling Prices ($2011-12), Gold Coast


$0

$100,000

$200,000

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48

Figure B.3 Real Median Dwelling Prices ($2011-12), Ipswich


Figure B.4 Real Median Prices ($2011-12), Lockyer Valley


$0

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49

Figure B.5 Real Median Prices, ($2011-12), Logan


Figure B.6 Real Median Prices, ($2011-12), Moreton Bay


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50

Figure B.7 Real Median Prices, ($2011-12), Noosa


Figure B.8 Real Median Prices, ($2011-12), Redland


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51

Figure B.9 Real Median Prices, ($2011-12), Scenic Rim


Figure B.10 Real Median Prices, ($2011-12), Somerset


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52

Figure B.11 Real Median Prices, ($2011-12), Sunshine Coast


Figure B.12 Real Median Prices, ($2011-12), Toowoomba


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53

Appendix C: Data Construction

South East Queensland House Prices

This study is concerned with house prices in South East Queensland (SEQ). SEQ is defined

as the area covered by twelve local government areas. Corelogic RP Data has provided the number of sales and median sales price on a monthly basis for the twelve LGAs from January 1990 to July 2015.

To calculate a median price for SEQ a weighted average was calculated as follows:

𝑝𝑡 = ∑𝑞𝑖𝑡 ∗ 𝑝𝑖𝑡

𝑛

𝑖=1

where,

𝑝𝑡 = median price at time t (month)

𝑖 = Local government area

𝑞𝑖𝑡 = number of sales for LGA i / all transaction at time t

𝑝𝑖𝑡 = median price for LGA i at time t

The monthly data is then rolled up to quarters based on the year to the end of the quarter. The number of sales is a simple sum whereas the median price is a weighted average of the number of sales and median price in the preceding 12 months. So for each quarter q:

𝑞𝑞 = ∑ 𝑤𝑚

−11

𝑚=0

𝑝𝑞 = ∑ 𝑞𝑚 ∗ 𝑝𝑚

−11

𝑚=0

SEQ Population

The SEQ population figures from 2001Q2 are obtained by summing SEQ LGA population estimates. Prior to 2001Q2 SEQ Population estimates are obtained from the Queensland population using the formula:

𝑃𝑆𝐸𝑄𝑡 = 𝑃𝑆𝐸𝑄2001𝑄2

𝑃𝑂𝑃2001𝑄2× 𝑃𝑂𝑃𝑡

where,

𝑃𝑆𝐸𝑄2001𝑄2 = 2,476,136 and 𝑃𝑂𝑃2001𝑄2 = 3,571,469 (From ABS: 3218.0 and 3201.0, Table 3)

The same approach is used for LGAs prior to 2001Q2.

Dwelling Stock

The housing stock for SEQ was constructed by the using the Perpetual Inventory Method (PIM) formula:

𝐻𝑆𝑡 = (1 − 𝛿)𝐻𝑆𝑡−1 + 𝐶𝑂𝑀𝑃𝑡

where,

𝐻𝑆1991𝑄3 = 734,126, the figure is the total dwellings of Brisbane and Moreton

Statistical Divisions and Toowoomba (from ABS census data August 1991).

𝐶𝑂𝑀𝑃 = quarterly dwelling units completed in Queensland.

𝛿 = 0.00320 = the depreciation rate calibrated using data from ABS census data

for 1986, 1991, 1996, 2001 and 2006.


54

Gross Disposable Income

Quarterly gross disposable income is calculated from annual estimates using the same quarterly share as the national gross disposable income.


55

Appendix D: Lot Production Process

The following description of lot production is adapted from Queensland Government (2015b) Residential land development activity profile, Brisbane City, December Quarter 2014.

The process for developing residential land is given in Figure B.1 and each step is explained

below:

Figure B.1 Residential land development stages

Source: Queensland Government (2015b)

Broadhectare Land Supply

Broadhectare land refers to residential greenfield land and brownfield (greater than 2,500 m²) that is currently suitable for residential development.

Material Change of Use

Where there is a development proposal that changes the use of land (includes starting a new use, increasing the intensity of an existing use or re-commencing an abandoned use) a material change of use (MCU) application is made to the relevant local authority.

Residential Lot Approval

Before residential lots can be created, an applicant must first obtain a development permit

approval for reconfiguring a lot (RaL) from the local government authority.

Operational works approval

Before an approved development proceeds, detailed engineering drawings and specifications for civil engineering and landscaping (or "operational works") must be approved by council. Such works may not be required for small projects.

Lot Production (lot certification)

Once council is satisfied with all aspects of the development being implemented, it will then

issue a certificate of compliance.

Lot registration

New lots within plans certified by council do not legally exist until the titles have been registered by the Department of Natural Resources and Mines. This registration is the final stage in the development of new lots.


56

Appendix E: LGA Supply Side Model Estimations

Lot Registration per capita, Houses

Table E.1a Supply Model for Lot Registrations per capita (change in real median house prices per sqm), Brisbane


1-2 bedrooms


Constant -12.153** -12.290 -11.476 -12.299*

Change in real median house prices (ln,Δ1) (+) 3.197** 3.525** 3.783** 3.708**

Real loan rate (-) 0.081** ! 0.082** ! 0.0829** ! 0.080** !

Producer prices (ln) (-) 1.051 1.079 0.900 1.082

D11 (b) -0.140* -0.125 -0.130 -0.150*

Quarter 2 0.083 0.085 0.082 0.085

Quarter 3 0.035 0.035 0.038 0.039

Quarter 4 0.220** 0.228** 0.232** 0.238**

R2 0.663 0.652 0.653 0.652



2012q4. (c) = D11 not included. na = not applicable. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC

Table E.1b Supply Model for Lot Registrations per capita (change in real median house prices per sqm), Gold Coast


1-2 bedrooms


Constant -13.051 -20.497* -18.900 -15.359

Change in real median house prices (ln,Δ1) (+) 1.604 6.833** 7.695* 3.831

Real loan rate (-) 0.094** ! 0.085** ! 0.088** ! 0.089** !


D11 (b) -0.555 -0.382 -0.386 -0.489**

Quarter 2 0.110 0.128** 0.117 0.108*

Quarter 3 0.069 0.081 0.067 0.066

Quarter 4 0.299** 0.369** 0.362** 0.327**

R2 0.602 0.655 0.654 0.619





57

Table E.1c Supply Model for Lot Registrations per capita (growth in real median house

prices per sqm), Ipswich


1-2 bedrooms


Constant -22.211 -29.664 -24.866 -12.405

Change in real median house prices (ln,Δ1) (+) 4.679** 8.309** 7.004** -1.262 !

Real loan rate (-) -0.088** -0.096** -0.092* -0.101


D11 (b) -0.200 -0.037 -0.140 -0.522

Quarter 2 0.289 0.319** 0.345** 0.289*

Quarter 3 0.014 0.012 0.040 0.030

Quarter 4 0.351 0.426** 0.427** 0.314*

R2 0.245 0.290 0.269 0.201




Source: AEC

Table E.1d Supply Model for Total Lot Registrations per capita (change in real median house prices per sqm), Moreton Bay


1-2 bedrooms


Constant -14.712 -12.967 -12.267 -11.608

Change in real median house prices (ln,Δ1) (+) 3.111 3.915** 3.385* 2.210**

Real loan rate (-) -0.002 0.002 0.002 0.001


D11 (b) -0.150 -0.173 -0.191 -0.235**

Quarter 2 -0.008 -0.011 -0.019 -0.021

Quarter 3 0.078 0.081 0.075 0.074

Quarter 4 0.261** 0.263** 0.256** 0.249**

R2 0.248 0.247 0.216 0.220




Source: AEC

Table E.1e Supply Model for Total Lot Registrations per capita (change in real median house prices per sqm), Logan


1-2 bedrooms


Constant -20.334 -22.372 -23.691 -20.302

Change in real median house prices (ln,Δ1) (+) 0.042 1.847 2.214 0.121

Real loan rate (-) 0.016 0.017 0.014 0.016


D11 (b) -0.077 -0.003 -0.010 -0.074

Quarter 2 -0.041 -0.037 -0.033 -0.041

Quarter 3 0.171 0.173 0.169 0.168

Quarter 4 0.041 0.065 0.073 0.044

R2 0.157 0.172 0.174 0.157





58

Table E.1f Supply Model for Lot Registrations per capita (change in real median house prices

per sqm), Sunshine Coast


1-2 bedrooms


Constant -7.493 -14.731 -16.578* -8.549

Change in real median house prices (ln,Δ1) (+) 2.423** 6.011** 6.660** 3.002**

Real loan rate (-) 0.062* ! 0.052** ! 0.046** ! 0.053** !


D11 (b) -0.784** -0.650** -0.633** -0.796**

Quarter 2 0.105 0.111 0.101 0.096

Quarter 3 -0.022 0.048 -0.041 -0.052

Quarter 4 0.180** 0.200* 0.219* 0.168

R2 0.558 0.583 0.592 0.543


Notes: (a) In logarithms. (b) =1 if 2011q1-2012q4. (c) = D11 not included. na = not applicable. *Significant at the 10% level. **

Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC


59

Building Approvals per capita, Houses

Table E.2a Supply Model for Building Approvals per capita (change in real median house prices per sqm), Brisbane


1-2 bedrooms


Constant 43.511** 45.046** 44.902** 41.719**

Change in real median house prices (ln,Δ1) (+) 1.768 9.370** 9.127** 4.599

Real loan rate (-) 0.041 0.002 0.011 0.029

Producer prices (ln) (-) -10.919** -11.192** -11.174** -10.513**

Quarter 2 0.255** 0.246** 0.233** 0.249*

Quarter 3 0.255** 0.269** 0.277** 0.260**

Quarter 4 0.315** 0.440** 0.427** 0.364**

R2 0.511 0.637 0.610 0.533




Table E.2b Supply Model for Building Approvals per capita (change in real median house prices per sqm), Gold Coast


1-2 bedrooms


Constant -20.070 -24.891 -22.460 -19.250

Change in real median house prices (ln,Δ1) (+) 1.282 8.651** 6.485 0.738

Real loan rate (-) 0.149** ! 0.117* ! 0.135 0.157** !


Quarter 2 0.111 0.123 0.123 0.104

Quarter 3 -0.056 -0.012 -0.020 -0.063

Quarter 4 0.0611 0.181** 0.140 0.038

R2 0.410 0.465 0.428 0.401




Table E.2c Supply Model for Building Approvals per capita (change in real median house prices per sqm), Ipswich


1-2 bedrooms


Constant -30.621 -27.240** -28.744** -32.313**

Change in real median house prices (ln,Δ1) (+) 0.206 4.016* 4.652** 1.761**

Real loan rate (-) 0.103** ! 0.078** ! 0.084** ! 0.094** !

Producer prices (ln) (-) 5.166 4.475* ! 4.789* ! 5.543** !

Quarter 2 0.082 0.080 0.090 0.087*

Quarter 3 0.218** 0.217* 0.231** 0.238**

Quarter 4 0.019 0.085 0.085 0.066

R2 0.628 0.669 0.665 0.685





60

Table E.2d Supply Model for Building Approvals per capita (change in real median house

prices per sqm), Moreton Bay


1-2 bedrooms


Constant -25.897* -23.072 -24.202* -22.776*

Change in real median house prices (ln,Δ1) (+) 2.123 5.642 8.361** 5.838**

Real loan rate (-) 0.057* ! 0.033 0.038 0.049


Quarter 2 0.012 0.003 -0.014 0.005

Quarter 3 0.126 0.145 0.125 0.134

Quarter 4 0.013 0.066 0.100 0.067

R2 0.395 0.408 0.475 0.494




Source: AEC

Table E.2e Supply Model for Building Approvals per capita (change in real median house prices per sqm), Logan


1-2 bedrooms


Constant -12.334 -11.309 -13.482 -20.231

Change in real median house prices (ln,Δ1) (+) -0.044 0.995 -2.306 -2.630*

Real loan rate (-) 0.076** ! 0.069** ! 0.088** ! 0.073** !


Quarter 2 0.171 0.165 0.172 0.193

Quarter 3 0.049 0.054 0.048 0.103

Quarter 4 0.132** 0.144 0.090 0.142

R2 0.236 0.236 0.243 0.344




Source: AEC

Table E.2f Supply Model for Building Approvals per capita (change in real median house prices per sqm), Sunshine Coast


1-2 bedrooms


Constant 19.047 -23.319** -24.189* -22.520**

Change in real median house prices (ln,Δ1) (+) 0.149 7.856** 10.761** 2.832

Real loan rate (-) 0.111** ! 0.082** ! 0.080** ! 0.086**

Producer prices (ln) (-) 2.584 3.563 3.757* ! 3.379* !

Quarter 2 0.165** 0.153** 0.138* 0.161*

Quarter 3 0.119** 0.145** 0.145** 0.123

Quarter 4 0.044 0.146** 0.210** 0.083

R2 0.429 0.554 0.600 0.464




Source: AEC


61

Lot Registrations per Capita, Units

Table E.3a Supply Models for Lot Registrations per capita (change in real median unit prices per sqm), Brisbane


1 Bedroom 2 Bedroom 3 Bedroom

Constant -8.464 -11.381 -9.434

Change in real median unit prices (ln,Δ1) (+) 0.029 2.115 3.255

Real loan rate (-) 0.083** ! 0.080** ! 0.083** !

Producer prices (ln) (-) 0.260 0.891 0.465

D11 (b) -0.236* -0.192** -0.196*

Quarter 2 0.081 0.088 0.083*

Quarter 3 0.034 0.042 0.038

Quarter 4 0.187** 0.215** 0.220**

R2 0.618 0.628 0.645


Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) In logarithms. (b) =1 if 2011q1-2012q4. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

Table E.3b Supply Model for Lot Registrations per capita (change in real median unit prices per sqm), Gold Coast


1 Bedroom 2 Bedroom 3 Bedroom

Constant -4.884 -13.906 -4.797

Change in real median unit prices (ln,Δ1) (+) 1.488 5.030** 6.653*

Real loan rate (-) 0.097** ! 0.096** ! 0.107** !

Producer prices (ln) (-) -0.457 1.485 -0.504

D11 (b) -0.604** -0.454** -0.490*

Quarter 2 0.106 0.116 0.114

Quarter 3 0.067 0.077 0.062

Quarter 4 0.299** 0.347** 0.354**

R2 0.597 0.640 0.639



2012q4. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC

Table E.3c Supply Models for Lot Registrations per capita (change in real median unit prices per sqm), Sunshine Coast


2 bedrooms 3 bedrooms

Constant -8.147 -4.249

Change in real median unit prices (ln,Δ1) (+) 1.613 0.564

Real loan rate (-) 0.058* ! 0.061* !

Producer prices (ln) (-) 0.336 -0.511

D11 (b) -0.806* -0.853**

Quarter 2 0.114 0.114

Quarter 3 -0.046 -0.044

Quarter 4 0.156** 0.145*

R2 0.518 0.508

Sample Size 78 78

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) In logarithms. (b) =1 if 2011q1-2012q4. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC


62

Panel Estimation, Houses, Units & Land

Table E.4 Panel Supply Model for Lot Registrations per capita and Building Approvals per capita (change in real median house prices, land prices and land prices per sqm)

Variable Lot Registrations per capita (a) Building Approvals per capita (a)

House Land Land per sqm

House Land Land per sqm

Constant -33.385** -25.165 -25.613 -7.530 -3.467 -2.772

Change in real median prices (ln,Δ1) (+)

6.147** 0.776** 0.440 2.724* 0.269 0.303

Real loan rate (-) -0.000 0.005 0.006 0.068** ! 0.081** ! 0.081** !

Producer prices (ln) (-) 5.796** ! 4.022** ! 4.117** ! 0.169 -0.732 -0.884

D11 (b) -0.138** -0.321** -0.319** -0.223** -0.278** -0.273**

R2 0.083 0.058 0.054 0.206 0.120 0.204

No. of Cross section 12 12 12 12 12 12

Total Panel observations 934 934 934 384 384 384


2012q4. *Significant at the 10% level. ** Significant at the 5% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC


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Appendix F: Modelling Using Alterative Quarterly Price Data Series

As in AEC (2010) the measure of real median prices is taken as year to the end of the quarter in the modelling as described in Appendix C. This means of calculation was

selected so as to smooth the price data. It does, however, produce a forward phase shift. To test the significance of the phase shift, models using the unmodified quarterly data have been estimated and are presented below.

Long-run Demand Model for SEQ

Although the coefficients have changed, the only real difference of any significance is that the housing stock variable (highlighted) is now significant in the house price model. There is no important difference in the real median unit and land price models. We conclude therefore that the forward phase shift is not an important difference for the demand

modelling.

Table F.1 Alternative Long-run Model for Real Median House, Unit and Land Prices, South East Queensland


Real Median House Prices

Real Median Unit Prices

Real Median Land Prices

Constant 14.750** 14.744** 14.205**


Real loan rate (-) (a) -0.030** -0.024** -0.027**

Log Real gross disposable income pc (+) 0.194 0.261 0.343

Log Trade weighted exchange rate (-) 0.053 0.379** ! 0.239


Log Consumer price index (+) -0.015 -0.445** ! -0.021

Log Housing stock pc (-) -1.198* 0.801* ! 1.019

R2 0.985 0.990 0.978

Notes: Standard errors are the Newey-West HAC standard errors computed with 3 lags. (a) This is a semi-logarithmic term, so the coefficient is not an elasticity. pc = per capita. ** Significant at the 5% level. * Significant at the 10% level. ! indicates a significant coefficient, but with an incorrect sign.

Source: AEC

Table F.2 Comparison of Long-run Models for Real Median House Prices, South East Queensland


Original Real Median House

Prices

Revised Real Median Unit

Prices

Constant 11.311** 14.750**


Real loan rate (-) (a) -0.026** -0.030**

Log Real gross disposable income pc (+) 0.614 0.194



Log Consumer price index (+) -0.164 -0.015

Log Housing stock pc (-) -0.533 -1.198*

R2 0.989 0.985


coefficient is not an elasticity. pc = per capita. ** Significant at the 5% level. * Significant at the 10% level. ! indicates a significant coefficient, but with an incorrect sign. Source: AEC


64

Supply Side Model for SEQ

Given the almost halving of the real median house price coefficient it was decided to investigate including up to five lags of the change in real median house prices to capture any phase shift in the specification. One of the problems of doing this was the declining degrees of freedom given that building approvals per capita in particular had only 32 observations.

Table F.3 Alternative Supply Model (change in real median house prices), South East Queensland




Building Approvals

per capita (a,c)

Constant -25.133** -16.859 -9.156

Change in real median house prices (ln,Δ1) (+) 1.316** 2.002** 5.393**

Real loan rate (-) 0.010 0.047** ! 0.063** !

Producer prices (ln) (-) 4.049** ! 2.176* ! 0.486

D11b -0.151** -0.335** na

Quarter 2 0.144** 0.097 0.208**

Quarter 3 0.165** 0.082 0.182**

Quarter 4 0.260** 0.252** 0.333**

R2 0.732 0.649 0.594




Observation of the five lag structure proved difficult to interpret so a final supply model was constructed with just second and third lags in the real median house price.

Table F.4 Alternative Supply Model (change in real median house prices), South East

Queensland (with 2 and 3 lags of changes in real median house price)




Building Approvals

per capita (a,c)

Constant -23.842** -18.105** -11.417

Change in house prices (ln,Δ2) (+) 1.407** 2.518** 6.059**

Change in house prices (ln,Δ3) (+) 1.396** 1.742** 0.597

Real loan rate (-) 0.014 0.047** ! 0.041**!

Producer prices (ln) (-) 3.763** ! 2.455** ! 1.061

D11b -0.098 -0.257** na

Quarter 2 0.117** 0.038 -0.074

Quarter 3 0.123** -0.003 -0.020

Quarter 4 0.210** 0.148** -0.0672**

R2 0.764 0.708 0.653




In summary, if we compare this latest run with the original Table 4.21 using the smoothed

real median house price, these results (adding together the second and third lag coefficients of real median house prices) come close for the two models with longer sample size, and “closer” for the Building Approvals model, i.e.:

For dwelling completions per capita: 2.803 (original was 2.961).

For lot registrations per capita: 4.26 (original was 4.667).

For building approvals per capita: 6.656 (original was 10.003).


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