An interdependence analysis of Australian house prices using variance decomposition

Purpose - The purpose of this paper is to analyse the interdependencies of the house price growth rates in Australian capital cities.
Design/methodology/approach - A vector autoregression model and variance decomposition are introduced to estimate and interpret the interdependences among the growth rates of regional house prices in Australia.
Findings - The results suggest the eight capital cities can be divided into three groups: Sydney and Melbourne; Canberra, Adelaide and Brisbane; and Hobart, Perth and Darwin.
Originality/value - Based on the structural vector autoregression model, this research develops an innovative interdependence analysis approach of regional house prices based on a variance decomposition method.

Residential auction clearance rates – what do they really mean?

The residential market in Melbourne is often referred to as the ‘auction capital of the world’ with approximately 30-35% of housing transfers undertaken via the auction process, most of which are conducted on the weekend and then reported in the media the following day. The most quoted measurement of auction success is via the clearance rate which simply indicates the proportion of signed contracts of sale within the auction process. At the same time the clearance rate can have a relatively large variance where the residential market can traditionally range from very good (i.e. a high clearance rate) to very poor (i.e. a low clearance rate). The subsequent effect on the market can directly increase or decrease demand, predominantly based only on this single measure of the perceived level of auction clearance rates only.

This paper examines the concept of the auction clearance rates and the heavy reliance on the only one measure of success (i.e. the clearance rates), regardless of other variables. The emphasis is placed on the auction clearance rate as one measure of demand in the housing market but within the context of the definition of market value i.e. willing buyer-willing seller. This is supported by a discussion about other variables including the asking price, the auction process itself, marketing considerations and seasonal adjustments. The findings provide an insight into how to correctly interpret the auction clearance rate in the context of the overall supply-demand interactions. Whilst the auction process is clearly an integral part of the residential transfer process it is essential that the auction clearance rate is used with caution and also in conjunction with other variables.

Investigating relationships of construction prices in Australia using cointegration analysis

Research on construction prices is significant for contractors and traders. A comprehensive understanding of construction prices may influence crucial decisions in business operation and arbitrage activities. This study focuses on the cointegration relationships of regional construction prices in Australia by using a range of econometric techniques including the stationarity test, the Engle-Granger cointegration approach examines the long run equilibrium relationships within the regional markets, and the error correction models explore the short run disequilibrium relationships. Finds of this study reveal that the economic system in which the construction industry participants operate is characterized by a highly competitive, integrated marketplace, Especially in Melbourne and Sydney. But exclude Northern Territory and Queensland. Furthermore, the results of long term relationships estimation suggest that there are 15 pairs of regional construction prices have long term equilibrium relationships. Additionally, the causalities and diffusion among the construction price indices in six states and two territories of Australia are estimated in this study. These outcomes suggest that causal links between regions mainly exist among adjoining states.

An economic analysis of construction industries producer prices in Australia

Research on construction prices is significant for contractors and traders. A comprehensive understanding of construction prices may influence crucial decisions in business operation and arbitrage activities. This study focuses on the cointegration relationships of regional construction prices in Australia by using a range of econometric techniques including the stationarity test, the Engle-Granger cointegration approach and error correction model. The cointegration relationships amongst the regional construction prices are detected in this study. The application of the Engle-Granger cointegration approach examines the long run equilibrium relationships within the regional markets, and the error correction models explore the short run disequilibrium relationships. Results of this study suggest that the economic system in which construction industry participants operate is characterised by a highly competitive integrated marketplace. Furthermore, the causalities and diffusion patterns among the construction price indices in six states and two territories of Australia are drawn by the cointegration analysis. These outcomes reveal a pattern of diffusion paths and network linkages among the six states and two territories, and then expose the regional price linkages.

Are shocks to commodity prices persistent?

This paper considers the issue of whether shocks to ten commodity prices (gold, silver, platinum, copper, aluminum, iron ore, lead, nickel, tin, and zinc) are persistent or transitory. We use two recently developed unit root tests, namely the Narayan and Popp (NP) [14] test and the Liu and Narayan (LN) [26] test. Both tests allow for two structural breaks in the data series. Using the NP test, we are able to reject the unit root null for iron ore and tin. Using the GARCH-based unit root test of LN, we are able to reject the unit root null for five commodity prices (iron ore, nickel, zinc, lead, and tin). Our findings, thus, suggest that only shocks to gold, silver, platinum, aluminum, and copper are persistent.

Spatial and temporal variation of house prices

This thesis originally developed a spatial and temporal analysis approach. This approach was applied to investigate the variation of house prices in Australia. The findings discovered the behaviours of house prices and the interconnections between them across the Australian cities.

Volatility decomposition of Australian housing prices

This study examines the volatility pattern of Australian housing prices. The approach for this research was to decompose the conditional volatility of housing prices into a “permanent” component and a “transitory” component via a Component-Generalized Autoregressive Conditional Heteroskedasticity (C-GARCH) model. The results demonstrate that the shock impact on the short-run component (transitory) is much larger than the long-run component (permanent), whereas the persistence of transitory shocks is much less than permanent shocks. Moreover, both permanent and transitory volatility components have different determinants. The results provide important new insights into the volatility pattern of housing prices which has direct implications for investment in housing by owner-occupiers and investors.

A panel regression method for quantifying the convergence of house prices

The convergence among house prices has attracted much attention from researchers. Previous research mainly utilised a time-series regression method to investigate convergences of house prices, which may ignore the heterogeneity of houses across cities. This research developed a panel regression method, by which the heterogeneity of house prices can be captured. Seemingly unrelated regression estimators were also adapted to deal with the contemporary correlations across cities. Investigation of the convergence among house prices in the Australian capital cities was carried out by using the developed panel regression method. Results suggested that house prices converge in Sydney, Adelaide and Hobart but diverge in Darwin.

Reading house prices in Australian capital cities

House prices in the Australian capital cities have been increasing over the last two decades. An over 10% average annual increase arises in the capital cities. In Melbourne, Brisbane and Perth, the house prices increased by more than 15% annually, while the house prices in Darwin increased by even higher at about 21%. It is surprising that, after a decrease in 2008, the house prices in the Australian capital cities show a strong recovery in their last financial year’s increase. How to read the house prices in cities across a country has been an issue of public interest since the late 1980s. Various models were developed to investigate the behaviours of house prices over time or space. A spatio-temporal model, introduced in recent literature, appears advantages in accounting for the spatial effects on house prices. However, the decay of temporal effects and temporal dynamics of the spatial effects cannot be addressed by the spatio-temporal model. This research will suggest a three-part decomposition framework in reading urban house price behaviours. Based on the spatio-temporal model, a time weighted spatio-temporal model is developed. This new model assumes that an urban house price movement should be decomposed by urban characterised factors, time correlated factors and space correlated factors. A time weighted is constructed to capture the temporal decay of the time correlated effects, while a spatio-temporal weight is constructed to account for the timevaried space correlated effects. The house prices of the Australian capital cities are investigated by using the time weighted spatio-temporal model. The empirical findings suggest that the housing markets should be clustered by their geographic locations. The rest parts of this paper are organised as follows. The following section will present a principle for reading urban house prices. The next section will outline the methodologies modelling the time weighted spatio-temporal model. The subsequent section will report the relative data and empirical results, while the final section will generate the conclusions.

Can the choice of interpolation method explain the difference between swap prices and futures prices?

The standard model linking the swap rate to the rates in a contemporaneous strip of futures interest rate contracts typically produces biased estimates of the swap rate. Institutional differences usually require some form of interpolation to be employed and may in principle explain this empirical result. Using Australian data, we find evidence consistent with this explanation and show that model performance is greatly improved if an alternative interpolation method is used. In doing so, we also provide the first published Australian evidence on the accuracy of the futures-based approach to pricing interest rate swaps.