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.

Big pharma in legal battles for monopoly prices in India

Two large multinational pharmaceutical companies are fighting for patents and monopoly pricing in Indian courts. The outcomes of the cases – involving Novartis and Bayer – are likely to determine the country’s future as a major global supplier of low-cost essential medicines.

lnvestigating spatial linkages of construction prices in Australia's regions

Linkages among different construction markets have attracted great of attention from the construction economist. With notable exceptions, most of this infestation has carried out by using input-output analysis. Interactions among regional construction markets have been discussed in few studies and none of them investigate spatial effects on the regional construction markets. This study employed spatial econometric techniques, spatial autocorrelation and convergence tests, to analysis interactions and linkages among construction price indices in Australian six states and two territories. The empirical results indicate the presence of significant positive spatial correlation between the construction prices in Australian eight construction markets and the degree of dependence decrease sufficiently quickly as the space between units increase. The results of convergence test further present evidence on "ripple effect" in the construction prices in Australian regional markets and the changes in regional construction price would positively influence neighboring states first, and then spread out into others, and then the regional prices converge and reach a long-run equilibrium in the following quarters. Urban development policymakers and construction developers could benefit from the analysis of spatial linkages in regional construction markets.

Better off dead? Prices realised for Australian paintings sold at auction

Using auction sales data on Australian paintings over the period 1995 and 2003 we investigate the relationship between artists‟ living status and the price of paintings sold at auction. For deceased artists we consider the time since their death and for living artists their conditional life expectancy. Hedonic regression analysis is applied separately to the data on Indigenous and non-Indigenous paintings. Comparing the modelling results across Indigenous and non-Indigenous paintings we see evidence of two different patterns of response to an artist‟s living status. Both yield non-linear impacts but for Indigenous paintings these are quadratic and for non-Indigenous they are quartic. Thus the response to living status in the more recent market for Indigenous paintings is different to the more established market for non-Indigenous paintings. Whilst the responses differ for the two types of paintings, in answer to the question posed and in terms of the price of a painting at auction an artist is better off long dead or close to death.