Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously

Book review : Managing government property assets by Olga Kaganova and James McKellar (eds)

Managing Government Property Assets, edited by Olga Kaganova and James McKellar, is reviewed.<br /

Impacts of monetary policies on housing affordability in Australia

Housing affordability has become a major policy issue in many countries across the world since the rapid inflation of house prices. This paper empirically investigates how monetary policies affect housing affordability in Australia from 1998 to 2009. Three primary variables associated with the housing sector and monetary policy, which are money supply, interest rates and house prices, are studied for all eight capital cities in Australia in this research. Shocks of such variables are identified by a structural vector autoregression (SVAR) model with restrictions that are consistent with economic theoretical framework. Based upon the analysis using the structural decomposition of impulse response on quarterly data, it can be discovered that the monetary policy plays an active role in housing affordability via adjustments of money supply and interest rates during the observed period in Australia. The empirical results from this research may be used for decision makers to determine money supply and interest rates from the perspective of housing affordability.<br /

Do spatial effects drive house prices away from the long-run equilibrium?

Long-run equilibrium of house prices has been investigated by researchers in multiple countries. The identification of this equilibrium not only provides references against contemporary house price levels, but also contributes to creation of stable-development policies and healthy investment strategies. However, there is little research investigating the factors that drive house prices away from the long-run equilibrium.Based on a framework of the conventional stationarity test process, this research develops a panel regression model and a spatial regression model to investigate the roles of spatial heterogeneity and correlations on house prices preceding the long-run equilibrium, respectively. Housing data generated from the capital cities in Australia are used to illustrate the models. Spatial effects can have a strong influence in the long-run performance of house prices, while the short-run performance of house prices is not influenced by the spatial effects

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&rsquo;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

An input-output approach for measuring real estate sector linkages

This research aims to measure and compare the total, backward, forward, internal and sectoral linkages of the real estate sector using the hypothetical extraction method over 30 years and explore the role of this sector in national economies and the quantitative interdependence between the real estate sector and the remaining sectors from a new angle. Empirical results show an increasing trend of these linkages, which confirms the increasing role of the real estate sector with economic maturity over the examined period. On the other hand, the significant rank correlations in the linkages imply that the importance of real estate remained fairly stable among highly developed economies over the examined period. This may supply a tool to signal the maturity of an entire economy. Furthermore, the findings can aid both governments making relative policies and businesses choosing strategic partners and location strategies

Ripple effects of house prices : considering spatial correlations in geography and demography

Purpose: Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial-temporal interconnections between house price movements across the Australian capital cities. Design/methodology/approach: Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects. Findings: The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information. Originality/value: This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted

The decomposition of housing market variations : a panel data approach

Purpose &ndash; This paper develops a new decomposition method of the housing market variations to analyse the housing dynamics of the Australian eight capital cities.Design/methodology/approach &ndash; This study reviews the prior research on analysing the housing market variations and classifies the previous methods into four main models. Based on this, the study develops a new decomposition of the variations, which is made up of regional information, homemarket information and time information. The panel data regression method, unit root test and F test are adopted to construct the model and interpret the housing market variations of the Australian capital cities.Findings &ndash; This paper suggests that the Australian home-market information has the same elasticity to the housing market variations across cities and time. In contrast, the elasticities of the regional information are distinguished. However, similarities exit in the west and north of Australia or the south and east of Australia. The time information contributes differently along the observing period, although the similarities are found in certain periods.Originality/value &ndash; This paper introduces the housing market variation decomposition into the research of housing market variations and develops a model based on the new method of the housing market variation decomposition.<br /