Sustainable construction : reliable forecasting for building construction prices

A reliable forecasting for future construction costs or prices would help to ensure the budget of a construction project can be well planned and limited resources can be allocated more appropriately in construction firms. Although many studies have been focused on the construction price modelling and forecasting, few researchers have considered the impacts of the global economic events and seasonality in price modelling and forecasting. In this study, an advanced multivariate modelling technique, namely the vector correction (VEC) model with dummy variables was employed and the impacts of the global economic event and seasonality were factored into the forecasting model for the building construction price in the Australian construction market. Research findings suggest that a long-run equilibrium relationship exists among the price, levels of supply and demand in the construction market. The reliability of forecasting models was examined by mean absolute percentage error (MAPE) and The Theil's inequality coefficient U tests. The results of MAPE and U tests suggest that the conventional VEC model and the VEC model with dummy variable are both acceptable for forecasting building construction prices, while the VEC model that considered external impacts achieves higher prediction accuracy than the conventional VEC model does.

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.

Directed search, coordination failure, and seller profits: an experimental comparison of posted pricing with single and multiple prices

We experimentally examine posted pricing and directed search. In one treatment, capacity-constrained sellers post fixed prices, which buyers observe before choosing whom to visit. In the other, firms post both “single-buyer” (applied when one buyer visits) and “multibuyer” (when multiple buyers visit) prices. We find, based on a 2 × 2 (two buyers and two sellers) market and a follow-up experiment with 3 and 2 × 3 markets, that multibuyer prices can be lower than single-buyer prices or prices in the one-price treatment. Also, allowing the multibuyer price does not affect seller profits and increases market frictions.

Do oil prices predict economic growth? New global evidence

In this paper, we test whether oil price predicts economic growth for 28 developed and 17 developing countries. We use predictability tests that account for the key features of the data, namely, persistency, endogeneity, and heteroskedasticity. Our analysis considers a large number of countries, shows evidence of more out-of-sample predictability with nominal than real oil prices, finds in-sample predictability to be independent of the use of nominal and real prices, and reveals greater evidence of predictability for developed countries.

Market effects on forecasting construction prices using vector error correction models

Construction price forecasting is an essential component to facilitate decision-making for construction contractors, investors and related financial institutions. Construction economists are increasingly interested in seeking a more analytical method to forecast construction prices. Although many studies have focused on construction price modelling and forecasting, few have considered the impacts of large-scale economic events and seasonality. In this study, an advanced multivariate modelling technique, namely the vector correction (VEC) model with dummy variables, was employed. The impacts of global economic events and seasonality are factored into the model to forecast the construction price in the Australian construction market. Research findings suggest that both long-run and dynamic short-term causal relationships exist among the price and levels of supply and demand in the construction market. These relationships drive the construction price and supply and demand, which interact with one another as a loop system. The reliability of forecasting models was examined by the mean absolute percentage error (MAPE) and the Theil's inequality coefficient U tests. The test results suggest that the conventional VEC model and the VEC model with dummy variable are both acceptable for forecasting the construction price, while the VEC model considering external impacts achieves higher prediction accuracy than the conventional VEC model. © 2014 © 2014 Taylor & Francis.

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.

Generic drug prices and policy in Australia: room for improvement? A comparative analysis with England

To assess the degree to which reimbursement prices in Australia and England differ for a range of generic drugs, and to analyse the supply- and demand-side factors that may contribute to these differences.

Changes in use of types of tobacco products by pack sizes and price segments, prices paid and consumption following the introduction of plain packaging in Australia

Objectives: To describe changes among smokers in use of various types of tobacco products, reported prices paid and cigarette consumption following the standardisation of tobacco packaging in Australia.
Methods: National cross-sectional telephone surveys of adult smokers were conducted from April 2012 (6 months before transition to plain packaging (PP) to March 2014 (15 months afterwards). Multivariable logistics regression assessed changes in products, brands and pack types/sizes; multivariable linear regression examined changes in inflation-adjusted prices paid and reported cigarette consumption between the pre-PP and three subsequent periods – the transition phase, PP year 1 and PP post-tax (post a 12.5% tax increase in December 2013).
Results: The proposition of current smokers using roll-your-own (RYO) products fluctuated over the study period. Proportions using value brands of factory-made (FM) cigarettes increased from pre-PP (21.4%) to PP year 1 (25.5%; p=0.002) and PP post-tax (27.8%; p<0.001). Inflation-adjusted prices paid increased in the PP year 1 and PP post-tax phases; the largest increases were among premium FM brands, the smallest among value brands. Consumption did not change in PP year 1 among daily, regular or current smokers declined significantly in PP post-tax (mean=14.0, SE=0.33) compared to PP year 1 (mean=14.8, SE=0.17; p=0.037).
Conclusions: Introduction of PP was associated with an increase in use of value brands, likely due to increased numbers available and smaller increases in prices for value relative to premium brands. Reported consumption declined following the December 2013 tax increase.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.