Forecasting construction demand : a vector error correction model with dummy variables

Modelling the level of demand for construction is vital in policy formulation and implementation as the construction industry plays an important role in a country’s economic development process. In construction economics, research efforts on construction demand modelling and forecasting are various, but few researchers have considered the impact of global economy events in construction demand modelling. An advanced multivariate modelling technique, namely the vector error correction (VEC) model with dummy variables, was adopted to predict demand in the Australian construction market. The results of prediction accuracy tests suggest that the general VEC model and the VEC model with dummy variables are both acceptable for forecasting construction economic indicators. However, the VEC model that considers external impacts achieves higher prediction accuracy than the general VEC model. The model estimates indicate that the growth in population, changes in national income, fluctuations in interest rates and changes in householder expenditure all play significant roles when explaining variations in construction demand. The VEC model with disturbances developed can serve as an experimentation using an advanced econometrical method which can be used to analyse the effect of specific events or factors on the construction market growth.

Econometric techniques for estimating construction demand in Australia

This study aims to develop reliable demand estimation models, at both national and regional levels, for the Australia’s construction market. The developed models would benefit the industry by serving as a reliable aid to policy in the areas of tendering, pricing, resource allocating, labour and workload planning.

A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.

The effects of the late 2000s global financial crisis on Australia's construction demand

An accurate measurement of the impacts of external shocks on construction demand will enable construction industry policymakers and developers to make allowances for future occurrences and advance the construction industry in a sustainable manner. This paper aims to measurethe dynamic effects of the late 2000s global financial crisis on the level of demand in the Australian construction industry. The vector error correction (VEC) model with intervention indicators is employed to estimate the external impact from the crisis on a macro-level construction economic indicator, namely construction demand. The methodology comprises six main stages to produce appropriate VEC models that describe the characteristics of the underlying process. Research findings suggestthat overall residential and non-residential construction demand were affected significantly by the recent crisis and seasonality. Non-residentialconstruction demand was disrupted more than residential construction demand at the crisis onset. The residential constructionindustry is more reactive and is able to recover faster following the crisis in comparison with the non-residential industry. The VEC model with intervention indicators developed in this study can be used as an experiment for an advanced econometric method. This can be used to analyse the effects of special eventsand factors not only on construction but also on other industries.

Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study

This study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.

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.

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.

Towards a Better Modelling and Assessment of Construction Risk: Insights from a Literature Review

This paper reviews the literature of construction risk modelling and assessment. It also reviews the real practice of risk assessment. The review resulted in significant results, summarised as follows. There has been a major shift in risk perception from an estimation variance into a project attribute. Although the Probability–Impact risk model is prevailing, substantial efforts are being put to improving it reflecting the increasing complexity of construction projects. The literature lacks a comprehensive assessment approach capable of capturing risk impact on different project objectives. Obtaining a realistic project risk level demands an effective mechanism for aggregating individual risk assessments. The various assessment tools suffer from low take-up; professionals typically rely on their experience. It is concluded that a simple analytical tool that uses risk cost as a common scale and utilises professional experience could be a viable option to facilitate closing the gap between theory and practice of risk assessment.

Identifying determinants of demand for construction using an econometric approach

Economic variation and its effects on construction demand have received a great deal of attention in construction economics studies. An understanding of future trends in demand for construction could influence investment strategies for a variety of parties, including construction developers, suppliers, property investors and financial institutions. This paper derives the determinants of demand for construction in Australia using an econometric approach to identify and evaluate economic indicators that affect construction demand. The forecasting contribution of different determinants of economic indicators and their categories to the demand for construction are further estimated. The results of this empirical study suggest that changes in consumer’s expectation, income and production, and demography and labour force are closely correlated with the movement of construction demand; and 14 economic indicators are identified as the determinants for construction demand. It was found that the changes in construction price, national income, size of population, unemployment rate, value or export, household expenditure and interest rates play key roles in explaining future variations in the demand for construction in Australia. Some “popular” macroeconomic indicators, such as GDP, established house price and bank loans produced inconclusive results.

Supporting pre-tender construction planning with virtual prototyping

Purpose – Virtual prototyping technologies linked to building information models are commonplace within the aeronautical and automotive industries. Their use within the construction industry is now emerging. The purpose of this paper is to show how these technologies have been adopted on the pre-tender planning for a typical construction project. Design/methodology/approach – The research methodology taken was an “action research” approach where the researchers and developers were actively involved in the production of the virtual prototypes on behalf of the contractor thereby gaining consistent access to the decisions of the planning staff. The experiences from the case study were considered together with similar research on other construction projects. Findings – The findings from the case studies identify the role of virtual prototyping in components modelling, site modelling, construction equipment modelling, temporary works modelling, construction method visualization and method verification processes. Originality/value – The paper presents a state-of-the-art review and discusses the implications for the tendering process as these technologies are adopted. The adoption of the technologies will lead to new protocols and changes in the procurement of buildings and infrastructure.

Transit users' route-choice modelling in transit assignment : a review

This paper reviews the main studies on transit users’ route choice in thecontext of transit assignment. The studies are categorized into three groups: static transit assignment, within-day dynamic transit assignment, and emerging approaches. The motivations and behavioural assumptions of these approaches are re-examined. The first group includes shortest-path heuristics in all-or-nothing assignment, random utility maximization route-choice models in stochastic assignment, and user equilibrium based assignment. The second group covers within-day dynamics in transit users’ route choice, transit network formulations, and dynamic transit assignment. The third group introduces the emerging studies on behavioural complexities, day-to-day dynamics, and real-time dynamics in transit users’ route choice. Future research directions are also discussed.

Economic fluctuations and productivity in the Malaysian construction sector

Growth in productivity is the key determinant of the long-term health and prosperity of an economy. The construction industry being one of major strategic importance, its productivity performance has a significant effect on national economic growth. The relationship between construction output and economy has received intensive studies, but there is lack of empirical study on the relationship between construction productivity and economic fluctuations. Fluctuations in construction output are endemic in the industry. In part they are caused by the boom and slump of the economy as a whole and in part by the nature of the construction product. This research aims to uncover how the productivity of construction sector is influenced in the course of economic fluctuations in Malaysia. Malaysia has adopted three economic policies – New Economic Policy (1971-1990), National Development Policy (1991-2000) and the National Vision Policy (2001-2010) since gaining independence in 1959. The Privatisation Master Plan was introduced in 1991. Operating within this historical context, the Malaysian construction sector has experienced four business cycles since 1960. A mixed-method design approach is adopted in this study. Quantitative analysis was conducted on the published official statistics of the construction industry and the overall economy in Malaysia between 1970 and 2009. Qualitative study involved interviews with a purposive sample of 21 industrial participants. This study identified a 32-year long building cycle appears in 1975-2006. It is superimposed with three shorter construction business cycles in 1975-1987, 1987-1999 and 1999-2006. The correlations of Construction labour productivity (CLP) and GDP per capita are statistically significant for the 1975-2006 building cycle, 1987-1999 and 1999-2006 construction business cycles. It was not significant in 1975-1987 construction business cycles. The Construction Industry Surveys/Census over the period from 1996 to 2007 show that the average growth rate of total output per employee expanded but the added value per employee contracted which imply high cost of bought-in materials and services and inefficient usage of purchases. The construction labour productivity is peaked at 2004 although there is contraction of construction sector in 2004. The residential subsector performed relatively better than the other sub-sectors in most of the productivity indicators. Improvements are found in output per employee, value added per employee, labour competitiveness and capital investment but declines are recorded in value added content and capital productivity. The civil engineering construction is most productive in the labour productivity nevertheless relatively poorer in the capital productivity. The labour cost is more competitive in the larger size establishment. The added value per labour cost is higher in larger sized establishment attributed to efficient in utilization of capital. The interview with the industrial participant reveals that the productivity of the construction sector is influenced by the economic environment, the construction methods, contract arrangement, payment chain and regulatory policies. The fluctuations of construction demand have caused companies switched to defensive strategy during the economic downturn and to ensure short-term survival than to make a profit for the long-term survival and growth. It leads the company to take drastic measures to curb expenses, downsizing, employ contract employment, diversification and venture overseas market. There is no empirical evidence supports downsizing as a necessary step in a process of reviving productivity. The productivity does not correlate with size of firm. A relatively smaller and focused firm is more productive than the larger and diversified organisation. However diversified company experienced less fluctuation in both labour and capital productivity. In order to improve the productivity of the construction sector, it is necessary to remove the negatives and flaws from past practices. The recommended measures include long-term strategic planning and coordinated approaches of government agencies in planning of infrastructure development and to provide regulatory environments which encourage competition and facilitate productivity improvement.

Assessing the significance of climate and community factors on urban water demand

Ensuring adequate water supply to urban areas is a challenging task due to factors such as rapid urban growth, increasing water demand and climate change. In developing a sustainable water supply system, it is important to identify the dominant water demand factors for any given water supply scheme. This paper applies principal components analysis to identify the factors that dominate residential water demand using the Blue Mountains Water Supply System in Australia as a case study. The results show that the influence of community intervention factors (e.g. use of water efficient appliances and rainwater tanks) on water demand are among the most significant. The result also confirmed that the community intervention programmes and water pricing policy together can play a noticeable role in reducing the overall water demand. On the other hand, the influence of rainfall on water demand is found to be very limited, while temperature shows some degree of correlation with water demand. The results of this study would help water authorities to plan for effective water demand management strategies and to develop a water demand forecasting model with appropriate climatic factors to achieve sustainable water resources management. The methodology developed in this paper can be adapted to other water supply systems to identify the influential factors in water demand modelling and to devise an effective demand management strategy.

Evacuation modelling analysis within the operational research context: a combined approach for improving enclosure designs

Evacuation models have been playing an important function in the transition process from prescriptive fire safety codes to performance-based ones over the last three decades. In fact, such models became also useful tools in different tasks within fire safety engineering field, such as fire risks assessment and fire investigation. However, there are some difficulties in this process when using these models. For instance, during the evacuation modelling analysis, a common problem faced by fire safety engineers concerns the number of simulations which needs to be performed. In other terms, which fire designs (i.e., scenarios) should be investigated using the evacuation models? This type of question becomes more complex when specific issues such as the optimal positioning of exits within an arbitrarily structure needs to be addressed. Therefore, this paper presents a methodology which combines the use of evacuation models with numerical techniques used in the operational research field, such as Design of Experiments (DoE), Response Surface Models (RSM) and the numerical optimisation techniques. The methodology here presented is restricted to evacuation modelling analysis, nevertheless this same concept can be extended to fire modelling analysis.