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.

[1]  S. T. Ng,et al.  Predicting construction market growth for urban metropolis: An econometric analysis , 2011 .

[2]  David Allen,et al.  Investigating other leading indicators influencing Australian domestic tourism demand , 2011, Math. Comput. Simul..

[3]  Deepak Nayyar The Financial Crisis, the Great Recession and the Developing World , 2011 .

[4]  S. T. Ng,et al.  Reliability of the Box–Jenkins model for forecasting construction demand covering times of economic austerity , 2010 .

[5]  J. G. Palma,et al.  Introduction: the global financial crisis , 2009 .

[6]  Byron Gangnes,et al.  Modeling tourism: A fully identified VECM approach , 2009, International Journal of Forecasting.

[7]  Albert P.C. Chan,et al.  Forecasting construction manpower demand: A vector error correction model , 2007 .

[8]  David H. Picken,et al.  Granger Causality Among House Price and Macroeconomic Variables in Victoria , 2007 .

[9]  Shen Yue,et al.  Housing Price Bubbles in Hong Kong, Beijing and Shanghai: A Comparative Study , 2006 .

[10]  S. Johansen,et al.  MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY , 2009 .

[11]  B. Goh The dynamic effects of the Asian financial crisis on construction demand and tender price levels in Singapore , 2005 .

[12]  J. Meikle A review of recent trends in house construction and land prices in Great Britain , 2001 .

[13]  Jamal Ameen,et al.  Discussion of “Earthmoving Productivity Estimation Using Linear Regression Techniques” by Richard Neale and Jamal Ameen , 2001 .

[14]  Michael Ball,et al.  Competition and the persistence of profits in the UK construction industry , 2000 .

[15]  Teo Ho Pin,et al.  Forecasting construction industry demand, price and productivity in Singapore: the BoxJenkins approach , 2000 .

[16]  Raymond Y. C. Tse,et al.  Matching housing supply and demand: an empirical study of Hong Kong's market , 1999 .

[17]  Bee-Hua Goh,et al.  Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques , 1998 .

[18]  Akintola Akintoye,et al.  Macro-economic leading indicators of construction contract prices , 1998 .

[19]  Goh Bee Hua Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression , 1996 .

[20]  Akintola Akintoye,et al.  Models of UK private sector quarterly construction demand , 1994 .

[21]  V. Ramey,et al.  How Important is the Credit Channel in the Transmission of Monetary Policy? , 1993 .

[22]  Clive W. J. Granger,et al.  Long-Run Economic Relationships: Readings in Cointegration , 1991 .

[23]  John C.S. Tang,et al.  Thai construction industry: Demand and projection , 1990 .

[24]  George Ofori,et al.  Construction industry and economic growth in Singapore , 1988 .

[25]  P. Phillips Testing for a Unit Root in Time Series Regression , 1988 .

[26]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[27]  Michael D. Geurts,et al.  Time Series Analysis: Forecasting and Control , 1977 .