Goh, B H (1997) Construction demand modelling: a systematic approach to using economic indicators and a comparative study of alternative forecasting approaches. PhD thesis, University College London, UK.
Abstract
The published literature abounds with evidence of a close relationship between the construction industry and the national economy. This study reinforces the strength of this relationship by proposing the use of economic indicators to model demand for construction. Alternative forecasting approaches are applied, comprising both traditional and state-of-the-art techniques. The aim is to establish the most theoretically significant and statistically adequate indicators, and the most accurate forecasting technique for modelling and predicting construction demand. A systematic approach is proposed to identify and select economic indicators that relate to demand for construction. It involves four distinct stages and they are: (1) theoretical identification: (2) data collection and pre-processing; (3) statistical selection; and (4) usage. This stage-by-stage process is illustrated on residential, industrial and commercial-type construction in Singapore. The findings confirm that demand in the construction industry is significantly related to a wide range of economic measures. A comparative study of regression and non-regression approaches of forecasting is earned out using Singapore's residential sector as a case-study. The techniques include the Multiple Linear Regression, the Multiple Log-linear Regression, the Autoregressive Non-linear Regression Algorithm and the Artificial Neural Network (ANN). Seven economic indicators have been selected to build the demand models, and they are: Building tender price index; Bank lending for housing; Population size; Housing stock (additions); National savings; Gross fixed capital formation for residential buildings; and Unemployment rate. Quarterly time-series data over the period 1975 - 1994 are used. Several conclusions are drawn. Firstly, non-linear methods produce more accurate forecasts. Secondly, the Multiple Log-linear is the most accurate regression technique. Thirdly, the ANN technique, a non-regression approach, performs outstandingly better than the regression approach. Keywords: Demand, economic indicators, forecasting, regression, artificial neural network.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | artificial neural network; construction demand; economic indicator; employment; forecasting; residential; Singapore; tender price index |
| Index terms: | tender price index, artificial neural network, employment, population, unemployment, housing, savings, residential building, Singapore, modelling, comparative study, construction industry, economic indicator, construction demand, national economy, housing stock, state of the art, forecasting, evidence |
| Subjects: | industry analysis, market analysis, economic analysis, demography, construction type, research dissemination and communication, prediction and forecasting, Geography, quantity surveying, research design and methodology, data analysis and analytics, modelling and simulation, evaluation and assessment methods, analytical methods, management |
| Topics: | Construction Technology, Research Practice, Human Resources, Geographical Context, Procurement, Business Strategy, Urban Studies, Engineering Principles |
| Descriptive scope: | 4 PCTA |
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