Construction Price Prediction Using Vector Error Correction Models

Jiang, Heng; Xu, Youquan; Liu, Chunlu

doi:10.1061/(asce)co.1943-7862.0000729

Cited by 26 publications

(10 citation statements)

References 41 publications

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“…the ability to predict previously unseen data. The time ordered approach adopted in this study is consistent with those used in similar previous research ( Jiang, Xu and Liu, 2013;Wong and Ng, 2010). Figure 1 shows the time series plot for tender price index data.…”

Section: Datasupporting

confidence: 57%

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Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

Oshodi

Ejohwomu

Famakin

et al. 2017

CEB

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The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

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Section: Datasupporting

confidence: 57%

“…Wong and Ng (2010) Building cost index, gross domestic product and construction sector's gross domestic output. Jiang, Xu and Liu (2013) National income, population, unemployment rate and interest rate. Shahandashti and Ashuri (2015) Crude oil prices and average hourly earnings.…”

Section: Introductionmentioning

confidence: 99%

Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

Oshodi

Ejohwomu

Famakin

et al. 2017

CEB

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“…The factors were money supply, consumer price index, producer price index, unemployment rate, GDP, national consumption expenditure, and debt-to-GDP ratio [41]. Heng (2013) [42] in developing construction price prediction model considered national income, population, unemployment rate, and interest rate as the main macroeconomic variables. Kim et al (20110) [43] in their study used current ratio of liquidity ratio, and debt ratio for leverage ratio.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards the Establishment of Relationship between Macroeconomic Indicators and Cost of Public Educational Buildings in Ghana

Asamoah

Baiden

Nani

2019

Journal of Engineering

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Cost of building is usually influenced by several factors; one of such is frequent changes in macroeconomic variables. The purpose of this study is to establish the need to conduct further research on the impact of changes in macroeconomic components on the cost of public educational buildings. The study adopted the qualitative research approach; purposive and snowballing techniques were used in selecting respondents. Questionnaire survey was used to obtain primary data from respondents who were Quantity Surveyors and Estimators. The questionnaires were analyzed through descriptive analysis. Secondary data was obtained through literature review. The study revealed that respondents were satisfied with cost management procedures and practices and mainly relied on cash flow, progress reporting, and project cost control methods as means of monitoring and managing project cost. Relative important index, prime rate, interest rate, and inflation were some of the macroeconomic components that professionals considered having impact on cost. The respondents also recommended further studies on the impact of macroeconomic variability on cost of public buildings.

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“…A VECM is a restricted vector autoregressive model that has cointegration restrictions built into specification [39]. Hence, the VECM includes an examination of the dynamic comovement among variables and the adjustment process toward long-term equilibrium [40].…”

Section: Vecm For Interval-valued Agricultural Commodity Futures Pricesmentioning

confidence: 99%

A combination method for interval forecasting of agricultural commodity futures prices

Xiong

Bao

et al. 2015

Knowledge-Based Systems

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Construction Price Prediction Using Vector Error Correction Models

Cited by 26 publications

References 41 publications

Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

Towards the Establishment of Relationship between Macroeconomic Indicators and Cost of Public Educational Buildings in Ghana

A combination method for interval forecasting of agricultural commodity futures prices

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