Dynamic Regression Models for Prediction of Construction Costs

Hwang, Seokyon

doi:10.1061/(asce)co.1943-7862.0000006

Cited by 63 publications

(37 citation statements)

References 8 publications

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“…Moreover, Ashuri and Lu [11] created univariate time series models to forecast the CCI whereas Shahandashti and Ashuri [12] used multivariate time series models to forecast the CCI. Hwang [13] proposed two dynamic univariate time series models to predict the CCI. Moon and Shin [14] concluded that Vector Error Correction Model (VECM) showed better predictive ability than a cointegrated vector autoregression model and the advantages of query frequency over conventional economic indices could prove to be beneficial for forecasting purposes.…”

Section: Research Backgroundmentioning

confidence: 99%

Using historical data of economic variables for investigating variations in building construction cost index

Atabeyli¹,

Kuru²,

Çalış³

2018

Creative Construction Conference 2018 - Proceedings

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The construction economy is exceptionally dynamic. Any variation in the prices of raw materials, labor, regulatory costs, and energy can have a major impact in the construction industry and make it difficult to control costs especially in large scale, long term and complex projects. Building Construction Cost Index (BCCI) is used in cost estimation of buildings and is defined as the measure of the changes in the cost of construction input items relative to the average price level of a base year. Therefore, it is important to understand the influencing factors and the impact of input variations in BCCI for limiting potential financial risks in construction projects. This study aims at investigating whether the historical data of different economic variables is useful to figure out the variations in BCCI. Data is obtained from the publications of the Turkish Statistical Institute to understand the relationship between BCCI and three factors including domestic producer price index, consumer price index and construction labor input index. In addition, the strength of the relationship between BCCI and aforementioned factors are analyzed via statistical analyses.

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Section: Research Backgroundmentioning

confidence: 99%

Using historical data of economic variables for investigating variations in building construction cost index

Atabeyli¹,

Kuru²,

Çalış³

2018

Creative Construction Conference 2018 - Proceedings

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“…Many studies have examined the factors influencing building development costs [15]; however, the influence levels of the identified factors vary due to differences in the regulatory regimes of the construction industries. Furthermore, these studies only focused on preliminary influential factors such as building cost components, project character, stakeholders' influence, and new technology innovations.…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

Exploratory Factors Influencing Building Development Costs in New Zealand Using SEM

Zhao¹,

Mbachu²,

Domingo³

2017

Preprint

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Identification of costs drivers and their influence level on building development costs play a key role in the development of construction models and improve the efficiency and effectiveness of any project. Forty-five indicators influencing building development costs in New Zealand are explored by literature review and pilot interviews. These indicators are grouped into seven categories. The determination and ranking of the cost drivers are carried out by a questionnaire survey distributed to key professionals working in New Zealand's construction industry. Structural equation modeling (SEM) software was employed for analysis of the collected data. One of the key advantages of this powerful software is to provide the p-value according to the structure of the research model. Findings of this study indicate that the property market and construction industry factor, statutory and regulatory factor, and socio-economic factors are major factor affecting building development costs in New Zealand.

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“…Examples are regression and time-series forecasting for short-term demand planning in tourism [14], time-series for pricing of financial assets [15] in financial markets, multiple linear regressions for cash-flow forecasting in the construction industry [16], or dynamic regression models for cost forecasting in the construction industry [17].…”

Section: Business Performance Managementmentioning

confidence: 99%