Forecasting
            <i>Engineering News-Record</i>
            Construction Cost Index Using Multivariate Time Series Models

Shahandashti, Mohsen; Ashuri, Baabak

doi:10.1061/(asce)co.1943-7862.0000689

Cited by 76 publications

(31 citation statements)

References 22 publications

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“…Consequently, to develop effective risk strategies, industry professionals can evaluate the effects of the factors that influence project cost. In study [28], the vector error correction model (VECM) that incorporated the producer price index (PPI) was used to forecast the construction cost index. e results indicated that the VECM can provide accurate cost index forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Residential Building Costs in New Zealand: A Time-Series Transfer Function Approach

Zhao

Mbachu

Liu

2020

Mathematical Problems in Engineering

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Cost estimating based on a building cost index plays an important role in project planning and cost management by providing accurate cost information. However, an effective method to predict the building cost index of New Zealand is lacking. This study proposes a transfer function method to improve the forecasting accuracy of the building cost index. In this study, the New Zealand house price index is included in the transfer function models as an explanatory variable to produce cost forecasts. The proposed method is used to estimate the building cost index of residential buildings including one-story houses, two-story houses, and town houses in New Zealand. To demonstrate the effectiveness of the proposed method, this study compares the cost forecasts generated from the transfer function models and the autoregressive integrated moving average (ARIMA) models. The results indicate that the proposed transfer function method can achieve better outcomes than ARIMA models by considering the time-lag causality between building costs and New Zealand house prices. The proposed method can be used by industry professionals as a practical tool to predict project costs and help the professionals to better capture the inherent relationships between cost and house prices.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling Residential Building Costs in New Zealand: A Time-Series Transfer Function Approach

Zhao

Mbachu

Liu

2020

Mathematical Problems in Engineering

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“…Ashuri et al [10] conducted a research to empirically examine whether the time series information on the economic, energy, and market variables is useful to explain the CCI variations.The results of the study showed that the information available from historical data on consumer price index, gross domestic product, crude oil price, housing starts and employment level in construction is useful to explain variations of the CCI. 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.…”

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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“…Panel data consists of not only time series data but also cross section data, the introduction of cross section data increases the degrees of freedom and the reliability of statistics tests. Considering the sensitivity of time series modelling against external changes, construction sector and study period are thereby divided into several categories or stages [28] , for better exploring causal relationship between construction activities and economic development within sub-study periods via Granger causality tests [29,30] . Using the technique of panel data regression develops original error correction model (ECM) into a panel error correction model (P-ECM), which is able to outline the short-run dynamics associated with unexpected shocks of the economy, and account for the regional disparities based on long-run equilibrium function [31] .…”

Section: Panel Data Modelling For Economic Analysismentioning

confidence: 99%

Comparative analysis of regional labor cost variations in China’s construction industry: Panel data modeling and analysis

Luo¹,

Fan²,

Liu³

2018

Creative Construction Conference 2018 - Proceedings

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Labour cost estimation, control, and regulations are of great importance to the final success of construction project, and sustainable development of construction industry. Over the recent years, the affordability and availability of construction workforce, underpinned by rich labour resources across the Mainland China, has gradually become the history and exerted ripple effects on the construction manpower recruitment, cost management, and even industry development. However, little research has been found with focus on labour cost fluctuations at regional level in China. This study attempts to explore the major factors affecting regional construction labour cost variations over the past two decades, from 1995 to 2015. Panel data analysis, and time series econometric modelling, is thereby applied to identify critical determinants of construction labour cost fluctuations across three regions of different levels of economic development, i.e. underdeveloped west region, developing central region, and developed east region, within Mainland China's construction industry. Empirical results indicate that gross domestic product (GDP), unemployment rate, construction labour productivity, construction technical equipment ratio, and construction profit rate are five key factors determining the variations of unit labour cost in Mainland China's construction industry. GDP, construction labour productivity and unemployment rate are three common factors that affect regional construction labour cost; Besides, construction profit rate is found to be another dominant determinant of construction labour cost in west region, while construction technical equipment ratio acts as a significant but negative factor in central and east regions, with incremental effects towards construction unit labour cost from west region to central region, then east region. For the evolving construction market, these principal findings provide valuable insights for construction enterprises to formulate forward-looking market strategies, and for governments to fine tune economic policies.

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Forecasting Engineering News-Record Construction Cost Index Using Multivariate Time Series Models

Cited by 76 publications

References 22 publications

Modelling Residential Building Costs in New Zealand: A Time-Series Transfer Function Approach

Modelling Residential Building Costs in New Zealand: A Time-Series Transfer Function Approach

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

Comparative analysis of regional labor cost variations in China’s construction industry: Panel data modeling and analysis

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