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

Abstract

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.

İsti̇hdam, Güven Endeksi̇ Ve Mali̇yet Endeksi̇ İli̇şki̇si̇: Türki̇ye İnşaat Sektörü İçi̇n Bi̇r Uygulama

“…In their study, Canöz and Erdoğdu (2019) aimed to investigate the relationship between sectoral confidence indices, including the construction confidence index, and BIST sector indices. Atabeyli et al (2018) aimed to understand the effects of the historical data of different economic variables on the Construction Cost Index. In the light of the data of the Turkish Statistical Institute for the 2005 and 2017 periods, the relationship between the three factors (construction labor input index, domestic producer price index and consumer price index) and the Construction Sector Cost Index was explained.…”

Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

“…Using selective empirical test, it is proved that CPI, Construction and Operation Plan (COP), Gross Domestic Product (GDP) and Building Plan (BP) are the most leading indicators of predicting CCI [10]. The relationship strength, between BCCI and aforementioned factors, is analysed with the help of historical data [11]. Consumer price index, crude oil price and production price index are considered as most consistent explanatory variables on applying for the vector of CCI using correction models [7].…”