Construction Cost Index Research Articles

Forecasting Construction Cost Indices: Methods, Trends, and Influential Factors

The Construction Cost Index (CCI) is an important tool that is widely used in construction cost management to monitor cost fluctuations over time. Numerous studies have been conducted on CCI development and forecasting models, including time series, artificial intelligence, machine learning, and hybrid models. Therefore, this study seeks to reveal the complexity of CCI forecasting and identify the leading indicators, trends, and techniques for CCI prediction. A bibliometric analysis was conducted to explore the landscape in the CCI literature, focusing on co-occurrence, co-authorship, and citation analysis. These analyses revealed the frequent keywords, the most cited authors and documents, and the most productive countries. The research topics and clusters in the CCI forecasting process were presented, and directions for future research were suggested to enhance the prediction models. A case study was conducted to demonstrate the practical application of a forecasting model to validate its prediction reliability. Furthermore, this study emphasizes the need to integrate advanced technologies and sustainable practices into future CCI forecasting models. The findings are useful in enhancing the knowledge of CCI prediction techniques and serve as a base for future research in construction cost estimation.

Development of a Prediction Model for Construction Project Cost in India: an Analytical Approach A Review

The fundamental problem in the construction industry is that building projects are completed attaken a tolls significantly higher than estimated venture taken a tolls consequently, it is fundamental todevelop a taken a toll forecast show that detain all components influencing the extend taken a toll bymeans of relapse examination. Development costs are continuously inclined to vacillations, with a longtermdesign of expanding, making the estimating prepare a difficult errand. Vacillations within the fetchedof building materials have a noteworthy affect on foreseeing the esteem of a venture and, as a result, onthe project's great conclusion It is well known that civil engineering projects are prone to cost overrunsand schedule delays. Lack of project scope and frequent changes leads to a negotiation process interms of cost and time between the first partyowner and the second partycontractors. In the project lifecycle, cost forecast is a precursor to budget prices and resource allocation, thus it is critical for anyorganization. The following objectives are framed based on the literature study identify the constructioncost predictability of various tools, identify the various influencing factors to predict the material price ofcement and steel, model the Construction Cost Index (CCI), predict the construction duration (Especiallyfor highway projects, as compared to other kind of projects. They are having more obstacles to completethe task), predict the land value, Regarding this issue this paper find the best methodology for thePrediction Model for Construction Project Cost by analysising the different researchers papersresearch.

Analysis of Infrastructure Spending, Construction Cost Index, Population Density on Road Stability

Analysis of Infrastructure Spending, Construction Cost Index, Population Density on Road Stability

The Swedish Production of Apartments as a Function of GNP, Building Costs and Population Changes: Generation of Intelligent Media Content via Big Data Analytics

The production of new apartments in Sweden has varied strongly during the period from 1975 to 2021. A new statistical function, which explains these production changes, has been developed. This function is designed, based on a set of hypotheses of how the production level should be affected by different explaining factors, such as the GNP, the size of the population, the growth of the population, and the cost of construction. The following hypotheses could not be rejected: the apartment production is a strictly increasing and strictly convex function of GNP, and a strictly increasing function of the size of the population and the growth of the population, and a strictly decreasing function of the cost of construction. The parameters of the statistical function have been estimated with high precision, via multiple regression analysis. It was not possible to detect heteroscedasticity via residual analysis. Furthermore, no indications that nonlinear transformations would improve the selected model were found. The apartment production model contains a strongly significant negative time trend. The estimated function is used to predict the future apartment production until the year 2050. The predictions are based on assumed growth levels of GNP and the population, and on alternative future time trends of the construction cost index. If the real construction cost index continues to grow with the same average trend as from the year 1993 to 2021, the future apartment construction level will stay almost constant at 40,000 apartments per year until 2050. If the future real construction cost index stays constant at the level in 2022, the production of new apartments will grow to almost 90,000 apartments per year in 2050. If the real construction cost index can be decreased to the level in 1993, the production of new apartments will grow to almost 130,000 apartments per year in the year 2050.

A novel weighted approach for time series forecasting based on visibility graph

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance.

Two ENR cost indexes react differently to labour conditions

ABSTRACT Building Cost Index (BCI), unlike Construction Cost Index (CCI), had not been thoroughly explored in the existing body of literature. Both BCI and the CCI are two similar time series, with the only difference lying in the labour components used in their calculations. This study utilizes impulse-response functions to observe the different reactions of the BCI and CCI concerning labour conditions. The results indicate that the BCI consistently displays heightened sensitivity to fluctuations in labour supply and unemployment, whereas the CCI remains insensitive to such labour-related fluctuations.

Predicting construction cost index using fuzzy logic and machine learning in Jordan

Purpose The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques. Design/methodology/approach The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula. Findings The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively. Originality/value The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.

Research on Comprehensive Evaluation Model of Regional Power Grid Construction Cost Based on Combined Weight

With the introduction of the "carbon peak, carbon neutrality" action plan, it has become an inevitable trend to invest in promoting supply-side structural adjustment and demand-side response to achieve bilateral development. Therefore, the construction expansion and construction cost assessment of key areas of power grid in the future are the focus of power enterprises. In this paper, a comprehensive evaluation model of regional power grid construction cost suitable for various environments is proposed. The ANP-CRITIC method, which combines the analytic hierarchy process with the CRITIC objective evaluation method, is used to determine the subjective and objective weights of the indicators. The comprehensive evaluation results are obtained by calculating the combined weights based on Euclidean distance. In terms of model verification, based on PEST theory, this paper constructs a regional power grid construction cost index system from 4 levels: political environment, economic environment, social environment and technical environment. Through the evaluation and analysis of regional examples, the rationality of the model is verified, and the lowest expected construction cost area is selected to assist enterprise decision-making and ensure the effective realization of power grid construction benefits.

İnşaat Maliyet Endeksi Tahmininde Holt-Winters Üstel Düzeltme Parametrelerinin PSO ve İleri Walk-Forward Cross-Validation ile Optimizasyonu

This research aims to enhance the accuracy of Construction Cost Index (CCI) forecasting using Holt-Winters exponential smoothing (ES) by optimizing its parameters, focusing on minimizing the Mean Absolute Percentage Error (MAPE) for precise CCI forecasts. To reach this aim, The Holt-Winters model parameters are optimized through Particle Swarm Optimization (PSO) and Walk-Forward Cross-Validation (WFCV). PSO, a metaheuristic optimization algorithm, is being applied to search for optimal values of the smoothing parameters (alpha, beta, and gamma) that determine the weightage of past observations, trends, and seasonality, respectively. WFCV is assessed the model's performance and ensures robustness. Reduced MAPEs of 22 for CCI forecasts and 2 for training data are the findings of the optimized Holt-Winters model. The obtained alpha, beta, and gamma values are 0.99, 0.77, and 0, respectively, highlighting the importance of while neglecting seasonality. Convergence graphs demonstrate the superiority of the optimization approach over conventional parameter values or random selections. By employing PSO and WFCV, the study efficiently fine-tunes the Holt-Winters model for precise CCI forecasting. Optimized parameter values enable data driven decision-making in construction project cost estimation and budget management. This research contributes a reliable and robust optimization methodology for CCI forecasting, supporting advancements in the field.

Economic Variables as an Early Warning Sign for Monitoring Construction Cost Indices – “An Empirical Study on Egypt”

The construction industry plays a crucial role in any nation's economic growth and prosperity through the implementation of the SDGs. Researchers reveal that the 17 SDGs are directly dependent by 17% and indirectly reliant by 27% on the construction industry's activities. In addition, the construction projects, whatever their type or components, continue for several years, during which many price changes and sharp economic fluctuations occur, whether at the local or international level, reflected in overall economic performance and the construction industry's health, leading to the project's cost changes and a shortage in the allocated budgets. Due to the difficulties this industry faces in reaching these goals, including the Egypt Vision 2030, we chose to look into these effects by using the ENR methodology to develop an explanatory construction cost indices model in Egypt similar to models used in other countries while making appropriate changes for the Egyptian markets. To illustrate the relationship between these selected economic variables and construction costs in Egypt and to act as an early warning sign to quantify, track, and predict the construction cost movements and trends in Egypt's construction sectors through a reliable model. A mixed research methodology consisted of semi-structured interviews with 15 industry experts and an empirical study covering 1990 through 2020 for selected local and international economic variables. We concluded that national and international economic variables significantly impacted as explanatory and predictive variables in Egypt's construction industry's cost indices; these variables are as follows: gross domestic product (GDP), consumer price index (CPI), USD to EGP exchange rate ($), inflation rate, lending rate, money supply, and unemployment rate, as well as international crude oil prices, gold prices, and copper prices. Nevertheless, further investigation is required with more economic variables across various construction industry sectors over various time intervals and different time series statistical and econometric modeling techniques.

Forecasting Construction Cost Index through Artificial Intelligence

This study presents a novel approach for forecasting the construction cost index (CCI) of building materials in developing countries. Such estimations are challenging due to the need for a longer time, the influence of inflation, and fluctuating project prices in developing countries. This study used three techniques—a modified Artificial Neural Network (ANN), time series, and linear regression—to predict and forecast the local building material CCI in Pakistan. The predicted CCI is based on materials, including bricks, steel, cement, sand, and gravel. In addition, the swish activation function was introduced to increase the accuracy of the associated algorithms. The results suggest that the ANN model has superior prediction results, with the lowest Mean Error (ME), Mean Absolute Error (MAE), and Theil’s U statistic (U-Stat) values of 0.04, 28.3, and 0.62, respectively. The time series and regression models have ME values of 0.22 and 0.3, MAE values of 30.07 and 28.3, and U-Stat values of 0.65 and 0.64, respectively. The proposed models can assist contractors, project managers, and owners through an accurately estimated cost index. Such accurate CCIs help correctly estimate project budgets based on building material prices to mitigate project risks, delays, and failures.

A systematic literature review on price forecasting models in construction industry

This paper summarizes a list of previously used forecasting models in the construction industry, using a three-stage review process. Specifically, articles published between 2012 to 2022 (inclusive) are investigated, and 34 documents are selected for further analysis. The results present a fluctuating number of publications from 2012 to 2016 and a significant increase since 2016. The distribution line between 2016 to 2020 indicates the growing interests in this topic. In addition, fluctuating material prices, construction costs and construction cost index were top concerns of researchers. Previous literatures frequently employed three forecasting models: Vector error correction model, Artificial Neural Network and Autoregressive Integrated Moving Average. Time series and machine learning techniques were widely used in prediction. Future studies could consider the hybrid model as a research method to improve the forecast accuracy. Additionally, future studies should take the effects of natural disasters into account. Findings of this study are expected to help researchers and industry practitioners select a suitable forecasting model and implement precise prediction in practice.

The Costs of Construction and Housing Prices: A Full-Cost Pricing or Tendering Theory?

While construction costs and housing prices are implicitly examined in the construction economics literature, dedicated studies on their theoretical underpinning are rare. In this study, we investigated the application of different pricing theories in Auckland by testing the relationship between house prices and construction costs in Auckland from 1995 to 2021. The results contrast the tendering pricing theory, which posits that construction prices are optimal mark-ups unaffected by market demand, with the full-cost pricing theory, which acknowledges the market-dependent nature of pricing. By using the Toda-Yamamoto’s granger-causality test and Pesaran’s Autoregressive Distributive Lag (ARDL) bound tests, we analysed the relationship between the house price index (HPI) and construction cost index (CCI). The result suggests a significant relationship between housing prices and construction costs in both the short and long term, supporting the predominance of the full-cost pricing theory in Auckland’s housing market. The finding highlights the potential need for property industry participants to evaluate the market structure of the construction industry, fostering a more competitive environment and paving the way for more effective supply-related housing policies.

Time-Series Forecasting Based on Fuzzy Cognitive Visibility Graph and Weighted Multisubgraph Similarity

This article aims to address the problem of time-series forecasting. Current state-of-the-art forecasting models lack the ability to mine the spatiotemporal dependence. How to mine more useable features of time series to make accuracy predictions is still an open issue. To address these challenges, from the perspective of fuzzy interaction between nodes, we propose a novel network constructing model called fuzzy cognitive visibility graph (FCVG) for time series to convert the time series into a pair of directed weighted graphs. To calculate the similarity between nodes in the FCVG, we develop the weighted multisubgraph similarity (WMSS). With these tools, we introduce the prediction based on fuzzy similarity distribution (PFSD), a novel forecasting method for time series, that can efficiently capture the spatiotemporal dependence. A time series is converted into a network by the FCVG, and the similarity scores between nodes are calculated through the WMSS. Based on the normalized similarity distribution, the predictions of time series are made. Extensive experiments on different datasets confirm the benefits of leveraging fuzzy interaction in time-series forecasting. Moreover, the construction cost index is predicted to show how to apply PFSD to forecast a specific time series.

Bayesian Modeling of Labor Earnings in Construction

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor’s average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor’s 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENJUALAN ENERGI LISTRIK PT. PLN DI KALIMANTAN BARAT TAHUN 2012-2020

Electricity is an important need for people in all regions of Indonesia in this era. The purpose of this study was to determine the effect of population, economic growth, foreign investment, domestic investment, and the construction cost index on electricity sales in all cities or districts in West Kalimantan Province. The data used in this study are secondary data in the form of time series and cross-section data sourced from the Central Bureau of Statistics and the Dukcapil of West Kalimantan Province in 2012-2020. The method used in this research is panel data analysis method.

Developing a Chained Fisher Construction Cost Index for Structural Steel Girder Bridges

The construction cost index is widely used by state departments of transportation (DOTs) for budgeting purposes, and to arrive at engineers’ estimates on projects for bid. State DOTs typically develop cost indexes at the construction program level. There is little information available on developing cost indexes for bridge types. This paper outlines how such a bridge-level cost index can be developed. The bridge-level cost index was developed from 19 weighted bid items aggregated into four weighted components (groups) of bid items, and then aggregated to the weighted bridge system. Using publicly available data from Ohio DOT, on historical bid items, chained Fisher indexes were developed for typical Ohio DOT structural steel girder bridges. In addition, forecasting beyond 2021 was computed using a well-fitted regression model. The findings from this research could help Ohio DOT to accurately estimate the cost to construct structural steel girder bridges based on price changes and price trends. In addition, the result of this research allows for long-term planning and budgeting of costs for similar bridge types within the state of Ohio. In their simplest form, the cost indexes generated from this research will help paint a picture of changes in the cost of construction from one specific year to another at a granular level. This research contributes to the body of knowledge by offering detailed steps on how to develop construction cost indexes at a granular level and specifically for structural steel girder bridges.

Time series analysis of building construction cost index in Türkiye

Time series analysis of building construction cost index in Türkiye

The asymmetric effects of real variables on real housing prices: a nonlinear ARDL analysis for Turkey

PurposeThis study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.Design/methodology/approachThis study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.FindingsThe real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.Originality/valueThis study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

PurposeThe present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.Design/methodology/approachThe cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.FindingsThe findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.Originality/valueThis research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.