Table 1. Literature review matrix...

Table 1.

Literature review matrix

Authors	Purpose	Methods	Predictors	Key findings	Output
AlTalhoni et al. (2025)	Propose highway construction cost index (HCCI) prediction models	VECM, long short-term memory (LSTM) networks and ARIMA	Oil prices, asphalt (PPI), concrete (PPI), hot-finished steel bars (PPI), construction sand, gravel and crushed stone (PPI), number of building permits	VECM is the most effective model for short-term forecasting under volatile conditions, achieving the lowest average mean squared error	HCCI
Alzara et al. (2025)	Create a CCI predictive model for Egypt’s construction industry	Long short-term memory (LSTM) and gated recurrent unit (GRU)	PPI, CPI, foreign reserves, oil prices, money supply and EGX30 (Egyptian Exchange)	Oil is the key factor that affects CCI in Egypt	CCI
Akpolat (2024)	Examine the impact of real variables on housing prices	Nonlinear autoregressive distributed lag (NARDL)	Exchange rates, mortgage rates, money supply, CCI	The real exchange rate has a positive and symmetric effect	Housing prices
Maqsoom et al. (2024)	Analyse the correlation between the construction price index (CPI) or prices of all commodities, lumber and wood products, cement and iron products and inflation in Thailand	ARIMA, Spearman correlation	Inflation rate	Iron products showed a significant relationship with inflation	CPI
Al Kailani et al. (2024)	Develop a construction cost index (CCI) for Jordan	Fuzzy analytic hierarchy process (FAHP), ML techniques	Cost of concrete, cement, steel, aggregate, diesel	Random forest model had the lowest MAPE (1.09%)	CCI
Zhang et al. (2024)	Observe building cost index (BCI) and CCI reactions concerning labour conditions	Vector autoregression (VAR), granger causality test	BCI, CCI	BCI shows sensitivity to labour supply and unemployment, CCI remains insensitive	BCI, CCI
Aslam et al. (2023)	Forecast CCI of building materials in developing countries	ANN, time series, linear regression	Cost of bricks, steel, cement, sand and gravel	ANN model has superior results with the lowest errors	CCI
El Said and Stammer (2023)	Identify significant bid items and develop highway construction cost index (HCCI) model	Multiple linear regression, time series analysis	Bid item price	Bituminous material is highly significant for cost estimation	HCCI
Hu and Xiao (2022d)	Propose fuzzy cognitive visibility graph (FCVG) for time series forecasting	FCVG, weighted multi-subgraph similarity (WMSS)	M1, M2 and M4 datasets	Leveraging fuzzy interaction improves time-series forecasting	CCI
Hu and Xiao (2022a)	Propose multi-subgraph similarity (MSS) for time series forecasting	ARIMA, SARIMA, Holt ES, MSS	CCI	MSS method provides more accurate predictions	CCI
Hu and Xiao (2022c)	Propose a forecasting method based on a directed visibility graph	ARIMA, SARIMA, Holt ES, Holt-Winter ES, visibility graph	CCI, GDP	The proposed method offers robust and accurate predictions	CCI, GDP
Hu and Xiao (2022b)	Propose a model using recurrent neural network (RNN) and network self-attention	SES, TBATS, ARIMA, CatBoost, transformer, RNN	CCI, M1, M3 data sets	The proposed method performs better for certain time series and shows robustness	CCI
Jiang et al. (2022)	Analyse construction costs using multivariate models	SMA, ARIMA, Holt ES	CPI, unemployment rate, employment rate, PPI, crude oil prices, GDP, building permits, import price index, money supply	ARIMA is the best forecasting model with key influencing factors identified	CCI
Kim et al. (2022)	Propose a hybrid ARIMA-ANN model for forecasting construction costs	ARIMA, ANN, hybrid ARIMA-ANN	CCI	Hybrid model outperforms individual ARIMA or ANN models for longer-term forecasts	CCI
Choi et al. (2021)	Develop city-level CCI models	ARIMA, VECM	CPI, effective federal funds rate, unemployment rate, construction employee ratio, average weekly hours of production, new building permits, M2 money supply, average hourly earnings in construction, S&P 500 stock index, crude oil prices, PPI, housing starts, real personal income, personal consumption expenditures	Significant city-level differences were found; national CCI causes forecast errors	CCI
Liu et al. (2021)	Develop a holistic framework for automated HCCI development using inconsistent pay items	Fisher index formula	Pay items	Provides reliable insights into construction market conditions	HCCI
Mao et al. (2021)	Forecast CCI using visibility graph network method	Node visibility, network structures	CCI	The method improves prediction accuracy	CCI
Cao and Ashuri (2020)	Explore models for volatile cost data prediction	ARIMA, LSTM	HCCI	LSTM outperforms other time-series models	HCCI
Zhao et al. (2020)	Propose a time-series transfer function to forecast BCI	ARIMA, time-series transfer function	House price index	Transfer function improves forecasting accuracy by considering time-lag causality	BCI
Kissi et al. (2019)	Identify key economic indicators that influence the tender price index prediction in the building industry of Ghana	Mean score ranking, Wilcoxon signed rank	–	The study identifies five significant economic indicators: CPI, PPI, GDP, currency exchange rate and interest rate	–
Elem-Uche et al. (2019)	Develop a VECM to examine the relationship between Nigeria’s GDP and key monetary policy variables (credit, exchange rate, and interest rate)	VECM	Money supply, interest rate, domestic credits, real effective exchange rate	Real output depends on money supply, credit and exchange rate channels in the short run and on interest rate in the long run	GDP
Elfahham (2019)	Estimate CCI for concrete structures	Neural networks, linear regression, time series	Prices of structural steel, Portland cement, bricks, sand and gravel	The autoregressive time-series prediction method is the most accurate	CCI
Mao and Xiao (2019)	Propose a CCI forecasting method using a visibility graph	Visibility graph	CCI	Improves prediction accuracy, contributing to cost-saving in construction	CCI
Oteng-Abayie and Dramani (2019)	Examine co-movements between building cost index and inflation and exchange rate in Ghana	Wavelet technique	Non-food CPI, exchange rate	There are co-movements between building cost index and inflation and exchange rate	BCI
Zhao et al. (2019)	Forecast residential building costs in New Zealand	Exponential smoothing, ARIMA	BCI	ARIMA outperforms exponential smoothing for townhouses and apartments	BCI
Kissi et al. (2018)	Apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling tender price index (TPI) in Ghana	ARIMAX, ARIMA	Composite consumer price indices (CCPI), gross domestic product – construction (GDPC), exchange rate (ER), GDP, interest rate and PPI	The ARIMAX model demonstrated better predictive accuracy compared to a single approach such as ARIMA	TPI
Moon et al. (2018a, 2018b)	Refine CCI prediction by applying long memory	ARFIMA, ARIMA	CCI	ARFIMA outperforms ARIMA	CCI
Moon and Shin (2018a)	Forecast CCI using interrupted time-series model	Interrupted time-series	CCI	The interrupted model performs better than ARIMA and Holt-Winters models	CCI
Moon and Shin (2018b)	Propose a CCI forecasting model based on VECM with search query frequencies	VECM	CCI	VECM model shows better predictive ability than cointegrated VAR	CCI
Zhang et al. (2018)	Forecast CCI using a visibility graph approach	Visibility graph,	CCI	Proposed method is easier to implement and can forecast CCI with fewer errors	CCI
Zhang et al. (2017)	Improve CCI forecasts using fuzzy logic in a visibility graph	Fuzzy logic and visibility graph	CCI	Fuzzy logic improves prediction accuracy by using appropriate rules	CCI
Joukar and Nahmens (2016)	Develop predictive model for CCI considering volatility	ARCH, GARCH	CCI	Models show persistent CCI volatility, especially during economic shocks	CCI
Shahandashti and Ashuri (2016)	Forecast national highway CCI using multivariate time series models	VECM	CPI, unemployment rate, employment rate in construction, average weekly hours, prime loan rate, PPI, crude oil prices, GDP, GDP implicit price deflator, building permits, construction spending, money supply, average hourly earnings, Dow Jones industrial average, housing starts	Multivariate models are more accurate than univariate ones	HCCI
Wang and Ashuri (2017)	Predict CCI using a modified K-nearest neighbours (KNN) algorithm	KNN	CPI, crude oil price, GDP, number of building permits	KNN yields small prediction errors	CCI
Cao et al. (2015)	Create a self-adaptive structural radial basis neural network intelligence machine (SSRIM) model to forecast Taiwan CCI	MARS, RBFNN, EL-SVM, GLR, ABC	Wholesale Price Index, CPI, bank lending rates, oil prices, exchange rates, stock indices, Nikkei 225	Nikkei 225, and bank lending rates are key predictors for Taiwan CCI accuracy	CCI
Shahandashti (2014)	Analyse the temporal relationships between highway construction costs and macroeconomic indicators	Pearson correlation, unit root test, granger causality test	PPI, GDP, GDP implicit price deflator, Dow Jones industrial average, money supply, prime loan rate, unemployment rate, federal funds rate, CPI, number of housing starts, number of building permits, construction spending, average hourly earnings, average weekly hours, and employment rate in construction, crude oil price	Crude oil prices and average hourly earnings are leading indicators of highway construction costs	HCCI
Cheng et al. (2013)	Establish a hybrid intelligence system (ELSVM) for modelling construction price variations	LS-SVM, DE	Wholesale Price Index, CPI, bank lending rates, oil prices, exchange rates, stock indices, Nikkei 225	ELSVM model achieves low MAPE in modelling CCI fluctuations	CCI
Shahandashti and Ashuri (2013)	Create multivariate time series models for CCI forecasting	Multivariate time series (VEC), granger causality tests	CPI, employment, building permits, money supply, oil price, housing starts	VEC models are suitable for CCI forecasting	CCI
Xu and Moon (2013)	Present a VAR model for forecasting the construction cost trend	Cointegrated vector autoregression (VAR)	CCI, CPI	The cointegrated VAR model provides more accurate forecasts	CCI
Ashuri et al. (2012)	Identify leading indicators of CCI through empirical tests	Granger causality tests, johansen’s cointegration tests	CPI, oil price, PPI, GDP, employment, building permits, housing starts	Leading indicators include CPI, oil prices, PPI, GDP and employment levels	CCI
Ashuri and Lu (2010)	Compare various time series approaches for CCI forecasting	SMA, Holt ES, Holt-Winters ES, ARIMA	CCI	Seasonal ARIMA is the most accurate for in-sample, and Holt-Winters is best for out-of-sample forecasting	CCI
Hwang (2009)	Propose dynamic regression models for predicting construction cost index	Linear regression, categorical regression, dynamic regression	CCI	Models generate more objective forecasts and are more accurate than existing models	CCI
Williams (1994)	Develop back-propagation neural network (NN) models to predict CCI changes	NN, exponential smoothing and simple linear regression	Housing starts, prime lending rate, CCI changes	Neural networks showed greater error than exponential smoothing and linear regression	CCI

Authors	Purpose	Methods	Predictors	Key findings	Output
AlTalhoni et al. (2025)	Propose highway construction cost index (HCCI) prediction models	VECM, long short-term memory (LSTM) networks and ARIMA	Oil prices, asphalt (PPI), concrete (PPI), hot-finished steel bars (PPI), construction sand, gravel and crushed stone (PPI), number of building permits	VECM is the most effective model for short-term forecasting under volatile conditions, achieving the lowest average mean squared error	HCCI
Alzara et al. (2025)	Create a CCI predictive model for Egypt’s construction industry	Long short-term memory (LSTM) and gated recurrent unit (GRU)	PPI, CPI, foreign reserves, oil prices, money supply and EGX30 (Egyptian Exchange)	Oil is the key factor that affects CCI in Egypt	CCI
Akpolat (2024)	Examine the impact of real variables on housing prices	Nonlinear autoregressive distributed lag (NARDL)	Exchange rates, mortgage rates, money supply, CCI	The real exchange rate has a positive and symmetric effect	Housing prices
Maqsoom et al. (2024)	Analyse the correlation between the construction price index (CPI) or prices of all commodities, lumber and wood products, cement and iron products and inflation in Thailand	ARIMA, Spearman correlation	Inflation rate	Iron products showed a significant relationship with inflation	CPI
Al Kailani et al. (2024)	Develop a construction cost index (CCI) for Jordan	Fuzzy analytic hierarchy process (FAHP), ML techniques	Cost of concrete, cement, steel, aggregate, diesel	Random forest model had the lowest MAPE (1.09%)	CCI
Zhang et al. (2024)	Observe building cost index (BCI) and CCI reactions concerning labour conditions	Vector autoregression (VAR), granger causality test	BCI, CCI	BCI shows sensitivity to labour supply and unemployment, CCI remains insensitive	BCI, CCI
Aslam et al. (2023)	Forecast CCI of building materials in developing countries	ANN, time series, linear regression	Cost of bricks, steel, cement, sand and gravel	ANN model has superior results with the lowest errors	CCI
El Said and Stammer (2023)	Identify significant bid items and develop highway construction cost index (HCCI) model	Multiple linear regression, time series analysis	Bid item price	Bituminous material is highly significant for cost estimation	HCCI
Hu and Xiao (2022d)	Propose fuzzy cognitive visibility graph (FCVG) for time series forecasting	FCVG, weighted multi-subgraph similarity (WMSS)	M1, M2 and M4 datasets	Leveraging fuzzy interaction improves time-series forecasting	CCI
Hu and Xiao (2022a)	Propose multi-subgraph similarity (MSS) for time series forecasting	ARIMA, SARIMA, Holt ES, MSS	CCI	MSS method provides more accurate predictions	CCI
Hu and Xiao (2022c)	Propose a forecasting method based on a directed visibility graph	ARIMA, SARIMA, Holt ES, Holt-Winter ES, visibility graph	CCI, GDP	The proposed method offers robust and accurate predictions	CCI, GDP
Hu and Xiao (2022b)	Propose a model using recurrent neural network (RNN) and network self-attention	SES, TBATS, ARIMA, CatBoost, transformer, RNN	CCI, M1, M3 data sets	The proposed method performs better for certain time series and shows robustness	CCI
Jiang et al. (2022)	Analyse construction costs using multivariate models	SMA, ARIMA, Holt ES	CPI, unemployment rate, employment rate, PPI, crude oil prices, GDP, building permits, import price index, money supply	ARIMA is the best forecasting model with key influencing factors identified	CCI
Kim et al. (2022)	Propose a hybrid ARIMA-ANN model for forecasting construction costs	ARIMA, ANN, hybrid ARIMA-ANN	CCI	Hybrid model outperforms individual ARIMA or ANN models for longer-term forecasts	CCI
Choi et al. (2021)	Develop city-level CCI models	ARIMA, VECM	CPI, effective federal funds rate, unemployment rate, construction employee ratio, average weekly hours of production, new building permits, M2 money supply, average hourly earnings in construction, S&P 500 stock index, crude oil prices, PPI, housing starts, real personal income, personal consumption expenditures	Significant city-level differences were found; national CCI causes forecast errors	CCI
Liu et al. (2021)	Develop a holistic framework for automated HCCI development using inconsistent pay items	Fisher index formula	Pay items	Provides reliable insights into construction market conditions	HCCI
Mao et al. (2021)	Forecast CCI using visibility graph network method	Node visibility, network structures	CCI	The method improves prediction accuracy	CCI
Cao and Ashuri (2020)	Explore models for volatile cost data prediction	ARIMA, LSTM	HCCI	LSTM outperforms other time-series models	HCCI
Zhao et al. (2020)	Propose a time-series transfer function to forecast BCI	ARIMA, time-series transfer function	House price index	Transfer function improves forecasting accuracy by considering time-lag causality	BCI
Kissi et al. (2019)	Identify key economic indicators that influence the tender price index prediction in the building industry of Ghana	Mean score ranking, Wilcoxon signed rank	–	The study identifies five significant economic indicators: CPI, PPI, GDP, currency exchange rate and interest rate	–
Elem-Uche et al. (2019)	Develop a VECM to examine the relationship between Nigeria’s GDP and key monetary policy variables (credit, exchange rate, and interest rate)	VECM	Money supply, interest rate, domestic credits, real effective exchange rate	Real output depends on money supply, credit and exchange rate channels in the short run and on interest rate in the long run	GDP
Elfahham (2019)	Estimate CCI for concrete structures	Neural networks, linear regression, time series	Prices of structural steel, Portland cement, bricks, sand and gravel	The autoregressive time-series prediction method is the most accurate	CCI
Mao and Xiao (2019)	Propose a CCI forecasting method using a visibility graph	Visibility graph	CCI	Improves prediction accuracy, contributing to cost-saving in construction	CCI
Oteng-Abayie and Dramani (2019)	Examine co-movements between building cost index and inflation and exchange rate in Ghana	Wavelet technique	Non-food CPI, exchange rate	There are co-movements between building cost index and inflation and exchange rate	BCI
Zhao et al. (2019)	Forecast residential building costs in New Zealand	Exponential smoothing, ARIMA	BCI	ARIMA outperforms exponential smoothing for townhouses and apartments	BCI
Kissi et al. (2018)	Apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling tender price index (TPI) in Ghana	ARIMAX, ARIMA	Composite consumer price indices (CCPI), gross domestic product – construction (GDPC), exchange rate (ER), GDP, interest rate and PPI	The ARIMAX model demonstrated better predictive accuracy compared to a single approach such as ARIMA	TPI
Moon et al. (2018a, 2018b)	Refine CCI prediction by applying long memory	ARFIMA, ARIMA	CCI	ARFIMA outperforms ARIMA	CCI
Moon and Shin (2018a)	Forecast CCI using interrupted time-series model	Interrupted time-series	CCI	The interrupted model performs better than ARIMA and Holt-Winters models	CCI
Moon and Shin (2018b)	Propose a CCI forecasting model based on VECM with search query frequencies	VECM	CCI	VECM model shows better predictive ability than cointegrated VAR	CCI
Zhang et al. (2018)	Forecast CCI using a visibility graph approach	Visibility graph,	CCI	Proposed method is easier to implement and can forecast CCI with fewer errors	CCI
Zhang et al. (2017)	Improve CCI forecasts using fuzzy logic in a visibility graph	Fuzzy logic and visibility graph	CCI	Fuzzy logic improves prediction accuracy by using appropriate rules	CCI
Joukar and Nahmens (2016)	Develop predictive model for CCI considering volatility	ARCH, GARCH	CCI	Models show persistent CCI volatility, especially during economic shocks	CCI
Shahandashti and Ashuri (2016)	Forecast national highway CCI using multivariate time series models	VECM	CPI, unemployment rate, employment rate in construction, average weekly hours, prime loan rate, PPI, crude oil prices, GDP, GDP implicit price deflator, building permits, construction spending, money supply, average hourly earnings, Dow Jones industrial average, housing starts	Multivariate models are more accurate than univariate ones	HCCI
Wang and Ashuri (2017)	Predict CCI using a modified K-nearest neighbours (KNN) algorithm	KNN	CPI, crude oil price, GDP, number of building permits	KNN yields small prediction errors	CCI
Cao et al. (2015)	Create a self-adaptive structural radial basis neural network intelligence machine (SSRIM) model to forecast Taiwan CCI	MARS, RBFNN, EL-SVM, GLR, ABC	Wholesale Price Index, CPI, bank lending rates, oil prices, exchange rates, stock indices, Nikkei 225	Nikkei 225, and bank lending rates are key predictors for Taiwan CCI accuracy	CCI
Shahandashti (2014)	Analyse the temporal relationships between highway construction costs and macroeconomic indicators	Pearson correlation, unit root test, granger causality test	PPI, GDP, GDP implicit price deflator, Dow Jones industrial average, money supply, prime loan rate, unemployment rate, federal funds rate, CPI, number of housing starts, number of building permits, construction spending, average hourly earnings, average weekly hours, and employment rate in construction, crude oil price	Crude oil prices and average hourly earnings are leading indicators of highway construction costs	HCCI
Cheng et al. (2013)	Establish a hybrid intelligence system (ELSVM) for modelling construction price variations	LS-SVM, DE	Wholesale Price Index, CPI, bank lending rates, oil prices, exchange rates, stock indices, Nikkei 225	ELSVM model achieves low MAPE in modelling CCI fluctuations	CCI
Shahandashti and Ashuri (2013)	Create multivariate time series models for CCI forecasting	Multivariate time series (VEC), granger causality tests	CPI, employment, building permits, money supply, oil price, housing starts	VEC models are suitable for CCI forecasting	CCI
Xu and Moon (2013)	Present a VAR model for forecasting the construction cost trend	Cointegrated vector autoregression (VAR)	CCI, CPI	The cointegrated VAR model provides more accurate forecasts	CCI
Ashuri et al. (2012)	Identify leading indicators of CCI through empirical tests	Granger causality tests, johansen’s cointegration tests	CPI, oil price, PPI, GDP, employment, building permits, housing starts	Leading indicators include CPI, oil prices, PPI, GDP and employment levels	CCI
Ashuri and Lu (2010)	Compare various time series approaches for CCI forecasting	SMA, Holt ES, Holt-Winters ES, ARIMA	CCI	Seasonal ARIMA is the most accurate for in-sample, and Holt-Winters is best for out-of-sample forecasting	CCI
Hwang (2009)	Propose dynamic regression models for predicting construction cost index	Linear regression, categorical regression, dynamic regression	CCI	Models generate more objective forecasts and are more accurate than existing models	CCI
Williams (1994)	Develop back-propagation neural network (NN) models to predict CCI changes	NN, exponential smoothing and simple linear regression	Housing starts, prime lending rate, CCI changes	Neural networks showed greater error than exponential smoothing and linear regression	CCI

Note(s):

ABC = artificial bee colony; ARFIMA = autoregressive fractional integrated moving average; ARIMA = autoregressive integrated moving average; CatBoost = categorical boosting; DE = differential evolution; EL-SVM = ensemble learning support vector machine; GLR = generalized likelihood ratio; GARCH = generalized autoregressive conditional heteroscedasticity; Holt ES = Holt’s exponential smoothing; LSTM = long short-term memory; LS-SVM = least squares support vector machine; LS-SVM = least squares support vector machine; MARS = multivariate adaptive regression splines; RBFNN = radial basis function neural network; MA = simple moving average; SARIMA = seasonal autoregressive integrated moving average; SES = simple exponential smoothing; TBATS = trigonometric Box-Cox ARIMA trend seasonal; VECM = vector error correction model; VEC = vector error correction

Source(s): Author’s own work

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