1. Introduction
The construction industry has an essential role in meeting the ever-changing needs of societies and has a significant impact on the transformation of the built environment. In dealing with vital environmental challenges such as decarbonization, comprehensive growth, and sustainability, societies struggle to identify construction industry dynamics, where the construction cost index plays a significant role.
Construction costs, over time, are reflected by the construction cost index, which represents the cost dynamics in the related industries and covers factors such as labor, energy, and material costs, the inflation rate, and other economic factors. The stakeholders, such as policymakers, industrial participants, and investors, heavily rely on the construction cost index to evaluate factors affecting competitiveness, such as the cost, economic viability, and profitability of construction projects.
In the face of rapid changes in the global built environment, the European Commission’s EU 2050 strategy addressing the Transition [1] identified the construction sector as one of the most effective tools to achieve climate neutrality by 2050. The construction sector is being positioned as a key driver of sustainable development in the European Union (EU), in line with the ambitious goals of the European Green Deal [2], primarily through initiatives such as “Renewal Wave and the Circular Economy Action Plan”.
Construction developments during the pandemic had negative impacts on supply chain management as well as on mega construction projects, which is noted by Gartomi et al. [3]. According to Idrissi et al. [4], COVID-19 broke out when the architecture, engineering, and construction industry was suffering from several shortcomings, mainly the poor level of technology utilized, as cited in the literature, which weakened the industry but provided a real opportunity to adjust and understand the role of building information modeling technology.
The global supply chain disruption caused by the COVID-19 pandemic and geopolitical conflicts resulted in a shortage in construction material supply and caused a significant increase in the overall cost of construction. In particular, small- and medium-sized suppliers have been struggling to adapt to the worsening conditions in the industry. Moreover, considering the construction sector’s dependence on energy and petroleum products, it can easily be said that the increase in energy prices has had a significant impact on construction costs [5]. The UN’s SDG 9 is related to promoting sustainable industrialization. According to SDG 9, one of the sustainable development objectives is to invest in technology and innovation. Therefore, the EU should provide funds for improving and enhancing scientific research, upgrading technological capabilities, and supporting Research and Development projects for industry organizations that require support, such as small- and medium-sized suppliers, who have difficulty adapting to industry challenges. In addition, EU funds can also be used to support the development of reliable and resilient infrastructure to increase access to small-scale industries and enterprises in developing countries. Additionally, vulnerable suppliers, such as SMEs, may benefit from government support to ensure equal opportunities and inequalities in a perfectly competitive market.
In addition to the economic factors, political and financial risks due to geopolitical tensions and the threat of inflationary pressures created by wars impacting construction costs are also influencing the construction industry. Migration from conflict regions, the focus on reformation effects and sustainability practices in the EU, and the impact of climate changes such as drought conditions and heat waves have all contributed to the cost challenges in construction industry activities.
In recent years, the construction cost index in the European Union reported by Eurostat displayed a substantial rising trend, reaching its peak value in 2020 [6]. An unprecedented rise in costs is expected if the mortgage interest rates in Europe increase. Considering the current global crisis in addition to the instability in the construction sector in Europe, it is also of great importance to understand the relationship between the construction cost index and risk factors. Hence, the main purpose of this study is to evaluate the correlation between the construction cost index and multifaceted effects such as political, economic, and financial risk indices occurring across Europe. Our research aims to provide insights into the impact of risk factors on building costs by examining the delicate balance between existing variables and to ensure a detailed understanding of the contribution of the construction industry to European economic growth. The construction cost index is a market-based basket that combines the price value of selected building materials, such as bricks, steel, cement, sand, and gravel production, etc., as well as the price of human resources used only by the construction sector. It is widely used to estimate project costs. According to Cao et al. [7], the construction cost index is a weighted aggregate index of the real prices of constant quantities of building materials. The index provides information about changes occurring in costs in both the short and long terms to obtain more accurate bids. This index is used by both property owners and contractors. The property owners require this index to estimate probable project costs, whereas contractors use it for tender phases to produce financial proposals.
There are other similar studies in the literature that analyze the relationship between the construction cost index and risk factors. Odeyinka et al. [8] stated that political and financial risks ranked as the most significant risk impact factors that affect construction costs. Another similar study undertaken by Ahmad et al. [9] investigated the group of qualitative and quantitative risk factors that have an impact on the construction sector in Yemen. The study undertaken by Ahmad et al. [9] is in line with our entire work, and their results are in agreement with our findings. Increases in inflation rates, fluctuations in material prices, political instability, delays in delivery of materials to project sites, and foreign currency fluctuations were found to be the top-ranked risk factors affecting project costs and causing cost overruns in Yemen. Among all the risk factors, political and economic instability were identified as the top-ranked risk factors, which is in agreement with our findings.
Another study undertaken by Oyewobi et al. [10] also reported similar findings in the case of Nigeria. An analysis carried out on financial risks revealed results that are similar to our study, supporting our finding that inflation, inadequate attention to cash flow, and exchange rate variations are the major risks identified under financial risks that have significant impacts on contractors’ estimates. As an economic risk, inflation has been observed as having a negative impact on contractors’ tender sums. In addition, impacts related to political uncertainty were found to occur the most, whereas government regulations have had the greatest impact on contractors’ tender figures. In the case of Nigeria, such findings are in line with our study, thus indicating strong empirical evidence in support of our results.
This article examines the factors affecting the construction industry in detail and tries to emphasize the importance of the construction cost index as a vital indicator of the sustainability of the sector. We compiled a schematic diagram depicting the dependencies of these intricacies of the construction sector with reference to their relationships as shown in Figure 1 below. This study also seeks to analyze the correlation between the construction cost index and risk variables in order to offer significant insights into construction costs and the European construction industry’s impact on economic growth.
Most of the building materials used by the construction sector are classified as critical raw materials (CRMs). Such materials are depleted and becoming more expensive as they are nonrenewable, which is one of the reasons that the construction cost index continues to rise. The UN’s 2050 net-zero horizon plan aims to enhance resource management by promoting the reduction in CRMs and fostering technological solutions. Key messages have been delivered within the UNECE Annual Report 2023, such as the need for decreased reliance on fossil fuels, increased reliance on renewable energy, promotion of the diversification of CRM supply, and encouragement of the recycling and recovery of CRMs. Additionally, the UNECE report suggested stimulating investment in innovation to reduce demand for CRMs as well as promoting the sustainable and responsible consumption of CRMs. The UN’s SDG 13 relates to climate actions, with the objective of promoting actions to combat climate change and its impacts. The UN’s goal is to mobilize USD 100 billion annually from all sources to address the need for developing countries with the implementation of a framework convention on climate change. The UN promotes mechanisms for raising capacity for effective climate change-related planning and management in the least-developed countries as well as small island developing states. Such climate change factors in the construction industry aim to affect the cost of building materials, which will be more challenging for construction developers to implement.
With the expected increase in mortgage rates across Europe, the construction cost index is likely to continue its rise and perhaps reach new record levels. Considering the global crises that the world is currently undergoing, some developing countries’ construction sector bubbles are beginning to collapse. There is even a high risk of a chain reaction caused by instability in the sector. Even the Chinese building construction sector, which initially demonstrated reliable and sustainable development, is showing signs of struggle as one of its biggest developers has faced the threat of bankruptcy; struggling to remain in business, this developer is now receiving support from the Chinese government [11]. While developers are trying to regain the public’s trust as a last resort to return to normal, they are faced with numerous lawsuits filed by citizens because they have failed to complete contractual obligations. In some cases, the buildings for which citizens are seeking mortgages have not been built.
This study aimed to empirically evaluate the impact of risk factors on the construction cost index. The results of this study may assist property owners in assessing project valuations and support contractors in estimating project costs for tender submissions. The main objective of this study is to shed light on the stakeholders of the construction sector regarding the risk of the occurrence of events related to uncertainties in the industry that may affect project objectives; the risk factors represent variables that can negatively or favorably impact a project.
The global COVID-19 pandemic that followed the war between Ukraine and Russia created major supply chain problems for global trade since many small- and medium-sized suppliers in the industry failed to adapt to the situation quickly enough. The volume of construction experienced an unprecedented sudden drop, particularly during the COVID-19 period, as shown in Figure 2 [6]. The construction sector, which relies heavily on energy and other petroleum products, also saw higher prices due to increasing energy prices. As the wages of construction workers rose with inflation, transportation costs increased as raw materials became more expensive. In addition, fears of war spreading to Europe and the rest of the world and rising global inflation have forced many people from third world countries and countries close to war zones to move to Europe in hopes of a better future. As people began to migrate, the demand for construction increased with the demand for more housing. When renovations began in the EU, instead of constructing new buildings, it was thought that it would be more economical to renovate existing buildings; however, almost all old buildings contain toxic substances such as asbestos.
The Y-axis in Figure 2 indicates the value of the indices with 2019 as the base year (2019 = 100). As drought hit in the summer of 2022, parts of Europe were exacerbated by heat waves. On 9 August 2022, a senior European Commission researcher declared 2022 to be Europe’s worst drought in five hundred years. A report from the Global Drought Observatory confirmed this. The drought severely affected the cooling systems of hydroelectric and nuclear power plants as the amount of river water available for cooling dropped. European agriculture was also affected by the drought [13]. This also affected the construction sector since clean water is essential to produce cement and other building materials. There has been a significant increase in the European Union construction cost index throughout European countries, with the value reaching EUR 130.00 points in June of 2022, according to Eurostat.
2. Theoretical Review of the Literature
In the construction of price prediction models, several variables have been considered. Ashuri and Shahandashti [14] focused solely on macroeconomic variables that have an impact on the price of construction raw materials. Similarly, Feng et al. [15] identified a relationship between exogenous variables and the housing market cycle in China, considering factors such as house prices, GDP, urban population, per capita disposable income, fixed asset investment, consumer price index, loans of financial institutions, and average construction cost. Padilla [16] identified oil prices, exchange rates, employment levels, and interest rates as primary determinants of housing prices in Canada. Danso and Obeng-Ahenkora [17] identified the variables of critical raw materials used in the construction sector along with labor wages and the cost of energy as well as transportation, which increases building materials. These findings demonstrate the broad range of factors influencing construction costs, underscoring the need for comprehensive analysis, such as the one conducted in this study.
Ashuri and Shahandashti [14] stated that major macroeconomic indicators play a significant role in the determination of construction input prices, especially in developing countries. Feng et al. [15] and Padilla [16] noted that the cost of Brent petrol, relative exchange rates, and changing interest rates cause changes in the cost of dwellings in Canada. This has been supported by Feng et al. [15] in the case of the housing market cycle in China.
Yorucu and Bahramian [18] state that "panel unit root, heterogeneous panel cointegration, panel fully modified, and panel dynamic regression procedures are commonly employed empirical techniques for panel estimations”. Biagi et al. [19] report that an increase in tourism activity or a country being a tourist destination has a positive effect on house prices, as most tourist destinations are in warmer parts of the world. These effects can be observed in Italy, Spain, and Greece in Europe. In addition, the living costs in these three countries are lower compared with Germany and France, making them a more viable option for Europe’s domestic migration. The overall eurozone construction prices and costs are shown in Figure 3.
2.1. Construction-Led Growth Theory and the Bon Curve Analysis
The construction sector is commonly known as the engine of economic growth in many nations and acts as a catalyst for the determination of the economic prosperity of a nation. Sectoral interdependence-centered construction at the core of business activities is supported by the traditional Bon curve theorem. Bon [20] defined construction as “an activity entails the assembly of building materials (and definitely the changes in prices of the construction raw materials) and components on site; the materials and components are supplied by a variety of industries in the manufacturing sector; they are delivered to the site by the transportation and trade sectors; the assembly proceeds in accordance with plans, designs, and management procedures supplied mainly by the business services industry in the service sector; most of the funds required for construction are supplied by the financial services industry in the service sector; and the significant part of the output supplied by the construction sector is delivered to the real estate industry in the service sector” [20].
Akintoye [21] emphasized that during the expansionary phase of the business cycle, when the construction sector sits at the peak of business activities, construction value added is greater than the real GDP growth. When there is a recession in the economy, there is a rapid decline in construction investment activities, which leads to a contraction in the real GDP of a nation. This reality is widely accepted in both developed and developing countries. The implementation of the Bon curve theorem shows an extra emphasis on long-term macroeconomic patterns, which was also adapted by Rostow [22] and later amended by Myers [23] as a new model of economic growth. The implementation of the Bon curve theorem and the construction-led growth hypothesis have been thoroughly discussed in the literature, and the studies undertaken by Ruddock and Lopes [24], Myers [23], Mehmet and Yorucu [25], and Mehmet and Yorucu [26] provide empirical evidence that countries at different stages of economic development have experienced different Bon curve shapes. Bon’s traditional inverted U-shape has become an inverted V-shape in the case of North Cyprus, Taiwan, and Singapore. Nations with limited land and fast economic growth have experienced rapid construction development and grew vertically compared with other industrialized economies.
The construction sector in the EU is the dynamo of economic growth. It holds one of the greatest employment shares (48%), especially in the repair and maintenance sector [23]. The COVID-19 pandemic caused detrimental effects on the EU economy, and the construction sector has been negatively affected by post-pandemic economic activities. Rising interest rates have increased construction material prices as well as labor costs. This has motivated us to empirically investigate the main causes of the negative impacts of rising construction costs within the EU area. Several risk indices have been chosen to investigate their individual impact on construction costs. For example, the political risk index chosen in our study includes several indicators, such as political instability, changes in regulations and laws, changes in scope due to government influence, accidents during construction, strikes, disorder, etc. The economic risk indicators that are chosen include inflation rate, high taxation and tax change, recession, unemployment, etc. Moreover, the financial risk index includes foreign currency fluctuation, an increase in interest rates, the default situation of the banking sector, etc. The industry risk index includes delays in the delivery of materials to project sites, fluctuations in material prices, poor-quality construction materials, shortages of labor and equipment at work sites, increases in labor costs, low productivity levels, personal conflicts among laborers, lack of communication, poor site safety, poor project site management, ineffective planning, etc. Regarding the low productivity level of construction activities, Idrissi et al. [4] stated that the economic size of a country and the complexity of project design are parameters that hinder the development of the construction industry. They also highlighted that the construction industry’s current global situation of low productivity indicates the presence of other constraints, such as the complexities of project management, cost, time, and quality requirements, with a significant focus on meeting sustainable development requirements to achieve the objectives behind the construction of megaprojects, as described in the UN’s SDG 11. We explore a common understanding of how the repair and maintenance sector, which constitutes almost 50 percent of overall construction activity in the EU, is handling the increasing cost of construction raw materials as reported by Euroconstruct [1]. Inflationary pressures in the EU, which are also reported by Eurostat in the national accounts, indicate that in several advanced industrialized countries, the share of the repair and maintenance of construction is reaching one-half of total construction, and yet the volume of construction continues to decline over time due to limited technology use. Sustainable development of new cities, such as smart cities, which are composed of technology-based societies, is categorized as the final stage of growth, which is characterized by mass consumption. In this stage of the broader concept of sustainable development, construction growth results in better quality of development rather than quantity, as is implicit in the idea of mass consumption [27]. Sustainability can be achieved by minimizing the environmental degradation per unit of GDP or by maximizing net public benefits. As Mehmet and Yorucu [25] noted, the construction sector is diverse and has many divisions, such as civil and public institutes that have been supported by the Bon curve theorem. During economic development, the relative significance of these construction sub-sectors is altered in tandem with the Colin Clark hypothesis [28] that with a rising GDP, the structure of the economy changes from being agrarian to industrial and then to service-based economic activity. As highlighted by Mehmet and Yorucu [26], the purpose is to eliminate risks and create more safe, inclusive, resilient, and sustainable smart urban cities, which supports the UN’s SDGs 11.
2.2. Construction and Development
Strassman [29] argued that the construction sector follows a pattern of change that shows all stages of economic and social development, in which the construction sector plays a role in increasing the income level of a society. Turin [30] has argued that the net output of construction per capita helps to measure the economic inequality among nations. As technology improves, the inequality between rich and poor widens, which is referred to as the divergence of income. Thus, construction technological standards between rich and poor countries vary according to the intensity of modern technology. Upgrading technology increases efficiency in construction and lowers the average unit costs and hourly construction wages.
The level of welfare among nations, especially within the EU, matters when there is high inflation and rapidly changing prices of construction raw materials. It is therefore equally important to discuss the European Union construction cost index to understand the microeconomic impact of the construction sector.
The European Union construction cost index (HICP-CT) tracks the average fluctuations in the price of various services and goods that are used in the construction industry of the eurozone. This index is a significant economic benchmark that is used to determine the total cost of construction projects in a zone and has important meaning for the construction industry and the economy.
The main goal of the European Union construction cost index is to track fluctuations in construction costs over a period. It can serve as an asset to politicians, economists, and businesspeople. Moreover, the European Union construction cost index can be used to predict the patterns and trends of construction prices. Furthermore, this index can be used to make comparisons between eurozone nations and identify regional disparities. The European Union’s statistical body, Eurostat, records, calculates, and publishes this data for the euro region.
In the past decade, the European Union construction cost index has been growing, which suggests that construction costs in the EU have increased. Growth in the sector and its profitability suggest that in the future, material prices will continue to increase as there is high demand, and this might further push the construction cost index even higher. As the construction sector relies on a global supply chain for essential materials such as steel, concrete, wood, etc., they are affected by both global and local economic factors.
Some of the factors affecting these prices are trade disputes between nations, natural disasters and their effects on the supply chain, and increasing demand for specific products in emerging economies, all of which have significant impacts on commodity prices. As inflation increases, labor costs are also increasing, which increases construction costs in the eurozone. The construction industry heavily relies on human labor, and finding skilled workers is also a struggle as new construction companies enter into the sector.
3. Data and Methodology
This study is novel in its field since no previous study has used the same indices to measure the correlation between the construction cost index and risk factors in the case of a group of countries, such as the EU area. This research uses data from the political risk index (PRI), industrial production index (IND), financial risk index (FRI), and economic risk index (ERI) from 2000 to 2022 with quarterly data. For example, in modeling the cost of risk in international construction projects, similar studies have been undertaken by Hashem Al-Tabtabai and Alex [31] and Perera et al. [32]. These indices are used to find the correlation between risk indicators and construction costs in the EU area. After careful investigation of the related literature, we identified previous academic studies undertaken by Odeyinka et al. [8] that also empirically investigated whether the risk factors mentioned herein have any negative impact on construction cost indices. Their results for the case of Nigeria revealed that financial and political risks ranked as the highest parameters that have significant impacts on construction costs. We aimed to investigate the same research question based on a group of countries; thus, we selected the euro area as our research subject. We only selected three variables to investigate because the other variables, such as data on industry index, physical risk, logistic risk, and environmental risk, have heterogeneity due to individual country varieties. After checking the industry risk index, we determined that our findings did not reveal statistically robust results. Data regarding the political risk index, industrial production index, financial risk index, and economic risk index on a quarterly basis (2002–2022) for the eurozone were gathered from the source of the PRS Group, named the International Country Risk Guide (ICRG). The political risk index includes 12 components, whereas the financial and economic risk indices have 5 factors each, respectively (for details, see the International Country Risk Guide Methodology [33] “. Among some components, the important ones can be summarized as political instability, government stability, changes in regulations and laws, changes in scope due to government influence, accidents during construction, strikes, disorder, etc. Decisions by the government regarding regulatory changes, such as introducing new cabinet decrees, may reduce the supply of construction materials, cause disruption of the supply chain, delay the completion of projects, cause interruptions pertaining to receiving cash installments from the buyers, and delay new upcoming investments. Unemployment problems occur in the construction sector, which increases construction costs due to a lack of availability of resources and a reduction in government tax revenues.
The economic risk indicators include inflation rate, high taxation and tax change, recession, unemployment, etc. The financial risk index includes foreign currency fluctuation, increases in interest rates, the default situation of the banking sector, etc. The industry risk index includes delays in the delivery of materials to project sites, fluctuations in material prices, poor-quality construction materials, shortages of labor and equipment at work sites, increases in labor costs, low productivity levels, personal conflicts among laborers, lack of communication, poor site safety, poor management of project sites, ineffective planning, etc.
The quarterly data about the construction cost index were collected from the International Country Risk Guide (ICRG) PRS group. For the preparation of figures, data were collected from Eurostat and ING Research for the period of 2005–2022 with 2015 constant prices (Figure 3) and 2019–2022 with 2019 constant prices (Figure 2), respectively. The data include the price value of selected building materials, such as bricks, steel, cement, sand, and gravel production, etc., as well as the cost of employing human resources used by the construction sector.
The European Construction Sector Observatory provides comprehensive data, reports, and analysis on the construction industry in Europe. It covers topics such as construction costs, economic indicators, market trends, and policy developments. Euroconstruct is a network of research institutes that provides forecasts and analysis on the construction industry in Europe. They produce regular reports on construction trends, market developments, and economic indicators [34].
Our investigation showed that there could be some limitations with the data, such as measurement inaccuracy and potential biases, which might influence the validity and accuracy of the data used. Moreover, the research conducted in this study is exclusive to the EU area. Furthermore, as previously mentioned, one of the limitations of this study is that we were unable to investigate the impact of other risk factors, such as environmental and technological risks, on construction costs because panel data are not yet available for the EU area. However, we think that green building technology will enable developers to reduce construction costs and increase energy efficiency. Moreover, technological innovation and advanced methods in building technology will reduce the need for human resources in the construction sector. When the cost of labor declines, the construction cost index will be more favorable for both households and contractors. Recent research undertaken by Yitmen et al. [35] emphasized the opportunities and challenges of implementing Construction 5.0 models to create environmental and economic sustainability in construction and to build more resilient urban cities. This is in agreement with the objective of the UN’s SDG 9, which is to generate sustainable industry, innovation, and infrastructure. To achieve such a goal, the UN promotes policies and objectives to build resilient infrastructure, inclusive and sustainable industrialization, and foster innovation. The variables’ descriptive statistics are shown in Table 1. The IND index has a leptokurtic distribution and is favorably skewed, according to the skewness and kurtosis values. On the other hand, all indices show leptokurtic distributions, and the PRI and ERI indices are negatively skewed, suggesting the occurrence of extreme values.
The Industrial Production Panel estimation method was used in this study, which employs statistical analysis in the eurozone to predict the construction cost index. This allows the analysis to forecast future price fluctuations of commonly used goods and services over time while also considering different variables that have a direct effect on the cost of production. In addition, the Industrial Production Panel regression model was used for predicting the relationship between the construction cost index and other factors that have a direct effect on it. The explanatory factors encompass labor, material, and financing costs, as well as macroeconomic variables such as inflation and GDP growth.
The ability to control the influence of subject-specific traits, such as the geographical location and scale of a construction company, on the construction cost index is one of the main advantages of panel estimates for analysts. It is also important to note the limitations of panel estimates. One limiting factor is its reliance on panel homogeneity, which assumes there is a significant relationship between variables that are used for the construction cost index for every organization or individual in the panel dataset. However, if there are significant variations between firms and individuals, this may not always hold true. Omitted variable bias is another panel estimation limitation, as this occurs when an important explanatory variable is excluded from the model. If the excluded variable has a correlation with the construction cost index and other explanatory variables, this might lead to biased estimations.
Regardless of its limitations, the panel estimate method is a very important tool and an effective method for generating the construction cost index in the eurozone. Using a panel regression model and panel data allows analysts to further enhance their understanding of the pattern and connections in construction costs, thus allowing them to identify potential influencing variables. This can aid economists, legislators, and companies in predicting and strategizing in response to changes in construction expenses and in making sensible choices regarding activities.
4. Empirical Estimations
Before applying the panel tests, we examined whether or not the data were stationary. Then, the KAO cointegration test was applied to determine whether there was a long-term relationship among the variables. In the next step, FMOLS and DOLS tests were applied in line with the purpose of this study. Finally, the Dumitrescu–Hurlin Panel Causality Test was applied to identify causal links among the variables. We used the construction cost index because the most reliable and long-term data are in the PRS group. There has been a significant decline in overall economic activity after the COVID-19 pandemic, and the construction industry has entered a stagnation phase. Moreover, a continuous rise in interest rates has increased the costs of construction materials and labor. Furthermore, borrowing money has also become more expensive for new mortgage deals. It is in our interest to investigate the group of countries that comprise the EU to identify the main causes of this economic decline. We believe that identifying the reasons for individual risk factors enables us to make recommendations to policymakers in order to regenerate the dynamism of the construction sector, which will lead to the construction-led growth of the EU economy.
This study provides valuable insight into factors affecting the construction cost index (CCI) and risk indices associated with European economics, politics, and finance. The DOLS and FMOLS panel estimating techniques, as well as the Dumitrescu–Hurlin Panel Causality Test, were employed to analyze data and reveal important conclusions about the influence of risk factors on construction costs [36,37].
The FMOLS cointegration results of this study show a significant negative relationship between construction cost indices and political, economic, and financial instability in the eurozone. Due to the instability caused by the aforementioned factors, we observed that the construction value-added score deteriorated. This inverse correlation between PRI, FRI, and ERI and the FMOLS estimation is supported by the derived coefficients, as they are all negative. An adverse relationship was detected by using the DOLS estimation technique between the risk factors mentioned above and construction material prices. Along with an increase in the index value of the risk factors, the construction industry indices also decrease accordingly. The negative values of the elasticity coefficients of the parameters used in our DOLS model also validate our findings. The robust results obtained by the panel causality test also confirm the findings with the earlier panel regression estimations. In addition, we found that IND and ERI are bidirectionally causal; therefore, if we change one variable, the other also changes. However, the results of the causality test reveal that changes in the financial risk index (FRI) induce changes in the industrial production index (IPI), and not the reverse.
The empirical evidence reveals strong correlations between financial, political, and economic risk indices and cost indices in Europe. Moreover, this research provides evidence that risk factors have a significant influence on construction costs. Policymakers, investors, and corporations may employ these findings to gain a deeper comprehension of how risk variables influence construction costs and thus develop policies accordingly.
Overall, the empirical data show strong and economically significant findings that are consistent with the expectations provided by the construction-led growth theory. These results bolster our comprehension of the building sector’s contribution to economic growth, lending support to the construction-led growth theory for European nations. This study illuminates the influence of risk variables on building costs, offering significant insights for industry stakeholders and European politicians.
4.1. Panel Unit Root Test Results
A Breitung t-stat test was conducted in this study as the panel unit root test method. The Breitung t-stat test is frequently used for evaluating the stability of time series data. It suggests that different panel portions have a unique unit root process. The Breitung t-stat test was selected because, compared with traditional unit root test methods, this test provides more reliable data, as it checks the variability in panel data and their cross-sectional dependencies.
A substantial p-value (0.00) indicates that the IND series became stable following initial differencing, which is consistent with the findings of the panel unit root tests. This result is clearly shown in Table 2. After the first difference, the PRI, FRI, and ERI series likewise showed stationarity. Because of the p-values’ significance, it is possible to rule out the null hypothesis that these series have a unit root, which suggests that they are nonstationary.
4.2. The FMOLS and DOLS Estimations
The dynamic OLS (DOLS) approach developed by Stock and Watson [38] has several advantages. First, it addresses endogeneity problems by using lags of first difference regressors as well as serial correlation. Second, the DOLS approach allows for variables to be integrated into different orders. In addition, Stock and Watson [38] have shown that the DOLS approach performs better in small samples; however, one major disadvantage is that the use of lags consumes degrees of freedom and may require a longer time dimension. On the other hand, as reported by Narayan and Narayan [39], the FMOLS approach is equipped to correct for endogeneity and various forms of serial correlation problems as well as errors arising from sampling bias. Another advantage is that it works well in large samples and is less sensitive to model specifications compared with the DOLS approach. However, the FMOLS approach relies on strict assumptions on the error terms and is thus sensitive to the violations of these assumptions and bandwidth selection. In this framework, we use both approaches as a robust check-in method to examine the long-term relationship between the construction cost index and risk indices. The application of the DOLS approach eliminates the correlation between regressions and the error term. Similar results obtained from studies that employed the DOLS approach show the robustness of our results. However, previous studies have neglected short-term and long-term causality. For this reason, our work attempts to fill this gap by applying the Granger causality test, which is based on the Panel Vector Error Correction Model (VECM). For further studies, additional tests for sensitivity to sample size could be conducted by considering sub-sample analysis; however, in this study, there were limitations in gathering such data and time constraints.
In line with Harris and Sollis [40], “FMOLS is a non-parametric approach to cope with corrections for serial correlation, while DOLS is a parametric approach where lagged first-differenced terms are clearly estimated. By using DOLS, the residuals are augmented with lags, leads, and contemporaneous values of the regressors”. Pedroni [41] argues that the between-group estimators are preferable to the within-group estimators for a few reasons. Regarding the superiority of each model, more detailed information is available in Pedroni [41] and Harris and Sollis [40]. Maeso-Fernandez et al. [42] pointed out that the FMOLS test requires fewer assumptions than the DOLS test; therefore, the FMOLS test is likely to deliver more robust results. According to Harris and Sollis [40], the empirical evidence is conflicting as to whether the FMOLS or DOLS approach is preferred. Regarding the superiority of the tests, the type of empirical modeling, the number of variables used, the amount of data included in the model, the possibility of adding deterministic dummies in a model, etc., matter a great deal and may play a significant role in producing robust outcomes. Dumitrescu and Hurlin [43] developed a “traditional Granger causality test to permit the element of heterogeneity by adding cross-sectional units based on homogenous non-causality, indicating that under the null hypothesis there is no causality among the variables of the panel while the heterogeneous noncausality indicates a causal linkage for any of the variables of the panel”.
The Kao Residual Cointegration Test [44] was used to assess the prolonged equilibrium relationship between the PRI, FRI, ERI, and IND indices. The Kao Residual Cointegration Test, which is commonly employed in econometric analysis to determine the nature of the relationship between variables in time, suggests that the variable under consideration consists of the same order and shows a long-term equilibrium relationship. To verify the presence of cointegration, the ADF test statistic was employed, and the results proved to be significant at a 1% level, as shown in Table 3. Hence, the null hypothesis of no cointegration may be eliminated. Thus, the long-term equilibrium relationship between PRI, FRI, ERI, and IND indices is confirmed.
The cointegration test results reveal crucial implications in determining the dynamics between the indices PRI, FRI, ERI, and IND. The implication of this study is that by understanding the effect of risk factors on the construction cost index, more accurate estimation for bidding prices can be achieved to improve competitiveness with accurately valued construction projects. This will enable fair competition among the stakeholders, and thus markets will be more efficient. The research findings provide a useful reference for those countries that have not yet established construction cost indices to estimate the impact of risks in the valuation of assets and estimate the value of tenders.
The long-term equilibrium relationship suggested that changes in the financial, economic, and political risk factors have significant impacts on the industrial production index. Along with Arellano and Bond [36], our panel estimation results, which were also found to be negative, also elaborate on the long-run elasticity coefficients, which are tabulated in Table 4 below. This finding indicates that as political, economic, and financial risks increase, construction costs tend to decrease, and vice versa. The DOLS panel estimation further underscores this finding, providing evidence that the construction cost index is significantly and inversely impacted by these risk factors. The regression results obtained by using the FMOLS technique provided statistically significant elasticity coefficients and revealed that the economic risk index was found to be statistically significant at 1% with an elasticity coefficient of 0.242, whereas both the political risk index and financial risk index provided elasticity coefficients of 0.231 and 0.228 at a 10% significance level, respectively. The estimated elasticity coefficients indicate that the economic risk index shows a stronger and more straightforward impact on the construction cost index when comparing political and financial risks. The results are robust and show strong empirical evidence that the risk factors have negative impacts on the construction cost index within the EU area, which is in agreement with the related literature. Similar results were also obtained by Odeyinka et al. [8]. The results with the DOLS estimation methodology were found to be significant only at a 10% significance interval for financial and economic risk parameters, with 0.244 and 0.183 elasticity values, respectively. In the case of Yemen, similar results were found by Ahmad et al. [9], who reported that such risk factors have a negative impact on the construction cost index. Oyewobi et al. [10] also found that financial and economic risks have significant impacts on construction costs in Nigeria.
The FMOLS and DOLS methods were implemented in this study to analyze the long-run equilibrium relationship, which is suitable when panel data are used. For nonstationary data, the Canonical Cointegration Regression (CCR) technique can also be used for nonstationary estimations. As mentioned by Stock and Watson [38], dynamic models address endogeneity problems by using lags at first difference regressors. Moreover, Harris and Sollis [40] stated that the FMOLS approach enables the use of large samples of panel data and the addition of time lags, and the DOLS approach permits the addition of both lags and lead time for estimations. Both methods check the serial correlation with the individual means of each parameter used in the model; however, with the DOLS technique, serial correlation can be detected with a group mean of the parameters. The implementation of the DOLS technique eliminates the correlation between regression and error terms. As noted by Montalvo [45], the CCR estimator provides a smaller bias than the OLS and the fully modified models, which emphasizes that the DOLS estimator provides systematically better results than the CCR estimator. For further research, other studies are available in the literature. The studies of Banerjee [46], Baltagi [47], and Baltagi and Peseran [48] can be examined to estimate regression with large panel data as a supplement to this study.
4.3. Dumitrescu–Hurlin Panel Causality Test
Panel unit root tests were conducted to verify the stationarity of the series upon initial differencing. The cointegration test results shown in Table 5 suggest a long-run equilibrium link between the indices. There is a reverse relationship between the construction cost indices and risk variables, which is expressed by the long-term coefficients using FMOLS and DOLS techniques estimates. The results of the Dumitrescu–Hurlin Panel Causality Test [43] demonstrated a strong bidirectional link between the construction cost index and the financial, economic, and political risk indices. Investors and the major construction sector policymakers may utilize these results for a better understanding of how risk variables affect building costs. Based on the Dumitrescu–Hurlin Panel Causality Test, we estimated that the political risk index, economic risk index, and financial risk index directly affect and have significant impacts on the industrial production index. This is why the dynamism of the construction sector has slowed down and caused the unemployment rate to increase and the economic growth rate to decline. An increase in interest rates and contractionary monetary policy in the EU has increased the cost of borrowing money and caused a rapid increase in the inflation rate. In addition to inflation, a recession in the EU economy signaled stagnation problems in the EU economy in general. Movements in asset prices are the main source of changes in the return to capital and affect the value of entrepreneurs’ net worth. Changes in monetary policy and a decline in the short-term interest rate raise asset prices and therefore entrepreneurs’ net worth. To sustain economic growth and reduce unemployment, a monetary transmission mechanism or risk stratification framework policies must be implemented. As stated by Bardsen et al. [49], a decrease in short-term interest rates will reduce the external finance premium and boost investment and output more than through the traditional monetary transmission mechanism. Policymakers and investors should consider these risk indices when analyzing and forecasting industrial production trends. In summary, the data analysis offers solid insights into how different risk variables in Europe relate to building cost indices.
5. Conclusions and Policy Recommendations
Many countries in the EU area, as well as in other parts of the world, have suffered from economic recession due to the COVID-19 pandemic. Economic activities during and after the pandemic have slowed down, and, as a result, budget deficits and debt stocks have sharply risen. Furthermore, unemployment and inflation have become major economic problems everywhere. Economic growth rates in OECD countries as well as the EU area have sharply declined. One of the sectors that has been most affected by the economic depression in the EU area is construction. The prices of building materials used by the construction sector increased very rapidly following the COVID-19 pandemic due to interruptions in the supply chain, causing increases in interest rates, inflation rates, and wage rates, as well as changes in tax rates. This has resulted in a contraction of construction activities in the euro area that warrants investigation. The statistical results obtained in this study using panel estimation techniques are robust and provide ample evidence that the political, economic, and financial risk factors have significant negative impacts on construction costs, which enable estate prices to increase even further and result in a cost-push inflation spiral. Prior studies did not investigate similar empirical work; therefore, this study is novel in its field. Further studies are expected to contribute to the related literature and thus enable both academics and practitioners to compare the results of this study with other research.
It was found that the construction cost index in the EU area has consistently reported a steady increase over the last two decades, especially during the post-COVID-19 pandemic period due to increasing political, economic, and financial risks.
The research findings presented in this work reveal insights into European construction cost indices and their risk factors, employing the current data and empirical findings. The Dumitrescu–Hurlin Panel Causality Test results and the data analysis utilizing the FMOLS and DOLS estimation methodologies emphasize the noteworthy trends and their correlations on the risk variables and their influences on construction costs. The results obtained also demonstrate a statistically sound inverse association between the risks resulting from the European political, economic, and financial instability and the building cost indices. An inverse relationship was observed between the construction cost and political, economic, and financial instability. The role played by the construction sector in economic growth was confirmed by the research results, which also verified the theoretical assumptions. Therefore, our method can be employed by policymakers, investors, and major players in the construction industry in strategy development to reduce the effect of risk factors on construction costs. Furthermore, the main results of the causality test demonstrate a strong positive correlation between the European construction cost index and the financial, economic, and political risk indices in both directions. The results also suggest that these risk variables affect building costs, as well as variations in building prices. The knowledge of these imperative relationships and dynamics between the construction costs and the risk variables could enhance decision-making and risk management capabilities in the construction sector [50].
The implication is that an understanding of the effect of risk factors on the construction cost index will enable a more accurate estimation of bidding prices, which will improve competitiveness by enabling the accurate valuation of construction projects. This will enable fair competition among the stakeholders, and thus markets will be more efficient. The research findings provide a useful reference for estimating the value of tenders and the impact of risks in the valuation of assets for countries that have not yet established construction cost indices.
The research findings also draw special attention to the need to reduce interest rates and lower inflation in the EU area. The economy of Europe is in stagnation, which is threatening the entire EU economy in general. The unemployment rate is also very high in the EU area. The research findings reveal that risk factors have influenced the construction sector negatively, and immediate actions can be employed by policymakers and stakeholders in strategy development to reduce the effect of the risk factors on construction costs. Policymakers can also change tax regulations to lower costs, which will reduce risks and promote a more resilient and sustainable construction industry. The aim here is to eliminate risks and create more safe, inclusive, resilient, and sustainable smart urban cities, which supports the UN’s SDG 11 [27].
This study’s conclusions might have a significant effect on decision-making for European investors, legislators, and business stakeholders. Policymakers may create effective regulations to reduce risks and promote a more stable and resilient construction industry by considering how risk variables affect construction prices. This study’s findings may be utilized by investors to evaluate risk profiles and make well-informed choices about their building project investments. The lack of availability of data are the major limitation of this study. Only 27 countries within the EU were examined in this study because there are no disaggregated regional data available to conduct a cluster analysis within the EU. The distribution of the regions or countries is not balanced with grouped data, and, therefore, the risk factors in different nations vary and show heterogeneity, which limited us to adopting various indices to examine the impact of risks on construction costs in different regions within the EU area. In future studies, additional tests for sensitivity to sample size by considering sub-sample analysis could be conducted; however, in this study, there were limitations in gathering such data and time constraints. For example, the FMOLS and DOLS approaches may lack the sensitivity required to account for nonlinear relationships or sudden structural shocks between variables, which could compromise the robustness of the findings. For nonlinear estimations, different methodologies are recommended rather than the FMOLS and DOLS approaches, which is another limitation of this study. For further study, similar academic research can be implemented for different country groups or regions.
Conceptualization, Y.A. and V.Y.; methodology, D.K. and Y.A.; software, D.K.; validation, V.Y. and Y.A.; formal analysis, D.K.; investigation, Y.A.; resources, Y.A.; data curation, D.K.; writing—original draft preparation, Y.A., D.K. and V.Y.; writing—review and editing, D.K. and V.Y.; visualization, D.K.; supervision, V.Y.; project administration, V.Y.; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The variables used in this paper are collected from the database of the ICRG and Eurostat.
The authors declare no conflicts of interest.
Footnotes
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![View Image - Figure 1. The European Commission’s “The European Green Deal” project outline (European Commission, 2019) [2].](https://media.proquest.com/media/hms/THUM/2/wnkYb?_a=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%3D%3D&_s=a6EqSG48HRJIw0bm5Yt9a5l9m2E%3D "View Image - Figure 1. The European Commission’s “The European Green Deal” project outline (European Commission, 2019) [2].")
Enlarge this image.Figure 1. The European Commission’s “The European Green Deal” project outline (European Commission, 2019) [2].
![View Image - Figure 2. Eurostat, ING Research (December 2022) Development EU construction sector volume (Index December 2019 = 100, SA) [12].](https://media.proquest.com/media/hms/THUM/4/wnkYb?_a=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%3D%3D&_s=gOxDMS2gHWNojf2Vf0HYirVEd30%3D "View Image - Figure 2. Eurostat, ING Research (December 2022) Development EU construction sector volume (Index December 2019 = 100, SA) [12].")
Enlarge this image.Figure 2. Eurostat, ING Research (December 2022) Development EU construction sector volume (Index December 2019 = 100, SA) [12].
![View Image - Figure 3. Construction producer price and construction cost indices overview (Eurostat, 2022) [6].](https://media.proquest.com/media/hms/THUM/6/wnkYb?_a=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%3D%3D&_s=XSYxNRNHa33BMXEpiYEBxMZoQSg%3D "View Image - Figure 3. Construction producer price and construction cost indices overview (Eurostat, 2022) [6].")
Enlarge this image.Figure 3. Construction producer price and construction cost indices overview (Eurostat, 2022) [6].
Descriptive statistics analysis of the political risk index (PRI), industrial production index (IND), financial risk index (FRI), and economic risk index (ERI).
| IND | PRI | FRI | ERI | |
|---|---|---|---|---|
| Mean | 92.73107 | 78.69151 | 37.35470 | 37.81033 |
| Median | 96.40000 | 78.50000 | 37.00000 | 38.16667 |
| Maximum | 487.7000 | 97.00000 | 48.50000 | 48.50000 |
| Minimum | 13.20000 | 50.33333 | 22.00000 | 7.500000 |
| Std. Dev. | 21.58045 | 8.147665 | 4.270399 | 5.085398 |
| Skewness | 3.618988 | −0.437701 | −0.064833 | −0.738856 |
| Kurtosis | 59.56485 | 3.317740 | 3.280443 | 4.947943 |
| Jarque–Bera | 378,990.1 | 101.0751 | 11.12522 | 696.6997 |
| Probability | 0.000000 | 0.000000 | 0.003839 | 0.000000 |
| Sum | 259,368.8 | 220,100.2 | 104,481.1 | 105,755.5 |
| Sum sq. dev. | 1,302,141. | 185,610.9 | 50,988.71 | 72,308.13 |
| Observations | 2797 | 2797 | 2797 | 2797 |
Panel unit root tests.
| IND | PRI | FRI | ERI | |
|---|---|---|---|---|
| At level | ||||
| Breitung t-stat test | 11.9691 | −0.01843 | −1.03788 | 0.68279 |
| At first difference | ||||
| −9.61076 | −9.43762 | −17.9299 | −9.58852 | |
Kao Residual Cointegration Test.
| t-Statistic | Prob. | |
|---|---|---|
| ADF | 8.587940 | 0.0000 |
| Residual variance | 1.417827 | |
| HAC variance | 2.899203 |
FMOLS and DOLS tests.
| Method: Panel Fully Modified Least Squares (FMOLS) | ||||
|---|---|---|---|---|
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| PRI | −0.231688 | 0.122748 | −1.887508 | 0.0592 |
| FRI | −0.228943 | 0.121085 | −1.890765 | 0.0588 |
| ERI | −0.242483 | 0.085562 | −2.834016 | 0.0046 |
| R-squared | 0.783331 | Mean dependent var | 93.05215 | |
| Adjusted R-squared | 0.778035 | S.D. dependent var | 21.41208 | |
| S.E. of regression | 10.08791 | Sum squared resid | 274,768.0 | |
| Long-run variance | 246.2511 | |||
| Method: Panel Dynamic Least Squares (DOLS) | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| PRI | −0.187024 | 0.141700 | −1.319860 | 0.1870 |
| FRI | −0.244199 | 0.143958 | −1.696321 | 0.0899 |
| ERI | −0.183230 | 0.102870 | −1.781187 | 0.0750 |
| R-squared | 0.791716 | Mean dependent var | 93.09935 | |
| Adjusted R-squared | 0.775054 | S.D. dependent var | 21.37354 | |
| S.E. of regression | 10.13715 | Sum squared resid | 262,042.6 | |
| Long-run variance | 283.8039 | |||
Pairwise Dumitrescu–Hurlin Panel Causality Test estimation results.
| Scheme 2000. Q1 to 2022. Q1. | |||
|---|---|---|---|
| Lags: 2 | |||
| Null Hypothesis: | W-Stat. | Zbar-Stat. | Prob. |
| PRI does not homogeneously cause IND | 3.26119 | 3.25330 | 0.0011 |
| IND does not homogeneously cause PRI | 5.61282 | 9.57549 | 0.0000 |
| FRI does not homogeneously cause IND | 5.25469 | 8.61268 | 0.0000 |
| IND does not homogeneously cause FRI | 2.09254 | 0.11147 | 0.9112 |
| ERI does not homogeneously cause IND | 4.39504 | 6.30156 | 0.0000 |
| IND does not homogeneously cause ERI | 3.84801 | 4.83092 | 0.0000 |
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1 Faculty of Business and Economics, Eastern Mediterranean University, Famagusta 99628, Turkey
2 Faculty of Economics and Administrative Sciences, European University of Lefke, Beirut 1102 2801, Lebanon;