Identifying Dominant Inflation Risks in Residential Construction Projects Using Fuzzy Truth Qualification

Abstract

Persistent inflation has intensified uncertainty in the construction industry, particularly in volatile economies. Inflation-driven risks affecting Turkish residential projects are examined in this study, focusing on rising costs, fluctuating labor and material prices, and associated risks. The power-based linguistic hedges were used to quantify dominant severity levels under uncertainty based on descriptive statistics and standard deviation thresholds. Results indicate that inflation mostly impacts projects through budget overruns and wage inflation, which exhibit the highest severity and crisis-level risk behaviors. A number of factors drive material price volatility, particularly macroeconomic instability, currency depreciation, and supply-chain disruptions. There is a sustained pressure on contractor profitability due to wage inflation. In contrast, inflation-related effects on schedule, quality, safety, and contractual disputes are secondary and context-dependent. The findings indicate a structural shift in the risk profile of Turkish residential construction, indicating a need for inflation-resilient cost management, adaptive contracting, and proactive labor planning.

1. Introduction

The construction industry plays a significant role in economic development by building homes and infrastructure [1], creating jobs, and supporting economic stability [2], but it is severely impeded by inflation and instability [3]. Inflation has a significant impact on the construction industry directly or indirectly [4]. Both developed and developing countries are prone to over-budgeting due to inflation [5]. Several researchers have studied the impact of inflation on the construction industry [6,7,8,9,10]. Building material prices, labor wages, and equipment rental costs are directly affected by inflation [11], resulting in a deviation from the project’s initial budget [12]. Several factors influence materials costs, including supply and demand, market conditions, transport and energy costs, raw materials, labor costs, crude oil prices, exchange rates, import duties, and inflation, resulting in a revision in the initial budget and projects’ outputs [11,12]. Another major resource for construction projects that influences budgets is labor wages [13]. Construction labor productivity is an important economic performance indicator in many countries because labor costs account for almost 30 to 60% of total construction costs [14]. According to Moyanga and Saka [3], to stabilize the construction industry, the government should implement fiscal and monetary measures to control inflation. In addition, it should increase local content to reduce construction costs to protect the sector from foreign exchange market fluctuations.

The housing sector has experienced a drop in investment due to rising interest rates and the withdrawal of public support schemes, which are significantly influencing construction investments.

In recent years, several macroeconomic factors, including rising interest rates, high inflation, and currency volatility, have contributed to a decline in housing investment and production in Türkiye. For instance, housing investment is projected to decrease by 7.7% in 2024 and by 3.9% in 2025 [15]. The Turkish government has played a significant role in the housing sector. The Housing Development Administration of the Republic of Türkiye (TOKİ) has historically supported large-scale social housing for low- and middle-income groups, delivering over one million units since the early 2000s; however, recent demand-stimulating policies have been constrained by high financing costs and ongoing macroeconomic volatility.

There have been numerous studies on the effects of inflation on construction, but relatively few have addressed the effects on residential construction, despite its heightened sensitivity to cost volatility and affordability constraints. Over the past five years, construction costs in Türkiye have increased substantially because of inflation and economic fluctuations. Accordingly, this study evaluates the impact of inflation on residential construction projects in Türkiye during the period 2020–2025, using data collected through a questionnaire survey administered to construction professionals, suppliers, and clients/owners. This study analyzes how inflationary pressures affect project costs, schedules, and resource allocation in residential construction projects, and examines the challenges faced by stakeholders during high inflation periods. It also examines strategies and risk management approaches to mitigate inflation’s negative impacts.

Most empirical studies on inflation impacts in construction rely on descriptive rankings or single-point statistical comparisons, which identify relevant factors but provide limited insight into their severity under prolonged and volatile inflationary conditions. Furthermore, inflation impacts are often treated as discrete events without accounting for the uncertainty and gradation inherent in perception-based assessments. The study contributes to the literature by examining residential construction projects, which are underrepresented and highly sensitive to inflation. It also integrates Relative Importance Index (RII) analysis with fuzzy truth qualification to allow severity-based interpretation under uncertain conditions.

Besides offering practical insights for policymakers, project managers, and industry professionals, the study also provides evidence-driven recommendations to enhance residential construction resilience and decision-making. Since severity assessments in construction management surveys are inherently subjective and influenced by experience and context, a fuzzy logic approach is used to improve interpretability and reliability. Rather than relying on rigid thresholds, the proposed framework operationalizes linguistic hedges as truth qualifiers using power-based operators. However, the study is limited to residential construction projects and does not address non-residential sectors like industrial or infrastructure construction, nor does it analyze global inflation trends beyond their local impacts.

2. Materials and Methods

A quantitative, questionnaire-based methodology is employed to examine how inflation affects residential construction projects. Responses were rated on a 5-point Likert scale according to how much they agreed with statements describing inflation’s impact on residential construction projects. Statistical analysis was conducted using Excel to determine the impact of inflation on these projects. The following are descriptions of the questionnaire, sampling strategy, data collection procedures, and statistical methods used to analyze Likert-scale data.

2.1. Research Design

The impact of inflation on residential construction projects was evaluated by developing 30 questions and dividing them into seven sections based on a thorough literature review and interview with experts: (A) Respondents’ profile, (B) Inflation’s impact overview, (C) Inflation impacts on budgets, (D) Inflation impacts on material prices, (E) Inflation impacts on labor wages, (F) Inflation-related project delays, and (G) Mitigation measures.

An online questionnaire was chosen as the most convenient method to deliver and collect responses in this study. This questionnaire was created using Google Forms and emailed to building construction professionals, suppliers, and clients/owners in Türkiye. There are four dominant methods of surveys extensively discussed in the literature: mail surveys, personal surveys, telephone surveys, and online surveys [16]. Data collection through online surveys has become more popular due to their ease of use [17], and online surveys can be significantly more effective than other formats when conducted properly [16]. Online surveys have many advantages, such as global reach, flexibility, speed, timeliness, technological innovations, convenience, ease of data entry and analysis, a diverse list of questions, low administration costs, and easy follow-up [16,18].

In this study, the majority of questions are closed-ended with Likert scales in this study: 18 have closed-ended with 1–5 point Likert scale, 1 has open-ended, 4 have multiple-response, and 7 have single-response.

2.2. Ethics Considerations

Zonguldak Bulent Ecevit University’s Human Research Ethics Committee approved this study on 25 June 2025/616484, protocol number 218. The online questionnaire included a consent form explaining the study’s purpose and explaining confidentiality terms.

2.3. Data Collection

A questionnaire designed to analyze the impact of inflation on residential building projects during the 2020–2025 period, characterized by high inflation, exchange-rate volatility, rising interest rates, and pandemic-related economic shocks, was distributed to 700 stakeholders involved in residential construction projects in Türkiye, yielding 363 valid responses. This resulted in a response rate of 52%. To ensure construct validity, data were cleaned up by excluding respondents who answered ‘no idea’ to more than 40% of items, and by excluding respondents who had variances less than or equal to 0.10. The remaining 264 samples were then analyzed.

2.4. Data Analysis

A summated Likert scale (multi-item Likert scale) was proposed by Likert [19] as a method of assessing survey respondents’ attitudes. It is composed of several Likert-type items that measure the same latent construct, usually by summing or averaging to produce one overall score. Likert-type items, on the other hand, are single questions that utilize some aspect of the original Likert response alternatives [20,21].

Likert-type and Likert scale data are analyzed differently. A variable can be measured on four different scales (the Steven’s scale): nominal, ordinal, interval, and ratio [22]. Nominal scales are used for categorical variables without order, such as gender, type of building, type of contract, or occupation. Categorization numbers serve only as labels. Ordinal scales are used when responses have a meaningful order and are ranked by some measure of magnitude. A number assigned to a group expresses a “greater than” relationship, but how much greater is not stated. Interval scale data also use numbers to indicate order and reflect a meaningful relative distance between points with equal intervals on a scale without an absolute zero. Ratio scales are similar to interval scales except that they have an absolute zero, such as age, years of experience, cost, duration, or height [22,23]. The interval data provides more information than the ordinal data, and the ordinal data provides more information than the nominal data [22]. In empirical research, Likert scales are commonly used to capture human attitudes and perceptions [24]. A Likert-type item falls into the ordinal measurement category. However, Likert scale data are analyzed on an interval measurement scale [20]. It is recommended by Boone and Boone [20] that Likert-type items be analyzed using the mode or median for central tendency, frequencies for variability, Kendall’s Tau-B or Tau-C for associations, and the chi-square test. Likert scale data, on the other hand, can be analyzed using the mean for central tendency, standard deviations for variability, Pearson’s r for associations, and t-test, ANOVA, and regression.

In this study, survey data were analyzed using descriptive and exploratory statistical techniques. This included calculating the means and standard deviations to identify central tendencies and variability. Outlier analysis was used to detect extreme responses, which improved the reliability of the results. Also, threshold-based comparisons (e.g., M ± SD) were performed to classify items based on their relative importance, allowing the study to distinguish between dominant, average, and low-priority inflation-related factors.

2.5. Fuzzy Decision-Support System

Fuzzy logic-based representations were used to address the inherent vagueness and subjectivity of expert judgments. Power-based operators were applied to fuzzy membership values to implement linguistic hedges, allowing nuanced differentiation between dominant and non-dominant severity levels [25]. The approach allows partial memberships to be systematically strengthened or weakened, providing a more interpretable and robust severity classification than crisp threshold-based approaches. Figure 1 illustrates the application of power-based truth qualifiers (Equations (1) and (2)) to fuzzy membership values for severity interpretation.

$$S\left(\mu \right)=\left\{\begin{array}{cc}{\mu }^2& \mathrm{V}\mathrm{e}\mathrm{r}\mathrm{y}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}\\ \mu & \mathrm{T}\mathrm{r}\mathrm{u}\mathrm{e}\\ \sqrt{\mu }& \mathrm{F}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{l}\mathrm{y}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}\end{array}\right.$$

(1)

$$S\left(\mu \right)=\left\{\begin{array}{cc}(1-{\mu )}^2& \mathrm{V}\mathrm{e}\mathrm{r}\mathrm{y}\mathrm{f}\mathrm{a}\mathrm{l}\mathrm{s}\mathrm{e}\\ 1-\mu & \mathrm{F}\mathrm{a}\mathrm{l}\mathrm{s}\mathrm{e}\\ \sqrt{1-\mu }& \mathrm{F}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{l}\mathrm{y}\mathrm{f}\mathrm{a}\mathrm{l}\mathrm{s}\mathrm{e}\end{array}\right.$$

(2)

The fuzzy framework converts aggregated perception scores into linguistic severity classes, namely negligible, low, moderate, high, and critical. This approach distinguishes them based on their strength and dominance of membership degrees instead of treating risks with similar Relative Importance Index (RII) scores equally. Under crisp statistical measures, risks can be differentiated based on their decision urgency and management priority.

A five-level fuzzy risk severity classification was developed using statistical landmarks, with triangular membership functions centered around the sample mean and its standard deviation. These membership functions form a symmetric fuzzy partition of severity levels, ranging from negligible to critical. Figure 2 illustrates the proposed fuzzy severity classification framework.

Linguistic truth levels are determined using a hierarchical decision mechanism, as described in Equation (3). The associated membership functions are evaluated sequentially from the strongest linguistic hedge (‘very true’) to ‘not true’, and the first satisfied condition determines the assigned linguistic truth level.

$$\begin{array}{cc}\mathrm{i}\mathrm{f}{\mu }^2\ge 0.75,& \mathrm{V}\mathrm{e}\mathrm{r}\mathrm{y}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}\\ \mathrm{E}\mathrm{l}\mathrm{s}\mathrm{e}\mathrm{i}\mathrm{f}\mu \ge 0.50,& \mathrm{T}\mathrm{r}\mathrm{u}\mathrm{e}\\ \mathrm{E}\mathrm{l}\mathrm{s}\mathrm{e}\mathrm{i}\mathrm{f}{\mu }^{0.5}\ge 0.25,& \mathrm{F}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{l}\mathrm{y}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}\\ \mathrm{E}\mathrm{l}\mathrm{s}\mathrm{e},& \mathrm{N}\mathrm{o}\mathrm{t}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}\end{array}$$

(3)

Although conventional descriptive statistics and RII rankings are effective for identifying and ordering inflation-related risks, they provide limited insight into severity dominance and decision relevance under uncertainty. The fuzzy truth qualification framework does not modify the ranking hierarchy produced by RII analysis, but rather it introduces an additional analytical layer that characterizes the degree of severity associated with each risk. Gradual reasoning provides a more realistic basis for adaptive risk management than threshold-based approaches in prolonged inflationary environments where costs are volatile and forecasting precision is limited.

The fuzzy framework adds value by identifying crisis-level risks more effectively because it uses dominant severity memberships rather than an arbitrary threshold, and it treats inflation-related risks as continuous phenomena rather than discrete events. This approach enhances interpretability and supports decision-making, particularly in contexts where quantitative forecasts are uncertain and qualitative judgments are required.

2.6. Frequency Index (FI)

Survey data can be summarized using a frequency index (Equation (4)) by expressing how often each response occurs in relation to the total number of responses. A frequency index converts raw counts into a relative measure, often expressed as a proportion or percentage, which helps standardize comparisons. The most common opinions or trends in survey data are often highlighted in this way.

$${FI}_i={\displaystyle \frac{n_i}{N}},\mathrm{o}\mathrm{r}{FI}_i(\%)=100{\displaystyle \frac{n_i}{N}},$$

(4)

where FI_i is the frequency index of category i, yielding a value between 0 and 1, or 0% and 100%, n_i is the number of respondents choosing category i, and N is the total number of respondents or total responses, in this case N indicates the total number of respondents.

2.7. Relative Importance Index (RII)

A relative or normalized score index, sometimes called a relative importance index (Equation (5)), is often used for comparing responses across items. It converts multiple items into a 0–1 or 0–100% value that can be compared easily. It is useful if Likert items have different numbers of response options, and is often used in construction, project management, and social science surveys to rank importance or impact.

$$RII={\displaystyle \frac{1}{5N}}\sum _{i=1}^5{a}_i{n}_i,\mathrm{o}\mathrm{r}RII\left(\%\right)={\displaystyle \frac{100}{5N}}\sum _{i=1}^5{a}_i{n}_i,$$

(5)

where RII is the relative important index or normalized score index, yielding a value between 0 and 1, or 0% and 100%, a_i is a numeric value of Likert category i (e.g., for a 5-point scale, i = 1, …, 5), n_i is the number of responses in category i, and N is the total number of respondents.

2.8. Validity and Reliability Assessment

A researcher’s data collection and analysis are evaluated for reliability and validity [26]. Validity and reliability are the two validation strategies widely used in measuring instrument evaluation. Validity describes how well measures define the concept, while reliability describes how consistent the measures are. Most widely accepted forms of construct validation involve assessing measures for convergent validity, discriminant validity, and nomological validity [27,28], and one form of construct validity is content or face validity [28]. The Coefficient (Cronbach’s) Alpha is probably the most commonly used reliability measurement [28,29].

2.9. Content Validity

Content validity assessment involves applying both development and judgment processes to determine whether an item represents or is relevant to content. Using this two-stage process to determine and qualify content validity is fundamental to instrument validation [30]. Researchers usually evaluate content validity qualitatively, through an expert committee assessment, and then quantitatively, using the content validity index [31]. The questionnaire’s content in this study was determined through a literature review, then discussed and finalized with industry practitioners and academic experts to ensure its validity. However, even if content validity is a fundamental requirement of all assessment instruments, measures cannot be validated solely based on content validity [32].

2.10. Reliability

Even when validity is assured, the researcher must still consider the reliability of the measurements. Reliability refers to a measure’s consistency. Generally, more reliable measures are more consistent than less reliable measures [28]. Cronbach [33] introduced Coefficient Alpha (Cronbach’s α) to measure internal consistency reliability, and it has become the most commonly used measure [28]. A general formula used in the study by Cronbach [33] was as follows (Equation (6)).

$$\alpha ={\displaystyle \frac{n}{n-1}}\left(1-{\displaystyle \frac{\sum _i{V}_i}{V_t}}\right),$$

(6)

where n is the number of items (in this case, k is used to indicate the number of items instead of n), i represents an item, V_i is the variance of item scores after weighting, and V_t is the variance of test scores. It is generally accepted that Cronbach’s alpha should not be lower than 0.70 [34].

3. Results

This section presents the empirical results of the questionnaire survey on the perceived impacts of the recent five-year inflation on residential construction projects, including material prices, labor wages, cost and time performance, and mitigation strategies adopted by practitioners. Descriptive statistics are presented first to summarize the profile of respondents and their organizations, then results are discussed in detail, including inflation perceptions, project impacts, and risk management practices.

3.1. Questionnaire Reliability Analysis

A reliability analysis was conducted to evaluate the internal consistency of the Likert-scale items across eight thematic categories. The Cronbach’s alpha values in

Table 1. Cronbach’s Alpha values based on various categories.

C	k	ΣVi	Vt	Cronbach’s Alpha	Interpretation
(a)	6	17.60	90.50	0.97	Excellent
(b)	12	31.71	285.71	0.97	Excellent
(c)	8	23.31	156.17	0.97	Excellent
(d)	7	21.63	127.42	0.97	Excellent
(e)	6	17.87	87.50	0.95	Excellent
(f)	6	15.42	75.00	0.95	Excellent
(g)	12	16.76	48.54	0.71	Acceptable
(h)	57	144.74	4494.81	0.99	Excellent

C: categories; k: number of scale items; V_t: variance of test scores; ΣV_i: sum of the variances of item scores after weighting.

ranged from 0.71 to 0.99, indicating that the study was generally reliable. Categories (a) to (f), which assessed the price impact, consequences, and drivers of inflation, all achieved alpha values between 0.95 and 0.97, demonstrating excellent internal consistency. This category, which encompassed a wider set of inflation-related items, had an alpha of 0.71, which indicates acceptable reliability. Lastly, the overall reliability of all the 57 items in category (h) was highly reliable, with a Cronbach’s alpha of 0.99. These results confirm that the questionnaire exhibits strong internal consistency across its measurement dimensions.

3.2. Profile of Respondents

Participants in the survey represented a broad cross-section of stakeholders involved in residential construction projects, reflecting perspectives from both the supply side, such as contractors, technical staff, and laborers and the demand side, such as clients, project developers, and regulators. The sample is dominated by practitioners in their early careers and mid-careers, while retaining a significant portion of highly experienced professionals. Respondents reported primarily working on low-rise and medium-rise building projects. The majority of respondents were employed in small businesses, followed by medium-sized companies and large companies, reflecting the prevalence of small and medium-sized businesses in the residential sector. Most respondents are actively engaged in multiple ongoing projects and are well-positioned to assess inflation’s impacts. The majority of respondents were employed in small businesses, followed by medium-sized companies and large companies, reflecting the prevalence of small and medium-sized businesses in the residential sector. Respondents are actively engaged in ongoing projects and are well-positioned to assess inflation’s impacts on residential construction projects. Detailed data are presented in

Table 2. Survey participants’ demographics.

Profile		ni	FIi (%)	Profile		ni	FIi (%)
Position or role:	Project developer	30	11	Experience:	Less than 5 years	86	33
	Client/Owner	26	10		5–10 years	72	27
	Contractor	23	9		11–20 years	82	31
	Consultant/Building inspector	30	11		More than 20 years	24	9
	Government/Building control officer	33	13	Project types:	Low-rise housing	104	39
	Supplier	22	8		Medium-rise apartments	122	46
	Project manager	18	7		High-rise buildings	38	14
	Site engineer/Architect	27	10	Organization size:	Small (1–50 employees)	77	29
	Site technician	11	4		Medium (51–200 employees)	123	47
	Skilled laborer/Foreman	11	4		Large (201+ employees)	64	24
	Quantity surveyor	3	1	Projects Handled each year (avg.):	13	97	37
	Safety officer	13	5		4–6	109	41
	Human resource/Payroll manager	13	5		More than 6	58	22
	Others	4	2

n_i; number of respondents choosing category i; FI_i; frequency index of category i.

.

3.3. Overall Perceived Impact of Inflation

Statistical analysis indicates that inflation over the past five years has had a substantial impact on residential construction, with a predominant perception ranging between very high and moderate impacts (‘very high’ = 21%, ‘high’ = 35%, and ‘moderate’ = 0.25).

3.4. Most Affected Cost Categories and Components

Inflation has the greatest impact on direct construction expenses, especially those involving materials and resources. Inflation is perceived to have the greatest impact on building materials costs, as evidenced by the highest mean score and RII. The relatively high variance and SD indicate a notable dispersion of opinions, reflecting differing project types or market exposures. Machine operation and energy-related costs are sensitive to inflation, as evidenced by the second-place ranking for equipment rental and fuel. As a result of escalating fuel prices and supply chain disruptions, logistics and transportation ranked third. Figure 3 illustrates this sensitivity of cost structures, reinforcing the dominance of material and energy. On the other hand, although labor costs are affected by inflation, they are perceived as less sensitive than material and equipment costs. Consultancy/professional fees and permit and compliance costs rank fifth and sixth, respectively. According to their lower RII scores and mean values, these costs are less affected by inflation than other costs.

A comprehensive statistical summary of inflation-affected cost categories is presented in Appendix A,

Table A1. Most affected cost categories by inflation.

i	Item Names	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
9(1)	Impact of inflation on building material costs	81	23	37	45	23	55	183	3.273	3.650	1.910	0.654	1
9(3)	Impact of inflation on equipment rental and fuel costs	39	53	51	31	38	52	225	2.933	3.019	1.738	0.586	2
9(4)	Impact of inflation on logistics and transportation costs	23	62	50	41	32	56	241	2.876	2.752	1.659	0.575	3
9(2)	Impact of inflation on labor wage costs	30	88	24	30	30	62	234	2.803	3.247	1.802	0.560	4
9(5)	Impact of inflation on permit and regulatory compliance costs	23	61	59	42	34	45	241	2.763	2.524	1.589	0.552	5
9(6)	Impact of inflation on consultant and professional service fees	21	64	55	53	28	43	243	2.716	2.411	1.553	0.543	6

1 = not significant, …, 5 = very significant; M: average of the items; s²: variance; SD: standard deviation; RII: relative importance index; R: rank.

.

3.5. Inflation-Related Consequences Affecting Residential Projects

Figure 4 indicates that financial and schedule-related impacts dominate quality-, safety-, and dispute-related consequences. Price increases directly translate into project budgets exceeding initial estimates, causing cost overruns to be the most significant inflation-induced consequence. Elevated costs and increased uncertainty regarding resource availability are also associated with substantial schedule disruptions. As a result of persistent inflation, contractors’ cash flow and profitability are threatened, potentially posing a threat to the continuity of their projects.

Inflation has also an indirect effect on supply chains and construction progress. The workforce is also affected by inflation, though to a lesser extent than material- and finance-related factors. Contractual, organizational, and quality consequences are perceived to be moderately significant. There is no primary inflationary effect in these outcomes; they are rather adaptive or reactive responses to rising costs. Inflation-related quality control failures and suspended or canceled projects indicate that while serious, these consequences are less frequent.

On-site theft, damage, or loss of materials, and safety and health issues rank lowest in the RII and mean, suggesting respondents perceive these impacts as relatively minor. These findings emphasize the need for robust cost control, contingency planning, and financial risk management strategies to mitigate inflation-related risks in residential construction.

Table A2. Inflation-related consequences affecting residential projects.

i	Item Names	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
10(01)	Cost overruns attributable to inflation	74	44	31	33	34	48	190	3.058	3.537	1.881	0.611	1
10(02)	Project delays associated with inflation	36	75	42	30	42	39	228	2.684	2.819	1.679	0.536	2
10(08)	Contractor financial instability	30	78	50	36	29	41	234	2.594	2.644	1.626	0.518	3
10(04)	Material shortages, delivery delays, or unavailability	44	65	52	39	37	27	220	2.586	2.527	1.590	0.517	4
10(05)	Labor shortages or reduced productivity	29	82	41	40	39	33	235	2.574	2.542	1.594	0.514	5
10(06)	Inflation-driven revisions to material specifications	36	83	45	39	22	39	228	2.513	2.651	1.628	0.502	6
10(09)	Subcontractor performance deficiencies	26	85	48	39	34	32	238	2.496	2.416	1.554	0.499	7
10(10)	Contractual disputes arising from inflation	36	76	56	37	29	30	228	2.478	2.425	1.557	0.495	8
10(07)	Quality control failures under inflationary conditions	40	85	42	36	28	33	224	2.473	2.614	1.617	0.494	9
10(03)	Project suspension or cancelation due to inflation	30	87	44	43	30	30	234	2.453	2.403	1.550	0.490	10
10(12)	Material loss, damage, or theft	37	93	45	28	22	39	227	2.423	2.685	1.639	0.484	11
10(11)	Safety and health issues under inflationary pressures	39	93	40	33	34	25	225	2.369	2.445	1.563	0.473	12

provides a quantitative overview of the perceived consequences of inflation on residential construction projects.

3.6. Budget Impacts and Budgeting Practices

The findings indicate that inflation plays a decisive role in budget escalation in residential construction projects. Respondents demonstrated a high level of confidence in evaluating inflation’s budgetary implications. Notably, inflation alone accounts for approximately one-quarter to one-third of total budget increases in a substantial share of projects, as reflected by the highest frequency index (FI = 35.61%). In addition, a considerable proportion of projects experienced budget overruns in the range of 11–20% (FI = 28.41%), underscoring the significant contribution of inflation to overall cost growth. Although less prevalent, a noteworthy number of projects were subjected to severe cost escalation exceeding 30% (FI = 21.97%), highlighting the pronounced impact of inflationary pressures on residential construction budgets.

3.7. Material Price Escalation and Its Drivers

Figure 5 indicates that core structural materials—most notably cement—together with aluminum and glass products are perceived as the most affected by price increases, reflecting pervasive inflationary pressures within the construction industry. This finding underscores the critical role of cement in construction activities and its pronounced sensitivity to inflation, energy costs, and supply–demand imbalances. Similarly, aluminum and glass products exhibit substantial price escalations, particularly for materials used in façades, windows, and finishing applications, which are strongly influenced by global market conditions and energy-intensive production processes. In contrast, steel and reinforcement bars demonstrate moderate to high inflationary effects, largely attributed to volatility in global steel markets.

Collectively, these findings emphasize the importance of targeted cost control and strategic procurement measures focused on high-impact material categories to effectively manage inflation risks in residential construction projects.

Table A3. Ranking of the major material categories affected by price increases.

i	Item Names	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
14(1)	Cement price escalation	72	27	37	49	25	54	192	3.219	3.496	1.870	0.643	1
14(8)	Aluminum and glass product price escalation	22	50	50	48	26	68	242	3.050	2.795	1.672	0.609	2
14(5)	Bricks and blocks price escalation	32	44	60	38	35	55	232	2.987	2.821	1.680	0.597	3
14(7)	Plumbing and electrical material price escalation	25	47	63	35	38	56	239	2.971	2.709	1.646	0.594	4
14(2)	Escalating prices for sand and aggregate	38	62	30	37	46	51	226	2.973	3.101	1.761	0.594	4
14(3)	Steel and reinforcement bar price escalation	23	51	65	32	31	62	241	2.950	2.776	1.666	0.590	5
14(6)	Paint, finishing, and coating material price escalation	25	58	54	39	26	62	239	2.916	2.855	1.690	0.583	6
14(4)	Timber and plywood price escalation	23	58	55	39	31	58	241	2.900	2.754	1.659	0.580	7

reports the quantitative ranking of major material categories affected by price increases.

3.8. Factors Contributing to Material Price Increases

The RII-based ranking of key drivers of material price inflation in residential construction is shown in Figure 6, which provides further information on perceived drivers of material price inflation in residential construction. Respondents overwhelmingly perceive broader economic conditions and global inflationary trends as the most influential contributors to rising material prices, followed by rising raw material costs and trade-related factors. Regulatory constraints and trade policies play a significant role in driving up material costs, particularly for imported construction materials. Global or local supply chain disruptions reflect ongoing logistical bottlenecks that affect product availability and pricing. The domestic market also contributes to price increases, though to a lesser extent than the broader economy. According to the findings, increasing transportation and energy costs have a moderate impact on material prices. Housing demand-side pressures are seen as less significant than macroeconomic, supply-side, and regulatory factors. These findings emphasize that developing mitigation strategies for inflation-induced price escalation requires construction stakeholders to account for external economic volatility and supply chain risks.

Statistical analysis of key factors influencing material price inflation in residential construction can be found in

Table A4. Ranking of the main reasons leading to material price increases.

i	Item Names	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
15(1)	Global inflation or currency devaluation	74	36	31	34	28	61	190	3.247	3.791	1.947	0.649	1
15(5)	Increased raw material costs	32	63	43	22	36	68	232	3.013	3.263	1.806	0.602	2
15(6)	Import tariffs or regulatory constraints	36	63	45	24	38	58	228	2.925	3.163	1.778	0.585	3
15(3)	Global or local supply chain disruptions	30	62	57	18	38	59	234	2.893	3.038	1.743	0.578	4
15(4)	Local market deficiencies or demand–supply imbalances	21	70	50	27	43	53	243	2.831	2.787	1.669	0.566	5
15(2)	Increases in logistics and fuel costs	24	77	43	19	53	48	240	2.800	2.880	1.697	0.560	6
15(7)	Increased housing demand	21	80	47	30	39	47	243	2.695	2.707	1.645	0.539	7

.

3.9. Issues About Rising Material Costs

The most common challenges encountered by residential construction projects are in response to rising building material costs. The listed challenges reflect financial, operational, contractual, and client-related difficulties that emerge when material prices increase. It was found that payment disputes with clients and reduced profit margins were among the most critical challenges with FI = 40% and FI = 36%, respectively. It indicates that contractors’ financial stability and ability to fulfill contractual obligations are greatly affected by escalating material prices.

Operational challenges are also highly evident. It is difficult to secure suppliers (FI = 33%), and procurement delays (FI = 29%) complicate purchasing decisions and scheduling when inflationary conditions prevail. The resulting budget overruns (FI = 31%) further exacerbate financial risks and undermine the feasibility of projects.

Furthermore, project redesign or scope reduction (FI = 22%) emerged as a notable response to rising material costs, suggesting that project teams often resort to value engineering or design modifications to contain escalating expenses. Such adjustments, however, may negatively affect the quality or functionality of the project.

From a client perspective, client dissatisfaction (FI = 20%) and project abandonment (FI = 20%) indicate that sustained material price increases have broader effects on stakeholder relationships. These outcomes reflect the cumulative effect of cost overruns, delays, and compromised project scope.

Consequently, rising material costs present a number of challenges, including financial strain, supply chain disruptions, and client-related issues.

3.10. Labor Wage Inflation and Its Drivers

Figure 7 presents respondents’ perceptions of the key factors contributing to labor wage increases in construction projects. It appears that macroeconomic conditions and cost-of-living pressures are the primary drivers of wage increases, with market competition and policy-driven wage adjustments playing secondary roles. Strong consensus among respondents indicates that general inflationary trends directly translate into higher wage demands and labor costs in the construction sector. As living expenses rise, workers are forced to seek higher wages to maintain purchasing power. It appears that competition among contractors for available labor contributes to upward pressure on wages. Government-mandated wage increases suggest that regulatory interventions play a moderate but notable role in shaping wage levels. Additionally, shortages of skilled workers contribute to wage escalation to a lesser extent than broader economic and regulatory factors. The influence of union negotiations on wage increases is perceived as having the least impact.

Table A5. Ranking of main reasons leading to labor wage increases.

i	Item Names	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
19(1)	General inflationary conditions	82	30	29	39	23	61	182	3.308	3.868	1.967	0.661	1
19(4)	Increases in living expenses	26	68	44	27	34	65	238	2.933	3.082	1.756	0.586	2
19(6)	Competitive pressure among contractors	28	62	46	43	32	53	236	2.864	2.810	1.676	0.572	3
19(5)	Government-mandated wage increases	29	64	52	37	42	40	235	2.753	2.629	1.621	0.550	4
19(2)	Shortage of skilled labor	25	81	42	20	57	39	239	2.711	2.758	1.661	0.542	5
19(3)	Union negotiations	33	72	54	34	27	44	231	2.641	2.724	1.651	0.528	6

contains a statistical analysis of the main reasons for labor wage increases.

3.11. Consequences of Rising Labor Wages on Project Performance

Figure 8 indicates that financial and schedule-related impacts are the most prominent consequences of rising labor wages, although the overall mean scores suggest moderate significance levels. Increasing labor wages primarily affects project profitability, confirming that labor cost escalation directly erodes contractors’ margins. High labor costs can also disrupt project schedules, possibly due to workforce adjustments or cost-related decision-making delays. Modifying labor allocation, such as resizing crews or changing labor intensity are common responses to wage increases by contractors. The increased use of subcontractors reflects the trend toward outsourcing certain activities as a cost-management strategy. By substituting less experienced workers for skilled ones, labor costs could be contained, but quality and productivity may suffer. Scope reduction, although still relevant, is perceived as having the lowest impact on labor wage increases.

A detailed statistical analysis of the impacts of rising labor wages on project performance can be found in

Table A6. Impacts of rising labor wages on project performance.

i	Item Names	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
20(4)	Reductions in profit margins	19	74	52	31	33	55	245	2.767	2.748	1.658	0.553	1
20(1)	Project delays	68	53	42	46	20	35	196	2.704	2.912	1.707	0.540	2
20(3)	Workforce reductions	28	68	60	34	31	43	236	2.665	2.610	1.615	0.533	3
20(6)	Increased reliance on subcontractors	24	76	50	41	36	37	240	2.617	2.480	1.575	0.523	4
20(5)	Substitution with less-experienced labor	22	77	53	40	38	34	242	2.583	2.385	1.544	0.516	5
20(2)	Project scope reductions	20	91	44	40	42	27	244	2.467	2.286	1.512	0.493	6

.

3.12. Client Reactions to Wage-Related Cost Increases

A further study examined how clients react to wage-related cost increases, indicating that rising labor wages frequently result in disputes and project delays or cancelations, rather than cooperative responses such as budget acceptance or redesign. Respondents to wage-related cost escalation exhibit a variety of contractual, managerial, and behavioral reactions. Around half of respondents reported that wage-driven cost escalation often leads to disagreements between clients and contractors regarding payment responsibilities, contract adjustments, or claims. It is likely (FI = 39%) that budget constraints or increased financial uncertainty are leading some clients to postpone project initiation or suspend ongoing work due to higher labor costs. A significant proportion of respondents (FI = 23%) also reported no significant reaction, indicating that some clients are either financially resilient or contractually prepared to accept wage-related costs without major changes to the project. Additionally, some respondents (FI = 22%) requested a redesign of the project in an attempt to reduce labor costs through scope modification or value engineering measures, while 15% accepted revised budgets, a more cooperative response in which clients acknowledge and accommodate the increase. A relatively small percentage of respondents (FI = 14%) were uncertain about how wage-related cost increases would affect clients’ behavior.

3.13. Impact of Rising Labor Wages on Project Budgets

Labor wage increases commonly lead to moderate to severe budget escalations in construction projects, with the majority of projects experiencing cost increases exceeding 10%. About 39% of projects have experienced labor wage increases, resulting in an 11–20% increase in budget over the initial estimate. In almost half (46.22%) of the projects, budget overruns exceeded 20%, demonstrating the severity of labor cost inflation. Only a minority of respondents (7.20%) reported that labor wage increases did not affect their project budgets. This finding highlights the importance of incorporating wage inflation allowances and labor cost risk management strategies into the project budgeting and cost planning processes.

3.14. Inflation-Related Project Delays

The findings indicate that schedule delays are prevalent in residential construction projects, although their severity and frequency vary. The majority of respondents (approximately 65%) reported that schedule delays occurred from sometimes or often, whereas a smaller proportion, nearly 8%, reported that their projects were always delayed. In contrast, only about 7% indicated that schedule delays never occurred.

Survey respondents were asked to estimate the percentage increase in contract duration attributed to labor wage fluctuations. The results indicated that approximately 83% of contracts experienced schedule delays associated with labor wage–related factors. Among these, about 7% reported minor duration extensions of less than 10%, while 5% reported severe extensions exceeding 100%. In contrast, 17% of respondents indicated no schedule delay.

Schedule delays associated with material price fluctuations show a broadly similar pattern. The results indicated that approximately 77% of contracts experienced project delays caused by material price fluctuations. Among these, about 6% reported minor duration extensions of less than 10%, while 3% reported severe extensions exceeding 100%. In contrast, 23% of respondents indicated no schedule delay.

Schedule delays are common in residential construction, with cost-related factors amplifying their occurrence. Labor wage fluctuations have the strongest impact, affecting approximately 83% of projects, followed by material price fluctuations at 77%. In contrast, almost 65% of projects experience general delays from sometimes or often. Severe delays are relatively rare, while a notable share of projects, particularly those affected by material price fluctuations, experience no delay, indicating variability in cost sensitivity and project management effectiveness.

3.15. Mitigation Measures and Their Perceived Effectiveness

Practitioners adopt a range of budget management strategies to mitigate inflationary pressures. Early procurement and bulk purchasing were reported by 34% of respondents, reflecting a proactive approach to securing materials before further price escalation. An equivalent proportion indicated the use of alternative or locally sourced materials and the regular forecasting and review of project budgets. In addition, 31% explicitly incorporated inflation allowances into their cost estimates. These reports suggest that many organizations attempt to anticipate inflationary effects and update their financial plans as revised price information becomes available. Nearly one-third of respondents employed re-measurable or cost-plus contract arrangements to partially transfer inflation risk between clients and contractors. Schedule adjustments (25%) and cost-driven redesign measures (20%) were less frequently applied, whereas contract renegotiation was relatively uncommon (12%). In general, the findings indicate that practitioners predominantly rely on procurement-based and financial planning strategies, complemented by contractual and design interventions, to manage inflation-induced budget uncertainty.

Practitioners have adopted a range of labor management strategies in response to rising wage levels. Labor-saving techniques and labor contract renegotiation were the most frequently reported measures (36%), reflecting efforts to reduce on-site labor intensity and manage wage escalation. The use of prefabricated components and multi-skilled workers was reported by 28% of respondents, indicating a shift toward improved workforce efficiency and flexibility. Workforce scheduling optimization (25%) and client-side cost-sharing arrangements (20%) were applied less frequently to mitigate labor cost pressures.

Respondents also assessed the effectiveness of their inflation mitigation strategies. Overall, 70% rated these measures as moderately to very highly effective, while 17% perceived them as only slightly beneficial. These results indicate that most practitioners consider inflation management strategies to provide measurable benefits, although a notable proportion views their impact as limited. Approximately one-third regard the measures as highly or very highly effective at mitigating inflation-related cost and schedule pressures.

3.16. Key Cost Components and Inflation Perceptions

A consolidated view of respondents’ perceptions of inflation impacts on key cost components, project budgets, schedule performance, and mitigation measures can be found in Figure 9. It appears that inflation exerts a strong influence on project budgets, particularly through direct cost escalation. Inflation-related factors are widely perceived to increase residential project budgets, reflecting a strong consensus on inflation’s budgetary impact. A five-year inflation impact on labor wages and an overall five-year inflation impact on residential construction were both rated as highly significant, illustrating labor cost escalation as a major inflationary pressure. Materials prices have also risen over the past five years, affecting project budgets. Unfortunately, the effectiveness of inflation mitigation strategies received the lowest ranking, implying that although mitigation measures are in place, their perceived effectiveness in offsetting inflation-induced cost and time pressures remains limited.

Schedule-related impacts, on the other hand, were perceived as less severe. Schedule delays caused by labor and material price fluctuations were less frequent than those caused by inflation, suggesting inflation primarily affects cost performance rather than time performance. There is limited consensus regarding a direct correlation between inflation and project delays triggered by fluctuations in material prices or labor wages, with more “no idea” responses and larger variances in these responses.

The findings clearly demonstrated a clear hierarchy of inflation impacts, with budget escalation, driven mainly by labor and material cost increases, perceived as the most critical issue, while schedule impacts and the effectiveness of mitigation measures are viewed as secondary and more uncertain. This pattern reinforces the dominance of cost-related inflation risks in residential construction projects.

Detailed statistics can be found in

Table A7. Rank-ordered analysis of respondents’ perceptions of inflation impacts on key cost components.

i	Items Name	No Idea	Likert Scale					Statistics				Ranking
		0	1	2	3	4	5	N	M	s2	SD	RII	R
11	Inflation-related factors influencing project budgets	0	17	20	75	94	58	264	3.5909	1.2236	1.1062	0.718	1
22	Five-year inflation impact on labor wages	0	13	29	77	92	53	264	3.5417	1.1694	1.0814	0.708	2
8	Five-year overall impact of inflation	0	21	29	66	93	55	264	3.5	1.3688	1.17	0.700	3
17	Five-year inflation-related increases in material prices	0	15	35	82	81	51	264	3.447	1.2443	1.1155	0.689	4
23	Impact of rising labor wages on project budgets	0	19	33	90	61	61	264	3.4242	1.3935	1.1805	0.684	5
18	Impact of rising material prices on project budgets	0	17	34	100	76	37	264	3.3106	1.1427	1.069	0.662	6
13	Five-year inflation impact on project budgets	0	25	58	74	61	46	264	3.1705	1.5032	1.226	0.634	7
24	Frequency of schedule delays	0	18	53	92	80	21	264	3.125	1.0832	1.0408	0.625	8
26	Project delays attributable to material price inflation	60	15	26	100	56	7	204	3.0686	2.3027	1.5175	0.613	9
12	Frequency of inflation-adjusted budgets	0	14	64	102	70	14	264	3.0227	0.9348	0.9669	0.604	10
25	Project delays attributable to labor wage inflation	46	19	41	97	48	13	218	2.9771	2.1047	1.4508	0.595	11
29	Effectiveness of inflation mitigation strategies	0	35	46	97	64	22	264	2.9697	1.2842	1.1332	0.593	12

.

3.17. Analyzing Critical Outliers and Severity Differentiation

Figure 10 presents the distribution, central tendency, and dispersion of mean scores for individual Likert-scale items across the questionnaire. Statistical outliers are observations that deviate substantially from the majority of data in a survey-based analysis, whereas values within the distribution but showing substantially higher or lower means reflect differences in perceived severity rather than statistical anomalies [35,36].

Outliers are items whose mean values exceed the upper or lower bounds of ‘M ± 2SD’ (or, more conservatively, ‘M ± 3SD’) [37]. Consistent with standard statistical conventions, this study defines statistical outliers as items whose mean values exceed the upper or lower bounds of ‘M ± 2SD’. Such values represent extreme departures from the central tendency and may indicate atypical or crisis-level conditions rather than normal variation in respondent perceptions.

In contrast, items with mean values falling between ‘M ± SD’ and ‘M ± 2SD’ are not treated as statistical outliers. They are instead interpreted as items with high- or low-severity items, reflecting perceptions significantly stronger or weaker than the dataset average while still within the expected range of variation. It is especially important for Likert-scale data, which capture subjective judgments rather than sharp statistical discontinuities.

Accordingly, items with mean values above ‘M + SD’ are considered high-severity issues, indicating that respondents perceive these factors as significantly more serious than the average issue. It is therefore important to prioritize these items in inflation management strategies. Items exceeding ‘M + 2SD’ are classified as extreme statistical outliers, suggesting exceptional conditions that may reflect systemic or crisis-level impacts rather than routine inflationary effects.

Conversely, items with mean values below ‘M − SD’ are interpreted as low-severity issues, indicating that respondents generally do not see inflation as a major threat. These items are considered a lower priority in inflation mitigation efforts. Items in the present dataset do not fall below ‘M − 2SD’, indicating that the responses are neither extremely low nor statistically anomalous.

This severity-based interpretation directly informs the Fuzzy Severity Classification Framework (Figure 2), which models graduated transitions in perceived impact rather than detecting statistical outliers. This framework relies on empirical thresholds derived from item means distributions.

For this survey,

Table 3. Likert scale items with brief descriptions.

i	Likert Scale Items	i	Likert Scale Items	i	Likert Scale Items
8	Five-year overall impact of inflation	11	Inflation-related factors influencing project budgets	18	Impact of rising material prices on project budgets
9(1)	Impact of inflation on building material costs	12	Frequency of inflation-adjusted budgets	19(1)	General inflationary conditions
9(2)	Impact of inflation on labor wage costs	13	Five-year inflation impact on project budgets	19(2)	Shortage of skilled labor
9(3)	Impact of inflation on equipment rental and fuel costs	14(1)	Cement price escalation	19(3)	Union negotiations
9(4)	Impact of inflation on logistics and transportation costs	14(2)	Escalating prices for sand and aggregate	19(4)	Increases in living expenses
9(5)	Impact of inflation on permit and regulatory compliance costs	14(3)	Steel and reinforcement bar price escalation	19(5)	Government-mandated wage increases
9(6)	Impact of inflation on consultant and professional service fees	14(4)	Timber and plywood price escalation	19(6)	Competitive pressure among contractors
10(01)	Cost overruns attributable to inflation	14(5)	Bricks and blocks price escalation	20(1)	Project delays
10(02)	Project delays associated with inflation	14(6)	Paint, finishing, and coating material price escalation	20(2)	Project scope reductions
10(03)	Project suspension or cancelation due to inflation	14(7)	Plumbing and electrical material price escalation	20(3)	Workforce reductions
10(04)	Material shortages, delivery delays, or unavailability	14(8)	Aluminum and glass product price escalation	20(4)	Reductions in profit margins
10(05)	Labor shortages or reduced productivity	15(1)	Global inflation or currency devaluation	20(5)	Substitution with less-experienced labor
10(06)	Inflation-driven revisions to material specifications	15(2)	Increases in logistics and fuel costs	20(6)	Increased reliance on subcontractors
10(07)	Quality control failures under inflationary conditions	15(3)	Global or local supply chain disruptions	22	Five-year inflation impact on labor wages
10(08)	Contractor financial instability	15(4)	Local market deficiencies or demand–supply imbalances	23	Impact of rising labor wages on project budgets
10(09)	Subcontractor performance deficiencies	15(5)	Increased raw material costs	24	Frequency of schedule delays
10(10)	Contractual disputes arising from inflation	15(6)	Import tariffs or regulatory constraints	25	Project delays attributable to labor wage inflation
10(11)	Safety and health issues under inflationary pressures	15(7)	Increased housing demand	26	Project delays attributable to material price inflation
10(12)	Material loss, damage, or theft	17	Five-year inflation-related increases in material prices	29	Effectiveness of inflation mitigation strategies

includes short descriptions of Likert scale items. Figure 2 illustrates the distribution, central tendency, and dispersion of mean scores for individual Likert-scale items across the questionnaire. The following items are all above the ‘M + SD’: ‘(8) Five-year overall inflation impacts’, ‘(9_1) Building materials’, ‘(14_1) Cement’, ‘(15_1) Currency devaluation or global inflation’, ‘(17) Five-year inflation-related material price increases’, ‘(18) Impact of rising material prices on project budgets’, ‘(19_1) General inflation’, and ‘(23) Impact of rising labor wages on project budgets’. These issues should be treated as urgent, pressing, and dominating issues because they are considered much more severe than the average issues in the dataset. There are, however, two extreme outliers above ‘M + 2SD’: the items ‘(11) Inflation-related factors causing budget increases’ and ‘(22) Five-year inflation impact on labor wages’. It appears that these points are more indicative of a systemic crisis than of normal fluctuations.

On the other hand, ‘(10_3) Suspended or canceled projects’, ‘(10_6) Material specification changes’, ‘(10_7) Failures in quality control’, ‘(10_9) Subcontractor failure or performance issues’, ‘(10_10) Contractual disputes’, ‘(10_11) Safety and health issues’, ‘(10_12) On-site theft, damage, or loss of materials’, and ‘(20_2) Reduction of scope’ are all below the ‘M − SD’ line, indicating that respondents do not consider these major inflation-related threats as major threats, and they are not priorities in managing inflation. It should, however, be noted that there is no point below ‘M − 2SD’.

3.18. Fuzzy Severity Classification Using Linguistic Hedges

Expert judgments are often expressed with inherent uncertainty and gradation rather than precise numerical values. This characteristic can be addressed through fuzzy logic-based transformations of membership values that use linguistic hedges as truth qualifiers. It preserves the gradual nature of expert evaluations while enabling a more realistic representation of risk severity by identifying dominant and non-dominant severity levels.

To account for the uncertainty and subjectivity inherent in construction management survey responses, a fuzzy logic–based approach was adopted in which linguistic hedges were used as truth qualifiers through power-based transformation of membership values to determine predominant severity levels.

The study uses linguistic hedges as truth qualifiers by applying power-based operators to fuzzy membership values. The power-based linguistic hedge operators are applied to membership values and interpreted as truth qualifiers (‘very true’, ‘true’, and ‘fairly true’).

Table 4. Non-dominant and dominant severity classes with linguistic hedges.

i	Non-Dominant Severity	Non-Dominant Hedge	Dominant Severity	Dominant Hedge	i	Non-Dominant Severity	Non-Dominant Hedge	Dominant Severity	Dominant Hedge
8	-	-	Critical	Very True	14(8)	Moderate	Fairly True	High	True
9(1)	Critical	Fairly True	High	True	15(1)	Critical	Fairly True	High	True
9(2)	Low	Fairly True	Moderate	True	15(2)	Low	Fairly True	Moderate	True
9(3)	High	Fairly True	Moderate	True	15(3)	High	Not True	Moderate	Very True
9(4)	Low	Not True	Moderate	Very True	15(4)	Low	Fairly True	Moderate	True
9(5)	Low	Fairly True	Moderate	True	15(5)	High	Fairly True	Moderate	True
9(6)	Moderate	Fairly True	Low	True	15(6)	High	Fairly True	Moderate	Very True
10(01)	Moderate	Fairly True	High	True	15(7)	Moderate	Fairly True	Low	True
10(02)	Moderate	Fairly True	Low	True	17	High	Fairly True	Critical	True
10(03)	Negligible	Fairly True	Low	True	18	Critical	Fairly True	High	True
10(04)	Moderate	Not True	Low	Very True	19(1)	Critical	Fairly True	High	True
10(05)	Negligible	Not True	Low	Very True	19(2)	Moderate	Fairly True	Low	True
10(06)	Negligible	Fairly True	Low	True	19(3)	Moderate	Fairly True	Low	True
10(07)	Negligible	Fairly True	Low	True	19(4)	High	Fairly True	Moderate	True
10(08)	Moderate	Not True	Low	Very True	19(5)	Low	Fairly True	Moderate	True
10(09)	Negligible	Fairly True	Low	True	19(6)	Low	Fairly True	Moderate	Very True
10(10)	Negligible	Fairly True	Low	True	20(1)	Moderate	Fairly True	Low	True
10(11)	Low	Fairly True	Negligible	True	20(2)	Negligible	Fairly True	Low	True
10(12)	Low	Fairly True	Negligible	True	20(3)	Moderate	Fairly True	Low	True
11	-	-	Critical	Very True	20(4)	Low	Fairly True	Moderate	True
12	High	Fairly True	Moderate	True	20(5)	Moderate	Not True	Low	Very True
13	Moderate	Fairly True	High	Very True	20(6)	Moderate	Fairly True	Low	Very True
14(1)	Critical	Fairly True	High	Very True	22	-	-	Critical	Very True
14(2)	High	Fairly True	Moderate	True	23	High	Fairly True	Critical	True
14(3)	High	Fairly True	Moderate	True	24	Moderate	Fairly True	High	True
14(4)	High	Not True	Moderate	Very True	25	High	Fairly True	Moderate	True
14(5)	High	Fairly True	Moderate	True	26	Moderate	Fairly True	High	True
14(6)	High	Fairly True	Moderate	Very True	29	High	Fairly True	Moderate	True
14(7)	High	Fairly True	Moderate	True

summarizes the non-dominant and dominant severity classifications for each item, together with their associated linguistic hedges, as determined using the maximum membership (max − μ) principle applied to the hedge operators μ², μ, and μ^0.5. The complete fuzzy hedge membership values are reported in

Table A8. Fuzzy severity memberships and dominant linguistic hedge identification.

i	Non-Dominant Severity	Very True	True	Fairly True	Non-Dominant Hedge	Dominant Severity	Very True	True	Fairly True	Dominant Hedge	i	Non-Dominant Severity	Very True	True	Fairly True	Non-Dominant Hedge	Dominant Severity	Very True	True	Fairly True	Dominant Hedge
8	-	-	-	-	-	Critical	1.00	1.00	1.00	Very True	14(8)	Moderate	0.22	0.47	0.69	Fairly True	High	0.28	0.53	0.73	True
9(1)	Critical	0.07	0.26	0.51	Fairly True	High	0.54	0.74	0.86	True	15(1)	Critical	0.03	0.18	0.42	Fairly True	High	0.68	0.82	0.91	True
9(2)	Low	0.08	0.28	0.53	Fairly True	Moderate	0.52	0.72	0.85	True	15(2)	Low	0.08	0.29	0.54	Fairly True	Moderate	0.51	0.71	0.84	True
9(3)	High	0.02	0.15	0.39	Fairly True	Moderate	0.72	0.85	0.92	True	15(3)	High	0.00	0.02	0.13	Not True	Moderate	0.96	0.98	0.99	Very True
9(4)	Low	0.00	0.04	0.20	Not True	Moderate	0.92	0.96	0.98	Very True	15(4)	Low	0.03	0.18	0.43	Fairly True	Moderate	0.66	0.82	0.90	True
9(5)	Low	0.17	0.41	0.64	Fairly True	Moderate	0.35	0.59	0.77	True	15(5)	High	0.17	0.41	0.64	Fairly True	Moderate	0.35	0.59	0.77	True
9(6)	Moderate	0.19	0.44	0.66	Fairly True	Low	0.32	0.56	0.75	True	15(6)	High	0.02	0.12	0.35	Fairly True	Moderate	0.77	0.88	0.94	Very True
10(01)	Moderate	0.20	0.44	0.67	Fairly True	High	0.31	0.56	0.75	True	15(7)	Moderate	0.14	0.37	0.61	Fairly True	Low	0.40	0.63	0.79	True
10(02)	Moderate	0.11	0.33	0.58	Fairly True	Low	0.44	0.67	0.82	True	17	High	0.03	0.17	0.41	Fairly True	Critical	0.69	0.83	0.91	True
10(03)	Negligible	0.18	0.42	0.65	Fairly True	Low	0.33	0.58	0.76	True	18	Critical	0.15	0.38	0.62	Fairly True	High	0.38	0.62	0.78	True
10(04)	Moderate	0.00	0.01	0.12	Not True	Low	0.97	0.99	0.99	Very True	19(1)	Critical	0.14	0.38	0.61	Fairly True	High	0.39	0.62	0.79	True
10(05)	Negligible	0.00	0.03	0.16	Not True	Low	0.95	0.97	0.99	Very True	19(2)	Moderate	0.18	0.42	0.65	Fairly True	Low	0.33	0.58	0.76	True
10(06)	Negligible	0.05	0.23	0.48	Fairly True	Low	0.60	0.77	0.88	True	19(3)	Moderate	0.04	0.19	0.44	Fairly True	Low	0.65	0.81	0.90	True
10(07)	Negligible	0.13	0.36	0.60	Fairly True	Low	0.41	0.64	0.80	True	19(4)	High	0.02	0.15	0.38	Fairly True	Moderate	0.73	0.85	0.92	True
10(08)	Moderate	0.00	0.04	0.20	Not True	Low	0.92	0.96	0.98	Very True	19(5)	Low	0.19	0.44	0.66	Fairly True	Moderate	0.31	0.56	0.75	True
10(09)	Negligible	0.08	0.28	0.53	Fairly True	Low	0.51	0.72	0.85	True	19(6)	Low	0.01	0.08	0.28	Fairly True	Moderate	0.85	0.92	0.96	Very True
10(10)	Negligible	0.12	0.34	0.58	Fairly True	Low	0.43	0.66	0.81	True	20(1)	Moderate	0.16	0.40	0.63	Fairly True	Low	0.36	0.60	0.78	True
10(11)	Low	0.09	0.30	0.55	Fairly True	Negligible	0.49	0.70	0.84	True	20(2)	Negligible	0.14	0.38	0.61	Fairly True	Low	0.39	0.62	0.79	True
10(12)	Low	0.23	0.48	0.69	Fairly True	Negligible	0.27	0.52	0.72	True	20(3)	Moderate	0.07	0.27	0.52	Fairly True	Low	0.53	0.73	0.85	True
11	-	-	-	-	-	Critical	1.00	1.00	1.00	Very True	20(4)	Low	0.16	0.39	0.63	Fairly True	Moderate	0.37	0.61	0.78	True
12	High	0.20	0.44	0.66	Fairly True	Moderate	0.31	0.56	0.75	True	20(5)	Moderate	0.00	0.00	0.03	Not True	Low	1.00	1.00	1.00	Very True
13	Moderate	0.01	0.07	0.27	Fairly True	High	0.86	0.93	0.96	Very True	20(6)	Moderate	0.01	0.11	0.34	Fairly True	Low	0.79	0.89	0.94	Very True
14(1)	Critical	0.01	0.08	0.29	Fairly True	High	0.84	0.92	0.96	Very True	22	-	-	-	-	-	Critical	1.00	1.00	1.00	Very True
14(2)	High	0.08	0.28	0.53	Fairly True	Moderate	0.52	0.72	0.85	True	23	High	0.06	0.24	0.49	Fairly True	Critical	0.57	0.76	0.87	True
14(3)	High	0.04	0.20	0.45	Fairly True	Moderate	0.63	0.80	0.89	True	24	Moderate	0.05	0.22	0.47	Fairly True	High	0.60	0.78	0.88	True
14(4)	High	0.00	0.04	0.20	Not True	Moderate	0.92	0.96	0.98	Very True	25	High	0.09	0.29	0.54	Fairly True	Moderate	0.50	0.71	0.84	True
14(5)	High	0.11	0.33	0.57	Fairly True	Moderate	0.46	0.67	0.82	True	26	Moderate	0.17	0.41	0.64	Fairly True	High	0.35	0.59	0.77	True
14(6)	High	0.01	0.09	0.31	Fairly True	Moderate	0.82	0.91	0.95	Very True	29	High	0.07	0.27	0.52	Fairly True	Moderate	0.54	0.73	0.86	True
14(7)	High	0.07	0.27	0.52	Fairly True	Moderate	0.53	0.73	0.85	True

.

The interpretation of dominant severity classifications based on Table 4 can be exemplified as follows.

(1)

Item 8’s dominant severity membership is critical, which is ‘very true’.

(2)

Item 9(1)’s non-dominant severity membership is critical, which is ‘fairly true’, whereas the dominant severity membership is high, which is ‘true’.

Similar patterns are observed for the remaining items, where the dominant severity classes are identified by the highest membership values after applying linguistic hedge operators.

4. Discussion and Limitations

During the last five years, Turkish residential construction projects have experienced profound and systemic inflation, primarily due to cost-escalation mechanisms rather than schedule and quality degradation. The findings indicate that inflation-related cost increases, particularly budget escalation and wage inflation, pose the greatest challenge to residential construction. It appears that respondents perceive these factors as crisis-level risks rather than as normal project variability since their mean values are highest and observations exceed the upper variability bounds. Continuous inflation fundamentally undermines the predictability of costs and the reliability of fixed-price estimations under such conditions.

The findings demonstrate that inflation substantially compromises the accuracy of initial cost estimates, as cumulative increases in material prices, labor wages, and indirect costs generate significant deviations over the project lifecycle. The limited integration of inflation-adjusted cost estimation into standard financial planning frameworks exacerbates exposure to budget overruns and financial instability. This observation is consistent with prior studies highlighting the vulnerability of fixed-price construction contracts under prolonged inflationary conditions, particularly when cost escalation is driven by macroeconomic rather than project-level factors [12,38].

Material price volatility emerged as the most influential contributor to inflation-related risk, with cement, aluminum, and glass products identified as the most affected material categories. Participants consistently attribute these increases to macroeconomic and global supply-chain forces, including currency devaluation, global inflation, and rising raw material costs, rather than project-level inefficiencies. The high concentration reinforces the characterization of material price volatility as a structural and systemic condition affecting the residential construction sector as a whole. These effects are exacerbated by energy-intensive production processes, imported inputs, and international market fluctuations. Similar impacts have been observed in other construction domains facing inflationary and supply-chain disruptions, including transportation infrastructure projects [11,39,40,41].

Contract design and risk allocation are directly affected by the findings in this context. According to research [42] from the highway construction sector, price adjustment clauses (PACs) can be more effective when used within construction contracts to manage material price volatility, demonstrating that the vast majority of U.S. state transportation agencies use PACs to mitigate material price uncertainty, particularly for fuel, asphalt, steel, and cement. Owners and contractors can apply these mechanisms to reduce the speculative risks embedded in bids, improve price transparency, and allocate inflation risks more equitably. Residential construction projects differ in scale and institutional structure, but similar inflation dynamics identified in this study can enhance cost resilience and reduce adversarial risk transfer in residential procurement contexts.

The second major inflationary pressure identified in this study is wage inflation, driven primarily by general inflation and rising living costs. Due to their continual and cumulative impact on contractor cash flow throughout project execution, labor-related risks rank among the most severe impacts. Contrary to material costs, which can be partially mitigated through early procurement or supplier diversification, wage inflation continues throughout the entire construction process. It appears that contractors respond by reducing profit margins, restructuring labor deployment, and relying more on subcontractors rather than by suspending projects or significantly compromising quality. Although these adaptive strategies may preserve short-term project continuity, they also pose long-term risks relating to workforce stability, productivity, and skill retention [13,43]. It appears that labor cost indexing or hybrid contracting approaches, such as limited costs-plus components for labor-intensive activities, are complementary tools to material-based price adjustments.

By contrast, schedule delays, quality failures, contractual disputes, and safety-related issues are perceived as secondary inflation-induced consequences. Although delays linked to labor wage and material price fluctuations are reported, their lower mean values and higher dispersion suggest variability driven by project-specific sensitivity and managerial effectiveness. These results indicate that inflation primarily manifests as financial pressure, while time and quality impacts remain contingent on mitigation capacity and operational control [44].

Although various mitigation measures, such as early procurement, alternative material selection, inflation contingencies, flexible contracting arrangements, and labor-saving technologies, are reportedly employed, their perceived effectiveness remains limited. Although various mitigation measures, such as early procurement, alternative material selection, inflation contingencies, flexible contracting arrangements, and labor-saving technologies, are reportedly employed, their perceived effectiveness remains limited. Under prolonged and volatile inflationary conditions, these measures tend to provide short-term relief but are insufficient. It is evident from the findings that there is no integrated and systematic framework for managing inflation risk in the residential construction sector. In the construction sector, more robust strategies such as dynamic cost indexing, inflation-adjusted contracts, and proactive labor market planning can reduce uncertainty, discourage speculative pricing, and improve financial sustainability [39,40,45].

Methodologically, graded linguistic reasoning within a fuzzy decision support framework enables inflation-related risks to be evaluated as continuous and severity-dependent phenomena rather than binary or threshold events. Using power-based linguistic hedges as truth qualifiers, the proposed approach supports prioritization under uncertainty and reveals not only which risks are critical, but also how severe they are. Considering inflationary environments to be inherently volatile and ambiguous, rigid classification schemes may not be sufficient to make effective decisions.

There is no doubt that inflation has fundamentally altered the risk profile of residential construction projects in Türkiye. Material price volatility and labor wage inflation dominate cost escalation dynamics, while schedule and quality impacts remain secondary and context-dependent. The limited perceived effectiveness of existing mitigation measures underlines the need for more robust, strategic, and inflation-responsive project management approaches. Under prolonged inflationary conditions, residential construction projects will remain highly vulnerable unless advanced cost forecasting, inflation-sensitive contracting, and structured labor management strategies are integrated, resulting in significant implications for contractor sustainability and affordability [12,39,46].

Inflation-related risks are not uniformly vulnerable across stakeholders; however, the observations presented in this study reflect perceptions rather than statistically validated differences. It appears that organizational size, professional role, and experience influence perceptions of material price volatility and wage increase, but no inferential subgroup analysis (e.g., ANOVA, Chi-square, or non-parametric equivalents) was conducted to confirm statistically significant differences between organizational categories. Therefore, the findings should be considered descriptive rather than causal or predictive.

However, the study also has several limitations. The findings may be influenced by recall bias and recency effects inherent in retrospective inflation assessments, as well as anchoring effects arising from recent conditions of extreme inflation. In addition, perception-based evaluations may vary due to heterogeneity among respondents in terms of firm size, professional role, and experience. Accordingly, this study aims to capture perceived inflation-related risks rather than quantify objective cost magnitudes, and the findings should be interpreted within this perceptual and contextual framework. Research in the future should include inferential subgroup analysis designed a priori to formally evaluate stakeholder heterogeneity and validate perceived differences.

5. Conclusions

This study provides a comprehensive assessment of inflation-related risks in residential construction projects in Türkiye during a period of sustained economic volatility. An integrated approach to interpreting inflation-related risks under uncertainty using fuzzy severity classification and linguistic hedges is flexible and provides a decision-support structure. The graded linguistic approach supports more realistic risk prioritization in ambiguous and volatile environments.

The findings clearly demonstrate that inflation has emerged as a dominant and systemic risk, fundamentally reshaping the cost structure and financial stability of residential construction projects. The main impact of inflation on projects is cost escalations rather than schedule delays or construction quality degradations. Material price volatility and labor wage inflation are identified as the most critical drivers of budget overruns. Both exhibit extreme outlier behavior that reflects crisis-level severity rather than routine project uncertainties. In particular, cement, aluminum, and glass products are highly sensitive to macroeconomic factors, including currency depreciation, global inflationary pressures, and disruptions in supply chains. The persistent financial burden associated with labor wage inflation erodes contractor profitability throughout the project lifecycle and influences workforce restructuring decisions. Even though such adaptive responses may improve short-term financial resilience, they also present long-term risks related to productivity, workforce stability, and skill retention.

Inflation-induced impacts on schedules, quality performance, contractual disputes, and safety outcomes are perceived as secondary and context-dependent. Although labor and material cost fluctuations can contribute to project delays, their severity varies across projects. This suggests that managerial capability and operational flexibility play a moderating role. Overall, inflation is perceived primarily as a financial risk, while time and quality impacts can be partially mitigated through effective project management practices.

Despite the widespread adoption of mitigation measures, such as early procurement, alternative materials, inflation allowances, flexible contracts, and labor-saving techniques, their perceived effectiveness remains limited in periods of prolonged inflation. Such measures are predominantly tactical and short-term, indicating the absence of comprehensive and systematic frameworks for managing inflation-related risks in residential construction practice. These mitigation measures are constrained by structural and stakeholder-specific factors under prolonged inflationary conditions. The majority of commonly adopted strategies rely on short-term flexibility and assume relatively stable contractual and financial environments; however, in Türkiye these assumptions are undermined by persistent exchange-rate volatility, rapid cost pass-through, and rigid contract structures. Moreover, vulnerability to inflation-related risks is not uniform across stakeholders. Smaller firms with limited financial buffers are perceived to be more susceptible to material price shocks and wage escalations, whereas larger firms may absorb short-term volatility but remain vulnerable to cumulative cost pressures and margin erosion over longer project cycles. Consequently, organizational size, professional role, and experience, as reflected in the respondent profiles (Table 2), shape both the perceived effectiveness of mitigation measures and adaptive capacity, underscoring the need for a differentiated and scale-sensitive approach to inflation risk management.

This study shows that inflation-related risks in residential construction are mostly financial in nature and are best understood as continuous and severity-driven phenomena rather than discrete events. The proposed approach improves interpretability under uncertainty and supports more informed decision-making in prolonged inflationary environments by integrating conventional ranking methods with fuzzy truth qualification frameworks. Furthermore, the findings highlight the importance of inflation-sensitive contracting mechanisms and proactive labor market planning in enhancing sector resilience. Unless such strategies are implemented, residential construction projects will remain highly vulnerable, which will negatively impact contractor sustainability and housing affordability in the long run. Importantly, this study goes beyond acknowledging inflation-driven cost increases by identifying the predominant inflation drivers under extreme market volatility. The results demonstrate that currency-sensitive materials and labor wage escalation consistently dominate project financial performance under sustained inflationary conditions, while several commonly cited project risks become secondary. This severity-based insight enables more effective risk prioritization in highly volatile construction markets such as Türkiye.

Several factors contributed to the study’s limitations. The reliance on self-reported perceptions introduces potential respondent bias, sentiment effects, and retrospective interpretations, and the limited focus on residential construction practitioners limits the generalizability of the findings to the infrastructure, industrial, or commercial sectors. Moreover, perception-based assessments may differ depending on the firm size, role, and experience of respondents. Therefore, the study focuses on perceived inflation-related risks rather than objective cost magnitudes, and its conclusions should be interpreted accordingly.