Short- and Long-Term Assessments of ESG Risk in Mexican Mortgage Institutions: Combining Expert Surveys, Radar Plot Visualization, and Cluster Analysis

Abstract

The recent debate on Environmental, Social, and Governance (ESG) factors has focused primarily on financial decision making and risk management from the perspectives of developed economies. However, in most developing countries, ESG risk models for mortgage lenders are very limited. In most of these countries, ESG-rating providers employ widely varying methodologies and disclosure policies, often resulting in divergent assessments of the same organization. This research develops a pilot statistical-analysis, dual-horizon ESG risk model specific to the Mexican mortgage industry, which provides a better understanding of how ESG risk could evolve over time across financial, operational, regulatory, and reputational dimensions in Mexico. This dual-horizon ESG framework considers a two-year short-term risk assessment and a ten-year long-term risk assessment. This research integrates expert opinions with a scoring system that improves on traditional methods. Dependability and internal consistency are tested using the Intraclass Correlation Coefficient (ICC) and Cronbach’s alpha. Radar chart visualization and cluster analysis are used to visualize the empirical results. The empirical findings show that environmental risk has strong temporal effects, and the perceived severity is 20% higher over the longer time horizon. Furthermore, social risk exhibits high variability, identifying it as a critical risk for financial stability and regulatory compliance. Cluster analysis identifies systematic patterns in expert opinions that determine two groups, making the qualitative findings derived from radar plots more robust. Group 0 (75% of experts) has an institutional view about ESG risks. Group 1 (25% of experts) aligns with an affiliation to large financial institutions. Finally, this research identifies three key sustainability challenges for the mortgage sector in Mexico: exposure to climate-induced stress, fragmented regulatory frameworks, and social inequality.

1. Introduction

Environmental, Social, and Governance (ESG) factors have become increasingly important in financial decision making and the management of sustainability-related risks. Financial institutions have systematically integrated ESG factors into their risk management systems to gauge the long-term sustainability and resilience of companies. In this sense, the authors of [1] stated that performance in ESG metrics is a key driver of an organization’s capacity to react to evolving risks and seize opportunities arising from shifts in the business environment.

Despite increasing interest in this area, serious methodological issues remain concerning the quantification of ESG risks. As noted in [2], establishing standardized and comparable metrics across industries (especially within the financial sector) requires reconfiguring traditional risk assessment models to incorporate non-financial elements that substantially influence institutional resilience and performance. Applying different methodologies to investigate ESG matters has resulted in broad differences in approaches and findings in most studies.

Significant evolution has taken place in the methods of measuring ESG risks for the financial industry. For instance, the authors of [3] presented a survey of cross-border investors who witnessed the systematic processes utilized for incorporating ESG information into investment decisions and risk management, thus highlighting the growing function of systematic methods for quantifying sustainability risks within the industry. Likewise, the research in [4] demonstrated the successful incorporation of sustainability practices in credit risk management systems by showing empirical proof of the benefits of incorporating ESG factors into risk model assessments in the financial industry.

Several international organizations have developed standardized approaches, such as those set out by the recommendations of the Task Force on Climate-Related Financial Disclosures (TCFD) stated in [5], which provide consistent frameworks for assessing and disclosing climate-related financial risks across sectors. Similarly, the United Nations Environment Program Finance Initiative (UNEPFI), as stated in [6], creates guidelines enabling financial institutions to address climate-related risks and opportunities in their lending portfolios effectively.

Assessment of ESG risks heavily depends upon the input of experts, especially in situations where quantifiable data may not be available. As noted in [3], the complex characteristics of ESG issues call for a heavy dependence upon experts’ opinions to produce comprehensive assessments of quantitative and qualitative aspects of risks tied to sustainability. Specialists’ opinions can clarify potential hazards and their likely effects in various periods and organizational setups.

Recent developments in ESG integration within financial institutions have all pointed to the need for organized risk assessment frameworks. As demonstrated in [7], investors are calling for more organized ESG assessment approaches to financial institutions’ climate stress and social transition exposure. Similarly, the authors of [8] emphasized multi-perspective assessment frameworks that determine short-term operational risks and longer-term strategic dilemmas.

The mortgage industry displays differences in the measurement of ESG risks, especially in emerging economies. Analyzing real estate lending, the authors of [8] stated that (1) environmental risks play a significant role in property values and loan performance and that (2) climate-related stress is a key driver of mortgage default rates. Concerning developing countries, the authors of [9] noted that housing affordability and access to financing add to the complexities that mortgage institutions face regarding ESG considerations, since sustainable housing programs in Latin American markets face implementation challenges based on institutional capacity and market structure limitations.

The Mexican housing market is exposed to numerous risks stemming from ESG concerns. As the authors of [10] stressed, institutions face the complex challenge of promoting holistic sustainability plans while ensuring that a range of housing options are available at reasonable prices. This complexity is intensified by the predominant presence of public mortgage institutions, whose active involvement in fostering inclusive and environmentally responsible housing initiatives has been emphasized in [11].

The modern framework addressing ESG risks concerning Mexican mortgage institutions is considered through the following four critical dimensions: financial considerations, operational issues, legal or regulatory aspects, and reputational consequences. As supported by the authors of [4,7], this approach provides a holistic consideration of various temporal horizons for measuring the impacts of ESG factors across various operational levels and time horizons. Through simultaneous consideration of several dimensions, the proposed model provides more accurate results than one-dimensional methods based solely on financial or regulatory aspects, thus avoiding the drawbacks of traditional ESG rating models, as stated in [2].

The prevailing state of the Mexican mortgage market shows a lack of clearly defined frameworks for including ESG considerations, which presents a fundamental limitation of risk assessment models [11,12]. Other developing countries also compromise the sector’s readiness to correctly identify, assess, and control sustainability risks, as described in [10]. These shortcomings are significant, given that the industry is relatively sensitive to environmental risks posed by climate change [9], housing affordability-related social risks [13], and governance risks related to regulatory compliance and transparency [14].

This research addresses known gaps through an all-encompassing dual-horizon methodology for ESG risks tailored to Mexican mortgage institutions and with transdisciplinary methods coupled with rigorously crafted expert surveys and radar plot visualizations. The ESG risks are assessed along different dimensions, specifically financial, operational, regulatory/legal, and reputational. The proposed methodology provides short- and long-term horizons, thus providing the financial industry with an integrated tool to examine the temporal impacts of sustainability risks.

This study employs statistical validation and multivariate analysis. The ICC and Cronbach’s alpha are used to measure the reliability of the dual-horizon ESG model. Cluster analysis exposes patterns of expert judgment missed by conventional techniques. Based on [15], PCA and K-means clustering are used to detect underlying expert rating groupings to enhance the interpretation of ESG data. For instance, the authors of [16] demonstrated that cluster analysis can expose underlying interdependencies between ESG factors and institutional characteristics, particularly in non-regular data contexts such as emerging markets.

While there is growing concern about integrating ESG considerations into risk management and financial decision making, there are limited frameworks that address the challenges facing mortgage institutions in emerging economies, such as that of Mexico. The challenges include a shortage of sector-wide ESG benchmarks, heightened exposure to climate risks, and continued issues with social equality. This research develops a pilot statistical-analysis, dual-horizon ESG risk model that stands out from the existing literature in the following ways: (1) it is specific to the mortgage industry in Mexico, (2) it explains how risks develop over time, (3) it considers financial, operational, regulatory, and reputational dimensions, and (4) it integrates expert opinions with normalized scores and radar plot visualizations to improve upon traditional methods.

The rest of this article is organized as follows: Section 2 offers an extended discussion of the dynamics and issues faced by the Mexican mortgage market; Section 3 discusses the methods used to evaluate ESG in various industries; Section 4 presents the data and key determinants that underpin the proposed framework for ESG ratings; Section 5 presents radar charts with empirical results, complemented with a discussion and an explanation of broader implications; and, finally, Section 6 provides the conclusions.

2. Literature Review on ESG Risk Measurement Frameworks and Applications in the Mortgage Sector

In recent years, ESG risks have been gaining relevance in financial decision making within sectors that directly impact sustainable development. The following is a brief review of the literature on ESG risk measurement frameworks, highlighting the heterogeneity of methodologies, the integration of ESG criteria into the mortgage sector, and the comparative approaches used by global standards and considerations about developing a dual-horizon ESG risk assessment tailored to Mexican mortgage institutions.

2.1. ESG Heterogeneity

ESG measures face a significant challenge in defining uniform and comparable indicators applicable to similar institutions in different sectors. As stipulated in [1], ESG methodologies and information disclosure policies vary considerably, along with many disparate assessments of comparable institutions. The qualitative nature of many aspects of sustainability represents a challenge compared with traditional quantitative financial metrics. As noted in [2], three principal sources of variation in ESG ratings (scope, approach, and weighting) can result in considerably different assessments of an organization’s ESG performance, with subsequent impacts on investment decisions, business strategies, and risk management.

Industry-specific characteristics necessitate tailored ESG approaches. While environmental metrics such as carbon emissions are relatively standardizable across industries [17], social and governance aspects typically require more complex assessment frameworks. Governance evaluations must address safety management systems, risk management, and regulatory compliance, which are often outside traditional ESG metrics. As argued in [18], effective ESG measurement requires methodologies incorporating sector-specific indicators to enhance comparability.

The allocation of the different ESG components is critical for enabling accurate assessments. Existing methodologies often employ estimated weights (for example, Refinitiv EIKON attributes 34% to environmental considerations, 35.5% to social, and 30.5% to governance). Still, standardized weightings may fail to reflect industry-specific priorities or stakeholder needs. In this sense, the authors of [3] found the significance of the ESG factors to be both industry- and location-dependent, thus requiring bespoke measurement approaches. Several international frameworks have been devised to address these challenges, such as the following.

(a)

The European Union’s Green Taxonomy provides a detailed framework for defining environmentally sustainable activities, especially in the energy, transport, and building sectors. In addition, it prescribes minimum standards for green mortgages, focusing on building energy performance and environmental goals [19]. This is a good example of regulatory coherence regarding standards for sustainable home finance.

(b)

The European Sustainable Finance Disclosure Regulation (ESFDR) requires that financial institutions disclose information about the ESG risks involved in their investments, promoting accountability and transparency, as stated in [20,21]. This regulation is expected to trigger similar developments in the Mexican mortgage sector, ultimately improving governance and disclosure standards.

(c)

The United Nations Principles for Responsible Investment (UNPRI) encourage financial institutions worldwide to integrate ESG principles into investment decision making. These guidelines apply to mortgage institutions addressing housing finance risks, including equal credit access and climate resilience [22].

(d)

Japan’s Green Building Program (JGBP) provides incentives such as grants and low-interest loans for energy-efficient construction, demonstrating how financial mechanisms can promote sustainable housing practices [23].

(e)

The United States has seen increased green bond issuance for energy-efficient housing projects, with agencies such as Fannie Mae integrating ESG into mortgage-backed securities, setting precedents for linking financial products to sustainability goals [24].

(f)

Brazil has launched ESG-themed credit products for sustainable agriculture and housing initiatives meeting social equality and environmental resilience requirements, emphasizing the need for regionally prioritized ESG frameworks [25].

More recent developments in the quantitative measurement of ESG factors have moved from historical performance measurements to the inclusion of forward-looking indicators; however, with that movement, there is an additional layer of complexity regarding standardization. Hence, incorporating ESG measurements in financial models for assessment and performance is critical to addressing sustainability comprehensively. In this sense, the authors of [26] pointed to instances where high-ESG-rated companies have lower capital costs, benefit from better assessments, and demonstrate operational excellence in markets that reward sustainability. Likewise, the authors of [27] pointed to the role of ESG ratings in financial institutions’ willingness to take risks and create value. ESG ratings are critical determinants of financial performance that are not traditional indicators for measuring sustainability [28]. Economic performance strongly correlates with ESG indicators, especially in capital-intensive industries requiring significant capital expenditures on sustainable practices and technologies. Finally, according to [29], ESG performance shapes financial assessments in sectors with substantial impacts to varying degrees, with deep sustainability assessments showing capabilities in risk management functions and long-term economic resilience.

2.2. Mortgage Sector Financing and ESG Integration

Mortgage lending is linked to the energy efficiency of built environments. The building industry is a major polluter, with buildings being responsible for 20% of CO₂ emissions and consuming over 40% of yearly output [30]. Financial institutions are increasingly willing to finance sustainable initiatives that meet rigorous environmental standards.

Legislative models such as the EU Sustainable Finance Disclosure Regulation and the Green Taxonomy establish standards for environmentally sustainable mortgage products and those closest to zero-energy building standards [31]. However, like many other developing countries, Mexico has financial and information asymmetries in the green housing market.

In terms of social dynamics, mortgage loans are key in promoting equal access to home finance and improving fairness, yet persistent inequalities remain evident. The findings in [32] revealed that banks with high ESG scores have reduced lending activities in areas with financial exclusion and prone to disasters. This phenomenon is generally described as “social washing”, showing the inconsistency between virtuous statements of intention and lending activities in practice.

2.3. Comparative Analysis of ESG Risk Assessment Methodologies

The dual-horizon approach presented in this study strengthens the existing frameworks for evaluating ESG risks and incorporates new elements that are specially designed for the mortgage sector in emerging markets. In what follows, the proposed framework is compared with traditional ones.

(a)

Task Force on Climate-Related Financial Disclosures (TCFD) Framework: This proposal follows the guidelines provided by the TCFD for disclosing climate-related financial risks, specifically physical and transition risks [5]. The proposed methodology is complemented by explicitly including governance and social risks and adapting impact categories to suit Mexican mortgage market conditions. While the TCFD recommends impact analyses across uncertain time horizons, the dual-horizon framework sets clear temporal boundaries (2 and 10 years) to foster consistent evaluations.

(b)

United Nations Environment Program Finance Initiative (UNEPFI) Methodology: The UNEPFI laid a framework for financial institutions to address climate risks and opportunities in their lending decisions effectively, as stated in [6]. The proposed methodology aligns with this framework but includes additional elements beyond climate considerations, including social and governance elements, and it utilizes radar plot visualizations to improve the interpretation of complex results. Moreover, while the UNEPFI mainly focuses on the portfolio level for impact, the proposed methodology evaluates risks across different operating dimensions, including financial, operational, regulatory, and reputational elements.

(c)

Models for Climate Stress Testing in Mortgage Lending: The current research builds on and extends previous foundational research related to climate stress testing models in mortgage finance based on significant contributions from [30,33]. While current models have focused mainly on physical and transition risks related to individual properties and entire portfolios, the proposed methodology integrates social and governance considerations relevant to the Mexican market, namely, housing affordability and compliance with regulatory requirements. Extending the range of issues considered allows for integrated consideration of sustainability risks in emerging markets.

(d)

Commercial ESG frameworks: The proposed methodology shows several valuable differences from traditional commercial ESG rating systems, including MSCI ESG and Sustainalytics [34]. While these conventional systems primarily compare a company’s past or existing performance with predefined benchmarks, the dual-horizon methodology takes a forward-looking stance, incorporating projections about likely changes to these risks over time.

In the present research, the dual-horizon methodology allows each factor to be determined through expert judgment. Unlike existing mortgage-specific standards, such as those of the Sustainability Accounting Standard for Mortgage Finance of 2018, set by the Sustainability Accounting Standards Board (SASB), as in [35], the proposed approach provides more remarkable thoroughness by analyzing different operational facets and time horizons. While the authors of [35] focused mainly on accounting measures, the proposed approach includes proactive risk assessments that ascertain existing vulnerabilities and future challenges.

These comparisons highlight the strengths of the dual-horizon framework, most significantly its ability to provide a richer measurement of ESG risks tailored to those in the mortgage market of an emerging country such as Mexico. Hence, after combining elements of traditional frameworks with conceptual innovations, including radar plot visualizations, effects over time, and cluster analysis, the proposed approach provides an enhanced tool for measuring the sustainability risks specifically applicable to Mexico.

Recent research using clustering methodologies has significantly improved the understanding of ESG trends and their multidimensional nature. Clustering allows for studying the evolution of ESG risks in specific sectors such as housing finance. Different tools, such as VOSviewer and Biblioshiny, allow for the categorization of ESG topics, from greenwashing detection to reputational risk and governance failures [15,16]. Clustering may thus clarify how ESG issues cluster uniquely within financial institutions, enabling risk managers to tailor ESG strategies more effectively. This also helps in matching ESG disclosures with robust, sectoral standards that may, in turn, reduce data asymmetry and make regulation more effective according to [17].

3. ESG Risk Landscape in the Mexican Mortgage Sector

While international ESG methodologies can offer useful benchmarks for emerging economies, Mexico requires special consideration due to its institutional structures, housing challenges, and regulatory dynamics. This section builds on the previous literature, highlighting the key factors influencing the perception and application of ESG risk in the Mexican mortgage industry landscape.

Mexico’s dependence on state-sponsored mortgage institutions such as INFONAVIT and FOVISSSTE underlines the relevance of fair lending practices. However, the availability of mortgages often exacerbates pre-existing disparities, since private sector financial institutions usually channel money to wealthy urban projects, sometimes hampering attempts to build cheaply available dwelling units [11].

Strong governance structures related to ESG investments are essential to uphold the integrity of ESG policy in the mortgage industry [36]. Access to high-quality ESG information and reporting to globally accepted standards are necessary to reduce risks from “greenwashing” and enable evidence-based investor decision making. Nevertheless, inconsistencies in ESG measures and unsatisfactory third-party audits represent tremendous risks, especially where there are weaker regulatory conditions.

Implementing ESG standards into the mortgage market offers a significant chance to redefine housing finance as a driver for sustainable development; current approaches to managing ESG risks for mortgage lending vary in methodology and priority. The framework outlined in [33] is a reference point for creating innovative green mortgage products by analyzing economic and environmental impacts for mortgage book holdings. In [29], the authors called for an integrated approach to managing climate risks for mortgage lending, including physical risks at an individual property level and transitional risks at a portfolio level. The authors of both [33] and [37] created climate stress tests for mortgage lending and called for inclusion within operational risk management frameworks for different climate scenarios.

There are public and private stakeholders in the mortgage loan market in Mexico. Most notably, the Institute of the National Housing Fund for Workers (INFONAVIT) and the Housing Fund for State Workers (FOVISSSTE) have become key enablers for low- and middle-income families to achieve homeownership. These actors have facilitated access to financial capital through the development of mortgage-backed products, including fiduciary stock certificates insured with INFONAVIT mortgages (CEDEVIS) and the INFONAVIT Housing Fund (TFOVIS), thus promoting growth for the private housing industry [14]. Nevertheless, issues still exist within housing markets, mainly related to financing, with housing being treated as a financial asset, as discussed in [12].

On the other hand, the authors of [13,14] stated that Mexico’s secondary mortgage finance industry is heavily impacted by international requirements, which makes it vulnerable to fluctuations in major macroeconomic indicators because of its dependence upon outside capital. Mortgage market regulation faces challenges in implementing and providing inter-institutional policies, which hinder adequate urban development and widen disparities between housing quality and affordability. There are continuing challenges even after national housing law ratification [38]. The situation, therefore, demands that business approaches be infused with Environmental, Social, and Governance (ESG) factors, since ESG considerations constitute a formalized paradigm for evaluating the profound influence of the mortgage industry on such important factors as efficiency in terms of energy use, social fairness, and transparency in governance. Implementing this approach will enhance the mortgage sector’s ability to harmonize its financial operation with sustainable development. The following section considers several ESG indicators that can be used across different sectors and how these measures can affect the readiness of the mortgage industry to respond to sustainability needs.

This research integrates social and governance dimensions relevant to a Mexican scenario, including regulatory and housing affordability concerns, through a dual-horizon approach that allows for a deeper analysis of ESG risks applicable to Mexican mortgage institutions.

Disparities inherent in ESG ratings highlight the urgent need for a regulation-driven, industry-segment methodology to assess ESG risks for the Mexican mortgage market. The proposed method addresses the problem of evaluating different aspects of risk through a dual-horizon framework incorporating industry experts’ input. In this sense, this study avoids some of the inadequacies of overly generalizing ESG measures through a context-oriented approach by taking the unique features of Mexico’s mortgage lending market into account while, at the same time, ensuring comparability across institutions. This helps bridge the gap between ESG theory and its application in real-world risk management.

4. Data and ESG Risk Score Structure

4.1. ESG Risk Score and the Dual-Horizon Structure

Risk data collection was conducted through a systematic survey that utilized five-point Likert scales to measure the probability and severity of ESG risks. The survey was framed around three main categories—ESG risks—with four impact dimensions considered within each category, as follows: financial, operational, regulatory/legal, and reputational. For each of the pairs, experts were asked to consider both short (2-year) and long (10-year) periods, leading to a total of 24 estimates for each expert (three types of ESG × 4 impact dimensions × 2 time horizons).

The survey employed specially crafted Likert scales created for each impact dimension. Concerning severity, five-point standardized scales were used, with response categories running from “Minor” (0.2) to “Critical” (1.0) and with clear thresholds specified for financial consequences (from below MXN 100,000 to above MXN 100 million) and regulatory penalties (from below MXN 738,380 to above MXN 2,953,521). To facilitate probability ratings, a frequency scale was used, with values running from “Never” (0) to “Four or more times” (1.0). The entire survey tool, including sample items and scale definitions, is included in Appendix A.

Several methodological precautions were taken to counteract potential subjectivity bias. First, twelve representatives of different types of Mexican mortgage market institutions, including public institutions such as INFONAVIT and FOVISSSTE, private banks, regulators, and government agencies, were chosen to be included to ensure a range of institutional backgrounds. Second, minimum experience requirements were set for all participating experts, requiring at least ten years of experience and an intimate understanding of sustainability issues. Third, specific definitions and concrete examples were given for each type of risk to create a shared understanding for all experts involved.

The risk assessment utilizes a normalized scoring system based on three important variables. The fundamental equation for risk quantification is as follows:

$$R_{ijk}=f\left(S_{ijk},{ P}_{ijk},{ T}_i\right)$$

(1)

where $R_{ijk}$ stands for the risk score for dimension $i$ with impact type $j$ and assessment category $k$; $S_{ijk}$ is the severity score; $P_{ijk}$ is the probability score; and $T_i$ is the ESG risk-type coefficient. The data were gathered via detailed questionnaires completed by twelve industry specialists in line with rigorous confidentiality guidelines to protect respondents’ anonymity and opinions. The specialists gave in-depth assessments of different dimensions of risk, highlighting the need to evaluate both the probability and severity of each ESG factor. For severity scoring ($S_{ijk}$), the severity is quantified on a normalized scale [0,1], with the following discrete intervals; the probability assessment ($P_{ijk}$) is evaluated on the following frequency-based scale [0,1]; and there is a separate dimension that captures the relevance scale [0,1] of the impact of each risk to the respondent’s area, as follows (

Table 1. Scoring scales used for ESG risk quantification.

Dimension	Category	Score
$\mathrm{Severity} \mathrm{scoring} (S_{ijk}$)	Minor	0.20
	Low	0.40
	Moderate	0.60
	High	0.80
	Critical	1.00
$\mathrm{Probability} \mathrm{assessment} (P_{ijk}$)	Never	0.00
	Once	0.25
	Twice	0.50
	Three times	0.75
	Four or more times	1.00
The relevance of the impact	No impact	0.00
	Minimal impact	0.25
	Significant impact	0.50
	Urgent	0.75
	Critical	1.00

):

These intervals are applied across four impact categories, each with specific quantification approaches, as shown in

Table 2. Risk impact categories.

Type of Impact	Quantification	Thresholds	Likert Scale
Financial	$$\begin{gathered} F_i \\ = S_{i,financial} \\ \times P_{i,financial} \end{gathered}$$	* Minor: MXN 1–99,999	Minor: 0.2
		Low: MXN 100,000–999,999	Low: 0.4
		Moderate: MXN 1M–24.9M	Moderate: 0.6
		High: MXN 25M–99.9M	High: 0.8
		Critical: MXN 100M+	Critical: 1
Operational	$$\begin{gathered} O_i \\ =S_{i,operational} \\ \times P_{i,operational} \end{gathered}$$	Minor: 0.2	Minor: 0.2
		Low: 0.4	Low: 0.4
		Moderate: 0.6	Moderate: 0.6
		High: 0.8	High: 0.8
		Critical: 1	Critical: 1
Regulatory	$L_i=S_{i,legal}\times P_{i,legal}$	** Minor: MXN 1–738,3800.2	Minor: 0.2
		Low: MXN 738,381–1,476,760	Low: 0.4
		Moderate: MXN 1,476,761–2,215,140	Moderate: 0.6
		High: MXN 2,215,141–2,953,520	High: 0.8
		Critical: MXN 2,953,521+	Critical: 1
Reputational	$$\begin{gathered} R_i \\ =S_{i,reputational} \\ \times P_{i,reputational} \end{gathered}$$	No impact	Minor: 0.2
		<25% impact	Low: 0.4
		>25% impact	Moderate: 0.6
		>50% impact	High: 0.8
		>75% impact	Critical: 1
Probability (applied to all impact types)	Frequency of occurrence within the assessment period	0 times	Never: 0
		1 time	Once 0.25
		2 times	Twice: 0.50
		3 times	Three times: 0.75
		4 or more times	Four or more: 1.0

* Severity thresholds defined by monetary values in Mexican pesos. “M” refers to millions of pesos. ** Severity thresholds aligned to regulatory penalty ranges. Monetary values are in Mexican pesos.

.

Converting qualitative expert insights into quantitative risk scores relies on well-established risk analysis frameworks that integrate severity and likelihood within a standardized scale. This methodology is based on the works in [4,7] and provides several techniques for numerically capturing expert judgment in sustainability-related financial risks. Using a normalized [0,1] scale with set intervals enables consistent comparison across various risk categories while preserving the detail needed to reflect meaningful distinctions in expert opinions.

Pre-established categories of severity, from minor to critical, and predefined ranges of probability serve as cognitive anchors that help experts facilitate comparable assessments to eliminate persistent subjective ambiguities. This method works particularly well in ESG scenarios, where the unavailability of past information elevates the role of expert foresight [5].

The core of the scoring framework mirrors conventional risk assessment logic, with Risk (R) being calculated as the product of Severity (S) and Probability (P), or R = S × P. This approach is widely recognized in the risk management literature [39,40] and has been adapted specifically for sustainability risk analysis [4]. It is essential to point out that incorporating a dual-horizon perspective further enhances the method’s relevance for long-term strategic planning.

Subsequently, the ESG risk score incorporates the dual horizon, short term (2 years), and long term (10 years) across four key impact dimensions—financial, operational, regulatory/legal, and reputational—to assess the probability and severity. Statistical analyses are used to examine the scoring framework’s internal consistency and dependability. Inter-rater agreement is also determined with the Intraclass Correlation Coefficient (ICC), explicitly referring to the ICC3k values (fixed average raters relevant to the present study design).

$$ICC={\displaystyle \frac{M{S}_R-M{S}_E}{M{S}_R+\left(k-1\right)M{S}_E}}$$

(2)

where $M{S}_R$ is the mean square for rows, $M{S}_E$ is the mean square for error, and $k$ is the number of raters. The results for each risk are reported in

Table 3. Intraclass correlation coefficient.

Risk Type	ICC3k	F	df1	df2	p-Value	95% CI
Environmental	0.7356	3.7820	4	44	0.010	[0.18, 0.97]
Social	0.7332	3.7478	6	66	0.003	[0.30, 0.95]
Governance	0.7384	3.8224	4	44	0.009	[0.19, 0.97]

.

The results of the ICC3k were 0.738 for governance risks, 0.733 for social risks, and 0.736 for environmental risks. These outcomes show substantial agreement among the experts for all ESG dimensions. As defined, ICC values above 0.70 imply high reliability, confirming that expert ratings are consistent and dependable. Each ICC estimate is supplemented with an F-statistic, Degrees of Freedom (df1 and df2), p-value, and the 95% Confidence Interval (CI). The F-statistic is used to test for the null hypothesis, which states that there is no inter-rater agreement beyond what is expected to happen by chance. Degrees of freedom, represented by df1 and df2, relate to the values used to calculate the F-distribution, which are specific to the number of raters and risk items scored. A p-value below 0.05 represents a statistically significant degree of observed agreement. Finally, the 95% confidence interval presents a range where the accurate ICC estimate will be found, with smaller intervals reflecting higher estimate precision. Together, these results validate the reliability and consistency of the measurement approach used to gauge ESG risks, backed by a high level of inter-rater agreement for all three dimensions of risk.

In addition, according to the experts, a repeated-measure design using paired t-tests was implemented to determine if there were differences between short-term (2-year time frame) and long-term (10-year time frame) horizons regarding ESG risks. Statistically significant differences were found in all three categories of ESG. As usual, the t-distribution satisfies the following:

$$t={\displaystyle \frac{\overline{X_d}}{s_d/\sqrt{n}}}$$

(3)

where $X_d$ is the mean difference between paired observations, $s_d$ is the standard deviation of differences, and n is the number of pairs.

  I.

Environmental risks (t = −9.993, p < 0.001);

 II.

Social risks (t = −11.075, p < 0.001);

III.

Governance risks (t = −9.145, p < 0.001).

Empirical evidence supports that experts reliably distinguish between risks estimated for short- and long-term horizons. The very large t-statistics and small p-values, all under 0.001, strongly suggest a systematic difference in perceptions of risks linked to different temporal horizons. The result supports the proposed model of two horizons, thus confirming that temporal perspectives impact experts’ ratings of ESG risks and should be included in risk measurement models.

Finally, Cronbach’s alpha (α) is used to assess the internal consistency of the measurement instrument used.

$$\mathsf{\alpha }={\displaystyle \frac{k}{k-1}}\left(1-{\displaystyle \frac{{\sum }_{i=1}^k{\mathsf{\sigma }}_i^2}{{\mathsf{\sigma }}_{total}^2}}\right)$$

(4)

where k is the number of items, ${\mathsf{\sigma }}_i^2$ is the variance of item $i$, and ${\mathsf{\sigma }}_{total}^2$ is the total score variance. The results showed remarkably high coefficients of reliability, as shown in the following:

  I.

Governance: 0.983;

 II.

Social: 0.964;

III.

Environmental: 0.930.

All values exceeded the generally accepted minimum of 0.70, as defined in [41]. The values confirm that the instrument used to assess each class of ESG risk has a high level of internal consistency, thus confirming that a set of questions or instruments for a given dimension is, indeed, measuring the same concept. These levels of reliability provide strong empirical confirmation of the concordance represented within the system of ESG ratings, once again supporting both instrument construction and agreement among experts’ viewpoints regarding items for a specific class of risks.

4.2. Radial Plot of ESG Risk Scores

Table 4. ESG expert panel summary.

Expert	Institutional Affiliation	Years of Experience	Expertise
E.1	Public mortgage	18	Risk evaluation, housing credit
E.2	Regulatory agency	20	Risk governance, compliance
E.3	Development bank & SOFOM *	10	Sustainable lending, credit risk
E.4	Commercial bank	11	Credit modeling, ESG policy integration
E.5	Technology (Data provider)	12	ESG systems, operational risk
E.6	Independent ESG consultant	14	Sustainability benchmarking, ESG scoring
E.7	ESG modeling (Research firm)	13	Climate risk modeling
E.8	Urban housing policy agency	16	Housing policy, impact assessment
E.9	Oversight & auditing body	19	Institutional risk auditing
E.10	Development bank	13	ESG financing frameworks
E.11	Commercial bank	12	Mortgage ESG implementation
E.12	Regulatory agency	11	Financial supervision, ESG policy

* SOFOM is the Spanish acronym for “Sociedad Financiera de Objeto Múltiple”; Multiple Purpose Financial Company is a type of non-bank financial institution in Mexico that provides credit, leasing, and financial factoring services. They are regulated under Mexican financial law but operate differently from traditional banks.

outlines the aggregated background of these experts by institutional affiliation and expertise. This panel includes professionals from public mortgage institutions, regulatory bodies, development and commercial banks, technology service providers, and independent ESG consultants.

For the visualization of ESG risk, a radar plot is implemented using polar coordinates, which are represented as follows:

$${\theta }_i={\displaystyle \frac{2\pi i}{n}}$$

(5)

$$r_i=v_i$$

(6)

where ${\theta }_i$ is the angular position, and n is the number of dimensions. Likewise, $r_i$ is the radial value, and $v_i$ is the normalized score for dimension $i$. Coordinate transformation is given, as usual, by $x_i=r_i\cos \left({\theta }_i\right)$ and $y_i=r_i\sin \left({\theta }_i\right)$. The radar plots include a normalized scale [0, 1] for all dimensions and color-coding for ESG categories. The type of impact is circled at 0.2 intervals with corresponding severity labels. Finally, there is an angular arrangement optimized for dimension readability. The analysis maintains the granularity of the five-point scale while enabling comparative analysis across different risk dimensions and categories. The results of the first two experts surveyed are shown in Figure 1.

The radar plot highlights the distribution of ESG risks across financial, operational, regulatory/legal, and reputational dimensions, evaluated over short-term (2 years) and long-term (10 years) horizons. The focus on mortgage and financial sector experts ensures that the findings are highly relevant to these industries, where ESG risks have unique implications.

In this way, the plot traces color-coded lines, setting apart risks relating to the environment in blue, social risks in pink, and governance risks in yellow. The radial scale corresponds with the gravity and likelihood of risks, and it is higher when the intensity is higher. Based on the shape and extension of the area colored in each, it is possible to identify dimensions, types of risks, and their relative urgency and long-term implications.

The comparative analysis of the radar plots from Expert 1 and Expert 2 reveals significant differences in their risk perceptions across the ESG dimensions. Expert 1 identifies social risks as the most concerning, precisely in the financial and regulatory/legal dimensions in the short run, with critical severity and high probabilities. Environmental factors are highly important but show a more disseminated distribution among dimensions, reflecting their long-term features. On the contrary, the radar plot for Expert 2 is more homogeneously distributed, placing governance risks as a high concern for all dimensions but primarily within financial and operational categories.

While Expert 1 focuses more on controlling social risks in the short run, Expert 2 emphasizes governance risks and points in the direction of system-wide and structural issues within the organization. Specifically, compared with some regulatory and legal areas, Expert 2 ranks social risks as much less severe and probable, which indicates a difference in the perceived power of forces emanating from society. Moreover, for Expert 2’s judgment, environmental risks are toned down, showing a less critical view of their long-term consequences. Expert 2 classifies social risks as much less serious and probable than Expert 1, indicating a difference in the perceived power of forces emanating from social responsibility.

Finally, Expert 1 focuses on social urgency and environmental persistence, contrasting with Expert 2’s prioritization of governance stability. This shows how individual expertise and focus areas shape the risk listing. Figure 2 shows the results of Experts 3 and 4 to continue with the results and their comparison.

As shown in Figure 2, the radar plots show striking contrasts in the perceived distribution between Expert 3 and Expert 4. Expert 3 has a broad, spiky assessment with large spikes in environmental (blue) and social (pink) risks across most dimensions. Environmental risks top the operational and reputational categories, in which dominating high-severity scores reflect long-term implications. Social risks make their mark in the financial and regulatory/legal dimensions, and the scores indicate both high severity and high probabilities in the short term. Present but subdued governance risks in yellow display a more subdued yet consistent pattern, which suggests that these are secondary priorities in the risk landscape. Expert 4’s radar plot shows a centralized and uniform structure, indicating a far lower risk perception in all dimensions of ESG.

However, Expert 4’s radar plot shows a centralized and uniform structure, indicating a far lower risk perception in all dimensions of ESG. All of the scores of these categories are between “minor” and “low”, meaning that there is limited perceived severity and probability. More balanced E, S, and G risk distributions suggest that there are no dominant drivers, indicating a rather non-pressing or more optimistic assessment of the ESG aspects.

The two experts diverge; these multiple estimates must be combined for a sound decision. Whereas Expert 3 has put more weight on high-severity risks for some categories, Expert 4 perceives risks as generally low, reflecting individual experiences, focus areas, or organizational contexts.

Figure 3 presents the results of the radar plots of the two experts, 5 and 6. The radar plots for Experts 5 and 6 reveal differing perspectives on ESG risks, both in terms of magnitude and distribution across dimensions. Expert 5’s plot is characterized by a strong emphasis on governance risks (yellow), with high severity scores, particularly in long-term regulatory/legal dimensions. The risk of governance fills the overall risk profile, implying a sense of systemic weaknesses in regulation and compliance. The environmental, blue, and pink social risks are relatively moderate; their scores are bunched at the “minor” to “low” severity level in each category, pointing toward a secondary emphasis on these aspects.

In contrast, Expert 6’s radar plot shows a more balanced distribution of risks among the three ESG dimensions. Environmental risks display noticeable spikes in the operational and financial dimensions, while governance risks maintain a steady presence across most categories. Social risks stand out in the reputational and economic dimensions, with the severity levels suggesting these are critical areas for short-term and long-term consideration. Compared with Expert 5, Expert 6 presents a broader evaluation, capturing diverse risk types more uniformly and avoiding any single dimension’s dominance.

These two experts underline variation in the main prioritization of ESG risk. Expert 5’s focus on governance risks underlines a focused approach to regulatory and compliance issues. In contrast, Expert 6 offers a more diffuse risk perception, underlining how ESG factors interact.

It can be seen in Figure 4 that the plots of Experts 7 and 8 portray two sets of ESG risk assessments. Expert 7 states that the radar plot has an expanded structure, placing governance risks in the regulatory, legal, and reputational dimensions. The environmental and social risks come out rather well, with these plots depicting an inclination to stay within low to moderate levels of severity. Hence, it would be presumed that the interlinking consequences of the ESG factors are identified, although there seems to be a search for compliance and reputational stability.

In contrast, Expert 8’s graph is very compact, with a low probability and minor magnitude for all tested ESG dimensions. Such a compact, symmetrical pattern should indicate a perception of limited ESG impact, where no single dimension is critical. Contrary to Expert 7, Expert 8 shows an extremely low perceived level of urgency or risk across the categories assessed, perhaps reflecting high confidence in existing mitigation strategies or simply being conservative about risk.

These scoring contrasts underline the role of individual expertise and context in forming their ESG risk assessment. If Expert 7’s approach underlines some important specifics, particularly regarding governance, Expert 8’s low-risk, flat profile expresses general optimism or minimizes focus on ESG challenges.

Next, Figure 5 depicts the results for Experts 9 and 10. The radar plots of Experts 9 and 10 reflect two different stances in rating ESG risks. Expert 9’s extensive, somewhat elliptical assessment is dominated by environmental hazards, especially in the operational and reputational categories. Social risks are less severe yet very financially dimmed, while governance risks are almost immaterial and appear stable. The general shape is an enormous weighing of long-term environmental and operational concerns.

Expert 10’s plot is also relatively balanced but represents a more equal spread among all ESG risk dimensions. The environmental and governance risks have a wider spread among all dimensions of finance and regulation, while their severity stands in the middle range from 0.4 to 0.6. The social risks are more focused on the short-term dimension regarding immediate operational and financial stability. This wider spread illustrates a more holistic approach toward ESG risk than Expert 9 employs, without the sharp spikes for specific categories.

This comparison of these two assessments brings out the different priorities in the ESG risk assessment. While there is a strong focus on environmental risks by Expert 9, signaling a forward-looking perspective underlined by considerations of sustainability and long-term consequences, Expert 10 shows a relatively balanced distribution, outlining an approach in which weights are set between short-term operational challenges and long-term stability in governance. Figure 6 summarizes the outcome of the recent expert survey.

The radar plots of Experts 11 and 12 represent the last contrast in the ESG risk assessment series and highlight a differently emphasized and perceived notion of risk. Expert 11’s plot displays a different pattern, with large spikes for environmental risk (in blue), particularly in the financial and reputational dimensions. In pink, Expert 11 rated social risks as moderate and grouped them around the economic and regulatory/legal categories, while governance risks are uniformly there for every category but at a much lower level. This setting indicates that Expert 11 considers environmental risk as primary and of long-lasting impact, while social and governance risks are secondary priorities.

In contrast, the plot for Expert 12 shows extreme compactness and centrality with very low scores regarding the severity and probability for all ESG dimensions. These lower, consistent scores across dimensions reflect perceptions of relatively constrained impacts on both the short-term and long-term risks without any outstanding dimensions; hence, Expert 12’s assessment is considered relatively conservative compared with that of Expert 11. This could be interpreted with an assumed belief in the current sufficiency of mitigation strategies against risks or low levels of perceived vulnerability due to ESG factors.

These two assessments underline the variability in expert evaluations and their consequences for strategic planning. While Expert 11 emphasizes urgent actions on environmental risks with medium-level concerns about social dimensions, Expert 12 presents a very optimistic view of ESG impacts, emphasizing stability in all dimensions.

All radar plots highlight the heterogeneity of ESG risk perceptions across the expert panel since the scores reflect their institutional roles and professional background.

4.3. The Dual-Horizon ESG Risk Assessment Framework

The proposed dual-horizon model of ESG risks offers a comprehensive framework for measuring sustainability risks in Mexican mortgage institutions, which is of immense significance for developing countries. As confirmed by Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6, the approach involves several dimensions to formulate an integral risk estimation framework.

The suggested model utilizes a matrix framework to assess ESG risks while considering temporal and organizational materiality factors. The temporal factor differentiates between the short term (2 years) and long term (10 years) to identify key issues and future risks. Moreover, the organizational materiality factor divides risks into four categories: financial impact, operational disruptions, regulatory compliance issues, and reputation loss.

Radar charts serve both as a tool of analysis and a means of presenting different dimensions of risk estimation in terms of what each axis represents. ESG dimensions are distinguished by coloring, whereas radial distance from the center measures the severity and probability of accompanying risks. This radially organized visualization facilitates deeper insight into observed risk patterns obscured by conventional measurement systems.

The proposed methodology combines the probability and significance of impacts to compile meaningful risk rankings, thus enabling meaningful comparisons among ESG criteria.

(a)

Well-ordered assessment frameworks have important practical implications for mortgage institutions and regulating bodies. They set a consistent benchmark for decision making and provide strategies for reducing risk;

(b)

The framework enables the recognition and prioritization of the most significant ESG risks in different operational areas and time horizons;

(c)

Strategic planning: classifying risks as short-term and long-term allows for proper strategic planning and resource distribution;

(d)

Complying with regulations: since there is no standardized ESG metric, a proper ESG risk tool allows organizations to adapt proactively to evolving regulatory scenarios;

(e)

Stakeholder engagement: radar plot visualization easily communicates complex ESG risk analyses to stakeholders.

The proposed framework, which was tested with twelve practitioners, captured essential risk perception trends, including governance topic prioritization, the increasing importance of environmental factors over longer horizons, and divergent opinions regarding social risks.

4.4. ESG Risk Score Clustering Analysis

The complex nature of ESG risk assessments in different dimensions and time horizons requires sophisticated analytical approaches to uncover underlying patterns in expert opinions. This investigation now uses cluster analysis combined with the obtained descriptive radar plots to find underlying structures in expert scores. The cluster approach is consistent with conventional multivariate statistical analyses in adopting a mix of dimensionality reduction strategies and partitioning algorithms to enable automated pattern recognition in high-dimensional data.

The application of dimensionality reduction and clustering methodologies to the examination of ESG risks is especially relevant when considering sustainability risk due to its potential multicollinearity. As Gao et al. [15] noted, ESG assessments are often characterized by overlapping informational variables in different dimensions, suggesting that methodologies based on principal components are best positioned to outline underlying trends. The justification for combining Principal Component Analysis (PCA) and clustering is based on the following three main reasons: (1) ESG data are often characterized by a dimensionality in conjunction with correlated characteristics to which PCA is well suited; (2) sampling of expert opinions is fairly limited (n = 12), so dimension reduction is a necessity to avoid overfitting; (3) potential institution-related bias in ESG assessments is best discerned through unsupervised learning rather than a priori classification [17]. Previous work in [16] demonstrated the effectiveness of such an integrated methodology in uncovering underlying structures in sustainability risk analyses in conditions of low levels of standardization.

In employing multivariate statistical techniques on a small sample size (n = 12), it is of utmost importance to be aware of the limitations, as highlighted in this pilot study. In a similar vein, the PCA method and consequent clustering in this pilot study were used in the exploration of the dataset rather than the reporting of the findings for the whole population. The main observational aspects of the local pattern recognition and heuristic cluster decision are to be made within this expert panel.

Since the purpose was only exploration with these limited data sizes, preliminary verification tests for PCA, such as KMO and Bartlett’s test, were first considered but then overlooked. These tests are without enough statistical power when n is small, and, hence, their results might be unreliable. In this sense, PCA is utilized as a practical method of dimensionality reduction, helping to avoid overfitting when applying a clustering algorithm to the dataset, which has many variables compared with the number of observations.

The clustering methodology involves a two-stage process. PCA is first used to reduce the dimensionality without losing the underlying structure of the dataset, followed by a K-means clustering application to extract unique expert groups. The PCA transformation is defined as follows:

$$Z=XW$$

(7)

where Z represents the transformed data (principal components), X is the standardized input data matrix of the expert evaluations, and W is the matrix of eigenvectors. This transformation maximizes the variance, which is explained along orthogonal dimensions, with each successive component capturing the maximum remaining variance. The K-means clustering algorithm is subsequently applied to the PCA-transformed data. This method partitions observations into K clusters by minimizing the within-cluster sum of squares:

$${ arg min}_C\sum _{i=1}^k\sum _{x\in C_i}{‖x-{\mu }_i‖}^2$$

(8)

where $C$ represents the set of clusters, $x$ represents each data point, and ${\mu }_i$ is the centroid of cluster $i$. The optimal number of clusters is determined using silhouette analysis, which measures how well the resulting clusters are separated [42].

The application of PCA to the expert opinion rating dataset showed that two principal components explained a combined total of 92.83% of the overall variability (Principal Component 1, PC1: 85.95% and PC2: 6.88%). The large percentage of the variance explained indicates that a two-dimensional representation captures the underlying trends in expert risk assessments. Clustering analysis identified two distinct groups of experts in the pool of twelve, as shown in Figure 7.

Cluster 0, which was formed by Experts 1, 2, 3, 5, 6, 7, 9, 10, and 11, accounts for 75% of the total expert population and has higher scores in the perception of risk in most ESG dimensions. Specifically, experts in this cluster show a notable difference in short-term and long-term risk assessments that align with firm statistical findings of long-term effects. Cluster 1 includes Experts 4, 8, and 12, accounting for 25% of the overall group of experts. This cluster has overall perception scores that are much lower than average for all ESG factors. The negative pattern along Principal Component 1 indicates a generally divergent attitude toward risk assessment with consistently lower assessments regarding severity and probability.

The sharp separation of clusters on PC1, which explains 85.95% of the variance, suggests a fundamental split in the intensity of risk perception more than a differing emphasis on specific ESG dimensions. This finding indicates that the primary source of variation among expert assessments lies in their general threshold of risk perception more than in the significance attached to different risk categories.

The cluster configuration identified provides additional contextual information to improve the interpretation of the results. The professionals working for state-owned housing institutions, namely, INFONAVIT and FOVISSSTE, are primarily positioned in Cluster 0, enhancing their potential to make more risky appraisals. This has been confirmed in the previous work by [11], which set out specific profiles of risk perception distinguishing between state-owned and private financial institutions in Mexico.

The location of Experts 4, 8, and 12 (Cluster 1) in the negative part of PC1 aligns with their membership in important, well-funded financial institutions with sophisticated risk management systems. This is also in line with the result in [43], which demonstrates systematic differences in the ESG risk scores associated with the sophistication of such institutions’ risk management approaches.

An analysis of the contributions of the different features to the PCA indicates that environmental-risk-related metrics are the most important in separating clusters, particularly concerning the time dimension. The substantial contribution of long-term environmental risk variables to PC1 (coefficient = 0.76) confirms the earlier finding of an increase of 20% in perceived environmental risk severity over 10 years.

The cluster analysis findings constitute a strong quantitative foundation for identifying systematic patterns in expert opinions on risks, thereby supporting the qualitative findings derived through radar plot visualization. The occurrence of clear clusters of experts with different profiles of risk perception deepens the understanding of institutional and contextual factors that are most likely to affect ESG risks in Mexican mortgage markets.

5. Discussion of the Results

5.1. ESG Risk Assessment Results

The previous statistical analyses reveal exceptionally high internal consistency across all risk categories, with Cronbach’s alpha values of 0.983 for governance, 0.964 for social, and 0.930 for environmental risks. The Intraclass Correlation Coefficient (ICC) values further validate the proposed modeling, with ICC3k values (appropriate for this study design with 12 raters) of 0.738 for governance risks, 0.733 for social risks, and 0.736 for environmental hazards. These values demonstrate substantial agreement among experts across all ESG dimensions, contrasting with the high variability in ESG ratings reported in [2].

A particularly significant finding emerges from the proposed temporal analysis comparing short- and long-term risk assessments. Paired t-tests reveal highly significant differences (p < 0.001) between the two temporal horizons for all three ESG categories: environmental (t = −9.993), social (t = −11.075), and governance (t = −9.145). These results provide preliminary evidence for the dual-horizon approach, suggesting what has been observed in the literature regarding the increasing importance of ESG risks over time and aligning with the recommendations of the TCFD, which are provided in [5], for incorporating different time scales in ESG risk assessment. The consistent statistical significance across all ESG dimensions argues against the notion that environmental risks would be the only category with significant temporal variation. Instead, the empirical findings suggest that all ESG risks are perceived to intensify over longer time horizons, with the most substantial temporal effect being observed for social risks (t = −11.075).

The high level of measurement consistency observed in all three risk categories (as evidenced by Cronbach’s alpha values exceeding 0.93) supports the inference made in [4] that governance factors often serve as predictors of overall risk management capability. The firm internal consistency found in the social risk assessments (α = 0.964) is noteworthy, as it challenges the view expressed in the prior literature about the difficulties in standardizing social impact measures.

The proposed integrated approach, which examines different impact dimensions (financial, operational, regulatory/legal, and reputational), proved valuable for capturing the multidimensional nature of ESG risks. The statistical significance of temporal differences across all categories indicates the claim in [7] that any realistic assessment of ESG risks should consider a range of potentially interacting impact pathways.

The results are compared with established international benchmarks to contextualize the findings within the global landscape of ESG risk assessment. The high internal consistency for governance risks (α = 0.983) and strong inter-rater agreement (ICC = 0.738) align with patterns observed in MSCI ESG ratings for Latin American financial institutions, where governance challenges often receive greater weight than environmental or social factors due to differences in regulatory frameworks and institutional development; see [34].

The significant temporal variation in risk perceptions (with t-values ranging from −9.145 to −11.075 and p < 0.001) reflects trends similar to those observed in the Sustainalytics sector risk reports for the financial sector. These reports highlight the increasing materiality of all ESG risks for financial sustainability as policies and regulations become more stringent over time, particularly climate transition risks, as noted in [44]. Hence, the strong statistical significance suggests the evolutionary nature of ESG risks.

The high degree of internal consistency in assessing social risk (α = 0.964) is in striking contrast to the prevailing emphasis in global frameworks such as the Task Force on Climate-Related Financial Disclosures (TCFD), which sometimes overlooks social aspects. This contrasts with a need to balance context-related social factors in emerging economies, such as in the Mexican case, where persistent disparity and housing affordability pose unique calls to mortgage institutions that are poorly captured by universal global frameworks.

The enduring significance of time differentiations across the three pillars of ESG aligns with the conclusions drawn in the World Economic Forum’s Global Risks Report [44], identifying governance failure, social unrest, and climate change inaction among the most pressing global risks expected in the next decade. These global views are warranted by evidence documented in [43,45], and are supplemented with relevant information concerning Mexico’s housing market and compliance with ESG guidelines. These comparative studies highlight similarities and divergent stances between the suggested model in evaluating Mexico’s mortgage market and other global ESG risk measurement methodologies.

5.2. Expert Variability Analysis

A comparison of differences between evaluations made by experts reveals patterns akin to trends linked to professional backgrounds and associations. Significantly, specialists with expertise in regulatory compliance and governance (Experts 5 and 7) strongly tended to stress risks specific to governance, giving much importance to regulatory and legal issues. Experts with long-term strategic planning expertise (Experts 3 and 11) placed great emphasis on environmental risks, specifically concerning long-term time horizons.

The results of this research show significant differences by institutional type. The state-owned mortgage institutions INFONAVIT and FOVISSSTE reflect intense stress on social risks, consistently with wide-ranging mandates that include financial and non-financial purposes, e.g., social policy purposes. In contrast, private financial institutions show greater sensitivity to governance risks, especially those specific to regulatory compliance and reputation management.

Institutional arrangements in Mexico influence varied understandings of environmental risk. Changes to sustainable housing laws made in 2023 can explain differences in the ecological assessments of risks. Additionally, the differences between perceived and actual environmental risks, especially for persons living in coastal areas or areas that suffered from long-term drought, could be due to unequal geographical distribution of institutional funds; housing in these settings tends to be more sensitive to such risks.

The consistent tendency to make lower evaluations of risks for certain experts (4, 8, and 12) for all categories can be interpreted as an implicit suggestion that, when one is affiliated with more prominent and older financial institutions with advanced risk management structures, perceptions regarding ESG exposure are strongly influenced by the degree of maturity in institutional risk management. Conversely, experts affiliated with newer, specialized establishments show a heightened sensitivity to specific categories of risks depending on their operational agendas.

5.3. Policy and Operation Implications

The conclusion drawn from this study has important policy-making recommendations for strategic ESG risk assessment in mortgage markets for the Mexican case and other emerging economies. There is a general view concerning the need for supervisory requirements to build resilience, improve transparency, maintain compliance with regulations, and encourage sound lending. The environmental risk analysis outlined in this report is a critical element in residential housing policy development, specifically through green mortgage product implementation and sustainable construction initiatives. In addition, the different levels of social risk highlight the need for policy responses designed to enhance access to low-cost housing and support the availability of credit to socially and economically marginalized groups.

These findings are meant to be integrated into the ESG criteria to enhance the overall level of risk management initiatives and to foster greater confidence in stakeholders. The dual-horizon methodology provides policymakers with essential insights into the time dynamics of ESG factors so that they can design emergency responses based on long-term sustainability targets (p < 0.001 for all ESG dimensions).

5.4. Methodological Limitations

In response to the limitations of expert-based approaches, different adjunctive approaches are used in both data collection and data analysis. At first, these shortcomings were addressed by synergizing multiple perspectives through collaborations involving experts from other institutional and professional backgrounds, which made it possible to identify converging trends independently of various organizational contexts. The interrater reliability assessments were analyzed using statistical procedures involving the calculation of the Intraclass Correlation Coefficient (ICC) and Cronbach’s alpha, producing acceptable values.

The survey design includes precise definitions and explanations for every risk sector, producing reduced variations in experts’ understanding of the questions being evaluated. Future research can improve this process by incorporating qualitative inputs from experts based on market and climate conditions, quantitative data analysis, and the development of specific sector-oriented risk indicators that can be systematically tracked over time. Future research may also involve more extensive and more heterogeneous sets of experts, permitting the observation of changes in perceptions of ESG risk over time.

5.5. Synchronizing Dual-Horizon ESG

This subsection presents several actions to synchronize dual-horizon ESG.

(1)

Implementation of a transition program that systematically structures initiatives from initial compliance through progressive positioning to sustainable practices;

(2)

Implementation of strategies that bridge short-term performance measures with long-term sustainability outcomes for resource allocation into different temporal priorities.

These mechanisms enable mortgage institutions to reduce short-term operating risks, particularly in governance areas, while preparing for mounting long-term environmental difficulties, as demonstrated by the 20% increase in perceived severity over the 10-year time horizon.

5.6. Technological Solutions

Advanced technologies offer significant opportunities for enriching ESG risk frameworks, especially as practice shifts toward methodologies based upon data-driven approaches instead of expertise-informed qualitative appraisals. Techniques such as those of advanced analytics enable quicker management of unstructured ESG data, thus enabling the consideration of these inherently subjective elements, especially within the social pillar, where the expert judgment found wide variations during this investigation.

The previous factors are especially relevant to emerging economies, such as that of Mexico, whose development is impeded by substantial limitations regarding dataset quality and the rate of change in institutional ESG maturity. By integrating these technologies in a dual-horizon model, mortgage institutions improve operating efficiency in gathering information, facilitate risk management automation, improve environmental scenario modeling, and increase the accuracy of ESG assessments across various industries.

5.7. Stakeholders’ Perspectives

Given the lack of standardized metrics for ESG management regarding Mexico’s mortgage market, this study is a key guide for stakeholders. However, it is essential to recognize that ESG risks in mortgage markets integrate various stakeholders whose individual priorities differ and whose perspectives also diverge considerably. For example, homebuyers evaluate environmental risks through lenses such as maintaining property values and energy costs, which are considerations quite different from those at the portfolio level, which concern most institutional analyses. Regulators are most interested in resilience and monitoring policy compliance, and they may view governance risks differently. Community organizations typically prioritize social considerations, such as housing affordability and fair access to credit (elements that may not be fully considered within traditional risk management).

The varied perceptions expressed should be considered when designing short- and long-run ESG risk assessments. While the experts in this study highlight the significance of governance risks, neighborhood residents will likely only see environmental risks through immediate livability concerns and long-run climate change considerations. Social risks, which have high variability in rank across the experts’ points of view, will likely be given relatively consistent priority by community stakeholders interested in housing affordability and access.

These distinctions highlight the necessity of placing the findings obtained in this research in the broader stakeholder context. Broadening the dual-horizon approach to cover further stakeholders is projected to identify noteworthy differences in perceived risks, illuminate issues that earlier approaches had not adequately explored, and enable policymakers to draw on a more unified and effective policy formulation system.

5.8. Cluster Analysis of Expert Assessment Patterns

The PCA-driven clustering resulted in a two-dimensional representation explaining 92.83% of expert assessments (Figure 7). By optimizing silhouette values in K-means clustering, two distinct clusters of experts were identified. Cluster 0 (n = 9) is characterized by elevated perception in all ESG dimensions, particularly environmental and regulation risks. Cluster 1 (n = 3) has much lower risk assessments with consistently low values in all categories reviewed.

The significant difference among the groups in Principal Component 1 (PC1), which is responsible for 85.95% of the variability, reveals a marked difference in the degree of risk perception rather than a varied emphasis on individual dimensions of ESG factors. This pattern suggests that professional assessments differ primarily in terms of their overall risk perception criteria rather than the relative emphasis on individual risk categories.

The results provided through the cluster analysis provide additional insight into the statistical findings presented in Section 5.1. Experts with a high degree of internal consistency in their assessments, reflected through Cronbach’s alpha, belong to a homogenous group and, thus, emphasize the reliability of measurements in line with particular institutional views about ESG risk. Experts 4, 8, and 12 (Cluster 1), whose unique positions in the negative range of PC1 align with their affiliations with large financial institutions with sophisticated risk management systems, validate the conclusions introduced in [17], addressing institutional maturity and their ability to understand ESG risk.

Regarding the ESG risk perception in the mortgage sector, a key institutional factor influencing it lies in the distinct mandates of financial entities (public and private) in relation to operational environments. Public mortgage institutions such as INFONAVIT and FOVISSSTE, whose constitutional missions include promoting social housing and supporting vulnerable populations, often emphasize long-term governance and social risks, reflecting their developmental mandates and public accountability. In comparison to private commercial banks and SOFOMs, these tend to prioritize short-term environmental and reputational risks, specially the probability of default, which is a risk that could affect investors’ perception. These distinctions are not merely strategic but also shaped by regulatory asymmetries, as follows: public institutions are often subject to different transparency, reporting, and performance requirements compared to private actors. Considering these institutional differences is crucial for plotting differentiated ESG supervisory frameworks that align risk assessments with institutional incentive structures, goals, and capacities. Hence, is necessary to consider that policymakers and regulators in Mexican financial sector should, therefore, consider flexible ESG guidelines that preserve comparability in the mortgage sector but allow for contextualized application across the public–private spectrum.

6. Conclusions

At present, there is no unique or standardized ESG measurement for financial institutions and even less so for the mortgage sector. This study offers proactive tools for addressing current and future issues related to ESG risk management in the mortgage business. The statistical results exhibit an extremely high degree of internal consistency for all three risk categories (Cronbach’s alpha coefficients: governance: 0.983; social: 0.964; and environmental: 0.930) and a high inter-rater agreement (ICC values above 0.73), thus confirming the validity of Cronbach’s test.

The identified governance risks highlight the importance of complex compliance frameworks and strong control platforms in properly addressing regulatory and reputational issues. An environmental long-term risk analysis showed a statistically significant increase in perceived severity (t = −9.993, p < 0.001), reinforcing demands for strategic measures promoting climate resilience, including environmental mortgage products and green housing programs. Finally, social risks showed the most variation for professional opinions (t = −11.075, p < 0.001), reflecting demands for targeted measures that address housing affordability and fair access to capital resources.

Using cluster analysis considerably adds to the understanding of the dynamics involved in ESG risk perception in Mexico’s mortgage market. The discrimination between two clusters of experts—with one group demonstrating greater sensitivity to risks (representing 75% of experts) and the other group having more conservative assessments (representing 25% of experts)—provides important insight into key institutional factors driving sustainability risk assessments. The prevalence of state-related experts in the cluster expecting high risk suggests that state-controlled mortgage institutions might place a primacy on long-term ESG factors, especially environmental risks. This is a critical insight with important implications for policy consistency across Mexico’s mortgage market, since it reveals a possible disparity in assessing risk between state-owned and independent lenders. A future line of inquiry involves examining whether these systematic assessments in each cluster are associated with differences in operational risk management strategies and lending policies, resulting in uneven compliance with sustainability measures across Mexico’s mortgage market.

It is important to point out that this investigation provides a complete framework for assessing ESG risks for the financial industry through a dual-horizon approach. The approach utilizes expert ratings of quantitative and qualitative elements applicable to mortgage credit institutions. This approach’s differentiation between short-term (2-year) and long-term (10-year) views allows for the fundamental comprehension of the temporal dynamics involving ESG risks. Paired t-tests indicated significant differences between both timeframes across all areas of ESG.

It is also essential to mention some limitations associated with the analyses of the research findings. The specific focus on Mexican mortgage institutions introduces limiting factors, such as translating the findings to possibly different contexts, while professional judgments include subjective determinations. Future research could build upon this methodology by incorporating quantitative market statistics with professional evaluations, developing industry-focused risk indicators, and tracking ESG risks across changing market conditions and regulatory environments.

The dual-horizon ESG risk framework addresses a critical gap in the analysis of sustainability risks in the mortgage markets of developing countries. An initial survey involving twelve industry experts confirmed the framework’s effectiveness in identifying key trends in perceived risks. Most importantly, the results indicate that perceptions of environmental risks have evolved while governance issues remain persistent. Unlike traditional approaches, this framework is specifically designed to capture the unique features of mortgage lending in emerging markets, incorporating aspects such as housing affordability, exposure to climate risks, and challenges in regulatory compliance.

Based on the differentiated ESG risk perceptions identified among institutions and the dual-horizon analysis conducted, it is necessary to propose a set of operational and institution-specific policy recommendations. First, financial regulators should establish a standardized ESG risk framework with short- and long-term risk indicators, while public mortgage institutions (such as INFONAVIT, FOVISSSTE, and CONAVI) should prioritize environmental criteria in loan origination and support ESG-compliant housing programs. Commercial banking and the SOFOMs sector should integrate ESG scores into credit risk models and establish internal ESG governance structures. To support these implementations, a phased regulatory roadmap is recommended, starting with basic ESG disclosure to the mortgage sector and progressing toward full integration with supervisory and capital adequacy frameworks. Finally, the creation of a national ESG risk observatory and specific capacity-building initiatives are required to strengthen institutional capacities and improve data-driven decision making in the Mexican mortgage ecosystem.

Future research studies could build upon this pilot study by including the opinions of a greater variety of stakeholders in the mortgage industry, including owners of properties, community associations, building experts, and environmental bodies, as well as state and local authorities. Comparative analyses across regions with different institutional and cultural structures will make the dual-horizon model applicable globally. Above all, the proposed model must be regularly updated to include developments in research on sustainability and advances in data availability. Likewise, future studies could involve more experts, combine qualitative and quantitative methods, and make repeated measurements to track changing ESG-linked risks over time.