Institutional theory conceptualizes institutions as the “rules of the game” that shape economic and social behavior through formal rules (laws, regulations, contracts) and informal constraints (norms, conventions) (North, 1990). In this perspective, policy outcomes depend not only on the content of regulations but also on how institutional arrangements allocate authority, define incentives, and structure enforcement. A critical concept for this study is that of “institutional voids,” gaps, inconsistencies, or weaknesses in the institutional framework that hinder the translation of policy into effective practice (Sydow et al., 2020; Hasan and Bondy, 2025).

In the context of Saudi social housing, institutional voids may appear as missing mandates for post-occupancy monitoring, fragmented oversight over sustainability performance, or misaligned financial regulations that fail to reward long-term efficiency and resilience. Rather than assuming that more or stricter regulation will automatically strengthen sustainability, an institutional perspective directs attention to how existing rules interact, how responsibilities are distributed, and how incentives are structured along the housing delivery chain.

Complementing this, the concept of the “accountability trap” captures situations in complex, multi-actor governance systems in which responsibility for outcomes becomes so diffuse that no single organization can be meaningfully held to account (Eilstrup-Sangiovanni and Hofmann, 2024; Lee and Ospina, 2021; Papadopoulos, 2024). In such settings, overlapping mandates, shared competences, and unclear performance metrics can lead to a focus on short-term deliverables (e.g., units constructed) rather than long-term outcomes (e.g., energy performance, resident wellbeing), undermining policy ambitions even when technical regulations are in place.

In Saudi Arabia’s social housing sector, where MOMAH leads policy, NHC orchestrates implementation, ROSHN and private developers execute large-scale projects, and municipalities and utility providers manage local services, the risk of an accountability trap is high. Without clearly assigned responsibilities for post-occupancy performance, data collection, and disclosure, sustainability requirements can be formally met at the design stage but eroded during procurement, construction, or operation. This theoretical lens helps explain why sophisticated regulatory instruments may co-exist with persistent implementation gaps.

To operationalize these institutional and accountability concepts for empirical analysis, the study develops a six-factor model of the enabling environment for sustainable and climate-smart social housing governance. The enabling environment is defined here as the set of institutional, regulatory, financial, technological, and learning conditions that collectively determine whether sustainability and resilience goals are integrated into housing delivery at scale. The six factors are:

Institutional Integration and Role Clarity: the degree to which mandates and responsibilities for planning, financing, delivering, and operating social housing are clearly allocated and coordinated across ministries, agencies, and developers.

These six factors are analytically distinct but empirically intertwined. Technological and digital infrastructure (Factor 4) underpins effective monitoring and transparency (Factor 5), while performance-linked finance (Factor 3) can only be operationalized where roles and standards (Factors 1 and 2) are sufficiently clear. Together, they provide the scaffolding for the four pillars of the S-CSHGM presented in the Discussion, which integrates these governance factors into a coherent reform pathway.

Recent advances in AI, Internet of Things (IoT), and building automation systems have profound implications for housing governance. Smart and self-learning buildings can adaptively manage energy consumption, indoor environmental quality, and maintenance schedules based on real-time data and predictive algorithms, thereby reducing operational costs and improving comfort (Ademowo et al., 2024; Azzi et al., 2024; Ding and Sun, 2024; Shamsuddin and Srinivasan, 2020). Digital twins and portfolio-level analytics can further support planning and investment decisions by simulating scenarios and identifying systemic inefficiencies (Alnaser et al., 2024; Arsecularatne et al., 2025).

From a governance perspective, these technologies expand what can be measured, attributed to, and optimized across social housing portfolios. AI-enabled analytics depend on standardized data infrastructures (Factor 4). However, once established, they can significantly strengthen monitoring and accountability (Factor 5) by generating real-time performance indicators, anomaly alerts, and evidence for compliance or non-compliance. They also create new demands for skills and organizational learning (Factor 6), as planners, developers, and regulators must interpret and respond to digital evidence.

In the context of Vision 2030 and the National Strategy for Data and AI, integrating AI-ready digital infrastructures into the enabling environment for social housing is therefore not merely a technical enhancement but a governance necessity. The analytical framework developed here explicitly incorporates technological and digital infrastructure as a core factor in understanding how institutional voids and accountability traps manifest—and how they might be overcome—in Saudi Arabia’s social housing system.

The data from the three methods were analyzed both independently and collectively.

Policy Documents: Coded data from NVivo 14 were analyzed using frequency and co-occurrence analyses to identify the most and least developed policy domains, with particular attention to how each of the six governance factors was addressed or left incomplete.

This triangulated design is appropriate for complex governance domains where experimental or large-N statistical designs are not feasible and where the primary aim is to build and refine an analytical model rather than to produce statistically generalizable estimates. In this study, the Delphi component is therefore used as one pillar of a broader evidence-based, complementary systematic policy analysis and structured international benchmarking—rather than as a stand-alone decision-making tool.

The systematic analysis of 42 policy documents confirms a sophisticated yet fragmented institutional architecture for sustainable housing delivery. The policy intent is clear and ambitious, but two critical, interrelated issues weaken its operationalization across institutional integration and role clarity (Factor 1), procurement and finance alignment (Factor 3), technological and digital infrastructure (Factor 4), and monitoring, transparency, and accountability (Factor 5).

The two-round Delphi survey robustly validated the gaps identified in the document analysis. It yielded a prioritized list of interventions mapped to the six governance factors of the analytical framework.

The structured benchmarking against Singapore, Abu Dhabi, and Qatar provided a clear mirror for Saudi Arabia’s governance landscape, revealing both the scale of the gap and the viability of the solutions proposed by the Delphi panel. The comparison also demonstrated how mature systems integrate technological and digital infrastructures into their governance regimes, using data and digital tools as enablers of enforcement, transparency, and learning.

A key distinction underpins this study: the difference between the diagnostic analytical framework and the prescriptive governance model. The six-factor framework (Section 2.3) served as our analytical lens to systematically diagnose gaps across institutional, regulatory, financial, digital, accountability, and learning dimensions. In contrast, the S-CSHGM is an operational governance model. It is a prescriptive, integrated blueprint that specifies the concrete pillars, actors, mechanisms, and feedback loops required to reconfigure the system. While the framework identifies what is broken, the model proposes how to fix it, translating diagnostic findings into a structured template for policy design and implementation.

Conceptually, the S-CSHGM integrates and tailors broader governance theories to the specific context of state-led housing delivery. It operationalizes the concept of polycentric governance by designing specific coordination bodies (e.g., the National Housing Sustainability Council) and data flows to manage multi-actor complexity. It moves beyond generic performance-based regulation by bundling performance mandates with performance-linked finance and performance-based data transparency, creating a mutually reinforcing incentive system explicitly designed to overcome the identified accountability trap. Its distinctive contribution lies in its holistic architecture and its treatment of Technological and Digital Infrastructure as a core governance pillar, making it an AI-ready model for climate-smart implementation.

The analysis of the documents reveals a significant discrepancy between policy objectives and institutional capacity, a common manifestation of what institutional scholars identify as “institutional voids” – gaps in the governing rules that impede the effective implementation of policies. Saudi Arabia’s regulatory framework, primarily founded on Mostadam and SBC 601, demonstrates substantial technical sophistication. However, this regulatory progress has not been matched by parallel developments in governance structures.

The core problem is the misalignment between broad sustainability mandates and narrow, weakly enforced accountability mechanisms. While MOMAH establishes ambitious policies, neither NHC nor ROSHN has formal accountability for post-occupancy performance, leading to what governance experts call the “accountability trap”. This explains why sustainability compliance peaks during design and construction but dissipates during operations–the institutional incentives are structured around delivery milestones rather than lifecycle performance. The finding that only 38% of policy documents specify enforceable performance indicators underscores this systemic gap between aspiration and enforcement, reflecting a governance system that prioritizes output (units built) over outcome (sustainable communities). Viewed through the six-factor framework, this gap is most visible in weak institutional integration and role clarity (Factor 1) and underdeveloped monitoring, transparency, and accountability mechanisms (Factor 5), which together prevent the regulatory instruments (Factor 2) from translating into verifiable performance at scale.

The Delphi results provide strong empirical evidence that financial and procurement mechanisms represent the most potent–and feasible–intervention points for systemic reform. The expert consensus around performance-linked procurement aligns with transaction cost economics, which emphasizes how contractual arrangements can shape organizational behavior. Currently, the dissociation between financial flows and sustainability outcomes creates a form of “moral hazard”—developers bear no financial consequences for long-term underperformance.

The proposed integration of Mostadam certification into PPP payment milestones represents a fundamental shift from input-based to outcome-based contracting. This approach has proven transformative in Singapore’s Green Mark model, where performance-linked contracts have driven continuous innovation. Similarly, the recommendation for green mortgage products applies behavioral economics principles to create market-driven demand for sustainability, effectively leveraging REDF’s substantial financial influence to reshape both supply and demand dynamics.

When interpreted as reforms to the “Procurement and Finance Alignment” (Factor 3), these measures directly address the institutional voids identified in Section 4.1.2 by linking public support and revenue flows to lifecycle performance rather than to delivery speed alone. Moreover, performance-linked contracts can be designed to require not only compliance with energy and water benchmarks, but also the deployment of smart meters, building management systems, and digital reporting tools, thereby creating a bridge to technological and digital infrastructure (Factor 4).

The high priority given to unified data platforms and public KPI dashboards (Delphi Levers 2 and 5) highlights the importance of information transparency in modern governance. Current data silos between the Balady, Mostadam, and NHC platforms represent “digital fragmentation”—a situation where technological tools exist but are disconnected from governance processes. In terms of the analytical framework, this fragmentation weakens both the “Technological and Digital Infrastructure” factor (Factor 4) and the “Monitoring, Transparency, and Accountability” factor (Factor 5), limiting the system’s capacity to detect underperformance, attribute responsibility, and learn over time.

The proposed integrated dashboard would serve multiple functions: an accountability mechanism through public disclosure, a performance management tool for agencies, and a feedback loop for continuous policy improvement. International experience from Qatar’s GSAS public reporting demonstrates how transparency can create “reputational markets” that incentivize developers to exceed minimum standards. Furthermore, systematic performance data collection is a prerequisite for evidence-based policy refinement, addressing the current reliance on anecdotal evidence.

Crucially, standardized digital data streams also create necessary preconditions for using AI-enabled analytics—such as anomaly detection, predictive maintenance, and portfolio-level optimization in smart buildings—which recent studies show can significantly reduce energy use while maintaining comfort. In this sense, AI and digital technologies do not simply add technical efficiency; they operate as governance tools that enlarge the informational base on which accountability and adaptive learning depend, directly strengthen Factors 4 and 5, and connect housing governance to the broader Vision 2030 and SDAIA-led digital transformation agenda.

Building on these empirical findings, we propose the Saudi Climate-Smart Social Housing Governance Model (S-CSHGM) (Figure 1). This framework is predicated on the understanding that sustainable outcomes emerge from a coherent governance ecosystem that connects policy, finance, data, and learning within a continuous feedback loop. The model operationalizes the six governance factors identified in the analytical framework by clustering them into four interdependent pillars that together address the institutional voids and accountability traps revealed in the results. Each of these four pillars is translated into specific institutional reforms, contractual mechanisms, digital platforms, and capacity-building initiatives, and is sequenced in the implementation roadmap in Table 8. Together, they move the analysis from identifying gaps to outlining a realistic, time-bound pathway for climate-smart, AI-ready implementation.

Institutional Integration and Role Clarity - addressing governance fragmentation

While the S-CSHGM provides a coherent roadmap, its implementation faces significant, non-trivial barriers that must be acknowledged and managed:

Political and Bureaucratic Inertia: The proposed NHSC would require unprecedented cross-ministerial cooperation. Overcoming entrenched silos and competing mandates will require sustained high-level political sponsorship, likely from the Council of Economic and Development Affairs (CEDA).

To address these challenges pragmatically, Table 8 sets out a phased implementation roadmap (2025–2028 and beyond) that moves from institutional foundation (Phase 1), through digital integration and dashboard prototyping (Phase 2), to activation of performance-linked procurement and finance (Phase 3) and full operationalization with annual reporting and continuous learning (Phase 4). Each phase is designed to tackle specific barriers: Phase 1 focuses on creating the NHSC, HPU, and cross-agency KPIs; Phase 2 on resolving data-governance and interoperability issues through pilots; Phase 3 on gradually embedding PLF clauses and green mortgage products; and Phase 4 on scaling up training, audits, and public transparency. This sequenced approach mirrors the logic of polycentric governance by strengthening multiple centers of decision-making and accountability over time, rather than relying on a single, one-off reform. In doing so, it mirrors the logic of “polycentric governance,” gradually strengthening multiple centers of decision-making and accountability rather than relying on a single top-down directive.

This study makes specific and substantive contributions to the scholarly literature on housing and urban governance in the Gulf Cooperation Council (GCC) states. While existing GCC research has valuably mapped policy trajectories, institutional arrangements, and the technical aspects of rating systems, it has not previously operationalized institutional theory and the accountability trap into a measurable, multi-factor governance model for the housing sector. By diagnosing gaps across six interlinked dimensions and proposing the integrated S-CSHGM, this research provides a novel, holistic lens to understand the ‘implementation gap’ common in state-led, rapid-development contexts. Furthermore, by centralizing digital infrastructure and AI-readiness within its governance model—connecting it directly to national strategies like Saudi Arabia’s National Strategy for Data and AI—the study addresses a critical omission in regional literature, which has largely overlooked the governance implications of digital transformation for sustainable housing outcomes. Thus, the study shifts the analytical focus from assessing isolated policy instruments to designing connected governance ecosystems capable of delivering verifiable performance.

Beyond its regional contributions, this study advances governance theory and housing scholarship more broadly. First, it extends the concept of the “implementation gap” from a technical challenge to a governance system failure, demonstrating how institutional misalignments can persist even amidst regulatory sophistication. By operationalizing institutional voids and accountability traps through six concrete governance factors, the study shows how abstract institutional concepts can be translated into diagnosable weaknesses and actionable reform levers in a specific sectoral context. Second, it provides a rare empirical analysis of housing governance in the Gulf context, challenging the presumption that strong state capacity automatically ensures policy implementation. Third, the study advances debates on digital transformation and AI in the built environment by framing technological and digital infrastructure as a governance factor rather than a purely technical domain. AI-enabled smart-building systems, digital twins, and integrated dashboards are conceptualized here as instruments that expand what can be known, measured, and governed in social housing portfolios, thereby reinforcing accountability and adaptive learning rather than simply improving operational efficiency.

The research further illuminates the limitations of top-down policy approaches in complex multi-stakeholder environments. The findings advocate that the provision of sustainable housing necessitates polycentric governance arrangements—a framework comprising multiple, formally independent yet functionally interdependent centers of decision-making, which the S-CSHGM aims to coordinate. Future research should explore several pathways emerging from this study. Longitudinal analysis could track the implementation of the S-CSHGM framework to identify adaptation patterns. Comparative studies could examine governance transitions in other GCC countries to identify regional patterns. Quantitative research could model the economic impacts of performance-linked financing, while ethnographic studies could examine how frontline bureaucrats interpret and implement these new sustainability mandates. Further work could also investigate how AI-enabled tools, such as predictive maintenance algorithms or occupant-centric comfort optimization, are integrated into governance arrangements and how they reshape professional practices, resident experiences, and the politics of accountability in climate-smart social housing.

As a qualitative, governance-oriented study, this research design has several methodological limitations that shape how the findings should be interpreted. First, the Delphi component, while employing a diverse, purposively sampled panel, is subject to the inherent limitations of elite-expert consultation. This method intentionally engages senior, influential stakeholders, whose perspectives—though essential for understanding high-level policy and implementation barriers—may not fully capture ground-level operational challenges or citizen experiences. The consensus reflects the synthesized judgments of these informed professionals. Although the panel’s composition across five key sectors (government, development, finance, academia, civil society) was designed to mitigate sectoral bias and provide a holistic view, the results cannot be statistically generalized to the wider population. Consequently, the Delphi findings should be understood as a structured synthesis of prioritized professional judgment, which was subsequently triangulated with documentary analysis and international benchmarking to strengthen validity.

Second, Delphi-based consensus is inherently subjective. Expert views may be influenced by individual professional backgrounds, institutional affiliations, and sectoral interests. An alternative panel with a different composition might have prioritized certain governance levers differently. The prioritized levers presented here therefore represent a strong, empirically derived indication of reform priorities for the current stakeholder ecosystem, but not a definitive or exclusive ranking.

Third, the benchmarking analysis draws on secondary data from official publications and technical manuals. While these sources are authoritative, they may not fully capture informal practices, implementation nuances, or very recent, unpublished reforms in the comparator systems. Furthermore, the simplified 0–2 scoring system is intended as a comparative heuristic to highlight relative strengths and gaps, rather than as a detailed, weighted performance rating.

Taken together, these limitations affirm that the proposed S-CSHGM should be treated as a theory-informed and empirically grounded framework for guiding governance reform. Its assumptions and proposed instruments constitute a robust starting point for policy redesign but will require iterative testing, adaptation, and further validation in practice.