Motivation

OpenFacet was conceived following analytical findings from the master’s dissertation “Strategic Analysis of Pricing Mechanisms in the Diamond Trade: A Modern Perspective on Traditional Practices,”¹ submitted to Salford University. The research dissected valuation conventions in the loose natural polished diamond (LNPD) market, identifying systemic opacity, dependence on unobservable inputs, price discontinuities at carat thresholds, and an absence of replicable valuation logic. While such deficiencies have been acknowledged informally within the trade, this work formalized them into a structured critique grounded in data science and economic modeling.

Rather than remain an academic exercise, the findings catalyzed a coordinated effort to construct a viable pricing methodology. A cross-functional team was assembled—including experts in data modeling, gemology, trade operations, and platform architecture—augmented by input from industry practitioners. OpenFacet emerged not as a price-setting benchmark, but as an auditable system for reconstructing diamond price surfaces using observable listing data.

Designed to reflect modern consumer-facing market dynamics, the platform deliberately excludes personal branding; its logic and methodologies are intended to stand independent of identity or authority, reinforcing its neutrality and reproducibility. OpenFacet provides a modular, neutral framework for deriving economically coherent price indices that adjust in real-time to listing behavior. While some market actors may see transparency as a threat to legacy negotiation margins, OpenFacet does not prescribe pricing. It reflects observable conditions—real-time, retail-visible listing data—allowing stakeholders to adapt strategies based on consumer-aligned references. Scarcity modeling and size interpolation preserve the flexibility needed for nuanced B2B pricing while reducing reliance on opaque conventions.

By embedding scarcity-awareness, correcting for artificial pricing thresholds, and allowing for attribute overlays, the system replaces intuition-led valuation with algorithmically reproducible logic. As the methodology propagates, wholesale pricing is expected to stabilize around narrower discount bands off retail-observable references—reducing arbitrariness without removing flexibility. The goal is not disruption for its own sake, but the replacement of secrecy with structure, enabling informed participation across retail, wholesale, and institutional layers.

Methodology Scope: From Listing Data to Price Surface

OpenFacet was developed in response to these analytical concerns. It is not a benchmark per se, but a transparent methodology for reconstructing diamond price surfaces from observable listing data. Its architecture is deliberately modular and fully documented, enabling replication and adaptation by any party with access to structured pricing data—whether from retail, trade, or institutional channels.

By processing publicly available, competitive retail listings from diverse sources, OpenFacet provides a method for deriving an Open Market Price (OMP) reference. This addresses the challenge that true B2B transaction data is often opaque and negotiated, making open price discovery difficult. By formalizing the process of extracting structured price signals from the most accessible layer of the value chain, OpenFacet aims to make pricing observable and auditable.

The system models price as a smooth, log-linear function of carat, color, and clarity within standardized bands. It corrects for local irregularities through low-rank residual modeling (ALS) and applies post-model smoothing and monotonic regression to enforce price continuity across carat weight and internal economic logic. The resulting matrices are updated daily and expressed in per-carat terms, using geometric means to reflect the multiplicative nature of diamond pricing.

Design Principles: Addressing the Academic Critique

The model is structured around principles that directly address the academic critique:

• Replicability: All transformations are documented. Anyone with comparable data inputs can reconstruct the matrices using published logic. There are no proprietary coefficients or concealed calibration steps. This directly counters the issue of unobservable inputs and lack of independent verification.

• Continuity and Smoothing: Price jumps at arbitrary thresholds (e.g., 1.00ct) are corrected using regression-based modeling and kernel interpolation. This rigorously addresses the discontinuity problem noted in empirical studies by enforcing smooth, economically rational gradients across carat weights.

• Adaptability: While the baseline model operates on the 4Cs, it is designed to support overlays for additional attributes (fluorescence, certificate annotations, issuer variation) when data availability permits. These can be estimated independently and layered onto the base surface, providing a framework to incorporate the broader range of price-relevant factors discussed in the research. Furthermore, this modular design allows for the development of analytical overlays, such as macro-economic adjustments², which provide the kind of insights into external market forces missing from traditional, static benchmarks.

• Economic Coherence: Monotonicity enforcement ensures that higher carat, color, or clarity grades are not priced below lower-quality counterparts, preventing economically illogical outputs, a key requirement for any reliable valuation system.

• Appropriate Aggregation (Geometric Mean): Reflecting insights that traditional averages distort pricing due to the multiplicative nature of quality and size, OpenFacet employs geometric interpolation and a geometric mean calculation for its derived index. This aligns with methodologies used in economic indices (like Jevons-style CPI) and accurately models how perceived value compounds in the diamond market, rather than simply adding linearly.

The Role of the Index: DCX Composite

The DCX Composite is a synthetic price index derived from OpenFacet matrices. By aggregating transparently modeled price points for a high-turnover basket of diamond specifications using a weighted geometric mean, DCX provides a benchmark for retail diamond pricing, algorithmic strategies, synthetic asset valuation, and quantitative market analysis.

This index serves as an example of how a transparent, data-driven methodology can formalize price signals, even when transaction data is not directly observable. It aims to offer a more reliable reference point than benchmarks based on opaque dealer channels, similar in principle to how indices for other assets are derived from diverse, publicly observable trading venues.

This formalized, auditable structure is a prerequisite for referencing by financial instruments and venues—as evidenced by futures markets for Bitcoin and commodities. CME Bitcoin contracts use the CF Benchmark’s reference rate built from vetted spot exchange data³, while Cboe Digital similarly aggregates spot prices to support its crypto derivatives⁴. In both cases, transparent, replicable indices enable financial instruments on opaque or fragmented underlying markets. OpenFacet applies these principles to diamonds.

Intended Use and Limitations

The current implementation draws from retail-visible pricing data and is thus scoped for use cases where wholesale data is inaccessible or proprietary. It does not capture negotiated B2B discounts, nor does it currently incorporate attributes such as exact cut proportions or certificate-specific commentary in its primary basket.

However, the methodology’s design for replication means it is intentionally built for adaptation by B2B platforms, trade consortia, or institutional stakeholders who control more granular pricing data. The framework supports internal benchmarking, automated valuation pipelines, synthetic asset pricing, and quantitative modeling of diamond-backed instruments, enabling the industry to build transparent systems tailored to their specific data flows.

Replication and Alignment

Replication is encouraged. The utility of OpenFacet lies not in centralization, but in formalizing a coherent statistical treatment of diamond pricing—enabling independent construction of aligned matrices across the value chain. Organizations that operate pricing platforms, manage polished inventory, or support valuation tools can adapt the model to their own datasets with full methodological control.

This approach aligns with the direction proposed in emerging academic literature: separating the concept of a price reference from proprietary access, and replacing legacy opacity with tractable, model-driven treatments of value grounded in observable market signals.

Conclusion

The problem of non-transparent diamond pricing has been clearly articulated in recent research. OpenFacet provides one response: a formalized, transparent, and modular methodology for reconstructing diamond price surfaces from structured data, enabling open market price discovery. It does not claim to set prices—it makes structure visible. The next phase of progress lies in replication. Stakeholders with access to relevant pricing feeds are invited to adopt or adapt the methodology, refine its parameters, and contribute to the establishment of coherent, auditable, and economically rational pricing infrastructure for a market that has long operated without it.