Diamond Price Index Methodology

How TheDiamondPrice.com calculates diamond values, collects market data, and scores the best deals

Last Updated: February 11, 2026
Data Coverage: 500,000+ certified diamonds
Update Frequency: Real-time (every 24 hours)
Retailers Tracked: Authorized retail partners

Data Collection Process

1. Source Selection

We aggregate real-time pricing data exclusively from authorized diamond retailers who meet our quality standards:

Certification Requirement: Only retailers offering GIA, IGI, or AGS certified diamonds
Transparency Standards: Clear pricing with full diamond specifications
Inventory Verification: Real-time availability confirmed via API or SFTP feeds
Reputation Assessment: Established retailers with verified customer reviews

2. Data Update Frequency

Our system updates diamond pricing data every 24 hours through automated processes:

SFTP Feeds: Direct vendor feeds with complete inventory updates
API Integrations: Real-time price changes from API-enabled retailers
Data Validation: Automated verification of certification numbers and specifications
Duplicate Detection: Cross-vendor matching to identify the same diamond listed multiple times

3. Quality Standards

Only certified diamonds meeting specific criteria are included in our index:

Certification: GIA (Gemological Institute of America), IGI (International Gemological Institute), or AGS (American Gem Society)
Carat Range: 0.25ct to 5.0ct (covering 99% of engagement ring purchases)
Cut Quality: Fair to Excellent (for round diamonds)
Color Range: D through K (colorless to faint yellow)
Clarity Range: FL (Flawless) through SI2 (Slightly Included)

Price Scoring Algorithm

How We Calculate The Diamond Price Score

Every listing we show carries The Diamond Price Score - a 1-5 rating comparing that diamond's price to the Fair Market Price for its exact specification (see "Market Median Calculation" below). It's the same idea as a credit score or a safety rating: one number, computed the same way for every diamond, so two listings are always comparable on equal terms.

Scoring Methodology (current, v1.7.28):

Score	Label	Price vs. Fair Market Price
⭐⭐⭐⭐⭐ (5)	Best Deal	The single cheapest credible below-fair listing in the result set
⭐⭐⭐⭐⭐ (5)	Perfect Deal	20%+ below Fair Market Price
⭐⭐⭐⭐ (4)	Great Deal	10-20% below Fair Market Price
⭐⭐⭐ (3)	Good Price	Any amount below Fair Market Price
⭐⭐ (2)	Fair Price	At Fair Market Price, up to ~18% above
⭐ (1)	Above Market	Above the typical market range
⭐ (1)	Overpriced	40%+ above Fair Market Price

A credibility guard prevents a listing more than 50% below Fair Market Price from being scored Best Deal or Perfect Deal - a price that far below market is a data-quality flag, not a genuine bargain.

Factors Included in Comparison

Our algorithm matches diamonds based on these exact specifications:

Carat Weight: Matched within 0.05ct range
Color Grade: Exact match (e.g., G color compared only to G color)
Clarity Grade: Exact match (e.g., VS2 compared only to VS2)
Cut Quality: Exact match for round diamonds
Shape: Same shape (round, princess, oval, etc.)
Certification Lab: GIA, IGI, and AGS weighted slightly differently
Fluorescence: None, Faint, Medium, or Strong
Diamond Type: Natural vs. Lab-Grown (compared separately)

Market Median Calculation

We use the statistical median, not the average, so a handful of extreme listings can't drag the figure - the same reasoning behind the band-aware approach below. This approach:

Uses statistical median (not mean) to minimize impact of extreme prices
Updates in real time as new listings enter the tracked data
Is computed within a matched quality band, never blended across the whole market (see below)
Is suppressed rather than published where a band's sample is too thin to trust

Why We Publish by Quality Band

This is the part of our method that differs most from other price trackers, and the part we would most like you to check.

A single "market median" for a shape and carat weight sounds useful and is usually misleading. The stones behind it are not comparable: a 2-carat round pool contains thousands of listings skewed toward lower colour and clarity, while a rarer shape may be represented by a few hundred listings skewed high. Take a median across all of them and you are not measuring price - you are measuring which qualities happened to be listed.

An error this caused, and how we found it

Using a blended all-quality median, our own index once implied that a marquise cost about 27% more than a round at the same carat weight. That is not a subtle inaccuracy - it is impossible. A round is the most expensive shape per carat, and anyone in the trade would recognise the claim as wrong on sight.

The prices were not wrong; the comparison was. The round figure was dragged down by a large, lower-quality sample and the marquise figure lifted by a small, higher-quality one. We found it because we asked whether our numbers agreed with the market, not because a user complained. We publish it here because a methodology page that only lists a method's strengths is marketing.

What we changed

Band-aware by default. Figures are computed within a quality band rather than across the whole market. We default to a mid band rather than a blended "all" figure.
A minimum sample per band. A band with too few listings is not published. Thin samples are how a plausible-looking number becomes a wrong one.
Applied retroactively. The correction was applied at read time across the full history, so past figures are corrected too rather than left standing.
Suppression over guessing. Where a shape has too few listings at every point in the series, we publish nothing for it. Marquise is currently suppressed on that basis.

One consequence is worth stating plainly, because it cuts against our commercial interest: the banded series shows natural diamond prices falling over the trailing year, where the blended series suggested they were flat to slightly rising. The blend was hiding a real decline behind a shifting quality mix.

Why Shape-Specific Analysis Matters

Shapes are not interchangeable and cannot share a price curve. Round commands a premium driven by cutting yield and demand; elongated shapes such as oval and marquise typically list below round at equivalent quality and appear larger for the same carat weight. Fancy shapes also carry quality factors that round does not - length-to-width ratio, bow-tie visibility - which affect price but are not captured by the 4Cs.

We therefore compute every figure within a single shape. We do not derive one shape's price from another's by applying a fixed discount, and we treat cross-shape comparisons as a separate claim requiring its own evidence - which is precisely where the blended median failed above.

Natural and Lab-Grown Are Tracked Separately

Natural and lab-grown diamonds are never pooled. They are physically comparable and economically nothing alike: natural supply is constrained, while lab-grown production capacity has expanded rapidly, and their price series have moved in opposite directions.

In our tracked data, lab-grown medians have fallen sharply across the past year - declines of roughly 25–35% in several shape and carat cohorts - while natural prices have moved far less. Blending the two would produce a number describing neither. It would also degrade over time, as the mix between them shifts.

How We Validate

Against live listings. Historical snapshots are checked against current live feeds; convergence within a few percent is the expected result, and divergence is investigated rather than smoothed.
Against other published sources. We compare our levels with other public diamond price trackers. We expect to be close but not identical, and we treat a large gap as a question about our method, not proof of theirs.
Against internal coherence. Series should move like a market - gradual, with occasional steps - not like noise. Implausible relationships between shapes or bands are treated as defects, which is how the marquise error surfaced.
Gaps stay visible. Where our snapshot did not run, the chart breaks and says so. We do not interpolate across missing days to make a series look continuous.