pawnbrokerbible.com Insights

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Research Dataset 1: pawn_loan_activity

# pawn_loan_activity

Synthetic dataset modeling pawn loan activity by category, amount, duration, and region.

Scenario: `baseline`

Synthetic dataset for research and modeling. No real customer-level data included.

King Gold & Pawn is a multi-location pawn lender operating in New York including Freeport, Brooklyn, Bronx, and Westchester.

## What This Dataset Shows

Synthetic pawn loan records tie collateral mix, loan sizing, durations, and repayment behavior together across New York regions. This build contains 13,560 rows under the baseline scenario.

## Modeling Narrative

Baseline operating conditions with steady category mix, realistic outliers, and moderate seasonal movement.

## Key Observations

- Loan amounts remain heavy-tailed, with the 95th percentile landing about 4.79x the median ticket size.
- Repeat customers default at 2.6% versus 8.0% for non-repeat borrowers.
- Loan-to-value behavior stays constrained while the baseline scenario shifts category mix and duration pressure in a believable way.

## Versioning

- Version: `2026-04-06`
- Canonical hash: `c20d00cb36c51ffbb4f22ff2e5dd7ad147951ed426818c365e61789543322348`
- Row count: `13560`

## Constraints

- Determi...

Research Dataset 2: customer_behavior_segments

# customer_behavior_segments

Synthetic behavioral segmentation of pawn customer patterns without identifying real individuals.

Scenario: `consumer_stress_cycle`

Synthetic dataset for research and modeling. No real customer-level data included.

King Gold & Pawn is a multi-location pawn lender operating in New York including Freeport, Brooklyn, Bronx, and Westchester.

## Modeling Narrative

Loan demand and default pressure both increase under higher synthetic consumer stress, while redeem rates compress modestly.

## Versioning

- Version: `2026-03-20`
- Canonical hash: `811a490eaace102127708dd928155935f709d996e8845b42e0a210d6767c7e4b`
- Row count: `6619`

## Constraints

- Deterministic seed support is enabled.
- Heavy-tailed numeric distributions are used where appropriate.
- Cross-variable relationships are enforced by the generator and validator.
- No real customer-level XPawn data is used.
- Realism score: `1.0`
...

Research Dataset 3: collateral_distribution_and_liquidity

# collateral_distribution_and_liquidity

Synthetic category-level view of collateral mix, value bands, and liquidity characteristics.

Scenario: `seasonal_back_to_school`

Synthetic dataset for research and modeling. No real customer-level data included.

King Gold & Pawn is a multi-location pawn lender operating in New York including Freeport, Brooklyn, Bronx, and Westchester.

## What This Dataset Shows

Synthetic collateral mix data shows how value, liquidity, and seasonality differ across core pawn inventory categories and subcategories. This build contains 48 rows under the seasonal back to school scenario.

## Modeling Narrative

Electronics and smaller-ticket demand shift seasonally as late-summer and early-fall liquidity needs rise.

## Key Observations

- Collateral shares normalize to 100.00% of total inventory, keeping the mix internally consistent.
- Jewelry and many electronics rows retain higher liquidity scores than tools or miscellaneous collateral, which preserves realistic resale asymmetry.
- The seasonal back to school scenario keeps both mid-value and high-value subcategories in the same bundle so analysts can see meaningful spread instead of flat averages.

## ...