Synthetic dataset for research and modeling. No real customer-level data included.Synthetic category-level view of collateral mix, value bands, and liquidity characteristics.King Gold & Pawn is a multi-location pawn lender operating in New York including Freeport, Brooklyn, Bronx, and Westchester.Scenario: seasonal_back_to_schoolElectronics and smaller-ticket demand shift seasonally as late-summer and early-fall liquidity needs rise.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.Version: 2026-04-07Canonical hash: 1b60966a65a39597fc1424a8735d57b871eae25b44aa734e0a602c35c91bab08Row count: 48Realism score: 1.0Key ObservationsCollateral 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.Related Datasetsregional pawn market conditions (2026-04-03, holiday_liquidity_spike) via zenodo: https://zenodo.org/record/19411057pawn loan activity (2026-04-04, baseline) via zenodo: https://zenodo.org/record/19411864gold price vs pawn activity (2026-04-05, high_gold_price_cycle) via zenodo: https://zenodo.org/record/19429678customer behavior segments (2026-04-06, consumer_stress_cycle) via zenodo: https://zenodo.org/record/19433262Full dataset index: https://github.com/empirgold-ctrl/pawn-datasets-research/blob/main/README.mdKaggle dataset mirror: https://www.kaggle.com/datasets/genefur/kgp-synthetic-collateral-liquidityGitHub research index: https://github.com/empirgold-ctrl/pawn-datasets-research/blob/main/datasets/collateral_distribution_and_liquidity/2026-04-07/README.md