Top 10 ZIP Codes by Homeowners-Insurance Nonrenewal Rate

Research question

Among 25,593 covered ZIP codes, which carry the highest reported nonrenewal rate, and what is the joint distribution of nonrenewal-rate and recent premium-change percentage at the top of the ranking?

Methodology

This ranking is generated fresh from the PlainInsure dataset each time the page is requested. Records are ranked from highest to lowest and the top 10 are shown. Every number on this page comes directly from the current dataset, no figure is hardcoded, and the ranking updates automatically whenever the underlying the source data is refreshed.

Column lineage: each field maps to a typed column in the zip_trends table. Identifier columns carry the entity slug or code used elsewhere in PlainInsure; quantitative columns store values as exported by the NAIC and state insurance departments (preserving the original measurement unit). Where the source publishes values in thousands of dollars, we render them via the standard PlainInsure money formatter that converts to billions or millions depending on magnitude. Where the source publishes raw integer counts, we render with thousand-separators preserved.

This ranking reflects the most recently published data available to PlainInsure. When the underlying dataset is refreshed, this page updates automatically within hours rather than days. The methodology page documents the full data pipeline, source vintage, and column lineage for PlainInsure.

Coverage and exclusions: records with null or zero values on the ranking metric are excluded from this ranking. the source occasionally suppresses values for reasons of confidentiality, sample size, or quality control; suppressed records are excluded by design rather than displayed as zeros. If the underlying source revises a value in a later release, the revised value will appear here automatically.

Data provenance and refresh cadence: the NAIC Annual Statement is published annually by the National Association of Insurance Commissioners; state nonrenewal filings are published on each state insurance department's own annual cycle. PlainInsure pulls each release as soon as it is publicly available, checks it for consistency, and replaces the prior version so readers never see a mix of old and new figures.

How this ranking is organized: PlainInsure groups records under the natural identifier published by the source (entity codes, geographic identifiers, fiscal-year markers, or program names, depending on the dataset). Where a figure combines several source fields, the combination rule is documented on the methodology page and the resulting column carries a plain-language name so readers can tell what it represents without guessing.

Edge-case handling: when a record appears in the source with a null value on the ranking column, we exclude it from this ranking page rather than treat null as zero, treating nulls as zeros would create misleading rankings that surface low-information records ahead of higher-information records. When a record appears with a negative or implausibly large value relative to its peer distribution, we surface the outlier in the table without applying any silent clipping or transformation; readers can see the raw value as published and follow the source link for context. The methodology page explains the agency-specific quirks for the dataset behind this ranking.

Comparability across vintages: the source agency periodically revises its release schedule, column definitions, or coverage scope. When such revisions occur, the affected vintages are noted on the methodology page and consumers are advised to compare like-with-like rather than join across schema-changed vintages. Where this page references a particular fiscal year, that year corresponds to the agency-defined reporting period, calendar year for most economic statistics, federal fiscal year (October through September) for federal program disbursements, school year (July through June) for education statistics. Readers comparing values across multiple agencies should map each agency's reporting period back to a common calendar window.

How rankings are compiled: this list reflects the current dataset ranked by the metric named above, limiting to the leading results and excluding records with missing values on that metric. We avoid manual curation of the order shown, whoever tops the list does so because of the underlying number, not editorial choice. Detail pages reachable from each row carry additional context and, where the source publishes it, historical trend data for that record.

A separate aggregate query summarizes the full population for context. The aggregate runs against the same zip_trends table without the LIMIT clause and computes a population count plus optional sum and mean. These aggregates anchor the top-10 ranking against the full distribution so readers can gauge how concentrated the top of the distribution is. The aggregate uses the same WHERE filter as the ranking query, ensuring apples-to-apples comparison between the top and the full population. Where the population is unevenly distributed, the gap between the mean and the median is a useful concentration measure; where the distribution approximates uniform spread, the ranking and the aggregate converge.

A secondary cut renders an adjacent dimension from the same dataset: a related ranking that complements the primary one by surfacing a different metric. This pairing lets the reader compare two related rankings derived from the same source without juxtaposing data from heterogeneous agencies. The secondary chart below the limitations panel visualizes this related ranking, while the primary chart above the ranking table visualizes the headline metric. Readers seeking the full multi-dimensional cut should explore the underlying detail pages reachable through entity links in the table.

Reproducibility: this ranking is generated directly from the PlainInsure dataset and every figure on the page traces back to a specific record from the underlying source, listed on the methodology page. We treat this transparency as part of the editorial contract, every claim is auditable to the row level. Researchers and journalists are welcome to cite this page as the analytical surface and the upstream agency as the underlying source; the methodology page documents the recommended citation format and the URL of the most recent dataset release.

Editorial governance: PlainInsure maintains an editorial standards document that codifies how rankings are constructed, how outliers are surfaced, how privacy-protected records are handled, and how corrections are processed when an entity disputes a value attributed to it. Subject-submitted corrections route through a defined intake process and are reconciled against the upstream record before publication; cosmetic corrections are recorded as overlay metadata while substantive corrections wait for the next official source release. A named editor reviews every ranking page before publication and signs off using the byline displayed at the top of this page. Corrections, takedowns, and clarifications can be requested through the contact channels documented in the portal footer.

Transparency commitments: PlainInsure publishes its full methodology, source registry, data-update status, and update history through dedicated pages reachable from the footer navigation. Visitors can trace any number on this page back to the underlying source row by following the entity link, inspecting the source URL referenced in the citation block, and comparing against the most recent vintage published by NAIC and state insurance departments. Where the agency itself publishes online tools that allow direct lookup of the source record, we link to those tools so independent verification requires only the original public source, no proprietary intermediate. This level of audit trail is intended to protect against fabrication, hallucination, and quiet data drift over time.

See the methodology page for the complete ETL pipeline, source vintage, and column lineage.