ZipCrawl ZCTA5 (ZIP Code) Data File

Version: v1.0
Last Updated: November 23, 2025
Geography: Zip Code (ZCTA5)
Source Datasets: ACS 5-Year Estimates, USPS ZIP Code reference, ZipCrawl curation pipeline

This dataset is available as a standalone file, or as part of our curated U.S. Geography bundle.

You can also view data for a specific zip code here: Zip Code (ZCTA5) Lookup (U.S.)

Overview

This dataset provides curated demographic, economic, housing, and geographic indicators for all ZIP Codes in the United States. This is based on U.S. Census zip code equivalents: ZCTA5s. ZCTAs are Census-defined statistical regions approximating USPS ZIP Codes.

ZipCrawl combines raw ACS estimates with USPS reference data and applies essential data-quality safeguards to ensure that values are interpretable, consistent, and appropriate for analytical use.

How to Use This Dataset

ZCTA vs. USPS ZIP Codes

ZCTAs are not identical to USPS ZIP Codes.
Each row represents a ZCTA5, not a postal ZIP.

Understanding Null Values

null in this dataset never means zero.

A null indicates:

unreliable ACS estimate (high margin of error), or
data that was not available in the source data set, or
measure suppressed due to group-quarters dominance

Do not treat null as zero in analysis unless explicitly intended.

Join Logic

ZCTAs are stable and uniquely identified by: zcta5_geoid (string)

You can join ZIP-level metrics from multiple ZipCrawl products using this field.

Recommended Use Cases

ZIP-level segmentation
Data enrichment for consumer, real estate, or policy analytics
Modeling markets, demographics, or economic conditions
Geographic clustering
Building ZIP code dashboards or maps

Common Pitfalls

⚠️ Pitfall 1 — Treating USPS ZIP Codes and ZCTAs as identical

They overlap but differ.
This dataset uses ZCTAs, not USPS ZIPs.

⚠️ Pitfall 2 — Misinterpreting NULL values

NULL = “statistically unreliable,” not “zero.”

⚠️ Pitfall 3 — Percentages may not sum to 100%

ACS universes differ across concepts (age, employment, households, etc.).

⚠️ Pitfall 4 — College towns, prisons, and military bases distort data

Group-quarters dominance invalidates many economic and household fields.

⚠️ Pitfall 5 — Comparing tiny ZIP Codes to large ones

Small ZCTAs can have volatile ACS estimates.
Use caution in ranking ZIPs by small counts or derived metrics.

Methodology Summary

ZipCrawl applies a small number of targeted adjustments to ensure dataset quality while preserving ACS integrity.

Margin-of-Error (MOE) Filtering

A value is set to NULL when: MOE ≥ Estimate * 0.5 AND the target value ≥ 100

This follows best practices to ensure that unreliable estimates on small population sizes are not included in the final data. On sample sizes closer to zero, even with a large MOE, we can be more confident the true value is close to zero. We never suppress these columns to null where a value exists, due to their outsized importance:

total population We use a higher threshold (1000) on ethnic breakdowns, since they collectively add up to one. We only want to suppress these if they are grossly, grossly unreliable.

Group-Quarters–Dominated ZIP Codes

An area is considered "group-quarters dominated" when group buildings like dorms, prisons, barracks, etc. dominate the population of that area.

A ZIP is flagged as:

group_quarters_dominated = TRUE when (group_quarters_population / total_population) ≥ 0.50

In these areas, ACS economic and household measures are structurally unreliable, because ACS excludes group-quarters residents from these calculations.

ZipCrawl therefore suppresses to NULL:

median household income
per-capita income
labor force participation
unemployment rate
poverty metrics
household occupancy and costs
any estimate derived strictly from household populations

Demographic distributions remain valid and are not suppressed.

Universe Alignment for Percentages

ACS percent calculations use different universes (e.g., population 3+, population 18+, civilian labor force, household population).
ZipCrawl ensures:

numerator and denominator universes match
no percentage exceeds logical bounds
questionable ratios are suppressed or corrected

Range Validation

Percent fields are validated to ensure: 0 ≤ pct ≤ 1

Values outside this range indicate universe mismatch or erroneous sources and are suppressed.

Field Groups (High-Level Structure)

Fields are organized into the following groups:

Geography & Identification

zcta5_geoid
state
USPS correspondence fields

Population

total_population
age structure
sex ratios

Race & Ethnicity

Standard ACS race and Hispanic origin distributions.

Education

educational attainment
enrollment
universe-aligned percentages

Economics (NULL when group-quarters-dominated)

income
poverty
employment & labor force metrics

Housing (NULL when group-quarters-dominated)

occupancy
tenure
rent & costs
household characteristics

ZipCrawl-Specific Derived Fields

group-quarters dominance flag
universe-aligned percentages
cleaned income metrics
MOE-filtered versions

Versioning and Stability

This dataset is updated annually following the Census Bureau's ACS 5-Year release schedule. There may be periodic updates as other sources are added or updates.

Each release includes:

updated ACS estimates
stable ZipCrawl methodology
version notes summarizing methodological changes (if any)

Changes to methodology are expected to be rare and will be documented clearly.

Unusual ZCTA5s

The following are some outlier and unusual ZCTA5 areas that may help with data validation or understanding.

01063: Northampton, MA. Campus of Smith College, nearly 100% female, 100% group-quarters housing, and many other suppressed estimates.
10279: NYC Financial district. Very low population, mean income over $1 million.
32830: Lake Buena Vista / Walt Disney World Area, FL. Practically zero population.
53940: Lake Delton, WI. Total population of 0.
99505: Anchorage, AK. Huge margin-of-error on population due to presence of Fort Wainwright (U.S. Army).

Contact

For questions or feedback:

ZipCrawl Support Team
support@zipcrawl.com