ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS and are available for Tax Years 1998, 2001, and 2004 through 2015. The data include items, such as:Number of returns, which approximates the number of householdsNumber of personal exemptions, which approximates the populationAdjusted gross income Wages and salariesDividends before exclusionInterest received

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

Context

Learning how to create a data pipeline.

Content

https://www.irs.gov/pub/irs-soi/16zpdoc.doc

Individual Income Tax ZIP Code Data

ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS and are available for Tax Years 2016. The data include items, such as:

-Number of returns, which approximates the number of households -Number of personal exemptions, which approximates the population -Adjusted gross income -Wages and salaries -Dividends before exclusion -Interest received

Acknowledgements

https://www.irs.gov/statistics

This layer shows an approximation of the percent of households in the US might see a full refund check from the stimulus bill being passed to help deal with the COVID-19 economic impact. The criteria used for this map was:The amount of the credit allowed by subsection (a) (determined without regard to this subsection and subsection (e)) shall be reduced (but not below zero) by 5 percent of so much of the taxpayer’s adjusted gross income as exceeds—‘‘(1) $150,000 in the case of a joint return,‘‘(2) $112,500 in the case of a head of household, and‘‘(3) $75,000 in the case of a taxpayer not described in paragraph (1) or (2).‘‘(d) ELIGIBLE INDIVIDUAL.—For purposes of thissection, the term ‘eligible individual’ means any individualother than—‘‘(1) any nonresident alien individual,‘‘(2) any individual with respect to whom a deduction under section 151 is allowable to another taxpayer for a taxable year beginning in the calendar year in which the individual’s taxable year beginsThe data comes from the IRS SOI Tax Stats by ZIP Code from 2017 tax returns. The calculation for this map was calculated using the following aggregated income categories:$1 to under $25,000$25,000 to under $50,000$50,000 to under $75,000$75,000 to under $100,000$100,000 to under $200,000$200,000 or moreThese were used to estimate how many individuals who filed tax returns would warrant a full $1,200 from the stimulus bill. This was based on if a filer was single, joint, or head of household. Because some of the cutoffs for the bill fall within these categories, they were split to estimate refunds that fall within that range.

Wealth Segmentation of U.S. ZIP Codes Based on IRS Data

This dataset provides a wealth-tier classification of U.S. ZIP codes for high income brackets using IRS income data and multivariate KMeans clustering. It can help with regional targeting, CRM enrichment, market analysis, or any data science task that benefits from understanding high income distribution across the U.S.

💡 Source

• IRS SOI ZIP Code Data (open source)
• Aggregated across AGI brackets (stub 3–6)

🧠 What’s Inside

Each row represents a ZIP code with:

• AGI (A00100), Total Income (A00200)
• Capital Gains, Business Income, Tax Paid
• Cluster assignment (0–2)
• Wealth Tier label: Low, Medium, or High

The cluster assignments are refined using distance to cluster centroids in normalized feature space to improve accuracy.

💼 Use Cases

• Segmenting markets for B2B/B2C outreach
• CRM lead enrichment
• Territory planning and resource allocation
• Visualization and dashboard overlays

📬 Contact

Created by Namrata Nyamagoudar(LinkedIn) for open-source analysis and enrichment use cases.