Getting Income Statistics by Zip Code


Understanding the income statistics of specific areas can be incredibly valuable for businesses, researchers, and policymakers. Detailed income data by zip code can offer insights into economic conditions, consumer behavior, and regional disparities. Thanks to the Zip Code API by Metadapi, getting access to detailed statistics and utilizing this data has never been easier. In this blog post, we'll explore how to obtain income statistics by zip code, highlight some sample use cases, and delve into one detailed use case to illustrate its practical application.

Understanding Income Statistics API

The statistics of income endpoint from the Zip Code API offer a wealth of data, including median household income, per capita income, income distribution, total returns, and much more (Over 160 data points). This API reconciles data from the "Individual Income Tax Statistics" dataset provided by the IRS.  We provide a consolidated view into this dataset to ensure accuracy and comprehensiveness. By using this API, users can easily integrate income statistics data into their applications, websites, or research projects.

Sample Use Cases for Income Statistics by Zip Code

  1. Business Market Analysis: Businesses can use income data to identify potential markets, tailor marketing strategies, and make informed decisions about store locations.
  2. Real Estate Investment: Real estate investors can assess neighborhood affluence to determine property values and investment potential.
  3. Public Policy and Planning: Policymakers can analyze income disparities across regions to allocate resources and design programs aimed at reducing poverty.
  4. Academic Research: Researchers can study the correlation between income levels and various social factors, such as education and health outcomes.
  5. Nonprofit Fundraising: Nonprofit organizations can identify areas with lower income levels to target for fundraising and outreach programs.

Detailed Use Case: Business Market Analysis

Let's dive deeper into how a retail business might use income statistics by zip code to enhance its market analysis and decision-making process.

Scenario: Opening a New Retail Store

Objective: A retail chain wants to open a new store in a metropolitan area. They need to determine the best location based on the income levels of different neighborhoods to ensure profitability and sustainability.


  1. Identify Potential Locations: The business identifies several zip codes in the metropolitan area where they might open the new store.
  2. Retrieve Income Data: Using an income statistics API, the business retrieves detailed income data for each identified zip code. The API provides information such as median household income, income distribution.
  3. Analyze Data: The business analyzes the retrieved data to compare the economic conditions of the different zip codes. They focus on metrics such as higher median household income and lower poverty rates, which indicate greater purchasing power and economic stability.
  4. Determine Target Demographics: The business identifies the target demographics that align with their products and services. For example, if they sell high-end electronics, they might prioritize areas with higher income levels.
  5. Make Informed Decision: Based on the income data analysis, the business selects the zip code with the most favorable economic conditions for their new store. They also use the data to tailor their marketing strategies, such as offering promotions that appeal to higher-income customers.
  6. Monitor and Adjust: After opening the store, the business continues to monitor income data and other economic indicators. They use this ongoing data analysis to adjust their strategies and operations to maximize profitability.
As an example, let's use the following Python code to extract and save into a csv file the number of returns and wages for 3 zip codes.

import requests
import csv

zips = ['33009','33967','90210']
file = r'<>'

def fetch_data_from_api(zipCode):
    #Replace the string with a valid API key.
    vheaders = {"Ocp-Apim-Subscription-Key": "<>"} 
    url = f"{zipCode}/soi"
    response = requests.get(url,headers=vheaders)    
    if response.status_code == 200:
        return response.json()
        print(f"Failed to fetch data. Status code: {response.status_code}")
        return None

def main():
    with open(file, 'w', newline='') as zipfile:
        writer = csv.writer(zipfile)
        header = ['zipCode', 'year', 'agiGroupId', 'returns', 'salariesWagesAmount']
        for zip in zips :
                response_api_data = fetch_data_from_api(zip)
                print('called api with success')
                for data in response_api_data['data']:
                    row = [data['zipCode'], data['year'], data['agiGroupId'], data['returns'],data['salariesWagesAmount']]
                print('There was an exception calling the API.')

if __name__ == "__main__":

This code will save a csv file with the returns and wages for the zip code provided.   Check out the full zip code statistics of income schema and take advantage of the hundreds of data elements available for analysis and application development. 
Using your favorite data reporting tool (Excel, PowerBI, Tableau, etc.) create a visualization that will help in your analysis of the extracted data.
Graph with wages by zip code


Income statistics by zip code provide a granular view of economic conditions, enabling businesses, policymakers, and researchers to make data-driven decisions. By leveraging the Zip Code API that offers detailed income data, users can gain valuable insights into local economies, tailor their strategies, and achieve their objectives more effectively. Whether you're opening a new store, investing in real estate, or designing public policies, understanding income statistics at the zip code level can significantly enhance your decision-making process.

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