This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

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.

A dataset listing the richest zip codes in New York per the most current US Census data, including information on rank and average income.

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2024 about households, median, income, and USA.

This service provides spatial data and information on Difficult Development Areas (DDAs) used for the Low Income Housing Tax Credit program. DDAs are designated by U.S. Department of Housing and Urban Development (HUD) and defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI). DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs in non-metropolitan areas are designated along county boundaries. DDAs may not contain more than 20% of the aggregate population of metropolitan and non-metropolitan areas, which are designated separately.

A dataset listing the richest zip codes in Virginia per the most current US Census data, including information on rank and average income.

This is the subset of parcels that meet the FIRST TWO of the “specified criteria” in the King County Code 26.12.003J definition of “Opportunity Areas.” Areas within King County that: (a) “are located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County;” (b) “are located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP Codes in King County.”

A dataset listing the richest zip codes in North Carolina per the most current US Census data, including information on rank and average income.

Graph and download economic data for Estimate of Median Household Income for Orange County, CA (MHICA06059A052NCEN) from 1989 to 2023 about Orange County, CA; Los Angeles; CA; households; median; income; and USA.

This feature dataset contains a snapshot of all King County parcels from September 2020, with all of the "additional relevant criteria" data used in Method 2 of the LCI opportunity area determination described below.There are two methods by which a property may qualify as being in an opportunity area:Method 1. Property meets all three of the following "specified criteria" in King County code 26.12.003.(a) Areas "located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County; (b) "located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP codes in King County; and (c) "are within the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within one-quarter mile of a residence, or are outside the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within two miles of a residence." (King County Code 26.12.003)Data results related to Method 1 are shown in the LCI Opportunity Areas dataset on the King County GIS Open Data site. In this dataset, the parcels where the "CriteriaAllYN" column is equal to "Y" also represents those parcels.Method 2. If a property does not qualify under Method #1, a project may qualify if: "the project proponent or proponents can demonstrate, and the advisory committee determines, that residents living in the area, or populations the project is intended to serve, disproportionately experience limited access to public open spaces and experience demonstrated hardships including, but not limited to, low income, poor health and social and environmental factors that reflect a lack of one or more conditions for a fair and just society as defined as "determinants of equity" in KCC 2.10.210." (King County Code 26.12.003)Conservation Futures (CFT) values the use of multiple sources of data and information to demonstrate that a property is in an opportunity area. Applicants are welcome to provide additional criteria and data sources not identified in this report to demonstrate that a property is in an opportunity area. These sources are provided in the document here: Understanding the Data Report.

A dataset listing the richest zip codes in Mississippi per the most current US Census data, including information on rank and average income.

New Upload:

Added +32,000 more locations. For information on data calculations please refer to the methodology pdf document. Information on how to calculate the data your self is also provided as well as how to buy data for $1.29 dollars.

What you get:

The database contains 32,000 records on US Household Income Statistics & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 348,893 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to up vote. Up vote right now, please. Enjoy!

Household & Geographic Statistics:

• Mean Household Income (double)
• Median Household Income (double)
• Standard Deviation of Household Income (double)
• Number of Households (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The dataset originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

Sources, don't have 2 dollars? Get the full information yourself!

2011-2015 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved August 2, 2017, from https://www2.census.gov/programs-surveys/acs/summary_file/2015/data/5_year_by_state/

Found Errors?

Please tell us so we may provide you the most accurate data possible. You may reach us at: research_development@goldenoakresearch.com

for any questions you can reach me on at 585-626-2965

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Check our data's accuracy: Census Fact Checker

Access all 348,893 location records and more:

Don't settle. Go big and win big. Optimize your potential. Overcome limitation and outperform expectation. Access all household income records on a scale roughly equivalent to a neighborhood, see link below:

Website: Golden Oak Research Kaggle Deals all databases $1.29 Limited time only

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Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about the Low-Income Housing Tax Credit Program visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low Income Tax Credit Program

Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: Q2 2025

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This dataset provides the location and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Development FAQs - IMS/PIC | HUD.gov / U.S. Department of Housing and Urban Development (HUD), for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Buildings Date Updated: Q2 2025

It contains three columns:i) "Municipality concept," which includes the name of the municipality and the postal code (truncated to 5 digits and enclosed in brackets),ii) "Foreigners from low- or middle-income countries (%)", andiii) "Average income per person (€)".For data processing, the first column should be split into two, with one containing the name of the municipality and the other containing the truncated postal code without brackets. txt file, size 31.4 KB

The Climate Leadership and Community Protection Act (CLCPA) directs the Climate Justice Working Group (CJWG) to establish criteria for defining disadvantaged communities. This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.