About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in San Diego: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 21,786(4.17%) households where the householder is under 25 years old, 215,194(41.21%) households with a householder aged between 25 and 44 years, 171,107(32.77%) households with a householder aged between 45 and 64 years, and 114,059(21.84%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of San Diego, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Diego median household income by age. You can refer the same here

This dataset gives a comprehensive view of demographic details from the 2010 Census, zooming in on smaller regions called census tracts, specifically within the City of Carlsbad. It includes important factors like population distribution, racial makeup, family structures, household types, and housing details. Tailored for visualizing and analyzing local demographics, it's especially helpful for understanding trends within specific regions. The dataset aims to support informed decision-making by providing a detailed look at the social and housing landscape at a more localized level within the City of Carlsbad.It's important to note that the original dataset was sourced from the San Diego Geographic Information Source (SANGIS). Initially covering census tracts for the entire San Diego County, we refined the dataset by clipping it to the specific boundary line of the City of Carlsbad. This process was undertaken to tailor the dataset to the unique demographic profile of Carlsbad, making it more pertinent for local analyses and mapping.For additional details and metadata regarding the broader San Diego Countywide layer, it is recommended to visit the SanGIS website. Accessing the metadata on the SANGIS website will provide users with a deeper understanding of the dataset's origins, characteristics, and any additional information relevant to the broader county context.Point of Contact:Point of Contact Operations Manager, Operations Manager U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch 4600 Silver Hill Road, Stop 7400 Washington, DC. 20233-7400 geo.tiger@census.gov 301-763-1128

Site codes for City-owned property in relation to each parcel that was donated or sold to the City. City sites as identified by site code may comprise multiple parcels.

Certificate number and property addresses for active Transient Occupancy Tax (TOT) vacation type certificates. Properties located in the City of San Diego that are rented to Transients for less than one month are required to obtain a Transient Occupancy Registration Certificate. This includes short-term residential occupancy properties of any kind (i.e. houses, condos, rooms, or spaces) rented directly by the owner/operator, by property management companies or via internet travel services. The short-term residential occupancy properties are generally issued a “Vacation” type TOT Certificate. In general, the data available in this dataset is the same information that is displayed on the TOT Certificate. For additional information on Transient Occupancy Registration Certificates and the Transient Occupancy Tax, please visit our website at sandiego.gov/treasurer/taxesfees/tot/.

Parcels represent taxable pieces of property. A parcel is created by the San Diego County Assessor/Recorder/County Clerk (ARCC) to identify a specific portion of real property that is taxed at a certain rate for a certain owner. Tax parcels are typically the same as a legally subdivided lot but are not necessessarily so. For example, a single owner may own a legally subdivided piece of property but there may be two or more tax parcels covering that property. Legal subdivisions are shown in the LOTS layer.Parcels are keyed to the Assessor Parcel Number (APN) and the parcel polygon identifier (PARCELID).The SanGIS parcel layers are “stacked” parcels. That means that for any piece of ground there may be multiple parcels. For example, a condominium building in downtown San Diego may have 200 individual condos. Each condo is a separate taxable parcel. All 200 parcels will be associated with the same physical lot on the ground. When the SanGIS parcel layer is created each individual condo has a polygon representing the physical location of the parent parcel. In this example there will be 200 polygons all stacked on top of each other that represent the taxable parcels and each polygon will have the same physical characteristics (shape, size, area, location) – they are, essentially, copies of each other. However, other associated information (owner, document numbers, etc) will be different for each. In this case, each condo unit will have its own parcel number and there will be no single parcel representing the lot on the ground. Besides condominiums there are two other cases where you will see stacked parcels – possessory interest and mobile homes. Possessory interests have Assessor Parcel Numbers (APNs) that start with 76x. A possessory interest (or PI) parcel represents a taxable interest in the underlying, or parent, parcel but not necessarily ownership. For instance, a private company may have an arrangement with a University to operate a business on campus – a coffee shop or gift shop for example. The private business is taxable and is assinged a 76x APN and that APN is associated with the parent parcel which is owned by the University. Possessory intestests do not represent ownership on the parcel, only a taxable interest in the underlying parent parcel.Mobile home parcel APNs start with 77x. In a manner similar to the possessory interests, mobile home owners own their home (coach) but not the underlying property on which the house sits. The actual mobile home is a separate taxable parcel associated with the mobile home park parent parcel. These taxable parcels all have the same polygon as the underlying parent parcel and will show as stacked parcels as well.This dataset contains parcels as shown on the Assessor Parcel Maps (APM). However, parcels shown in this layer may lag that of the official APM by a number of weeks due to how SanGIS is notified of the newly created parcel and the timing of publication of the parcel layer.This dataset contains the parcel polygon and associated parcel information provided by the County ARCC in thier Master Property Record (MPR file) and Parcel Assessment Record (PAR file). In addition to the MPR and PAR data assigned by ARCC, SanGIS may add situs address information if it has been provided by the addressing authority in which the parcel is situated. The situs address information provided by SanGIS may not be the same as the SITUS address data in the MPR.This dataset contains site address information along with owner names and addresses, and other property information. Key fields in this dataset include:Land use information provided in the NUCLEUS_USE_CD field (225 types with a 3-digit domain). The ASR_LANDUSE field is an older version of this field but comprises more generalized land uses (91 types). Generalized land use zoning information is provided in the NUCLEUS_ZONE_CD field. The ASR_ZONE field is an older version of this field. Land use zoning is generalized comprising 9 zone types. This can provide a useful approximation for parcels that are outside of the San Diego City and County zoning jurisdictions.Please note that land use and zoning fields are not regularly maintained by the Assessor's Office and should only be used as an approximate guide. Updates are only made when there is new construction, or a change in ownership. They are not updated when the County and Local Cities update their zoning data or when permit changes to properties are completed. Please refer to city and County official zoning datasets for official zoning information, and to SANDAG for more current land use data. NOTE: If the name of this layer includes "_NORTH", "_SOUTH", or "_EAST" it represents a subset of the entire San Diego County Parcel Base. That is, the "_NORTH" layer includes only parcels generally in the Northwestern portion of the County. The "_SOUTH" layer includes parcels in the Southwestern portion. And the "_EAST" layer includes parcels in the approximate Eastern half of the County.

In New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of 47 nights per stay, with an average price of 119 U.S. dollars per night. Meanwhile, the average income per property was 7,062 U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in San Francisco. This was followed by Los Angeles and San Diego. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in San Francisco. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than 39.7 thousand properties accommodating to the different needs of visitors to the city. There are a variety of types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered to longer-term stays, in light of city regulations on housing.

This collection presents survey data from 12 cities in the United States regarding criminal victimization, perceptions of community safety, and satisfaction with local police. Participating cities included Chicago, IL, Kansas City, MO, Knoxville, TN, Los Angeles, CA, Madison, WI, New York, NY, San Diego, CA, Savannah, GA, Spokane, WA, Springfield, MA, Tucson, AZ, and Washington, DC. The survey used the current National Crime Victimization Survey (NCVS) questionnaire with a series of supplemental questions measuring the attitudes in each city. Respondents were asked about incidents that occurred within the past 12 months. Information on the following crimes was collected: violent crimes of rape, robbery, aggravated assault, and simple assault, personal crimes of theft, and household crimes of burglary, larceny, and motor vehicle theft. Part 1, Household-Level Data, covers the number of household respondents, their ages, type of housing, size of residence, number of telephone lines and numbers, and language spoken in the household. Part 2, Person-Level Data, includes information on respondents' sex, relationship to householder, age, marital status, education, race, time spent in the housing unit, personal crime and victimization experiences, perceptions of neighborhood crime, job and professional demographics, and experience and satisfaction with local police. Variables in Part 3, Incident-Level Data, concern the details of crimes in which the respondents were involved, and the police response to the crimes.

Code Enforcement Violations that were reported to the Development Services Department prior to January 2018 and were and closed out between 2015 and 2018. For more recent data on code enforcement violations, please visit OpenDSD.

A dataset listing California counties by population for 2024.

The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. NOTE: This map has been updated as of 1/16/24 to use a newer version of the data layer which contains more cities than it previously did. As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. Map opens in Sacramento, CA. Use bookmarks or the search bar to get to other cities.Cities included in this mapAlabama: Birmingham, Mobile, MontgomeryArizona: PhoenixArkansas: Arkadelphia, Batesville, Camden, Conway, El Dorado, Fort Smith, Little Rock, Russellville, TexarkanaCalifornia: Fresno, Los Angeles, Oakland, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: Boulder, Colorado Springs, Denver, Fort Collins, Fort Morgan, Grand Junction, Greeley, Longmont, PuebloConnecticut: Bridgeport and Fairfield; Hartford; New Britain; New Haven; Stamford, Darien, and New Canaan; WaterburyFlorida: Crestview, Daytona Beach, DeFuniak Springs, DeLand, Jacksonville, Miami, New Smyrna, Orlando, Pensacola, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Columbus, Macon, SavannahIowa: Boone, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Dubuque, Sioux City, WaterlooIllinois: Aurora, Chicago, Decatur, East St. Louis, Joliet, Peoria, Rockford, SpringfieldIndiana: Evansville, Fort Wayne, Indianapolis, Lake County Gary, Muncie, South Bend, Terre HauteKansas: Atchison, Greater Kansas City, Junction City, Topeka, WichitaKentucky: Covington, Lexington, LouisvilleLouisiana: New Orleans, ShreveportMaine: Augusta, Boothbay, Portland, Sanford, WatervilleMaryland: BaltimoreMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Fall River, Fitchburg, Haverhill, Holyoke Chicopee, Lawrence, Lexington, Lowell, Lynn, Malden, Medford, Melrose, Milton, Needham, New Bedford, Newton, Pittsfield, Quincy, Revere, Salem, Saugus, Somerville, Springfield, Waltham, Watertown, Winchester, Winthrop, WorcesterMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Austin, Duluth, Mankato, Minneapolis, Rochester, Staples, St. Cloud, St. PaulMississippi: JacksonMissouri: Cape Girardeau, Carthage, Greater Kansas City, Joplin, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Elizabeth City, Fayetteville, Goldsboro, Greensboro, Hendersonville, High Point, New Bern, Rocky Mount, Statesville, Winston-SalemNorth Dakota: Fargo, Grand Forks, Minot, WillistonNebraska: Lincoln, OmahaNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen County, Camden, Essex County, Monmouth, Passaic County, Perth Amboy, Trenton, Union CountyNew York: Albany, Binghamton/Johnson City, Bronx, Brooklyn, Buffalo, Elmira, Jamestown, Lower Westchester County, Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Schenectady, Staten Island, Syracuse, Troy, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorain, Portsmouth, Springfield, Toledo, Warren, YoungstownOklahoma: Ada, Alva, Enid, Miami Ottawa County, Muskogee, Norman, Oklahoma City, South McAlester, TulsaOregon: PortlandPennsylvania: Allentown, Altoona, Bethlehem, Chester, Erie, Harrisburg, Johnstown, Lancaster, McKeesport, New Castle, Philadelphia, Pittsburgh, Wilkes-Barre, YorkRhode Island: Pawtucket & Central Falls, Providence, WoonsocketSouth Carolina: Aiken, Charleston, Columbia, Greater Anderson, Greater Greensville, Orangeburg, Rock Hill, Spartanburg, SumterSouth Dakota: Aberdeen, Huron, Milbank, Mitchell, Rapid City, Sioux Falls, Vermillion, WatertownTennessee: Chattanooga, Elizabethton, Erwin, Greenville, Johnson City, Knoxville, Memphis, NashvilleTexas: Amarillo, Austin, Beaumont, Dallas, El Paso, Forth Worth, Galveston, Houston, Port Arthur, San Antonio, Waco, Wichita FallsUtah: Ogden, Salt Lake CityVirginia: Bristol, Danville, Harrisonburg, Lynchburg, Newport News, Norfolk, Petersburg, Phoebus, Richmond, Roanoke, StauntonVermont: Bennington, Brattleboro, Burlington, Montpelier, Newport City, Poultney, Rutland, Springfield, St. Albans, St. Johnsbury, WindsorWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Madison, Milwaukee County, Oshkosh, RacineWest Virginia: Charleston, Huntington, WheelingAn example of a map produced by the HOLC of Philadelphia:

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in San Antonio: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 10(1.89%) households where the householder is under 25 years old, 134(25.28%) households with a householder aged between 25 and 44 years, 265(50%) households with a householder aged between 45 and 64 years, and 121(22.83%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of San Antonio, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Antonio median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in San Antonio: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 30,838(5.63%) households where the householder is under 25 years old, 210,558(38.43%) households with a householder aged between 25 and 44 years, 186,354(34.01%) households with a householder aged between 45 and 64 years, and 120,133(21.93%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of San Antonio, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Antonio median household income by age. You can refer the same here