In the vibrant and continuously evolving landscape of Tennessee, understanding the intricate layers of socio-economic and demographic data is paramount for any entity seeking to establish, expand, or refine its operations. From bustling metropolitan hubs like Memphis and Nashville-Davidson to dynamic urban centers such as Knoxville, Chattanooga, and Clarksville, the Volunteer State presents a rich tapestry of communities, each with unique characteristics.
The strategic value derived from granular, data-driven insights cannot be overstated in today’s competitive environment. A robust data infrastructure provides a critical lens through which businesses, policymakers, and researchers can analyze market dynamics, identify growth opportunities, and address specific community needs with unparalleled precision.
Tennessee’s extensive data framework encompasses a multitude of indicators, offering a comprehensive view of its residents and localities. These detailed metrics are crucial for crafting informed decisions that resonate with local populations and contribute to sustainable development across the state, from the largest cities to the smallest villages.
Demographic profiling forms a foundational component of this extensive data landscape. It provides crucial insights into the human composition of Tennessee’s diverse cities and towns, including populations reported for Adamsville, Alamo, Alcoa, Algood, Arlington, Athens, Atoka, Bartlett, Brentwood, Brighton, Bristol, Brownsville, Chattanooga, Church Hill, Clarksville, Cleveland, Clinton, Collegedale, Collierville, Columbia, Cookeville, Covington, Crossville, Dayton, Dickson, Dyersburg, Elizabethton, Erwin, Fairview, Fayetteville, Farragut, Franklin, Gallatin, Gatlinburg, Germantown, Goodlettsville, Greeneville, Harriman, Henderson, Hendersonville, Hohenwald, Humboldt, Huntingdon, Indian Mound-Bumpus Mills, Jackson, Jasper, Jefferson City, Johnson City, Jonesborough, Kingsport, Kingston, Knoxville, La Follette, La Vergne, Lafayette, Lakeland, Lawrenceburg, Lebanon, Lenoir City, Lewisburg, Lexington, Livingston, Loudon, Manchester, Martin, Maryville, McMinnville, Medina, Memphis, Metropolitan Government, Milan, Millersville, Millington, Monteagle, Monterey, Morristown, Mount Carmel, Mount Juliet, Mount Pleasant, Munford, Murfreesboro, Nashville-Davidson, Newbern, Newport, Nolensville, Oak Ridge, Oakland, Oliver Springs, Oneida, Paris, Pigeon Forge, Pikeville, Portland, Powell, Pulaski, Red Bank, Ripley, Rockwood, Rogersville, Savannah, Selmer, Sevierville, Shelbyville, Signal Mountain, Smithville, Smyrna, Soddy-Daisy, Somerville, South Pittsburg, Sparta, Spring City, Spring Hill, Springfield, Sweetwater, Tullahoma, Union City, White House, Whiteville, Winchester, Woodbury, among many others.
Further demographic precision is achieved through data points such as ‘Population – Males (%)’ and ‘Population – Females (%)’, alongside granular breakdowns by race for both genders, including ‘Population – Males (%) – White’, ‘Population – Males (%) – Black or African American’, ‘Population – Males (%) – Asian’, ‘Population – Males (%) – Hispanic or Latino’, ‘Population – Males (%) – American Indian and Alaska Native’, ‘Population – Males (%) – Multirace’, and ‘Population – Males (%) – Other Race’. Similar detailed percentages are available for female populations across these racial categories.
Understanding the racial composition is vital, with categories such as ‘Races – White alone (%)’, ‘Races – Black alone (%)’, ‘Races – American Indian alone (%)’, ‘Races – Asian alone (%)’, ‘Races – Hispanic (%)’, ‘Races – Native Hawaiian and Other Pacific Islander alone (%)’, ‘Races – Two or more races (%)’, and ‘Races – Other race alone (%)’. The data also tracks ‘% change since 2000’ for many of these racial categories, allowing for an analysis of demographic shifts over time, complemented by ‘Racial diversity’ metrics.
Age distribution, captured by ‘Median resident age’ and ‘Resident age diversity’, is further refined by ‘Median resident age – Males’ and ‘Median resident age – Females’, as well as median ages for specific racial groups and by gender within those groups. This granular age data is invaluable for understanding generational trends and consumer cohorts.
The economic landscape is meticulously documented through various income metrics, providing a detailed financial portrait of Tennessee’s communities. ‘Median household income ($)’ is a cornerstone, complemented by its ‘(% change since 2000)’, offering insights into economic progression or stagnation. The ‘Household income diversity’ metric adds another layer of understanding.
Further, income is analyzed through the lens of housing, with ‘Median household income for houses/condos with a mortgage ($)’ and ‘Median household income for apartments without a mortgage ($)’, revealing different financial capacities tied to housing tenure. The ‘Ratio of average income to average house value (%)’ and ‘Ratio of average income to average rent’ provide critical affordability and market health indicators.
Median income data is also disaggregated by race and ethnicity, including ‘Median household income ($) – White’, ‘Median household income ($) – Black or African American’, ‘Median household income ($) – Asian’, ‘Median household income ($) – Hispanic or Latino’, ‘Median household income ($) – American Indian and Alaska Native’, ‘Median household income ($) – Multirace’, and ‘Median household income ($) – Other Race’. Similar breakdowns are available for ‘Median family income ($)’ and ‘Median per capita income ($)’, often with ‘(% change since 2000)’ for trend analysis.
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Unemployment figures are equally comprehensive, tracking ‘Unemployment (%)’ and its ‘(% change since 2000)’. This vital economic health indicator is also available for various racial and ethnic groups, including ‘Unemployment (%) – White’, ‘Unemployment (%) – Black or African American’, ‘Unemployment (%) – Asian’, ‘Unemployment (%) – Hispanic or Latino’, ‘Unemployment (%) – American Indian and Alaska Native’, ‘Unemployment (%) – Multirace’, and ‘Unemployment (%) – Other Race’, highlighting disparities in employment opportunities.
Furthermore, the data includes ‘Residents with income below the poverty level (%)’ and ‘Residents with income below 50% of the poverty level (%)’, alongside ‘Children below poverty level (%)’. Poverty status is also analyzed by family type, such as ‘Poor families by family type – Married-couple family (%)’, ‘Poor families by family type – Male, no wife present (%)’, and ‘Poor families by family type – Female, no husband present (%)’. This granular view extends to ‘Poverty status for native-born residents (%)’ and ‘Poverty status for foreign-born residents (%)’, and poverty among different educational attainment levels like ‘Poverty among high school graduates not in families (%)’ and ‘Poverty among people who did not graduate high school not in families (%)’. Racial breakdowns of poverty rates mirror those of income and employment.
The housing market in Tennessee is meticulously detailed within this data framework, offering insights critical for real estate development and urban planning. Key metrics include ‘Median house or condo value ($)’ and its ‘($ change since 2000)’, alongside ‘Median gross rent ($)’ and ‘Median contract rent ($)’. The ‘Median monthly housing costs ($)’ provides a comprehensive view of housing affordability.
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The data also segments housing values by structure type, such as ‘Mean house or condo value by units in structure – 1, detached ($)’, ‘1, attached ($)’, ‘2 ($)’, ‘3 or 4 ($)’, ‘5 or more ($)’, ‘Boat, RV, van, etc. ($)’, and ‘Mobile home ($)’. This level of detail is invaluable for assessing housing stock composition and market segments. Furthermore, ‘Median house or condo value ($)’ is reported across all racial categories, similar to income data, offering insights into wealth distribution and housing equity.
Information on housing characteristics extends to ‘Median number of rooms in houses and condos’ and ‘Median number of rooms in apartments’, as well as ‘Median number of bedrooms in owner occupied houses’ and ‘Mean number of bedrooms in owner occupied houses’, with similar data for renter-occupied units. These details inform understanding of living standards and family size accommodation.
Occupancy status is covered by ‘Occupied housing units (%)’ and ‘Vacant housing units (%)’, further broken down by ‘Owner occupied housing units (%)’ and ‘Renter occupied housing units (%)’. Vacancy status is elaborated with categories like ‘For rent (%)’, ‘For sale only (%)’, ‘Rented or sold, not occupied (%)’, ‘For seasonal, recreational, or occasional use (%)’, ‘For migrant workers (%)’, and ‘Other vacant (%)’, providing a granular view of market availability.
The age of housing stock is also captured through ‘Median year house/condo built’ and ‘Median year apartment built’, along with percentages of units built in specific periods, ranging from ‘2005 or later (%)’ back to ‘1939 or earlier (%)’. This data is crucial for assessing infrastructure age and potential renovation markets. Details on ‘Mortgage status’ including ‘with mortgage (%)’, ‘with second mortgage (%)’, ‘with home equity loan (%)’, and ‘without a mortgage (%)’ offer a financial snapshot of homeowners.
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Understanding the social fabric of Tennessee’s communities is facilitated by data on marital status, including ‘Never married (%)’, ‘Now married (%)’, ‘Separated (%)’, ‘Widowed (%)’, and ‘Divorced (%)’. These statistics provide insights into household formations and social support structures.
Geographical mobility is tracked through ‘Same house 1 year ago (%)’, ‘Moved within same county (%)’, ‘Moved from different county within same state (%)’, ‘Moved from different state (%)’, and ‘Moved from abroad (%)’. This data is essential for understanding population churn and regional migration patterns, influencing both labor supply and consumer bases.
Furthermore, the data delineates ‘People in Group quarters’ across various categories, including ‘Institutionalized population (%)’ (e.g., correctional institutions, nursing homes, hospitals) and ‘Noninstitutionalized population (%)’ (e.g., college dormitories, military quarters, group homes, religious group quarters). This comprehensive overview provides a clear picture of the non-traditional living arrangements within communities.
Educational attainment is a key indicator of human capital and workforce readiness. The data provides detailed percentages for residents across the educational spectrum, from ‘No schooling completed (%)’ to ‘Doctorate degree (%)’, with intermediate categories like ‘Less than high school (%)’, ‘High school or equivalent (%)’, ‘Associate degree (%)’, and ‘Bachelor’s degree (%)’.
School enrollment figures further refine this picture, covering ‘Nursery, preschool (%)’, through ‘Grade 1 to 4 (%)’, ‘Grade 5 to 8 (%)’, ‘Grade 9 to 12 (%)’, and on to ‘College undergrad (%)’ and ‘Graduate or professional (%)’, alongside the percentage ‘Not enrolled in school (%)’. This is complemented by a breakdown of ‘Public schools (%)’ and ‘Private schools (%)’ enrollment.
The workforce composition and commuting patterns are thoroughly documented, vital for understanding labor markets and infrastructure demands. ‘Occupation diversity’ and ‘Industry diversity’ are key metrics. The data identifies ‘Most Common Occupations’ such as ‘Management, business, science, and arts occupations’, ‘Service occupations’, ‘Sales and office occupations’, ‘Natural resources, construction, and maintenance occupations’, and ‘Production, transportation, and material moving occupations’, each with sub-categories for finer analysis.
Similarly, ‘Most Common Industries’ are detailed, including ‘Agriculture, forestry, fishing and hunting, and mining’, ‘Construction’, ‘Manufacturing’, ‘Retail trade’, ‘Transportation and warehousing, and utilities’, ‘Information’, ‘Finance and insurance, and real estate and rental and leasing’, ‘Professional, scientific, and management, and administrative and waste management services’, ‘Educational services, and health care and social assistance’, ‘Arts, entertainment, and recreation, and accommodation and food services’, and ‘Public administration’. These categories, along with their respective percentages, provide a robust framework for sector-specific analysis.
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Commute patterns are captured by ‘Commute – mean travel time to work (minutes)’ and a percentage breakdown of travel times, from ‘Less than 5 minutes (%)’ to ’90 or more minutes (%)’. The ‘Means of transportation to work’ is also detailed, including ‘Drove car alone (%)’, ‘Carpooled (%)’, ‘Public transportation (%)’ (with various modes like ‘Bus or trolley bus (%)’, ‘Subway or elevated (%)’, ‘Railroad (%)’, ‘Ferryboat (%)’, ‘Taxicab (%)’), ‘Motorcycle (%)’, ‘Bicycle (%)’, ‘Walked (%)’, and ‘Other means (%)’. The prevalence of ‘Working at home (%)’ is also tracked, reflecting modern work trends.
Beyond socio-economic metrics, the data framework extends to crucial aspects of public safety and environmental quality. Crime statistics, provided as ‘per 100,000 population’ for categories like ‘Murders’, ‘Rapes’, ‘Robberies’, ‘Assaults’, ‘Burglaries’, ‘Thefts’, ‘Auto thefts’, and ‘Arson’, offer a snapshot of community safety, often with 5-year averages for trend analysis. These also contribute to ‘City-data.com crime index’, ‘Violent crime index’, and ‘Property crime index’.
Environmental data includes ‘Air pollution – Air Quality Index (AQI)’ and specific pollutant levels for ‘CO’, ‘NO2’, ‘SO2’, ‘Ozone’, ‘PM10’, ‘PM25’, and ‘Pb’. Such information is critical for assessing environmental health and its implications for residents and businesses.
The political landscape is also reflected in the data through ‘Presidential Elections Results (%)’ from 1996 through 2020, broken down by ‘Democratic Party’, ‘Republican Party’, and ‘Other’ candidates. This offers a historical perspective on political leanings within various communities.
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Cultural and ancestral roots are illuminated by ‘Ancestries Reported’, including percentages for ‘Arab’, ‘Czech’, ‘Danish’, ‘Dutch’, ‘English’, ‘French’, ‘French Canadian’, ‘German’, ‘Greek’, ‘Hungarian’, ‘Irish’, ‘Italian’, ‘Lithuanian’, ‘Norwegian’, ‘Polish’, ‘Portuguese’, ‘Russian’, ‘Scotch-Irish’, ‘Scottish’, ‘Slovak’, ‘Subsaharan African’, ‘Swedish’, ‘Swiss’, ‘Ukrainian’, ‘United States’, ‘Welsh’, ‘West Indian’, and ‘Other’ ancestries. Language spoken at home is also tracked, with ‘Residents speaking English at home (%)’ and ‘Residents speaking Spanish at home (%)’ detailed by place of birth (born in US, native born elsewhere, foreign born).
Finally, an interesting social indicator is the prevalence of ‘Likely homosexual households (%)’, further segmented into ‘Lesbian couples (%)’ and ‘Gay men (%)’, with ‘(% change since 2000)’ available for trend analysis, offering a glimpse into evolving social demographics.
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In summation, the depth and breadth of data available for communities across Tennessee provide an unparalleled resource for strategic analysis. Whether the focus is on market entry, operational scaling, workforce planning, or community engagement, this comprehensive framework offers the factual basis for informed decision-making. The ability to dissect socio-economic, demographic, housing, and lifestyle indicators at a granular level empowers organizations to navigate Tennessee’s diverse landscape with precision and foresight.
