Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Donuka offers a simple, reliable property data solution to power innovation and create seamless business solutions for companies of all sizes. Our data covers more than 155+ million properties spread out across the U.S. that can be accessed in bulk-file format or through our APIs.

We offer access to data ONLY in selected states and counties

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300+ data sources in total
2. 4 billion records (listed in the "data types" block above) in total
3. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

In 2023, jewelry and precious metals worth about 1,786.88 million U.S. dollars were stolen in the United States. Only about 37.21 million U.S. dollars worth of stolen jewelry and precious metals was recovered in the same year.

In 2024, the finance, insurance, real estate, rental, and leasing industry contributed the highest amount of value to the GDP of the U.S. at 21.2 percent. The construction industry contributed around four percent of GDP in the same year.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show value of owner-occupied housing units by Georgia House in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes:SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameOwnOcc_e# Owner-occupied housing units, 2017OwnOcc_m# Owner-occupied housing units, 2017 (MOE)ValLt50k_e# Owner-occupied units valued less than $50,000, 2017ValLt50k_m# Owner-occupied units valued less than $50,000, 2017 (MOE)pValLt50k_e% Owner-occupied units valued less than $50,000, 2017pValLt50k_m% Owner-occupied units valued less than $50,000, 2017 (MOE)Val50_100k_e# Owner-occupied units valued $50,000 to $99,999, 2017Val50_100k_m# Owner-occupied units valued $50,000 to $99,999, 2017 (MOE)pVal50_100k_e% Owner-occupied units valued $50,000 to $99,999, 2017pVal50_100k_m% Owner-occupied units valued $50,000 to $99,999, 2017 (MOE)Val100_150k_e# Owner-occupied units valued $100,000 to $149,999, 2017Val100_150k_m# Owner-occupied units valued $100,000 to $149,999, 2017 (MOE)pVal100_150k_e% Owner-occupied units valued $100,000 to $149,999, 2017pVal100_150k_m% Owner-occupied units valued $100,000 to $149,999, 2017 (MOE)Val150_200k_e# Owner-occupied units valued $150,000 to $199,999, 2017Val150_200k_m# Owner-occupied units valued $150,000 to $199,999, 2017 (MOE)pVal150_200k_e% Owner-occupied units valued $150,000 to $199,999, 2017pVal150_200k_m% Owner-occupied units valued $150,000 to $199,999, 2017 (MOE)Val200_300k_e# Owner-occupied units valued $200,000 to $299,999, 2017Val200_300k_m# Owner-occupied units valued $200,000 to $299,999, 2017 (MOE)pVal200_300k_e% Owner-occupied units valued $200,000 to $299,999, 2017pVal200_300k_m% Owner-occupied units valued $200,000 to $299,999, 2017 (MOE)Val300_500k_e# Owner-occupied units valued $300,000 to $499,999, 2017Val300_500k_m# Owner-occupied units valued $300,000 to $499,999, 2017 (MOE)pVal300_500k_e% Owner-occupied units valued $300,000 to $499,999, 2017pVal300_500k_m% Owner-occupied units valued $300,000 to $499,999, 2017 (MOE)Val500k_1m_e# Owner-occupied units valued $500,000 to $999,999, 2017Val500k_1m_m# Owner-occupied units valued $500,000 to $999,999, 2017 (MOE)pVal500k_1m_e% Owner-occupied units valued $500,000 to $999,999, 2017pVal500k_1m_m% Owner-occupied units valued $500,000 to $999,999, 2017 (MOE)Val1mP_e# Owner-occupied units valued $1,000,000 or more, 2017Val1mP_m# Owner-occupied units valued $1,000,000 or more, 2017 (MOE)pVal1mP_e% Owner-occupied units valued $1,000,000 or more, 2017pVal1mP_m% Owner-occupied units valued $1,000,000 or more, 2017 (MOE)ValLt100k_e# Owner-occupied units valued less than $100,000, 2017ValLt100k_m# Owner-occupied units valued less than $100,000, 2017 (MOE)pValLt100k_e% Owner-occupied units valued less than $100,000, 2017pValLt100k_m% Owner-occupied units valued less than $100,000, 2017 (MOE)Val100_300k_e# Owner-occupied units valued $100,000 to $299,999, 2017Val100_300k_m# Owner-occupied units valued $100,000 to $299,999, 2017 (MOE)pVal100_300k_e% Owner-occupied units valued $100,000 to $299,999, 2017pVal100_300k_m% Owner-occupied units valued $100,000 to $299,999, 2017 (MOE)Val300kPlus_e# Owner-occupied units valued $300,000 or more, 2017Val300kPlus_m# Owner-occupied units valued $300,000 or more, 2017 (MOE)pVal300kPlus_e% Owner-occupied units valued $300,000 or more, 2017pVal300kPlus_m% Owner-occupied units valued $300,000 or more, 2017 (MOE)mMedHUValue_eMedian value of owner-occupied unit (dollars), 2017mMedHUValue_mMedian value of owner-occupied unit (dollars), 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 217 data sources in Washington, D.C.
3. 315,000+ properties in Washington, D.C.
4. 4 billion records (listed in the "data types" block above) in total
5. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 155,000,000 properties in total
3. 4 billion records (listed in the "data types" block above) in total
4. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

About this dataset

Context

The dataset tabulates the Cogan House township population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Cogan House township. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 513 (57% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Cogan House township population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Cogan House township is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Cogan House township is shown in the following column.

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 Cogan House township Population by Age. You can refer the same here

This dataset represents all City of Bend permit applications from 1993 to present as well as many historic permit applications from 1990-1993. Applications are generated using tax lot data from the time of submission combined with application attributes derived from City of Bend permitting software. Please note data is updated nightly and subject to change as applications are received and reviewed.Attribute Information:Field Name DescriptionObjectIDFor internal use.GNMasterProjectIDFor internal use.GNCommonIDFor internal use.PR_RecordIDFor internal use.ApplicationNumberThe tracking number for this application in The City of Bend permitting system.ApplicationDateThe date the application was submitted for review. IssueDateThe date the City of Bend issued the permit. If there is an Application Date but no Issue Date, this generally means the application is still under review.DateFinaledThe date the application had all its inspections completed. If there is an Issue Date but not a Date Finaled, this generally means the application is still under construction.SQFTThe estimate square footage of the work being proposed. The estimated square footage (if any) is supplied by the applicant and not verified by the City. UnitsThe number of housing units that will be constructed. Please note units are not verified until permit issuance. Data is subject to change.Affordable HousingIdentifier for permits related to affordable housing projects. OnSewerIdentifier for permits on properties served by City sewer. ProjectValuationThe estimated project cost of the work being proposed based on the fair market value. The estimate cost (if any) represents the best available information and is subject to change. ApplicationTypeThe application type code by category, such as new construction, demolition, renovation, addition, etc. TypeDescThe application type description by category, such as new construction, demolition, renovation, addition, etc. ApplicationStatusThe current status code for the application. Updated nightly. StatusDescThe current status description for the application. Updated nightly. BldgUseThe building use code by category, such as single family dwelling, duplex, multifamily, commercial or industrial, etc.UseDescThe building use description by category, such as single family dwelling, duplex, multifamily, commercial or industrial, etc.BuildingCategoryA description of whether the permit is for a residential or non-residential project.DeptCodeThe lead department managing the application review.DepartmentThe lead department managing the application review.OwnerThe owner of the property associated with this permit at the time of application. CensusStructureCodeThe census structure code. Permits for new housing units are classified into US Census Bureau-defined classifications.CensusStructureDescThe census structure description. Permits for new housing units are classified into US Census Bureau-defined classifications.AddressThe site address for the application. Please note if a project includes multiple addresses, only one is visible in this field.LOCIDFor internal use.SITADDIDFor internal use.TAXLOTThe tax lot for the application. Please note if a project includes multiple tax lots, only one is visible in this field.CENTERLINIDFor internal use.LocationFinaledFor internal use.GlobalIDFor internal use.CREATEDBYFor internal use.CREATEDDATEFor internal use.UPDATEDBYFor internal use.LASTUPDATEFor internal use.InfoFinaledFor internal use.OverallStatusFor internal use.Shape.STArea()For internal use.Shape.STLength()For internal use.For questions regarding permit applications, please visit The City of Bend Online Permit Center or call 541-388-5580. For questions related to the data please email GIS@bendoregon.gov.

Graph and download economic data for Housing Inventory: Active Listing Count in Florida (ACTLISCOUFL) from Jul 2016 to Jun 2025 about active listing, FL, listing, and USA.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the birth and citizenship status by Georgia House in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, Super District, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

TotPop_e

# Total population, 2017

TotPop_m

# Total population, 2017 (MOE)

Native_e

# U.S. Native, 2017

Native_m

# U.S. Native, 2017 (MOE)

pNative_e

% U.S. Native, 2017

pNative_m

% U.S. Native, 2017 (MOE)

BornUS_e

# Born in the United States, 2017

BornUS_m

# Born in the United States, 2017 (MOE)

pBornUS_e

% Born in the United States, 2017

pBornUS_m

% Born in the United States, 2017 (MOE)

BornState_e

# Born in state of residence, 2017

BornState_m

# Born in state of residence, 2017 (MOE)

pBornState_e

% Born in state of residence, 2017

pBornState_m

% Born in state of residence, 2017 (MOE)

BornDiffState_e

# Born in different state, 2017

BornDiffState_m

# Born in different state, 2017 (MOE)

pBornDiffState_e

% Born in different state, 2017

pBornDiffState_m

% Born in different state, 2017 (MOE)

BornTerr_e

# Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017

BornTerr_m

# Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017 (MOE)

pBornTerr_e

% Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017

pBornTerr_m

% Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017 (MOE)

ForBorn_e

# Foreign born, 2017

ForBorn_m

# Foreign born, 2017 (MOE)

pForBorn_e

% Foreign born, 2017

pForBorn_m

% Foreign born, 2017 (MOE)

Naturalized_e

# Naturalized U.S. citizen, 2017

Naturalized_m

# Naturalized U.S. citizen, 2017 (MOE)

pNaturalized_e

% Naturalized U.S. citizen, 2017

pNaturalized_m

% Naturalized U.S. citizen, 2017 (MOE)

NotNaturalized_e

# Not a U.S. citizen, 2017

NotNaturalized_m

# Not a U.S. citizen, 2017 (MOE)

pNotNaturalized_e

% Not a U.S. citizen, 2017

pNotNaturalized_m

% Not a U.S. citizen, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population by race/ethnicity and change data by Georgia House in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameTotPop_e# Total population, 2017TotPop_m# Total population, 2017 (MOE)Hisp_e# Hispanic or Latino (of any race), 2017Hisp_m# Hispanic or Latino (of any race), 2017 (MOE)pHisp_e% Hispanic or Latino (of any race), 2017pHisp_m% Hispanic or Latino (of any race), 2017 (MOE)Not_Hisp_e# Not Hispanic or Latino, 2017Not_Hisp_m# Not Hispanic or Latino, 2017 (MOE)pNot_Hisp_e% Not Hispanic or Latino, 2017pNot_Hisp_m% Not Hispanic or Latino, 2017 (MOE)NHWhite_e# Not Hispanic, White alone, 2017NHWhite_m# Not Hispanic, White alone, 2017 (MOE)pNHWhite_e% Not Hispanic, White alone, 2017pNHWhite_m% Not Hispanic, White alone, 2017 (MOE)NHBlack_e# Not Hispanic, Black or African American alone, 2017NHBlack_m# Not Hispanic, Black or African American alone, 2017 (MOE)pNHBlack_e% Not Hispanic, Black or African American alone, 2017pNHBlack_m% Not Hispanic, Black or African American alone, 2017 (MOE)NH_AmInd_e# Not Hispanic, American Indian and Alaska Native alone, 2017NH_AmInd_m# Not Hispanic, American Indian and Alaska Native alone, 2017 (MOE)pNH_AmInd_e% Not Hispanic, American Indian and Alaska Native alone, 2017pNH_AmInd_m% Not Hispanic, American Indian and Alaska Native alone, 2017 (MOE)NH_Asian_e# Not Hispanic, Asian alone, 2017NH_Asian_m# Not Hispanic, Asian alone, 2017 (MOE)pNH_Asian_e% Not Hispanic, Asian alone, 2017pNH_Asian_m% Not Hispanic, Asian alone, 2017 (MOE)NH_PacIsl_e# Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017NH_PacIsl_m# Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017 (MOE)pNH_PacIsl_e% Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017pNH_PacIsl_m% Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017 (MOE)NH_OthRace_e# Not Hispanic, some other race alone, 2017NH_OthRace_m# Not Hispanic, some other race alone, 2017 (MOE)pNH_OthRace_e% Not Hispanic, some other race alone, 2017pNH_OthRace_m% Not Hispanic, some other race alone, 2017 (MOE)NH_TwoRace_e# Not Hispanic, two or more races, 2017NH_TwoRace_m# Not Hispanic, two or more races, 2017 (MOE)pNH_TwoRace_e% Not Hispanic, two or more races, 2017pNH_TwoRace_m% Not Hispanic, two or more races, 2017 (MOE)NH_AsianPI_e# Non-Hispanic Asian or Pacific Islander, 2017NH_AsianPI_m# Non-Hispanic Asian or Pacific Islander, 2017 (MOE)pNH_AsianPI_e% Non-Hispanic Asian or Pacific Islander, 2017pNH_AsianPI_m% Non-Hispanic Asian or Pacific Islander, 2017 (MOE)NH_Other_e# Non-Hispanic other (Native American, other one race, two or more races), 2017NH_Other_m# Non-Hispanic other (Native American, other one race, two or more races), 2017 (MOE)pNH_Other_e% Non-Hispanic other (Native American, other one race, two or more races), 2017pNH_Other_m% Non-Hispanic other (Native American, other one race, two or more races), 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

This dataset represents all City of Bend permit applications from 1993 to present as well as many historic permit applications from 1990-1993. Applications are generated using address point data from the time of submission combined with application attributes derived from City of Bend permitting software. Please note data is updated nightly and subject to change as applications are received and reviewed.Attribute Information:Field NameDescriptionObjectIDFor internal use.GNMasterProjectIDFor internal use.GNCommonIDFor internal use.PR_RecordIDFor internal use.ApplicationNumberThe tracking number for this application in The City of Bend permitting system.ApplicationDateThe date the application was submitted for review. IssueDateThe date the City of Bend issued the permit. If there is an Application Date but no Issue Date, this generally means the application is still under review.DateFinaledThe date the application had all its inspections completed. If there is an Issue Date but not a Date Finaled, this generally means the application is still under construction.SQFTThe estimate square footage of the work being proposed. The estimated square footage (if any) is supplied by the applicant and not verified by the City. UnitsThe number of housing units that will be constructed. Please note units are not verified until permit issuance. Data is subject to change.Affordable HousingIdentifier for permits related to affordable housing projects. OnSewerIdentifier for permits on properties served by City sewer. ProjectValuationThe estimated project cost of the work being proposed based on the fair market value. The estimate cost (if any) represents the best available information and is subject to change. ApplicationTypeThe application type code by category, such as new construction, demolition, renovation, addition, etc. TypeDescThe application type description by category, such as new construction, demolition, renovation, addition, etc. ApplicationStatusThe current status code for the application. Updated nightly. StatusDescThe current status description for the application. Updated nightly. BldgUseThe building use code by category, such as single family dwelling, duplex, multifamily, commercial or industrial, etc.UseDescThe building use description by category, such as single family dwelling, duplex, multifamily, commercial or industrial, etc.BuildingCategoryA description of whether the permit is for a residential or non-residential project.DeptCodeThe lead department managing the application review.DepartmentThe lead department managing the application review.OwnerThe owner of the property associated with this permit at the time of application. CensusStructureCodeThe census structure code. Permits for new housing units are classified into US Census Bureau-defined classifications.CensusStructureDescThe census structure description. Permits for new housing units are classified into US Census Bureau-defined classifications.AddressThe site address for the application. Please note if a project includes multiple addresses, only one is visible in this field.LOCIDFor internal use.SITADDIDFor internal use.TAXLOTThe tax lot for the application. Please note if a project includes multiple tax lots, only one is visible in this field.CENTERLINIDFor internal use.LocationFinaledFor internal use.ShapeFor internal use.GlobalIDFor internal use.CREATEDBYFor internal use.CREATEDDATEFor internal use.UPDATEDBYFor internal use.LASTUPDATEFor internal use.InfoFinaledFor internal use.OverallStatusFor internal use.For questions regarding permit applications, please visit The City of Bend Online Permit Center or call 541-388-5580. For questions related to the data please email GIS@bendoregon.gov.

This dataset represents all City of Bend permit applications connected to street segment(s) from 2019 to present. Permits are generated using road centerlines data from the time of submission combined with application attributes derived from City of Bend permitting software. Please note data is updated nightly and subject to change as applications are received and reviewed.Attribute Information: Field NameDescriptionOBJECTIDFor internal use.GNMasterProjectIDFor internal use.GNCommonIDFor internal use.PR_RecordIDFor internal use.ApplicationNumberThe tracking number for this application in The City of Bend permitting system.ApplicationDateThe date the application was submitted for review. IssueDateThe date the City of Bend issued the permit. If there is an Application Date but no Issue Date, this generally means the application is still under review.DateFinaledThe date the application had all its inspections completed. If there is an Issue Date but not a Date Finaled, this generally means the application is still under construction.SQFTThe estimate square footage of the work being proposed. The estimated square footage (if any) is supplied by the applicant and not verified by the City. UnitsThe number of housing units that will be constructed. Please note units are not verified until permit issuance. Data is subject to change.Affordable HousingIdentifier for permits related to affordable housing projects. OnSewerIdentifier for permits on properties served by City sewer. ProjectValuationThe estimated project cost of the work being proposed based on the fair market value. The estimate cost (if any) represents the best available information and is subject to change. ApplicationTypeThe application type code by category, such as new construction, demolition, renovation, addition, etc. TypeDescThe application type description by category, such as new construction, demolition, renovation, addition, etc. ApplicationStatusThe current status code for the application. Updated nightly. StatusDescThe current status description for the application. Updated nightly. BldgUseThe building use code by category, such as single family dwelling, duplex, multifamily, commercial or industrial, etc.UseDescThe building use description by category, such as single family dwelling, duplex, multifamily, commercial or industrial, etc.BuildingCategoryA description of whether the permit is for a residential or non-residential project.DeptCodeThe lead department managing the application review.DepartmentThe lead department managing the application review.OwnerThe owner of the property associated with this permit at the time of application. CensusStructureCodeThe census structure code. Permits for new housing units are classified into US Census Bureau-defined classifications.CensusStructureDescThe census structure description. Permits for new housing units are classified into US Census Bureau-defined classifications.AddressThe site address for the application. Please note if a project includes multiple addresses, only one is visible in this field.LOCIDFor internal use.SITADDIDFor internal use.TAXLOTThe tax lot for the application. Please note if a project includes multiple tax lots, only one is visible in this field.CENTERLINIDFor internal use.LocationFinaledFor internal use.GlobalIDFor internal use.CREATEDBYFor internal use.CREATEDDATEFor internal use.UPDATEDBYFor internal use.LASTUPDATEFor internal use.InfoFinaledFor internal use.OverallStatusFor internal use.Shape.STLength()For internal use.For questions regarding permit applications, please visit The City of Bend Online Permit Center or call 541-388-5580. For questions related to the data please email GIS@bendoregon.gov.

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 155,000,000 properties in total
3. 4 billion records (listed in the "data types" block above) in total
4. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

We are also including tabular version that’s slightly more comprehensive (would include anything that didn’t join to the parcel basefile due to lot alterations or resubdivisions since 2023 and/or due to parcels comprised of condos). There are approximately 200 records that don't join to the spatial parcel file and some additional that are null in the spatial parcel file, altogether around 560 total. This Excel file can be downloaded HERE, and does not contain the latitude and longitude information.Data Dictionary:Attribute LabelDefinitionSourceTAX_IDUnique 26 character property tax identification numberOnondaga County PlanningPRINTKEYAbbreviated tax identification number (section-block-lot)Onondaga County PlanningADDRESSNUMProperty’s physical street addressOnondaga County PlanningADDRESSNAMProperty’s physical street nameOnondaga County PlanningTAX_ID_1City Tax ID number (26 digit number used for parcel mapping)City of Syracuse - AssessmentSBLProperty Tax Map Number (Section, Block, Lot)City of Syracuse - AssessmentPNUMBRProperty Number (10 digit number)City of Syracuse - AssessmentStNumParcel street numberCity of Syracuse - AssessmentStNameParcel street nameCity of Syracuse - AssessmentFullAddressStreet number and street nameCity of Syracuse - AssessmentZipParcel zip codeCity of Syracuse - Assessmentdesc_1Lot description including dimensionsCity of Syracuse - Assessmentdesc_2Lot description including dimensionsCity of Syracuse - Assessmentdesc_3Lot description including dimensionsCity of Syracuse - AssessmentSHAPE_IND City of Syracuse - AssessmentLUC_parcelNew York State property type classification code assigned by assessor during each roll categorizing the property by use. For more details: https://www.tax.ny.gov/research/property/assess/manuals/prclas.htmCity of Syracuse - AssessmentLU_parcelNew York State property type classification nameCity of Syracuse - AssessmentLUCat_OldLegacy land use category that corresponds to the overarching NYS category, i.e. all 400s = commercial, all 300s = vacant land, etc.NAland_avLand assessed valueCity of Syracuse - Assessmenttotal_avFull assessed valueCity of Syracuse - AssessmentOwnerProperty owner name (First, Initial, Last, Suffix)City of Syracuse - AssessmentAdd1_OwnPOBoxProperty owner mailing address (PO Box)City of Syracuse - AssessmentAdd2_OwnStAddProperty owner mailing address (street number, street name, street direction)City of Syracuse - AssessmentAdd3_OwnUnitInfoProperty owner mailing address unit info (unit name, unit number)City of Syracuse - AssessmentAdd4_OwnCityStateZipProperty owner mailing address (city, state or country, zip code)City of Syracuse - AssessmentFRONTFront footage for square or rectangular shaped lots and the effective front feet on irregularly shaped lots in feetCity of Syracuse - AssessmentDEPTHActual depth of rectangular shaped lots in feet (irregular lots are usually measured in acres or square feet)City of Syracuse - AssessmentACRESNumber of acres (where values were 0, acreage calculated as FRONT*DEPTH)/43560)City of Syracuse - Assessmentyr_builtYear built. Where year built was "0" or null, effective year built is given. (Effective age is determined by comparing the physical condition of one building with that of other like-use, newer buildings. Effective age may or may not represent the actual year built; if there have been constant upgrades or excellent maintenance this may be more recent than the original year built.)City of Syracuse - Assessmentn_ResUnitsNumber of residential unitsNA - Calculated fieldIPSVacantIs it a vacant structure? ("Commercial" or "Residential" = Yes; null = No)City of Syracuse - Division of Code EnforcementIPS_ConditionProperty Condition Score assigned to vacant properties by housing inspectors during routine vacant inspections (1 = Worst; 5 = Best)City of Syracuse - Division of Code EnforcementNREligibleNational Register of Historic Places Eligible ("NR Eligible (SHPO)," or "NR Listed")City of Syracuse - Neighborhood and Business DevelopmentLPSSLocally Protected Site Status ("Eligible/Architecturally Significant" or "Local Protected Site or Local District")City of Syracuse - Neighborhood and Business DevelopmentWTR_ACTIVEWater activity code ("I" = Inactive; "A" = Active)City of Syracuse - WaterRNIIs property located in Resurgent Neighborhood Initiative (RNI) Area? (1 = Yes; 0 = No)City of Syracuse - Neighborhood and Business DevelopmentDPW_QuadGeographic quadrant property is located in. Quadrants are divided Northwest, Northeast, Southwest, and Southeast based on property location in relation to I-81 and I-690. DPW uses the quad designation for some types of staff assignments.City of Syracuse - Department of Public WorksTNT_NAMETNT Sector property is located inCity of Syracuse - Neighborhood and Business DevelopmentNHOODCity NeighborhoodSyracuse-Onondaga County Planning Agency (SOCPA)NRSAIs property located in Neighborhood Revitalization Strategy Area (NRSA)? (1 = Yes; 0 = No)City of Syracuse - Neighborhood and Business DevelopmentDOCE_AreaGeographic boundary use to assign Division of Code Enforcement casesCity of Syracuse - Neighborhood and Business DevelopmentZONE_DIST_PREVFormer zoning district codeSyracuse-Onondaga County Planning Agency (SOCPA)REZONEReZone designation (adopted June 2023)City of Syracuse - Neighborhood and Business DevelopmentNew_CC_DISTCurrent Common Council District property is located inOnondaga County Board of ElectionsCTID_2020Census Tract ID (2020)U.S. Census BureauCTLAB_2020Census Tract Label (2020)U.S. Census BureauCT_2020Census Tract (2020)U.S. Census BureauSpecNhoodIs property located in a special Neighborhood historic preservation district? (1 = Yes; 0 or null = No)Syracuse-Onondaga County Planning Agency (SOCPA)InPDIs property located in preservation district? (1 = Yes; 0 or null = No)Syracuse-Onondaga County Planning Agency (SOCPA)PDNAMEPreservation District nameSyracuse-Onondaga County Planning Agency (SOCPA)ELECT_DISTElection district numberOnondaga County Board of ElectionsCITY_WARDCity ward numberOnondaga County Board of ElectionsCOUNTY_LEGOnondaga County Legislative District number (as of Dec 2022)Onondaga County Board of ElectionsNYS_ASSEMBNew York State Assembly District number (as of Dec 2022)Onondaga County Board of ElectionsNYS_SENATENew York State Senate District number (as of Dec 2022)Onondaga County Board of ElectionsUS_CONGRUnited States Congressional District numberOnondaga County Board of Elections

DEFINITION:Tax Law POINT is a generalized point representation of lands enrolled in the Managed Forest and Forest Crop Law Programs, collectively referred to as Tax Law Layers. Points are located at the center point of each 40-acre quarter-quarter section in which land is enrolled. Points do not identify specific enrollment location. Acreage enrolled from fractional or government lots are located either to the most approximate QQ, Q or S as possible. (Enrolled parcels are represented by the PLSS shape they lie within; however, the actual size of the enrolled property may be as small as 0.1 acres). The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.FEATURE TYPE(S):PointGEOGRAPHIC EXTENT:StatewideSOURCE SCALE:VariedPROJECTION:Wisconsin Transverse Mercator NAD 1983/1991 (WTM83/91)WKID: 3071PURPOSE/BACKGROUND:Wisconsin’s forest tax laws encourage sustainable forest management on private lands by providing a property tax incentive to landowners. Both the Managed Forest Law (MFL) and Forest Crop Law (FCL) encourage proper management of woodlands not only in their purposes and policies, but through a written management plan for a landowner’s property. The management plan incorporates landowner objectives, timber management, wildlife management, water quality and the environment as a whole to create healthy and productive forest. In exchange for following a written management plan and program rules, landowners pay forest tax law program rates in lieu of regular property taxes.FCL lands are open to the public for the following activities: hunting and fishing.MFL lands enrolled as open are open to the public for the following activities: hunting, fishing, hiking, sight-seeing, and cross-country skiing.Additional rules regarding public access may be reviewed here: https://dnr.wisconsin.gov/topic/forestlandowners/mflThe GIS feature class was created to be used in the Open Private Forest Lands web mapping application (Private Forest Lands Open to Public Recreation).Open Private Forest Lands (OPFL) Project Background:Provide a simple GIS web mapping application to display the approximate representations of over 1.3 million acres of Forest Tax Law lands (Managed Forest and Forest Crop Law) open to the public for hunting, hiking, fishing, cross-country skiing, and sightseeing. Display information to allow the public to access the lands without spending a lot of time cross checking plat books or contacting local county offices or the county Land Information Offices.Update Frequency:Semi-Annual (January, September). Edits to Tax Law entries can occur throughout the year, but most changes are not effective until January 1 except for landowner information. Landowner information edits are updated in the spatial views on a weekly basis. In addition, Forestry will re-generate taxlaw shapes as significant improvements to the data are completed. In January of each year, the feature class is re-generated to reflect new entries, changes to access, etc. effective January 1. January update: Update to reflect enrollments as of January.September update: Pre-hunting season update.The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.ATTRIBUTES:Field Descriptions:ORDER_NO: (c, 12) The Forestry property code of the feature. (Use as join field for if linking to landowner table information.)Format: 2-digit cnty – 3 digit seq no – 4 digit year of entryEx. 11-234-2013DNR_CTY_NO: (n, 2) The 2-digit DNR county code representing the predominant county in which the DTRSQQ falls.Format: Numbers, No commasEx: 37 (Marathon County)CNTY_NAME: (T, 11) County name of the predominant county in which the DTRSQQ falls.Ex: MarathonENTRY_YEAR: (t, 4) The year in which the order number was entered into the taxlaw program.Format: YYYYEx: 1999TAX_TYPE: (t, 3) Indicates whether the polygon is enrolled in MFL or FCL.Format: ALL CAPSPossible Values:MFL: Managed Forest LawFCL: Forest Crop LawAC_OP_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as open. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_OP_ORD for total open acreage associated with this order number). NOTE: This is not the total number of acres open with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_CL_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as closed. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_CL_ORD for total closed acreage associated with this order number). NOTE: This is not the total number of acres closed with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_TOT_PLS: (double) Total acres, associated to the identified order number, that are enrolled in the identified PLSS. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_TOT_ORD for total acreage associated with this order number). NOTE: This is not the total number of acres enrolled within this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.ORDER_YRS: (t, 2) Total number of years the order will be enrolled in the program (under the associated order number). Format: Plan or order lengths are either 25 or 50 yearsEx: 50ORDER_EXP: (t, 20) Date that order number expires. All orders end on December 31. Format: December 31, YYYYEx: December 31, 2015OWNER_TEXT: (t, 30) Type of ownership. Ownership could be: Individual, Joint, Corporation, LLC, Partnership, LLP, Trust, etc.ACCNT_TYPE: (t, 1) Type of account.Possible Values:S: Small Account – landowners generally have less than 1,000 acres of forest land and the accounts are managed by DNR field foresters.L: Large Account – landowners generally have 1,000 acres or more of forest land and the accounts are managed by DNR Forest TaxAC_OP_ORD: (double) Total open acreage associated with the order number. AC_CL_ORD: (double) Total closed acreage associated with the order number.AC_TOT_ORD: (double) Total acreage associated with the order number.DTRSQQ_CO: (long) A concatenation of direction, township, range, section, quarter section, and quarter-quarter section used to approximate the location of the order number (or part of the order number). Each order number has separate records for each DTRSQQ where the order number resides. (Data source: 24K Landnet Spatial Database Technical Documentation)Format:1st Digit = Direction2nd & 3rd Digits = Township4th & 5th Digits = Range6th & 7th Digits = Section8th Digit = Quarter9th Digit = Quarter-QuarterEx: 441012812LEGAL_D_CO: (t, 5) Code describing legal description identified by order number.Format: 1st character:Blank = Entire (Govt Lot)D = Entire (PLSS)P = Part ofE = Entire Excluding ROW2nd character:L = Govt LotBlank = PLSSCharacters 3-5:If PLSS, 001-016 are StandardIf PLSS, 017-060 are FractionalIf Govt Lot, this is the Govt Lot #Ex: PL003LEGAL_DESC: (t, 100) Translated legal description code. Ex: GOV LOT 3, PART OFDTRSLD_TXT: (t, 2380) Field generated to convert DTRSQQ and legal description codes to a text description of the PLSS where the enrollment is located. Includes a note indicating if a record includes a fractional correction.Ex: T02-R01W-S05, Part of the NE of the NW (fractional correction)PARCEL_NO: (t, 255) County created parcel number. (Parcel level information not yet available for all records.)Format: Varies by countyEx: 07-04-59MCD_NAME: (t, 50) Municipal Civil Division (MCD) name.Ex: Solon SpringsMCD_TYPE_C: (t, 1) Type of Municipal Civil Division (MCD). Format: ALL CAPSPossible Values:T: TownV: VillageC: CityPLSS_LEVEL: (t, 2) PLSS level to which the record is located. Format: ALL CAPSPossible Values:QQ: Quarter-quarterQ: QuarterS: SectionCHNG_BY: (c, 30) The user who last updated the record.Ex: klauscCHNG_DATE: (date) Date the record was last changed.Format: MM/DD/YYYY Ex: 10/23/2012ACCESS: (t, 1) Indicates whether the quarter-quarter contains areas which are open to the public, closed to the public, or both.Format: ALL CAPSPossible Values:O: QQ contains areas that are Open to the publicC: QQ contains areas that are Closed to the publicB: QQ contains Both open and closed areas.ADDITIONAL INFORMATION:Tax law programs: https://dnr.wisconsin.gov/topic/forestlandowners/mflWeb mapping application: https://dnr.wisconsin.gov/topic/forestlandowners/opentopublicappCONTACT PERSON(S):GIS contact: Laura Waddle - GIS Specialist, (608) 320-4648, Laura.Waddle@wisconsin.govResource contact: <>R.J. Wickham - Tax Law Section Chief, (920) 369-6248, Richard.Wickham@wisconsin.govCOPYRIGHT:The material is for the noncommercial use of the general public. The fair use guidelines of the U.S. copyright statutes apply to all material on the Department of Natural Resources Webpages and linked agency Webpages. The Department of Natural Resources shall remain the sole and exclusive owner of all rights, title and interest in and to all specifically copyrighted information created and posted for inclusion in this system. Photographs and graphics on the Department website are either the property of the state department or the state agency that holds a license to use and display the material. For copy or use of information on the Department website that is outside of the fair use provisions of copyright law, please seek permission from the individual listed as responsible for the page. If you have any questions on using material on the Department web pages please e-mail the specific

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 217 data sources in New York City
3. 4 billion records (listed in the "data types" block above) in total
4. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

U.S. Home Services Market size was valued at USD 211.71 Billion in 2023 and is projected to reach USD 893.18 Billion by 2031, growing at a CAGR of 19.59% from 2024 to 2031.

U.S. Home Services Market Overview

The U.S. Home Services Market is poised for continued growth, driven by a combination of evolving consumer preferences, technological advancements, and demographic trends. A key driver of this market is the increasing demand for home maintenance and improvements. With homeownership rates on the rise, particularly among millennials and baby boomers, more individuals are investing in home repairs, remodeling, and upgrades to enhance comfort, functionality, and property value. The aging housing stock in the U.S. also contributes significantly to this demand, as older homes require frequent repairs and updates to meet modern standards and codes.

This is particularly true in regions with older infrastructure, where demand for plumbing, electrical, and HVAC services remains high. One of the most influential trends in the U.S. Home Services Market is the shift toward digitalization and online platforms. As consumers become more tech-savvy, they are increasingly turning to mobile apps and online marketplaces to find, compare, and schedule home services. This has not only improved accessibility and convenience for homeowners but also allowed service providers to reach new audiences.

About this dataset

Context

The dataset tabulates the Charlotte Court House population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Charlotte Court House. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 413 (62.39% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Charlotte Court House population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Charlotte Court House is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Charlotte Court House is shown in the following column.

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 Charlotte Court House Population by Age. You can refer the same here