Occupancy status, Units, Year built, Owner/Renter (Tenure), Mortgage/Rent costs variables from 1-Year ACS.Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: District of ColumbiaCurrent Vintage: 2023ACS Table(s): DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 2, 2025National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Data processed using R statistical package and ArcGIS Pro.Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana 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)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Comprehensive dataset of 1 Renter's insurance agencies in Aiko District, Kanagawa, Japan as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Occupancy status, Units, Rooms, Year built, Owner/Renter (Tenure), Mortgage/Rent costs, and more. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP04. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Comprehensive dataset of 1 Renter's insurance agencies in Mie District, Mie, Japan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The median rent for one- and two-bedroom apartments in Washington, D.C., amounted to about 2,121 U.S. dollars by the end of 2023. Rents decreased by over 11.6 percent annually in December 2020, but this trend quickly reversed and as of December 2021, rental growth was 12.7 percent. Among the different states in the U.S., the District of Columbia ranks as one of the most expensive rental markets.

Experimental Occupancy status, Units, Year built, Owner/Renter (Tenure), Mortgage/Rent costs variables. Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data. This includes a limited number of data tables for the nation, states, and the District of Columbia. Please visit the following webpage for details. https://www.census.gov/programs-surveys/acs/data/experimental-data.htmlContact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2020. ACS Table(s): Housing - Experimental. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: March 18, 2022. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

This data collection contains information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), urban/rural status, household relationship, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry into the United States, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, occupation and industry, class of worker, income, and poverty status. Housing items include vacancy status, tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, household size, occupants per room, number of units in structure, year structure was built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent. With subject content identical to that provided in Summary File 3, the information is presented in 813 tables which are tabulated for every upper and lower chamber state legislative district and smaller geographic units within the districts: counties, county subdivisions, places, consolidated cities, and American Indian Areas/Alaska Native Areas/Hawaiian Home Lands. There is one variable per table cell, plus additional variables with geographic information. Like Summary File 3, the collection contains 4,004 data files:77 for each state, the District of Columbia, and Puerto Rico. The collection is supplied in 54 ZIP archives. There is a separate ZIP file for each state, the District of Columbia, and Puerto Rico, and for the convenience of those who need all of the data, a separate ZIP archive with all 4,004 data files. The codebook and other documentation are located in the last ZIP archive.

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 2011-2015, to show various demographic data by House district in the state of Georgia (including the following categories: total population, age, race/ethnicity, household composition, grandparents, school enrollment, educational attainment, veteran status, disability, foreign born status, linguistic isolation, unemployment, commuting mode, occupation, income, health insurance, poverty, housing characteristics, vehicle availability, housing values, and housing affordability).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. The Census Bureau also calculates a corresponding margin of error (MOE) for ACS measures (although margins of error are not included in this dataset).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 2011-2015). 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, refer to Census Bureau documentation.- - - - - -Base Attributes:DISTRICT = GA House DistrictPOPULATION = District Population (2010 Census)Name = GA House District NameTotal_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)profile_url = Web address of district profile- - - - - -Attributes from ACS:Workers_16_years_and_over= Number, Workers, 16 years and overCar_Truck_or_Van_drove_alone= Number, Car, truck, or van – drove alonePct_Car_Truck_Van_drove_alone= Percent, Car, truck, or van – drove aloneCar_truck_or_van_carpooled= Number, Car, truck, or van – carpooledPct_Car_Truck_Van_carpooled= Percent, Car, truck, or van – carpooledPublic_Transport_excluding_Taxi= Number, Public transportation (excluding taxicab)Pct_Public_Transp_exclude_Taxi= Percent, Public transportation (excluding taxicab)Worked_at_home= Number, Worked at homePct_Worked_at_home= Percent, Worked at homeMean_Travel_Time_to_Work_min= Mean travel time to work (minutes)- - - - - -Civilian_nonInstitutional_Pop= Total Civilian Noninstitutionalized PopulationCiv_nonInstitution_Pop_wDisabil= #, Civilian Noninstitutionalized Population With a disabilityPct_Civ_nonInstitut_Pop_wDisab= %, Civilian Noninstitutionalized Population With a disabilityCiv_nonInstitut_Pop_under_18yrs= #, Civilian Noninstitutionalized Population Under 18 yearsCiv_nonInst_under18_wDisab= #, Civilian Noninstitutionalized Under 18 years With a disabilityPct_Civ_nonInst_under18_wDisab= %, Civilian Noninstitutionalized Under 18 years With a disabilityCiv_nonInst_Pop_18_to_64= #, Civilian Noninstitutionalized Population 18 to 64 yearsCiv_nonInst_18_to_64_wDisab= #, Civilian Noninstitutionalized 18 to 64 years With a disabilityPct_Civ_nonInst_18to64_wDisab= %, Civilian Noninstitutionalized 18 to 64 years With a disabilityCiv_nonInst_Pop_65years_up= #, Civilian Noninstitutionalized Population 65 years and overCiv_nonInst_65up_wDisab= #, Civilian Noninstitutionalized 65 years and over With a disabilityPct_Civ_nonInst_65up_wDisab= %, Civilian Noninstitutionalized 65 years and over With a disability- - - - - -Population_25_years_and_over= #, Population 25 years and overLess_than_HS_or_GED= #, Less than HS or GEDPercent_Less_than_HS_or_GED= %, Less than HS or GEDBA_or_Higher= #, BA or HigherPercent_BA_or_Higher= %, BA or Higher- - - - - -US_Native= #, U.S. NativePercent_US_Native= %, U.S. NativeUSnative_Born_in_US= #, U.S. Native, Born in the United StatesPct_USnative_Born_US= %, U.S. Native, Born in the United StatesUSnative_Born_State_Resid= #, U.S. Native, Born in State of ResidencePct_USnative_Born_State_Resid= %, U.S. Native, Born in State of ResidenceUS_Native_Born_Diff_State= #, U.S. Native, Born in Different StatePct_US_Natv_Born_inDiff_State= %, U.S. Native, Born in Different StateForeign_Born= #, Foreign BornPercent_Foreign_Born= %, Foreign BornForBorn_Nat_UScitizen= #, Foreign Born, Naturalized U.S. CitizenPct_ForBorn_Nat_UScitizen= %, Foreign Born, Naturalized U.S. CitizenForeignBorn_notUS_Citizen= #, Foreign Born, Not a U.S. CitizenPct_ForBorn_notUS_Citizen= %, Foreign Born, Not a U.S. Citizen- - - - - -GParents_Liv_wOwn_GChild_und18= #, Grandparents living with own grandchildren under 18 yearsGParents_RespFor_Gchildren= #, Grandparents Responsible for grandchildrenPct_GPar_RespFor_Gchildren= %, Grandparents Responsible for grandchildren- - - - - -Pop_wHealth_Insurance= #, Civilian noninstitutionalized population with health insurance coveragePct_Pop_wHealth_Ins= %, Civilian noninstitutionalized population with health insurance coveragePop_wPriv_Health_Ins= #, Civilian noninstitutionalized population with private health insurancePct_Pop_wPriv_Health_Ins= %, Civilian noninstitutionalized population with private health insurancePopulation_with_public_coverage= #, Civilian noninstitutionalized population with public coveragePct_Pop_with_public_coverage= %, Civilian noninstitutionalized population with public coveragePop_wNo_Health_Ins= #, Civilian noninstitutionalized population with no health insurance coveragePct_Pop_wNo_Health_Ins= %, Civilian noninstitutionalized population with no health insurance coveragePop_u18_wNo_Health_Ins= #, Civilian Noninstitutionalized Population Under 18 years with no health insurancePct_Pop_u18_wNo_Health_Ins= %, Civilian Noninstitutionalized Population Under 18 years with no health insurancePop_18to64_Employed= #, Civilian noninstitutionalized ages 18 to 64, employedPop_18to64_Empl_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePct_Pop_18to64_Emp_wNo_Hlth_Ins= %, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePop_18to64_Unemployed= #, Civilian noninstitutionalized ages 18 to 64, unemployedPop_18to64_Unemp_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePct_Pop_18to64_Unemp_No_HlthIns= %, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePop_18to64_Not_in_Labor_Force= #, Civilian noninstitutionalized ages 18 to 64, not in labor forcePop_18to64_Not_LabFor_NoHlthIns= #, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurancePctPop_18to64_NotLFor_NoHlthIns= %, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurance- - - - - -HousUnits_MonthOwnerCosts_toInc= #, Housing units for which Selected Monthly Owner Costs as % of income are computedSel_Mo_Own_Costs_30pct_of_Incom= #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomePct_Sel_Mo_Own_Costs_30pct_Inc= %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomeHousUnits_Compute_RentPctIncome= #, Housing units for which Gross rent as a percentage of income is computedRent_Pct_of_Inc_More30Pct= #, Gross rent as a percentage of household income (GRAPI) is 30% or morePctRent_PctIncome_More30Pct= %, Gross rent as a percentage of household income (GRAPI) is 30% or moreHousUnits_OwnRent_Compute= #, Housing units for which SMOCAPI or GRAPI are computedHousCosts_Units_30pctMore_Inc= #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household incomePctHousCost_30pctMore_Income= %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income- - - - - -Total_housing_units= Total housing unitsOccupied_housing_units= #, Occupied housing unitsPercent_Occupied_housing_units= %, Occupied housing unitsVacant_housing_units= #, Vacant housing unitsPercent_Vacant_housing_units= %, Vacant housing unitsHomeowner_vacancy_rate= Homeowner vacancy rateRental_vacancy_rate= Rental vacancy rateOne_unit_detatched_housing_unit= #, 1-unit detached housing unitsPercent_1Unit_Detached= %, 1-unit detached housing unitsHousing_units_built_since_2000= #, Housing units built since 2000Pct_Units_Built_Since_2000= %, Housing units built since 2000Units_Built_1980_to_1999= #, Housing units built 1980 to 1999Pct_Units_Built_1980_to_1999= %, Housing units built 1980 to 1999Units_Built_1979_or_Earlier= #, Housing units built 1979 or earlierPct_Units_Built_1979_or_Earlier= %, Housing units built 1979 or earlierOwner_occupied_housing_units= Housing Tenure: #, Owner occupied housing unitsPct_Owner_Occ_HousUnits= Housing Tenure: %, Owner occupied housing unitsRenter_occupied_housing_units= Housing Tenure: #, Renter occupied housing unitsPct_Renter_Occ_Units= Housing Tenure: %, Renter occupied housing units- - - - - -OwnOcc_units_valued_less_100k= #, Owner occupied housing units valued less than $100,000Pct_OwnOcc_units_val_less_100k= %, Owner occupied housing units valued less than $100,000OwnOcc_units_valued_100k_300k= #, Owner occupied housing units valued $100,000-$299,999Pct_OwnOcc_units_val_100k_300k= %, Owner occupied housing units valued $100,000-$299,999OwnOcc_units_valued_300k_more= #, Owner occupied housing units valued $300,000 or morePct_OwnOcc_units_val_300k_more= %, Owner occupied housing units valued $300,000 or moreMedian_value_own_occ_units= Median value, owner occupied housing units- - - - - -Income_Total_households = Income: Total householdsHousehold_inc_less_35k= #, Household income less than $35,000Pct_Household_inc_less_35k= %, Household income less

Abstract (en): This data collection contains information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), urban/rural status, household relationship, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry into the United States, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, occupation and industry, class of worker, income, and poverty status. Housing items include vacancy status, tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, household size, occupants per room, number of units in structure, year structure was built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent. With subject content identical to that provided in Summary File 3, the information is presented in 813 tables that are tabulated for every geographic unit represented in the data. There is one variable per table cell, plus additional variables with geographic information. The data cover more than a dozen geographic levels of observation (known as "summary levels" in the Census Bureau's nomenclature) based on the 110th Congressional Districts, e.g., the 110th Congressional Districts, themselves, Census tracts within the 110th Congressional Districts, and county subdivisions within the 110th Congressional Districts. There are 77 data files for each state, the District of Columbia, and Puerto Rico. The collection is supplied in 54 ZIP archives. There is a separate ZIP file for each state, the District of Columbia, and Puerto Rico, and for the convenience of those who need all of the data, a separate ZIP archive with all 4,004 data files. The codebook and other documentation are located in the last ZIP archive. All persons and housing units in the United States and Puerto Rico. Every person and housing unit in the United States was asked basic demographic and housing questions (for example, race, age, and relationship to householder). A sample of these people and housing units was asked more detailed questions. The sampling unit for Census 2000 was the housing unit, including all occupants. There were four different housing unit sampling rates, 1-in-8, 1-in-6, 1-in-4, and 1-in-2, designed to yield an overall average of about 1-in-6. mail questionnaireICPSR has not checked this data collection.

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 comparison of housing ownership costs and rental costs to income by Super District 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, SuperDistrict, 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

HUM_SMOCAPI_e

# Housing units with a mortgage, costs as a percentage of income computed, 2017

HUM_SMOCAPI_m

# Housing units with a mortgage, costs as a percentage of income computed, 2017 (MOE)

MSMOCAPI30PctPlus_e

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017

MSMOCAPI30PctPlus_m

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pMSMOCAPI30PctPlus_e

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017

pMSMOCAPI30PctPlus_m

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

HUNM_SMOCAPI_e

# Housing units without a mortgage, costs as a percentage of income computed, 2017

HUNM_SMOCAPI_m

# Housing units without a mortgage, costs as a percentage of income computed, 2017 (MOE)

NMSMOCAPI30PctPlus_e

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017

NMSMOCAPI30PctPlus_m

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pNMSMOCAPI30PctPlus_e

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017

pNMSMOCAPI30PctPlus_m

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

OccGRAPI_e

# Occupied units for which rent as a percentage of income can be computed, 2017

OccGRAPI_m

# Occupied units for which rent as a percentage of income can be computed, 2017 (MOE)

GRAPI30PctPlus_e

# Gross rent 30.0 percent of income or greater, 2017

GRAPI30PctPlus_m

# Gross rent 30.0 percent of income or greater, 2017 (MOE)

pGRAPI30PctPlus_e

% Gross rent 30.0 percent of income or greater, 2017

pGRAPI30PctPlus_m

% Gross rent 30.0 percent of income or greater, 2017 (MOE)

HousingCost30PctPlus_e

# All occupied units for which costs exceed 30 percent of income, 2017

HousingCost30PctPlus_m

# All occupied units for which costs exceed 30 percent of income, 2017 (MOE)

PayingForHousing_e

# Total households paying for housing (rent or owner costs), 2017

PayingForHousing_m

# Total households paying for housing (rent or owner costs), 2017 (MOE)

pHousingCost30PctPlus_e

% Occupied units for which costs exceed 30 percent of income, 2017

pHousingCost30PctPlus_m

% Occupied units for which costs exceed 30 percent of income, 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.

Summary Tape File (STF) 1 consists of six sets of computer-readable data files containing detailed tabulations of the nation's population and housing characteristics produced from the 1980 Census. This series is comprised of STF 1A, STF 1B, STF 1C, STF 1D, STF 1E, and STF 1F. All six groups of files in the STF 1 have identical tables and formats presented in 59 tables consisting of 321 cells. The data items contained in the STF 1 files were also tabulated from the complete count or "100-percent" questions included on the 1980 Census. The data files differ only in geographic coverage. STF 1F, the School Districts file, is a special tabulation that provides summary level data for school districts by state (summary level 40) including the District of Columbia, and by county or county equivalent (summary level 41). Population items tabulated include age, race (provisional data), sex, marital status, Spanish origin (provisional data), household type, and household relationship. Housing items tabulated include occupancy/vacancy status, tenure, contract rent, value, condominium status, number of rooms, and plumbing facilities. Selected aggregates, means, and medians are also provided.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.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)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)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 2018-2022). 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.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

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)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

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 2016-2020). 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.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.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)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)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 2019-2023). 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.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

NOTE: For information on confidentiality protection, nonsampling error, and definitions, see http://www.census.gov/prod/cen2010/doc/cd113.pdf..Source: U.S. Census Bureau, 2010 Census..X Not applicable. .[1] Other Asian alone, or two or more Asian categories. .[2] Other Pacific Islander alone, or two or more Native Hawaiian and Other Pacific Islander categories. .[3] One of the four most commonly reported multiple-race combinations nationwide in Census 2000. .[4] In combination with one or more of the other races listed. The six numbers may add to more than the total population, and the six percentages may add to more than 100 percent because individuals may report more than one race. .[5] This category is composed of people whose origins are from the Dominican Republic, Spain, and Spanish-speaking Central or South American countries. It also includes general origin responses such as "Latino" or "Hispanic." .[6] "Spouse" represents spouse of the householder. It does not reflect all spouses in a household. Responses of "same-sex spouse" were edited during processing to "unmarried partner." .[7] "Family households" consist of a householder and one or more other people related to the householder by birth, marriage, or adoption. They do not include same-sex married couples even if the marriage was performed in a state issuing marriage certificates for same-sex couples. Same-sex couple households are included in the family households category if there is at least one additional person related to the householder by birth or adoption. Same-sex couple households with no relatives of the householder present are tabulated in nonfamily households. "Nonfamily households" consist of people living alone and households which do not have any members related to the householder. .[8] The homeowner vacancy rate is the proportion of the homeowner inventory that is vacant "for sale." It is computed by dividing the total number of vacant units "for sale only" by the sum of owner-occupied units, vacant units that are "for sale only," and vacant units that have been sold but not yet occupied; and then multiplying by 100. .[9] The rental vacancy rate is the proportion of the rental inventory that is vacant "for rent." It is computed by dividing the total number of vacant units "for rent" by the sum of the renter-occupied units, vacant units that are "for rent," and vacant units that have been rented but not yet occupied; and then multiplying by 100. . Source: U.S. Census Bureau, Congressional District Summary File (113th Congress), Tables P5, P6, P8, P12, P13, P17, P19, P20, P25, P29, P31, P34, P37, P43, PCT5, PCT8, PCT11, PCT12, PCT19, PCT23, PCT24, H3, H4, H5, H11, H12, and H16.

Abstract (en): Summary File 4 (SF 4) from the United States 2000 Census contains the sample data, which is the information compiled from the questions asked of a sample of all people and housing units. Population items include basic population totals: urban and rural, households and families, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status, year of entry, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include basic housing totals: urban and rural, number of rooms, number of bedrooms, year moved into unit, household size and occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, monthly rent, and shelter costs. In Summary File 4, the sample data are presented in 213 population tables (matrices) and 110 housing tables, identified with "PCT" and "HCT" respectively. Each table is iterated for 336 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), 39 Hispanic or Latino groups, and 86 ancestry groups. The presentation of SF4 tables for any of the 336 population groups is subject to a population threshold. That is, if there are fewer than 100 people (100-percent count) in a specific population group in a specific geographic area, and there are fewer than 50 unweighted cases, their population and housing characteristics data are not available for that geographic area in SF4. For the ancestry iterations, only the 50 unweighted cases test can be performed. See Appendix H: Characteristic Iterations, for a complete list of characteristic iterations. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. All persons in housing units in the District of Columbia in 2000. 2013-05-25 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 342 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 341 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 340 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 339 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 338 and flagged as study-level files, so that they will accompany all downloads. Because of the number of files per state in Summary File 4, ICPSR has given each state its own ICPSR study number in the range ICPSR 13512-13563. The study number for the national file is 13570. Data for each state are being released as they become available.The data are provided in 38 segments (files) per iteration. These segments are PCT1-PCT4, PCT5-PCT16, PCT17-PCT34, PCT35-PCT37, PCT38-PCT45, PCT46-PCT49, PCT50-PCT61, PCT62-PCT67, PCT68-PCT71, PCT72-PCT76, PCT77-PCT78, PCT79-PCT81, PCT82-PCT84, PCT85-PCT86 (partial), PCT86 (partial), PCT87-PCT103, PCT104-PCT120, PCT121-PCT131, PCT132-PCT137, PCT138-PCT143, PCT144, PCT145-PCT150, PCT151-PCT156, PCT157-PCT162, PCT163-PCT208, PCT209-PCT213, HCT1-HCT9, HCT10-HCT18, HCT19-HCT22, HCT23-HCT25, HCT26-HCT29, HCT30-HCT39, HCT40-HCT55, HCT56-HCT61, HCT62-HCT70, HCT71-HCT81, HCT82-HCT86, and HCT87-HCT110. The iterations are Parts 1-336, the Geographic Header File is Part 337. The Geographic Header File is in fixed-format ASCII and the table files are in comma-delimited ASCII format. A merged iteration will have 7,963 variables.For Parts 251-336, the part names contain numbers within parentheses that refer to the Ancestry Code List (page G1 of the codebook).

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

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 2014-2018). 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 a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

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)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

The 110th Congressional District Summary File (100-percent) (110CD100) contains the 100- percent data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino, household relationship, and group quarters. Housing items include occupancy status, vacancy status, and tenure (owner occupied or renter occupied). The file contains subject content identical to that shown in Summary File 1 (SF 1).

All licenses for the occupation of Crown lands and leases of Crown lands required the payment of rent in amounts and at intervals as stated by legislation or regulations made under the authority of legislation. Rents could be paid either by post or personally to the Melbourne office of the Department of Crown Lands and Survey (VA 538) or to local Receivers and Paymasters as designated for each parish and Land District (subsequent to the formation of the Occupation Branch in c 1874). Receivers and Paymasters were often local Clerks of Courts.

Previous to the passage of the Land Act of 1869, the payment of rents had been recorded in Registers of Licensees and Lessees. These continued for Section 33 of the Land Act 1869 and at the offices of local Receivers and Paymasters. Within the Department of Crown Lands itself and the Occupation Branch these Registers were superseded by the Rent Rolls.

Details given in the rent rolls are the name of the licensee or lessee, the details of the location and size of the land, details of the payments of fees and of the date and amount of regular periodic payments of rent. Remarks include details of subsequent purchase of the land, of any transfers of leases or licenses to other holders and the subsequent payments made by those persons, any cancellation or revocation or instances of abandonment of the land by the occupier.

Notifications of rents due at a particular date were circulated by notice or by lists published in the Government Gazette. The latter allowed local officers to be aware of the rents due in their areas. When the rents were paid to these officers, the payments were recorded in the local records and returns forwarded to the Department. Examples of these records may be seen in VPRS 809 Returns of Pastoral Rents Received. At the Occupation Branch, clerks (the rent rollers) were employed whose sole duties were the updating and maintenance of the rent rolls and preparation of certificates documenting payments where these were to be credited against the purchase price of land. Originally from about 1877, a rent roll clerk was attached to each "District Land Office" within the Occupation Branch.

Rent rolls, like registers of applications, were arranged according to sections of a specific Land Act. For major provisions such as Sections 19 and 20 Land Act 1869 or Section 29 Land Act 1898 and Section 35 Land Act 1901, the rent roll recorded only payments relating to that section . Payments for obligations under other sections of the Land Acts could be included together in one roll. Separate rolls were kept for payments made in each Land District.

Section 42 of the Land Act 1884 (as confirmed in the consolidated Land Act 1890) provided for the issue to grazing area lessees (under Section 32 of the same Act) of licences to occupy for agricultural allotments not exceeding 320 acres in extent. . Persons who had selected that amount of land under previous Land Acts were not eligible for this provision. Those who had selected less than the 320 acres could select the amount of land necessary to make it up to 320 acres.

Rent was set at one shilling per acre per annum with the licensee to reside on the allotment and make improvements to it. During the period of this license the land could be resumed by the Crown for a number of specified purposes with the repayment of any rentals or if the terms of the license were not complied with If these conditions and conditions relating to the control of vermin and fencing were complied with, at the end of this time a lease for up to 14 years was able to be applied for at the rental of one shilling per acre per annum or a Crown grant could be obtained by the payment of the full purchase price of fourteen shillings per acre. Lessees could obtain a Crown grant at any time during this fourteen year period by the payment of the difference between the rent already paid under the lease and the set price of fourteen shillings per acre.

This Section was amended in Section 44 of the Land Act 1898 to divide lands into three classes for the purpose of the licensing of these agricultural allotments. No more than 200 acres of first-class lands were to be licensed at the rent of one shilling per acre per annum; no more than three hundred and twenty acres of second-class land at the annual rental of ninepence per acre. Both types of land were to be licensed for no more than six years.

In the 1898 Act, Sections 58 and 59 provided for the extension of the licensing and leasing provisions for agricultural allotments to grazing allotments. Sections 59 and 61 of the Land Act 1898 allowed for the issue of residential or non-residential licenses for grazing allotments on third class land. A license to occupy could initially be issued for up to six years for 640 acres. If conditions relating to the provision of fencing and the destruction of vermin were met, a lease for 14 years could be obtained at a cost of sixpence per acre. Rent payments could be used to defray the cost of purchase at ten shillings per acre.

Under the consolidated Land Act of 1901, agricultural allotments were dealt with under Sections 47 (licensing) and 49 (leasing) and grazing allotments by Sections 54 (licensing) and Section 56 (leasing).

From late 1907 the Department of Crown Lands and Survey began changing to cards for its recordkeeping systems with the rent roll being reported as mainly on cards by 1917.

VPRS 13750 / P, Units 1 and 3 were previously registered as Units 212 and 217 of VPRS 631 / P Rent Rolls.