The residential vacancy rate is the percentage of residential units that are unoccupied, or vacant, in a given year. The U.S. Census Bureau defines occupied housing units as “owner-occupied” or “renter-occupied.” Vacant housing units are not classified by tenure in this way, as they are not occupied by an owner or renter.

The residential vacancy rate serves as an indicator of the condition of the area’s housing market. Low residential vacancy rates indicate that demand for housing is high compared to the housing supply. However, the aggregate residential vacancy rate is lacking in granularity. For example, the housing market for rental units in the area and the market for buying a unit in the same area may be very different, and the aggregate rate will not show those distinct conditions. Furthermore, the vacancy rate may be high, or low, for a variety of reasons. A high vacancy rate may result from a falling population, but it may also result from a recent construction spree that added many units to the total stock.

The residential vacancy rate in Champaign County appears to have fluctuated between 8% and 14% from 2005 through 2022, reaching a peak near 14% in 2019. In 2023, this rate dropped to about 7%, its lowest value since 2005. However, this rate was calculated using the American Community Survey’s (ACS) estimated number of vacant houses per year, which has year-to-year fluctuations that are largely not statistically significant. Thus, we cannot establish a trend for this data.

The residential vacancy rate data shown here was calculated using the estimated total housing units and estimated vacant housing units from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

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 in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Occupancy Status.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (4 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table SB25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation.This latest data was release in December of 2024. POP_UNI Total Population

POP_MIN Population in all race / ethnic categories other than 'white, not hispanic'

POP_HISLAT Population that is hispanic or latino

POP_65UP Population 65 and older

DISHH_UNI Households (aka Occupied Housing Units)

DISHH Households with one or more persons with one or more disabilities

ZCARHH_UNI Households (aka Occupied Housing Units)

ZCARHH Households with access to zero cars

UNEMP_UNI Population age 16+ who are in the labor force

UNEMP Population in the labor force who are unemployed

BBHH_UNI Households (aka Occupied Housing Units)

LINTHH Households with internet via dial-up only

ZINTHH Households with no internet

ZCOMHH Households with no computer

POV_UNI Population for whom poverty status is determined

POV_100 Population at or below 100% Federal Poverty Level

POV_200 Population at or below 200% Federal Poverty Level

HHInc_UNI Households (aka Occupied Housing Units)

HHIncL25k Household Income under $25,000

HHInc25_50k Household Income between $25,000 and $50,000

HHInc50_75k Household Income between $50,000 and $75,000

HHInc75_100K Household Income between $75,000 and $100,000

HHInc100_150K Household Income between $100,000 and $150,000

HHInc150_200k Household Income between $150,000 and $200,000

HHInc200plus Household Income above $200,000

TRANS_UNI Workers 16 years and older

TRANS_CAR Workers who use a car as their means of transportation

TRANS_POOL Workers who carpool as their means of transportation

TRANS_PUB Workers who use public transportation

TRANS_BUS Workers who take a bus as their means of transportation

TRANS_BIKE Workers who bicycle as their means of transportation

TRANS_WALK Workers who walk as their means of transportation

TRANS_WFH Workers who work from home

ED_UNI Population 25 years and over (Ed. Universe)

ED_LESS_TWEL Less than a twelfth grade education

ED_HS_GRAD High School graduate

ED_GED_EQ GED or alternative credential

ED_COL_SOME Some college

ED_ASSOC Associate's degree

ED_BACH Bachelor's degree

ED_MAST_P Master's, Professional, or Doctorate degree

PER_CAP Per capita income

MED_INC Median income

HU_UNI Total housing units

HU_OCC Occupied housing untis

HU_VAC Vacant housing units

VET_UNI Veteran Universe

VET_YES Veterans

VET_NO Non-Veterans

HU_SF Single family housing unit

HU_MF Multifamily housing unit

HU_OTH Other housing unit type

TEN_UNI Occupied housing units

TEN_RENT Renter occupied housing unit

TEN_OWN Owner occupied housing unit

PCT_POV_100 Percent of population at or below 100% Federal Poverty Level

PCT_POV_200 Percent of population at or below 200% Federal Poverty Level

PCT_MIN Percent of population in all race / ethnic categories other than 'white, not hispanic'

PCT_HISLAT Percent of population that is hispanic or latino

PCT_65UP Percent of Population over 65

PCT_DISHH Percent of Households with one or more persons with one or more disabilities

PCT_ZCARHH Percent of Households with access to zero cars

PCT_UNEMP Percent fo Population in the labor force who are unemployed

PCT_LINTHH Percent of Households with internet via dial-up only

PCT_ZINTHH Percent of Households with no internet

PCT_ZCOMHH Percent of Households with no computer

PCT_L25K Percent of Households with Income under $25,000

PCT_25_50k Percent of Households with Income between $25,000 and $50,000

PCT_50_75k Percent of Households with Income between $50,000 and $75,000

PCT_75_100k Percent of Households with Income between $75,000 and $100,000

PCT_100_150k Percent of Households with Income between $100,000 and $150,000

PCT_150_200K Percent of Households with Income between $150,000 and $200,000

PCT_200kPlus Percent of Households with Income above $200,000

PCT_CAR_ALONE Percent of Workers who use a car as their means of transportation

PCT_Walk_Bike Percent of Workers who walk or Bike as their means of transportation

PCT_WFH Percent of Workers who work from home

PCT_BACH Percent of Population with Bachelors Degree

PCT_MAST_P Percent of Population with Master's, Professional, or Doctorate Degree

PCT_OCC Percent of Housing Units that are occupied

PCT_SF_HU Percent of Single Family Housing Units

PCT_MF_HU Percent of Multi Family Housing Units

PCT_RENT Percent of Tenants that are renters

PCT_OWN Percent of Tenanats that own

PCT_VET Percentage of population that are veterans

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 2012-2016, to show comparison of housing ownership costs and rental costs to income, by census tract 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 2012-2016). 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, click here.Attributes: GEOID10 = 2010 Census tract identifier (combination of Federal Information Processing Series (FIPS) codes for state, county, and census tract) County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county) Area_Name = 2010 Census tract name- - - - - -Total_Population = # Total Population, 2016 Total_Population_MOE_2016 = # Total population (Margin of Error), 2016- - - - - -Units_Owner_Costs_Computed = # Housing units for which Selected Monthly Owner Costs as a percentage of household income are computed, 2016 Units_Owner_Costs_Computed_MOE = # Housing units for which Selected Monthly Owner Costs as a percentage of household income are computed (Margin of Error), 2016 Units_OwnCost_30pct_Income = # Owner-occupied housing units, costs 30.0% of income or more, 2016 Units_OwnCost_30pct_Income_MOE = # Owner-occupied housing units, costs 30.0% of income or more (Margin of Error), 2016 Pct_OwnCost_30pct_Income = % Owner-occupied housing units, costs 30.0% of income or more, 2016 Pct_OwnCost_30pct_Income_MOE = % Owner-occupied housing units, costs 30.0% of income or more (Margin of Error), 2016 Units_RentToIncome_Computed = # Occupied units for which rent as % of income can be computed, 2016 Units_RentToIncome_Computed_MOE = # Occupied units for which rent as % of income can be computed (Margin of Error), 2016 Units_Rent_More30Pct_Income = # Gross rent 30.0% of income or greater, 2016 Units_Rent_More30Pct_Income_MOE = # Gross rent 30.0% of income or greater (Margin of Error), 2016 Pct_Rent_More30Pct_Income = % Gross rent 30.0% of income or greater, 2016 Pct_Rent_More30Pct_Income_MOE = % Gross rent 30.0% of income or greater (Margin of Error), 2016 Num_Tot_HH_RentOwnCosts = # Total households paying for housing (rent or owner costs), 2016 Num_Tot_HH_RentOwnCosts_MOE = # Total households paying for housing (rent or owner costs) (Margin of Error), 2016 Units_HsCosts_30pct_Income = # Occupied units for which costs exceed 30% of income, 2016 Units_HsCosts_30pct_Income_MOE = # Occupied units for which costs exceed 30% of income (Margin of Error), 2016 Pct_HsCosts_30pct_Income = % Occupied units for which costs exceed 30% of income, 2016 Pct_HsCosts_30pct_Income_MOE = % Occupied units for which costs exceed 30% of income (Margin of Error), 2016- - - - - -Planning_Region = Planning region designation for ARC purposes AcresLand = Land area within the tract (in acres) AcresWater = Water area within the tract (in acres) AcresTotal = Total area within the tract (in acres) SqMi_Land = Land area within the tract (in square miles) SqMi_Water = Water area within the tract (in square miles) SqMi_Total = Total area within the tract (in square miles) TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively. CountyName = County Name last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.

This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.

Temporal extent

The data contains annual maps from 1985 to 2018.

Data format and units

Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.

All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.

The dataset features

• population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)
• mass (in tons) of …
  • total material stock
    • … material stock in buildings
      • … in commercial and industrial buildings
      • … in multi-family residential buildings
      • … in single-family residential buildings
      • … in high-rise buildings
      • … in lightweight buildings
    • … material stock in road infrastructure
    • … material stock in rail infrastructure
    • … material stock in other infrastructure

Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness.

Further information

For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Corresponding publication

Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology

Funding

This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2010-2014 to show housing affordability data (housing costs relative to income) by city for the State of Georgia.

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. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2010-2014). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

NAME = Name of city or municipality

Acres = Area in acres

Sq_Miles = Area in square miles

County20 = Within ARC 20-county region

County10 = Within ARC 10-county region

HousUnits_MonthOwnerCosts_toInc = #, Housing units for which Selected Monthly Owner Costs as % of Income are computed

Sel_Mo_Own_Costs_30pct_of_Incom = #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household income

Pct_Sel_Mo_Own_Costs_30pct_Inc = %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household income

HousUnits_Compute_RentPctIncome = #, Housing units for which Gross Rent as a Percentage of income is computed

Rent_Pct_of_Inc_More30Pct = #, Gross rent as a percentage of household income (GRAPI) is 30% or more

PctRent_PctIncome_More30Pct = %, Gross rent as a percentage of household income (GRAPI) is 30% or more

HousUnits_OwnRent_Compute = #, Housing units for which SMOCAPI or GRAPI are computed

HousCosts_Units_30pctMore_Inc = #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income

PctHousCost_30pctMore_Income = %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income

last_edited_date = Last date feature was edited by ARC

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

Date: 2010-2014

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.