Name: HOUSING &amp; INFRASTRUCTURE
SKU: 5855
Availability: InStock

C. HOUSING & INFRASTRUCTURE

DATAFIELDS

C1. Future of Driving

Index_code	Index_name
Region	Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census Bureau
State	Abbreviation of 50 U.S. states and the District of Columbia
Z_Code	ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name	ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name	Name for county and county equivalents that the ZIP code resides in or most closely associated with
CAR_per_capita	Number of cars divided by ZIP code total population
CAR_per_hhld	Number of cars divided by ZIP code total households
Agg_CARS	Aggregate total number of cars by ZIP code; including automobiles, vans, and light trucks of one-ton capacity or less that are intended for household members to use
%County_Agg_CARS_ZIP	Number of cars by ZIP code residents as a share of total number of cars in the county
‰State_Agg_CARS_ZIP	Number of cars by ZIP code residents as a share of total number of cars in the state
%Agg_CARS_Commute	%Total number of cars by ZIP code residents used for commuting purposes
%Agg_CARS_OWN_OHU	%Total vehicles in the ZIP code belong to home-owning households
%Agg_CARS_RTR_OHU	%Total vehicles in the ZIP code belong to home-renting households
Tot_Hhlds	Total households
%Tot_Hhlds_XCAR	%Total households without any vehicle
%Tot_Hhlds_1CAR	%Total households with one vehicle
%Tot_Hhlds_2CAR	%Total households with two vehicles
%Tot_Hhlds_3CAR	%Total households with three vehicles
%Tot_Hhlds_MCAR	%Total households with four or more vehicles
Total workers 16+	Total workers age 16 and over
%TotWorkers16+_CAR	%Total workers 16+ drove to work
%TotWorkers16+_CARalone	%Total workers 16+ drove to work —while driving alone
%TotWorkers16+_CARpooled	%Total workers 16+ drove to work —while car-pooling with other individual(s)
5YAvgAggTT_CAR	2016-2020 Five-year average of aggregate time —one-way in minutes— used on traveling for the purpose of driving to work
5YAvg_CARs_commute	2016-2020 Five-year average number of cars used to commute to work
%5YAvg_CARs_commute_M	%2016-2020 Five-year average number of cars used to commute to work —driven by male drivers
%5YAvg_CARs_commute_F	%2016-2020 Five-year average number of cars used to commute to work —driven by female drivers
5YAvgDailyTT_CAR	2016-2020 Five-year average of daily time —one-way in minutes— used on traveling for the purpose of driving to work
MedianAge_ALL	Median age of all resident population combined
MedianAge_NHW	Median age of resident population self-identified as Non-Hispanic White
MedianAge_HSP	Median age of resident population self-identified as Hispanic
MedianAge_ASN	Median age of resident population self-identified as Asian
MedianAge_BLK	Median age of resident population self-identified as Black or African-American
MedianAge_AIAN	Median age of resident population self-identified as American Indian and Alaska Native
MedianAge_NHPI	Median age of resident population self-identified as Native Hawaiian and Pacific Islander
MedianAge_TWM	Median age of resident population self-identified as having two or more races/ethnic background
MHhld$_ALL	Median household income – All households
MHhld$_NHW	Median household income – Non-Hispanic White households
MHhld$_HSP	Median household income – Hispanic households
MHhld$_ASN	Median household income – Asian households
MHhld$_BLK	Median household income – Black or African-American households
MHhld$_AIAN	Median household income – American Indian and Alaska Native households
MHhld$_NHPI	Median household income – Native Hawaiian and Pacific Islander households
MHhld$_TWM	Median household income – Two or more races households
County_CAR_dealers_p10SQMI	County-based indicator: total number of car dealerships —new and old combined— for every ten square miles within the county limit. Note dealerships classified in this category are primarily engaged in retailing vehicles but may also provide repair and maintenace services.
County_CAR_repairs_p10SQMI	County-based indicator: total number of car repair shops for every ten square miles within the county limit. Note shops classified in this category provide repair, maintenace, diagnostic, paint, interior technical services of automobiles.
2019_Avg_PROP$	Average personal property taxes by ZIP code is the total personal property taxes amount, divided by the number of returns with personal property taxes; Based on individual income tax returns filed with the IRS between January 2019-December 2019. Personal property usually refers to movable property (in contrast to immovable ones such as land and house), some of the most common examples include cars, boats, motorcycles and personal planes.

C2. Inflation Reduction Act & Local Homes

Index_code	Index_name
Region	Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST	Abbreviation of U.S. states (includes DC)
Z_Code	ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name	ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name	Name for county and county equivalents that the ZIP code resides in or most closely associated with
TOT_Hhlds	Total households (ZIP codes with less than ten household counts were excluded)
%OWNU_TOT_Hhlds	Owner-occupied units as a percentage of total households
OWNU	Owner-occupied units
%OWNU_B_<=YR1980	% Owner-occupied units built in 1980 or earlier
%OWNU_B_>=YR2000	% Owner-occupied units built in 2000 or later
%OWNU_B_>=YR2010	% Owner-occupied units built in 2010 or later
%OWNU_B_YR2000-2009	% Owner-occupied units built between 2000 and 2009
%OWNU_B_YR1980-1999	% Owner-occupied units built between 1980-1999
%OWNU_B_YR1960-1979	% Owner-occupied units built between 1960-1979
%OWNU_B_YR1940-1959	% Owner-occupied units built between 1940-1959
%OWNU_B_<=YR1939	% Owner-occupied units built in 1939 or earlier
MAX_OWNU_B%	Predominant shares of time frame during which local homes were built – in percentage
MAX_OWNU_B	Predominant shares of time frame during which local homes were built – in one of the six broad demarcations: 1. Year 2010 or later, 2. 2000-2009, 3. 1980-1999, 4. 1960-1979, 5. 1940-1959, 6. 1939 or earlier. *Equal shares of two or more time frames met the criteria
%HFUEL_Type1	%Total households heated by: utility gas (via underground pipes)
%HFUEL_Type2	%Total households heated by: bottled, tank, or liquid propane gas
%HFUEL_Type3	%Total households heated by: electricity
%HFUEL_Type4	%Total households heated by: fuel oil, kerosene, etc.
%HFUEL_Type5	%Total households heated by: coal or coke
%HFUEL_Type6	%Total households heated by: wood
%HFUEL_Type7	%Total households heated by: solar energy
%HFUEL_Type8	%Total households heated by: other fuel
%HFUEL_Type9	%Total households heated by: no fuel used
Max_%HFUEL_Type	Predominant home-heating fuel of choice – in percentage
MAX_HFUEL_Type	Predominant home-heating fuel of choice – by fuel type.
*MAX_HFUEL_Type	Predominant home-heating fuel of choice – by fuel type. *Equal shares of two or more fuel sources met the criteria
%TOT_RTNS_ECRED	%Total individual tax returns filed that included those claiming energy credits; Based on individual income tax returns filed with the IRS between January 2019-December 2019
2019_Avg_ECRED$	Average energy credits amount by ZIP code is the total energy credits, divided by the number of returns with such a claim; Based on individual income tax returns filed with the IRS between January 2019-December 2019
Hhlds_per_SQMI	ZIP code-based households per square mile of land area, rounded to full integer
	The following analytics —derived from the National Risk Index by FEMA— is provided for reference. Each ZIP code is assigned the risk likelihood score linked to that of the county encompassing the ZIP code. For full access, see: https://www.fema.gov/flood-maps/products-tools/national-risk-index. Possibility of occurrence score: 5=Very High 4=Relatively High 3=Relatively Moderate 2=Relatively Low 1=Very Low 0=Not applicable, no rating, or insufficient data
AVLN_Score=0-5	County-level possibility of occurrence — Avalanche
CFLD_Score=0-5	County-level possibility of occurrence — Coastal Flooding
CWAV_Score=0-5	County-level possibility of occurrence — Cold Wave
DRGT_Score=0-5	County-level possibility of occurrence — Drought
ERQK_Score=0-5	County-level possibility of occurrence — Earthquake
HAIL_Score=0-5	County-level possibility of occurrence — Hail
HRCN_Score=0-5	County-level possibility of occurrence — Hurricane
HWAV_Score=0-5	County-level possibility of occurrence — Heat Wave
ISTM_Score=0-5	County-level possibility of occurrence — Ice Storm
LNDS_Score=0-5	County-level possibility of occurrence — Landslide
LTNG_Score=0-5	County-level possibility of occurrence — Lightning
RFLD_Score=0-5	County-level possibility of occurrence — Riverine Flooding
SWND_Score=0-5	County-level possibility of occurrence — Strong Wind
TRND_Score=0-5	County-level possibility of occurrence — Tornado
TSUN_Score=0-5	County-level possibility of occurrence — Tsunami
VLCN_Score=0-5	County-level possibility of occurrence — Volcanic Activity
WFIR_Score=0-5	County-level possibility of occurrence — Wildfire
WNTW_Score=0-5	County-level possibility of occurrence — Winter Weather
MAX=5_NDSTR_Type	The natural disaster scored as “very high” (score=5) for the county or county equivalents that the ZIP code resides in or is most closely associated with. *More than one type of natural disaster was scored as having very high possibility to occur (score=5) for the county. “-” None scored 5.

C3. Utility Report

Index_code	Index_name
Region	Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST	Abbreviation of U.S. states (includes DC)
Z_Code	ZIP Code Tabulation Areas (ZCTAs) — Census Bureau
Z_Name	ZIP code location name. Note: common location names only; Neighborhood modifications/adjustments are NOT included in the dataset.
C_Name	Name for county and county equivalents
2020 Total households	Total households (ZIP codes with less than ten household counts were excluded)
%HFUEL_Type1	%Total households heated by: utility gas (via underground pipes)
%HFUEL_Type2	%Total households heated by: bottled, tank, or liquid propane gas
%HFUEL_Type3	%Total households heated by: electricity
%HFUEL_Type4	%Total households heated by: fuel oil, kerosene, etc.
%HFUEL_Type5	%Total households heated by: coal or coke
%HFUEL_Type6	%Total households heated by: wood
%HFUEL_Type7	%Total households heated by: solar energy
%HFUEL_Type8	%Total households heated by: other fuel
%HFUEL_Type9	%Total households heated by: no fuel used
%Hhlds_XPlmg	%Total households without plumbing (derived from survey question(s) asking to identify the below items in the household: hot and cold running water, bathtub/shower, sink with a faucet, stove/range, refrigerator etc)
OWN_Hhlds_XPlmg	Owner-households without plumbing facilities
RTR_Hhlds_XPlmg	Renter-households without plumbing facilities
%Hhlds_XKitn	%Total households without complete kitchen facilities (derived from survey question(s) asking to identify the below items in the household: hot and cold running water, bathtub/shower, sink with a faucet, stove/range, refrigerator etc)
OWN_Hhlds_XKitn	Owner-households without kitchen facilities
RTR_Hhlds_XKitn	Renter-households without kitchen facilities
2019_TOT_RTNS	Total individual tax returns filed with the IRS between January 2019-December 2019
2019_RTNS_ECRED	Total individual tax returns, that claimed energy credits, filed with the IRS between January 2019-December 2019
%TOT_RTNS_ECRED	%Total individual tax returns filed that included those claiming energy credits
2019_Avg_ECRED$	Average energy credits amount by ZIP code is the total energy credits, divided by the number of returns with such a claim; Based on individual income tax returns filed with the IRS between January 2019-December 2019
%Hhlds_INTSUB	%Households with internet subscription of any type
%Hhlds_INTSUB_DAL_ONLY	%Households with internet subscription – dial-up only subscription
%Hhlds_INTSUB_BDB	%Households with broadband internet subscription of any type
%Hhlds_INTSUB_BDBCEL	%Households with broadband internet subscription – cellular data plan
%Hhlds_INTSUB_BDBOTH	%Households with broadband internet subscription – fiber optic, DSL (digital subscriber line), cable
%Hhlds_INTSUB_BDBSTL	%Households with internet subscription – broadband satellite service
%Hhlds_INT_XSUB	%Households have internet access while without a subscription
%Hhlds_XINT	%Households without any type of internet access
%POP_Poverty	%Population in poverty
%POVPOP_M	%Population in poverty who are male
%POVPOP_F	%Population in poverty who are female
%POVPOP_Age<5	%Population in poverty who are children under the age of five
%POVPOP_Age65+	%Population in poverty who are seniors age 65 and more
MHhld$_ALL	Median household income – All households

C4. Commuting – Profile by County

Index_code	Index_name
Region	Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST	Abbreviation of U.S. states (includes DC)
C_code	County_report_geocode
C_name	Full name of the U.S. county including state in which it resides
M_name	Metropolitan area name
R_scale	Scale of rural level based on the cross-attributes of population density and whether or not a county is part of a metropolitan area: 1.M_500+: county has at least 500 population per square mile and is part of a metropolitan area 2.M_250-499: county has 250-499 population per square mile and is part of a metropolitan area 3.M_100-249: county has 100-249 population per square mile and is part of a metropolitan area 4.M_<50-99: county has 50-99 population per square mile and is part of a metropolitan area 5.M_<50: county has less than 50 population per square mile and is part of a metropolitan area 6.NonM_50+: county has at least 50 population per square mile and is not part of a metropolitan area 7.NonM_25-49: county has 25-49 population per square mile and is not part of a metropolitan area 8.NonM_<25: county has less than 50 population per square mile and is not part of a metropolitan area
Pop_density	County population per square mile of county land area, rounded to full integer
TotWorkers16+	Total workers age 16 and over
%TotWorkers16+_BLK	% Total workers age 16+ who are Black/African-American
%TotWorkers16+_ASN	% Total workers age 16+ who are Asian
%TotWorkers16+_HSP	% Total workers age 16+ who are Hispanic
%TotWorkers16+_NHW	% Total workers age 16+ who are Non-Hispanic White
TotWorkers16+_WFH	Total workers age 16+ worked from home
%TotWorkers16+_WFH	% Total workers age 16+ worked from home
%TotWorkers16+_WFH_BLK	% Total workers age 16+ worked from home who are Black/African-American
%TotWorkers16+_WFH_ASN	% Total workers age 16+ worked from home who are Asian
%TotWorkers16+_WFH_HSP	% Total workers age 16+ worked from home who are Hispanic
%TotWorkers16+_WFH_NHW	% Total workers age 16+ worked from home who are Non-Hispanic White
TotWorkers16+_PubTr	Total workers age 16+ went to work by public transportation
%TotWorkers16+_PubTr_ALL	% Total workers age 16+ went to work by public transportation
%TotWorkers16+_PubTr_BLK	% Total workers age 16+ went to work by public transportation who are Black/African-American
%TotWorkers16+_PubTr_ASN	% Total workers age 16+ went to work by public transportation who are Asian
%TotWorkers16+_PubTr_HSP	% Total workers age 16+ went to work by public transportation who are Hispanic
%TotWorkers16+_PubTr_NHW	% Total workers age 16+ went to work by public transportation who are Non-Hispanic White
CommutingWFH_gapBLK	CommutingWFH gap – Black/African-American
CommutingWFH_gapASN	CommutingWFH gap – Asian
CommutingWFH_gapHSP	CommutingWFH gap – Hispanic
CommutingWFH_gapNHW	CommutingWFH gap – Non-Hispanic White
CommutingPubTr_gapBLK	CommutingPubTr gap – Black/African-American
CommutingPubTr_gapASN	CommutingPubTr gap – Asian
CommutingPubTr_gapHSP	CommutingPubTr gap – Hispanic
CommutingPubTr_gapNHW	CommutingPubTr gap – Non-Hispanic White
CommutingWFH_qBLK	CommutingWFH disparity quotient – Black/African-American
CommutingWFH_qASN	CommutingWFH disparity quotient – Asian
CommutingWFH_qHSP	CommutingWFH disparity quotient – Hispanic
CommutingWFH_qNHW	CommutingWFH disparity quotient – Non-Hispanic White
CommutingPubTr_qBLK	CommutingPubTr disparity quotient – Black/African-American
CommutingPubTr_qASN	CommutingPubTr disparity quotient – Asian
CommutingPubTr_qHSP	CommutingPubTr disparity quotient – Hispanic
CommutingPubTr_qNHW	CommutingPubTr disparity quotient – Non-Hispanic White
EstTotWorkers16+_PubTr_60+	Estimated total workers age 16+ went to work by public transportation who are older (age 60 and above)
EstTotWorkers16+_PubTr_LANGAssist	Estimated total workers age 16+ went to work by public transportation who lack English proficiency
EstTotWorkers16+_PubTr_	Estimated total workers age 16+ went to work by public transportation below poverty threshold
EstTotWorkers16+_PubTr_IST	Estimated total workers age 16+ went to work by public transportation commuted within the state
EstTotWorkers16+_PubTr_IC	Estimated total workers age 16+ went to work by public transportation commuted within county of residence
EstTotWorkers16+_PubTr_OC	Estimated total workers age 16+ went to work by public transportation commuted outside county of residence
EstTotWorkers16+_PubTr_OST	Estimated total workers age 16+ went to work by public transportation commuted outside the state
EstTotWorkers16+_DISAB_PubTr	Estimated total workers age 16+ with at least one disability who went to work by public transportation
%TotWorkers16+_PubTr_60+	% Total workers age 16+ went to work by public transportation who are older (age 60 and above)
%TotWorkers16+_PubTr_LANGAssist	% Total workers age 16+ went to work by public transportation who lack English proficiency
%TotWorkers16+_PubTr_	% Total workers age 16+ went to work by public transportation below poverty threshold
%TotWorkers16+_PubTr_IST	% Total workers age 16+ went to work by public transportation commuted within the state
%TotWorkers16+_PubTr_IC	% Total workers age 16+ went to work by public transportation commuted within county of residence
%TotWorkers16+_PubTr_OC	% Total workers age 16+ went to work by public transportation commuted outside county of residence
%TotWorkers16+_PubTr_OST	% Total workers age 16+ went to work by public transportation commuted outside the state
%TotWorkers16+_DISAB_PubTr	% Total workers age 16+ with at least one disability who went to work by public transportation
%TotWorkers16+_PubTr_TTTW<30	% Total workers age 16+ went to work by public transportation on average spending less than 30 minutes —per trip— to commute
%TotWorkers16+_PubTr_TTTW30-49	% Total workers age 16+ went to work by public transportation on average spending more than 30 minutes but less than one hour —per trip— to commute
%TotWorkers16+_PubTr_TTTW60+	% Total workers age 16+ went to work by public transportation on average spending one hour or more —per trip— to commute
TotWorkers16+_PubTr_TTTWAvg	Average travel time to work —minutes per trip: workers age 16+ went to work by public transportation
Median$_Workers16+_PubTr	Median earnings of workers age 16+ who went to work by public transportation
TotWorkers16+_CTV_alone	Total workers age 16+ went to work driving a car/truck/van alone
TotWorkers16+_CTV_carpooled	Total workers age 16+ went to work carpooling a car/truck/van
%TotWorkers16+_CTV_alone_ALL	% Total workers age 16+ went to work driving a car/truck/van alone
%TotWorkers16+_CTV_carpooled	% Total workers age 16+ went to work carpooling a car/truck/van
%TotWorkers16+_CTValone_BLK	% Total workers age 16+ went to work driving a car/truck/van alone who are Black/African-American
%TotWorkers16+_CTValone_ASN	% Total workers age 16+ went to work driving a car/truck/van alone who are Asian
%TotWorkers16+_CTValone_HSP	% Total workers age 16+ went to work driving a car/truck/van alone who are Hispanic
%TotWorkers16+_CTValone_NHW	% Total workers age 16+ went to work driving a car/truck/van alone who are Non-Hispanic White
TotWorkers16+_CTValone_BLK	Total workers age 16+ went to work driving a car/truck/van alone who are Black/African-American
TotWorkers16+_CTValone_ASN	Total workers age 16+ went to work driving a car/truck/van alone who are Asian
TotWorkers16+_CTValone_HSP	Total workers age 16+ went to work driving a car/truck/van alone who are Hispanic
TotWorkers16+_CTValone_NHW	Total workers age 16+ went to work driving a car/truck/van alone who are Non-Hispanic White
EstTotWorkers16+_CTValone_IST	Estimated total workers age 16+ went to work driving a car/truck/van alone commuted within the state
EstTotWorkers16+_CTValone_IC	Estimated total workers age 16+ went to work driving a car/truck/van alone commuted within county of residence
EstTotWorkers16+_CTValone_OC	Estimated total workers age 16+ went to work driving a car/truck/van alone commuted outside county of residence
EstTotWorkers16+_CTValone_OST	Estimated total workers age 16+ went to work driving a car/truck/van alone commuted outside the state
%TotWorkers16+_CTValone_IST	% Total workers age 16+ went to work driving a car/truck/van alone commuted within the state
%TotWorkers16+_CTValone_IC	% Total workers age 16+ went to work driving a car/truck/van alone commuted within county of residence
%TotWorkers16+_CTValone_OC	% Total workers age 16+ went to work driving a car/truck/van alone commuted outside county of residence
%TotWorkers16+_CTValone_OST	% Total workers age 16+ went to work driving a car/truck/van alone commuted outside the state
TotWorkers16+_CTV_alone_TTTWAvg	Average travel time to work —minutes per trip: workers age 16+ went to work driving a car/truck/van alone
TotWorkers16+_CTV_carpooled_TTTWAvg	Average travel time to work —minutes per trip: workers age 16+ went to work carpooling a car/truck/van
Median$_Workers16+_CTV_alone	Median earnings of workers age 16+ who went to work driving a car/truck/van alone
Median$_Workers16+_CTV_carpooled	Median earnings of workers age 16+ who went to work carpooling a car/truck/van

C5. Digital Access – Profile by County

Index_code	Index_name
Region	Four U.S. regions – West, South, Midwest and Northeast — as broadly defined by the U.S. Census
ST	Abbreviation of U.S. states (includes DC)
C_code	County_report_geocode
C_name	Full name of the U.S. county including state in which it resides
M_name	Metropolitan area name
R_scale	Scale of rural level based on the cross-attributes of population density and whether or not a county is part of a metropolitan area: 1.M_500+: county has at least 500 population per square mile and is part of a metropolitan area 2.M_250-499: county has 250-499 population per square mile and is part of a metropolitan area 3.M_100-249: county has 100-249 population per square mile and is part of a metropolitan area 4.M_<50-99: county has 50-99 population per square mile and is part of a metropolitan area 5.M_<50: county has less than 50 population per square mile and is part of a metropolitan area 6.NonM_50+: county has at least 50 population per square mile and is not part of a metropolitan area 7.NonM_25-49: county has 25-49 population per square mile and is not part of a metropolitan area 8.NonM_<25: county has less than 50 population per square mile and is not part of a metropolitan area
Pop_density	County population per square mile of county land area, rounded to full integer
TotPopH	Total population in households
%PopH_BLK	% Total population in households who are Black/African-American
%PopH_ASN	% Total population in households who are Asian
%PopH_HSP	% Total population in households who are Hispanic
%PopH_NHW	% Total population in households who are Non-Hispanic White
PopHDA_ALL	Total Population in households with computer AND broadband access
%PopHDA_BLK	% All population in households with computer AND broadband access who are Black/African-American
%PopHDA_ASN	% All population in households with computer AND broadband access who are Asian
%PopHDA_HSP	% All population in households with computer AND broadband access who are Hispanic
%PopHDA_NHW	% All population in households with computer AND broadband access who are Non-Hispanic White
DA_gapBLK	Digital access gap – Black/African-American
DA_gapASN	Digital access gap – Asian
DA_gapHSP	Digital access gap – Hispanic
DA_gapNHW	Digital access gap – Non-Hispanic White
DA_qBLK	Digital access disparity quotient – Black/African-American
DA_qASN	Digital access disparity quotient – Asian
DA_qHSP	Digital access disparity quotient – Hispanic
DA_qNHW	Digital access disparity quotient – Non-Hispanic White
%ALLPopH_DA	% All population in households with computer AND broadband access
%BLKPopH_DA	% Black/African-American population in households with computer AND broadband access
%ASNPopH_DA	% Asian population in households with computer AND broadband access
%HSPPopH_DA	% Hispanic population in households with computer AND broadband access
%NHWPopH_DA	% Non-Hispanic White population in households with computer AND broadband access
EstHhlds_INT_subs_100	Estimated broadband subscriptions needed to achieve 100% internet access
EstHhlds_INT_subs_90	Estimated broadband subscriptions needed to achieve 90% internet access
PopH_X-INT\|W-CMPT_ALL	Estimated population in households without internet service (but with computer)
PopH_X-INT\|W-CMPT_BLK	Estimated population in households without internet service (but with computer) who are Black/African-American
PopH_X-INT\|W-CMPT_ASN	Estimated population in households without internet service (but with computer) who are Asian
PopH_X-INT\|W-CMPT_HSP	Estimated population in households without internet service (but with computer) who are Hispanic
PopH_X-INT\|W-CMPT_NHW	Estimated population in households without internet service (but with computer) who are Non-Hispanic White
PopH_X-INT\|W-CMPT_NHOP+AIAn	Estimated population in households without internet service (but with computer) who are Native Hawaiian, other Pacific Islander, American Indian and Alaska Native (alone) combined
EstHhlds_X-CMPT_ALL	Estimated number of households without a computing device
PopH_X-CMPT_ALL	Estimated population in households without a computer
PopH_X-CMPT_BLK	Estimated population in households without a computer who are Black/African-American
PopH_X-CMPT_ASN	Estimated population in households without a computer who are Asian
PopH_X-CMPT_HSP	Estimated population in households without a computer who are Hispanic
PopH_X-CMPT_NHW	Estimated population in households without a computer who are Non-Hispanic White
PopH_X-CMPT_NHOP+AIAn	Estimated population in households without a computer who are Native Hawaiian, other Pacific Islander, American Indian and Alaska Native (alone) combined
EstHhlds_X-INT\|W-CMPT_BLK	Estimated number of households without internet service (but with computer) who are Black/African-American
EstHhlds_X-INT\|W-CMPT_ASN	Estimated number of households without internet service (but with computer) who are Asian
EstHhlds_X-INT\|W-CMPT_HSP	Estimated number of households without internet service (but with computer) who are Hispanic
EstHhlds_X-INT\|W-CMPT_NHW	Estimated number of households without internet service (but with computer) who are Non-Hispanic White
EstHhlds_X-CMPT_BLK	Estimated number of households without a computer who are Black/African-American
EstHhlds_X-CMPT_ASN	Estimated number of households without a computer who are Asian
EstHhlds_X-CMPT_HSP	Estimated number of households without a computer who are Hispanic
EstHhlds_X-CMPT_NHW	Estimated number of households without a computer who are Non-Hispanic White

Select	C1. Future of Driving, C2. Inflation Reduction Act & Local Homes, C3. Utility Report, C4. Commuting – Profile by County, C5. Digital Access – Profile by County

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