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Census Block 009707-1-028 in San Bernardino County, California

Population129 (2010), see rank
House Units110, see rank
RaceWhites:89.9%, Hispanics:9.3%, Blacks:0%, Asians:7.8%, Others:2.3%
Total Area:0.11 sq mi
Water Area:0.00 sq mi (0.00%)
State:California
Area:Riverside-San Bernardino-Ontario, CA
County:San Bernardino County
City:Apple Valley
Census Tract:009707
Census Block Group:009707-1

Census Block 009707-1-028 Map, Border, and Nearby Locations

Population by Races

White:116 (89.92%, see rank)
Black:0 (0.00%, see rank)
Hispanic:12 (9.30%, see rank)
Asian:10 (7.75%, see rank)
Native (American Indian, Alaska Native, Hawaiian Native, etc.):0 (0.00%, see rank)
One Race, Other:3 (2.33%, see rank)
Two or More Races:0 (0.00%, see rank)

Population by Gender

Male: 52 (40.31%, see rank)
Females: 77 (59.69%, see rank)

Household and Family

 Census Block 009707-1-028%
Total Households80100%
Average Household Size1.61, see rank-
1 Person Households3240.00%, see rank
2 or More Person Households4860.00%, see rank
Family Households (Families)4758.75%, see rank
Average Family Size2.02, see rank-
Married-Couple Family4657.50%, see rank
Nonfamily Households3341.25%, see rank

Housing Occupancy

 Census Block 009707-1-028%
Total Housing Units110100%
Occupied Housing Units8072.73%, see rank
Owner Occupied6760.91%, see rank
Owner Occupied with Mortgage3632.73%
Owner Occupied without Mortgage3128.18%
Renter Occupied1311.82%, see rank
Vacant Housing Units3027.27%, see rank
For Rent43.64%, see rank
For Sale Only21.82%, see rank
Rented or Sold, Not Occupied21.82%, see rank
For Seasonal, Recreational, or Occasional Use76.36%, see rank
For Migrant Workers00.00%, see rank
Other Vacant1513.64%, see rank

*Based on 2010 data.


*A census block is the smallest geographic area defined by the United States Census Bureau to take census. In total, there are more than 10 million census blocks in the United Status with an average of around 30 residents in each. A census block normally only covers a street block or a small community. It is ideal for studying data relating to the smallest geographic area.


 
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