About this application: This application provides summary profiles showing frequently requested data items from various US Census Bureau programs. Profiles are available for the nation, states, and counties.
Owner-occupied housing unit rate, 2014-2018 - (Percent)
County
Value
Ada
68.4
Adams
80.6
Bannock
68.2
Bear Lake
76.7
Benewah
73.1
Bingham
75.6
Blaine
69.7
Boise
83.9
Bonner
74.4
Bonneville
70.0
Boundary
75.0
Butte
84.2
Camas
75.1
Canyon
68.2
Caribou
79.7
Cassia
69.7
Clark
62.3
Clearwater
76.2
Custer
74.8
Elmore
58.1
Franklin
80.8
Fremont
81.3
Gem
73.5
Gooding
68.3
Idaho
77.9
Jefferson
79.7
Jerome
63.9
Kootenai
70.5
Latah
53.0
Lemhi
75.4
Lewis
71.5
Lincoln
69.1
Madison
46.4
Minidoka
71.7
Nez Perce
70.5
Oneida
80.7
Owyhee
70.6
Payette
72.6
Power
68.9
Shoshone
70.1
Teton
74.0
Twin Falls
68.6
Valley
78.7
Washington
70.3
Value for Idaho (Percent): 69.3%
Data item: Owner-occupied housing unit rate, 2014-2018
Sources: U.S. Census Bureau, American Community Survey (ACS) and Puerto Rico Community Survey (PRCS), 5-Year Estimates. The PRCS is part of the Census Bureau's ACS, customized for Puerto Rico. Both Surveys are updated every year.
Definition
Owner-Occupied - A housing unit is owner-occupied if the owner or co-owner lives in the unit, even if it is mortgaged or not fully paid for. The owner or co-owner must live in the unit and usually is Person 1 on the questionnaire. The unit is "Owned by you or someone in this household with a mortgage or loan" if it is being purchased with a mortgage or some other debt arrangement such as a deed of trust, trust deed, contract to purchase, land contract, or purchase agreement. The unit also is considered owned with a mortgage if it is built on leased land and there is a mortgage on the unit. Mobile homes occupied by owners with installment loan balances also are included in this category. For the complete definition, go to ACS subject definitions "Tenure."
The homeownership rate is computed by dividing the number of owner-occupied housing units by the number of occupied housing units or households.
Source and Accuracy
This Fact is based on data collected in the American Community Survey (ACS) and the Puerto Rico Community Survey (PRCS) conducted annually by the U.S. Census Bureau. A sample of over 3.5 million housing unit addresses is interviewed each year over a 12 month period. This Fact (estimate) is based on five years of ACS and PRCS sample data and describes the average value of person, household and housing unit characteristics over this period of collection.
Statistics from all surveys are subject to sampling and nonsampling error. Sampling error is the uncertainty between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Measures of sampling error are provided in the form of margins of error for all estimates included with ACS and PRCS published products. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results. The data for each geographic area are presented together with margins of error at Using margins of error. A more detailed explanation of margins of error and a demonstration of how to use them is provided below.
For more information on sampling and estimation methodology, confidentiality, and sampling and nonsampling errors, please see the Multiyear Accuracy (US) and the Multiyear Accuracy (Puerto Rico) documents at "Documentation - Accuracy of the data."
Margin of Error
As mentioned above, ACS estimates are based on a sample and are subject to sampling error. The margin of error measures the degree of uncertainty caused by sampling error. The margin of error is used with an ACS estimate to construct a confidence interval about the estimate. The interval is formed by adding the margin of error to the estimate (the upper bound) and subtracting the margin of error from the estimate (the lower bound). It is expected with 90 percent confidence that the interval will contain the full population value of the estimate. The following example is for demonstrating purposes only. Suppose the ACS reported that the percentage of people in a state who were 25 years and older with a bachelor's degree was 21.3 percent and that the margin of error associated with this estimate was 0.7 percent. By adding and subtracting the margin of error from the estimate, we calculate the 90-percent confidence interval for this estimate:
Therefore, we can be 90 percent confident that the percent of the population 25 years and older having a bachelor's degree in a state falls somewhere between 20.6 percent and 22.0 percent.
