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
Abbeville
76.3
Aiken
71.7
Allendale
67.3
Anderson
70.2
Bamberg
71.6
Barnwell
69.7
Beaufort
71.6
Berkeley
71.7
Calhoun
81.0
Charleston
61.1
Cherokee
69.1
Chester
75.3
Chesterfield
71.0
Clarendon
76.3
Colleton
75.7
Darlington
69.7
Dillon
64.8
Dorchester
71.4
Edgefield
73.0
Fairfield
73.7
Florence
65.4
Georgetown
77.1
Greenville
66.5
Greenwood
64.2
Hampton
73.8
Horry
70.8
Jasper
68.5
Kershaw
82.2
Lancaster
78.2
Laurens
70.4
Lee
73.2
Lexington
74.0
Marion
70.6
Marlboro
65.8
McCormick
76.6
Newberry
73.0
Oconee
72.2
Orangeburg
68.0
Pickens
67.6
Richland
58.8
Saluda
73.7
Spartanburg
69.6
Sumter
64.7
Union
69.3
Williamsburg
74.6
York
71.5
Value for South Carolina (Percent): 68.9%
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.
