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.
Living in same house 1 year ago, percent of persons age 1 year+, 2014-2018 - (Percent)
County
Value
Allegany
84.3
Anne Arundel
85.8
Baltimore
87.6
Baltimore city
83.4
Calvert
88.3
Caroline
87.4
Carroll
91.1
Cecil
89.1
Charles
89.2
Dorchester
86.9
Frederick
87.6
Garrett
90.8
Harford
88.8
Howard
86.7
Kent
83.8
Montgomery
85.5
Prince George's
85.3
Queen Anne's
89.0
Somerset
81.9
St. Mary's
85.6
Talbot
86.1
Washington
86.1
Wicomico
80.9
Worcester
86.8
Value for Maryland (Percent): 86.2%
Data item: Living in same house 1 year ago, percent of persons age 1 year+, 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
Residence 1 year ago is used in conjunction with location of current residence to determine the extent of residential mobility of the population and the resulting redistribution of the population across the various states, metropolitan areas, and regions of the country. For the complete definition, go to ACS subject definitions "Residence 1 year ago."
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.
