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The purpose of the Building Permits Survey (BPS) is to provide national, state, and local statistics on the number and valuation of new privately-owned housing units authorized by building permits in the United States. The United States Code, Title 13, authorizes this survey and provides for voluntary responses.
The statistics from the Building Permits Survey are based on reports that are submitted by local building permit officials in response to a voluntary mail survey. The data are collected using Form C-404, "Report of Building or Zoning Permits Issued for New Privately-Owned Housing Units." Annual respondents receive an introductory letter each year.
Building permits data are collected from individual permit offices, most of which are municipalities; the remainder are counties, townships, or New England and Middle Atlantic-type towns. Because building permits are public records, local area data can be published without any confidentiality concerns. From local area data, estimates are tabulated for counties, states, metropolitan areas, Census Divisions, Census Regions, and the United States. Data are also collected for Puerto Rico and U.S. territories, although these areas are excluded from the national estimates.
The Building Permits Survey covers all "permit-issuing places," which are jurisdictions that issue building or zoning permits. Zoning permits are used only for areas that do not require building permits but require zoning permits. Areas for which no authorization is required to construct a new privately-owned housing unit are not included in the survey.
Periodically, we use Form C-411 "Survey of Residential Building or Zoning Permit Systems" to canvass active governments in the United States. An introductory letter explains the survey. The Building Permits sampling frame or "universe" is updated by adding all places that reported the establishment of a new permit system since the last canvass. The universe is defined as all unique permit offices at the time the sample is selected. Unique permit offices are those jurisdictions that would not result in double reporting. For example, if a city issues zoning permits and its county issues building permits (including permits for buildings in the city), only the county office is included in the universe. The numbers associated with the various universes (e.g., 20,000-place universe) are rounded for ease of identification:
The impact of updating the universe of permit-issuing places is shown in the Building Permits Universe Overlap table. It shows the number of housing units authorized by building permits for both the new and the old universe in the year that the universe was updated, going back to 1963.
The list of jurisdictions from which permits data are collected is updated monthly to reflect ongoing changes in permit coverage reported to the Census Bureau by local governments. These updates are reflected in the data for individual permit-issuing places, but all other estimates include only areas that had permit coverage at the time the current universe was established. This provides data that can be compared over time without the need to account for changes in permit coverage.
About half of the permit-issuing places in the United States are surveyed monthly. The remainder of places are surveyed annually.
The design of the monthly sample that has been used since January 2005 is as follows:
The monthly estimates shown for the United States, Census Regions, Census Divisions, and states are derived from a sample of 9,000 permit-issuing places selected from a universe of 20,000 such places. Selection of the monthly sample was a multiple step process. All permit-issuing places in the 75 metropolitan areas having the greatest number of housing units authorized in 2002 were selected with certainty. All permit-issuing places in states with less than 50 permit-issuing places were selected with certainty. Permit-issuing places having special data reporting arrangements were selected with certainty. The remaining places were stratified by state. Within a state, places were ordered by a weighted average of the numbers of housing units authorized in 2000, 2001, and 2002. Places with a large weighted average, varying by state, were selected with certainty. Other places were selected at the rate of 1 in 10.
Edits are performed to review data received on survey forms; checks include high or low numbers of units, units per building, cost per unit, cost per building, etc. When a report is not received, missing housing unit data are either (1) obtained from the Survey of Construction (SOC), which is used to collect information on housing starts, sales, and completions, or (2) imputed. Data from SOC are available only for about 900 places for which Census Bureau field representatives list permits issued for new residential construction as part of the SOC sampling operation. (Please go to the Survey of Construction Methodology for more information.) If data are not reported and are not available from SOC, estimates are imputed based on the assumption that the ratio of authorizations for the current time period to the prior year total is the same for reporting and nonreporting jurisdictions in that Census Region. Data for the four types of structures (one unit, two units, 3-4 units, and 5 units or more) are imputed separately.
Monthly building permits data are available in four basic levels of aggregation: state, metropolitan area (MA), county, and permit-issuing place. The state data are also aggregated to create estimates for the Census Divisions, Census Regions, and the United States. Monthly data are tabulated for the current month and for the year to date. Year-to-date data include any late reports received or corrections made to reports from prior months in the year. Because the year-to-date estimates include corrections not reflected in the monthly data, summing the published monthly data will not generate the published year-to-date estimate. Monthly and year-to-date estimates for state and higher aggregates are sample-based estimates that represent the entire geographic area. A few states have complete coverage in the monthly sample.
Annual data are obtained by summing monthly data for places in the monthly sample and using annual data for annual reporters or places in the monthly sample that provided only annual totals. If both monthly and annual data exist, the annual data are used. If no annual data are received, but there were some months reported, the sum of the monthly reported and imputed data is used rather than the imputed annual data. Building permits data are not sample-based on an annual basis; annual data are tabulated from the entire universe of building permit offices. Monthly data are not revised except for the highest aggregates (U.S. and Census Regions) after annual processing. Monthly estimates of housing units authorized for the U.S. and Census Regions are revised using a benchmark process to sum to the final annual totals.
Monthly tables of data by MA show all MAs, but most do not include complete counts on a monthly basis because no estimate is made of monthly activity of areas not in the monthly sample. The MAs that are completely covered monthly include the 75 MAs having the greatest number of housing units authorized in 2002. The remaining are simply the sum of monthly reporters with no estimate for annual reporters. To provide a measure of sample coverage, monthly tables by MA show the percentage of housing units authorized in the previous year represented by those places in the monthly survey in each metropolitan area. This is referred to as the "monthly coverage percent." Annual MA tables include estimates for all permit-issuing areas in each MA.
Monthly county totals are the sum of the data for places requested to report monthly in a county; for counties not fully covered by monthly reporters, county totals will be incomplete. Annual county totals include estimates for all permit offices.
Monthly data by permit-issuing place include municipalities requested to report monthly. Data for all permit-issuing municipalities are available annually.
According to the release schedule, revised monthly estimates for the U.S. and Census Regions are released on approximately the 18th working day of the month. An analysis of the NRC revisions is updated with the release of each year's preliminary January and July data.
Estimates in the New Residential Construction release are not revised on the 18th working day; any revisions are shown in the following month's release.
On the same day that the revised monthly estimates for the U.S. and Census Regions are released, monthly estimates by Census Division, state, metropolitan area, county, and permit-issuing place are also released. The monthly estimates by Census Division, state, metropolitan area, county, and permit-issuing place are not revised after their initial release. However, the year-to-date estimates are revised each month to reflect late reports received or corrections made to reports from prior months in the year.
After the completion of the annual survey, final annual estimates for the previous year for the U.S. and by Census Region, Census Division, state, metropolitan area, county, and permit-issuing place are released on the first working day in May. With the release of April data on New Residential Construction, revised monthly estimates for the U.S. and Census Regions for the prior year which have been "benchmarked" to the final annual totals are released. Seasonally adjusted annual rates for the previous 27 months are also revised to reflect updated seasonal factors. No other not seasonally adjusted monthly data are revised after the completion of the annual survey. No annual data are revised after the release of the final annual estimates.
The portion of residential construction measurable from building permits records is inherently limited because such records obviously do not reflect construction activity outside of areas subject to local permits requirements. For the nation as a whole, less than 2 percent of all privately owned housing units are constructed in areas not requiring building permits. However, this proportion varies greatly from state to state and among metropolitan areas.
The reported statistics on building permits are influenced by the following factors:
To the extent that most of these limiting factors apply rather consistently over an extended period, they may not seriously impair the usefulness of building permit statistics as prompt indicators of trends in residential construction activity. However, the geographic limitations of the data need to be kept in mind. In addition, the dollar volume of residential construction should be used with caution. Because of the nature of the building permit application process, valuations may frequently differ from the true cost of construction. Any attempt to use these figures for inter-area comparisons of construction volume must, at best, be made cautiously and with broad reservations.
The monthly estimates shown for the United States, Census Regions, Census Divisions, and states (with the exception of a few states that have complete coverage in the monthly sample) are based on samples and may differ from statistics that would have been obtained from a complete census using the same schedules and procedures. For a particular estimate, statisticians define this difference as the total error of the estimate. When describing the reliability of survey results, total error is defined as the sum of sampling error and nonsampling error. Sampling error is the error arising from the use of a sample, rather than a census, to estimate population values. Nonsampling error encompasses all other factors that contribute to the total error of a survey estimate. The sampling error of an estimate can usually be estimated from the sample, whereas the nonsampling error of an estimate is difficult to measure and can rarely be estimated. Consequently, the actual error in an estimate exceeds the error that can be estimated. Further descriptions of sampling error and nonsampling error are provided in the following sections. Data users should take into account the estimates of sampling error and the potential effects of nonsampling error when using the published estimates.
Sampling error reflects the fact that only a particular sample was surveyed rather than the entire population. Each sample selected for the BPS is one of a large number of similar probability samples that, by chance, might have been selected under the same specifications. Estimates derived from the different samples would differ from each other. The standard error (SE), or sampling error, of a survey estimate is a measure of the variation among the estimates from all possible samples and, thus, is a measure of the precision with which an estimate from a particular sample approximates the average from all possible samples.
Estimates of the standard errors have been computed from the sample data for selected statistics. They are presented in the form of relative standard errors (RSEs). The relative standard error equals the standard error divided by the estimated value to which it refers. Estimates of the RSEs are available at the Building Permits variance web site.
The sample estimate and an estimate of its standard error allow us to construct interval estimates with prescribed confidence that the interval includes the average result of all possible samples with the same size and design. To illustrate, if all possible samples were surveyed under essentially the same conditions, and estimates calculated from each sample, then:
Thus, for a particular sample, one can say with specified confidence that the average of all possible samples is included in the constructed interval. For example, suppose that an estimated 100,000 housing units were authorized by building permits in a particular month and that the average relative standard error of this estimate is 1 percent. Multiplying 100,000 by .01, we obtain 1,000 as the standard error. This means that we are confident, with 68% chance of being correct, that the average estimate from all possible samples of housing units authorized during the particular month is between 99,000 and 101,000 homes. To increase the probability to a 90% chance that the interval contains the average value over all possible samples (this is called a 90-percent confidence interval), multiply 1,000 by 1.645, yielding limits of 98,355 and 101,645 (100,000 units plus or minus 1,645 units). The average estimate of housing units authorized during the specified month may or may not be contained in any one f these computed intervals; but for a particular sample, one can say that the average estimate from all possible samples is included in the constructed interval with a specified confidence of 90 percent. It is important to note that the standard error and the relative standard error only measure sampling error. They do not measure any systematic nonsampling errors in the estimates.
The annual estimates of building permits are not based on probability samples; annual data are tabulated from the entire universe of building permit offices. The statistics for counties and metropolitan areas are also not based on probability samples. Although not subject to sampling error, these estimates are subject to various nonsampling errors.
Nonsampling error encompasses all factors, other than sampling error, that contribute to the total error of a sample survey estimate and may also occur in censuses. It is often helpful to think of nonsampling error as arising from deficiencies or mistakes in the survey process. Nonsampling errors are usually attributed to many possible sources: (1) coverage error - failure to accurately represent all population units in the sample, (2) inability to obtain information about all sample cases (nonresponse), (3) response errors, possibly caused by definitional difficulties or misreporting, (4) mistakes in recording or coding the data obtained, and (5) other errors of coverage, collection, processing, or imputation for missing items or inconsistent data. Although nonsampling error is not measured directly, the Census Bureau employs quality control procedures throughout the process to minimize this type of error.
When a report is not received, missing housing unit data are either (1) obtained from the Survey of Construction (SOC), which is used to collect information on housing starts, sales, and completions, or (2) imputed. Data from SOC are available only for about 900 places for which Census Bureau field representatives list permits issued for new residential construction as part of the SOC sampling operation. (Please go to the Survey of Construction Methodology for more information.) If data are not reported and are not available from SOC, estimates are imputed based on the assumption that the ratio of authorizations for the current time period to the prior year total is the same for reporting and nonreporting jurisdictions in that Census Region. Data for the four types of structures (one unit, two units, 3-4 units, and 5 units or more) are imputed separately.
Many of our data products by county and permit-issuing place show "Reported only" data as well as "Estimates with Imputation." The "Reported Only" data are based only on reports received from permit offices and data obtained from the SOC, and do not include any imputation for missing data. "Estimates with Imputation" include both reported and imputed data.
The imputation rate is the percentage of an estimate that is based on imputation. For 2013, the average imputation rate for revised monthly estimates of the total number of housing units authorized by building permits was approximately 18%. After the conclusion of the 2013 Annual Survey, the imputation rate for the 2013 final annual estimate of the total number of housing units authorized by building permits was approximately 7%.
At the end of the year, a second request for data is mailed to delinquent monthly offices. If an office has not reported for up to 4 months during the year, a form is sent for each missing month; if an office has missed reporting for 5 months or more, an annual form is sent. Each office that is requested to report annually receives a second request if the annual report has not been received by the initial due date.
The unit response rate is the percentage of reports requested that were received. For 2013, the average unit response rate for revised monthly estimates of units authorized was approximately 72%. To combine monthly and annual requests to determine the annual unit response rate, requests for annual data from places not in the monthly sample are counted as 12 monthly requests and annual data received are counted as 12 monthly reports. After the conclusion of the 2013 Annual Survey, the unit response rate for all monthly and annual offices was approximately 85%.
The Census Bureau uses many additional methods to improve response rates for this voluntary survey. These include contacting nonrespondents by telephone or email, contacting other government jurisdictions such as counties or states to encourage response from permit offices in their areas or to obtain data for individual jurisdictions, and working with public and private organizations to encourage and facilitate response to the survey.
Some permit offices are able to report the number of housing units authorized, but not the valuation of construction for those units. Valuations for these offices are imputed based on the average cost per unit for the same type of structure and Census Region.
Seasonal adjustment is the process of estimating and removing seasonal effects from a time series to better reveal certain nonseasonal features such as underlying trends and business cycles. Seasonal adjustment procedures estimate effects that occur in the same calendar month with similar magnitude and direction from year to year. In series whose seasonal effects come primarily from weather, the seasonal factors are estimates of average weather effects for each month. Seasonal adjustment does not account for abnormal weather conditions or for year-to-year changes in weather. Seasonal factors are estimates based on present and past experience. Future data may show a different pattern.
The mechanics of seasonal adjustment involve breaking down a time series into a trend-cycle, a seasonal component, and an irregular component.
The trend-cycle is the long-term tendency of a series to grow or decline.
The seasonal component consists of seasonal effects that are reasonably stable in terms of timing, direction, and magnitude. Possible causes include natural factors (the weather), administrative measures, and social/cultural/religious traditions.
Monthly time series that are totals of daily activities can be influenced by each calendar month's weekday composition. This influence is revealed when monthly values consistently depend on which days of the week occur five times in the month. For example, building permit offices are usually closed on Saturday and Sunday. Thus, the number of building permits issued in a given month is likely to be higher if the month contains a surplus of weekdays and lower if the month contains a surplus of weekend days. Recurring effects associated with individual days of the week are called trading-day effects.
Trading-day effects can make it difficult to compare time series values or to compare movements in one series with movements in another. For this reason, when estimates of trading-day effects are statistically significant, they are adjusted out of the series. The removal of such estimates is referred to as trading-day adjustment.
Series may have moving-holiday effects. Economic effects from holidays such as Easter, Labor Day, and Thanksgiving may affect more than one month, so their timing is not strictly seasonal, but they are predictable calendar events. When these moving-holiday effects are statistically significant, they are adjusted out of the series. The removal of such estimates is referred to as moving-holiday adjustment.
The irregular component is anything not included in the trend-cycle or the seasonal effects (which include trading-day effects and moving-holiday effects). Its values are unpredictable with respect to timing, impact, and duration. It can arise from sampling error, nonsampling error, unseasonable weather, natural disasters, strikes, etc.
Most of the seasonally adjusted series are shown as seasonally adjusted annual rates (SAAR). The seasonally adjusted annual rate is the seasonally adjusted monthly value multiplied by 12. The benefit of the annual rate is that not only can one monthly estimate be compared with another, monthly data can also be compared to an annual total. The seasonally adjusted annual rate is neither a forecast nor a projection; rather it is a description of the rate of building permits in the particular month for which they are calculated.
The seasonal adjustment factors for these data are indexes, that is, they are the factor times 100. They were developed using X-13ARIMA-SEATS software. The X-13ARIMA-SEATS software improves upon the X-12-ARIMA seasonal adjustment software by providing enhanced diagnostics as well as incorporating an enhanced version of the Bank of Spain's SEATS (Signal Extraction in ARIMA Time Series) software, which uses an ARIMA model-based procedure instead of the X-11 filter-based approach to estimate seasonal factors. The X-13ARIMA-SEATS and X-12-ARIMA software produce identical results when using the X-11 filter-based adjustment methodology. The X-13ARIMA-SEATS software will be available from the Census Bureau's Internet site in the coming months. Note that BPS estimates continue to be adjusted using the X-11 filter-based adjustment procedure. For more information on X-12-ARIMA please refer to the Census Bureau's X-12 website.
Seasonally adjusted annual rates are developed each month for building permits by Region and type of structure. Each month, 10 series are run through the X-13ARIMA-SEATS program. The seasonally adjusted U.S. single-family total is the sum of the seasonally adjusted single-family structures in each of the four Census Regions. The seasonally adjusted U.S. total is the sum of the seasonally adjusted U.S. total single-family, U.S. total for two-to-four unit structures, and U.S. total for structures with five units or more. The totals for each of the four Regions are seasonally adjusted and modified so that the seasonally adjusted U.S total derived from the Region totals equals the seasonally adjusted U.S. total derived from the structures.
For further information on time series and seasonal adjustment, please refer to the Seasonal Adjustment Frequently Asked Questions.
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