The data for tenure were obtained from Housing Question 17 in the 2006 American Community Survey. The question was asked at occupied housing units. Occupied housing units are classified as either owner occupied or renter 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.
A housing unit is "Owned by you or someone in this household free and clear (without a mortgage)" if there is no mortgage or other similar debt on the house, apartment, or mobile home including units built on leased land if the unit is owned outright without a mortgage.

All occupied housing units which are not owner occupied, whether they are rented or occupied without payment of rent, are classified as renter occupied. "No cash paid" units are separately identified in the rent tabulations. Such units are generally provided free by friends or relatives or in exchange for services such as resident manager, caretaker, minister, or tenant farmer. Housing units on military bases also are classified in the "No cash paid" category. "Rented for cash rent" includes units in continuing care, sometimes called life care arrangements. These arrangements usually involve a contract between one or more individuals and a health services provider guaranteeing the individual shelter, usually a house or apartment, and services, such as meals or transportation to shopping or recreation. (For more information, see " Meals Included in Rent .")

From 1996-2006 the American Community Survey questions were the same. Starting in 2006, the instruction Mark (X) ONE box. was added following the question, and the instruction Include home equity loans. was added following the response category "Owned by you or someone in this household with a mortgage or loan?" Additional changes introduced In 2006 included revising the wording of two of the response categories from "Rented for cash rent?" to "Rented?" and "Occupied without payment of cash rent?" to "Occupied without payment of rent?"

The detailed tables provide basic distributions of characteristics. They are the foundation upon which other data products are built. These tables display estimates and the associated lower and upper bounds of the 90 percent confidence interval. They include demographic, social, economic, and housing characteristics, and provide 1-, 3-, or 5-year period estimates for the nation and the states, as well as for counties, towns, and other small geographic entities, such as census tracts and block groups.

The Census Bureau's goal is to maintain a high degree of comparability between ACS detailed tables and Census 2000 sample-based data products. In addition, characteristics not measured in the Census 2000 tables will be included in the new ACS base tables. The 2007 detailed table products include more than almost 600 tables that cover a wide variety of characteristics, and another 380 race and Hispanic-origin iterations that cover 40 key characteristics. In addition to the tables on characteristics, approximately 80 tables summarize allocation rates from the data edits for many of the characteristics. These provide measures of data quality by showing the extent to which responses to various questionnaire items were complete. Altogether, over 1,300 separate detailed tables are provided.

Both occupied and vacant mobile homes to which no permanent rooms have been added are counted in this category. Mobile homes used only for business purposes or for extra sleeping space and mobile homes for sale on a dealer's lot, at the factory, or in storage are not counted in the housing inventory.

This is a 1-unit structure detached from any other house, that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. A one-family house that contains a business is considered detached as long as the building has open space on all four sides. Mobile homes to which one or more permanent rooms have been added or built also are included.

This is a 1-unit structure that has one or more walls extending from ground to roof separating it from adjoining structures. In row houses (sometimes called townhouses), double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.

These are units in structures containing 2 or more housing units, further categorized as units in structures with 2, 3 or 4, 5 to 9, 10 to 19, 20 to 49, and 50 or more apartments.

This category is for any living quarters occupied as a housing unit that does not fit the previous categories. Examples that fit this category are houseboats, railroad cars, campers, and vans. Recreational vehicles, boats, vans, tents, railroad cars, and the like are included only if they are occupied as someone's current place of residence.

The 1996-1998 American Community Survey question provided the response category, "a mobile home or trailer." Starting in 1999, the ACS response category dropped "or trailer" to read as "a mobile home."

SPPs provide certain characteristics from the data profiles for a specific race or ethnic group (e.g., Alaska Natives) or some other selected population group (e.g., people aged 60 years and older). SPPs are provided every year for many of the Census 2000 Summary File 4 iteration groups. SPPs were introduced on a limited basis in the fall of 2005, using the 2004 sample. In 2008 (sample year 2007), this product was significantly expanded. The earlier SPP requirement was that a substate geographic area must have a population of at least 1,000,000 people. This threshold was reduced to 500,000, and congressional districts were added to the list of geographic types that can receive SPPs. Another change to SPPs in 2008 is the addition of many country-of-birth groups. Groups too small to warrant an SPP for a geographic area based on 1 year of sample data may appear in an SPP based on the 3- or 5-year accumulations of sample data. More details on these profiles can be found in Hillmer (2005), which includes a list of selected race, Hispanic origin, and ancestry populations.

Research has shown that respondents tended to overstate their expenses for electricity and gas when compared to utility company records. There is some evidence that this overstatement is reduced when yearly costs are asked rather than monthly costs. Caution should be exercised in using these data for direct analysis because costs are not reported for certain kinds of units such as renter-occupied units with all utilities included in the rent and owner-occupied condominium units with utilities included in the condominium fee.

The American Community Survey questions ask for monthly costs for electricity and gas, and yearly costs for water/sewer and other fuels. Since 1999, the words "or mobile home" were added to each question, and Question 14b, which asked "Last month, what was the cost of gas for this house, apartment, or mobile home?" had an additional response category, "included in electricity payment entered above."

Once plans are finalized for the ACS data products, the DRB reviews them to assure that confidentiality of respondents has been protected.

Title 13 of the United States Code (U.S.C.) is the basis for the Census Bureau's policies on disclosure avoidance. Title 13 says, "Neither the Secretary, nor any other officer or employee of the Department of Commerce may make any publication whereby the data furnished by any particular establishment or individual under this title can be identified . . ." The DRB reviews all data products planned for public release to ensure adherence to Title 13 requirements, and may insist on applying disclosure avoidance rules that could result in the suppression of certain measures for small geographic areas. (More information about the DRB and its policies can be found at . To satisfy Title 13 U.S.C., the Census Bureau uses several statistical methodologies during tabulation and data review to ensure that individually identifiable data will not be released.

These are vacant units offered "for rent," and vacant units offered either "for rent" or "for sale."

These are vacant units rented but not yet occupied, including units where money has been paid or agreed upon, but the renter has not yet moved in.

These are vacant units being offered "for sale only," including units in cooperatives and condominium projects if the individual units are offered "for sale only." If units are offered either "for rent" or "for sale" they are included in the "for rent" classification.

These are vacant units sold but not yet occupied, including units that have been sold recently, but the new owner has not yet moved in.

These are vacant units used or intended for use only in certain seasons or for weekends or other occasional use throughout the year. Seasonal units include those used for summer or winter sports or recreation, such as beach cottages and hunting cabins. Seasonal units also may include quarters for such workers as herders and loggers. Interval ownership units, sometimes called shared-ownership or time-sharing condominiums, also are included here.

These include vacant units intended for occupancy by migratory workers employed in farm work during the crop season. (Work in a cannery, a freezer plant, or a food-processing plant is not farm work.)

This chapter deals with the 1-year and 3-year data products. Future versions of this document will include a discussion of the 5-year data products. The American Community Survey (ACS) data products and supporting documentation are released in several series and at several Internet locations. The primary Web site for data dissemination is the American FactFinder (AFF); supporting documentation can be found on the ACS Web site and the Census Bureau's File Transfer Protocol (FTP) site.

Since 2000, the ACS has been tabulating and publishing single year estimates for specific areas with populations of 250,000 or more. In 2005, the ACS expanded its sample size to cover all of the United States and the Commonwealth of Puerto Rico. In summer 2006, the ACS started releasing data annually for areas with populations of 65,000 or more. In 2008, the ACS is releasing 3-year period estimates for areas with a population of 20,000 or more on an annual basis. For smaller areas, it will take 5 years to accumulate a large enough sample to produce releasable estimates. Once those data are collected, the Census Bureau will release tabulations annually, based on 5-year period data for areas as small as census tracts and block groups.

Federal agencies distribute billions of dollars among states, tribal governments, and population groups, based on social and economic data. In the past, the statistics that determined services locations and program funding came in large part from the long-form sample of the decennial census. As the ACS continues to grow, its data products will provide updated versions of many of the long-form products from Census 2000. Beginning in 2010, the decennial census no longer will include a long-form sample, and ACS data products will provide high-quality, updated annual statistics for comparisons of the demographic, social, economic, and housing characteristics of areas and population groups. The ACS statistics also will show trends and relative differences between areas and population groups. These data products will continue to meet the needs of those who previously used the decennial census sample statistics, and will provide more current statistics than those available from the census long-form sample, which reflect only one point in time.

By 2010, the information on social, demographic, economic, and housing characteristics previously available only once every 10 years will be available annually through the ACS for all areas. Each year thereafter, these areas will get new estimates based on the 5-year interval ending in the latest completed sample year.

Figure 14.1 summarizes the data products release schedule. In 2006, the first set of 1-year estimates was released for specific areas with populations of 65,000 and more. These areas will continue to receive 1-year estimates annually. In 2008, data collected over a 3-year period (2005− 2007) was released for areas with at least 20,000 people. These areas will continue to receive 3-year estimates annually. In 2010, the first 5-year products will be released based on data collected in 2005−2009. These products will be produced for areas down to census tracts and block groups. Once 3- and 5-year products are produced, annual updates will follow, as indicated by Table 14.1.arThe AFF Web site contains data maps, tables, and reports from a variety of censuses and surveys.
AFF lists these data sets by program areas and survey years. AFF contains data for a wide variety
of surveys including the Decennial Census, the ACS, the Population Estimates Program, the Economic
Census, and the Annual Economic Surveys.

Data product	Population threshold	Year of data release
		2006	2007	2008	2009	2010	2011	2012	2013
1-year estimates	65,000+	2005	2006	2007	2008	2009	2010	2011	2012
3-year estimates	20,000+			2005-	2006-	2007-	2008-	2009-	2010-
				2007	2008	2009	2010	2011	2012
5-year estimates	All areas*					2005-	2006-	2007-	2008-
						2009	2010	2011	2012

* All legal, administrative, and statistical geographic areas down to the tract and block group level.

The AFF Web site contains data maps, tables, and reports from a variety of censuses and surveys. AFF lists these data sets by program areas and survey years. AFF contains data for a wide variety of surveys including the Decennial Census, the ACS, the Population Estimates Program, the Economic Census, and the Annual Economic Surveys.

The AFF is the primary Web access tool for ACS data. Data products include detailed tables, data profiles, comparison profiles (1-year data only), narrative profiles, ranking tables and charts (single year data only), geographic comparison tables, thematic maps, subject tables, selected population profiles, and downloadable public use microdata sample (PUMS) files.

The ACS Web site contains a wealth of information, documentation, and research papers about ACS. The site contains important metadata, including more than 50 population concept definitions and more than 40 housing concept definitions. The ACS Web site can be found at .

Documentation on the accuracy of the data also is included, and provides information about the sample design, confidentiality, sampling error, nonsampling error, and estimation methodology. The errata section lists updates made to the data. The geography section gives a brief explanation of the Census Bureau's geographic hierarchy, common terms, and specific geographic areas presented.

The FTP site is intended for advanced users of census and ACS data. This site provides quick access to users who need to begin their analyses immediately upon data release. The data are downloaded into Excel, PDF, or text files. Users of the FTP site can import the files into the spreadsheet/database software of their choice for data analysis and table presentation. Documentation describing the layout of the site in the README file is available in the main directory on the FTP server. The FTP site can be accessed through the ACS Web site.

The variable "Selected Conditions" is defined for owner- and renter-occupied housing units as having at least one of the following conditions: 1) lacking complete plumbing facilities, 2) lacking complete kitchen facilities, 3) with 1.01 or more occupants per room, 4) selected monthly owner costs as a percentage of household income greater than 30 percent, and 5) gross rent as a percentage of household income greater than 30 percent.

Sampling error is the difference 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). Note that sample-based estimates will vary depending on the particular sample selected from the population. Measures of the magnitude of sampling error, such as the variance and the standard error (the square root of the variance), reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology. The American Community Survey (ACS) is committed to providing its users with measures of sampling error along with each published estimate. To accomplish this, all published ACS estimates are accompanied either by 90 percent margins of error or confidence intervals both based on ACS direct variance estimates. Due to the complexity of the sampling design and the weighting adjustments performed on the ACS sample, unbiased design-based variance estimators do not exist. As a consequence, the direct variance estimates are computed using a replication method that repeats the estimation procedures independently several times. The variance of the full sample is then estimated by using the variability across the resulting replicate estimates. Although the variance estimates calculated using this procedure are not completely unbiased, the current method produces variances that are accurate enough for analysis of the ACS data.

For Public Use Microdata Sample (PUMS) data users, replicate weights are provided to approximate standard errors for the PUMS-tabulated estimates. Design factors are also provided with the PUMS data, so PUMS data users can compute standard errors of their statistics using either the replication method or the design factor method.

To inflate selected monthly owner costs from previous years, the dollar values are inflated to the latest years dollar values by multiplying by a factor equal to the average annual Consumer Price Index (CPI-U-RS) factor for the current year, divided by the average annual CPI-U-RS factor for the earlier/earliest year.

Computation of replicate factors begins with the selection of a Hadamard matrix of order R (a multiple of 4), where R is the number of replicates. A Hadamard matrix H is a k -by- k matrix with all entries either 1 or −1, such that H'H = k I (that is, the columns are orthogonal). For ACS, the number of replicates is 80 ( R = 80). Each of the 80 columns represents one replicate.

Next, a pair of rows in the Hadamard matrix is assigned to each record (HU or group quarters (GQ) person). An algorithm is used to assign two rows of an 80×80 Hadamard matrix to each HU. The ACS uses a repeating sequence of 780 pairs of rows in the Hadamard matrix assigned to each record, in short order (Navarro, 2001a). The assignment of Hadamard matrix rows repeats every 780 records until all records receive a pair of rows from the Hadamard matrix. The first row of the matrix, in which every cell is always equal to one, is not used.

The replicate factor for each record then is determined from these two rows of the 80×80 Hadamard matrix. For record i ( i = 1, …, n , where n is sample size) and replicate r ( r = 1, …, 80), the replicate factor is computed as:


where R 1i and R 2i are respectively the first and second row of the Hadamard matrix assigned to the i -th HU, and a R l i, r and a R 2i,r are respectively the matrix elements (either 1 or −1) from the Hadamard matrix in rows R 1 i and R 2 i and column r . Note that the formula for ƒ i,r yields replicate factors that can take one of three approximate values: 1.7, 1.0, or 0.3. That is;

• If a R 1 i,r = +1 and a R 2 i,r = +1, the replicate factor is 1.

• If a R 1 i,r = −1 and a R 2 i,r = −1, the replicate factor is 1.

• If a R 1 i,r = +1 and a R 2 i,r = −1, the replicate factor is approximately 1.7.

• If a R 1 i,r = −1 and a R 2 i,r = +1, the replicate factor is approximately 0.3.

The expectation is that 50 percent of replicate factors will be 1, and the other 50 percent will be evenly split between 1.7 and 0.3 (Gunlicks, 1996). The following example demonstrates the computation of replicate factors for a sample of size five, using a Hadamard matrix of order four:


Table 12.1 presents an example of a two-row assignment developed from this matrix, and the values
of replicate factors for each sample unit.

Table 12.1 Example of Two-Row Assignment, Hadamard Matrix Elements, and Replicate Factors

Case # (i)	Row assignment		Hadamard matrix element								Approximate replicate factor
	R1i	R2i	Replicate 1		Replicate 2		Replicate 3		Replicate 4		fi,1	fi,2	fi,3	fi,4
			aR1i,1	aR2i,1	aR1i,2	aR2i,2	aR1i,3	aR2i,3	aR1i,4	aR2i,4
1	2	3	+1	+1	-1	+1	+1	-1	-1	-1	1	0.3	1.7	1
2	3	4	+1	+1	+1	-1	-1	-1	-1	+1	1	1.7	1	0.3
3	4	2	+1	+1	-1	-1	-1	+1	+1	-1	1	1	0.3	1.7
4	2	3	+1	+1	-1	+1	+1	-1	-1	-1	1	0.3	1.7	1
5	3	4	+1	+1	+1	-1	-1	-1	-1	+1	1	1.7	1	0.3

Note that row 1 is not used. For the third case ( i = 3), rows four and two of the Hadamard matrix are to calculate the replicate factors. For the second replicate ( r = 2), the replicate factor is computed using the values in the second column of rows four (−1) and two (−1) as follows:

Replicate weights are produced in a way similar to that used to produce full sample final weights. All of the weighting adjustment processes performed on the full sample final survey weights (such as applying noninterview adjustments and population controls) also are carried out for each replicate weight. However, collapsing patterns are retained from the full sample weighting and are not determined again for each set of replicate weights.

Before applying the weighting steps explained in Chapter 11, the set of replicate sampling weights is computed. With the replicate factor assigned, the replicate sampling weight for replicate r is computed by multiplying the full sample weight after computer-assisted personal interviewing (CAPI) subsampling factor ( WSSF - see Chapter 11 for the computation of this weight) by the replicate factor f i,r ; that is, RWSSF i,r = WSST i x f i,r, where RWSSF i,r is the replicate weight after CAPI subsampling factor for the i -th HU and the r -th replicate ( r = 1, ..., 80).

One can elaborate on the previous example of the replicate construction using five cases and four replicates: Suppose the full sample WSSF values are given under the second column of the following table (Table 12.2). Then, the replicate weight after CAPI subsampling factor ( RWSSF ) values are given in columns 7-10.

Table 12.2 Example of Computation of Replicate Weight After CAPI Subsampling Factor ( RWSSF )

Case #	WSSFi	Approximate replicate factor				Replicate weight after CAPI subsampling factor
		fi,1	fi,2	fi,3	fi,4	RWSSFi,1	RWSSFi,2	RWSSFi,3	RWSSFi,4
1	100	1	0.3	1.7	1	100	29	171	100
2	120	1	1.7	1	0.3	120	205	120	35
3	80	1	1	0.3	1.7	80	80	23	137
4	120	1	0.3	1.7	1	120	35	205	120
5	110	1	1.7	1	0.3	110	188	110	32

The rest of the weighting process (Chapter 11) then is applied to each replicate weight RWSSF i,r (starting from the adjustment for variation in monthly response ( VMS ) and proceeding to the population control adjustment or raking). Basically, the weighting adjustment process is repeated independently 80 times and the RWSSF i,r is used in place of WSSF i (as in Chapter 11). By the end of this process, 80 final replicate weights for each HU and person record are produced.

Given the replicate weights, the computation of variance for any ACS estimate is straightforward. Suppose that è is an ACS estimate of any type of statistic, such as mean, total, or proportion. Let Θ₀ denote the estimate computed based on the full sample weight, and Θ1, Θ2,... Θ80, denote the estimates computed based on the replicate weights. The variance of Θ₀ v( Θ₀) is estimated as the sum of squared differences between each replicate estimate Θ r ( r = 1, ..., 80) and the full sample estimate Θ₀. The formula is as follows:¹



This equation holds for count estimates as well as any other types of estimates, including percents, ratios, and medians.

There are certain cases, however, where this formula does not apply. The first and most important cases are estimates that are "controlled" to population totals and have their standard errors set to zero. These are estimates that are forced to equal intercensal estimates during the weighting process raking step-for example, total population and collapsed age, sex, and Hispanic origin estimates for weighting areas. Although race is included in the raking procedure, race group estimates are not controlled; the categories used in the weighting process (see Chapter 11) do not match the published tabulation groups because of multiple race responses and the "Some Other Race" category. Information on the final collapsing of the person post-stratification cells is passed from the weighting to the variance estimation process in order to identify estimates that are controlled. This is done independently for all weighting areas and then is applied to the geographic areas used for tabulation. Standard errors for those estimates are set to zero, and published margins of error are set to "*****" (with an appropriate accompanying footnote).

Another special case deals with zero-estimated counts of people, households, or HUs. A direct application of the replicate variance formula leads to a zero standard error for a zero-estimated count. However, there may be people, households, or HUs with that characteristic in that area that were not selected to be in the ACS sample, but a different sample might have selected them, so a zero standard error is not appropriate. For these cases, the following model-based estimation of standard error was implemented.

For ACS data in a census year, the ACS zero-estimated counts (for characteristics included in the 100 percent census ("short form") count) can be checked against the corresponding census estimates. At least 90 percent of the census counts for the ACS zero-estimated counts should be within a 90 percent confidence interval based on our modeled standard error.² Let the variance of the estimate be modeled as some multiple ( K ) of the average final weight (for a state or the nation). That is:

v(0) = K x (average weight)

Then, set the 90 percent upper bound for the zero estimate equal to the census count:



Solving for K yields:


K was computed for all ACS zero-estimated counts from 2000, which matched Census 2000 100 percent counts, and then the 90th percentile of those K s was determined. Based on the Census 2000 data, we use a value for K of 400 (Navarro, 2001b). As this modeling method requires census counts, the 400 value can next be updated using the 2010 Census and 2010 ACS data.

For publication, the standard error ( SE ) of the zero count estimate is computed as:



The average weights (the maximum of the average housing unit and average person final weights) are calculated at the state and national level for each ACS single-year or multiyear data release. Estimates for geographic areas within a state use that states average weight, and estimates for geographic areas that cross state boundaries use the national average weight.

Finally, a similar method is used to produce an approximate standard error for both ACS zero and 100 percent estimates. We do not produce approximate standard errors for other zero estimates, such as ratios or medians.

Footnote:

¹A general replication-based variance formula can be expressed as where c r is the multiplier related to the r -th replicate determined by the replication method. For the SDR method, the value of c r is 4 / R , where R is the number of replicates (see Fay and Train, 1995).
²This modeling was done only once, in 2001, prior to the publication of the 2000 ACS data.

This measure divides the selected monthly owner costs as a percentage of household income distribution into two equal parts: one-half of the cases falling below the median selected monthly owner costs as a percentage of household income and one-half above the median. Median selected monthly owner costs as a percentage of household income is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Derived Measures.") Median selected monthly owner costs as a percentage of household income is rounded to the nearest tenth. (For more information on medians, see "Derived Measures.")

Once the standard errors have been computed, margins of error and confidence bounds are produced for each estimate. These are the measures of overall sampling error presented along with each published ACS estimate. All published ACS margins of error and the lower and upper bounds of confidence intervals presented in the ACS data products are based on a 90 percent confidence level, which is the Census Bureau's standard (U.S. Census Bureau, 2008a).

A margin of error contains two components: the standard error of the estimate, and a multiplication factor based on a chosen confidence level. For the 90 percent confidence level, the value of the multiplication factor used by the ACS is 1.645. The margin of error of an estimate è can be computed as:



where se( Θ) is the standard error of the estimate Θ. Given this margin of error, the 90 percent confidence interval can be computed as:



that is, the lower bound of the confidence interval is [ Θ - margin of error ( Θ) ], and the upper bound of the confidence interval is [ Θ + margin of error ( Θ) ]. Roughly speaking, this interval is a range that will contain the true value of the estimated characteristic, with a known probability.

Users are cautioned to consider "logical" boundaries when creating confidence bounds from the margins of error. For example, a small population estimate may have a calculated lower bound less than zero. A negative number of people does not make sense, so the lower bound should be set to zero instead. Likewise, bounds for percents should not go below zero percent or above 100 percent. For other characteristics, like income, negative values may be legitimate.

Given the confidence bounds, a margin of error can be computed as the difference between an estimate and its upper or lower confidence bounds:

Margin of Error = max (upper bound - estimate, estimate - lower bound)

Using the margin of error (as published or calculated from the bounds), the standard error is obtained as follows:

Standard Error = Margin of Error / 1.645

For ranking tables and comparison profiles, the ACS provides an indicator as to whether two estimates are statistically significantly different at the 90 percent confidence level. That determination is made by initially calculating:



If Z < -1.645 or Z > 1.645, the difference between the estimates is significant at the 90 percent level. Determinations of statistical significance are made using unrounded values of the standard errors, so users may not be able to achieve the same result using the standard errors derived from the rounded estimates and margins of error as published. Only pairwise tests are used to determine significance in the ranking tables; no multiple comparison methods are used.

The Census Bureau cannot possibly predict all combinations of estimates and geography that may be of interest to data users. Data users can download PUMS files and tabulate the data to create estimates of their own choosing. The ACS PUMS contains a subset of the full ACS sample. Thus, estimates from the ACS PUMS file can be different from the published ACS estimates that are based on the full ACS sample.

Users of the ACS PUMS files can compute the estimated variances of their statistics using one of two options: (1) the replication method using replicate weights released with the PUMS data, and (2) the design factor method described below.

For the replicate method, direct variance estimates based on the SDR formula as described in Section B above can be implemented. Users can simply tabulate 80 replicate estimates in addition to their desired estimate by using the provided 80 replicate weights, and apply the variance formula:



Similar to methods used to calculate standard errors for PUMS data from Census 2000, the ACS PUMS provides tables of design factors for various topics such as age for persons or tenure for HUs. The 2007 ACS PUMS design factors are published at national and state levels (U.S. Census Bureau, 2008b), and were calculated using 2005 ACS data. PUMS design factors will be updated periodically, but not on an annual basis. The design factor approach was developed based on a model that uses a standard error from a simple random sample as the base, and then inflates it to account for an increase in the variance caused by the complex sample design. Standard errors for almost all counts and proportions of persons, households, and HUs are approximated using design factors. For single-year ACS PUMS files beginning with 2005, use:



for a total, and



for a percent,

where
Y = the estimate of total or a count.
p = the estimate of a percent.
DF = the appropriate design factor based on the topic of the estimate.
N = the total for the geographic area of interest (if the estimate is of HUs, the number of HUs is used; if the estimate is of families or households, the number of households is used; otherwise the number of persons is used as N ).
B = the base (denominator) of a percent.

The factor 99 in the formula is the value of the finite population correction factor for the PUMS, which is computed as (100 - f ) / f , where f (given as a percent) is the sampling rate for the PUMS data. Since the PUMS is approximately a 1 percent sample of HUs, (100 - f ) / f = (100 - 1) /1 = 99.

For 3-year PUMS files beginning with 2005−2007, the 3 years worth of data represent approximately a 3 percent sample of HUs. Hence, the finite population correction factor for 3-year PUMS is (100 - f ) / f = (100 - 3) / 3 = 97 / 3. To calculate standard errors from 3-year PUMS data, substitute 97 / 3 for 99 in the above formulas.

The design factor ( DF ) is defined as the ratio of the standard error of an estimated parameter (computed under the replication method described in Section B) to the standard error based on a simple random sample of the same size. The DF reflects the effect of the actual sample design and estimation procedures used for the ACS. The DF for each topic was computed by modeling the relationship between the standard error under the replication method ( RSE ) with the standard error based on a simple random sample ( SRSSE ); that is, RSE = DF x SRSSE , where the SRSSE is computed as follows:



The value 39 in the formula above is the finite population correction factor based on an approximate sampling fraction of 2.5 percent in the ACS; that is, 100 - 2.5) / 2.5 = 97.5 / 2.5 = 39.

The value of DF is obtained by fitting this (no intercept) regression model RSE = DF x SRSSE using standard errors ( RSE , SRSSE ) for various published table estimates at the national and state levels. The values of DF s by topic can be obtained from the PUMS Accuracy of the Data (2007) (U.S. Census Bureau, 2008b). The documentation also provides examples on how to use the design factor GVFs to compute standard errors for the estimates of totals, means, medians, proportions or percentages, ratios, sums, and differences.

The topics for the 2007 PUMS design factors are, for the most part, the same ones that were available for the Census 2000 PUMS. We recommend to users that, in using the design factor approach, if the estimate is a combination of two or more characteristics, the largest DF for this combination of characteristics is used. The only exceptions to this are items crossed with race or Hispanic origin; for these items, the largest DF is used, excluding race or Hispanic origin DF s.

Fay, R., and G. Train. (1995). "Aspects of Survey and Model-Based Postcensal Estimation of Income and Poverty Characteristics for States and Counties." Proceedings of the Section on Government Statistics . Alexandria, VA: American Statistical Association, pp. 154−159, .

Gbur, P., and L. Fairchild. (2002). "Overview of the U.S. Census 2000 Long Form Direct Variance Estimation." Proceedings of the Section on Survey Research Methods . Alexandria, VA: American Statistical Association, pp. 1139−1144.

Gunlicks, C. (1996). "1990 Replicate Variance System (VAR90-20)." Internal U.S. Census Bureau Memorandum for Documentation, June 4, 1996.

Judkins, D. R. (1990). "Fays Method for Variance Estimation." Journal of Official Statistics, Vol. 6, No. 3, 1990, pp. 223−239.

Navarro, A. (2001a). "2000 American Community Survey (ACS) Comparison County Replicate Factors (ACS-V-01)." Internal U.S. Census Bureau Memorandum to C. Alexander, Washington, DC, May 23, 2001.

Navarro, A. (2001b). "Estimating Standard Errors of Zero Estimates." Internal U.S. Census Bureau Draft Memorandum to C. Alexander, Washington, DC, November 6, 2001.

U.S. Census Bureau (2002). "Current Population Survey: Technical Paper 63RV-Design and Methodology." Washington, DC, 2002, .

U.S. Census Bureau (2008a). "Census Bureau Standard: Dissemination of Census and Survey Data Products." Washington, DC, 2008, .

U.S. Census Bureau (2008b). "PUMS Accuracy of the Data (2007)." Washington, DC, 2008, . Wolter, K. M. (1984). "An Investigation of Some Estimators of Variance for Systematic Sampling." Journal of the American Statistical Association , Vol. 79, 1984, pp. 781−790.

For the 1996-1998 American Community Survey, the question asked whether there was a telephone in the house or apartment. A telephone must be inside the house or apartment for the unit to be classified as having a telephone. Units where the respondent uses a telephone located inside the building but not in the respondent's living quarters are classified as having no telephone. The words "or mobile home" were added in the 1999 question to be more inclusive of the structure type. In 2004 instructions that accompanied the ACS mail questionnaire advised respondents to answer that the house, apartment, or mobile home had telephone service if cell phones were used by household members.

This chapter discusses the data products derived from the American Community Survey (ACS). ACS data products include the tables, reports, and files that contain estimates of population and housing characteristics. These products cover geographic areas within the United States and Puerto Rico. Tools such as the Public Use Microdata Sample (PUMS) files, which enable data users to create their own estimates, also are data products.

ACS data products will continue to meet the traditional needs of those who used the decennial census long-form sample estimates. However, as described in Chapter 14, Section 3, the ACS will provide more current data products than those available from the census long form, an especially important advantage toward the end of a decade.

Most surveys of the population provide sufficient samples to support the release of data products only for the nation, the states, and, possibly, a few substate areas. Because the ACS is a very large survey that collects data continuously in every county, products can be released for many types of geographic areas, including many smaller geographic areas such as counties, townships, and census tracts. For this reason, geography is an important topic for all ACS data products.

The first step in the preparation of a data product is defining the topics and characteristics it will cover. Once the initial characteristics are determined, they must be reviewed by the Census Bureau Disclosure Review Board (DRB) to ensure that individual responses will be kept confidential. Based on this review, the specifications of the products may be revised. The DRB also may require that the microdata files be altered in certain ways, and may restrict the population size of the geographic areas for which these estimates are published. These activities are collectively referred to as disclosure avoidance.

The actual processing of the data products cannot begin until all response records for a given year or years are edited and imputed in the data preparation and processing phases, the final weights are determined, and disclosure avoidance techniques are applied. Using the weights, the sample data are tabulated for a wide variety of characteristics according to the predetermined content. These tabulations are done for the geographic areas that have a sample size sufficient to support statistically reliable estimates, with the exception of 5-year period estimates, which will be available for small geographic areas down to the census tract and block group levels. The PUMS data files are created by different processes because the data are a subset of the full sample data. After the estimates are produced and verified for correctness, Census Bureau subject matter analysts review them. When the estimates have passed the final review, they are released to the public. A similar process of review and public release is followed for PUMS data.

While the 2005 ACS sample was limited to the housing unit (HU) population for the United States and Puerto Rico, starting in sample year 2006, the ACS was expanded to include the group quarters (GQ) population. Therefore, the ACS sample is representative of the entire resident population in the United States and Puerto Rico. In 2007, 1-year period estimates for the total population and subgroups of the total population in both the United States and Puerto Rico were released for sample year 2006. Similarly, in 2008, 1-year period estimates were released for sample year 2007. In 2008, the Census Bureau will, for the first time, release products based on 3 years of ACS sample, 2005 through 2007. In 2010, the Census Bureau plans to release the first products based on 5 years of consecutive ACS samples, 2005 through 2009. Since several years of samples form the basis of these multiyear products, reliable estimates can be released for much smaller geographic areas than is possible for products based on single-year data.

In addition to data products regularly released to the public, other data products may be requested by government agencies, private organizations and businesses, or individuals. To accommodate such requests, the Census Bureau operates a custom tabulations program for the ACS on a fee basis. These tabulation requests are reviewed by the DRB to assure protection of confidentiality before release.

Chapter 14 describes the dissemination of the data products discussed in this chapter, including display of products on the Census Bureau's Web site and topics related to data file formatting.

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.
A housing unit is "Owned by you or someone in this household free and clear (without a mortgage)" if there is no mortgage or other similar debt on the house, apartment, or mobile home including units built on leased land if the unit is owned outright without a mortgage.

All occupied housing units which are not owner occupied, whether they are rented or occupied without payment of rent, are classified as renter occupied. "No cash paid" units are separately identified in the rent tabulations. Such units are generally provided free by friends or relatives or in exchange for services such as resident manager, caretaker, minister, or tenant farmer. Housing units on military bases also are classified in the "No cash paid" category. "Rented for cash rent" includes units in continuing care, sometimes called life care arrangements. These arrangements usually involve a contract between one or more individuals and a health services provider guaranteeing the individual shelter, usually a house or apartment, and services, such as meals or transportation to shopping or recreation. (For more information, see " Meals Included in Rent .")

From 1996-2006 the American Community Survey questions were the same. Starting in 2006, the instruction Mark (X) ONE box. was added following the question, and the instruction Include home equity loans. was added following the response category "Owned by you or someone in this household with a mortgage or loan?" Additional changes introduced In 2006 included revising the wording of two of the response categories from "Rented for cash rent?" to "Rented?" and "Occupied without payment of cash rent?" to "Occupied without payment of rent?"

The detailed tables provide basic distributions of characteristics. They are the foundation upon which other data products are built. These tables display estimates and the associated lower and upper bounds of the 90 percent confidence interval. They include demographic, social, economic, and housing characteristics, and provide 1-, 3-, or 5-year period estimates for the nation and the states, as well as for counties, towns, and other small geographic entities, such as census tracts and block groups.

The Census Bureau's goal is to maintain a high degree of comparability between ACS detailed tables and Census 2000 sample-based data products. In addition, characteristics not measured in the Census 2000 tables will be included in the new ACS base tables. The 2007 detailed table products include more than almost 600 tables that cover a wide variety of characteristics, and another 380 race and Hispanic-origin iterations that cover 40 key characteristics. In addition to the tables on characteristics, approximately 80 tables summarize allocation rates from the data edits for many of the characteristics. These provide measures of data quality by showing the extent to which responses to various questionnaire items were complete. Altogether, over 1,300 separate detailed tables are provided.

Both occupied and vacant mobile homes to which no permanent rooms have been added are counted in this category. Mobile homes used only for business purposes or for extra sleeping space and mobile homes for sale on a dealer's lot, at the factory, or in storage are not counted in the housing inventory.

This is a 1-unit structure detached from any other house, that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. A one-family house that contains a business is considered detached as long as the building has open space on all four sides. Mobile homes to which one or more permanent rooms have been added or built also are included.

This is a 1-unit structure that has one or more walls extending from ground to roof separating it from adjoining structures. In row houses (sometimes called townhouses), double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.

These are units in structures containing 2 or more housing units, further categorized as units in structures with 2, 3 or 4, 5 to 9, 10 to 19, 20 to 49, and 50 or more apartments.

This category is for any living quarters occupied as a housing unit that does not fit the previous categories. Examples that fit this category are houseboats, railroad cars, campers, and vans. Recreational vehicles, boats, vans, tents, railroad cars, and the like are included only if they are occupied as someone's current place of residence.

The 1996-1998 American Community Survey question provided the response category, "a mobile home or trailer." Starting in 1999, the ACS response category dropped "or trailer" to read as "a mobile home."

SPPs provide certain characteristics from the data profiles for a specific race or ethnic group (e.g., Alaska Natives) or some other selected population group (e.g., people aged 60 years and older). SPPs are provided every year for many of the Census 2000 Summary File 4 iteration groups. SPPs were introduced on a limited basis in the fall of 2005, using the 2004 sample. In 2008 (sample year 2007), this product was significantly expanded. The earlier SPP requirement was that a substate geographic area must have a population of at least 1,000,000 people. This threshold was reduced to 500,000, and congressional districts were added to the list of geographic types that can receive SPPs. Another change to SPPs in 2008 is the addition of many country-of-birth groups. Groups too small to warrant an SPP for a geographic area based on 1 year of sample data may appear in an SPP based on the 3- or 5-year accumulations of sample data. More details on these profiles can be found in Hillmer (2005), which includes a list of selected race, Hispanic origin, and ancestry populations.

Research has shown that respondents tended to overstate their expenses for electricity and gas when compared to utility company records. There is some evidence that this overstatement is reduced when yearly costs are asked rather than monthly costs. Caution should be exercised in using these data for direct analysis because costs are not reported for certain kinds of units such as renter-occupied units with all utilities included in the rent and owner-occupied condominium units with utilities included in the condominium fee.

The American Community Survey questions ask for monthly costs for electricity and gas, and yearly costs for water/sewer and other fuels. Since 1999, the words "or mobile home" were added to each question, and Question 14b, which asked "Last month, what was the cost of gas for this house, apartment, or mobile home?" had an additional response category, "included in electricity payment entered above."

Once plans are finalized for the ACS data products, the DRB reviews them to assure that confidentiality of respondents has been protected.

Title 13 of the United States Code (U.S.C.) is the basis for the Census Bureau's policies on disclosure avoidance. Title 13 says, "Neither the Secretary, nor any other officer or employee of the Department of Commerce may make any publication whereby the data furnished by any particular establishment or individual under this title can be identified . . ." The DRB reviews all data products planned for public release to ensure adherence to Title 13 requirements, and may insist on applying disclosure avoidance rules that could result in the suppression of certain measures for small geographic areas. (More information about the DRB and its policies can be found at . To satisfy Title 13 U.S.C., the Census Bureau uses several statistical methodologies during tabulation and data review to ensure that individually identifiable data will not be released.

These are vacant units offered "for rent," and vacant units offered either "for rent" or "for sale."

These are vacant units rented but not yet occupied, including units where money has been paid or agreed upon, but the renter has not yet moved in.

These are vacant units being offered "for sale only," including units in cooperatives and condominium projects if the individual units are offered "for sale only." If units are offered either "for rent" or "for sale" they are included in the "for rent" classification.

These are vacant units sold but not yet occupied, including units that have been sold recently, but the new owner has not yet moved in.

These are vacant units used or intended for use only in certain seasons or for weekends or other occasional use throughout the year. Seasonal units include those used for summer or winter sports or recreation, such as beach cottages and hunting cabins. Seasonal units also may include quarters for such workers as herders and loggers. Interval ownership units, sometimes called shared-ownership or time-sharing condominiums, also are included here.

These include vacant units intended for occupancy by migratory workers employed in farm work during the crop season. (Work in a cannery, a freezer plant, or a food-processing plant is not farm work.)

This chapter deals with the 1-year and 3-year data products. Future versions of this document will include a discussion of the 5-year data products. The American Community Survey (ACS) data products and supporting documentation are released in several series and at several Internet locations. The primary Web site for data dissemination is the American FactFinder (AFF); supporting documentation can be found on the ACS Web site and the Census Bureau's File Transfer Protocol (FTP) site.

Since 2000, the ACS has been tabulating and publishing single year estimates for specific areas with populations of 250,000 or more. In 2005, the ACS expanded its sample size to cover all of the United States and the Commonwealth of Puerto Rico. In summer 2006, the ACS started releasing data annually for areas with populations of 65,000 or more. In 2008, the ACS is releasing 3-year period estimates for areas with a population of 20,000 or more on an annual basis. For smaller areas, it will take 5 years to accumulate a large enough sample to produce releasable estimates. Once those data are collected, the Census Bureau will release tabulations annually, based on 5-year period data for areas as small as census tracts and block groups.

Federal agencies distribute billions of dollars among states, tribal governments, and population groups, based on social and economic data. In the past, the statistics that determined services locations and program funding came in large part from the long-form sample of the decennial census. As the ACS continues to grow, its data products will provide updated versions of many of the long-form products from Census 2000. Beginning in 2010, the decennial census no longer will include a long-form sample, and ACS data products will provide high-quality, updated annual statistics for comparisons of the demographic, social, economic, and housing characteristics of areas and population groups. The ACS statistics also will show trends and relative differences between areas and population groups. These data products will continue to meet the needs of those who previously used the decennial census sample statistics, and will provide more current statistics than those available from the census long-form sample, which reflect only one point in time.

By 2010, the information on social, demographic, economic, and housing characteristics previously available only once every 10 years will be available annually through the ACS for all areas. Each year thereafter, these areas will get new estimates based on the 5-year interval ending in the latest completed sample year.

Figure 14.1 summarizes the data products release schedule. In 2006, the first set of 1-year estimates was released for specific areas with populations of 65,000 and more. These areas will continue to receive 1-year estimates annually. In 2008, data collected over a 3-year period (2005− 2007) was released for areas with at least 20,000 people. These areas will continue to receive 3-year estimates annually. In 2010, the first 5-year products will be released based on data collected in 2005−2009. These products will be produced for areas down to census tracts and block groups. Once 3- and 5-year products are produced, annual updates will follow, as indicated by Table 14.1.arThe AFF Web site contains data maps, tables, and reports from a variety of censuses and surveys.
AFF lists these data sets by program areas and survey years. AFF contains data for a wide variety
of surveys including the Decennial Census, the ACS, the Population Estimates Program, the Economic
Census, and the Annual Economic Surveys.

Data product	Population threshold	Year of data release
		2006	2007	2008	2009	2010	2011	2012	2013
1-year estimates	65,000+	2005	2006	2007	2008	2009	2010	2011	2012
3-year estimates	20,000+			2005-	2006-	2007-	2008-	2009-	2010-
				2007	2008	2009	2010	2011	2012
5-year estimates	All areas*					2005-	2006-	2007-	2008-
						2009	2010	2011	2012

* All legal, administrative, and statistical geographic areas down to the tract and block group level.

The AFF Web site contains data maps, tables, and reports from a variety of censuses and surveys. AFF lists these data sets by program areas and survey years. AFF contains data for a wide variety of surveys including the Decennial Census, the ACS, the Population Estimates Program, the Economic Census, and the Annual Economic Surveys.

The AFF is the primary Web access tool for ACS data. Data products include detailed tables, data profiles, comparison profiles (1-year data only), narrative profiles, ranking tables and charts (single year data only), geographic comparison tables, thematic maps, subject tables, selected population profiles, and downloadable public use microdata sample (PUMS) files.

The ACS Web site contains a wealth of information, documentation, and research papers about ACS. The site contains important metadata, including more than 50 population concept definitions and more than 40 housing concept definitions. The ACS Web site can be found at .

Documentation on the accuracy of the data also is included, and provides information about the sample design, confidentiality, sampling error, nonsampling error, and estimation methodology. The errata section lists updates made to the data. The geography section gives a brief explanation of the Census Bureau's geographic hierarchy, common terms, and specific geographic areas presented.

The FTP site is intended for advanced users of census and ACS data. This site provides quick access to users who need to begin their analyses immediately upon data release. The data are downloaded into Excel, PDF, or text files. Users of the FTP site can import the files into the spreadsheet/database software of their choice for data analysis and table presentation. Documentation describing the layout of the site in the README file is available in the main directory on the FTP server. The FTP site can be accessed through the ACS Web site.