The data on value (also referred to as "price asked" for vacant units) were obtained from Housing Question 19 in the 2006 American Community Survey. The question was asked at housing units that were owned, being bought, vacant for sale, or sold not occupied at the time of the survey. Value is the respondent's estimate of how much the property (house and lot, mobile home and lot, or condominium unit) would sell for if it were for sale. If the house or mobile home was owned or being bought, but the land on which it sits was not, the respondent was asked to estimate the combined value of the house or mobile home and the land. For vacant units, value was the price asked for the property. Value was tabulated separately for all owner-occupied and vacant-for-sale housing units, as well as owner-occupied and vacant-for-sale mobile homes.

Since value collected before 2006 is the only dollar amount captured on the questionnaire in specified intervals, the category boundaries for previous years are not adjusted for inflation. In the comparison profiles, however, the median value is adjusted for inflation by multiplying a factor equal to the average annual CPI-U-RS factor for the current year, divided by the average annual CPI-U-RS factor for the earlier/earliest year.

The median divides the value distribution into two equal parts: one-half of the cases falling below the median value of the property (house and lot, mobile home and lot, or condominium unit) and one-half above the median. Quartiles divide the value distribution into four equal parts. Median and quartile value are computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Derived Measures.") Median and quartile value calculations are rounded to the nearest hundred dollars. Upper and lower quartiles can be used to note large value differences among various geographic areas. (For more information on medians and quartiles, see "Derived Measures.")

To calculate aggregate value, the amount assigned for the category "Less than $10,000" is $9,000. The amount assigned to the category $1,000,000 or more" is $1,250,000. Aggregate value is rounded to the nearest hundred dollars. (For more information on aggregates, see " Derived Measures .")

The Census Bureau presents survey response and nonresponse rates as part of the ACS Quality Measures. The survey response rate is the ratio of the units interviewed after data collection to the estimate of all units that were eligible to be interviewed. Data users can find survey response and nonresponse rates on the AFF for ACS data for 2007 and beyond (including multiyear estimates). The same rates for data years 2000 to 2006 are available on the ACS Quality Measures Web site. The ACS Quality Measures provide separate rates for HUs and GQ persons. For the HU response rate, the numerator includes all cases that were interviewed after mail, telephone, and personal visit follow-up. For the GQ person response rate, the numerator includes all interviewed persons after the personal visit. For both rates, the numerator includes completed interviews as well as partial interviews with adequate information for processing.

To accurately measure unit response, the ACS estimates the universe of cases eligible to be interviewed and the survey noninterviews. The estimate of the total number of eligible units becomes the denominator of the unit response rate.

The ACS Quality Measures also include the percentage of cases that did not respond to the survey by the reason for nonresponse. These reasons include refusal, unable to locate the sample unit, no one home during the data collection period, temporarily absent during the interview period, language problem, insufficient data (not enough data collected to consider it a response), and other (such as "sample address not accessible;" "death in the family;" or cases not followed up due to budget constraints, which last occurred in the winter of 2004). For the GQ rates, there are two additional reasons for noninterview: whole GQ refusal, and whole GQ other (such as unable to locate the GQ).

The ACS Quality Measures provide information about item nonresponse. When respondents do not report individual data items, or provide data considered invalid or inconsistent with other answers, the Census Bureau imputes the necessary data. The imputation methods use either rules to determine acceptable answers (referred to as "assignment") or answers from similar people or HUs ("allocation"). Assignment involves logical imputation, in which a response to one question implies the value for a missing response to another question. For example, first name often can be used to assign a value to sex. Allocation involves using statistical procedures to impute for missing values. The ACS Quality Measures include summary allocation rates as a measure of the extent to which item nonresponse required imputation. Starting with the 2007 ACS data (including ACS multiyear data), the Quality Measures include only two item allocation rates: overall HU characteristic imputation rate and overall person characteristic imputation rate. These rates are available on the AFF at the national and state level. However, the ACS releases imputation tables on AFF that allow users to compute allocation rates for all published variables and all published geographies. Allocation rates for all published variables from 2000 to 2006 are available on the ACS Quality Measures Web site at the national and state level.

The 1996-1998 American Community Survey question provided a space for the respondent to enter the number of vehicles. Since 1999, the American Community Survey question provided pre-coded response categories.

The final component of nonsampling error is processing error-error introduced in the postdata collection process of turning the responses into published data. For example, a processing error may occur in keying the data from the mail questionnaires. The miscoding of write-in responses, either clerically or by automated methods, is another example. The degree to which imputed data differ from the truth also reflects processing error-specifically imputation error. A number of practices are in place to control processing error (more details are discussed in Chapters 7 and 10). For example:

• Data capture of mail questionnaires includes a quality control procedure designed to ensure the accuracy of the final keyed data.
• Clerical coding includes a quality control procedure involving double-coding of a sample of the cases and adjudication by a third keyer.
• By design, automated coding systems rely on manual coding by clerical staff to address the most difficult or complicated responses.
• Procedures for selecting one interview or return from multiple returns for an address rely on a review of the quality of data derived from each response and the selection of the return with the most complete data.
• After completion of all three phases of data collection (mail, CATI, and CAPI), questionnaires with insufficient data do not continue in the survey processing, but instead receive a noninterview code and are accounted for in the weighting process.
• Edit and imputation rules reflect the combined efforts and knowledge of subject matter experts, as well as experts in processing, and include evaluation and subsequent improvements as the survey continues to progress.
• Subject matter and survey experts complete an extensive review of the data and tables, comparing results with previous years data and other data sources.

Median year householder moved into unit divides the distribution into two equal parts: one-half of the cases falling below the median year householder moved into unit and one-half above the median. Median year householder moved into unit is computed on the basis of a standard distribution. (See the "Standard Distributions" section under "Derived Measures.") Median year householder moved into unit is rounded to the nearest calendar year. (For more information on medians, see "Derived Measures.")

Since 1996, the question provided two write-in spaces for the respondent to enter month and year the householder (person 1) moved into the house, apartment, or mobile home.