<b><font size=+1>APPENDIX B.  SOURCE AND ACCURACY OF ESTIMATES

REDESIGN OF THE CURRENT POPULATION SURVEY/Housing Vacancies and Homeownership

(CPS/HVS)</font></b>



Methodology Changes in 2003



     Population controls that reflect the results of the 2000 decennial census are used in the

CPS/HVS estimation process for the first time in the first quarter 2003.  This change has a slight

effect on vacancy and homeownership rates, as described below.  As a final additional step in 

our estimation process, the estimates are now controlled to independent housing counts used for

the first time in order to produce a more accurate estimate of housing units.  This should make

the CPS/HVS estimates of housing units more consistent with other Census Bureau 

housing surveys.  The new housing controls will affect the count of all housing units in the sense

that both occupied and vacant units will be ratio estimated to the new control total.  Vacancy

rates and homeownership rates are not be affected by this change.  



The CPS/HVS began computing first-stage factors (used for weighting purposes) based on

year-round and seasonal counts of housing units from the 2000 decennial census, beginning in

the first quarter 2003.  From 1980 to 2002, the CPS/HVS first-stage factors were based on

year-round estimates only.   We believe that this improves our counts of year-round and seasonal

units.  The shift from 1990-based to 2000-based population controls (including the weighting

revision) had a very slight effect on vacancy rates and homeownership rates.  Research has

shown that the new 2000-based controls dropped the rental vacancy rate in the first quarter 2002

from 9.14 percent to 9.08 percent---a difference of less than 1/10 of one percent.  The

homeowner vacancy rate was revised from 1.67 percent to 1.65 percent, while the

homeownership rate was revised from 67.82 percent to 67.81 percent. The questions on race on

the CPS are modified beginning in the first quarter 2003 to comply with the revised standards for

federal statistical agencies.  Respondents may now select more than one race.  The

Hispanic/Nonhispanic origin question continues to be asked separately.









Introduction of Computed-Assisted Survey Collection (CASIC) and 1990

census-based weighting (from 1995 to 2002)



     Major changes related to the Current Population Survey/Housing Vacancy Survey

(CPS/HVS) were effective beginning with the first quarter 1994 data.  First, a new weighting

procedure was implemented based on the 1990 decennial census.  The 1990-based weighting

produces, on average, estimates of the total housing inventory that are about 0.1 percent lower

than the 1980-based weighting.  Revised data are provided in the historical tables for 1993 to

show the effect of this change.  Generally, the vacancy rates are only minimally affected, while

the homeownership rate is about one-half of a percentage point lower with the new weighting

procedures.         



     A second change is that the CPS/HVS has become a totally computerized survey with the

implementation of the Computer Assisted Survey Information Collection (CASIC).  The CASIC

tools consist of state-of-the-art computer-assisted modules for data collection and processing. 

Although the concepts, definitions, and questionnaire items remain the same, the shift to CASIC

may affect vacancy rates and homeownership rates.  We are unable to determine the quantitative

effects of the use of CASIC on the vacancy and homeownership rates.  Data users should use

caution when comparing 1994 and later data with earlier data.  Beginning the second quarter of

1999, a change was made in the way data for housing units in structure are collected. In the past,

there was one category to show 1-unit in structure. Now that has been separated into two

categories: 1-unit attached and 1-unit detached.



 

Source of Data

 

     The estimates presented in this report are based on data obtained from two surveys

conducted by the Bureau of the Census. Data concerning vacancy rates and tenure of occupied

housing units are from the monthly sample of the Current Population Survey/Housing Vacancy

Survey (CPS/HVS).  Characteristics of occupied housing units are from the American Housing

Survey (AHS).  





CPS and AHS Designs

 

Since the inception of the CPS in 1940, the sample has been redesigned several times to upgrade

the quality and reliability of the data and to meet changing data needs. From July 1995 to March

2004, the CPS/HVS sample is selected from a frame based on the 1990 decennial census and is

spread over 754 sample areas, which represent 2,007 geographic areas in the United States.

Beginning in April 2004, the new sample drawn from the 2000 decennial census is phased in

over a 15-month period. From April 2004 to June 2005, the sample consisted of sample units

drawn from both the 1990 and 2000 decennial censuses.   The metropolitan/nonmetropolitan data

shown for 2005 report reflect 2000 census definitions, while data shown for 1995 to 2004 reflect

1990 metropolitan/nonmetropolitan definintions.

Beginning in the first quarter 1986, vacant seasonal mobile homes were included in the count of

vacant seasonal units. This change resulted in a 12 percent increase in the number of vacant

seasonal housing units. 

Beginning in 2002, the size of the CPS/HVS sample increased to approximately 72,000 housing

units. This expansion was one of the Census Bureau's plans to meet the requirements of the State

Children's Health Insurance Program (SCHIP) legislation. Of the 72,000 housing units contained

in the CPS/HVS sample, approximately 61,200 are eligible for interview each month; of this

number, 3,900 occupied units, on the average, are visited but interviews are not obtained because

occupants are not found at home after repeated calls or are unavailable for some other reason. In

addition to the 61,200, there are also about 10,800 sample units in an average month which are

visited but are found to be vacant or otherwise not to be interviewed. About half of the 10,800

are vacant and interviewed for the HVS. The CPS estimation procedure for occupied units

involves the inflation of the weighted sample results to independent estimates of the total civilian

noninstitutional population of the United States by age, race, sex, and Hispanic/non-Hispanic

categories. These independent estimates are based on statistics from the decennial censuses of

population; statistics on births, deaths, immigration, and emigration; and statistics on the strength

of the Armed Forces. The HVS estimation procedure for vacant units is similar to that used for

occupied units. Weighted sample results are adjusted at the state level using 2000 census vacant

counts. A second adjustment inflates these results based on the CPS coverage of occupied units

by geographic areas. As a final step for both the CPS and HVS, all housing unit counts are

adjusted to reflect independent housing control totals. This change is effective, beginning 2003.

 Data shown in all tables (except table 2) on vacancy rates and tenure of occupied units are based

on a 12-month average for 2005. The data concerning the distribution of characteristics for

occupied housing units, shown in table 2, are obtained primarily from the AHS national sample.

Distributions of characteristics of occupied housing units from the AHS estimates are applied to

CPS current housing inventory independent estimates to obtain the characteristics of occupied

housing units used in this report. The Survey of Construction (SOC) and the Consumer Price

Index also are used to improve estimates of the rent distribution. The 2003 AHS sample is spread

over 394 sample areas comprising 878 counties and independent cities with coverage in each of

the 50 States and the District of Columbia. Of the 55,600 housing units both occupied and vacant

contained in the AHS sample, 48,300 were interviewed and 5,600 were classified as "Type A

noninterviews" for various reasons. 2,200 units were visited but were not eligible to be

interviewed for the purposes of AHS. A detailed description of the AHS sample design and

estimation procedure can be found in the H-150 report for 2003. 



Comparability with Census of Housing Data 

      

     Most of the concepts and definitions are the same for items that appear in both the 1980

and 1990 censuses and the Housing Vacancy Survey.  However, there is one minor difference in

the housing unit definition between the CPS/HVS and the 1980 and 1990 decennial censuses. 

The difference is that, in the CPS/HVS, living arrangements containing five or more persons, not

related to the person in charge, were classified as group quarters; for the 1980 and 1990 census,

the requirement was raised to nine or more persons not related to the person in charge.  There

were some differences in what has been counted as housing units between the earlier censuses

and the CPS/HVS.  Descriptions of the differences between earlier censuses and the CPS/HVS

appear in the 1985 and earlier reports of this series. 



     Prior to 1990, there were significant differences between the CPS/HVS and the decennial

censuses.  The 1980 and 1990 decennial censuses included vacant mobile homes as housing

units, whereas prior to 1986 the CPS/HVS did not.  However, beginning in 1986, vacant seasonal

mobile homes were counted as housing units in the CPS/HVS.  In addition, year-round vacant

mobile homes were counted as housing units, beginning in 1990 in the CPS/HVS.  Another

difference in the housing unit definition between the CPS/HVS (prior to 1986) and the 1980 and

1990 censuses was that the CPS/HVS required units to be separate living quarters and have direct

access or have complete kitchen facilities.  For the 1980 and 1990 decennial censuses, the

complete kitchen facilities alternative was dropped with direct access required of all units. 

However, beginning in 1990, the CPS/HVS requirement for complete kitchen facilities was

dropped with direct access required of all units.  Thus, the earlier definitional differences were

eliminated.  



     In addition, there are differences between the methodologies used to collect data for the

CPS/HVS and the censuses.  These differences include interviewing procedures, staff experience

and training; differences in processing procedures and sample designs; the sampling variability

associated with the CPS/HVS and the sample data from the census; and the nonsampling errors

associated with the CPS/HVS and census data. 



     Research has shown that the CPS/HVS and the 1990 census produced significant

differences for vacancy characteristics.  The rental vacancy rate from the April 1990 census was

8.5 percent, whereas, the CPS/HVS reported the rental vacancy rate of 7.2 percent for the first

half of 1990.  The April 1990 census had a homeowner vacancy rate of 2.1 percent, while the

CPS/HVS had a vacancy rate of approximately 1.7 percent for the first half of 1990.  For

occupied housing, the April 1990 census produced a homeownership rate of 64.2 percent, while

for the first half of 1990 the CPS/HVS produced a rate of 63.9 percent.    These differences

illustrate that, for these characteristics as well as others, caution should be used when making

comparisons between the 1990 census and the CPS/HVS.

     

     Further research has shown that the CPS/HVS and the 2000 decennial census produced

significant differences for vacancy characteristics.  The rental vacancy rate from the April 2000

census was 6.8 percent, whereas the CPS/HVS reported the rental vacancy rate of 7.9 percent for

the first half of 2000.  The April 2000 census has a homeowner vacancy rate of 1.7 percent for

the first half of 2000.  For occupied housing, the April 2000 census produced a homeownership

rate of 66.2 percent, while for the first half of 2000, the CPS/HVS produced a rate of 67.2

percent.  These differences illustrate that, for these characteristics as well as others, caution

should be used when making comparisons between the 2000 census and the CPS/HVS.





Comparability with Earlier Data

 

     As stated earlier in this report, beginning in 1994 new weighting procedures based on the

1990 decennial census were implemented.  In addidition, the survey data collection procedures

became totally computerized.  Caution should be used when comparing current data with

unrevised data prior to 1994.



     In 1989, new edit procedures were implemented in the Current Population

Survey/Housing Vacancy Survey (CPS/HVS).  These new procedures were used to allocate cases

that would have been classified as "not reported" under previous procedures.  



     In 1990, year-round vacant mobile homes were included for the first time as part of the

year-round vacant count of housing units.  This change was made to make the composition of the

housing unit inventory for the CPS/HVS similar to the decennial census and other surveys, which

count all mobile homes as housing units when occupied or vacant (available for occupancy on

the site).  Research has shown that the inclusion of year-round vacant mobile homes increases the

vacancy rate significantly in some cases.  All of the 1989 data in this report have been updated to

include year-round vacant mobile homes.  Caution should be used when comparing unrevised

vacancy data prior to 1990 to data for later years.



     In addition to the above mentioned design and estimation changes, caution should be used

in comparing data for 1980 and beyond in this report with data from 1979 and earlier years.

Starting in 1980, several changes were implemented in the survey to improve the reliability of the

data presented.  These included adding a supplemental sample, refining the estimation

procedures, and changing the source of occupied characteristics from the Quarterly Housing

Survey (QHS) to the AHS. 



     Although the above mentioned changes have resulted in more reliable estimates, data for

1980 and later in this report are not completely comparable to data for 1979 and previous years, as

published in Housing Vacancies reports, series H-111.  Furthermore, unrevised data prior to 1990

are not completely comparable to 1990 data and beyond, due to the inclusion of year-round vacant

mobile homes, beginning in 1990.  Thus, particular caution should be observed in drawing

conclusions about trends that extend from before 1980 to 1980 and beyond, and also trends from

before 1990 to 1990 and later.  For comparative purposes, 1979 data in this report have been

revised to incorporate all changes made in 1980, and 1989 data have been revised to incorporate

all changes made in 1990. Unrevised 1989 and 1979  data are provided to show the magnitude of

the various changes.  



     The revised 1979 vacancy estimates are higher than the original 1979 estimates.  The

increase in vacancy rates was not the result of locating additional vacant units, but reflects the

increase in sample size and refinements in the estimation procedure.  It is safe to assume that prior

to the implementation of these new procedures (1955 through 1978) HVS produced

underestimates of vacant units.  Earlier reports in this series give more complete descriptions of

the original CPS sample, the QHS sample, and estimation procedures. 





Caution in Using Vacancy Rates for Characteristics in Table 2



     Vacancy rates in table 2 are based in part on forecasts of occupied housing units.  These

forecasts are periodically revised to incorporate more recent data and improved forecasting

procedures.  Data shown for 2005 and 2004, shown on table 2 are based on the 2003 AHS.

     For the occupied unit forecasts for the monthly rent categories, we update the AHS data

quarterly to reflect the rise in the cost of renting through the use of the residential rent index, and

the latest available asking rent data for newly constructed rental units.  

Caution in Using Seasonal Vacant Data 



     Analysis of seasonal vacant data prior to the 1987 has shown that estimates for these

characteristics have been underestimated by approximately 28 percent.  The estimates beginning

in 1987 are adjusted to reflect this.  This revision has an effect on other categories (especially the

percentage occupied) in addition to seasonal vacant units in the distributions shown in tables 7. 





Accuracy of the Estimates 



     Since the CPS/HVS estimates are based on a sample, they may differ somewhat from the

figures that would have been obtained if a complete census had been taken using the same

questionnaires, instructions, and enumerators.  There are two types of errors possible in an

estimate based on a sample survey:  sampling and nonsampling.  The accuracy of a survey result

depends on both types of errors, but the full extent of the nonsampling error is unknown. 

Consequently, particular care should be exercised in the interpretation of figures based on a

relatively small number of cases or on small differences between estimates.  The standard errors

provided for the CPS/HVS estimates primarily indicate the magnitude of the sampling error. 

They also partially measure the effect of some nonsampling errors in responses and enumeration;

but do not measure any systematic biases in the data.  (Bias is the difference averaged over all

possible samples, between the estimate and the desired value.) 





Nonsampling Variability



     Nonsampling errors can be attributed to many sources, e.g., inability to obtain information

about all cases in the sample, definitional difficulties, differences in the interpretation of

questions, inability or unwillingness on the part of respondents to provide correct information,

inability to recall information, errors made in collection such as recording or coding the data,

errors made in processing the data, errors made in estimating values for missing data, and failure

to represent all units with the sample (undercoverage).  Undercoverage in the CPS/HVS results

from missed housing units and misclassifying housing units. Ratio estimation to independent

controls, as described previously, partially corrects for the bias due to survey undercoverage. 

However, biases exist in the estimates to the extent that missed households have different

characteristics than interviewed households.  

 

Sampling Variability



 

     The standard errors given in the following tables are primarily measures of sampling

variability, that is, of the variations that occurred by chance because a sample rather than the

entire population was surveyed.  The sample estimate and its standard error enable one to

construct confidence intervals; ranges that would include the average results of all possible

samples with a known probability.  For example, if all possible samples were selected, each of

these being surveyed under essentially the same general conditions and using the same sample

design, and if an estimate and its standard error were calculated from each sample, then

approximately 90 percent of the intervals from 1.6 standard errors below the estimate to 1.6

standard errors above the estimate would include the average result of all possible samples. 



     The average estimate derived from all possible samples is or is not contained in any

particular computed interval.  However, for a particular sample, one can say with specified

confidence that the average estimate derived from all possible samples is included in the

confidence interval. 



     Standard errors may also be used to perform hypothesis testing, a procedure for

distinguishing between population parameters using sample estimates.  The most common types

of hypotheses appearing in this report are:  (1) the population parameters are identical, and, (2) the

population parameters are different.  An example of this would be comparing the vacancy rate in

MA's versus the vacancy rate outside MA's.  Tests may be performed at various levels of

significance, where a level of significance is the probability of concluding that the characteristics

are different when, in fact, they are identical. 



     To perform the most common test, let x and y be sample estimates for two characteristics

of interest.  Let the standard error on the difference x-y be SEDIFF.  If the ratio R = (x-y)/SEDIFF

is between -1.6 and +1.6, no conclusion about the difference between the characteristics is

justified at the 0.10 level of significance.  If, on the other hand, this ratio is smaller than -1.6 or

larger than +1.6, the observed difference is significant at the 0.10 level.  In this event, it is a

commonly accepted practice to say that the characteristics are different. Of course, sometimes this

conclusion will be wrong.  When the characteristics are, in fact, the same, there is a 10 percent

chance of concluding that they are different.  All statements of comparison in the text have passed

a hypothesis test at the 0.10 level of significance or better.  This means that, for most differences

cited in the text, the estimated difference between characteristics is greater than 1.6 times the

standard error of the difference. 



     Comparisons of characteristics of vacancies for 1990 (which include year-round vacant

mobile homes as part of the year-round vacant inventory for the first time) with previous

unrevised years reveal significant differences in some cases.  Thus caution should be used when

comparing current data with previous unrevised data prior to 1990.





Illustration of the Use of Tables of Standard Errors



The sample estimate and its standard error enable one to construct a confidence interval. A

confidence interval is a measure of an estimate's variability. The larger a confidence interval is in

relation to the size of the estimate, the less reliable the estimate. For example, the estimated

percent of all housing units vacant and available for rent in the Midwest is 3.5 percent (Table 7)

and the standard error on the estimate is 0.1 percentage points. Then the 90-percent confidence

interval (Table B-1) is calculated as 3.5 percent +/- (1.645 x 0.1), or 3.5 percent +/- 0.2, or from

3.3 percent to 3.7 percent. If all possible samples were surveyed under essentially the same

general conditions and the same sample design, and if an estimate and its standard error were

calculated from each sample, then approximately 90 percent of the intervals from 1.645 standard

errors below the estimate to1.645 standard errors above the estimate would include the average

result of all possible samples.



Standard errors are also used to perform hypothesis testing--a procedure for distinguishing

between population parameters (whose values are not known) using sample estimates. The most

common type of hypothesis is that two population parameters are different. These tests may be

performed at various significance levels. The significance level is the probability of concluding

that the parameters are different when, in fact, they are the same. For example, to conclude that

two parameters are different at the 0.10 level of significance, the absolute value of the difference

between their corresponding sample estimates must be greater than or equal to1.645 times the

estimated standard error of the difference.



The Census Bureau uses 90-percent confidence intervals and 0.10 levels of significance to

determine statistical validity. The 90-percent confidence intervals are shown in the text or in the

tables for selected items. The standard errors for other figures in this report are given in the tables.

In addition to sampling error, the figures in this report, both the estimates and their standard

errors, are also subject to rounding error.