U.S. Department of Commerce

Housing Vacancies and Homeownership (CPS/HVS)


Annual 1996: Source and Accuracy of Estimates 


REDESIGN OF THE CURRENT POPULATION SURVEY/HOUSING
VACANCY SURVEY (CPS/HVS)

          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.


 
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 Vacancies and Homeownership (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.  Beginning in April 1984, the
current design was phased in through a series of changes that were
completed in July 1985.  Prior to the redesign, sample cases were
selected from the 1970 census frame. 

     The CPS/HVS sample is spread over 729 sample areas, which represent
1,973 geographic areas in the United States.  The
metropolitan/nonmetropolitan data shown in this report reflect 1990
census definitions.  The CPS/HVS began a major geographic redesign in
April 1994.  The survey gradually replaced sample cases selected from the
1980 census over a 15-month phase-in period with new sample cases drawn
from the 1990 census.  The sample was fully phased in by June 1995.  For
the transitional period (1995), we have converted the remaining 1980
sample cases to reflect 1990 metropolitan/nonmetropolitan definitions.  
For 1996 data and beyond, the data will reflect 1990 definitions.


     In 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.   

     Of the 60,000 housing units both occupied and vacant contained in
the CPS sample, approximately 51,000 are occupied and eligible for
interview each month; of this number, 3,200 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 51,000 occupied units, there are also
about 9,000 sample units in an average month which are visited but are
found vacant or otherwise not to be interviewed.  About half of the 9,000
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 1990 census vacant counts.  A second adjustment
inflates these results based on the CPS coverage of occupied units by
geographic areas. 
     Data shown in all tables (except table 2) on vacancy rates and
tenure of occupied units for 1996 are from the CPS and are averaged over
the 12 months of the year.  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 1993 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 56,700 housing units both occupied
and vacant contained in the AHS sample, 51,100 were interviewed and 2,300
were classified as "noninterview" for various reasons.  In addition,
3,300 units were visited but were not eligible to be interviewed for the
purposes of AHS.    The AHS estimation procedure for occupied units is a
three-stage ratio estimation process.  The first-stage adjustment is made
by tenure, residence, and the census region using data from the 1980
census.  The second stage makes adjustments to independent estimates of
new construction obtained from the Survey of Construction which includes
a survey of housing starts and completions conducted monthly by the
Bureau of the Census.  The third stage adjusts to independent estimates
of the total housing inventory based on data obtained from the CPS.  A
more detailed description of the AHS sample design and estimation
procedure can be found in the AHS series H-150 report for 1993. 
 

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 Vacancies and Homeownership. 
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. 

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 Vacancies and Homeownership (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.  For 1996 and 1995
data shown on table 2, these forecasts are based on the 1993 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 
 
Standard errors are used to: 1) measure the accuracy of the survey
estimates, and 2) draw inferences from the survey data.  For example,
Table B-1 of this report shows that the percent of for-rent units outside
MAs for 1996 is estimated to be 2.2 percent.  Table B-1 also shows the
standard error of this estimate to be approximately 0.1 percentage
points. Consequently, the 90-percent confidence interval as shown by
these data is from 1.9 to 2.5; i.e., the interval 2.2 + (1.6 x 0.1)
percentage points.  Thus, one can say with about 90-percent confidence
that the average percent of for-rent units derived from all possible
samples is included in this confidence interval.  Statements about
differences are made only when the 90-percent confidence interval on the
estimated difference does not include zero.  The 90-percent confidence
intervals are shown in the text 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. 
  

ILLUSTRATION OF THE COMPUTATION OF THE STANDARD ERROR OF A
DIFFERENCE
 
     Table B-1 shows the rental vacancy rate for units in the Midwest is
7.9 percent and 8.6 percent in the South.  Thus, the apparent difference
between the two rates is 0.7 percent.  The standard error of 7.9 percent
and the standard error of 8.6 percent are both 0.2 as shown in table B-1. 
Therefore, the standard error of the estimated difference of 0.7 is about
0.3 percent.     

                         0.3 = sqrt ((0.2)2 + (0.2)2)
                                 
      Consequently, the 90 percent confidence interval for the 0.7
difference is from 0.2 to 1.2 percent; i.e., the interval 0.7 + (1.6 x 0.3)
percentage points.  Thus, one can say with about 90 percent confidence that
this interval includes the actual value that would have been obtained by
averaging the results from all possible samples of this type.  Thus, we can
conclude with 90 percent confidence that the rental vacancy rate in the
South is higher than the rate in the Midwest.

Go to Housing Vacancies and Homeownership Annual Statistics: 1996
Contact Bob Callis or Melissa Kresin at (301)763-3199 or visit ask.census.gov for further information on the Housing Vacancy Survey.

Source: U.S. Census Bureau, Housing and Household Economic Statistics Division
Last Revised: October 31, 2011