SOURCE AND ACCURACY OF ESTIMATES

REDESIGN OF THE CURRENT POPULATION SURVEY/Housing Vacancies and Homeownership
(CPS/HVS)

     Major changes related to the Current Population Survey/Housing
Vacancy Survey (CPS/HVS) are 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.  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 data for 1994 and later 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 (CPS).  Characteristics of
occupied housing units in table 3 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 1994, the
current design was phased in through a series of changes that were
completed in July 1995.  

     The CPS/HVS sample is selected from a frame based on the 1990 census
and is spread over 754 sample areas, which represent 2,007 geographic
areas in the United States.  Due to the new sample drawn from the 1990
census, metropolitan/nonmetropolitan data published in 1995 and later are
not directly comparable to data for 1994 and earlier.

     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.  
     Of the 60,000 housing units both occupied and vacant contained in
the CPS sample, approximately 51,000 are 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, there are also about 9,000 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 9,000 are vacant and
interviewed for 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 3) on vacancy rates and
tenure of occupied units for the fourth quarter 1996 are from the CPS/HVS
and are averaged for the 3 months, October, November, and December 1996. 
The data concerning the distribution of characteristics for occupied
housing units, shown in table 3, 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 region,
tenure, metropolitan area status, and urban or rural status 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 1990 census
data.  Then, the occupied portion and the vacant portion of the
independent control are allocated based on data from the CPS/HVS. 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 prior to 1983, 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 HVS appear in the
1985 and earlier reports of this series. 
     Prior to the first quarter 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 the first
quarter 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 the first quarter 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
non-sampling 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 with the first quarter
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 the fourth quarter 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 the first quarter 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 vacant mobile homes increased the vacancy
rate significantly in some cases.  We have revised 1989 data in this
report to reflect all changes.  Caution should be used when comparing
data from 1990 or later with unrevised data prior to 1990.
     In addition to the design and estimation changes mentioned in the
previous section, caution should be used in comparing data for 1980 and
beyond in this report with data from 1979 and earlier years. Starting
with the first quarter 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 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 the fourth quarter 1979 and previous quarters, as
published in Housing Vacancies reports, series H-111, Nos. 1 to 79-Q4. 
Furthermore, unrevised data prior to 1990 is not completely comparable to
1990 data and beyond, due to the inclusion of year-round vacant mobile
homes, beginning in the first quarter 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.  


VACANCY RATES FOR CHARACTERISTICS IN TABLE 3 
 
     Vacancy rates in table 3 are based in part on forecasts of occupied
housing units.  These forecasts are periodically revised to incorporate
more recent data and improved forecasting procedures.  Beginning in the
first quarter 1995, table 3 is based on data from 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 first quarter 1987 has
shown that estimates for these characteristics have been underestimated
by approximately 28 percent.  The estimates beginning with the first
quarter 1987 are adjusted to reflect this.  


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 non-sampling.  The accuracy of a survey
result depends on both types of errors, but the full extent of the non-
sampling 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 non-sampling 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.) 




NON-SAMPLING 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 shown in the 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 MAs versus the vacancy rate outside MAs. 
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 the vacancies for the first
quarter 1990, (which include year-round vacant mobile homes as part of
the year-round vacant housing inventory for the first time) with previous
unrevised quarters reveal significant differences in some cases.  Thus
caution should be used when comparing current data with 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 10 of this report shows that 2.6 percent of all housing units are
vacant and available for rent.  Table A-1 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 2.4 to
2.8; i.e., the interval 2.6 +/- (1.6 x 0.1) percentage points.  Thus, one
can say with about 90-percent confidence that the average rental vacancy
rate 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. 

Table A-1.  Standard Errors of Percentages in Tables 10 and 11:
            Fourth Quarter 1996

                                  Vacant for-rent units as a 
                                  percent of all housing units

                                                          Standard
                                    Standard    Differ-   error on
                             Rate      error     ence\1 difference
Area

United States..........       2.6        0.1         -         0.1
  Inside MAs...........       2.7        0.1       -0.1        0.1
  Outside MAs..........       1.9        0.1         -         0.2

 Northeast.............       2.6        0.1         -         0.2
 Midwest...............       2.4        0.1        0.1        0.2
 South.................       2.7        0.1         -         0.1
 West..................       2.7        0.1       -0.3        0.2


                                  Vacant for-sale-only units as a 
                                  percent of all housing units

                                                          Standard
                                    Standard    Differ-   error on
                             Rate      error     ence\1 difference
Area

United States..........       1.0        (z)        0.1        (z)
  Inside MAs...........       1.0        (z)        0.1        0.1
  Outside MAs..........       1.1        0.1         -         0.1

 Northeast.............       1.0        0.1        0.2        0.1
 Midwest...............       0.9        0.1         -         0.1
 South.................       1.1        0.1        0.1        0.1
 West..................       1.0        0.1        0.1        0.1

Z Less than 0.05.  - Represents zero.
\1 Represents difference in fourth quarter 1996 and fourth quarter 1995 rate