<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>

     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.

 
<b>SOURCE OF DATA</b> 
 
     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).  


<b>CPS AND AHS DESIGNS</b> 
 
     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 1998 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 1995 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 59,500 housing units both occupied and vacant contained in the
AHS sample, 51,700 were interviewed and 4,200 were classified as "noninterview"
for various reasons.  In addition, 3,600 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 1995.  


<b>COMPARABILITY WITH CENSUS OF HOUSING DATA</b> 
	 
     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. 

<b>COMPARABILITY WITH EARLIER DATA</b> 
 
     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. 


<b>CAUTION IN USING VACANCY RATES FOR CHARACTERISTICS IN TABLE 2</b> 

     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 1998 and 1997 data shown on table 2,
these forecasts are based on the 1995 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.  
  

<b>CAUTION IN USING SEASONAL VACANT DATA</b> 

     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. 


<b>ACCURACY OF THE ESTIMATES</b> 
 
     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.) 


<b>NONSAMPLING VARIABILITY</b> 
 
     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.  
 

<b>SAMPLING VARIABILITY</b> 
 
     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.


<b>ILLUSTRATION OF THE USE OF TABLES OF STANDARD ERRORS</b> 
 
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 1998 is estimated to be
2.1 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 2.3; i.e., the interval 2.1 +/-
(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. 
  

<b>ILLUSTRATION OF THE COMPUTATION OF THE STANDARD ERROR OF A DIFFERENCE</b>
 
Table B-1 shows the rental vacancy rate for units in the Midwest is  7.9 percent
and 9.6 percent in the South.  Thus, the apparent difference between the two
rates is 1.7 percent.  The standard error of 7.9 percent and the standard error
of 9.6 percent are both 0.2 as shown in table B-1.  Therefore, the standard error
of the estimated difference of 1.7 is about 0.3 percent.     

                                ----------------
                                |
                         0.3 = \|(0.2)2 + (0.2)2
                                 
     Consequently, the 90 percent confidence interval for the 1.7 difference is
from 1.2 to 2.2 percent; i.e., the interval 1.7 + (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.