<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 Vacancies and Homeownership (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 Vacancy
Survey (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 1997 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
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. 


<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 1997 and 1996 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 1997 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 8.0 percent and 9.1
percent in the South.  Thus, the apparent difference between the two rates is 1.1 percent.
The standard error of 8.0 percent and the standard error of 9.1 percent are both 0.2 as shown
in table B-1.  Therefore, the standard error of the estimated difference of 1.1 is about 0.3
percent.


                                 ----------------
                                |
                         0.3 = \|(0.2)2 + (0.2)2
                                 

      Consequently, the 90 percent confidence interval for the 1.1 difference is from 0.6 to
1.6 percent; i.e., the interval 1.1 + (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.