AHAR_APR_Supporting_Statement-Final Dec 2012(B)

Part B: Statistical Methods

No statistical methods are used to complete the Annual Performance Report. The discussion below refers to statistical methods for the Annual Homeless Assessment Report.

B1 Potential Respondent Universe for the Annual Homeless Assessment Report

Per the HMIS Data Standards, program providers in each CoC collect data on program participants at participant entry and exit and, for some data elements, at least once annually during program enrollment, if the period between program entry and exit exceeds one year.

Data are typically collected manually and then entered into the local HMIS by staff of the recipient organization. However, recipients may also collect and enter data into HMIS simultaneously or “in real time.” To complete a local Annual Homeless Assessment Report, recipients must aggregate data contained in HMIS into AHAR response tables associated with each question. In many instances this is accomplished automatically through a canned report in HMIS.

HUD expects and encourages all CoCs (approximately 448) to submit local AHAR reports. The burden estimates in section A.12 are based on this assumption. However, when HUD initiated this effort in 2002, some CoCs did not have a functional HMIS and many others were collecting information from only a portion of homeless assistance providers in their community. As a result, HUD developed a representative national sample of 102 communities to target for data collection until all CoCs could participate in the AHAR. Participation has steadily grown beyond the original sample. For the 2008 report, 222 CoCs contributed their data.

B2 Statistical Methods and Data Collection Procedures

B.2.1 Sampling Plan

This section describes the procedures for selecting a nationally representative sample of 102 jurisdictions for the AHAR.¹

CDBG Jurisdictions Are the Primary Sampling Units

The AHAR uses the geographic areas defined for the allocation of CDBG funds as the primary sampling unit. The four types of CDBG jurisdictions are:

• Principal cities²

• Cities with 50,000 or more persons (that are not principal cities)

• Urban counties

• Rural areas or non-entitlement jurisdictions

CDBG jurisdictions constitute the basic building blocks of CoCs. In some cases, the CDBG jurisdiction and the CoC represent the same geographic area (e.g., principal cities are often a single CoC), but, in other situations, the CDBG jurisdiction is a geographic subunit of the CoC (e.g., a small city with 50,000 or more persons may be a subunit of a countywide CoC). The selection of 102 CDBG jurisdictions ensures the inclusion of a wide range of sites in the AHAR as well as the reasonably precise measurement of the characteristics of homeless persons and their patterns of service use.

HUD provided a sampling frame for the selection of CDBG jurisdictions. The sampling frame is a list of all 3,142 CDBG jurisdictions within the 430 CoCs in the 50 states as of 2002.³ The next section describes the decision to stratify the sites based on geographic type, along with the procedures for selecting certainty and non-certainty sites.

Stratifying the Sample by Type of Geographic Area

A CDBG jurisdiction may be a large principal city of a metropolitan area, a smaller city with a population of 50,000 or more, one or more suburban or urban fringe counties, or a rural area. As such, the number of homeless persons in each jurisdiction varies considerably.

Using the relative size of the homeless population in each CDBG jurisdiction to select a sample may increase the precision of the estimates for any particular sample size. However, with the number of homeless persons in each CDBG jurisdiction unknown, the study team assumed that the total population in each CDBG jurisdiction provided a measure of relative size of the homeless population for purposes of sample selection. The study team premised the assumption on the likelihood that the number of homeless persons is correlated with the total population in the area served by the CDBG jurisdiction. The team further refined the assumption by dividing the sample into strata based on the expected rate of homelessness.⁴

Earlier research on homelessness indicates that the rate of homelessness varies by type of geographic area. For example, Burt (2001) found that 71 percent of the homeless persons using homeless-related services are located in principal cities but that only 30 percent of the total U.S. population lives in principal cities.⁵ By contrast, rural areas account for 9 percent of the homeless population, but 20 percent of the overall population. Further, suburban/urban fringe areas represent 21 percent of homeless persons, but 50 percent of the overall population. These findings suggest that, before using the total population as a proxy for the relative size of the homeless population, the CDBG jurisdictions should be stratified by type of geographic area to account for the fact that the ratio of homeless persons to the population varies across geographic areas. Hence, the study team divided the CDBG jurisdictions into four groups based on their classification for the allocation of CDBG funds: principal cities, other cities larger than 50,000, urban counties, and rural areas (i.e., counties that are part of non-entitlement areas). Such stratification increases the precision of estimates.

Very Large CDBG Jurisdictions Selected with Certainty

Given that the size of the population across CDBG jurisdictions is skewed by a few very large jurisdictions covering areas with several million residents, a useful strategy for reducing sampling variability in the estimated number and characteristics of homeless persons is to select very large jurisdictions in the sample with certainty. Selecting a CDBG jurisdiction with certainty means that the CDBG jurisdiction represents only itself in the sample estimates but ensures that the sample does not exclude the largest jurisdictions, whose number and characteristics of the homeless population could substantially affect national estimates. Exhibit B-1 lists the 18 CDBG jurisdictions selected with certainty.

For selecting the certainty sites, the study team divided the CDBG jurisdictions into the four geographic-type strata. Assuming the rate of homelessness was the same in each area within each stratum, the study team calculated the standard deviation (square root of the variance) of the number of homeless persons for the entire stratum. The team then recalculated the standard deviation by excluding the largest site (as if that site were taken with certainty) to obtain a relative estimate of the reduction in the variance of the estimates that would occur if that site were selected with certainty. In the event of substantial reduction in the variance due to the selection of the certainty unit, the overall variance of the sample estimates will be smaller as the variance contribution to the estimate from the certainty sites is zero. The process of selecting the next-largest site as a certainty site continued until the reduction of the variance or standard deviation was small or marginal. The process resulted in the identification of 11 certainty sites consisting of eight principal cities, one other city larger than 50,000, and two urban counties (but no non-entitlement areas).

Based on earlier research findings showing that homeless persons are disproportionately located in principal cities, the study team identified 7 additional principal cities as certainty sites, for a total of 15 principal cities in the certainty sample (and 18 certainty sites in total). The team selected the 7 additional principal cities with certainty because the cities had among the largest populations of persons living in emergency and transitional shelters in the 1990 and 2000 Census counts.⁶ All 7 certainty sites had one of the 10 largest counts in either 1990 or 2000.⁷ Given that so many homeless persons live in these cities, it is important to include them with certainty in a nationally representative sample.

Exhibit B-1: Geographic Characteristics and Population of 18 Certainty Sites
	Geographic Area	Type of CDBG Entity	Size of Housed Population	Census Region	CoC Name
1	NEW YORK CITY	Principal City	8,008,278	Northeast	New York City Coalition/CoC
2	LOS ANGELES	Principal City	3,694,820	West	County of Los Angeles, CA
3	CHICAGO	Principal City	2,896,016	Midwest	Chicago CoC
4	HOUSTON	Principal City	1,953,631	South	Houston/Harris County
5	PHILADELPHIA	Principal City	1,517,550	Northeast	City of Philadelphia
6	PHOENIX	Principal City	1,321,045	West	Maricopa CoC
7	SAN DIEGO	Principal City	1,223,400	West	City of San Diego Consortium
8	DALLAS	Principal City	1,188,580	South	Dallas Homeless CoC
9	DETROIT	Principal City	951,270	Midwest	City of Detroit CoC
10	SAN FRANCISCO	Principal City	776733	West	City and County of San Francisco
11	BOSTON	Principal City	589,141	Northeast	City of Boston
12	WASHINGTON, DC	Principal City	572,059	South	District of Columbia Homeless Services
13	SEATTLE	Principal City	563,374	West	Seattle-King County CoC
14	CLEVELAND	Principal City	478,403	Midwest	Cuyahoga County/Cleveland CoC
15	ATLANTA	Principal City	416,474	South	Atlanta Tri- Jurisdictional
16	LOS ANGELES COUNTY	Urban County	2,205,851	West	County of Los Angeles, CA
17	COOK COUNTY	Urban County	1,712,784	Midwest	Cook County CoC
18	ISLIP TOWN	City >50,000	322,612	Northeast	Suffolk County CoC Group

Selection of Non-Certainty Sample

The selection of the non-certainty sites occurred in two phases. Phase one was completed in 2005 and included 62 non-certainty sites. The 62 non-certainty sites and the 18 certainty sites (80 total sample sites) constituted the original sample for the 2005, 2006, and 2007 AHARs. Phase 2 was completed for the 2008 AHAR and added 22 non-certainty sites to the original sample.

Phase 1: Selecting 62 Non-Certainty Sites. To select the 62 non-certainty sites for the original sample, the study team divided the 3,124 CDBG jurisdictions into 16 strata based on the four types of geographic areas and Census regions. As discussed earlier, the team divided the sample into strata based on the type of geographic area because earlier research indicated that the rate of homelessness is higher in principal cities than in other areas. The team further divided the sample into Census regions because business cycles might affect regions differently and result in variation in rates of and trends in homelessness across regions. Dividing the sample into strata that are more similar in terms of the rate of homelessness and the characteristics of homeless persons than the overall population reduces the variance of the sample estimates for a particular sample size. Stratified sampling also eliminates the possibility of some undesirable samples. For example, with a simple random sample, one possible sample might include sites only in rural areas or sites only in the Northeast, both of which are undesirable samples.

One possibility considered for the non-certainty sample was allocation of the sample to the stratum in proportion to the population in each stratum. However, such an approach ignores the research indicating that a disproportionate share of the homeless is located in principal cities. Ignoring information on the location of the homeless population would lead to a relatively high degree of imprecision in national estimates such that 20 of the 62 non-certainty sites would be allocated to principal cities, 6 to non-principal cities, 16 to urban counties, and 20 to rural areas. The same number of rural areas as principal cities would be selected even though earlier research suggests that only 9 percent of the homeless population lives in rural areas whereas 70 percent lives in principal cities.

Another possibility under consideration for the non-certainty sample was allocation of the total non-certainty sample of 62 CDBG jurisdictions to each of the 16 strata in proportion to the adjusted population in each stratum, where the adjustment accounts for different rates of homelessness across geographic areas. This allocation method produces the highest degree of precision of national estimates for a given sample size. The adjusted population is the population of persons living in an area multiplied by an adjustment factor for the expected rate of homelessness in that area. With the rate of homelessness in principal cities roughly five times that of other areas, the study team multiplied the population in principal cities by five so that the adjusted populations would reflect the relative number of homeless persons expected in each stratum.⁸ If the adjusted population were used to allocate the non-certainty sites across the strata, 39 of the 62 original non-certainty sample sites would have been allocated to principal cities, 4 to non-principal cities, 8 to urban counties, and 11 to rural areas. While optimal for national estimates, the number of sites in the non–principal city stratum was too small for subnational estimates.

The sampling allocation procedure ultimately used for AHAR data collection strikes a balance between the most precise national estimates possible with a sample of 62 non-certainty sites and reasonably sized samples from each of the four types of geographic areas. The study team allocated the 62 original non-certainty sample sites across the 16 strata based on the square root of the adjusted population. The result is a sample allocation between the allocation in proportion to the population and the allocation in proportion to the adjusted population. Accordingly, 27 of the 62 original non-certainty sites are in principal cities, 8 are in non-principal cities, 13 are in urban counties, and 14 are in rural areas. The allocation means lower variances of the estimates than either simple random sampling or sampling in direct proportion to the population and provides better representation of non-principal city areas than the allocation in proportion to the adjusted population.

To select the non-certainty sites in each stratum, the study team divided the sites into groups based on size and then randomly selected one site from each group. The number of non-certainty sites allocated to each stratum determined the number of groups, and each group in a stratum contained the same number of sites. Sampling from groups based on population size is beneficial in that it ensures that the sample has a similar distribution of CDBG jurisdiction sizes as the population. Given that the size of the homeless population is expected to correlate with the total population within strata, similarity in distribution is an important feature of the sample.

Phase 2: Adding 22 Rural Non-Certainty Sites. The data collection results from the 2005-2007 AHAR reports indicated that many rural communities (or non-entitlement CDBG areas) did not have emergency shelters or transitional housing programs located in these jurisdictions. Among the few rural sample sites that did have emergency shelters and/or transitional housing programs, many of those programs were not entering data into an HMIS. As a result, previous AHAR reports did not capture information from many rural jurisdictions, and the lack of data increased the variance of the AHAR estimates and made the analysis of rural/suburban versus urban homelessness less reliable.

In 2008, 22 new rural communities were added to the AHAR sample, increasing the total number of rural jurisdiction to 36 and the total number of AHAR sample sites to 102. The new AHAR sample sites were selected in the same manner as the original non-certainty sample sites. The original 2002 sampling frame of 3,142 CDBG jurisdictions within the 430 CoCs in the 50 states was used to select the new rural communities. However, the original file was compared with an updated 2006 CDBG list of jurisdictions to remove from the sampling frame jurisdictions that had either merged with other jurisdictions since 2002 or had changed their status from non-entitlement (rural) areas to entitlement areas.

The sample was stratified to ensure that each of the four census regions was represented. The goal was to select at least three rural communities from each census region that had at least one emergency shelter or transitional housing program. In some cases, more than three communities for a particular region were selected if inventory information reported by CoC suggested that the communities did not have any emergency shelters or transitional housing programs. That is, from each region, we randomly selected rural jurisdictions until we had at least three rural jurisdictions with at least one emergency shelter or transitional housing program. In total, 22 new rural sample sites were added; three from the Northeast region; seven from the South region; seven from the Midwest region; and five from the West region.

The final AHAR sample contains 102 sample sites, and Exhibit B-2 shows the total number of certainty and non-certainty sites selected from each region-CDBG type stratum. The sample sites contain over 40 million persons, or approximately 16 percent of the population living within CoC communities and 14 percent of the U.S. population. The expectation is that the sample will contain an even higher proportion of the U.S. homeless population because the selection procedures intentionally oversampled areas with a high rate of homelessness (i.e., principal cities). About two-fifths of the selected sites (42 sites) are principal cities, even though only one-third of the total population lives there. The other 60 sample sites were distributed across the three remaining CDBG jurisdictions: non-principal cities with a population over 50,000 (9 sites), urban counties (15 sites), and nonentitlement/rural areas (36 sites).

Exhibit B-2: Number of Sites in Universe and Sample by Region-CDBG Type
Stratum	Number of Geographic Areas in Universe	Number of Certainty Sites in Sample	Number of Noncertainty Sites in Sample	Total Sample
Northeast Principal City	86	3	5	8
South Principal City	151	4	8	12
Midwest Principal City	124	3	7	10
West Principal City	106	5	7	12
Northeast City >50,000	81	1	2	3
South City >50,000	48	0	2	2
Midwest City >50,000	55	0	1	1
West City >50,000	114	0	3	3
Northeast Urban County	33	0	3	3
South Urban County	54	0	4	4
Midwest Urban County	33	1	3	4
West Urban County	34	1	3	4
Northeast Non-entitlement County	148	0	6	6
South Non-entitlement County	812	0	11	11
Midwest Non-entitlement County	890	0	11	11
West Non-entitlement County	373	0	8	8
Total	3,142	18	84	102

Addition of Contributing Sites

In addition to the 102 sample sites selected, many other communities volunteer to provide data for the AHAR to help produce more precise national estimates. The additional communities are entire Continuums of Care and are termed “contributing sites.” In the 2008 AHAR, 135 contributing communities provided data for use in the AHAR report. As with the sites selected with certainty, data from the contributing sites represent themselves in the national estimates.

B.2.2 AHAR Weighting and Analysis Procedures

This section describes the process used in 2008 to obtain national estimates from the raw HMIS data submitted by participating communities. The estimates of the number and characteristics of the homeless population using homelessness services are based on weighted data. The study team designed the sampling weights to produce nationally representative estimates from the sites that provided data. The steps for obtaining the final estimate are listed here and described in more detail below.

• Step 1: Staff from the AHAR sample sites filled out reporting categories with information (raw data) from emergency shelters and transitional housing providers that had entered data into their local HMIS.

• Step 2: The raw data were adjusted by reporting category within each site to account for providers that did not participate in the site’s HMIS.

• Step 3: Base sampling weights were developed for all selected sites based on the assumption that 100 percent of the AHAR sample sites provided information.

• Step 4: Base sampling weights were adjusted to account for contributing sites.

• Step 5: Weights were adjusted for nonresponse to determine the preliminary analysis weights.

• Step 6: Based on national totals of emergency and transitional housing beds, a post-stratification adjustment was made to arrive at the final analysis weights.

• Step 7: A final adjustment factor was derived to account for people who used more than one type of homeless service provider.

• Step 8: National estimates were calculated by using the final weight (Step 6) and the final adjustment factor (Step 7).

Step 1: Staff from AHAR sites filled out reporting categories with information from emergency shelters and transitional housing providers that had entered data into their local HMIS.

Participating communities logged into the AHAR Exchange—the web-based data collection tool designed for the AHAR—and entered the information (raw data) on the number of homeless persons, their characteristics, and their patterns of service use. The information was reported separately for each reporting category: individuals using emergency shelters (ES-IND); persons in families using emergency shelters (ES-FAM); individuals using transitional housing (TH-IND); and persons in families using transitional housing (TH-FAM). The information was then aggregated into a fifth set of tables, the summary tables, to provide total cross-program estimates for the site. A public version of the AHAR Exchange is available for viewing and local use: http://sandbox.hmis.info/.

Step 2: The raw data were adjusted by reporting category within each site to account for providers that did not participate in the site’s HMIS.

Where participation in the HMIS was less than 100 percent, the raw data at each site were upwardly adjusted to account for nonparticipating providers (i.e., providers that did not submit data to HMIS). This adjustment, or extrapolation, was carried out separately by reporting category within each site. The extrapolation technique assumes that nonparticipating providers serve the same number of unique persons per available bed as participating providers during the study period. It makes a small adjustment for the overlap between users of participating and nonparticipating providers.⁹

The post-extrapolation results for each site are estimates of the homeless population served by each reporting category and the total sheltered homeless population at all emergency shelters and transitional housing in the entire site during the study period.

Step 3: Base sampling weights were developed on the assumption that 100 percent of the AHAR sample sites provided information.

The study team selected the largest sites (i.e., the CDBG jurisdictions with the largest populations) with certainty. As such, each site’s base sampling weight is 1.0, meaning that each respective site’s data represent only that site. The study team divided the noncertainty sites into 16 strata based on the four Census regions (East, West, Midwest, and South) and four CDBG types (three types of entitlement communities—principal city, urban county, other city with population greater than 50,000—and one type of nonentitlement community). The base sampling weights for the noncertainty sites are the inverse of the probability of selection. For example, if 1 out of 100 sites was selected in a stratum, the base sampling weight for selected sites in that stratum would be 100 (the inverse of 1/100 = 100). Each noncertainty site in a stratum had the same chance of being selected; therefore, each has the same weight.

If all the sample sites provided full AHAR data (in the absence of contributing sites), national estimates of the homeless population would be calculated by multiplying each site’s base sampling weight by the extrapolated number of persons with each characteristic at the site and then aggregating across sites.

Step 4: Base sample weights were adjusted to account for contributing sites.

One hundred and thirty-five communities volunteered to provide their HMIS-based data for the 2008 AHAR. The data from these communities—or contributing communities—increase the reliability of the AHAR estimates. The 135 CoCs that are contributing communities represent 725 CDBG jurisdictions.¹⁰ The study team treated all of these sites as certainty sites and assigned them a weight of 1.0 such that each site would represent only itself in the national estimates. The study team adjusted the base sampling weights of the noncertainty sites downward to represent only the noncontributing sites in their respective stratum. For example, assume that there were two sample sites in a stratum and that both originally had a base weight of 100. If the contributing sites represented 10 CDBG jurisdictions in that stratum, the sample weight for each sample site would be downwardly adjusted to 95. In other words, the two sample sites originally represented 200 sites in their stratum, but, with the contributing sites now representing 10 of those 200 sites, the sample site needs to represent 190 sites. The addition of the contributing sites did not affect the base sampling weights of the certainty sites.

If all the sample sites and contributing sites provided full AHAR data, the study team would calculate national estimates of the homeless population by multiplying each site’s base weight by the extrapolated number of persons with each characteristic at the site and then aggregating across sites.

Step 5: The base weights were adjusted for nonresponse to derive the preliminary analysis weights.

The above base weights assume that all the sample and contributing sites provided data for all four reporting categories except for those for which they have no providers in their jurisdiction. Unfortunately, 15 sample sites were not able to provide any usable data, and 25 other sample sites were unable to provide data for all their reporting categories (i.e., they provided partial data). Eighty-eight contributing sites also provided only partial data. In addition, 29 sample sites had no providers (i.e., no emergency shelters or transitional housing programs). The ‘zero provider sites’ are part of the estimate (because they represent themselves and all nonsample zero provider sites in the population) but need to be treated differently from the other sites. Once the study team confirmed that the site had no providers, it needed no further information. Given that the zero provider sites did not have any information for the AHAR reporting categories, none of them was a nonrespondent.

Recognizing that some participating sites provided only partial data (i.e., data on some but not all of their reporting categories) and that the data proved useful for the AHAR report, the study team carried out the nonresponse adjustment to the weights separately for each of the four reporting categories. That is, each site contributing data to the AHAR has four analytic weights—one for each reporting category. However, for any reporting category for which a site was not able to provide data, the analytic weight is zero. The respondent sites for that reporting category represent the site. (Step 8 describes the procedure for aggregating across reporting categories to arrive at national estimates.)

Below is a description of how the weight for each type of site was adjusted for nonresponse to derive the final analysis weights.

1. The weights of the contributing sites did not change; each contributing site continued to represent itself with an analytic weight of 1.0 for each program-household type for which it provided data.

2. The weights of the no-provider sites did not change. Their weight remained the base weight calculated in Step 4 because all zero provider sites in the sample are considered respondents. In essence, the no-provider sites produced a response of 100 percent. Stated differently, since none of the nonresponse sites has no providers, the no-provider sites would not appropriately represent them.

3. For the certainty sites providing data, base weights were adjusted so that the analytic weights represented all certainty sites. The adjustment was made separately for each program-household type within four weighting classes based on region: North, South, East, and Midwest. ¹¹ The nonresponse adjustment was based on the relative number of shelter beds in the nonrespondent sites and accounts for the possibility of a high degree of size variation among certainty sites. The nonresponse adjustment formula follows:

TTotal number of beds within a reporting category at certainty sites in region

÷

Number of beds within reporting category at respondent certainty sites in region

For example, assume that six of the seven certainty sites in the West provided TH-IND data and that one site did not. If the nonrespondent certainty site had 1,000 TH-IND beds and the six participating certainty sites had 5,000 beds, the weight of the six participating certainty sites would be multiplied by 6/5 (6,000 divided by 5,000). The adjustment assumes that the nonrespondent certainty sites would serve approximately the same number of persons per bed as the participating certainty sites. The nonresponse adjustment for certainty sites was derived separately by region based on the judgment that homeless providers in principal cities in the same region were more likely than principal cities overall to serve persons with similar characteristics.

4. For the noncertainty sites, the weights of the participating sites were upwardly adjusted to represent all the sites meant to be represented by the nonrespondent sample sites. The adjustment was carried out separately for each program-household type within 16 weighting classes based on type of CDBG jurisdiction and region: (1) principal city, (2) city with greater than 50,000 population, (3) urban counties, and (4) and nonentitlement areas. The nonresponse adjustment was the same as that used for certainty sites--the ratio of total number of beds in the weighting class divided by number of beds in participating sites.

Step 6: A post-stratification adjustment was carried out to create final analysis weights.

A post-stratification adjustment based on national totals of emergency and transitional housing beds accounted for new CDBG jurisdictions added since 2002 as well as for any differences in the average size of sample and nonsample sites. This final adjustment to the analysis weights applied only to noncertainty sample sites. The preliminary analysis weight (from Step 5) is the final analysis weight for certainty sites, no-provider sites, and contributing sites.

The initial AHAR sample was drawn from the number of CDBG jurisdictions in existence in 2002. Since that time, however, the number of CDBG jurisdictions has increased from 3,142 to 4,115.¹² Therefore, the study team adjusted the analysis weights to account for the expansion. The increase in CDBG jurisdictions was not evenly distributed; most of the growth occurred in the South, particularly in the rural South. Thus, we adjusted the weights separately for each of the 16 strata. The adjustment factor was the ratio of total number of beds in the strata in 2008 (after excluding beds from certainty and contributing communities) to the weighted number of beds in the noncertainty sample sites in the strata providing usable data.¹³ The number of beds for the adjustment was based on the housing inventory chart submitted as part of the 2008 CoC application.

The adjustment both corrected for the difference in the number of CDBG jurisdictions in CoCs between 2002 and 2008 and adjusted for any differences in the number of beds per CDBG sample site and CDBG nonsample site in the same stratum.

The Step 6 weights are the final analysis weights for use with the sample and data provided to produce separate national estimates of the homeless population for each reporting category. However, to aggregate the data across reporting categories, a further adjustment is needed to account for persons who used more than one program type during the study period.

Step 7: Final adjustment factor was derived to account for users of several program types.

To calculate national estimates that require data aggregation across the four reporting c

categories, an adjustment is needed for persons who used more than one program-household type during the study period. That is, if a person used an emergency shelter for individuals and then a transitional housing program for individuals, the person will appear in more than one set of reporting categories for the study period; aggregation of the numbers from the four reporting categories would double count that person. The needed adjustment is the same type of adjustment embedded in the AHAR summary table for sites providing data on all four reporting categories. For the 80 participating sites (33 sample sites and 47 contributing communities) providing data on all four reporting categories, the adjustment factor was the actual adjustment factor calculated from how much overlap the sites reported with their HMIS data. However, for the 113 participating sites that provided only partial data, it was not possible to calculate the overlap adjustment factor from their data. Instead, for all partial reporting sites, the study team used the average overlap adjustment factor from the 80 sites providing full data. Thus, for partial reporting sites, the overlap adjustment factor was assumed to be 0.9622.

T he overlap adjustment factor was calculated as follows:

Total unduplicated number of persons served at the full-reporting sites

÷

Total number of persons served at the full-reporting sites before accounting for persons served by more than one program-household type

Step 8: Calculate national estimates.

To calculate national estimates, the study team first calculated the total number of persons with each characteristic within each of the four reporting categories. Then, within each reporting category, the team multiplied the final analysis weight (from Step 7) for each site by the number of persons with that characteristic in that site’s reporting category. Next, the team summed the number of persons in each site across sites to arrive at the estimated number of persons with that characteristic who were served in that reporting category. For estimates of the number of persons served by all four reporting categories, the team summed totals across the four reporting categories and then multiplied by the adjustment factor from Step 7. Percentage calculations followed the same procedures by calculating both the numerator and denominator of the desired percentage.

B.2.3 Use of Periodic Data Collection Cycles to Reduce Burden

Data collection is only required once per year. In order to monitor data quality and assess homelessness trends, HUD is requesting but not requiring communities to submit quarterly data.

B3 Maximizing Response Rates

B.3.1 Annual Performance Report for HUD’s Homeless Assistance Programs

Grantees of HUD’s Homeless Assistance Programs are required to submit the Annual Performance Report annually in order to be compliant with their grant requirements. Assistance in completing the Annual Performance Report will be available through Help Desk support to any grantees that need it. There are no additional efforts planned to maximize response rates.

B.3.2 Annual Homeless Assessment Report

A HUD contractor will work with all communities to provide technical assistance throughout the year to assure the highest participation rate possible. This technical assistance will focus on helping the community understand what is involved in producing the local AHAR report and address any data quality problems, working with each community’s unique system to produce the data necessary to submit to the AHAR, providing tools to check data quality, and on-site activities focused on improving HMIS implementation.

The following procedures will also be employed to maximize response rates:

• Each community will be assigned a staff person who is available to answer questions related to the AHAR, data quality, and strategies to increase HMIS coverage.

• Communities will have a window of eight weeks to submit the data after the data collection period ends.

Procedures for Dealing with Non-Response

HUD will attempt to minimize non-response by:

• first, providing hands-on technical assistance to communities participating in the AHAR;

• second, undertaking outreach to communities with mature HMIS implementations; and

• third, providing a web-based automated interface (the AHAR Exchange) for AHAR reporting to improve the efficiency of the collection process and the validity and reliability of the data.

B4 Tests of Procedures or Methods

No tests of procedures or methods were conducted for the Annual Performance Report.

AHAR data collection was piloted in two communities: Washington, DC and Montgomery County, MD. During the pilot a member of the AHAR research team walked through the report tables with CoC representatives. These representatives provided feedback to the research team. This feedback informed changes to the data collection, which included providing more explicit definitions and instructions.

B5 Statistical Consultation and Information Collection Agents

As stated in B2 above, there are no statistical methods to be employed in conjunction with the re-designed Annual Performance Report or the Annual Homeless Assessment Report.

For the Annual Homeless Assessment Report, the individuals listed in Exhibit B-3 below assisted the Department in the design of the AHAR research effort.

Exhibit B-3: Individuals Consulted on the AHAR Research Project

Name	Telephone Number	Email Address	Role
Dr. Larry Buron	301-634-1735	[email protected]	Project Director, Abt Associates
Dr. Alvaro Cortes*	301-634-1857	[email protected]	Project Team, Abt Associates
Paul Dornan	202-402-4486	[email protected]	Project Team, U.S. Department of Housing and Urban Development
Michael Roanhouse	202-402-4482	[email protected]	Project Team, U.S. Department of Housing and Urban Development

*Inquiries regarding the AHAR research project should be directed to Dr. Alvaro Cortes.

Attachment A

Federal Regulations Related to HUD’s Annual Progress Report for Homeless Programs

	Annual Performance Report for HUD’s Homeless Assistance Programs: Data Elements, Response Categories and Justification
Q #			Title of Question			Response Categories			Justification
Section 1: Grantee Information
1			Contact Information		Project Name Project Sponsor Grantee Contact Name Title Address Phone Number Fax Number Email Address			Allows HUD to identify primary grantee contact responsible for information contained in the report.
2			Authorizing Information		Name of Authorized Grantee Official Title/Position Name of Authorized Sponsor Official Title/Position			Allows HUD to identify the authorizing official representing the grantee and sponsor organizations.
3			Project Information		Type of Grant Program Components or Types Special Initiative Target Subpopulation CoC Number Program Identifier Operating Year Start Date Operating Year End Date Operating Year Covered by this Progress Report Is this an extension Performance Report? Is this a final Performance Report? Is this a corrected Performance Report? Does this project have a 20-year use requirement? If yes, in what year does the 20-year use requirement end?			Allows HUD to monitor and verify the type of grant, type of populations served, dates of operation, and basic project information per the Grant Agreement.
4			Site Information		Project Administrative Address Program Site Configuration Type Site Type Housing Type			Allows HUD to monitor and verify the location of HUD supported facility and type of building used for housing and services (dorm, apartment, etc.).
5			Current Bed and Unit Inventory (Households without children, Households with children)		Total current number of year-round bed/units (Beds, CH Beds, Units) Total current number of year-round beds/units (Beds, Units)			Allows HUD to monitor and verify conformance with bed and unit inventory identified in HUD Grant Agreement.
6			HMIS Bed Participation Rate		Is this project a victim service provider? HMIS-Beds (total number of year-round beds in HMIS for households without children, total number of year-round beds in HMIS for households with children) HMIS Bed Coverage Rate (for year-round beds for households without children, for year-round beds for households with children, Total for all year-round beds)			Allows HUD to monitor and verify whether project is classified as a victim service provider and, for non-victim service providers, verify conformance with HMIS participation requirement in Grant Agreement.
7			HMIS Data Quality		Universal Data Elements Program-Specific Data Elements			Allows HUD to monitor and verify conformance with HMIS data coverage requirement in Grant Agreement.
Section 2: Program Outputs
8			Persons Served During the Operating Year by Household Type (Total, Persons in households without children, Persons in households with children)		Total number of persons served during operating year Average number of persons served each night during the operating year Point-in-Time counts of persons during the operating year			Allows HUD to monitor and verify conformance with projected persons to be served by household type in Grant Agreement.
9			Households Served During the Operating Year (Total, Households without children, Households with children)		Total number of households served at any time during the operating year Point-in-Time counts of households during the operating year			Allows HUD to monitor and verify conformance with projected households served in Grant Agreement.
10			Bed Utilization Rate		Average daily bed utilization rate during the operating year Point-in-time bed utilization rate			Allows HUD to monitor bed utilization rate as an indicator of project performance.
11			Unit Utilization Rate		Point-in-time bed utilization rate			Allows HUD to monitor unit utilization rate as an indicator of project performance.
12			Client Contacts and Engagements (Street Outreach Programs Only - Persons identified as sleeping in places not meant for human habitation at the time of first contact, Persons identified as sleeping in a shelter/housing service site or other form of housing at the time of first contact, Persons whose living arrangements at the time of first contact are unknown, All Persons Contacted)		Of those persons contacted by the street outreach program during the operating year, how many persons were contacted… once, 2-5 times, 6-9 times, 10 or more times, Total. Of those persons contacted by the street outreach program during the operating year, how many persons were engaged after... one contact, 2-5 contacts, 6-9 contacts, 10 or more contacts, Total. Rate of Engagement			Allows HUD to monitor project contacts and engagements with clients and the rate of engagement as indicators of project performance.
Section 3: Client Characteristics
		3.1 Client Characteristics by Household Type (Total Persons, Persons in Households With Children, Persons in Households Without Children)
13			Gender (All Persons)		Gender of adults Gender of children Gender of persons missing age information			Allows HUD to monitor gender characteristics of clients served as a factor in understanding the client population served.
14			Age (All Persons)		Age Ranges			Allows HUD to monitor age characteristics of clients served as a factor in understanding the client population served.
15			Ethnicity and Race (All Persons)		Ethnicity Race (cross-tabulated with Ethnicity)			Allows HUD to monitor ethnicity and racial characteristics of clients served as factors in understanding the client population served.
16			Physical & Mental Health Condition (All Persons)		Physical and mental health condition Number of conditions			Allows HUD to monitor physical and mental health characteristics of clients served by household type as factors in understanding the client population served.
17			Domestic Violence (Adults and Unaccompanied Youth Only)		Status of Domestic Violence Experience When experience occurred			Allows HUD to understand domestic violence experience of clients served as a factor in understanding the client population served.
18			Residence Prior to Program Entry (All Persons)		Homeless Situations Institutional Settings Other Locations			Allows HUD to monitor and verify residence prior to program entry of clients served as a factor in understanding the client population served and to verify conformance with client eligibility requirements in Grant Agreement.
19			Veteran Status (Adults Only)		Veteran status			Allows HUD to monitor veteran status of clients served as a factor in understanding the client population served.
3.2 Client Characteristics by Exit Status (Total Persons, Persons Who Exited Program During Year, Persons who Remained in Program at End of Year)
20			Physical & Mental Health Condition by Exit Status (All Persons)		Number of conditions Physical and mental health conditions			Allows HUD to monitor physical and mental health characteristics of clients served by exit status as factors in understanding the client population served.
21			Client Monthly Cash-Income Amount by Entry and Exit Status (All Leavers Only)		Client monthly cash-income amount at program entry Client monthly cash-income amount at program exit			Allows HUD to monitor entry and exit monthly cash-income amounts received by clients who left the program as a factor in understanding the client population served and as an indicator of project performance.
22			Client Monthly Cash-Income Amount by Entry and Latest Status (All Stayers Only)		Client monthly cash-income amount at program entry Client monthly cash-income amount at most recent client assessment			Allows HUD to monitor entry and most recently assessed monthly cash-income amounts received by clients who remained in the program as a factor in understanding the client population served and as an indicator of project performance.
23			Clients' Cash Income Sources by Exit Status (All Persons)		Number of cash-income sources Types of cash-income sources			Allows HUD to monitor type and number of cash-income sources for clients who left the program and clients who stayed in the program as factors in understanding the client population served and as indicators of project performance.
24			Client Non-Cash Benefits by Exit Status (All Persons)		Number of non-cash income benefits Types of non-cash income benefits			Allows HUD to monitor type and number of non-cash benefits received by clients who left the program and clients who stayed in the program as factors in understanding the client population served and as indicators of project performance.
25			Length of Participation by Exit Status (Residential Programs Only; All Persons)		Length of participation ranges Average and Median Length of Participation (in days)			Allows HUD to monitor length of participation of residential program clients who left the program and clients who stayed in the program as a factor in understanding the client population served and as an indicator of project performance.
26			Destination by Household Type and Length of Stay (All Leavers Only)		Permanent destinations Temporary destinations Institutional settings Other destinations			Allows HUD to monitor destination of clients who left the program by household type as a factor in understanding the client population served and as an indicator of project performance.
Section 4: Financial Information
		4.1 Financial Information for the Supportive Housing Program (SHP)
27			SHP and Cash Match Expenditures During the Operating Year		Expenditure Type (Acquisition, Rehabilitation, New Construction, Supportive Services, Real Property Leasing, Operations, HMIS Activities, Administration)			Allows HUD to monitor and verify grantee expenditure of Supportive Housing Program and cash match funds for eligible activities and achievement of match requirements in Grant Agreement.
4.2 Financial Information for the Shelter Plus Care (S + C) Program
28			S+C and Supportive Services Match Expenditures During the Operating Year		Expenditure Amount (Rental Assistance, Supportive Services Match)			Allows HUD to monitor and verify grantee achievement of Shelter Plus Care match requirements in Grant Agreement.
29			Value of Supportive Services Received by S + C Clients During the Operating Year		Supportive Service Expenditure			Allows HUD to monitor value of specific in-kind services received by clients.
4.3 Financial Information for the Single Room Occupancy (SRO) Program
30			Value of Supportive Services Received by SRO Clients During the Operating Year		Supportive Service Expenditure Value			Allows HUD to monitor value of specific in-kind services received by clients.
		4.4 Share of HUD McKinney-Vento Funding
31			Percent of HUD McKinney-Vento Funding		What percentage of the project's total budget for the operating year reported on is represented by HUD McKinney-Vento funding?			Allows HUD to monitor percentage of HUD McKinney-Vento funding relative to the overall program budget.
Section 5: Program Performance
32a			Primary Performance Measures by Program Type (excluding HMIS-dedicated projects)		Permanent housing programs Transitional housing programs Street Outreach Programs Supportive Service Only Programs with a Housing Goal Safe Havens			Allows HUD to monitor and verify client change with respect to housing stability and income as indicators of project performance and conformance with Grant Agreement.
32b			Secondary Performance Measures: Service Linkage Measures (Street Outreach Programs Only)		# of persons who accomplished outcome Total # of persons in the program for whom the measure is appropriate			Allows HUD to monitor and verify client change with respect to service linkage as an indicator of project performance and conformance with Grant Agreement.
33			Program-Defined Performance Measures (Mandatory for Supportive Service Only Programs without a Housing Goal; Optional for Others)		# of persons who accomplished outcome Total # of persons in the program for whom the measure is appropriate			Allows HUD to monitor achievement of program-defined performance measures as a factor in understanding overall project performance.
Section 6: Narrative
34			Description of Optional Measure(s) (Any program that reported Program-defined measure(s) must complete this question)		Data source and method of data collected for optional performance measure Data elements and formula for calculating the optional performance measure Use of the optional performance measure			Allows HUD to monitor the data sources and methods of measurement used for optional performance measures reported in question 32a.
35			Explanation of Variance(s) Between Planned and Actual Performance		Narrative explanation			Allows HUD to monitor reasons for any significant variance (10% or greater) between planned and actual performance.
36			Significant Program Accomplishments		Describe any significant accomplishments achieved by your program during the operating year.			Allows HUD to monitor additional significant program accomplishments as a factor in understanding overall project performance.
37			Additional Comments (Optional)		Provide any additional comments on other areas of the Performance Report that need explanation, such as differences in anticipated and actual program outputs, bed utilization, etc.			Allows HUD to review additional grantee comments and explanations regarding one or more APR responses.
Section 7: HMIS Dedicated Projects
1a			Homeless Management Information System (HMIS) Lead Organization		Organization Name Street Address 1 Street Address 2 City State Zip Code			Allows HUD to verify the HMIS Lead Organization.
1b			Homeless Management Information System (HMIS) Contact Person		Prefix First name Last name Suffix Telephone number Extension Fax number Email address Confirm email address			Allows HUD to verify the contact person for an HMIS implementation.
1c.			Homeless Management Information System (HMIS)		Select your HMIS implementation type (Single CoC, Regional (multiple CoC), Statewide Coc) Select the CoC(s) covered by the HMIS implementation Type of HMIS Software you have (commercially available or custom designed) Name of HMIS Software			Allows HUD to verify the HMIS implementation type and CoCs included within the HMIS implementation.
1d.			HMIS Implementation		Scope of HMIS Implementation			Allows HUD to monitor the scope of an HMIS implementation.
2			HMIS Staffing		Indicate the staffing levels currently committed to managing the HMIS, as well as those planned within the next year, by percent FTE.			Allows HUD to monitor the labor allocation (measured by full-time equivalent) by functional category for the HMIS project.
3			HMIS Participation by Program Type		Identify the types of Contributory CoC and non-CoC programs that are included in HMIS Total number of programs in homeless system Total number of programs participating in HMIS			Allows HUD to monitor the number and type of Contributory CoC and non-CoC programs that are included in the HMIS.
4a.			HMIS Functionality		Indicate which system functionalities are currently part of your HMIS. General Functionality HUD Reporting Data Quality Security Interoperability.			Allows HUD to monitor the types of HMIS functionality presently available in the HMIS.
4b.			Explain plans to address any deficiencies in your HMIS system.		Narrative			Allows HUD to monitor plans to address deficiencies in HMIS systems.
5			Electronic Data Sharing between CHOs		Type of Training Training Requirements Frequency Number of CHO's that Completed Training			Allows HUD to monitor the level of electronic data sharing among CHOs.
6			User Training Requirements		Type of Training Training Requirements Frequency Number of users that Completed Training in the operating year			Allows HUD to monitor and verify HMIS training requirements, frequency and user completion rate by training types as indicators of conformance with Grant Agreement.
7			Follow-Up Training Requirements		Beyond the start-up training requirements specified in Q6, are HMIS users required to complete any refresher or additional HMIs training in later periods?			Allows HUD to monitor and verify HMIS training requirements as indicators of conformance with Grant Agreement.
8			HMIS Data Timeliness Procedures		Are CHOs required to enter HMIS data within a specific timeframe after client intake, contact, or exit?			Allows HUD to monitor and verify presence of procedures that address data entry and grantee description of those procedures as indicators of conformance with Grant Agreement.
9			HMIS Data Quality Procedures		Do you have standard operating procedures for monitoring the quality of data stored in HMIS? If so, please describe them.			Allows HUD to monitor HMIS bed coverage rate for all homeless assistance programs as an indicator of project performance and in conformance with Grant Agreement.
10a			HMIS Bed and Unit Participation Chart (Last Wednesday in January)		Point-in-Time counts Year round beds Year round beds in HMIS Year round units Year round units in HMIS Number of persons in HMIS participating in residential Number of households in HMIS participating in residential Bed coverage Bed utilization Unit utilization			Allows HUD to monitor point-in-time counts, HMIS bed coverage rates, bed utilization rates, and unit utilization rates for all homeless assistance programs as an indicator of project performance and in conformance with Grant Agreement.
10b.			HMIS Bed and Unit Participation Chart (Last Wednesday in July)		Point-in-Time counts Year round beds Year round beds in HMIS Year round units Year round units in HMIS Number of persons in HMIS participating in residential Number of households in HMIS participating in residential Bed coverage Bed utilization Unit utilization			Allows HUD to monitor point-in-time counts, HMIS bed coverage rates, bed utilization rates, and unit utilization rates for all homeless assistance programs as an indicator of project performance and in conformance with Grant Agreement.
10c.			If you did not have 100% bed coverage for all the above categories, please explain your barriers and plan for improving your bed coverage.		Narrative			Allows HUD to review the HMIS implementation’s plan for improving bed coverage rates.
11a			HMIS Data Quality across all Contributory Homeless System Programs		Universal Data Elements for Residential Programs Universal Data Elements for Outreach/SSO			Allows HUD to verify conformance with HMIS data coverage requirement in Grant Agreement.
11b.			HMIS Data Quality across all Contributory Homeless System Programs		Program Descriptor Elements			Allows HUD to verify conformance with HMIS data coverage requirement in Grant Agreement.
12			HMIS Funding		Please check appropriate funding sources that supported the HMIS during the operating year and for each source indicate the ($) amount. HUD SHP grant (dedicated HMIS project) HUD CDBG HUD ESG HUD HOPWA HUD SHP administration Local government Local private Participation fees from agencies Other			Allows HUD to monitor grantee funding sources that supported HMIS.
13			HMIS Expenditures by Type		Please indicate HMIS expenditure types and amounts for the operating year.			Allows HUD to monitor grantee HMIS expenditures by type.
14			HMIS Narrative (Optional)		Is there any other information that you think is important for understanding your HMIS implementation?			Allows HUD to review additional grantee comments and explanations regarding HMIS implementation.

Attachment C

Annual Homeless Assessment Report:

Data Elements, Response Categories and Justification

	Annual Homeless Assessment Report: Data Elements, Response Categories and Justification
Q #				Title of Question	Response Category				Justification
Section 1: Total Counts
1			Unduplicated number of persons that used Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing participating in HMIS			n/a		Provides HUD with an unduplicated count of homeless persons staying in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing programs selected time period.
2			Number of Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing, year-round equivalent shelter beds for persons included in HMIS			n/a		Informs HUD of the proportion of providers who have complied with the requirement to enter homeless data into an HMIS.
3			Number of Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing, year-round equivalent beds for persons at providers not participating HMIS			n/a		Informs HUD of the proportion of providers who have not complied with the requirement to enter homeless data into an HMIS.
4			Number of persons who used more than one HMIS-participating Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing Program			n/a		Allows HUD to track the patterns of shelter stays among homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
5			How many persons in families/individuals were using Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing on average per night during covered time period?			n/a		Allows HUD to assess the average bed utilization among Emergency Shelter, Transitional Housing, and Permanent Supportive Housing programs during selected time period.
6			How many persons in families/individuals were using Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing on:			Wednesday of the last week in October? Wednesday of the last week in January? Wednesday of the last week in April? Wednesday of the last week in July?		Allows HUD to track seasonal patterns in the use of Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period and to determine what percentage of available beds are filled at a given point in time.
7			Number of Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing beds included in HMIS and available on:			Wednesday of the last week in October? Wednesday of the last week in January? Wednesday of the last week in April? Wednesday of the last week in July?		Allows HUD to assess seasonal patterns in bed capacity among Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
8			How many persons in families/individuals used Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing at some time during the covered period and were also served as a person in a family/individual in Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing during covered time period?			n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
Section 2: Demographics
9			Age of Children/Adults			Children: Under 1 1 to 5 6 to 12 13 to 17 Adults: 18 to 30 31 to 50 51 to 61 62 or older Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
10			Gender of Children/Adults			Female Male Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
11			Ethnicity			Non-Hispanic/Non-Latino Hispanic/Latino Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
12			Race/Ethnicity			White, Non-Hispanic/Non-Latino White, Hispanic/Latino Black or African-American Asian American Indian or Alaska Native Native Hawaiian or Other Pacific Islander Multiple races Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
13			Persons by Household Size			1 Person 2 People 3 People 4 People 5 or more People Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
14			Veteran Status (Adults Only)			A veteran Not a veteran Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
15			Disability Status			Yes, disabled Not disabled Missing		Allows HUD to track and compare the characteristics of homeless persons and the magnitude of chronic homelessness among persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
16			Persons by Household Type			Individual adult male Individual adult female Adults in family, with child(ren) Children in families, with adults Unaccompanied youth Missing		Allows HUD to track and compare the characteristics of homeless persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
Section 3: Prior Living Situation
17			Living Arrangement the Night Before Program Entry for persons in families/individuals in Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing (adults only for families, all persons for individuals)			Emergency shelter Transitional housing Permanent supportive housing Psychiatric facility Substance abuse treatment center or detox Hospital (non-psychiatric) Jail, prison, or juvenile detention Rented housing unit Owned housing unit Staying with family Staying with friends Hotel or motel (no voucher) Foster care home Place not meant for human habitation Other living arrangement Missing		Allows HUD to track the paths into homelessness for persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
18			How long did persons in families/individuals stay in their living arrangement the night before program entry? (adults only for families, all persons for Individuals)			One week or less More than one week, but less than a month One to three months More than three months, but less than a year One year or longer Missing		Allows HUD to track the paths into homelessness for persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
19			Location of last permanent residence (adults only for families, all persons for individuals)			Zip code is within jurisdiction Zip code is not within jurisdiction Missing		Allows HUD to track the paths into homelessness for persons who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
Section 4: Length of Stay
20		Number of Nights in Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing for adults in families/individual adults (Females/Males/Missing Gender)				1 to 7 nights 8 to 30 nights 31 to 60 nights 61 to 90 nights 91 to 120 nights 121 to 150 nights 151 to 180 nights 181 to 210 nights 211 to 240 nights 241 to 270 nights 271 to 300 nights 301 to 330 nights 331 to 360 nights 361 to 366 nights Missing		Allows HUD to track and compare length of stay among adult homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
21		Median Number of Shelter Nights in Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing for adults in families/individual adults (Females/Males/Missing Gender)				n/a		Allows HUD to assess average length of stay among adult homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
22		Number of Nights in Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing for children in families/individual children (Females/Males/Missing Gender)				1 to 7 nights 8 to 30 nights 31 to 60 nights 61 to 90 nights 91 to 120 nights 121 to 150 nights 151 to 180 nights 181 to 210 nights 211 to 240 nights 241 to 270 nights 271 to 300 nights 301 to 330 nights 331 to 360 nights 361 to 366 nights Missing		Allows HUD to track and compare length of stay among youth homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
23		Median Number of Shelter Nights in Emergency Shelter/Safe Haven/Transitional Housing/Permanent Supportive Housing for Individual Children/Children in Families (Females/Males/Missing Gender)				n/a		Allows HUD to assess average length of stay among youth homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
Section 5: Household Counts (Families Only)
24		How Many Family Households Stayed in Emergency Shelter/Transitional Housing/Permanent Supportive Housing at any given time during the covered time period?				n/a		Allows HUD to assess the average unit utilization of families in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
25		How Many Family Households Stayed in Emergency Shelter/Transitional Housing/Permanent Supportive Housing on:				Wednesday of the last week in October? Wednesday of the last week in January? Wednesday of the last week in April? Wednesday of the last week in July?		Allows HUD to track seasonal patterns in unit utilization among families in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period and to determine what percentage of available family units are filled at a given point in time.
Section 6: Long-term Stayer Demographics
26		Age of Long-Term Stayer Children/Adults				Children: Under 1 1 to 5 6 to 12 13 to 17 Adults: 18 to 30 31 to 50 51 to 61 62 or older Missing		Allows HUD to track and compare the characteristics of long-term (6 months or more) homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
27		Ethnicity of Long-Term Stayers				Non-Hispanic/Non-Latino Hispanic/Latino Missing		Allows HUD to track and compare the characteristics of long-term (6 months or more) homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
28		Race/Ethnicity of Long-Term Stayers				White, Non-Hispanic/Non-Latino White, Hispanic/Latino Black or African-American Asian American Indian or Alaska Native Native Hawaiian or Other Pacific Islander Multiple races Missing		Allows HUD to track and compare the characteristics of long-term (6 months or more) homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
29		Household Size of Long-Term Stayers				1 Person 2 People 3 People 4 People 5 or more People Missing		Allows HUD to track and compare the characteristics of long-term (6 months or more) homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
30		Veteran Status of Long-Term Stayers (Adults Only)				A veteran Not a veteran Missing		Allows HUD to track and compare the characteristics of long-term (6 months or more) homeless clients in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
31		Disability Status of Long Term-Stayers				Yes, disabled Not disabled Missing		Allows HUD to track and compare the characteristics of long-term (6 months or more) homeless clients and the magnitude of chronic homelessness among long-term clients who stay in Emergency Shelter, Transitional Housing, and Permanent Supportive Housing during selected time period.
Section 7: Summary
32		Number of persons in your HMIS who appeared in ALL program-household types (ESIND, ESFAM, THIND, THFAM, PSHIND, AND PSHFAM, SHIND)				n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
33		Number of persons in your HMIS who appeared in 5 program-household types only				n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
34		Number of persons in your HMIS who appeared in 4 program-household types only				n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
35		Number of persons in your HMIS who appeared in 3 program-household types only				n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
36		Number of persons in your HMIS who appeared in 2 program-household types only				n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
37		Number of persons in your HMIS who appeared in ONE program-household type only ((ESIND, ESFAM, THIND, THFAM, PSHIND, PSHFAM, OR SHIND)				n/a		Allows HUD to track the patterns of homeless persons through different types of residential programs.
38		Number of emergency shelter year-round family units in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's family unit capacity in Emergency Shelters during selected time period.
39		Number of emergency shelter year-round family beds in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's family bed capacity in Emergency Shelters during selected time period.
40		Number of emergency shelter year-round individual beds in your current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's year-round individual bed capacity in Emergency Shelters during selected time period.
41		Number of emergency shelter seasonal beds in your current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's seasonal bed capacity in Emergency Shelters during selected time period.
42		Number of emergency shelter overflow/voucher beds in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's overflow and voucher bed capacity in Emergency Shelters during selected time period.
43		Number of emergency shelter year-round equivalent family beds in current inventory for the AHAR community during the covered time period				n/a		Informs HUD of the nation's overall family bed capacity in Emergency Shelters during selected time period.
44		Number of emergency shelter year-round equivalent individual beds in current inventory for the AHAR community during the covered time period				n/a		Informs HUD of the nation's overall individual bed capacity in Emergency Shelters during selected time period.
45		Number of safe haven year-round equivalent individual beds in current inventory for the AHAR community during the covered time period				n/a		Informs HUD of the nation’s overall individual bed capacity in Safe Havens during selected time period.
46		Number of transitional housing year-round family units in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's family unit capacity in Transitional Housing during selected time period.
47		Number of transitional housing year-round family beds in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's family bed capacity in Transitional Housing during selected time period.
48		Number of transitional housing year-round individual beds in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's individual bed capacity in Transitional Housing during selected time period.
49		Number of permanent supportive housing year-round family unit in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's family unit capacity in Permanent Supportive Housing during selected time period.
50		Number of permanent supportive housing year-round family beds in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's family bed capacity in Permanent Supportive Housing during selected time period.
51		Number of permanent supportive housing year-round individual beds in current inventory for the AHAR community at the start of the covered time period				n/a		Informs HUD of the nation's individual bed capacity in Permanent Supportive Housing during selected time period.
52		Number of People Served in HMIS-Participating Providers During Covered Time Period missing first name				n/a		Allows HUD to track missing rates among required HMIS universal data elements.
53		Number of People Served in HMIS-Participating Providers During Covered Time Period missing last name				n/a		Allows HUD to track missing rates among required HMIS universal data elements.
54		Number of People Served in HMIS-Participating Providers During Covered Time Period missing part or all of social security number				n/a		Allows HUD to track missing rates among required HMIS universal data elements.
55		Number of People Served in HMIS-Participating Providers During Covered Time Period missing month, day, or year of date of birth				n/a		Allows HUD to track missing rates among required HMIS universal data elements.
56		Number of People Served in HMIS-Participating Providers During Covered Time Period missing gender				n/a		Allows HUD to track missing rates among required HMIS universal data elements.

1 The initial AHAR sample consisted of 80 jurisdictions. Some jurisdictions from the original sample—especially jurisdictions representing rural areas—were unable to provide data to the AHAR because of HMIS implementation issues or other data quality concerns. In addition, several of the rural sample sites did not have any homeless residential service providers located in their jurisdiction. As a result, we were unable to report data by geography. In an effort to improve the scope and quality of data from rural jurisdictions, 22 additional rural jurisdictions were added to the AHAR sample. Thus, there are a total of 102 AHAR sample sites.

2 The original file from which the sample was selected used the category of “central city” for CDBG jurisdictions rather than “principal city.” However, the CDBG program moved to designation of principal city rather than central city following the OMB guidance, and the definition of central city and principal city are slightly different (see 24 CFR Part 570). Of the 482 CDBG central city jurisdictions that existed both before and after the definition change, 327 central city jurisdictions (68%) became principle cities with the definition change. A small number of non-central cities (85 out of 2,501) in the original file were categorized as principal cities in the 2007 CDBG file. In our analysis by CDBG jurisdiction and in procedures for adjusting the sampling weights, we used the community’s current CDBG jurisdiction to ensure that our results accurately represented the current system for designating CDBG jurisdictions.

3 HUD provided a file called “COC_GeoAreasInfo.xls” with a list of 3,219 CDBG jurisdictions, jurisdiction type, and population of each jurisdiction. Geographic areas in the U.S Territories and Puerto Rico and three duplicate records were eliminated, resulting in a sampling frame of 3,142 CDBG jurisdictions. In addition, four CDBG areas in Massachusetts and one in New Hampshire included overlapping geographic areas and double-counted the population; therefore, the population was evenly divided across the overlapping CDBG jurisdictions before sampling.

4 Sampling based on the expected rate of homelessness is an attempt to obtain more precise estimates than those yielded by a simple random sample. If the proxy for the expected rate of homelessness is not correlated with the actual rate of homelessness, the resulting estimates will still be unbiased; however, the extra precision gains go unrealized.

5 Burt, Martha. 2001. Homeless Families, Singles, and Others: Findings from the 1996 National Survey of Homeless Assistance Providers and Clients. Housing Policy Debate, V12 (4), 737-780. This report presents the share of the homeless population by urban/rural status. The share of the population in each type of geographic area comes from the author’s calculations based on March 1996 Current Population Survey data. The results from the Burt study were based on central cities rather than principal cities, but we refer to them as principal cities here because of the high degree of overlap and to make the discussion easier to follow.

6 For 1990 counts, see U.S. Department of Housing and Urban Development. “Allocating Homeless Assistance by Formula.” A Report to Congress, 1992. For 2000 counts, see U.S. Census Bureau. “Emergency and Transitional Shelter Population: 2000.” A Census 2000 Special Report.

7 The other 8 certainty sites in principal cities were all ranked in the top 15 in the 1990 or 2000 Census counts.

8 The ratio was determined as follows. Burt (2001) found that 71 percent of the homeless population lived in central cities in 1996. At the same time, Current Population Survey data indicate that only 30 percent of the overall population lived in central cities at that time. The ratio of the share of the homeless population to the share of the overall population in central cities is 2.36. The ratio is 0.42 for non-principal city portions of Metropolitan Statistical Areas and 0.46 for rural areas. Dividing the principal city ratio by the rural ratio (2.36/0.46) equal 5.1, suggesting that the rate of homelessness is about five times higher in central cities than in rural areas.

9 Given that data from nonparticipating providers were not available, it is impossible to verify this assumption. However, it is the most reasonable assumption in that it is accurate when nonparticipating providers are missing at random or at least not systematically missing in a way correlated with the number of people they serve per available bed.

10 The AHAR sample consists of CDBG jurisdictions that are either the same as the CoC or part of the area covered by the COC. CDBG jurisdictions are the building blocks of the CoC. The contributing sites volunteered as CoCs. For example, the Iowa State COC represents 104 CDBG jurisdictions: 96 nonentitlement communities and 8 principal cities. Most other contributing sites represent between 1 and 7 CDBG jurisdictions.

11 Fifteen of the 18 certainty sites are principal cities; therefore, the nonresponse adjustment essentially occurs within CDBG type.

12 The 4,115 CDBG jurisdictions also include nonfunded CDBG jurisdictions not part of the original sampling frame.

13 Several hundred beds on the 2008 CoC application (less than 1 percent of all beds) did not match a known geocode, making unclear the CDBG jurisdiction in which the beds were located--even after manual review. We assigned the beds to CDBG type within each region in the same proportion as the beds with valid geocodes.

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