AHAR APR Supporting Statement-Final

This request is for clearance of data collection and reporting to enable the U.S. Department of Housing and Urban Development (HUD) Office of Community Planning and Development (CPD) to continue to manage and assess the effectiveness of its homeless assistance programs on an annual basis. HUD uses two primary mechanisms to meet its oversight responsibilities for these programs. First, it requires grantees and project sponsors that receive funding through Continuum of Care Homeless Assistance Programs (authorized by the McKinney-Vento Homeless Assistance Act) to prepare and submit annual project-level reports on performance and spending. Second, it funds an annual research effort to estimate the total number of homeless persons served through homeless assistance programs nationwide each year, their characteristics, and their service needs. The Annual Homeless Assessment Report is based on the submission of aggregate community-wide reports.

Both reports rely on a primary data source in each community – a local Homeless Management Information System (HMIS). An HMIS is an electronic data collection system that stores person-level information about homeless persons who access a community’s homeless service system. Over the past decade, HUD has supported the development of local HMIS by funding their development and implementation, by providing technical assistance, and by developing national data standards that enable the collection of standardized information on the characteristics, service patterns and service needs of homeless persons within a jurisdiction and across jurisdictions. These standards are described in HUD’s Homeless Management Information Systems (HMIS) Data Standards, cleared by OMB with the HPRP reporting package (OMB Control Number 2506-0186). The data elements provide the basis for data collection for the 2506-0186 and the reporting requirements proposed in this package. In conjunction with the re-tooling of HUD reporting requirements described in this submission, HUD is revising the data elements in the HMIS Data Standards. The proposed revised data standards are provided as a supplement to this OMB package.

The need and legal basis for these reporting requirements are presented below.

A.1.1 Annual Performance Reports (APR) for Homeless Assistance Programs

The existing Annual Progress Report (APR) (OMB Approval No. 2506-0145; Expiration: November 30, 2009) is a reporting tool that tracks the progress and accomplishments of the following competitive Homeless Assistance Programs: the Supportive Housing Program (SHP), Shelter Plus Care (S+C), and Section 8 Moderate Rehabilitation for Single Room Occupancy Dwellings (SRO) Program. Recipients of funding under the SHP, S+C, and SRO programs must complete and submit a progress report for each year in which assistance is received. In addition to providing the Department with important information to monitor individual project performance, the reports inform the Department’s competitive process for homeless assistance funding and enable the Department to report on overall program performance via the Performance Assessment Rating Tool. The APR is also used at the local level to evaluate program performance and to inform the Continuum of Care (CoC) application process (OMB Control Number 2506-0112).

With this OMB request, HUD is proposing to re-design the APR to meet two key objectives. First, proposed changes to current data collection requirements will enable grantees and project sponsors to report more accurately on project accomplishment and outcomes. Second, HUD proposes to increase reporting efficiency by developing a web-based reporting tool. In the future, the APR will be part of a streamlined and integrated program management, reporting, and application system through e-snaps, HUD’s new electronic portal for the annual Continuum of Care competitive funding process. With the re-design, HUD also proposes to change the title of this reporting tool from the Annual Progress Report to the Annual Performance Report (APR).

Funding recipients are mandated to collect and report APR information to HUD per the following federal regulations (see Attachment A):

Supportive Housing Program (CFDA 14.235): 24 CFR section 583.300 (g): “Records and reports. Each recipient of assistance under this part must keep any records and make any reports (including those pertaining to race, ethnicity, gender, and disability status data) that HUD may require within the timeframe required.”
Shelter Plus Care (CFDA 14.238): 24 CFR section 582.300 (d)(1): “Records and reports. (1) Each recipient must keep any records and, within the timeframe required, make any reports (including those pertaining to race, ethnicity, gender, and disability status data) that HUD may require.”
Section 8 Moderate Rehabilitation for Single Room Occupancy Dwellings Program (CDFA 14.249): 24 CFR section 882.808 (p): “Records and reports. Each recipient of assistance under this subpart must keep any records and make any reports that HUD may require within the timeframe required.”

A.1.2 Annual Homeless Assessment Report

Beginning with the FY 1999 HUD Appropriations Act, Congress called upon HUD to collect data from communities across the country in order to better understand the nature and extent of homelessness nationwide. House Report 105-610 stated the rationale for and scope of this data collection effort:

…HUD is directed to work with a representative sample of jurisdictions to collect, at a minimum, the following data: the unduplicated count of clients served; client characteristics such as age, race, sex, disability status; units (days) and type of housing received (shelter, transitional, permanent); and services rendered. Outcome information such as housing stability, income and health status should be collected as well. Armed with information like this, HUD’s ability to assess the success of homeless programs and grantees will be vastly improved.

Subsequent Senate and House Appropriations Committee reports have reiterated Congress’s directive to HUD regarding the importance of collecting data on homeless persons and the opportunity to capture this information through local Homeless Management Information Systems (HMIS). In the FY 2001 HUD Appropriations Act, Congress made the cost of implementing and operating an HMIS an eligible activity under the Supportive Housing Program and directed HUD to take the lead in requiring every jurisdiction to have client-level reporting within three years. FY 2001 Senate Report 106-410 stated the following:

The Committee believes that HUD must collect data on the extent of homelessness in America as well as the effectiveness of the McKinney homeless assistance programs in addressing this condition. These programs have been in existence for some 15 years, and there never has been an overall review or comprehensive analysis on the extent of homelessness or how to address it. The Committee believes that it is essential to develop an unduplicated count of homeless people, and an analysis of their patterns of use of assistance (HUD McKinney homeless assistance as well as other assistance both targeted and not targeted to homeless people) including how they enter and exit the homeless assistance system and the effectiveness of assistance. The Committee recognizes that this is a long term effort involving many partners. However, HUD is directed to take the lead in approaching this goal by requiring client level reporting at the jurisdiction level within 3 years.

To improve the capacity of local providers and jurisdictions to collect data, the bill includes language that makes implementation of management information systems (MIS), as well as collection and analysis of MIS data, an eligible use of Supportive Housing Program funds. Further, the bill includes language allowing HUD to use 1 percent of homeless assistance grant funds for technical assistance, for management information systems, and to further its efforts to develop an automated, client-level APR system. Of this amount, at least $1,500,000 should be used to continue on an annual basis to provide a report on a nationally representative sample of jurisdictions whose local MIS data can be aggregated yearly to document the change in demographics of homelessness, demand for homeless assistance, to identify patterns in utilization of assistance, and to demonstrate the effectiveness of assistance. The Committee also expects HUD to use technical assistance funds to assist in the development of an unduplicated count. The Committee instructs HUD to use these funds to contract with experienced academic institutions to analyze data and report to the agency, jurisdictions, providers and the Committee on findings.

Most recently, Congress expressed support for the implementation of HMIS and the development of a national report on homelessness in conjunction with the passage of the Transportation, Treasury, Housing and Urban Development, the Judiciary, the District of Columbia, and Independent Agencies Appropriations Act of 2006 (PL 109-115). Senate Report 109-109 stated:

In order to improve efforts in addressing homelessness, it is critical for providers and government officials to have reliable data. To address this matter, the Committee began an effort in 2001 that charged the Department to collect homeless data through the implementation of a new Homeless Management Information System [HMIS]. The implementation of this new system would allow the Department to obtain meaningful data on the Nation's homeless population and develop annual reports through an Annual Homeless Assessment Report [AHAR].

. . . the Committee strongly urges the Department to ensure full participation by all CoCs in the HMIS effort and consider future CoC funding to be contingent upon participation in HMIS and AHAR.

In order to fulfill these Congressional directives, HUD contracted with Abt Associates Inc., a private research firm, to develop a process to collect and analyze aggregated HMIS data from communities across the country. A report to Congress has been designed to address the following questions:

How many people are homeless during a year in the United States?
Who is homeless?
What is the nation’s capacity to provide housing for homeless persons?
Where do homeless persons receive shelter?
What are the patterns of shelter use?

Three Annual Homeless Assessment Reports have been delivered to Congress thus far; the fourth report is currently in preparation.

A2 Information Users

How is the information collected and how is the information to be used?

A.2.1 Annual Performance Report for HUD’s Homeless Assistance Programs

The re-designed Annual Performance Report (APR) will be used by HUD’s grantees to report on projects funded through HUD’s Homeless Assistance Programs, including the Supportive Housing Program (SHP), the Shelter Plus Care (S+C) Program, and the Section 8 Moderate Rehabilitation for Single Room Occupancy Dwellings (SRO) Program. Programs are required to collect data and prepare reports as a condition of funding.

An Annual Performance Report must be submitted for each operating year in which HUD funding is provided. A separate report must be submitted for each HUD grant received. For Shelter Plus Care (S+C) grantees, a separate Performance Report must be submitted for each S+C component (tenant, sponsor, project and single room occupancy rental assistance).

All Annual Performance Reports will be submitted to HUD electronically via e-snaps. The data are used by HUD to assess the performance of individual projects and to determine project compliance with funding requirements, including use of HUD funds for approved purposes and procurement of required matching funds. APRs are also aggregated by program type to provide information on overall program performance and outcomes to HUD staff, other federal agencies, the Congress, and the Office of Management and Budget.

Item-by-Item Justification

Detailed justification of each data element requested in the Annual Performance Report is contained in Attachment B.

In general, the re-designed Annual Performance Report covers the following topics:

Grantee Information—basic information about the grantee and project including the target population, the facility type, the number of beds in the project, and the extent to which data on clients served in the project are entered into the community Homeless Management Information System.
Outputs—counts of persons and households served, bed and unit utilization rates (for residential programs), and the number of client contacts (for street outreach programs).
Client Characteristics—information about all clients served in a project by household type and exit status.
Financial Information—information about project funding and expenditure and matching amounts.
Program Performance—information on performance measures by program type (for example, Permanent Housing, Transitional Housing, Supportive Service Only Programs, and Safe Havens), as well as information on client progress and performance based on one or more “self-sufficiency” domains, service linkage measures, or optional program-defined measures.
Narrative—descriptive information about the project and accomplishments.
HMIS-dedicated Projects—basic information about HMIS implementation.

A.2.2 Annual Homeless Assessment Report (AHAR)

The information collected through the AHAR is used by HUD and the Congress to understand the nature and extent of homelessness, assess the effectiveness of homeless assistance programs, analyze service use patterns, and understand how programs can be improved. As directed by Congress, communities that participate in the AHAR collect client-level data on homeless persons through local Homeless Management Information Systems (HMIS). On an annual basis, they de-duplicate and aggregate this information and produce a report of local data using a standardized template. These aggregated reports are submitted electronically to a private research firm and are the basis for an annual national report on homelessness in the United States. Specifically, the report shows the number of persons experiencing homelessness during a year, the number of persons using shelters and permanent supportive housing, patterns of shelter use, and the characteristics of homeless persons.

For the CoCs gathering and aggregating the data, the local AHAR report is useful in several ways. First, it presents an opportunity to assess the quality of the HMIS data that are collected by homeless assistance providers in each community. Second, the information is useful to understanding homeless clients and service needs at the local level.

Item-by-Item Justification

Detailed justification of each AHAR data collection element is contained in Attachment C. The types of information collected for the AHAR include:

Person Counts—number of homeless persons served in residential programs at four points in time and over the course of a year.
Demographics—age, gender, race, ethnicity, household size, veteran status, and disability status of homeless persons served in residential programs.
Prior Living Situation—living arrangement the night before entering a residential program for persons in families and individuals; length of time in that living situation.
Length of Stay—number of nights that individuals and persons in families stay in residential programs.
Household Counts—number of homeless households served in residential programs at four points in time and over the course of a year.
Long-term Stayer Demographics—age, race, ethnicity, household size, veteran status, and disability status of homeless children and adults that stay in residential programs for more than six months.

A3 Improved Information Technologies

Describe whether, and to what extent, the collection of information is automated (item 13b1 of OMB form 83-i). If it is not automated, explain why not. Also describe any other efforts to reduce burden.

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

As described in Section A.2.1 above, HUD is implementing a major improvement in information technologies at this time. Annual Performance Reports will be submitted via a web-based reporting tool integrated into HUD’s e-snaps system. In order to increase the accuracy of the data and reduce burden on grantees, the system will have the following capabilities:

Some Performance Report information will be pre-filled. The Performance Report will be linked to information provided on the original application; portions of the Performance Report will be pre-filled with information from these sources.
Built-in data quality checks. The e-snaps system will check for data consistency and accuracy as grantees and/or project sponsors complete the Performance Report and will identify potential data issues for the user prior to submission.
Multiple reporting modules. The web-based Performance Report will contain reporting modules that are specific to each type of grant and program component. Upon entering information about a program’s type, the web-based Performance Report will display the questions that apply to that type only. For example, a transitional housing (TH) program will see the performance measures that apply to a TH program only.
Use of pick-lists or drop-down menus. The Performance Report will contain drop-down menus where applicable to facilitate reporting and improve data quality.
Automated calculations. Rows or columns shaded in grey will be automatically calculated and thus do not require data entry or manual calculations.
Other key features include:

Secure data entry (128 bit encryption).
User login, save/review, and submit—including user registration with authorizing grantee official verification; data entry, review, edit prior to final submission; and data submission date/time stamp.
Electronic signature/approval by authorizing grantee and sponsor officials.
Navigation to access different sections/tables sequentially or non-sequentially.
Help/look-up features—including highlight text linked to Performance Report instructions, definitions, or the HMIS Data Standards; a link to the full Performance Report instructions; and integrated Help Desk support.
Integrated method for HUD review and approval—including interactive messaging with grantee contact to address questions/corrections; and HUD review and approval date/time stamp with grantee notification.

A.3.2 Annual Homeless Assessment Report

Every effort has been made to reduce burden on communities that develop and submit local AHAR reports based on HMIS data. Most importantly, the AHAR relies on the HMIS standardized data elements, the same data elements that are used to generate HUD’s Annual Performance Report. The standardized data elements, which are outlined in the HMIS Data Standards, are collected and electronically maintained by all communities receiving HUD homeless assistance funding regardless of whether they are completing the AHAR. Communities that are developing local AHAR reports can use their existing HMIS to capture all the information needed to complete the local AHAR report without any additional data collection burden.

A web-based data collection tool for AHAR submission has been developed that improves both the efficiency of the collection process and the validity and reliability of the data. Reporting features also add value for communities. This tool—known as the AHAR Exchange—has the following key features:

Manual or Automated Data Submission Options. Communities can manually enter data or can upload data that have been exported as an XML file from their own HMIS.
Navigation. Communities can navigate the data collection system as a survey, or they can use menus to move directly to specific questions.
Messaging and Workflow. Users correspond with HUD's data collection contractor through built-in messaging linked to email. The workflow requires respondents to submit data for review, and for HUD contractors to review the data for errors.
Status Tracking. The database tracks the status of data collection efforts for each participating community and across communities.
Validation. The system validates the data both as the data are entered and in summary validation reports. Data are cross-validated against other data entered by communities to ensure consistency.
Notes. Users can provide notes containing additional context for the data submitted.
Reporting. Built-in reports summarize and extrapolate the data to make the information useful for participating communities.

Additional features include:

Sandbox Feature. A "sandbox" version of the tool is used for training and testing. Communities also use the sandbox for their own local needs.
Audit. The system audits every change to ensure data integrity.
Application Security Features. Data are stored in a secure, professional hosting environment and backed up nightly. Security features include session time out after 15 minutes of inactivity, lockout after 3 successive failed logins, and password change required every 45 days.
Section 508 Compliance. The site is accessible to those with visual impairments.

A4 Duplication of Similar Information

Is this information collected elsewhere? If so, why cannot any similar information already available be used or modified?

The Annual Performance Report for homeless assistance programs is the only annual report that HUD requires programs to submit in order for HUD to monitor project progress.

The Annual Homeless Assessment Report is not duplicative of any information currently available on the topic of homelessness. No other federal effort is under way to collect longitudinal, community-level data on homelessness. This reporting is not duplicative either because no other homeless data are available to HUD on a quarterly basis.

A5 Small Businesses

Does the collection of information impact small businesses or other small entities (item 5 of OMB form 83-i)? Describe any methods used to minimize burden.

No small businesses are involved as respondents to this data collection effort. HUD’s Annual Performance Reports are completed by grantees and project sponsors receiving HUD homeless assistance funding.

The local Annual Homeless Assessment Report is completed by local or state governments or nonprofit organizations that represent local Continuums of Care.

A6 Less Frequent Data Collection

Describe the consequence to Federal program or policy activities if the collection is not conducted or is conducted less frequently, as well as any technical or legal obstacles to reducing burden.

Historically, HUD has used the Annual Performance Report for homeless assistance programs to monitor all homeless assistance projects on an annual basis. Less frequent data collection would significantly reduce HUD’s ability to monitor program performance and ensure compliance with program requirements and federal regulations

With respect to the Annual Homeless Assessment Report, HUD is complying with Congressional directive to collect data on homeless persons on an annual basis. Further, collection of this data on a less frequent basis may compromise the quality of the data that are collected.

A7 Special Circumstances

The proposed data collection activities are consistent with the guidelines set forth in 5 CFR 1320.6 (Controlling Paperwork Burden on the Public—General Information Collection Guidelines). There are no special circumstances that require deviation from these guidelines.

A8 Federal Register Notice/Outside Consultation

Identify the date and page number of the Federal Register notice (and provide a copy) soliciting comments on the information. Summarize public comments and describe actions taken by the agency in response to these comments. Describe all efforts to consult with persons outside the agency.

In accordance with the Paperwork Reduction Act of 1995, the Department of Housing and Urban Development published a notice in the Federal Register on July 22, 2009 (74 FR 36244) announcing the agency’s intention to request an OMB review of data collection activities for the Annual Homeless Assessment Report and the Annual Performance Report for HUD’s Homeless Assistance Programs (including the Supportive Housing, Shelter Plus Care, Section 8 SRO). The notice provided a 60-day period for public comments. A copy of the Notice is in Attachment D.

The Department received 43 distinct responses representing 286 distinct comments. The comments referred to this supporting statement as well as the Draft Homeless Management Information Systems Data Standards. A table of comments and responses can be found in Attachment E.

Outside consultations related to the proposed data collection effort are described below.

A.8.1 Annual Performance Report for HUD’s Homeless Assistance Programs: Initial Consultation

In the course of re-designing the Annual Performance Report, HUD conducted extensive consultations with staff from HUD headquarters and its field offices and HUD grantees in August and September 2006. A total of 42 focus groups were conducted in 10 cities nationwide: Baltimore, Boston, Chicago, Denver, Fort Worth, Kansas City, Knoxville, New York, San Francisco, and Seattle. In each city, HUD convened three focus groups with grantees and one focus group with field office staff, except in San Francisco where five focus groups were held with grantees and one focus group was held with field office staff. Most of the participants provided in-person feedback, and some participated via telephone or video conferencing. Overall, more than 500 participants attended the focus groups, including 416 grantees and 86 field office staff.

A.8.2 Annual Homeless Assessment Report

HUD assembled a group of experts on August 27 and August 28, 2002 to provide guidance on the development of an Annual Homeless Assessment Report and data standards associated with that report. Attendees included representatives from CoCs with more advanced HMIS systems, experts from federal agencies that collect data on vulnerable populations (including the U.S. Department of Health and Human Services, the U.S. Department of Justice, and the Office of Management and Budget), researchers on homelessness, advocacy organizations, and providers of technical assistance to communities that have developed HMIS. A list of participants and their affiliations at the time of the meeting is provided in Exhibit A-1 below.

Exhibit A-1: AHAR/HMIS Data Standards Expert Meeting Participants (affiliations as of 8/2002)

1	Jacqueline	Brown	Atlanta Children's Shelter
2	Gloria	Townsend	City of Baltimore Department of Housing Community Development
3	Matthew	Berg	City of Philadelphia Office of Emergency Shelter & Services
4	Barbara	Ritter	City of Spokane Human Services
5	Brooke	Spellman	Consultant / Former City of Chicago Department of Human Services
6	Julie	Williams	Idaho Housing and Finance Association
7	Matt	White	Consultant
8	Nan	Roman	National Alliance to End Homelessness
9	Donald	Whitehead	National Coalition for the Homeless
10	Lyn	Rosenthal	National Network to End Domestic Violence
11	Jill	Berry	New York City Department of Homeless Services
12	Martha	Are	North Carolina Coalition to End Homelessness
13	Tedd	Kelleher	State Washington Office of Community Development - Housing Finance Division
14	Annetta	C. Smith	U.S. Census Bureau
15	Jean	Hochron	U.S. Department of Health and Human Services
16	John	Fanning	U.S. Department of Health and Human Services - HIPPA
17	Fran	Randolph	U.S. Department of Health and Human Services - Homeless Programs Branch
18	Patricia	Carlile	U.S. Department of Housing and Urban Development - CPD
19	Robyne	Doten	U.S. Department of Housing and Urban Development - CPD
20	John	Garrity	U.S. Department of Housing and Urban Development - CPD
21	Mark	Johnston	U.S. Department of Housing and Urban Development - CPD
22	Michael	Roanhouse	U.S. Department of Housing and Urban Development - CPD
23	David	Vos	U.S. Department of Housing and Urban Development - CPD/HOPWA
24	Paul	Dornan	U.S. Department of Housing and Urban Development - PD&R
25	Peter H.	Dougherty	U.S. Department of Veterans Affairs
26	Carol	Coleman	U.S. Federal Emergency Management Agency
27	Clinton	Jones	U.S. House Subcommittee on Housing & Opportunity
28	Philip	Mangano	U.S. Interagency Council on the Homeless
29	Steve	Redburn	U.S. Office of Management & Budget
30	Oscar	Gutierrez	University of Massachusetts - McCormack Institute
31	Michelle	Hayes	University of Massachusetts - McCormack Institute
32	Dennis	Culhane	University of Pennsylvania - CMHPSR
33	Stephen	Poulin	University of Pennsylvania - CMHPSR
34	Martha	Burt	Urban Institute
35	Robert	Rosenheck	Veterans Affairs North East Program Evaluation Center/ Yale University
36	Julie	Hovden	Wisconsin State Division of Housing & Intergovernmental Relations
37	Steve	Berg	National Alliance to End Homelessness
38	Fay	Nash	U.S. Census Bureau
39	Stan	Chappell	U.S. Department of Health and Human Services - Family and Youth Services Bureau
40	Polly	Cooper	U.S. Department of Housing and Urban Development - CPD
41	Susan	Corts	U.S. Department of Housing and Urban Development - CPD
42	Doris	Hill	U.S. Department of Housing and Urban Development - CPD
43	Marty	Horwath	U.S. Department of Housing and Urban Development - CPD
44	Rebecca	Wiley	U.S. Department of Housing and Urban Development - CPD
45	Patrick	Simien	U.S. Department of Housing and Urban Development - OPC
46	Bob	Gray	U.S. Department of Housing and Urban Development - PD&R
47	Jeff	Lubell	U.S. Department of Housing and Urban Development - PD&R
48	Seth	Marcus	U.S. Department of Housing and Urban Development - PD&R
49	Marge	Martin	U.S. Department of Housing and Urban Development - PD&R
50	Kevin	Neary	U.S. Department of Housing and Urban Development - PD&R
51	Rob	Wilson	U.S. Department of Labor
52	Allen	Taylor	U.S. Department of Veterans Affairs
53	Gayla	West	U.S. Interagency Council on the Homeless
54	Francisco	Balicao	U.S. Office of Management & Budget
55	Mary Joel	Holin	Abt Associates Inc.
56	Larry	Buron	Abt Associates Inc.
57	Alvaro	Cortes	Abt Associates Inc.
58	Jill	Khadduri	Abt Associates Inc.

A9 Payment/Gift to Respondents

Explain any payments or gifts to respondents, other than remuneration of contractors or grantees.

HUD does not provide remuneration to grantees for completion and submission of Annual Performance Reports or local AHAR reports.

A10 Confidentiality

Describe any assurance of confidentiality provided to respondents and the basis for assurance in statute, regulation or agency policy.

The Annual Performance Report and the Annual Homeless Assessment Report contain only aggregated data on the number and characteristics of persons receiving homeless assistance services. These reports do not contain any protected personal information.

A11 Sensitive Questions

Justify any questions of a sensitive nature, such as sexual, religious beliefs, and other matters that are commonly considered private.

The Annual Performance Report and the AHAR do not include questions of a sensitive nature for HUD grantees or subgrantees.

A12 Burden Estimate (Total Hours and Wages)

Estimate public burden: number of respondents, frequency of response, annual hour burden. Explain how the burden was estimated.

The exhibits below demonstrate how the public burden for the Annual Performance Report and the Annual Homeless Assessment Report were calculated. The total burden for data collection for both reports over a one year period is estimated at 207,944 hours.

When compared with the last clearance (OMB Approval No. 2506-0145), there is a drop in burden for the Annual Performance Report (from 29.35 hours per program under the current version of the APR, to 28 hours per program under the new automated version of the APR). However, because the AHAR has been added to this clearance, the overall burden will increase.

A.12.1 Burden Estimates for Annual Performance Report for HUD’s Homeless Assistance Programs

Exhibits A-2 and A-3 provide information on the estimated time and expenses necessary to compile data and complete the revised Annual Performance Reports for all homeless assistance programs for a one-year period. Total burden for data collection over one year for the Annual Performance Reports is estimated at 182,000 hours. The average annual burden for recipients of HUD Homeless Assistance Program funding (not-for-profit organizations and state and local governments) that complete an Annual Performance Report is 28 hours at a cost of $891.24.

The burden estimates for the re-designed Annual Performance Report represents an overall decrease in respondent burden for recipients of HUD’s homeless assistance funds compared to the current Annual Progress Report (OMB Approval No. 2506-0145). The current report had an estimated burden of 234,800 hours for 8,000 homeless assistance providers, or an average estimated burden of 29.35 hours per respondent.

Exhibit A-2: Estimated Annual Burden Hours for Annual Performance Reports

A	B	C	D	E
Recipient Category	Total Number of Annual Reports	Grantee or Subgrantee Burden per Annual Report (Minutes)	Total Burden (Minutes)	Total Burden (Hours)
			B*C	D/60
Not-for-Profit Recipients of HUD Homeless Assistance Funding*	3,250	1,680	5,460,000	91,000
State and Local Government Recipients of HUD Homeless Assistance Funding*	3,250	1,680	5,460,000	91,000
Total	6,500	3,360	10,920,000	182,000

*Includes Projects funded by the HUD Supportive Housing Program, Shelter Plus Care, and Section 8 Moderate Rehabilitation for Single Room Occupancy Dwellings (SRO) Program.

Exhibit A-3: Estimated Annualized Cost Per Respondent for Annual Performance Reporting

A	B	C	D
Recipient Category	Total Burden Hours per Annual Report	Hourly Wage Rate*	Total Respondent Costs
			B*C
Not-for-Profit Recipients of HUD Homeless Assistance Funding	28	$31.83	$891.24
State and Local Government Recipients of HUD Homeless Assistance Funding	28	$31.83	$891.24

*Hourly wage rates are based on the 2007 Occupational Employment and Wages published by the Department of Labor (5/9/08). The hourly wage rates in Exhibit 2 represent the average of “Business Operations Specialists, All Others” ($29.88/hr) and “Data Base Administrators” ($33.78), assuming an equal proportion of hours required to complete the Performance Report per occupational type.

A.12.2 Burden Estimates for the Annual Homeless Assessment Report

HUD expects that every Continuum of Care (CoC) (currently there are 448 CoCs nationwide) will submit a local AHAR report annually that provides a community-level snapshot of the homeless population. In addition, for purposes of monitoring AHAR data quality and data trends throughout the year, HUD is requesting that CoCs also begin to submit AHAR reports quarterly.

The effort involved in completing the AHAR report will vary from community to community depending on technology used and staff capacity. Fortunately, most communities now use systems that are able to produce automated reports. A relatively small number of communities must manually run reports to extract data for the AHAR. For this reason, the estimated annualized burden estimate is divided into two categories: 1) communities that use their HMIS software systems to produce automated reports; and 2) communities that must manually run reports to extract AHAR data.

The burden estimates for each group are as follows:

Group 1: Communities with automated reports: 16 hours for the annual report plus 8 hours for each quarterly report. A total of 48 hours per community per year.
Group 2: Communities that must manually run reports: 24 hours for each annual report plus 16 hours for each quarterly report. A total of 88 hours per community per year. Note that these are averages. Communities will spend more time in programming the initial report. Once it is programmed, the number of hours required to complete the report will be less.

The burden estimate for all 448 CoCs is outlined in Exhibit A-4. The total number of hours required to produce and submit local annual and quarterly AHAR reports is 25,944. It should be noted that the first time a CoC participates in the AHAR is the most burdensome. Once a community has contributed data, the burden will be reduced because the process will vary little from year to year. Additionally, the burden will be reduced as more software vendors develop automated AHAR reporting features.

Exhibit A-4: Estimated Annualized Burden Hours for the Annual Homeless Assessment Report

A	B	C	D	E
CoC Category	Number of Respondents	Number of Responses per Year	Average Burden per Respondent	Total Burden Hours
				B*D
Group 1: CoCs with Automated Software Report	425	1 annual 4 quarterly	48	20,400
Group 2: CoCs with Manual Software Report	63	1 annual 4 quarterly	88	5,544
Total	488			25,944

Exhibit A-5 provides estimates of annualized cost to respondents for the hour burdens for collection of information. The estimated annualized cost for a CoC that has software with an automated AHAR report function (Group 1) is $1,527.84. The estimated annualized cost for other CoCs (Group 2) is $2,801.94.

Exhibit A-5: Estimated Annualized Cost per AHAR Respondent

A	B	C	D
CoC Category	Average Burden Per Respondent	Hourly Wage Rate*	Total Costs per Respondent
			B*C
Group 1: CoCs with Automated Software Report	48	$31.83	$1,527.84
Group 2: CoCs with Manual Software Report	88	$31.83	$2,801.04

*Hourly wage rates are based on the 2007 Occupational Employment and Wages published by the Department of Labor (5/9/08). The hourly wage rates in Exhibit 5 represent the average of “Business Operations Specialists, All Others” ($29.88/hr) and “Data Base Administrators” ($33.78), assuming an equal proportion of hours required to complete the AHAR per occupational type.

A13 Capital Costs

Estimate the annual capital cost to respondents or record keepers.

There are no capital costs for respondents beyond customary or usual business practices or that are not otherwise required to achieve regulatory compliance not associated with the collection of information for purposes of completing the Annual Performance Reports or the Annual Homeless Assessment Report.

A14 Cost to the Federal Government

Estimate annualized costs to the Federal government.

The federal costs associated with the Annual Performance Reports are accounted for as part of overall program oversight and management. It is not possible to separate out any specific costs attributed to the data collection effort that are borne by the government.

HUD contracts with a private research firm to collect and analyze local Annual Homeless Assessment Report data, prepare a yearly report, and prepare quarterly interim updates. The estimated annual contractor cost is $700,000. The activities that are covered in this estimate include: conducting outreach to communities to participate in the AHAR process; providing technical assistance to communities to prepare for the AHAR and to submit their data; cleaning and analyzing the data; and preparing the reports.

A15 Program or Burden Changes

Explain any program changes or adjustments in burden.

This submission to OMB includes a request for approval of an automated version of the Annual Performance Report that will incorporate continued annual reporting on HUD’s competitive Homeless Assistance Programs. The automated version of the Annual Performance Report replaces the former paper version (OMB Approval No. 2506-0145). The change in overall respondent burden for Homeless Assistance Programs decreases from 29.35 hours per program under the current version of the Annual Progress Report, to 28 hours per program under the new automated version.

The AHAR data collection effort does not necessitate the collection of any additional information from homeless clients in a community. The community will aggregate client-level data that they are already required to collect for Performance Reports.

A16 Publication and Tabulation Dates

If the information will be published, outline plans for tabulation and publication.

A.16.1 Annual Performance Report for HUD’s Homeless Assistance Programs

Annual Performance Report data will be entered and stored in the e-snaps system. HUD staff will review and assess each Annual Performance Report individually to determine compliance with HUD regulations and grantee agreements. Aggregated data from the Annual Performance Reports will be used to report to Congress, OMB, and other stakeholders on program performance on an as requested basis.

A.16.2 Annual Homeless Assessment Report

The data collection period for the AHAR is October 1^st to September 30^thof each year. During this period, homeless assistance providers capture and record information about the homeless persons in their community using homeless services. At the CoC level, staff members work to ensure that providers submit data on every client served and that they submit all of the information on each client that is required. The providers typically update this information on a monthly basis. The monthly data will be aggregated to provide three-month snapshots for data quality assessments and to provide HUD with recent data on homelessness trends.

Once the annual data collection period ends on September 30th, data are submitted by providers to the CoC where an annual community level report is developed and submitted to HUD’s research contractor using a web-based system. Following the review of data, analysis and report writing commences by early January. Pending HUD review and approval, the AHAR report is released to Congress in the spring of each year. HUD expects that when the report is released it will be published on HUD’s website. It will also be publicly available once delivered to Congress.

A17 Expiration Date

Explain any request to not display the expiration date.

The OMB expiration date will be displayed on all data collection instruments. No exceptions are requested.

A18 Certification Statement

Explain each exception to the certification statement identified in item 19.

There are no exceptions to the certification.

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 cities^²
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.

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.
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.
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.

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	larry_buron@abtassoc.com	Project Director, Abt Associates
Dr. Alvaro Cortes*	301-634-1857	alvaro_cortes@abtassoc.com	Project Team, Abt Associates
Paul Dornan	202-402-4486	paul.dornan@hud.gov	Project Team, U.S. Department of Housing and Urban Development
Michael Roanhouse	202-402-4482	michael.roanhouse@hud.gov	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.

File Type	application/msword
File Title	Abt Single-Sided Body Template
Author	HerbertC
Last Modified By	Brett Gagnon
File Modified	2009-11-09
File Created	2009-11-09