Transcript Housing and Demographic Data: An Overview of Census Data

Preserving Affordable Rental Housing:
The Role of Data
Anne Ray, Consultant
Patricia Roset-Zuppa, Research Analyst
Florida Housing Data Clearinghouse
Shimberg Center for Affordable Housing, University of Florida
2007 Housing Policy Conference –
National Low Income Housing Coalition
February 26, 2007
Introduction
• Shimberg Center for Affordable Housing
• Florida Housing Data Clearinghouse
• MacArthur research by Shimberg and
Florida Housing Finance Corporation:
Research of assisted housing preservation data
and the development of risk assessment tools
Extensive vs. Intensive Data Collection
Extensive:
Portfolio-wide,
few key variables
Intensive:
Individual property,
detailed information
Extensive Data Collection
• Purposes: early warning, scope
• Data: owner type, funding, key dates,
rents, capital needs summary
Intensive Data Collection
• Purposes: tenant advocacy, subsidy
allocation, preservation transaction
• Data: financial detail, land use
restrictions, owner’s intent, rehab
needs
Moving Data to the Public
Agencies
collect data for
various
purposes
Data available,
useful for
preservationminded
organizations
Assisted Housing Inventory (AHI)
– Public database with development-level
information for subsidized rental housing
in Florida
– Since 2003
– Currently 2,244 properties with 279,201
units
AHI Data Users and Uses
• Policymakers, Planners, Developers,
Advocates
• Housing supply analysis for purpose of:
– Preservation of existing affordable stock
– Program analysis and legislation
– Housing Element of Local Comprehensive Plan
– Consolidated Plan
Assisted Housing Inventories
AHI-General
AHI-Preservation
HUD
USDA-RHS
FHFC
LHFAs
Data Variables
• Shim ID and link to map
• Development name and address details
• Unit count
• Bedroom breakdown
• Occupancy status
• Target population
• Funding source and program
• Approximate year built or year of funding
• Type of ownership
• Funding/affordability expiration dates
HUD Preservation Files
Quarterly Excel files with property data on:
• Prepaid mortgages
• Opt-outs
• Potential opt-outs (from notices)
• Refinanced mortgages
• Mark-to-Market
Limitations of Data from Sources
• Lack of unique property IDs
• Discrepancy in development data among
data sources
• Holes in essential data
• Reporting lags
From Raw Data to AHI:
Our Technical Approach
• Matching and merging of data files
• Unique Shim IDs
• Business rules
• Property-level ‘investigation’
AHI – What it Takes
• Cost
– Setup: 2 full-time equivalents for 2 years
– Maintenance: 1-1.25 full-time equivalents
– Database expert, housing expert, tech
support
• Clearinghouse funding:
– State 60-65%
– Internal funding 30%
– Grants and external contracts 5-10%
Strengths of AHI
• Comprehensive data sources and
variables
• Relationships with data providers
• Critical data fields for preservation:
– Funding and affordability expiration dates
– Type of ownership
– Year of construction
• Updated annually, some quarterly
• Public access
• Downloadable to Excel
Our Challenges/Opportunities
– Marketing the site
– Tracking of properties
– Accessing local data
– Accessing other critical preservationrelated variables
Website
Florida Housing Data Clearinghouse:
www.flhousingdata.shimberg.ufl.edu/
Assisted Housing Inventory:
www.flhousingdata.shimberg.ufl.edu/
AHI_introduction.html
Shimberg Center for Affordable Housing
University of Florida
203 Rinker Hall
PO Box 115703
Gainesville, FL 32611-5703
Tel: 352-273-1192
Fax: 352-392-4364
Anne Ray: [email protected]
Patricia Roset-Zuppa: [email protected]
Diep Nguyen: [email protected]