preserving affordable rental housing: the role of data anne ray, consultant patricia roset-zuppa,...
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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
Data available, useful for preservation-minded organizations
Agencies collect data for various purposes
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
• 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
Data Variables
Quarterly Excel files with property data on:
• Prepaid mortgages
• Opt-outs
• Potential opt-outs (from notices)
• Refinanced mortgages
• Mark-to-Market
HUD Preservation Files
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 preservation-related variables
Website
Florida Housing Data Clearinghouse:
www.flhousingdata.shimberg.ufl.edu/
Assisted Housing Inventory:www.flhousingdata.shimberg.ufl.edu/
AHI_introduction.html
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