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

Assisted Housing Inventories

AHI-General

AHI-Preservation

HUD

USDA-RHS

FHFC

LHFAs

• 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

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: annelaurieray@yahoo.com

Patricia Roset-Zuppa: roset@dcp.ufl.edu

Diep Nguyen: diep@ufl.edu