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Affordability and Availability of Rental Housing in Pennsylvania

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Report Sections

  • Executive Summary

    HTMLPDF PDF Document (5 pages, 258 KB)
  • Chapter 1: Introduction

    HTMLPDF PDF Document (3 pages, 198 KB)
  • Chapter 2: Housing Characteristics in Pennsylvania and Neighboring States in 2000

    HTMLPDF PDF Document (8 pages, 293 KB)
  • Chapter 3: Housing Conditions of Pennsylvania's Lower Income Renters in 2000

    HTMLPDF PDF Document (11 pages, 650 KB)
  • Chapter 4: A Mid-Decade Update: Housing Conditions in 2005-06

    HTMLPDF PDF Document (15 pages, 1.27 MB)
  • Chapter 5: Implications for Policymakers and Suggested Research

    HTMLPDF PDF Document (5 pages, 260 KB)
  • Appendix A: County Level Housing Characteristics in 2000

    PDF PDF Document (22 pages, 660 KB)
  • Appendix B: Measuring National Needs for Affordable Rental Housing: A Brief Review of Past Research and Strategy Recommendations

    PDF PDF Document (8 pages, 229 KB)
  • Appendix C: Methodology for Calculating Affordable and Available Rental Housing Units Using CHAS Data

    PDF PDF Document (4 pages, 194 KB)
  • Appendix D: County-Level Examination of Rental Housing Needs in 2000

    PDF PDF Document (18 pages, 381 KB)
  • Appendix E: Using 2005 and 2006 ACS Data to Assess Rental Housing Needs

    PDF PDF Document (6 pages, 206 KB)
  • Appendix F: Changes Between 1990 and 2000 by DCED Regions and Consolidated PUMAs

    PDF PDF Document (15 pages, 413 KB)
  • Appendix G: Changes Between 2000 and 2005-06 by DCED Regions and Consolidated PUMAs

    PDF PDF Document (10 pages, 354 KB)
  • Bibliography

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  • Glossary

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Chapter 5: Implications for Policymakers and Suggested Research

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Suggested Research

Some of the questions posed in the previous section can be addressed through existing research and further analysis of available data, while other questions will require additional research and analysis of new data as they become available. Still others, particularly those dealing with the effects of the foreclosure crisis, are moving targets, with conditions changing month by month.

One useful extension of this study would be to more thoroughly analyze who the ELI and VLI renters are within Pennsylvania and, having identified their salient features, better define the characteristics of housing that best meets their needs. Much of this can be accomplished using existing data, including indicators such as the distribution of units in the rental stock by number of bedrooms, or of households by type and size (i.e., large families, seniors, individuals with disabilities, etc.). Differentiating the data by these indicators would provide not only a more thorough analysis of housing needs at state and sub-state levels but also one of more use to local housing planners and developers.

Along similar lines, another useful extension of this study would be to use existing data to analyze housing affordability needs and conditions of Pennsylvania owners. Lowerincome homeowners also have cost burdens and face shortages of affordable housing. In a recent report, the Joint Center for Housing Studies at Harvard University noted that, nationally, over 60 percent of the bottom quartile of homeowners pay more than 30 percent of their incomes for housing.71

Where policymakers determine that additional affordable rental units are needed, that decision may trigger additional issues that may call for further locally oriented research and assessment. In addition to the traditional tools of site and land-use analysis, an analysis of where new units should be located in relation to available and projected jobs can be valuable, in light of the frequent mismatch between available jobs and affordable and available housing units.72

A further important area of research is to look at how affordable housing needs and shortages, for both owners and renters, are changing as a result of the mortgage foreclosure crisis. In light of the urgency of this issue and the time lag in the availability of much national data, local planners and researchers should explore locally generated data sources, such as county-level transaction and foreclosure filing data, to develop timely local assessments of these issues. A number of models are beginning to emerge around the United States, including the NEO CANDO (Northeast Ohio Community and Neighborhood Data for Organizing) system established at Case Western Reserve University in Cleveland.73

New data sets are becoming available that will further assist state and local policymakers in developing their affordable housing strategies. In December 2008, the Census Bureau introduced the first three-year estimates of ACS data, starting with the years 2005-07. These estimates are based on a larger sample size and are more reliable than data based on one or two years when analyzing information for areas with small populations.74 By 2010, the Census Bureau plans to release five-year estimates annually, beginning with 2005-09, for still greater accuracy at the small-area level. The methodology used in this study can easily be applied to the ACS multi-year data as they become available. Moreover, in the near future it will become easier for state and local planners to apply the methodology used in this study. HUD is also funding additional ACS data mining that will include data by HAMFI thresholds, similar to CHAS data. Once these special tabulations become available, it will become much easier to identify trends in housing affordability and availability on a regular basis. This study can be a valuable model for processing ACS micro-data in the future to investigate issues such as housing needs of the disabled that are not directly addressed by the special CHAS-like tabulations.

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  • 71See Joint Center for Housing Studies (2008), p. 23.
  • 72See Lipman (2006), p. iii. This study documents the extent and effects of the mismatch between job and housing locations, noting that “in their search for lower cost housing, working families often locate far from their place of work, dramatically increasing their transportation costs and commute times. Indeed, for many such families, their transportation costs exceed their housing costs.”
  • 73NEO CANDO External Link is a free social and economic data system of the Center on Urban Poverty and Community Development at Case Western.
  • 74For a more detailed discussion on ACS data and sample sizes refer to the Census Bureau. External Link
  • Last update: June 11, 2010