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Experimental Poverty Measures: Summary of a Workshop 7 Housing The workshop participants addressed two topics on housing costs and benefits in a new poverty measure: (1) whether and how differences in the amount of money owners and renters require to meet basic needs should be accounted for and (2) how to best estimate the value of housing subsidies. While the general issue of incorporating near-cash subsidies was not on the workshop agenda, given the overwhelming support for including them in a new poverty measure, part of this session was devoted to discussing housing subsidies because estimating their value can be challenging, and alternative methods for estimating them have been discussed in Census Bureau papers on experimental poverty measures (see Stern, 2004). ACCOUNTING FOR HOME OWNERSHIP One problem with the current official poverty measure is that thresholds do not vary by whether one rents or owns a house or apartment. If a person is an owner, there is no distinction between whether one has a mortgage or owns the property outright. The crux of the problem in having no such distinctions is that people who own a home outright or have low mortgages have more money to spend on other basic needs (such as food and clothing) than either renters or people with large mortgages. The 1995 National Research Council (NRC) report noted that consideration of approaches that account for these ownership distinctions in-
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Experimental Poverty Measures: Summary of a Workshop volves complex and highly technical issues. Many of the approaches involve accounting for the flow of services that owners obtain from their homes by adding a “rental equivalence value,” or “imputed rent,” to homeowners’ incomes that would also be consistent with the value of housing represented in the thresholds. These terms refer to the estimated amount of money owners would receive if they rented their homes. The value added is net of owners’ spending on their mortgages, property taxes, and maintenance costs. The thinking is that if the rental equivalence value is not added to the homeowners’ incomes, then people who own their homes with low or no mortgages would appear to be no better off than renters or homeowners with higher costs. Taking this value into account potentially affects the elderly the most, since they are the people most likely to own their homes. Recent research suggests that the elderly poverty rate is relatively lower when owner-occupied housing is accounted for. Given the uncertainty of data quality and the complexity of the calculations involved in estimating rental equivalence values, the 1995 NRC report did not recommend incorporating the value of housing in a new measure right away, but it urged that high priority be given to research to develop data and methods that could produce reasonable rental equivalence values. Since the NRC report, several alternatives for accounting for the value of owner-occupied housing in a new measure have been suggested and evaluated. One approach involves estimating the rental equivalence value for homes that are owned, as mentioned above. More specifically, it first involves determining the rental value of a home. This value is used in the construction of the thresholds (the portion for which housing needs are determined). Then, in order to create a measure of families’ resources that is consistent with the value of housing represented in the thresholds, “net implicit income” is added to homeowner’s incomes. Net implicit income equals the implicit rent homeowners would receive for their homes, minus the costs to maintain them, plus price appreciation. For homeowners with no mortgages, this method can potentially add substantial amounts to their computed incomes, making them less likely to be classified as poor. Several statistical techniques can be used to determine rental equivalence, such as using rental equivalence values reported in surveys or through statistical modeling (see Garner, 2004). A second approach for incorporating the housing value of owner-occupied housing is to determine the “user cost of capital.” When constructing thresholds, which are based on expenditure data, the user cost of
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Experimental Poverty Measures: Summary of a Workshop capital for renters is the rent they pay. For owners, the user cost of capital represents the rental equivalence value of the dwelling. Net implicit income is once again added to homeowners’ incomes. This method is therefore conceptually similar to the rental equivalence method: the main difference is that the user cost approach is designed to figure out what homeowners would pay for the home, net of financing, taxes, maintenance, and inflation. This method is a more refined and direct approach than the rental equivalence one, with the main drawback being its complexity. It is also difficult to implement with the data currently available. A third approach is called the out-of-pocket or payments approach. Its goal is to identify expenses associated with owning a home and accounting for them in the poverty thresholds of homeowners. Once a home is owned outright with no mortgage, out-of-pocket expenditures potentially fall. Under this method, no implicit income from owner-occupied housing is added to families’ resources. This method represents a relatively simple method of accounting for the value of owning a home, though, as noted by Garner (2004), one set of criticisms is that it ignores the opportunity costs of holding equity in a home, depreciation, and the effects of inflation on the interest paid. In the discussion, workshop participants tended to favor simpler approaches, as complex ones often end up having large margins of error due to data constraints. Two participants stated that when incorporating housing adjustments, the differences by geographic area would need to be addressed. Stephen Malpezzi (University of Wisconsin) advocated not adopting the more complex “user cost” method; he advocated constructing separate thresholds for owners and renters and adding net implicit rent to families’ resources (which would tend to add money to those families with no mortgages). Many workshop participants seemed to favor incorporating the value of housing to homeowners in a new poverty measure, making distinctions between the income needs of owners with mortgages, owners without mortgages, and renters. As Gary Burtless (The Brookings Institution) noted, “There is a very big difference between someone who owns a house outright, and a comfortable house, who is 80 years old, and someone who is 80 years old and has to pay rent. The fact that they have the same countable cash income does not make their situations the same, and that is very easy to explain to ordinary Americans.” Given the highly technical aspect of the alternative methods available, there was not much discussion concerning the best one.
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Experimental Poverty Measures: Summary of a Workshop ACCOUNTING FOR HOUSING SUBSIDIES The main challenge in estimating housing subsidies using Current Population Survey (CPS) data—the source of official poverty statistics—is that while the survey asks respondents whether they live in public housing or pay rent at a reduced rate (such as through the Section 8 program sponsored by the Department of Housing and Urban Development), no information is collected on the subsidy value or characteristics of the housing unit in which respondents live. However, the CPS does include an imputed estimate of the monetary value of a family’s housing subsidy in its annual files, but these estimates are based on 1985 American Housing Survey (AHS) data that have been updated for inflation using the CPI. Even the AHS does not have direct information on the dollar amount of subsidies, since renters, for example, are often unaware of the amount HUD reimburses owners who rent to them through the Section 8 program. The AHS, however, does identify subsidized housing units in a more detailed fashion than the CPS, and AHS renters report the amount of rent and utilities they pay. Using information on the reported characteristics of the housing unit, one can estimate what the market value of the unit would be without the subsidy. The difference between the estimated rental value of the unit and the actual amount paid as reported in the survey equals the estimated housing subsidy the family receives. Families in the CPS are then assigned a housing subsidy value by matching family characteristics to similar families in the AHS. This match is currently based on family income, family composition, and region of residence. The 1995 NRC report recommended that the Census Bureau conduct research on alternative ways to improve and update these housing subsidy estimates, and a few alternatives have since been implemented and evaluated. The two main alternatives include one that updates and refines the AHS-CPS match described above, and one that uses information on fair market rents (FMR) from HUD to estimate rental values and housing subsidy amounts. In the updated match method, 1999 AHS data are used (instead of 1985 data), and more household characteristics and greater geographic detail on location of residence are used in the match in order to more accurately impute housing subsidy values to CPS families. The second approach involves using FMRs for a large number of geographic areas, which HUD calculates annually. These FMRs usually represent estimates of the 40th percentile of rent for adequate units in the rel-
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Experimental Poverty Measures: Summary of a Workshop evant local housing market. These rents are used to administer Section 8 Housing Assistance Payments. Using the FMR method, the dollar amount of the housing subsidy a CPS family receives can thus be calculated as equaling the difference between the fair market rent where the family resides and 30 percent of that family’s total income (since families receiving the subsidy are required to spend 30 percent of their income on rent). The overall effect of alternative methods on estimated poverty rates is small—no more than 0.3 percentage points. Ronald Sepanik (Department of Housing and Urban Development), in his commentary on these two approaches, expressed reservations about using HUD FMRs for the purpose of estimating housing subsidies. Among other technical concerns, he noted that FMRs were not consistently set at the 40th percentile of rent. Rebecca Blank (University of Michigan) expressed concerns about the quality of data used in both of the methods. Kathleen Short (Census Bureau) expressed concerns about the quality of the subsidy estimates using the FMR approach, though the CPS-AHS statistical match method was more challenging to complete in a timely manner every year. Rebecca Blank stated that her sense from the paper presentation and discussion was that the statistical match method seemed to be the technically superior method, and many participants seemed to agree with her assessment.
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