How Much Nursing Home Care Can Home Equity Finance?

Stephan Lindner

Urban Institute

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ABSTRACT

Older adults who have income and assets have choices should they need long-term services and supports (LTSS). In this paper we estimate the extent to which older adults could potentially use home equity to help pay for nursing home care. By estimating the relationship between home equity and care costs, we provide an upper bound on how much assistance with LTSS home equity might purchase. Using a large sample of older adults from the American Community Survey in 2011, we compare their estimated level of home equity to the cost of nursing home care in their area. We find that the typical (median) older adult is able to purchase just over half a year of care in a private room. However, the median conceals a great deal of variation. For example, 28% of older adults are renters and therefore have no home equity to help pay for LTSS. In addition, those at greatest risk of needing LTSS, such as those age 85 and older, and those with one or more functional limitations, have lower ability to finance LTSS through home equity primarily because they have low rates of homeownership. High home equity levels--and a correspondingly high number of nursing home days that it could finance--are concentrated in a limited number of states and metropolitan areas such as Washington, California, Illinois, Massachusetts or the District of Columbia. By contrast, those living in states and metropolitan areas in the rustbelt and that were hit hard by the recession have low home equity values to spend on nursing home care.

DISCLAIMER: The opinions and views expressed in this report are those of the authors. They do not necessarily reflect the views of the Department of Health and Human Services, the contractor or any other funding organization.

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Older adults who have income and assets have choices should they need long-term services and supports (LTSS). In this paper we estimate the extent to which older adults could potentially use home equity to help pay for nursing home care. By estimating the relationship between home equity and care costs, we provide an upper bound on how much assistance with LTSS home equity might purchase. Using a large sample of older adults from the American Community Survey (ACS) in 2011, we compare their estimated level of home equity to the cost of nursing home care in their area. We find that the typical (median) older adult is able to purchase just over half a year of care in a private room. However, the median conceals a great deal of variation. For example, 28 percent of older adults are renters and therefore have no home equity to help pay for LTSS. In addition, those at greatest risk of needing LTSS, such as those age 85 and older, and those with one or more functional limitations, have lower ability to finance LTSS through home equity primarily because they have low rates of homeownership. High home equity levels--and a correspondingly high number of nursing home days that it could finance--are concentrated in a limited number of states and metropolitan areas such as the District of Columbia, Hawaii and California. By contrast, those living in states and metropolitan areas in the rustbelt and that were hit hard by the recession have low home equity values to spend on nursing home care. For these and other reasons, our estimate may overstate the amount of nursing home care that older adults most likely to use nursing home care could finance via home equity.

 

Introduction

One of the most important risks that older people in the United States face is the possibility of becoming disabled and no longer able to live independently. Many elders with a disability continue to reside at home alone or with family and rely exclusively on unpaid help, primarily from family caregivers, for the entirety or some part of the duration of their need for long-term care services and supports (LTSS). At higher disability levels, their likelihood of receiving supplemental paid care increases.¹ Using the Urban Institute's Dynamic Simulation of Income Model, Favreault and Dey (2015) estimated that about half (52 percent) of Americans turning 65 today will require high-level LTSS, either paid or unpaid, at some point before they die.² Many may under-estimate this risk.³ Most will need assistance for less than two years. Among those who ever use paid LTSS, at home or in a nursing home or other residential care facility, about half will receive it for less than a year, whereas a little more than 10 percent will receive it for five years or more. Women have LTSS needs of longer duration compared to men and lower-income elders also tend to need LTSS for longer periods compared to higher-income elders. Unmarried and low-income elders use more paid LTSS relative to married and higher-income elders.

The types and amounts of personal or family financial resources--income, savings, home equity, insurance⁴--that older persons can draw upon to pay out-of-pocket for LTSS depend not only on their differential income or wealth but also on the type of LTSS they use. Most older adults prefer to remain at home. Elders living at home may need to use much if not all of their monthly income to cover basic living expenses. If they are homeowners, they will likely wish to continue to live in the home, which limits their ability to use their home equity to pay for LTSS. Although a reverse equity mortgage or other home equity line of credit could potentially access some home equity to pay for home and community-based services (HCBS), that amount will likely fall considerably short of the full equity value.

Some older adults move to a residential care setting where they can obtain LTSS when needed. Because such assisted living is typically more expensive than living at home, they may need all of their income and some of their assets to pay for care. If homeowners with no spouse or other family members who need to reside in the home decide to move into an assisted living facility, using home equity--most often by selling the home--to help finance the cost of such care becomes a viable option or, in some cases, a necessity.⁵

When older adults need more care than can be provided at home or at an assisted living facility, they may move to a nursing home. Although for most this is the least preferred LTSS setting, it is necessary in some situations. Again, using home equity to help finance the cost of such care may be required. Because nursing home care is very expensive, all assets, including home equity, may be used if the stay is long enough. Besides the loss of funds, using home equity to pay for care will make it difficult to return to the community, should functioning improve.

This study examines the distribution of home equity and nursing home costs across individuals and geographic areas to shed light on the potential for home equity to finance LTSS needs.⁶ Similarly to other studies in this area (e.g., Davidoff 2010), we use conservative, stylized assumptions regarding the amount of equity accessible to homeowners, largely ignoring issues of how and at what cost equity would be accessed. Our results thus represent what is likely a best case scenario for leveraging home equity. At the national level, we investigate how many days of nursing home care older people at different percentiles in the housing equity distribution nominally could finance with home equity. We then explore geographic variation in housing equity and nursing home costs.

It is important to recognize that homeownership and home equity levels vary widely across the population, making it difficult to generalize the extent to which home equity could be used to pay for care. For instance, people with disabilities report much lower home values than people without disabilities (Johnson 2013). Moreover, home equity, housing costs, and LTSS costs vary dramatically across geographic regions within the United States. For example, the cost of nursing home care ranged from less than $200 to more than $400 per day in a nursing home in 2011 (MetLife 2011). Although it might be expected that geographic areas with home values under the national average would also have residential elder care costs lower than the national average, this is not always the case. For example, West Virginia has lower home equity values and higher nursing home costs compared to the national averages. It is also important to bear in mind that our analysis does not adjust for the likelihood of using paid LTSS in various settings, which varies by disability status, marital status, and income/wealth, although as we have seen, the probability of using high-cost LTSS and paying out-of-pocket for all or some of this care varies greatly according to such factors.

 

Data and Methods

We use the 2011 ACS to obtain home values in various geographic areas. We select people age 65 years of older who do not live in group quarters. The sample has 490,782 adults and includes both homeowners and renters. The ACS asks about ownership and home value at the household level, not the individual level. Renters have zero home equity. We assign ownership to either the head or the spouse of the head of the household. We assign half the home value to the head and half to the spouse if they live together; otherwise, we assign the full home value to the head.⁷ Although this allocation provides a useful way to compare couples with older adults who live alone, it has limitations. If one spouse is institutionalized, there often is no way to access the home equity without changing the housing arrangement of the spouse that remains in the community. This also applies to children or other caregivers who reside with the person who enters the institution.

We further reduce home values by 8 percent to account for transaction costs (e.g., real estate broker fees, moving expenses).⁸ Clearly, the decision to use house equity as a financing tool for nursing home care is much more complicated than we assume here. For instance, it might be difficult for homeowners to put their house on the market at a short notice, especially after onset of a serious disability. Because of such complications, we see our analysis as producing estimates that likely overstate the potential for equity financing. We return to possible caveats at the end of this study.

The ACS asks respondents if they have a mortgage on their home, but it does not inquire about the outstanding mortgage value. We therefore impute mortgage values for ACS respondents reporting a mortgage using data from the 2010 wave of the Health and Retirement Study (HRS).⁹ We then subtract the imputed mortgages from home value to obtain net equity.

We next match the ACS to MetLife's 2011 market survey of long-term care costs (MetLife 2011). MetLife divides the United States into metropolitan areas and "Rest of State" areas (i.e., nonmetropolitan areas not included among the state's metropolitan areas). MetLife conducts telephone interviews for at least ten nursing homes or 15 percent of nursing homes in each area, for a total sample of 2,003 nursing homes.¹⁰

The MetLife survey includes 83 metropolitan areas whereas the ACS has 284 metropolitan areas.¹¹ In most cases we are able to match a metropolitan area in the MetLife survey to a metropolitan area in the ACS. Most metropolitan areas are in just one state, but some extend over several states (e.g., District of Columbia or Kansas City). Most of these matches are straightforward because the metropolitan area names in the two surveys are identical or nearly identical.¹²

We use the matched estimates to calculate the number of days of nursing home care that a person's home value could pay for by dividing net home equity by local nursing home costs.¹³ We then calculate averages and percentile values separately for home equity, nursing home costs, and days of nursing home care that a person's net equity could pay for. We first calculate these statistics at the national level and then repeat them for each state and each metropolitan/nonmetropolitan area. We use ACS person weights for all of our calculations.

 

Sample Description

The average age of adults in the sample is 75 years (see Table 1). The majority are female and married. Blacks and Hispanics constitute 8.5 percent and about 7 percent of the sample, respectively. Although it is common for the elderly to be homeowners who have paid off their mortgage, more than one in four older adults do not own a home and more than one-third of homeowners still hold a mortgage. For homeowners with a mortgage, mortgage values are often substantial relative to home values, accounting on average for more than half of home values according to our imputations. The median home value net of mortgage debt is about $74,000 and the spread between the 25th and 75th percentile of home values is $100,000. Nursing home costs are distributed more evenly, and costs for semi-private rooms are somewhat lower than costs for private rooms. These statistics suggest that variation in home value is likely to be a more dominant factor than variation in nursing home costs in determining how much nursing home care home equity can finance.

The lower part of Table 1 shows how nursing home costs, housing values, homeownership rates, mortgage status and mortgage values as a fraction of home values vary across states and metropolitan/nonmetropolitan areas.¹⁴ Nursing home costs are essentially equally distributed at the state and metropolitan/nonmetropolitan area level as compared to the individual level because these costs only vary at the geographic level. The spread in homeownership between the 25th and 75th percentile is about 6.5 percentage points at the state level and 9 percentage points at the metropolitan/nonmetropolitan area level. Variation in home values is still substantial at the state and metropolitan/nonmetropolitan level, albeit not as large as at the individual level.

 

Nationwide Estimates

Table 2 presents the distribution of the population age 65 or older by of days of nursing home care that could be financed by net home equity using estimated local nursing home costs for private rooms.¹⁵ Twenty-five percent of the population has no equity or insufficient equity to pay for any care. Those at the 50th percentile could pay for about seven months of care, those at or above the 75th percentile could pay for more than a year of care, and those at or above the 90th percentile could pay for more than two years of care. The table also highlights how skewed the distribution is: adults in the 99th and 99.9th percentile can afford almost eight years and more than 21 years of care, respectively.¹⁶

Looking across the various subgroups, the number of days that could be financed by net home equity tends to be somewhat lower for those age 85 or older at lower percentile values but not at higher percentile values. At the 50th percentile, those who are age 85 or older could finance about three months less care than those who are younger. Homeownership for the oldest age group is much lower than for other age groups, contributing to this discrepancy. Married couples can finance more days of nursing home care at lower levels of the distribution than unmarried older adults but the reverse is true for higher values of the home equity distribution. Lower homeownership for nonmarried people appears to contribute much to this discrepancy. People with one or at least two functional limitations,¹⁷ African Americans, and especially Hispanics can purchase substantially fewer days of care. For these groups, we also observe lower homeownership rates as compared to the whole sample.

Among homeowners (shown in the lower panel of Table 2), the median number of days that net equity could finance is about one year, similar to the expected duration of nursing home use in remaining life at age 65. Differences between subgroups and the main sample are smaller because of low homeownership rates among these subgroups. For instance, the median value for African American homeowners is 226 days, still less than the 329 days for all homeowners, but a smaller gap between African Americans and nonHispanic Whites than we find for the full population including renters. Furthermore, people age 85 or older who are homeowners can purchase more days of nursing home care at all percentile values than younger homeowners. However, this oldest group of people has a relatively lower rate of homeownership.

Table 3 shows corresponding results using nursing home costs for semi-private rooms. Not surprisingly, choosing a semi-private room would increase the number of days that net home equity could finance, but the benefits are greatest at the upper end of the distribution, where choice of a semi-private room may be less likely. For instance, homeowners at the 90th percentile could pay for a little more than three years of care, about five months more than for a private room, while those at the 25th percentile could pay for about 6.5 months of care, less than a month more than when assuming a private room.

 

Estimates for States

Figure 1 displays the geographic distribution of median nursing home days that homeowners could finance with their home equity (Table A-2 shows corresponding numbers). The figure suggests a correlation between housing markets and nursing home days. In "rustbelt" states such as Michigan, people can finance fewer days of nursing home care than in states with strong housing markets, for example on the west coast and in the mid-Atlantic region. States with strong declines in home values such as Nevada or Florida also appear to rank toward the bottom of the nursing home days distribution.

Table 4 shows nursing home days, nursing home costs, home values and homeownership rates across states, focusing on the five states with the lowest and highest median days of nursing home care that net equity could finance (Table A-1 and Table A-2 show corresponding statistics for all states). The table shows a wide gap between states: median days that could be financed by net equity ranges from between 122 and 146 in the five lowest states to more than twice that in the five states at the top of the distribution. Comparing nursing home costs and home values (first and second columns), with the exception of Alaska, low per-person home values rather than high nursing home costs are the primary reason why people can buy relatively little nursing home care with their housing wealth in states like New York, West Virginia and Florida. The last column of the table, which shows homeownership rates, reveals that low percentages of people owning a home explain some of these low values, especially for New York.

Looking at the bottom part of the table, nursing home days for homeowners are higher for states at the top and bottom of the distribution compared to nursing home days for all older adults, with values especially high for the top five states. Because of such differences in homeownership across states, some states (like Alaska and West Virginia) appear in both parts of the tables but others do not. For instance, New York is among the bottom five of all states when all older adults are included but not among homeowners because of the low percentage of homeowners in the Empire State. Similarly, California and the District of Columbia are among the top five states for homeowners but not for all older adults because of their lower homeownership rates.

In Figure 2, we show the relationship between median net home equity and median days of potential nursing home care for homeowners across all states (see Table A-2 for corresponding numbers). Binary regression results indicate that a $1,000 increase in net home equity is associated with the ability to purchase slightly more than two days of additional nursing home care.¹⁸

Table 4 suggests that homeownership tends to be low in states with high net home equity. Figure 3 illustrates this relationship by displaying median home values for older homeowners and homeownership rates for all states (Table A-1 shows corresponding numbers for all states). The figure displays states with a higher number of homeowners in the sample using proportionately larger circles. A $1,000 higher home value is associated with slightly more than a 0.1 percent lower homeownership percentage, suggesting that high home values can be a barrier to acquiring a home in the first place.

Taken together, our analysis at the state level shows that the ability to use home equity to finance nursing home care differs markedly across states. Homeowners in states with high home values can potentially leverage their home value to finance more nursing home care if needed. However, higher home values also may act as a barrier to obtaining a home in the first place because homeownership rates are lower in states with high home values than in states with low home values.

 

Estimates for Metropolitan and NonMetropolitan Areas

So far we have looked at variation in home value and nursing home costs at the state level, but the MetLife survey also allows us to further examine metropolitan and nonmetropolitan areas (called "Rest of the State" areas in the MetLife survey).

Table 5 presents estimates for a few of these areas, focusing again on those with the highest and lowest number of nursing home days that median home values could finance (see Table A-3 and Table A-4 for corresponding statistics for all areas). Metropolitan areas are prevalent at both the top and the bottom of the distribution. At the bottom of the distribution are metropolitan areas located in the rustbelt (e.g., Rochester, New York) or hit hard by the decline in the housing market (e.g., Las Vegas, Nevada). Miami even has a median home value of zero because fewer than 50 percent of older adults own a house or report positive home equity in that area.¹⁹ By contrast, metropolitan and nonmetropolitan areas at the top of the distribution have high per-person home values ranging from $70,000 to over $250,000.

For homeowners, the contrast between areas at the top and the bottom of the distribution is even more marked. Median days of nursing home that could be financed by home equity ranges from 175 days to 200 days at the bottom of the distribution but exceeds 650 days at the top of the distribution. All of the top five areas are metropolitan areas with very high home values.²⁰

It is also noteworthy that metropolitan areas dominate both the top and the bottom of the distribution of median nursing home days. At the bottom, rustbelt cities such as Pittsburgh and metropolitan areas such as Las Vegas have been hit hard by a declining housing market. At the top, metropolitan areas such as the District of Columbia or San Diego are characterized by a high demand for houses due to a strong influx of people.

Another reason why nonmetropolitan areas are typically not found at the top or bottom of the distribution is that they often comprise a large part of a state. These areas are often very diverse in terms of housing values. For instance, for Florida the MetLife survey distinguishes between just three metropolitan areas (Jacksonville, Miami, and Orlando). Obviously, the rest of Florida is still both rural and urban, with a median net per capita home value of $61,000--lower than Jacksonville and Miami but higher than Orlando.

 

Caveats

Our analyses use a stylized set of assumptions to help inform policymakers about the extent to which home equity could finance needs for LTSS for older people in the United States, using nursing home care costs as the metric. We do not examine costs for receiving care at home or the cost of increasingly common residence in alternative supportive settings, either of which might be financed through access to home equity. Our analytic approach does not consider other potential financing sources, and implicitly assumes that individuals do not need to tap their home equity to meet other pressing expenses and does not consider other potential financing sources. For example, we assume in effect that individuals can finance their out-of-pocket expenses for health services other than LTSS through a combination of income and nonhome wealth. For any given individual or family, using home equity may or may not be feasible. Needs for LTSS often develop quickly and unexpectedly; houses can take a long time to sell, especially in depressed home markets and at certain points in the year or in the business cycle. For these reasons, many people facing a nursing home stay are likely to have lower financial resources at hand from selling their home than our estimates suggest. Moreover, for married couples, selling the home may not be a viable option when one spouse enters a nursing home.

Measurement of housing values may also be a problem, as some research suggests that survey respondents may misstate home values, and they may do so systematically. For instance, Benítez-Silva et al. (2010) find that people in the HRS overstate home values by an average of 6-10 percent and report a higher expected selling price if they bought their property during economic booms. Davidoff (2004) reports that older homeowners spend less on routine home maintenance than younger homeowners, suggesting that houses of older homeowners might be less valuable.

Ehrlich (2013) compares the ACS to the house price index of the Federal Housing Finance Administration, which includes all transactions in the housing market. His analysis reveals that homeowners systematically under-estimate the value of their house in areas with increasing house values (e.g., Hawaii) but over-estimate the value of their house in areas with decreasing house values (e.g., Michigan). These patterns would imply that the distribution of days in a nursing home that home equity could finance may have an even larger variance than reported here. Kingkade (2013) similarly compares ACS reports to CoreLogic data on tax information, and finds variation in estimated accuracy of reports by individual and household characteristics. Woodward, Wilson, and Chestnut (2007) conducted another related ACS validation study.

Aside from these measurement issues, how long one will need to stay in a nursing home is highly uncertain. Some may only need temporary care to hasten recovery from an acute illness or a serious injury, which would be covered in all or part by Medicare for the population age 65 or older we examine, or to provide respite for a caregiver providing care at home. Others can expect to spend years in a nursing home. Brown and Finkelstein (2007), summarizing studies in this area, report an average duration of nursing home stay of about one year but also find that between 10 percent and 20 percent of all nursing home patients spend more than five years in a nursing home.

On the other hand, some aspects of our analyses may lead us to understate the home equity resources available to finance LTSS for part of the population. For example, sensitivity analyses using HRS data suggest that net home equity estimates in that dataset are modestly higher than in the ACS. Figure A-1 plots the bottom 98 percentiles of net home equity in the two surveys. Although the patterns are broadly similar, values from the HRS are modestly higher in the middle of the distribution.²¹ We also did not include information about second homes, as the ACS does not include this information. Data from the 2010 wave of the HRS suggest that almost 15 percent of those ages 65 and older own a second home, with a median equity of $70,000 among these owners. These owners tend to have equity in their first home substantially above the median, so we hypothesize that those in higher percentiles of the distribution are more affected by this omission than those in lower percentiles of the distribution who are likely to have fewer alternatives to home equity for financing care.

Our analysis has also focused on nursing home costs and does not include estimates for other types of care arrangements especially residential care or care in other types of assisted living facilities. A comparison of assisted living costs to nursing home costs shows that nursing home costs are on average about double assisted living costs. However, reliance on the MetLife data on average monthly assisted living costs may understate the cost of assisted living relative to nursing home care. This is because nursing home rates are more likely to include all or almost all services, whereas it is more typical in assisted living facilities to bill extra for certain services only if and when they are actually used. Areas with higher nursing home costs also have higher assisted living costs but the latter increase more modestly. Overall, these differences suggest that residential care/assisted living may be an attractive alternative to nursing home care for those people with disabilities who need some assistance but not at the level offered by nursing homes.

A final limitation of the analyses is that we examine individuals at a single point in time. We therefore cannot make any inferences about the dynamics surrounding disability onset and housing choices. We cannot observe persons in our sample who already have sold their homes to relocate to community-based supportive settings or accessed equity to pay for LTSS at home through mechanisms such as home equity lines of credit or reverse mortgages. Further analyses using longitudinal data are needed to understand these dynamics.

 

Conclusion

Many older adults meet their LTSS needs through the help of family and friends, supplementing such help with paid care when necessary. For example, in 2011, 95 percent of disabled older Americans not residing in nursing homes reported receiving such unpaid help and one-third of them relied exclusively on unpaid family and friends (Spillman et al. 2014). However, those with low incomes and assets and those who need care for an extended period may require additional help. Medicaid provides nursing home care and some HCBS in all states. However, this help is strictly means-tested.²² Older adults must have little income and few assets, as well as be sufficiently disabled, to qualify for such help. Most persons who qualify for Medicaid have few financial resources in the first place. However, Weiner and colleagues (2013) estimated that about 13 percent of Americans not enrolled in Medicaid at age 65 or older in the mid-1990s had "spent-down" and become Medicaid eligible within the following decade because their medical and/or LTSS costs exceeded their Medicare coverage and ability to pay privately.²³ Once eligible for nursing home care, Medicaid residents are permitted to retain only a small personal needs allowance, which varies by state but is rarely greater than $50 per month.

Medicaid beneficiaries residing in the community may be required to contribute toward the cost of their HCBS or other Medicaid–covered services if they have income other than public cash assistance that exceeds the Federal Poverty Level. Elderly and disabled individuals who qualify for Medicaid HCBS at higher-income levels may retain all or some of their income to pay basic community living expenses; these amounts vary by state. The extent to which these monthly maintenance allowances are sufficient to cover community living costs is addressed in a separate Urban Institute brief (Johnson and Lindner 2016). The income and asset thresholds for Medicaid eligibility vary by marital status and other criteria. For example, older single adults typically qualify for Medicaid (and Supplemental Security Income [SSI]) if their income is less than $733 per month (in 2015) and the value of their assets is not greater than $2,000. In some states, older adults needing a nursing home level of care may have incomes up to 300 percent of SSI (about $2,200 per month). For married couples, Medicaid eligibility depends on whether one or both spouses enroll in Medicaid. If both are enrolled then the income and asset limits for a couple are slightly higher than for a single adult. If only one spouse receives Medicaid LTSS (either through a nursing home or a Medicaid HCBS waiver), then the nonMedicaid spouse may retain more income and assets, with the amounts depending on state and federal Medicaid rules. The most common sources of income for older adults with LTSS needs are Social Security and payments from defined-benefit pension plans. Regardless of marital status, no type of income is exempt when determining Medicaid eligibility.

For many older adults their home equity is their main asset. Before applying for Medicaid, some elders who owned a home may have already sold it, perhaps to move to a more convenient rental unit in the community or to help pay for assisted living or nursing home care. If a Medicaid applicant does own a home that is their "principal residence," most states consider it an exempt asset (also termed a noncountable resource) in determining Medicaid eligibility. This often occurs because a Medicaid beneficiary living "at home" needs a home to live in. A Medicaid beneficiary's home has traditionally been treated as an exempt asset even in the case of a nursing home resident so long as he or she states an "intent to return." Until ten years ago, no upper limit was placed on the home equity value of an applicant's home considered to be an exempt asset. However, the 2005 Deficit Reduction Act set a limit which is adjusted annually for inflation. As of 2015, under federal law, the applicant's equity value in a principal residence up to of $552,000 may be exempted, with states having the option of raising this limit to $828,000. Critics of Medicaid eligibility rules often interpret these rules as permanently exempting home equity assets.²⁴ This is not accurate because since 1993 federal Medicaid law requires states to implement estate recovery programs to offset LTSS expenditures made on behalf of Medicaid beneficiaries age 55 and older (and, at state option, estate recovery can be expanded to include Medicaid expenditures for other services and/or on behalf of younger beneficiaries). At a minimum, federal law requires mandatory estate recovery against assets that pass through probate, but states may elect to expand the definition of estates subject to recovery and many have done so. Some states have aggressive estate recovery programs, others less so (see, for example, statistics in Wood and Klem 2007; Thomson/Medstat 2005a, 2005b).

Our analyses suggest that the ability of older adults in the United States to use their homes to finance nursing home care varies widely, both by geography and by other key characteristics. Those at the top of the housing wealth distribution may have significant capacity to use net equity to finance lengthy spells of care and also may have significant other resources to draw upon. However, about a quarter of older Americans have no home equity. Those at the median have sufficient equity to cover less than a year of care--substantially less if they are African American, Hispanic, or already experiencing functional limitations. These findings underscore the importance of developing LTSS financing policies that recognize the divergent economic circumstances of older Americans.

In our stylized exercise, we have not considered a common case, where those needing LTSS prefer to remain at home and may need to access home equity over a long period to pay for services and maintain community residence (see related discussion in Johnson and Lindner 2015). Previous research has explored home equity as a resource for either pre-funding care through purchase of long-term care insurance (LTCI) or other pre-funding vehicles, or to pay directly for care. A concern, however, is the generally high transaction costs and wide variation on the amount of equity that could be accessed through reverse mortgages, as well as the inefficiency of layering of transaction costs for access to equity and pre-funding vehicles (Spillman and Murtaugh 2007). Policies to reduce costs and raise limits on the amount of equity that can be accessed almost certainly would be needed if housing equity is to make an important contribution to increased pre-funding of LTSS or to direct payment for expenses for LTSS among persons with more modest means.

 

Endnotes

1. Higher-level disability is defined as need for assistance with at least two activities of daily living (that is, requiring human help with personal care tasks) that is expected to last for at least 90 days or need for supervision due to severe cognitive impairment. Activities of daily living include tasks such as bathing, dressing, transferring from bed to chair, toileting, managing incontinence, and eating.

2. Several other studies have estimated similar risks (Brown and Warshawsky 2013; Stallard 2011). Other literature estimates probability of needing nursing home care specifically. Several studies suggest that a 65-year-old adult has about a 40 percent chance of eventually entering a nursing home, with an average duration of about one year (Kemper and Murtaugh 1991; Spillman and Lubitz 2002; Kemper, Komisar, and Alexcih 2005). One recent study suggests the risk may be higher (Friedberg et al. 2014). Hurd et al. (2013), using the HRS, report a higher lifetime risk of entering nursing home care than these previous studies. Specifically, they estimate that a 50-year-old has more than a 50 percent chance of receiving nursing home care before death. They speculate that recall bias and the use of multiple data sources may contribute to underreporting of nursing home stays in earlier studies. One challenge with the HRS data Hurd et al. use is that they do not enable one to readily differentiate strictly post-acute stays from custodial stays.

3. For discussion of perceived probabilities of nursing home use, see, for example Wiener, Khatutsky, Thach, Greene, Allaire, Brown, Lamont, Marton, and Shipley (2015). Many similarly under-estimate the financial risk of having to pay out-of-pocket for paid LTSS, especially because surveys show that many mistakenly believe that Medicare coverage is available and are unaware that Medicaid, the main source of public funding for nursing home care beyond short, post-hospital stays and for in-home aide services, is strictly means-tested (see, for example, Associated Press-NORC 2016; Brown, Goda, and McGarry 2012).

4. Johnson and Park (2011) estimate that about 10.7 percent of those ages 55 and older have LTCI, and that these individuals are disproportionately higher-income. LifePlans (2012) similarly documents a strong relationship between income and wealth and purchase of LTCI. Brown and Finkelstein (2007) discuss reasons why private LTCI is not prevalent.

5. A recent survey conducted among a representative sample of Americans aged 40-70 asked how they would be willing to access LTSS if they needed it. About 44 percent of respondents reported that they would be very or somewhat willing to use value in their home to pay for LTSS if they were to become disabled (Wiener, Khatutsky, Thach, Greene, Allaire, Brown, Lamont, Marton, and Shipley 2015). Thus, over half the survey respondents (none of whom were seeking to access paid LTSS at the time) expressed reluctance to use home equity to pay for LTSS. Low-income homeowners were significantly less willing to do so, compared to those with higher incomes and savings.

6. An earlier study by Houser (2012) made comparable calculations relating nursing home and home care costs to incomes rather than housing wealth.

7. Our assumption about splitting home equity recognizes that equity and the physical home itself may be needed to support a spouse, who ultimately also may need LTSS. Of course, in many couples a spouse with no or fewer significant health limitations may provide care to a disabled spouse that reduces his or her need for paid care in the community or institutional care, at least for some period of time. Also, it is important to bear in mind spousal protections that exist under current law, for example in the Medicaid program, that affect individuals' incentives to leverage home equity.

8. This assumption is geared toward accounting for the fact that one's home is typically not as liquid as most financial assets. We start with a value of 8 percent because standard brokerage fees often average from 5 percent to 6 percent (e.g., U.S. Department of Justice 2007), and the typical seller will have significant additional costs beyond this fee. Magnitudes of transaction costs are likely to vary significantly among households and across housing markets. The size of the transaction costs may also differ significantly based on the mechanism used to finance potential LTSS, for example a straight sale of the home compared to a reverse mortgage or home equity line of credit (for a discussion of transaction costs in reverse mortgages, see for example, U.S. Government Accountability Office 2009, which notes that transaction costs as a percentage of the mortgage vary based on the duration of the mortgage). Further, the timing of needs for LTSS is often very uncertain; this may add to transaction costs in some circumstances (i.e., one may need to pay double housing costs during transitional phases).

9. Specifically, we use the HRS to regress the square root of mortgage values for first and second mortgages on the primary home on the log of home values, its squared term, and other characteristics. We then use estimated coefficients to calculate expected mortgage values for each person reporting a mortgage in the ACS. We restrict the sample for the HRS regression to homeowners 65 years or older with a mortgage. Other covariates in the model include: age, log of total household income and its square term, total mortgage payment, and indicators for race/ethnicity, nativity, highest educational achievement, marital status, gender, employment status, and Census region. We experimented with other functional forms (e.g., using the log of mortgage values) and other variables (e.g., a squared term for age and a disability indicator) and chose from among these different specifications based on how well the variables predicted mortgage values and the overall explanatory power of the model (i.e., R-squared). Imputed mortgage values do not differ strongly across various functional form specifications.

10. Genworth (2015) provides similar data.

11. For determining metropolitan area in the 2011 ACS, we use the variable "metarea".

12. Although most metropolitan area matches are unproblematic, a few complications emerge. First, 11 areas in the ACS have multiple states. For instance, the metropolitan areas "Kansas City" includes people residing both in Missouri and in Kansas. For such cases, we have matched all of these households to the corresponding MetLife metropolitan area (Kansas City, Missouri in this case). Second, some MetLife metropolitan areas do not have an ACS metropolitan area with the same or similar name. Of these, we could assign five areas to larger metropolitan area close by that the ACS includes. For instance, the "Des Plaines Area" in Illinois is part of Chicago. Three MetLife metropolitan areas could not be matched to any metropolitan area in the ACS. These are: Bridgewater Area (New Jersey), Rapid City Area (South Dakota) and Charleston Area (West Virginia).

13. We use nursing home costs at the metropolitan and nonmetropolitan area level when such areas exist and state averages for states where the MetLife survey does not include any metropolitan areas (these are Alaska, Vermont and Wyoming) and for states where there is no match between MetLife and ACS metropolitan areas (these are New Jersey, South Dakota and West Virginia). For New Jersey, the MetLife survey has two metropolitan areas. We match the Cherry Hill Area to Philadelphia, Pennsylvania/New Jersey in the ACS and could not determine a match for the Bridgewater Area. For South Dakota, the MetLife survey has one metropolitan area (Rapid City Area) but the ACS has none. Similarly, for West Virginia, the MetLife survey but not the ACS includes one metropolitan area (Charleston Area).

14. For these statistics, we first calculate median values of nursing home costs and net home value and percentages of homeownership and mortgage status by state or metropolitan/nonmetropolitan area and then report averages and percentiles of these aggregates. Averages of homeownership rates and mortgage status are identical at the individual and state level because we use person weights in all calculations. Corresponding values at the metropolitan/nonmetropolitan area level differ slightly because not all states or areas are included at this level.

15. We start using costs for a private room, given that most older adults live relatively independently (i.e., alone or with just a spouse); movement to a semi-private arrangement represents a significant change in circumstances. We subsequently demonstrate the sensitivity of our estimates to this choice by showing values using estimated costs for semi-private rooms.

16. For this and all subsequent tables, we do not present averages because the distributions of nursing home days and home values are highly right-skewed (i.e., because a small number of people have very expensive homes, the mean ratio poorly represents the typical older adult's situation).

17. The disabilities that ACS respondents report that we use here include the following: (1) self-care difficulty (difficulties dressing/bathing); (2) independent living difficulty (difficulties doing errands alone); (3) ambulatory difficulty (difficulties walking of climbing stairs); (4) cognitive difficulty (difficulties learning, remembering, or concentrating); and (5) vision or hearing difficulty (difficulties/inability to hear or see).

18. The positive relationship between home equity and nursing home days is very similar for the 25th and 75th percentile values.

19. The homeownership rate in Miami is 50.2 percent but the median home value is zero because a few homeowners report zero house equity.

20. The District of Columbia is among these top five areas because we treat the District both as a state for the state-level analysis and as a metropolitan area in this part of the analysis. State-level estimates for the District only include people living within city limits whereas metropolitan area estimates include people in the larger metropolitan area (e.g., Silver Spring, Maryland), which explains the different estimates for the District in Table 4 and Table 5.

21. Further sensitivity analyses reveal that the match between the ACS and HRS vary by population subgroup. For example, distributions for married people line up nearly exactly, but distributions for single people are less similar.

22. For additional detail on Medicaid eligibility and cost sharing, see, for example, O'Keeffe, Saucier, Jackson, Cooper, McKenney, Crisp, and Moseley (2010).

23. Spillman and Waidmann (2015) similarly estimate four-year Medicaid transition probabilities for the Medicare-eligible population living in the community. They estimate a transition rate of roughly 5 percent, which appears broadly consistent with the estimates from Weiner and colleagues (2013). They further find that nursing home admission and low-income and assets are the most significant predictors of Medicaid enrollment. Other literature on related issues surrounding Medicaid eligibility includes Baird, Hurd, and Rohwedder (2014) and Greenhalgh-Stanley (2012).

24. For example, Warshawsky (2014) maintains that the Medicaid eligibility rules are so lax that they can allow "millionaires" to enroll.

 

References

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Baird, Matthew, Michael Hurd, and Susann Rohwedder. 2014. Medicaid Spend-down: The Importance of Strategic Asset Transfers to Reach Medicaid Eligibility. http://www.netspar.nl/files/Evenementen/2014-06-19%20ipw/papers/062%20rohwedder.pdf.

Benítez-Silva, Hugo, Eren Selcuk, Frank Heiland, and Sergi Jiménez-Martín. 2010. How Well do Individuals Predict the Selling Prices of their Homes? SUNY-Stony Brook.

Brown, Jason, and Mark Warshawsky. 2013. "The Life Care Annuity: A New Empirical Examination of an Insurance Innovation That Addresses Problems in the Markets for Life Annuities and Long-Term Care Insurance." Journal of Risk and Insurance, 80(3): 677-703.

Brown, Jeffrey R., and Amy Finkelstein. 2007. "Why is the Market for Long-Term Care Insurance so Small?" Journal of Public Economics, 91(10): 1967-1991.

Brown, Jeffrey R., Gopi Shah Goda, and Kathleen McGarry. 2012. "Long-Term Care Insurance Demand Limited By Beliefs About Needs, Concerns About Insurers, and Care Available from Family." Health Affairs, 31(6): 1294-1302.

Davidoff, Thomas. 2004. "Maintenance and the Home Equity of the Elderly." Fisher Center for Real Estate and Urban Economics, Paper No. 03-288.

Davidoff, Thomas. 2010. "Home Equity Commitment and Long-Term Care Insurance Demand." Journal of Public Economics, 94(1-2): 44-49.

Ehrlich, Gabriel. 2013. Price and Time to Sale Dynamics in the Housing Market: the Role of Incomplete Information. Washington, DC: Congressional Budget Office.

Favreault, Melissa, and Judith Dey. 2015. Long-Term Services and Supports for Older Americans: Risks and Financing Research Brief. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/basic-report/long-term-services-and-supports-older-americans-risks-and-financing-research-brief.

Friedberg, Leora, Wei Sun, Anthony Webb, Wenliang Hou, and Zhenyu Li. 2014. "New Evidence on the Risk of Requiring Long-Term Care." Working Paper No. 2014-12. Center for Retirement Research at Boston College.

Genworth. 2015. Genworth 2015 Cost of Care Survey: Home Care Providers, Adult Day Health Care Facilities, Assisted Living Facilities and Nursing Homes.

Greenhalgh-Stanley, Nadia. 2012. "Medicaid and the Housing and Asset Decisions of the Elderly: Evidence from Estate Recovery Programs." Journal of Urban Economics, 72(2-3): 210-24.

Houser, Ari. 2012. A New Way of Looking at Private Pay Affordability of Long-Term Services and Supports. AARP Public Policy Institute.

Hurd, Michael, Pierre-Carl Michaud, and Susann Rohwedder. 2013. "The Lifetime Risk of Nursing Home Use." In David A. Wise, ed., Discoveries in the Economics of Aging, Chicago, IL: University of Chicago Press.

Johnson, Richard W. 2013. "Income and Wealth of Older Adults Needing Long-Term Services and Support." Statement before the Commission on Long-Term Care.

Johnson, Richard W., and Stephan Lindner. 2016. "Older Adults' Living Expenses and the Adequacy of Income Allowances for Medicaid Home- and Community-Based Services." Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/basic-report/older-adults-living-expenses-and-adequacy-income-allowances-medicaid-home-and-community-based-services

Johnson, Richard W., and Janice S. Park. 2011. "Who Purchases Long-Term Care Insurance?" Older Americans' Economic Security, No. 29. Washington, DC: Urban Institute.

Kingkade, W. Ward. 2013. "Self-Assessed Housing Values in the American Community Survey: An Exploratory Analysis Using Linked Real Estate Records." Paper presented at the Annual Joint Statistical Meetings, August 3-8, Montreal.

LifePlans. 2012. Who Buys Long-Term Care Insurance in 2010-2011? A Twenty-Year Study of Buyers and Non-Buyers (in the Individual Market). Washington DC: America's Health Insurance Plans.

MetLife. 2011. Market Survey of Long-Term Care Costs: The 2011 MetLife Market Survey of Nursing Home, Assisted Living, Adult Day Services, and Home Care Costs. New York, NY.

O'Keeffe, Janet, Paul Saucier, Beth Jackson, Robin Cooper, Ernest McKenney, Suzanne Crisp, and Charles Moseley. 2010. Understanding Medicaid Home and Community Services: A Primer 2010 Edition. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/report/understanding-medicaid-home-and-community-services-primer-2010-edition.

Spillman, Brenda C., and Christopher M. Murtaugh. 2007. Simulation Modeling on Life Care Annuity: Final Report. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/basic-report/simulation-modeling-life-care-annuity-final-report.

Spillman, Brenda C., Jennifer Wolff, Vicki Freedman, and Judith Kasper. 2014. Informal Caregiving for Older Americans: An Analysis of the 2011 National Health and Aging Trends Survey. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/report/informal-caregiving-older-americans-analysis-2011-national-study-caregiving.

Spillman, Brenda C., and Timothy Waidmann. 2015. Beyond Spend-down: The Prevalence and Process of Transitions to Medicaid. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/report/beyond-spend-down-prevalence-and-process-transitions-medicaid.

Stallard, Eric. 2011. "Estimates of the Incidence, Prevalence, Duration, Intensity and Cost of Chronic Disability among the U.S. Elderly." North American Actuarial Journal, 15(1): 32-58.

Thomson/Medstat. 2005a. Medicaid Liens and Estate Recovery in Massachusetts: Policy Brief. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/basic-report/medicaid-liens-and-estate-recovery-massachusetts.

Thomson/Medstat. 2005b. Medicaid Estate Recovery Collections: Policy Brief. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/report/medicaid-estate-recovery-collections.

U.S. Department of Justice and Federal Trade Commission. 2007. Competition in the Real Estate Brokerage Industry.

U.S. Government Accountability Office. 2009. Reverse Mortgages: Product Complexity and Consumer Protection Issues Underscore Need for Improved Controls over Counseling for Borrowers. http://www.gao.gov/assets/300/291790.pdf.

Warshawsky, Mark. 2014. "Millionaires on Medicaid." Wall Street Journal. http://online.wsj.com/news/articles/SB10001424052702304325004579297052950416982.

Wiener, Joshua M., Wayne L. Anderson, Galina Khatutsky, Yevgeniya Kaganova, Janet O'Keeffe, Anne Tumlinson, Eric Hammelman, and Elana Stair. 2013. Medicaid Spend Down: New Estimates and Implications for Long-Term Services and Supports Financing. http://www.rti.org/pubs/tsf_ltc-financing_medicaid-spend-down-implications_wiener-tumlinson_3-20-13_0.pdf.

Wiener, Joshua M., Galina Khatutsky, Nga Thach, Angela M. Greene, Benjamin Allaire, Derek Brown, Helen Lamont, William Marton, and Samuel Shipley. 2015. Findings from the Survey of Long-Term Care Awareness and Planning Research Brief. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.gov/basic-report/findings-survey-long-term-care-awareness-and-planning-research-brief.

Wood, Erica F., and Ellen M. Klem. 2007. "Protections in Medicaid Estate Recovery: Findings, Promising Practices, and Model Notices." No. 2007-07. Washington, DC: AARP.

Woodward, Jeanne, Ellen Wilson, and John Chestnut. 2007. Evaluation Report Covering Property Value. 2006 American Community Survey Test Report H.7. Washington, DC: Bureau of the Census.

 

Figures and Tables

FIGURE 1. Geographic Distribution of Days of Private Room Nursing Home Care that Home Equity Could Finance for Homeowners
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011). NOTES: The figure displays the geographic distribution of days in nursing home paid for homeowners at the state level using median home values. Darker colors indicate fewer days of care.

 

FIGURE 2. Median Net Home Equity and Median Days of Private Room Nursing Home Care that Home Equity Could Finance for Homeowners, by States
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011). NOTES: The figure displays median net home equity for homeowners and days in nursing home that could be financed by the net home equity, adjusted for transaction costs, for each state. Circle sizes are proportionate to the weighted number of homeowners in the sample that live in the respective state.

 

FIGURE 3. Median Net Home Equity and Homeownership Rates by States
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011). NOTES: The figure displays median net home equity, adjusted for transaction costs, for homeowners and homeownership for each state. Circle sizes are proportionate to the weighted number of homeowners in the sample that live in the respective state. Vertical axis does not start at zero.

 

TABLE 1. Descriptive Statistics for Older Adults in the Sample
	Mean	25th Percentile	Median	75th Percentile
Distribution at the Individual Level
Age (years)	74.7	68.0	73.0	80.0
Male (percent)	43.7
Black (percent)	8.5
Hispanic (percent)	7.2
Married (percent)	54.1
Nursing home costs, private room (dollars)	245.0	195.0	235.0	272.0
Nursing home costs, semi-private room (dollars)	215.6	176.0	205.0	240.0
Homeownership (percent)	72.4
Per capita home value of homeowners prior to mortgage imputation (dollars)	144,285	51,520	92,000	165,600
Net per capita home equity of homeowners (dollars)	122,114	38,783	73,794	138,000
Homeowner with mortgage (percent)	34.8
Mortgage relative to home value among mortgage holders (percent)	58.9	38.4	45.9	55.3
Distribution at the State Level
Nursing home costs, private room (dollars)	245.3	195.0	235.0	272.0
Nursing home costs, semi-private room (dollars)	217.7	176.0	209.0	226.0
Homeownership (percent)	72.4	70.7	73.1	77.3
Per capita home value of homeowners prior to mortgage imputation (dollars)	104,543	69,000	82,800	138,000
Net per capita home equity of homeowners (dollars)	83,954	55,200	64,400	109,940
Homeowners with a mortgage (percent)	34.8	29.4	35.6	38.1
Mortgage relative to home value among mortgage holders (percent)	58.9	55.3	61.2	65.9
Distribution at the Metropolitan/NonMetropolitan Area Level
Nursing home costs, private room (dollars)	244.4	194.0	235.0	272.0
Nursing home costs, semi-private room (dollars)	214.6	173.0	205.0	229.0
Homeownership (percent)	72.2	69.5	75.0	78.3
Per capita home value of homeowners prior to mortgage imputation (dollars)	106,895	69,000	78,200	125,120
Net per capita home equity of homeowners (dollars)	86,957	55,200	65,285	100,934
Homeowners with a mortgage (percent)	34.9	28.4	34.5	39.9
Mortgage relative to home value among mortgage holders (percent)	59.4	53.7	58.8	65.2
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS data to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs. See text for further information on sample selection. Statistics at the state and metropolitan/nonmetropolitan area level are obtained by first calculating median values of nursing home costs and net home equity and percentages of homeownership and mortgage status by state or metropolitan/nonmetropolitan area and then reporting averages and percentiles of these aggregates. Averages of homeownership and mortgage status are identical at the individual and state level because we use person weights for all calculations. Corresponding values at the metropolitan/nonmetropolitan area level differ slightly because not all states are included at this level. Mortgage relative to home value is calculated by dividing mortgage values by home value for homeowners who report having a mortgage.

 

TABLE 2. Nationwide Distribution of Days of Private Room Nursing Home Care that Net Home Equity Could Finance for Older Adults
	Age Groups				Married		Functional Limitations			Race
	65+	65-74	75-84	85+	Yes	No	Zero	One	Two or More	White, NonHisp	Black, NonHisp	Hispanic
All Older Adults
10th percentile	0	0	0	0	0	0	0	0	0	0	0	0
25th percentile	0	1	0	0	109	0	45	0	0	32	0	0
50th percentile	208	214	220	116	255	59	255	182	39	245	55	0
75th percentile	460	453	475	451	456	468	507	420	309	495	256	249
90th percentile	828	807	846	872	757	909	896	755	637	873	506	550
95th percentile	1,186	1,162	1,196	1,258	1,078	1,299	1,278	1,076	930	1,247	744	823
99th percentile	2,853	2,908	2,788	2,845	2,888	2,844	3,158	2,401	2,208	3,069	1,545	1,691
99.9th percentile	7,741	7,208	7,943	8,930	5,866	9,500	7,741	7,475	7,741	7,855	6,354	5,123
At leat 1 year n.h. (%)	32.5	32.1	33.9	30.5	34.1	30.5	36.9	29.2	21.1	35.9	16.6	17.3
At least 3 years n.h (%)	5.8	5.6	6.1	6.5	4.9	7.0	6.8	4.8	3.7	6.5	2.2	2.9
Homeownership	72.4	76.1	72.1	56.4	88.1	53.8	78.7	71.1	53.3	77.7	56.3	48.3
Sample size	490,326	271,101	159,543	59,682	276,804	213,522	313,335	84,013	92,978	396,631	40,353	30,413
Older Homeowners Only
10th percentile	82	76	88	105	80	87	91	69	64	88	49	62
25th percentile	174	163	185	218	162	216	185	157	146	182	112	135
50th percentile	329	309	346	400	294	432	345	303	289	340	226	260
75th percentile	581	545	600	706	490	763	601	535	521	593	416	478
90th percentile	973	929	999	1,184	812	1,260	1,011	909	910	995	708	834
95th percentile	1,394	1,314	1,420	1,664	1,151	1,762	1,444	1,258	1,299	1,421	968	1,184
99th percentile	3,452	3,407	3,382	3,996	3,107	4,059	3,651	2,926	3,119	3,623	2,147	2,493
99.9th percentile	8,446	7,783	8,718	9,916	5,992	9,916	8,085	8,478	8,930	8,478	8,045	7,360
At leat 1 year n.h. (%)	44.9	42.2	47.0	54.0	38.7	56.8	46.9	41.1	39.6	46.3	29.6	35.9
At least 3 years n.h (%)	8.1	7.3	8.4	11.5	5.5	13.0	8.6	6.8	7.0	8.4	3.9	6.0
Sample size	380,652	218,588	123,628	38,436	250,133	130,519	259,310	64,110	57,232	324,436	25,412	17,537
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Home equity values are reduced to account for potential transaction costs. See text for further information.

 

TABLE 3. Nationwide Distribution of Days of Semi-Private Room Nursing Home Care that Net Home Equity Could Finance for Older Adults
	Age Groups				Married		Functional Limitations			Race
	65+	65-74	75-84	85+	Yes	No	Zero	One	Two or More	White, NonHisp	Black, NonHisp	Hispanic
All Older Adults
10th percentile	0	0	0	0	0	0	0	0	0	0	0	0
25th percentile	0	1	0	0	125	0	52	0	0	36	0	0
50th percentile	237	243	250	130	289	67	288	207	45	277	61	0
75th percentile	523	513	539	509	519	529	579	480	354	561	288	310
90th percentile	944	920	968	997	879	1,038	1,025	872	721	1,003	574	661
95th percentile	1,361	1,328	1,383	1,445	1,249	1,496	1,481	1,246	1,078	1,438	848	997
99th percentile	3,339	3,353	3,288	3,368	3,307	3,366	3,665	2,843	2,584	3,590	1,761	2,058
99.9th percentile	9,115	8,639	9,259	10,288	6,793	10,976	9,259	8,926	8,976	9,259	7,635	6,043
At leat 1 year n.h. (%)	36.9	36.7	38.4	33.9	39.9	33.3	41.8	33.6	24.2	40.7	19.5	21.3
At least 3 years n.h (%)	7.6	7.3	8.0	8.4	6.5	9.0	8.8	6.4	4.9	8.4	2.8	4.2
Homeownership	72.4	76.1	72.1	56.4	88.1	53.8	78.7	71.1	53.3	77.7	56.3	48.3
Sample size	490,326	271,101	159,543	59,682	276,804	213,522	313,335	84,013	92,978	396,631	40,353	30,413
Older Homeowners Only
10th percentile	93	87	100	119	91	99	103	79	72	100	54	76
25th percentile	198	186	212	245	184	244	212	180	166	207	126	170
50th percentile	374	352	392	452	333	489	390	345	329	383	254	322
75th percentile	660	627	683	802	561	871	685	617	601	673	465	584
90th percentile	1,122	1,072	1,146	1,353	931	1,438	1,164	1,044	1,045	1,140	793	1,010
95th percentile	1,608	1,540	1,643	1,981	1,321	2,039	1,683	1,448	1,500	1,643	1,085	1,438
99th percentile	4,051	4,013	3,948	4,577	3,547	4,718	4,220	3,414	3,625	4,182	2,352	2,992
99.9th percentile	9,695	9,275	9,841	12,085	7,461	12,085	9,480	9,595	9,963	9,726	8,908	9,440
At leat 1 year n.h. (%)	51.0	48.3	53.3	60.0	45.3	62.0	53.1	47.2	45.5	52.3	34.6	44.2
At least 3 years n.h (%)	10.6	9.6	11.0	14.9	7.4	16.7	11.2	9.0	9.2	10.9	4.9	8.7
Sample size	380,652	218,588	123,628	38,436	250,133	130,519	259,310	64,110	57,232	324,436	25,412	17,537
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values. The sample includes all adults age 65 or older. Home equity values are reduced to account for potential transaction costs for those who report holding a mortgage. See text for further information.

 

TABLE 4. States with Lowest and Highest Days of Private Room Nursing Home Care that Net Home Equity Could Finance
Area	Days in Nursing Home	Daily Cost of Nursing Home ($)	Per Capita Net Home Equity ($)	Home-Ownership (%)
All Older Adults
Five lowest states
New York	122	387	41,448	59.1
Alaska	122	655	79,918	74.8
Nevada	123	215	27,600	65.9
West Virginia	143	240	34,417	80.1
Florida	146	253	37,238	73.1
Five highest states
Montana	308	194	59,800	81.0
Hawaii	317	326	114,604	61.2
Utah	332	230	69,374	81.7
Delaware	337	260	87,400	77.9
Virginia	362	190	69,000	75.0
Older Homeowners Only
Five lowest states
Alaska	156	655	102,111	74.8
West Virginia	192	240	46,000	80.1
Indiana	212	237	47,840	77.7
Mississippi	231	199	46,000	77.7
Michigan	235	235	54,249	78.3
Five highest states
Louisiana	444	141	64,400	76.2
Virginia	484	190	95,037	75.0
California	538	272	156,737	62.8
Hawaii	668	326	230,000	61.2
District of Columbia	739	315	232,823	58.8
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs. See text for further information. All statistics are medians

 

TABLE 5. Metropolitan and NonMetropolitan Areas with Lowest and Highest Days of Private Room Nursing Home Care that Net Home Equity Could Finance
Area	Days in Nursing Home	Daily Cost of Nursing Home ($)	Per Capita Net Home Equity ($)	Home-Ownership (%)
All Older Adults
Five lowest areas
Florida: Miami	0	306	0	50.2
Nevada: Las Vegas	106	215	22,857	62.0
Florida: Orlando	110	256	28,038	69.9
New York: Rochester	132	344	45,483	74.6
New York except New York City, Rochester and Syracuse	135	340	46,000	75.1
Five highest areas
Minnesota: Rochester Area	369	187	69,000	81.5
District of Columbia	404	276	101,302	58.8
Hawaii: Honolulu	422	326	137,540	60.8
Rhode Island except Providence	458	276	126,500	77.7
Connecticut: Stamford Area	595	425	253,000	74.8
Older Homeowners Only
Five lowest areas
New York: Syracuse	173	329	57,040	74.8
New York: Rochester	174	344	59,800	74.6
Pennsylvania: Pittsburgh	191	301	57,500	74.4
Indiana except Fort Wayne and Indianapolis	194	237	46,000	80.0
New York except New York City, Rochester and Syracuse	203	340	69,000	75.1
Five highest areas
California: San Diego	655	271	177,462	64.3
District of Columbia	667	276	169,554	58.8
Hawaii: Honolulu	803	326	261,740	60.8
California: Los Angeles	822	250	205,551	56.2
Connecticut: Stamford Area	839	425	356,500	74.8
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs. See text for further information. All statistics are medians.

 

Appendix

FIGURE A-1. Comparison of Distributions of Per Capita Net Home Equity in the ACS and HRS, 2010: People Age 65 and Older
SOURCE: Authors' calculations from ACS and HRS. Per capita home equity values are reduced to account for potential transaction costs (see text).

 

TABLE A-1. Median Days of Private Room Nursing Home Care that Net Home Equity Could Finance, by States for All Older Adults
Area	Days in Nursing Home	Daily Cost of Nursing Home ($)	Per Capita Net Home Equity ($)	Home-Ownership (%)
Alabama	230	180	41,400	79.0
Alaska	122	655	79,918	74.8
Arizona	174	228	40,182	76.6
Arkansas	218	161	35,810	78.6
California	210	272	61,194	62.8
Colorado	302	235	69,000	74.7
Connecticut	236	370	92,000	70.5
Delaware	337	260	87,400	77.9
District of Columbia	256	315	80,500	58.8
Florida	146	253	37,328	73.1
Georgia	220	195	38,212	72.8
Hawaii	317	326	114,604	61.2
Idaho	221	204	46,000	79.0
Illinois	264	198	49,642	74.1
Indiana	163	237	37,235	77.7
Iowa	271	153	42,208	80.9
Kansas	225	161	36,800	77.7
Kentucky	193	195	40,340	79.1
Louisiana	294	141	41,400	76.2
Maine	176	270	48,707	72.8
Maryland	302	285	82,800	70.7
Massachusetts	264	356	92,000	67.2
Michigan	172	235	38,771	78.3
Minnesota	270	216	57,316	77.3
Mississippi	176	199	34,500	77.7
Missouri	247	157	41,400	77.3
Montana	308	194	59,800	81.0
Nebraska	218	162	36,800	78.0
Nevada	123	215	27,600	65.9
New Hampshire	255	299	77,560	74.4
New Jersey	283	387	92,000	67.3
New Mexico	217	212	46,823	77.0
New York	122	387	41,448	59.1
North Carolina	227	195	46,000	77.0
North Dakota	160	172	27,600	73.3
Ohio	183	219	40,391	76.2
Oklahoma	220	183	36,800	80.2
Oregon	258	256	65,320	73.8
Pennsylvania	178	285	46,000	72.7
Rhode Island	256	288	73,600	66.2
South Carolina	229	191	45,080	78.5
South Dakota	175	186	32,478	74.3
Tennessee	247	186	46,000	77.3
Texas	190	190	34,500	72.0
Utah	332	230	69,374	81.7
Vermont	264	279	73,600	80.2
Virginia	362	190	69,000	75.0
Washington	275	269	71,300	72.7
West Virginia	143	240	34,417	80.1
Wisconsin	229	228	54,883	75.7
Wyoming	278	212	59,012	78.7
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs. See text for further information.

 

TABLE A-2. Median Days of Private Room Nursing Home Care that Net Home Equity Could Finance, by States for Older Homeowners
Area	Days in Nursing Home	Daily Cost of Nursing Home ($)	Per Capita Net Home Equity ($)
Alabama	307	180	55,295
Alaska	156	655	102,111
Arizona	266	228	61,856
Arkansas	286	161	46,000
California	538	272	156,737
Colorado	413	235	93,066
Connecticut	342	370	136,179
Delaware	414	260	107,014
District of Columbia	739	315	232,823
Florida	246	253	62,944
Georgia	354	166	62,187
Hawaii	668	326	230,000
Idaho	292	204	64,061
Illinois	398	198	76,411
Indiana	212	237	47,840
Iowa	350	153	55,200
Kansas	299	161	47,840
Kentucky	257	195	54,954
Louisiana	444	141	64,400
Maine	272	270	74,520
Maryland	438	285	119,600
Massachusetts	425	356	149,500
Michigan	235	235	54,249
Minnesota	362	216	77,348
Mississippi	231	199	46,000
Missouri	346	157	56,908
Montana	417	194	80,500
Nebraska	284	162	46,000
Nevada	278	215	63,621
New Hampshire	323	299	97,980
New Jersey	418	387	144,900
New Mexico	326	212	73,012
New York	343	387	126,040
North Carolina	330	195	68,080
North Dakota	273	172	46,875
Ohio	252	211	55,200
Oklahoma	288	183	46,000
Oregon	379	256	94,951
Pennsylvania	266	285	71,472
Rhode Island	417	288	118,086
South Carolina	313	191	62,100
South Dakota	253	186	47,002
Tennessee	341	186	64,400
Texas	291	190	55,200
Utah	392	230	84,262
Vermont	330	279	92,000
Virginia	484	190	95,037
Washington	411	252	109,940
West Virginia	192	240	46,000
Wisconsin	303	228	73,155
Wyoming	380	212	80,500
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs. See text for further information.

 

TABLE A-3. Median Days of Private Room Nursing Home Care that Net Home Equity Could Finance, by Metropolitan and NonMetropolitan Areas for All Older Adults
Area	Days in Nursing Home	Daily Cost of Nursing Home ($)	Per Capita Net Home Equity ($)	Home-Ownership (%)
Alabama: Birmingham	274	185	50,600	79.0
Alabama: Montgomery	194	202	39,163	76.3
Alabama except Birmingham and Montgomery	225	180	40,480	79.3
Arizona: Phoenix	175	223	39,100	74.3
Arizona: Tucson	178	259	46,000	75.2
Arizona except Phoenix and Tucson	168	228	38,392	81.4
Arkansas: Little Rock	272	169	46,000	80.8
Arkansas except Little Rock	209	161	33,729	78.1
California: Los Angeles	246	250	61,485	56.2
California: San Diego	325	271	88,199	64.3
California: San Francisco	196	485	95,147	62.3
California except Los Angeles, San Diego and San Francisco	188	272	51,060	67.6
Colorado: Colorado Springs	267	225	60,048	76.0
Colorado: Denver	305	235	71,765	70.8
Colorado except Colorado Springs/Denver	309	223	69,000	78.6
Connecticut: Hartford	187	410	76,487	68.5
Connecticut: Stamford Area	595	425	253,000	74.8
Connecticut except Hartford Stamford	238	370	87,917	70.5
Delaware: Wilmington	340	257	87,400	75.8
Delaware except Wilmington	329	260	85,478	80.0
District of Columbia	404	315	101,302	58.8
Florida: Jacksonville	217	212	46,000	71.8
Florida: Miami	0	306	0	50.2
Florida: Orlando	110	256	28,038	69.9
Florida except Jacksonville, Miami and Orlando	164	253	41,400	76.8
Georgia: Atlanta	201	195	39,100	68.7
Georgia except Atlanta	222	166	36,900	75.8
Hawaii: Honolulu	422	326	137,540	60.8
Hawaii except Honolulu	195	413	80,500	62.1
Idaho: Boise	156	236	36,800	77.1
Idaho except Boise	243	204	49,595	79.9
Illinois: Chicago	323	198	63,940	69.9
Illinois: Peoria	230	240	55,200	84.2
Illinois except Chicago and Peoria	211	174	36,800	80.6
Indiana: Fort Wayne	196	235	46,000	80.8
Indiana: Indianapolis	161	229	36,800	74.4
Indiana except Fort Wayne and Indianapolis	155	237	36,800	80.0
Iowa: Des Moines	217	195	42,262	75.6
Iowa except Des Moines	276	153	42,208	81.5
Kansas: Wichita	186	177	32,959	74.3
Kansas except Wichita	200	161	32,200	79.7
Kentucky: Lexington	341	216	73,600	78.6
Kentucky: Louisville	193	240	46,267	74.6
Kentucky except Lexington and Louisville	189	195	36,800	80.0
Louisiana: Baton Rouge	331	153	50,600	80.4
Louisiana: Shreveport Area	254	145	36,800	79.5
Louisiana except Baton Rouge and Shreveport Area	294	141	41,400	75.1
Maine: Southern Maine	180	297	53,594	64.5
Maine except Southern Maine	176	270	47,617	74.4
Maryland: Baltimore	276	285	78,692	69.5
Maryland except Baltimore	290	258	74,765	71.8
Massachusetts: Boston	305	356	108,560	65.7
Massachusetts: Worcester	210	333	69,898	63.1
Massachusetts except Boston and Worcester	232	357	82,800	70.6
Michigan: Detroit	178	198	35,243	75.2
Michigan: Grand Rapids	168	273	46,000	77.9
Michigan except Detroit, Grand Rapids	170	235	40,020	80.9
Minnesota: Minneapolis/Saint Paul	309	216	66,700	73.7
Minnesota: Rochester Area	369	187	69,000	81.5
Minnesota except Minneapolis, Saint Paul and Rochester	232	206	47,840	80.9
Mississippi: Jackson	198	186	36,800	74.6
Mississippi except Jackson	173	199	34,500	78.4
Missouri: Kansas City	286	157	44,890	74.5
Missouri: Saint Louis	279	186	51,981	77.6
Missouri except Kansas City, Saint Louis	237	149	35,276	78.9
Montana: Billings	304	193	58,628	80.2
Montana except Billings	313	194	60,750	81.1
Nebraska: Omaha	213	211	44,932	75.6
Nebraska except Omaha	221	162	35,880	79.0
Nevada: Las Vegas	106	215	22,857	62.0
Nevada except Las Vegas	169	248	41,815	74.1
New Hampshire: Manchester	163	330	53,836	58.7
New Hampshire except Manchester	263	299	78,583	75.6
New Mexico: Albuquerque	244	226	55,200	72.7
New Mexico except Albuquerque	217	212	46,000	79.7
New York: New York	155	387	59,800	52.8
New York: Rochester	132	344	45,483	74.6
New York: Syracuse	135	329	44,540	74.8
New York except New York, Rochester and Syracuse	135	340	46,000	75.1
North Carolina: Charlotte	201	228	45,914	75.3
North Carolina: Raleigh/Durham	224	242	54,280	73.8
North Carolina except Charlotte and Raleigh/Durham	236	195	46,000	78.1
North Dakota: Fargo Area	188	182	34,127	67.5
North Dakota except Fargo Area	160	172	27,600	74.3
Ohio: Cincinnati	178	233	41,400	73.8
Ohio: Cleveland	178	241	42,909	73.9
Ohio: Columbus	191	219	41,849	71.5
Ohio except Cincinnati, Cleveland and Columbus	185	211	38,985	78.3
Oklahoma: Oklahoma City	198	209	41,400	74.3
Oklahoma: Tulsa	223	183	40,823	79.5
Oklahoma except Oklahoma City and Tulsa	230	150	34,500	82.8
Oregon: Eugene	242	257	62,237	74.5
Oregon: Portland	297	243	72,201	69.4
Oregon except Eugene and Portland	244	256	62,560	77.1
Pennsylvania: Philadelphia	242	285	69,000	68.9
Pennsylvania: Pittsburgh	138	301	41,400	74.4
Pennsylvania: Scranton	175	236	41,400	71.4
Pennsylvania except Philadelphia, Pittsburgh and Scranton	178	259	46,000	75.0
Rhode Island: Providence	222	288	64,023	62.6
Rhode Island except Providence	458	276	126,500	77.7
South Carolina: Charleston	296	194	57,488	77.3
South Carolina: Columbia	199	210	41,850	77.0
South Carolina except Charleston and Columbia	229	191	43,700	78.9
Tennessee: Memphis	183	191	34,966	66.9
Tennessee: Nashville	285	200	57,006	74.4
Tennessee except Memphis, Nashville	247	186	46,000	79.9
Texas: Austin	311	203	63,209	71.4
Texas: Dallas/Fort Worth	202	190	38,317	69.5
Texas: Houston	172	198	34,079	66.7
Texas except Austin, Dallas/Fort Worth and Houston	182	177	32,200	74.7
Utah: Salt Lake City	313	230	71,986	79.3
Utah except Salt Lake City	343	201	69,000	84.8
Virginia: Richmond Area	305	211	64,400	71.0
Virginia except Richmond Area	334	190	63,480	75.7
Washington: Seattle	323	285	92,000	67.4
Washington: Spokane	205	269	55,200	74.4
Washington except Seattle and Spokane	274	252	69,000	75.7
Wisconsin: Madison Area	321	251	80,500	80.4
Wisconsin: Milwaukee	218	274	59,800	67.1
Wisconsin except Madison Area and Milwaukee	222	228	50,600	78.4
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs. The following states have no metropolitan area in MetLife (2011): Alaska, Vermont and Wyoming. The following states have metropolitan areas in MetLife (2011) but no corresponding metropolitan areas in the ACS: New Jersey, South Dakota and West Virginia. These states are not displayed in the table.

 

TABLE A-4. Median Days of Private Room Nursing home Care that Net Home Equity Could Finance, by Metropolitan and NonMetropolitan Areas for Homeowners
Area	Days in Nursing Home	Daily Cost of Nursing Home ($)	Per Capita Net Home Equity ($)
Alabama: Birmingham	373	185	69,000
Alabama: Montgomery	285	202	57,500
Alabama except Birmingham and Montgomery	299	180	53,750
Arizona: Phoenix	289	223	64,400
Arizona: Tucson	284	259	73,600
Arizona except Phoenix and Tucson	238	228	54,306
Arkansas: Little Rock	381	169	64,400
Arkansas except Little Rock	286	161	46,000
California: Los Angeles	822	250	205,551
California: San Diego	655	271	177,462
California: San Francisco	474	485	230,000
California except Los Angeles, San Diego and San Francisco	389	272	105,800
Colorado: Colorado Springs	368	225	82,800
Colorado: Denver	470	235	110,400
Colorado except Colorado Springs and Denver	411	223	91,598
Connecticut: Hartford	280	410	115,000
Connecticut: Stamford Area	839	425	356,500
Connecticut except Hartford and Stamford Area	336	370	124,200
Delaware: Wilmington	447	257	115,000
Delaware except Wilmington	389	260	101,200
District of Columbia	667	315	169,554
Florida: Jacksonville	325	212	69,000
Florida: Miami	263	306	80,500
Florida: Orlando	216	256	55,194
Florida except Jacksonville, Miami and Orlando	241	253	60,905
Georgia: Atlanta	354	195	69,000
Georgia except Atlanta	344	166	57,142
Hawaii: Honolulu	803	326	261,740
Hawaii except Honolulu	404	413	166,980
Idaho: Boise	234	236	55,200
Idaho except Boise	338	204	69,000
Illinois: Chicago	488	198	96,600
Illinois: Peoria	268	240	64,400
Illinois except Chicago and Peoria	264	174	46,000
Indiana: Fort Wayne	232	235	54,610
Indiana: Indianapolis	241	229	55,200
Indiana except Fort Wayne and Indianapolis	194	237	46,000
Iowa: Des Moines	315	195	61,381
Iowa except Des Moines	361	153	55,200
Kansas: Wichita	286	177	50,600
Kansas except Wichita	271	161	43,700
Kentucky: Lexington	394	216	85,100
Kentucky: Louisville	279	240	66,994
Kentucky except Lexington and Louisville	236	195	46,000
Louisiana: Baton Rouge	362	153	52,440
Louisiana: Shreveport Area	457	145	64,400
Louisiana except Baton Rouge and Shreveport Area	310	141	92,000
Maine: Southern Maine	451	297	69,000
Maine except Southern Maine	256	270	69,000
Maryland: Baltimore	404	285	115,000
Maryland except Baltimore	389	258	100,459
Massachusetts: Boston	487	356	173,510
Massachusetts: Worcester	345	333	115,000
Massachusetts except Boston and Worcester	348	357	124,200
Michigan: Detroit	267	198	52,900
Michigan: Grand Rapids	227	273	62,100
Michigan except Detroit and Grand Rapids	224	235	52,529
Minnesota: Minneapolis/Saint Paul	414	216	89,517
Minnesota: Rochester Area	430	187	80,500
Minnesota except Minneapolis/Saint Paul and Rochester	313	206	64,400
Mississippi: Jackson	325	186	60,444
Mississippi except Jackson	231	199	46,000
Missouri: Kansas City	396	157	62,100
Missouri: Saint Louis	371	186	69,000
Missouri except Kansas City and Saint Louis	309	149	46,000
Montana: Billings	381	193	73,600
Montana except Billings	427	194	82,800
Nebraska: Omaha	270	211	56,929
Nebraska except Omaha	284	162	46,000
Nevada: Las Vegas	278	215	59,721
Nevada except Las Vegas	284	248	70,480
New Hampshire: Manchester	293	330	96,600
New Hampshire except Manchester	328	299	97,980
New Mexico: Albuquerque	361	226	81,683
New Mexico except Albuquerque	325	212	69,000
New York: New York	526	387	203,477
New York: Rochester	174	344	59,800
New York: Syracuse	173	329	57,040
New York except New York, Rochester and Syracuse	203	340	69,000
North Carolina: Charlotte	302	228	68,760
North Carolina: Raleigh/Durham	331	242	80,030
North Carolina except Charlotte and Raleigh/Durham	330	195	64,400
North Dakota: Fargo Area	379	182	69,000
North Dakota except Fargo Area	267	172	46,000
Ohio: Cincinnati	266	233	61,894
Ohio: Cleveland	256	241	61,640
Ohio: Columbus	291	219	63,707
Ohio except Cincinnati, Cleveland and Columbus	240	211	50,600
Oklahoma: Oklahoma City	275	209	57,500
Oklahoma: Tulsa	302	183	55,200
Oklahoma except Oklahoma City and Tulsa	293	150	43,924
Oregon: Eugene	358	257	92,000
Oregon: Portland	470	243	114,329
Oregon except Eugene and Portland	327	256	83,720
Pennsylvania: Philadelphia	381	285	108,668
Pennsylvania: Pittsburgh	191	301	57,500
Pennsylvania: Scranton	253	236	59,800
Pennsylvania except Philadelphia, Pittsburgh and Scranton	266	259	69,000
Rhode Island: Providence	383	288	110,400
Rhode Island except Providence	582	276	160,540
South Carolina: Charleston	448	194	86,940
South Carolina: Columbia	288	210	60,547
South Carolina except Charleston and Columbia	303	191	57,960
Tennessee: Memphis	335	191	63,908
Tennessee: Nashville	381	200	76,210
Tennessee except Memphis and Nashville	322	186	59,800
Texas: Austin	449	203	91,080
Texas: Dallas/Fort Worth	315	190	59,800
Texas: Houston	302	198	59,800
Texas except Austin, Dallas/Fort Worth and Houston	260	177	46,000
Utah: Salt Lake City	393	230	90,439
Utah except Salt Lake City	392	201	78,728
Virginia: Richmond Area	436	211	92,000
Virginia except Richmond Area	436	190	82,800
Washington: Seattle	516	285	147,200
Washington: Spokane	279	269	74,980
Washington except Seattle and Spokane	370	252	93,164
Wisconsin: Madison Area	367	251	92,000
Wisconsin: Milwaukee	326	274	89,358
Wisconsin except Madison Area and Milwaukee	293	228	66,700
SOURCE: Authors' calculations from 2011 ACS matched to MetLife (2011) using 2010 HRS to impute mortgage values for those who report holding a mortgage. The sample includes all adults age 65 or older. Per capita home equity values are reduced to account for potential transaction costs.The following states have no metropolitan area in MetLife (2011): Alaska, Vermont and Wyoming. The following states have metropolitan areas in MetLife (2011) but no corresponding metropolitan areas in the ACS: New Jersey, South Dakota and West Virginia. These states are not displayed in the table.

 

ABOUT THE AUTHOR:

Stephan Lindner a research associate in the Income and Benefits Policy Center at the Urban Institute and a senior research association in the Center for Health Systems Effectiveness at the Oregon Health and Science University (OHSU). His research examines social insurance and welfare programs--in particular, Social Security Disability Insurance, Supplemental Security Income, and unemployment insurance. He is interested in how economic circumstances, income sources, and program characteristics influence program participation for displaced and disabled workers. More recently, he has examined efforts to improve cardiovascular disease prevention in primary care clinics. He earned his M.A. and Ph.D. in economics from the University of Michigan.

The author thanks John Drabek and Pamela Doty of ASPE and Melissa Favreault, Richard Johnson, and Brenda Spillman of the Urban Institute for comments on earlier drafts. Melissa Favreault also provided support with the HRS data analyses.

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Where Do People with Disabilities Live?

This report was prepared under contract #HHSP23320100025W1 between the U.S. Department of Health and Human Services (HHS), Office of Disability, Aging and Long-Term Care Policy (DALTCP) and the Urban Institute. For additional information about this subject, you can visit the DALTCP home page at http://aspe.hhs.gov/office-disability-aging-and-long-term-care-policy-daltcp or contact the ASPE Project Officers, John Drabek and Pamela Doty, at HHS/ASPE/DALTCP, Room 424E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201; John.Drabek@hhs.gov, Pamela.Doty@hhs.gov.

Reports Available

How Much Nursing Home Care Can Home Equity Finance?

• HTML:  https://aspe.hhs.gov/basic-report/how-much-nursing-home-care-can-home-equity-finance
• PDF:  https://aspe.hhs.gov/pdf-report/how-much-nursing-home-care-can-home-equity-finance

Later-Life Household Wealth Before and After Disability Onset

• HTML:  https://aspe.hhs.gov/basic-report/later-life-household-wealth-and-after-disability-onset
• PDF:  https://aspe.hhs.gov/pdf-report/later-life-household-wealth-and-after-disability-onset

Older Adults' Living Expenses and the Adequacy of Income Allowances for Medicaid Home and Community-Based Services

• HTML:  https://aspe.hhs.gov/basic-report/older-adults-living-expenses-and-adequacy-income-allowances-medicaid-home-and-community-based-services
• PDF:  https://aspe.hhs.gov/pdf-report/older-adults-living-expenses-and-adequacy-income-allowances-medicaid-home-and-community-based-services