Reverse Mortgage Demographics and Collateral Performance

Reverse Mortgage Demographics and Collateral Performance Thomas Davidoff Sauder School of Business, University of British Columbia February 25, 2014 Abstract Home Equity Conversion Mortgage (HECM) data seem to confirm two concerns about these federally insured loans offered to older US homeowners. First, originations are rare, consistent with a familiar disinterest in extracting home equity through sale among older owners, even those with low wealth. Second, moral hazard and adverse selection appear to operate on HECM s implicit home price insurance. Demographics mitigate both concerns. Consistent with greater demand among those with low wealth, HECM loans are more common, more responsive to price appreciation, and more intensively used in neighborhoods where large fractions of homeowners are black and Hispanic, and where incomes and property values are below metropolitan averages. The correlation between minority share of homeowners and late-2000s home price busts explains most observed selection into HECM on price appreciation within metropolitan areas. Selection on price movements and demographics explains away roughly half of poor collateral performance in HECM loans that has been attributed elsewhere to strategic undermaintenance. Keywords: Mortgages, Housing Demand, Social Security and Pensions, Portfolio Choice, Insurance. JEL Classification: G21, R21, H55, G11, G22 1 Introduction Home Equity Conversion Mortgages (HECMs) offer US homeowners over 62 cash or lines of credit. Borrowers may defer repayment until they move or die, and their liability at loan termination is the lesser of the mortgaged home s net resale value or the outstanding loan balance. 1 The Federal Housing Administration (FHA) provides HECM lenders with 1 Industry participants express mixed opinions as to whether shortfalls trigger adverse credit score events when the loan terminates while a borrower is alive. There is no adverse credit event if the loan terminates with the death of the borrower(s). 1

insurance against shortfalls at termination between collateral value and the balance due, in exchange for fees due at loan origination and interest charges through the life of the loan. Insurance fee schedules and allowable loan to value ratios do not vary across markets, nor do they incorporate time series risk factors such as rent to price ratios. The limited liability feature is thus likely to be overpriced in some markets at some dates and underpriced in others. 2 HECM data seem to confirm economists concerns about the potential size of a market for home equity loans to older Americans and about moral hazard and adverse selection problems in the design of FHA HECM insurance. The stated intent of FHA intervention into the reverse mortgage market was to help the large number of retired homeowners with modest income to remain in their homes during retirement. 3 The fact that only roughly 2% of eligible homeowners participate (Consumer Financial Protection Bureau (2012)) appears consistent with the familiar disinterest among healthy older homeowners, even those with low wealth and incomes, in liquidating home equity through sale or forward mortgages. Writing near the inception of the HECM program, Venti and Wise (1989) and Feinstein and McFadden (1989) found low rates of mobility among healthy older homeowners generally in US panel data, and found little or no evidence that low wealth or illiquidity increases the propensity to liquidate home equity through selling a home. 4 Both analyses conclude that these facts suggest that the reverse mortgage market is likely to remain small. FHA has lost money guaranteeing HECM loans, in large part because HECMs were originated disproportionately near the recent price cycle peak in metropolitan areas that experienced large subsequent price declines. 5 Shan (2011) and Haurin et al. (2013) observe 2 Fees have risen and insurable loan to value ratio formulas have shrunk in the wake of the housing crisis. There are market-specific caps on loan size that limit loan to value ratios among higher value properties, and loan to value varies with the age of the borrower (the younger spouse if a couple). Davidoff (2012) presents simulations that show the implicit put option in lines of credit to be underpriced in many states near the home price cycle peak, under different assumptions about future price and interest rate movements. 3 HECM was enabled by the 1987 Housing and Community Development Act. See Mayer and Simons (1994) and Kutty (1998) for early estimates of the potential market size. 4 Venti and Wise (1989) and Venti and Wise (2001) do find that conditional on sale, poorer homeowners are relatively likely to downsize. 5 See Integrated Financial Engineering, Inc. (2013) for loss estimates and Davidoff and Wetzel (2013) for 2

that this geographic and temporal selection is consistent with Akerlof (1970)-type exploitation of unpriced variation in the value of the limited liability provision. For example, it is possible that the reason HECM demand exploded in the Sand States (Arizona, California, Florida, and Nevada) relative to elsewhere in the mid-2000s was that homeowners viewed markets in these states as the likeliest to see sufficient price declines to generate a positive expected value for HECM s implicit home price insurance feature. A new result, presented in Section 2.5 using Zip Code-level price index data from Zillow, is that there was also significant adverse selection on ex-post price performance within metropolitan areas during the home price boom. Consistent both with adverse selection across neighborhoods within metropolitan areas, and with moral hazard on maintenance, Capone et al. (2010) report that homes backing HECM loans have appreciated at a lower rate than metropolitan averages. Undermaintenance and procyclical terminations are now embedded in federal actuarial modeling described in Integrated Financial Engineering, Inc. (2013). Shiller and Weiss (2000) and Miceli and Sirmans (1994) highlight the incentive that HECM borrowers have to undermaintain their homes when potential loan balances exceed collateral value. This paper shows that HECM appears to have relatively strong appeal at the lower end of the wealth distribution. In particular, the ratio of HECM loan originations divided by estimated eligible homeowners, the sensitivity of originations to price increases, and use of lines of credit conditional on taking on HECM, are significantly larger in neighborhoods with high minority shares, low property values, and high poverty rates relative to metropolitan area means. As suggested in Mian and Sufi (2009), and documented in Section 2, neighborhoods with high minority population shares and lower than average property values saw larger than average price declines during the recent home price bust. This suggests that some of the poor collateral performance of HECM loans relative to metropolitan area means may be driven not by older homeowners consciously exploiting default options embedded in a discussion of the role of geographic and time series variation in originations in generating insurance losses. 3

HECM, but instead by a more innocuous incidental correlation through demographics. A glance at HECM loan data suggests a strong correlation among race, property values, and HECM originations. Figure 1 plots the fraction of homeowners who are black or Hispanic as of the 2000 US Census and the ratio of HECM loan originations between program inception in 1989 through 2011 to the number of homeowners over 62 as of 2010 by Zip Code for three metropolitan areas: Chicago; Los Angeles; and Washington, DC. Racial location patterns in all three markets are familiar: blacks are predominant in the South and East of Chicago; in the South and generally away from the Pacific Coast in metropolitan Los Angeles; and in the North and East of metropolitan Washington, DC. Property values are lower in these neighborhoods than elsewhere in these generally expensive metropolitan areas. The spatial pattern of HECM originations looks remarkably similar to the distribution of minority homeowners. Section 2.2 shows that the graphical correlation in Figure 1 extends nationally. Within metropolitan areas, HECM originations are more common, and more sensitive to price appreciation, in neighborhoods where property values are lower than average, and where a larger than average share of homeowners are black or Hispanic. 6 Black and Hispanic homeowners are less wealthy than others, so the relative prevalence of HECM loans in minority neighborhoods suggests that demand for home equity extraction is negatively correlated with wealth and income. For example, I find in the 2002 of the Health and Retirement/AHEAD study that the median ratio of non-housing wealth to home equity among homeowners identifying as either black or Hispanic and aged 70-75 is.13, median home value is $75,000, and the median ratio of conventional mortgage debt to home value is.16. For 70-75 year old homeowners who are not black or Hispanic, the medians are 1.05, 125,000 and.08. A correlation between low wealth and HECM originations would be consistent both with 6 One channel through which minority population shares might affect lending warrants remark: it has been argued that legislatively imposed affordable housing goals may have fed problematic lending practices in the forward mortgage market. Ghent et al. (2013) provide a critical review and empirical contribution. Such incentives should not have affected HECM loans directly, which are not reported under the Home Mortgage Disclosure Act. There could have been indirect effects through capital gains pressure on originations or expansion of originator presence. 4

program goals and with theoretical life cycle considerations laid out by Artle and Varaiya (1978). Housing demand is generally deemed increasing in wealth, but with an elasticity less than one, 7 so low non-housing wealth is correlated empirically with both low housing wealth and a high ratio of home equity to other wealth. For example, I find in the 2002 wave of HRS/AHEAD, among home owning households age 70 to 75, that the correlation between non-housing wealth and the ratio of non-housing wealth to primary residence equity is.61. Intuition and calibrations (De Nardi et al. (2010), Lockwood (2011)) suggest that bequests are superior goods. For poorer households, then, proceeds from any sale while alive thus are typically a larger fraction of resources, and wealth taken from the estate presumably relatively less valued relative to consumption while alive. Thus the ability to to borrow against proceeds from a sale while alive or from the value of housing left to heirs should benefit low wealth households more than higher wealth households. The same could be said about the benefit of selling a home earlier in retirement, though, and there is little evidence that selling homes has greater appeal at the low end of the income and wealth distribution. Medicaid rules provide reason to expect that HECM demand would be more skewed towards the bottom end of the income and wealth distribution than the sale of primary residences. Medicaid covers health expenditures for poor retirees, and its coverage of the significant risk of long-term care looms large in the economics of retirement, and home equity extraction in particular (see Davidoff (2009), Greenhalgh-Stanley (2011) and Nakajima and Telyukova (2013)). Medicaid places a high tax on non-housing income and assets, but allows retirees and sometimes their heirs to live in their homes and even retain home equity untaxed after funding a nursing home stay. Some states impose liens on the homes of Medicaid recipients, but others do not. Thus converting home equity to cash through sale when a Medicaid stay is likely or has occurred is costly. By contrast, whether HECM borrowing is implicitly taxed or subsidized by Medicaid is ambiguous, as outlined in Davidoff (2012). The prospect or presence of a lien may make a HECM line of credit more attractive, since if 7 Davis and Ortalo-Magne (2011) provide a review of the literature and a frequently cited counterargument. 5

not spent before a move out of the home or death, home equity may be taken by Medicaid. HECM proceeds cannot be saved, but unused credit is not subject to Medicaid recapture and credit may be used over time to stay under spending caps imposed by Medicaid. In Section 2 I make some effort to disentangle race from wealth and income as sources of HECM demand. Due to data and identification problems, the effort is necessarily incomplete. A full picture of the relationship between propensity to consume home equity through HECM and wealth, income, or liquidity would require detailed data on the wealth and incomes of HECM users. Unfortunately, given the small fraction of the eligible population that uses HECM, such relationships cannot be gleaned from surveys such as HRS/AHEAD. HECM loan data offers detail on mortgaged properties, but nothing other than age and gender or marital status regarding borrower characteristics. 8 Merging HECM data with census aggregate statistics at the Zip Code level enables descriptions of relationships among HECM originations per eligible household, ethnic and racial population shares, and income-based measures of poverty rates among older homeowners measures for the elderly. Data on nonhousing financial assets are not available. Naturally there is considerable heterogeneity of wealth and income among blacks and Hispanics, and the correlation between measured ethnicity and resources presumably varies across locations. Even if some of the difference in takeup rates across neighborhoods with different black and Hispanic shares of homeowners were explained away by available measures of wealth, wealth is in part a result of demand for savings, which appears to vary across race and Hispanic status conditional on observable characteristics. 9 8 Data on borrower incomes and assets was recorded early in the life of HECM, but since this information is not part of underwriting criteria (all that is required is that no borrower be under 62, and pre-existing mortgage debt be less than available loan proceeds), Rodda et al. (2000) observed many missing values and deemed the data unusable, while noting low use among non-whites. There is no current data on the distribution of income or non-housing assets among borrowers. Redfoot et al. (2007) describe results from a survey that oversamples borrowers, but notice that the survey responses come disproportionately from non-hispanic whites. Readers of an earlier version suggested that minority shares might be particularly large among single women. I did not find a strong effect in a cursory look. 9 See, for example, Blau and Graham (1990) and Altonji and Doraszelski (2005). Kain and Quigley (1975); Canner et al. (1991); Gabriel and Painter (2003); and Bayer et al. (2004), among many others discuss the identification of purely racial and ethnic characteristics versus financial factors in housing demand and mortgage outcomes. 6

Academic researchers do not have access to the resale performance of HECM collateral that Capone et al. (2010) and Integrated Financial Engineering, Inc. (2012) use to find that mortgaged homes have seen larger price declines that metropolitan averages. However, it is possible to observe whether the lender claimed a shortfall between the home s resale value and the outstanding loan balance at termination. Consistent with the results of FHA researchers, a large number of insurance claims arise when adjusting the initial appraised value by the change in the FHFA repeated sale index for the properties metropolitan area between origination and termination would imply no shortfall. Section 2.6 shows that between one-third and one-half of these shortfalls disappear when Zip Code and low-value property indexes from Zillow replace FHFA metropolitan indexes in the calculation of baseline market appreciation. Recognizing the geographic distribution of HECM loans within metropolitan areas attenuates the scope for strategic exploitation of default options through undermaintenance to explain poor collateral performance. However, this geographic distribution could have arisen through lemon selling among older homeowners. Regressions presented in Section 2.5 show that most of the within-metropolitan adverse selection on price declines can be explained by the appeal of HECM in neighborhoods where minority homeowners and inexpensive homes are concentrated. The remainder can be explained by lagged home price appreciation. These facts do not quite imply that adverse selection on price declines was caused by differences in demographic propensity to use HECM. However, any conscious exploitation of differences in the expected value of the limited liability feature of HECM were apparently driven chiefly by differences in predictable changes to supply and demand factors highly correlated with demographics (e.g. non-prime loan growth). 7

2 Empirical Analysis of HECM Loans The discussion in the Introduction motivates three sets of empirical questions. First, we wish to know if Figure 1 generalizes: are local differences in HECM market penetration and borrower credit use associated with differences in race and observable measures of poverty? Second, to what extent can demographics explain the relationship between HECM originations in the mid-2000s and the magnitude of the local housing bust? Third, to what extent can the underperformance of HECM collateral relative to metropolitan home price index performance be explained by within-metropolitan adverse selection? 2.1 Data Table 1 presents summary statistics at the Zip Code and individual level from: FHA HECM loan-level data, 2000 and 2010 Census data, and Zillow and FHFA repeated home price indexes. Summary statistics are presented for variables of primary interest in the cases where FHFA and Zillow data overlap. This is a more urbanized sample than would be a sample representative of the US as I consider metropolitan price data, and the Zillow data appears to be available unequally across states. I measure market penetration as the ratio of HECM originations to eligible homeowners over some period. Log market penetration might be easier to fit with characteristics, but I include in the analysis the non-trivial number of Zip Codes with no HECM originations in particular sub-periods of interest. FHA provides public access to data on each HECM loan originated through mid-2011. This data includes the appraised value of the home at origination, the borrower or borrowers age and gender or marital status; the date of origination (and termination if any); credit use by year of the loan s life; the mortgaged home s Zip Code (and state, county, and metropolitan area); and an indicator for whether the loan terminated with a balance in excess of realized property value net of selling costs. From the credit draws and formulaic interest charges (most HECM loans accumulate interest monthly at a spread over the one- 8

year treasury or LIBOR yield), I estimate the outstanding balance at termination. 10 The Decennial Census provides at the level of Zip Code Tabulation Area (an approximation of a Zip Code based on a collection of block groups) counts of homeowners by age, by race and Hispanic status, and by age and poverty status. Combining the Census and FHA data, we can observe conditional and unconditional correlations among Zip Code demographic characteristics and Zip Code HECM take-up rates. Unfortunately, the Census does not provide direct measures of non-housing asset wealth. Low Zip Code property values could suggest both low financial wealth and low ratios of housing to other wealth. Since most micro studies indicate wealth and price elasticities of housing demand below one, we might reasonably expect that high property values are more likely to signal a high ratio of housing wealth to other wealth using cross-metropolitan variation than within-metropolitan variation. Given demand for HECM is concentrated in low property value census tracts, I focus on Census estimates of the 25th percentile of home values as of 2000. Similar results apply with median values. 2000 is a natural baseline Census year, as approximately 95% of HECM loans were originated after 1999 (through mid-2011, the median origination year was 2007). The Federal Housing Finance Agency (FHFA) publishes a repeated sale home price index at the metropolitan level. Zillow publishes an index of representative home prices for all homes and for homes in the upper and bottom tercile of metropolitan home values both at the metropolitan and at the Zip Code level. At the HECM loan level, where data is available, multiplying appraised value at origination by the ratio of the FHFA or Zillow index at the date of termination to the value at origination yields an estimate of terminal collateral value (before selling costs) that can be compared to the outstanding loan balance. 2.2 HECM Penetration Rates and Demographics Tables 2 and 3 present regressions of the form: 10 Draw data is annual, but interest accrues monthly. Given that most loans feature very large initial draws, I assume for calculations that all draws are made in the first month of a loan year. 9

H zt ˆN zt =α + βminority z2000 + γ 1 poverty z2000 + γ 2 value z2000 (1) + m δ zm η m + x zt µ + φ p zt + ɛ zt (2) In specification (1), H zt is the number of HECM loans originated in period t in Zip Code z. I estimate the eligible population ˆN zt throughout as the number of homeowners over age 64 as of the 2010 Census. Recognizing that this approximation in part reflects growing population, I control for the log growth of older population between the 2000 and 2010 censuses. Not all household heads over 65 who own a home are HECM-eligible: those with spouses under 62 or with conventional mortgage debt above HECM loan limits are not eligible. In estimating the numerator H, I exclude HECM loans labeled refinances of existing HECMs and those flagged as used to purchase a home. The controls x also contain the fraction of homes that are single family in z because HECM rules limit lending to apartment owners. The poverty measure is the fraction of homeowners over age 64 that were deemed below poverty income levels in 1999. value is the 25th percentile of owner-assessed value as of the 2000 census. δ zm η m is the product of an indicator and a coefficient associated with Zip Code z lying within metropolitan area m. minority reflects the fraction of homeowners identifying as black or Hispanic as of the 2000 Census. The relationship between HECM originations and the black and Hispanic shares are naturally different, but these differences appear to vary across locations, so I adopt the parsimonious lumping into minority. p zt is Zip Code-specific growth in home prices in z as measured by Zillow over some period; in one specification, I interact this measure with demographic characteristics. The estimated regression coefficients reflect partial correlations at the neighborhood level. These coefficients may be quite different from the causal effects of right hand side variables on demand for HECM loans for several reasons. Among these reasons are the fact that we do 10

not have controls for non-housing wealth, so the effect of minority population share is likely overstated. Moreover, loan originators may have allocated marketing resources differentially to minority and poor neighborhoods. Recognizing that lending policies or personnel may be correlated within metropolitan areas, I cluster standard errors at that level. 11 Table 2 presents regression estimates of the ratio of HECM originations over the full period 1989-2011 to 2010 eligible population on 2000 Zip Code characteristics. The most consistent result is that there is a significantly positive relationship between HECM originations per approximate eligible homeowner and the fraction of homeowners (of all ages) with a respondent that identifies as black or Hispanic. The estimated effect of moving from zero minority to 100% minority drops significantly from roughly a 7.5% increase in penetration to roughly 5% when metropolitan area dummy variables are introduced. Either is a very large effect, given that the sample mean of the dependent penetration ratio is roughly 1.5% as indicated in Table 1. The roughly offsetting significant coefficients of estimated 2010 eligible owners (negative) and 2000 eligible owners (positive) suggests positive changes in elder population do not drive originations, or may simply be a symptom of approximation error in the denominator of the dependent variable. We find in specification (1) that conditionaly only on Zip Code population and single family share, the fraction of owners over 64 as of 2000 who are poor is positively and significantly associated with HECM originations as a fraction of 2010 estimated eligible owners. The coefficient on older homeowner poverty switches sign and significance conditional on the minority share, and then becomes indistinguishable from zero conditional on the log 25th percentile home value. The coefficient on log 25th percentile home values flips from a positive and significant sign without metropolitan area fixed effects to significantly negative conditional on metropolitan area dummies. This result is consistent with the level of home prices and low wealth both positively influencing originations: given a price elasticity of demand for owner housing less 11 Metropolitan dummies with no clustering may not be sufficient to capture phenomena such as a particular lender s propensity to market in black neighborhoods, for example. 11

than one, we expect to find higher home values in expensive metropolitan areas holding income and wealth constant. Within metropolitan areas there are theoretically offsetting wealth and housing to other wealth ratio effects of home values as described above. Specifications (5) and (6) of Table 2 repeat specification (4), but confine the sample to the top 20% of Zip Codes for black and Hispanic population share (5) and bottom 20% (6). The Zip Codes in specification (6) have less than 4% minority homeowner shares; those in the top 20% have more than 24%. In both the high and low minority samples, minority share remains significantly positively associated with originations. 12 The rate of income poverty among owners 65 and above in 2000 is significantly positively associated with demand in heavily minority neighborhoods, but negatively associated with demand in mostly non-hispanic white neighborhoods. By contrast, within metropolitan areas, lower quartile home prices are significantly negatively associated with HECM originations in largely nonminority neighborhoods, but not in neighborhoods with larger minority concentrations. In sum, HECM originations are clearly more common in neighborhoods with large minority population shares. There is more limited evidence that this relates to income or asset poverty. Poor whites evidently do not commonly use HECM loans. 2.3 Contemporaneous Appreciation and HECM Originations Table 3 presents regressions of the relationship between growth in HECM originations and growth in home prices. Given that HECM demand presumably responds with a lag to home price growth and given the concentration of growth in prices between 2004 and 2007, I present regressions of the origination share of eligible population 2004-2007 minus the share 1989-2003 on a measure of the home price boom: the ratio of price in 2006 to 2002. Using the notation in (1), the dependent variable is thus H 2004 through 2007 H 1989 through 2003 ˆN 2010. Specification (1) of Table 3 yields results very similar to those in specification (4) of Table 3: the significantly positive relationship between log home price boom and originations 12 For the full sample, a linear minority specification has a better fit than log. 12

during 2004-2007 does not affect the positive relationship between origination growth and minority share or low home values. A log point increase in home prices between 2002 and 2006 relative to the metropolitan mean is associated with an approximately 2% increase in the growth in originations. Specification (2) of Table 3 shows that originations are significantly more sensitive to price growth in minority and inexpensive neighborhoods within metropolitan areas. To the extent that variation in origination growth may be taken to reflect demand rather than supply growth, this result illustrates a greater marginal (with respect to price appreciation) propensity to consume home equity through HECM among minority and low wealth households. However, there is no significant relationship between income-based poverty rates for older homeowners and the sensitivity of growth in HECM originations to price growth. Specifications (3) and (4) of Table 3 show similar results in the heavily minority and heavily non-minority samples described with respect to Table 2. The interactive effects of minority share and low home prices with changes in home prices are stronger in highly minority Zip Codes than in low minority Zip Codes. Similar results arise to those presented in Table 3 if the ratio of originations to population 2004-2007 is considered in isolation (because originations were generally very low prior to 2004) or if the baseline share subtracted from boom period originations includes both the pre-boom period 1989-2003 and the period after 2008. This is illustrated in Table 5, which presents the mean minority share and log median home value by year across HECM Zip Codes. Let y z be either minority share or log median home value as of 2000, let h zt be the number of HECMs originated in z in year t, and let H t be the number of HECMs originated nationwide in t. Table 5 measures: z home value hit a trough around the cycle peak. y z h zt H t. Minority shares peaked and median Zip Code 13

2.4 Credit Use Table 4 presents regressions of individual level first-year credit divided by the initial credit limit for all HECM loans issued prior to 2008. Starting in 2008, a large fraction of loans required immediate withdrawal of all credit; prior to 2008, the large majority of loans were lines of credit. The regression controls are: the log appraised value, log credit limit, and dummies for metropolitan area, (younger) borrower age, and borrower gender or marital status. Specifications (1) and (2) differ in that the latter excludes loans for which there is a binding cap, so that the loan to value ratio is constrained by an absolute dollar amount. Specification (3) limits the sample to loans that can be merged with Zillow Zip Code price data. In all three cases, we find a significantly positive coeffcients on minority population share and the fraction of homeowners over 65 with poverty incomes on credit use. High log 25th percentile Zip Code home value and own home values are associated with significantly less credit use. Log Zillow price relative to January, 2001, is also significantly associated with more credit use. All effects are highly significant, except for poverty, which has marginal significance. The results in Table 4 provide a bit more support for a role for low income per se generating demand for HECM, as opposed race. to an incidental correlation through race. 2.5 Ex-post Zip Code Price Declines Davidoff and Wetzel (2013) and Shan (2011) show that HECM originations during the period of peak home prices in the mid-2000s predict subsequent price crashes across metropolitan areas. The first specification of Table 6 shows that this is true within metropolitan areas, too. A one percent difference in originations 2004-2007 divided by 2010 eligible households in a given Zip Code is associated with a 3% larger price crash as measured by Zillow between January, 2006 and January, 2011, both relative to metropolitan means. Note a larger ratio of 2006 to 2011 price implies a larger crash. Specification (2) of Table 6 adds controls for minority share and log 25th percentile home 14

value. These controls reduce the magnitude of the coefficient of HECM origination share by roughly half, to 1.5%. Allowing in a crude way for the likelihood that the relationship between originations and price crash magnitude is likely to be larger where the home price cycle had greater amplitude, specification (3) shows that when minority share and log 25th percentile home value are interacted with an indicator for a metropolitan area lying within the Sand States of Arizona, California, Florida, or Nevada, the coefficient on crash falls again by almost half. Thus the coefficient on HECM market penetration 2004-2007 falls from approximately 3 to approximately.8 with just two Zip Code controls interacted with an approximation of crash magnitude. Note that the main effect of being in a Sand State is captured by metropolitan area dummies in all specifications. Specifications (4) and (5) of Table 6 show that within the low and high minority share subsamples, there is no significant relationship of origination share and subsequent price crash. Specification (6), restoring the full sample, adds to specification (3) a control for log price appreciation 2002-2006. This control reduces the estimated relationship between HECM peak period market penetration and ex-post price decline to a value of roughly.2, statistically indistinguishable from zero. Evidently, controls for demographics substantially weaken the estimated relationship between ex-post price crashes and HECM originations. This does not preclude the possibility that HECM grew in particular Zip Codes because borrowers anticipated limited liability value there. The fact that controlling for lagged capital gains reduces the coefficient dramatically could simply mean that the unpriced information borrowers exploited in choosing whether or not to use HECM was mostly based on a (correct ex-post) belief that gains during the price boom 2002-2006 signalled future price declines. Similarly, the pool of potential borrowers may have recognized that home values in areas seeing the largest relaxation of credit constraints were likely to correct dramatically. 15

2.6 Collateral underperformance relative to market FHA has access to the transaction prices of mortgaged homes under HECM. They (Capone et al. (2010) and Integrated Financial Engineering, Inc. (2012)) find that relative to FHFA repeated sales home price indexes, HECM homes appreciate at a lower rate, and more so when the homes have low appraised value at origination. While there is no public access to collateral resale values, public FHA data provides a noisy signal of changes in home value. Public data includes an indicator of whether the loan servicer makes an insurance claim because the home is worth less than the outstanding balance at termination. 13 Credit use by year of the loan s life is also public. Given the formulaic interest rate calculation (the one-year treasury or LIBOR plus a lender s margin plus FHA interest rate charges for the mortgage insurance), we can estimate loans annual balances. Table 1 reports summary statistics for individual loan data among HECM loans that have terminated. Approximately 7.5% of all terminated loans feature a shortfall claim. I exclude from tabulation loans originated by Financial Freedom, the largest originator in the HECM data, because Davidoff and Wetzel (2013) reports highly peculiar claim behavior among these loans. This exclusion does not affect qualitatively the patterns described below. 14 Figure 2 plots the fraction of loans that generate a shortfall insurance claim by rounded estimated ratio of outstanding balance estimated to home value. The solid line presents the shortfall claim when loan to value ratios are estimated based on appreciation of the property at the FHFA estimated metropolitan average rate. The dashed line depicts the rate of shortfall claims by estimated loan to value when home price appreciation is based on Zillow s Zip Code level data. The dotted line shows the rate of shortfall claim when 13 There are two types of claims. An unusual type, that does not trigger the shortfall indicator I consider, occurs when the outstanding loan balance is close to the original appraised value of the home (or maximum insured value when that is lower due to caps on insured value and loan amounts). This type of claim only arises when the loan has been outstanding at a high interest rate long enough, and hence is rare among HECM loans, of which the median origination year in the data is 2000. I deem a shortfall claim only to occur when the home is sold for less than the outstanding balance and the insurer requests reimbursement. 14 Financial Freedom was a subsidiary of the failed IndyMac Bank, and appears to feature a very large rate of Type I errors in which I estimate the loan to have a outstanding balance to value ratio far greater than 100%, and yet there is no claim. 16

home price appreciation is based on Zip Code level data for homes in the bottom tercile of metropolitan home values. There is a substantial gap between the probability of a shortfall claim when the estimated loan to value ratio is below 100% depending on whether the resale value at termination is calculated based on the FHFA metropolitan home price index, the Zillow Zip Code index for all homes, or the Zillow Zip Code index for homes with values in the bottom tercile of all metropolitan area values. A shortfall claim when the outstanding balance at is above roughly 90% of true value may not be surprising, given general undermaintenance among older homeowners (Davidoff (2004)) and selling costs. Among non-financial Freedom loans with mark-to-market outstanding balance to value between 75% and 90% at termination based on FHFA metropolitanlevel price appreciation, I find that 33% feature a shortfall claim. By contrast, only 23% of such loans feature shortfall claims when home values are marked to market via Zillow Zip Code level appreciation. When homes are marked to market based on Zillow appreciation calculated only among bottom tercile homes, the fraction of terminated loans with a shortfall claim falls to 19.8%. Given the strong correlation between minority shares and price crashes and the likelihood that Zip Code level appreciation is measured with error, it is interesting to consider the shortfall claim rate among homes in Zip Codes with low minority shares. Confining the sample to terminated non-financial Freedom loans in neighborhoods with minority shares below the metropolitan mean and with median home values as of 2000 greater than the metropolitan mean, the rate of shortfall claims among homes with FHFA marked to market balance to value ratios between.75 to.9 falls below 19%, from 33% without the sample limitation. The shortfall claim rate based on balance to Zillow-based value for all Zip Code homes in the low minority, high value sample is 17.5%. The smaller decline in Zip Code based claim rates makes sense because that index already incorporates differences in price declines based on demographics. Interestingly, however, the shortfall rate falls from 19.8% to 9.6% using the bottom tercile Zip Code price index from Zillow. 17

Summarizing, when market price appreciation is calculated based on FHFA metropolitan level price growth, we find that almost a third of homes that would have had outstanding balances between 75% and 90% of home value if they had appreciated at market level appreciation generate shortfall claims. This suggests that owners substantially undermaintain homes. Between one-third and half of these anomalous shortfall claims disappear when the benchmark for appreciation is neighborhood level appreciation and when differences in appreciation based on initial home values and minority population shares are recognized. Even more disappear conditional on low minority shares when the local bottom tercile price index is the benchmark for market appreciation. 3 Conclusion HECM loans have been originated more frequently, and with more sensitivity to capital gains, in neighborhoods with high minority concentrations and property values below metropolitan averages. Among neighborhoods with high minority concentrations, the rate of poverty among 65 year olds is also significantly positively associated with high origination rates. Conditional on taking on a HECM loan, residents of minority, low income, and low property value neighborhoods, as well as owners of homes below neighborhood averages, use significantly more credit than others. There is thus reason to believe, contrary to evidence from the sale of homes, that there is substantially greater absolute and marginal propensity to consume home equity extraction among lower wealth households than others. Living in a high property value neighborhood within a metropolitan area is associated with less use of HECM loans, but living in expensive metropolitan areas and receiving capital gains are associated with significantly more use. Thus the ratio of home equity to wealth appears to increase demand for HECM loans. A conjecture is that Medicaid rules, which make owning a home attractive, but do not necessarily punish use of HECM loans, may explain part of the difference in appeal at the lower end of the income and wealth distribution between sale 18

and HECM as means of equity extraction. The same types of neighborhoods in which HECM loans were relatively popular saw large price declines after 2006. This correlation explains away essentially all of the newly documented within-metropolitan adverse selection on price, and between one-third and half of the seeming moral hazard on maintenance suffered by FHA in their HECM insurance program. Conceivably, this selection into HECM based on minority population share and relatively low property values could have been driven by a prevailing expectation that the home price insurance effectively offered by FHA would have most value in these neighborhoods. Life cycle theoretical considerations though, make it highly plausible that causality ran from liquidity-based demand to an incidental correlation with ex-post price declines. An interesting possibility mentioned by some industry participants is that errors or fraud in appraisals might explain some of the residual collateral underperformance. An ancillary result that warrants further research relates to wealth inequality. Within metropolitan areas, older homeowners who started the 2000s in neighborhoods with less housing wealth, more poverty, and presumably less non-housing wealth, saw considerably larger home price declines after 2007 than other homeowners. Given evidently non-trivial differences in appetites for spending home equity, and the possibility that changes in the income distribution will lead to continued underperformance of poorer neighborhoods prices relative to metropolitan area means (pursuant to Gyourko et al. (2004)), the distribution of resources across Americans of homeowning age may grow more considerably more unequal through retirement. References Akerlof, George A., The market for lemons : Quality uncertainty and the market mechanism, The Quarterly Journal of Economics, 1970, 84 (3), 488 500. Altonji, Joseph G. and Ulrich Doraszelski, The Role of Permanent Income and De- 19

mographics in Black/White Differences in Wealth, Journal of Human Resources, 2005, 40 (1), 1 30. Artle, Roland and Pravin Varaiya, Life Cycle Consumption and Homeownership, Journal of Economic Theory, 1978, 18 (1), 38 58. Bayer, Patrick, Robert McMillan, and Kim Rueben, What Drives Racial Segregation? New Evidence Using Census Microdata, Journal of Urban Economics, 2004, 56 (3), 514 535. Blau, Francine D. and John W. Graham, Black-White Differences in Wealth and Asset Composition, The Quarterly Journal of Economics, 1990, 105 (2), 321 339. Canner, Glenn B, Stuart A Gabriel, and J Michael Woolley, Race, Default Risk and Mortgage Lending: A Study of the FHA and Conventional Loan Markets., Southern Economic Journal, 1991, 58 (1). Capone, Charles A., Karen L. Chang, and Colin A. Cushman, Identification of Home Maintenance Risk in Reverse Mortgages: An Empirical Examination of Home Price Appreciation among HECM Borrowers, working paper, U.S. Department of Housing and Urban Development 2010. Consumer Financial Protection Bureau, Reverse Mortgages, Report to Congress, US Government 2012. Davidoff, Thomas, Maintenance and the Home Equity of the Elderly, Working Paper 03-288, Fisher Center for Real Estate, UC Berkeley 2004., Housing, Health, and Annuities, Journal of Risk and Insurance, 2009, 76 (1), 31 52., Can High Costs Justify Weak Demand for the Home Equity Conversion Mortgage?, Working Paper, Sauder School of Business, UBC 2012. 20

and Jake Wetzel, Do Reverse Mortgage Borrowers Use Credit Ruthlessly?, Working Paper, Sauder School of Business, UBC 2013. Davis, Morris A. and Francois Ortalo-Magne, Househould Expenditures, Wages, Rents, Review of Economic Dynamics, 2011, 14 (2), 248 261. Feinstein, Jonathan and Daniel McFadden, The Dynamics of Housing Demand by the Elderly: Wealth, Cash Flow and Demographic Effects, in David Wise, ed., The Economics of Aging, Chicago: University of Chicago Press, 1989. Gabriel, Stuart and Gary Painter, Paths to Homeownership: An Analysis of the Residential Location and Homeownership Choices of Black Households in Los Angeles, Journal of Real Estate Finance and Economics, 2003, 27 (1), 87 106. Ghent, Andra C., Ruben Hernanandez-Murillo, and Michael T. Owyang, Did Affordable Housing Legislation Contribute to the Subprime Securities Boom?, Working Paper, Arizona State University 2013. Greenhalgh-Stanley, Nadia, Medicaid and the Housing and Asset Decisions of the Elderly: Evidence from Estate Recovery Programs, Working Paper, Syracuse University 2011. Gyourko, Joseph, Christopher J. Mayer, and Todd Sinai, Superstar Cities, 2004. Working Paper, Wharton. Haurin, Donald, Chao Ma, Stephanie Moulton, and Jason Seligman, Spatial Variation in Reverse Mortgages Usage: House Price Dynamics and Consumer Selection, Working Paper, Ohio State University 2013. Integrated Financial Engineering, Inc., Actuarial Review of the Federal Housing Administration Mutual Mortgage Insurance Fund HECM Loans For Fiscal Year 2012, Technical Report, For US Department of Housing and Urban Development 2012. 21

, Actuarial Review of the Federal Housing Administration Mutual Mortgage Insurance Fund HECM Loans For Fiscal Year 2013, Technical Report, For US Department of Housing and Urban Development 2013. Kain, John F. and John M. Quigley, Housing Markets and Racial Discrimination 1975. Kutty, Nadinee, The scope for poverty alleviation among elderly homeowners in the US through reverse mortgages, Urban Studies, 1998, 35 (1), 113 130. Lockwood, Lee M., Incidental Bequests: Bequest Motives and the Choice to Self-Insure Late-Life Risks, Working Paper, NBER 2011. Mayer, Christopher and Katerina Simons, Reverse Mortgages and the Liquidity of Housing Wealth, Journal Of The American Real Estate And Urban Economics Association, 1994, 22 (2), 235 255. Mian, Atif and Amir Sufi, The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis, Quarterly Journal of Economics, 2009, 124 (4). Miceli, Thomas and C.F. Sirmans, Reverse Mortgage and Borrower Maintenance Risk, Journal of the American Real Estate and Urban Economics Association, 1994, 22 (2), 257 299. Nakajima, Makota and Irina A. Telyukova, Reverse Mortgage Loans: a Quantitative Analysis, Working Paper, UCSD 2013. Nardi, Mariacristina De, Eric French, and John Bailey Jones, Why do the Elderly Save? The Role of Medical Expenses, Journal of Political Economy, 2010, 118 (1), 39 75. Redfoot, Donald L., Ken Scholen, and S. Kathi Brown, Reverse Mortgages: Niche Product or Mainstream Solution? Report on the 2006 AARP National Survey of Reverse Mortgage Shoppers, Report 2007-22, AARP 2007. 22

Rodda, David T., Christopher Herbert, and Hin-Kin Lam, Evaluation Report of FHA s Home Equity Conversion Mortgage Insurance Demonstration, Prepared for US Department of Housing and Urban Development, Abt Associates 2000. Shan, Hui, Reversing the Trend: The Recent Expansion of the Reverse Mortgage Market, Real Estate Economics, 2011, forthcoming. Shiller, Robert and Allan Weiss, Moral Hazard in Home Equity Conversion, Real Estate Economics, 2000, 28 (1), 1 31. Venti, Steven F. and David A. Wise, Aging, Moving, and Housing Wealth, in David A. Wise, ed., The Economics of Aging, Chicago: University of Chicago Press, 1989, pp. 9 48. and, Aging and Housing Equity: Another Look, 2001. NBER Working Paper 8608. 23

Figure 1: Latitude and longitude of Zip Codes in Chicago; Washington, DC; and Los Angeles. Data point radii are proportional to HECM originations divided by homeowners over 64 reported in the 2010 US Census (left column), and to black and Hispanic homeowners divided by all homeowners in the 2010 Census (right column) 88.6 88.4 88.2 88.0 87.8 87.6 41.2 41.4 41.6 41.8 42.0 42.2 42.4 Chicago HECM penetration longitude latitude 88.6 88.4 88.2 88.0 87.8 87.6 41.2 41.4 41.6 41.8 42.0 42.2 42.4 Chicago minority concentration longitude latitude 78.0 77.5 77.0 76.5 38.2 38.4 38.6 38.8 39.0 39.2 39.4 Washington HECM penetration longitude latitude 78.0 77.5 77.0 76.5 38.2 38.4 38.6 38.8 39.0 39.2 39.4 Washington minority concentration longitude latitude 118.8 118.6 118.4 118.2 118.0 117.8 33.8 34.0 34.2 34.4 34.6 Los Angeles HECM penetration longitude latitude 118.8 118.6 118.4 118.2 118.0 117.8 33.8 34.0 34.2 34.4 34.6 Los Angeles minority concentration longitude latitude 24

Figure 2: Default rates by outstanding balance as a percentage of estimated property value, with property estimated from different data sources. Solid line: estimated value at termination based on zip code appreciation from Zillow zip code median sale price index. Dashed line: estimated value at termination based on FHFA metropolitan-level repeated sale index. Dotted line: resale based on Zillow Zip Code index for properties in the bottom tercile of metropolitan value. Fraction of loans with a shortfall claim 0.0 0.2 0.4 0.6 0.8 1.0 FHFA Zillow Zip Zillow Zip Bottom Tier 60 80 100 120 Outstanding balance to mark to market value 25

Table 1: Zip Code Level Summary Statistics Variable Obs Mean Std. Dev Min Max Zip Code Level Originations 2004-2007 Owners 65+ in 2010 6,832 0.016 0.016 0 0.168 Originations 1989-2011 6,832 0.04 0.03 0.002 0.3 Owners 65+ in 2010 Originations 2004-2007 - Originations 1989-2003 6,832 0.01 0.013-0.036 0.137 Owners 65+ in 2010 Poverty Rate among owners 65+ in 2000 6,832 0.121 0.083 0 0.657 Black+Hispanic share of all owners 2000 6,832 0.156 0.199 0.002 0.986 Median Home Value 6,832 150621 96,471 22,000 995,200 25th%ile Home Value 6,832 120930 76,138 11,200 795,200 Single Family % 6,832 0.872 0.129 0.012 1 Homeowners 65+, 2010 6,832 1,428 1,107 31 12,564 Homeowners 65+, 2000 6,832 1,262 1,104 14 13,834 Log Price 2006/2002 6,832 0.433 0.255-0.205 1.274 Log Price 2006/2011 6,832 0.246 0.263-0.404 1.206 Sand State (CA, AZ, FL, NV) 6,832 0.252 0.434 0 1 First year draw data appraisal 317,258 238,328 156,463 17,500 999,999 credit limit 317258 139,098 68,730 8399 485,957 First yr. credit/appraisal 317,258 0.662 0.277 0 1 Terminated Loans Data Outstanding Balance Zip indexed value 91,152.497.307 5.74e-07 3.3357 Outstanding Balance FHFA metro indexed value 116,111.495.271-2.146 2.537 Outstanding Balance Zip lower tercile indexed value 91,654.501 6.389-1,931 4.453 Shortfall claim 116,111.075.263 0 1 26

Table 2: Regressions of Ratio of HECM originations 1989-2010 to homeowners over 65 in 2010 on Fraction of homeowners over 65 with below-poverty incomes as of 2000, fraction of homeowners who are black or Hispanic, and log median value among owner occupied homes. Specifications (4)-(6) includes metropolitan area dummy variables ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) constant -0.0285* 0.0178** -0.1441** 0.0919** 1e-04 0.0862** ( 0.0134 ) ( 0.0074 ) ( 0.0241 ) ( 0.0304 ) ( 0.1288 ) ( 0.0358 ) poverty 0.0585* -0.0553** 0.0068 0.01 0.0558-0.0284** ( 0.031 ) ( 0.0168 ) ( 0.0167 ) ( 0.0163 ) ( 0.0344 ) ( 0.0121 ) single family % 0.0322** 0.0206** 0.0242** 0.0389** 0.0714** 0.0161** ( 0.0078 ) ( 0.006 ) ( 0.0059 ) ( 0.0054 ) ( 0.0164 ) ( 0.0051 ) Log 65+, 2000 0.0179** 0.0131** 0.0173** 0.0139** 0.027** 0.004** ( 0.0028 ) ( 0.0028 ) ( 0.0025 ) ( 0.0024 ) ( 0.009 ) ( 0.0017 ) Log 65+, 2010-0.0219** -0.0109** -0.0142** -0.014** -0.0129** -0.0116** ( 0.0034 ) ( 0.0039 ) ( 0.0037 ) ( 0.0024 ) ( 0.0053 ) ( 0.0031 ) Log owners, 2000-0.0143** -0.0099** -0.0104** -0.004-0.0205* 0.0068* ( 0.003 ) ( 0.0031 ) ( 0.0034 ) ( 0.0028 ) ( 0.0112 ) ( 0.003 ) Log owners, 2010 0.0212** 0.0075** 0.0061** 0.0035 0.0099 0 ( 0.0024 ) ( 0.0025 ) ( 0.0025 ) ( 0.0022 ) ( 0.0069 ) ( 0.003 ) minority 0.072** 0.0751** 0.0463** 0.0544** 0.1695** ( 0.0101 ) ( 0.009 ) ( 0.0077 ) ( 0.0086 ) ( 0.061 ) Log 25th %ile value 0.0142** -0.0095** -0.0098-0.0075** ( 0.002 ) ( 0.0027 ) ( 0.0129 ) ( 0.003 ) Adj. R-sq. 0.1 0.26 0.3 0.64 0.58 0.57 degrees of freedom 6825 6824 6823 6535 1189 1188 Metro Dummies N N N Y Y Y Minority Subset NA NA NA NA High Low Notes: In all regression tables, standard errors clustered at the metropolitan level and * denotes significant at 5%, ** at 1%. 27

Table 3: Regressions of Zip Code HECM originations 2004-2007 minus 1989-2003 divided by 2010 estimated eligible households on characteristics and Zillow Zip Code price growth estimate January 2002 to January 2006 ( 1 ) ( 2 ) ( 3 ) ( 4 ) constant 0.0769** -0.0166-0.1036* 0.0128 ( 0.0125 ) ( 0.0171 ) ( 0.0482 ) ( 0.0169 ) minority 0.0175** 0.0139** 0.0093* 0.0101 ( 0.0039 ) ( 0.0036 ) ( 0.0048 ) ( 0.0528 ) Log Price 2006/2002 0.0217** 0.2518** 0.449** 0.1726** ( 0.0036 ) ( 0.0456 ) ( 0.0876 ) ( 0.0622 ) Log 25th %ile value -0.0075** 0.0012 0.0063-5e-04 ( 0.0011 ) ( 0.0014 ) ( 0.0039 ) ( 0.0015 ) poverty -0.002-0.0082 0.0167-0.0111 ( 0.0073 ) ( 0.0152 ) ( 0.0322 ) ( 0.0123 ) single family % 0.0086** 0.0079** 0.0168** 0.004* ( 0.0023 ) ( 0.0022 ) ( 0.0057 ) ( 0.002 ) Log 65+, 2000-0.0016* -9e-04 0.0023-0.0018** ( 9e-04 ) ( 8e-04 ) ( 0.0027 ) ( 7e-04 ) Log 65+, 2010 6e-04 5e-04 0-2e-04 ( 0.0012 ) ( 0.0011 ) ( 0.0025 ) ( 0.0015 ) Log owners, 2000 0.0022* 0.0021* 0.0011 0.002* ( 0.001 ) ( 9e-04 ) ( 0.0038 ) ( 0.0012 ) Log owners, 2010-0.001-0.002* -0.0013-0.0011 ( 0.001 ) ( 9e-04 ) ( 0.0025 ) ( 0.0017 ) minority price growth 0.0026** 0.0129** 0.0014 ( 9e-04 ) ( 0.0039 ) ( 0.0028 ) Log 25th %ile value price growth -0.0201** -0.0359** -0.0146** ( 0.0039 ) ( 0.0079 ) ( 0.0051 ) poverty price growth 0.0181-0.0062 0.012 ( 0.0401 ) ( 0.0779 ) ( 0.0397 ) Adj. R-sq. 0.56 0.59 0.58 0.28 degrees of freedom 6534 6531 1185 1184 Metro Dummies Y Y Y Y Minority Subset NA NA High Low 28

Table 4: Regression of individual loan log ratio of credit used in first year of loan s life to initial principal limit on borrower, loan, and Zip Code characteristics. Specification (1) includes and (2) excludes loans where loan to value ratios are reduced by time- and metropolitan-varying caps on insured value. Specification (3) further excludes loans with missing Zillow price data. All three specifications control for dummy variables for age, gender, metropolitan area, and origination year are included. All HECM loans prior to 2008 (when fixed rate lump sum loans were initiated) merged with Zip Code data tabulated in Table 1. ( 1 ) ( 2 ) ( 3 ) constant 1.4991** 0.4669** 1.0118** ( 0.3345 ) ( 0.19 ) ( 0.2113 ) minority 0.2877** 0.2659** 0.2763** ( 0.0228 ) ( 0.0247 ) ( 0.0173 ) Log 25th %ile value -0.0535** -0.0559** -0.0505** ( 0.0102 ) ( 0.0106 ) ( 0.0135 ) Log appraised home value -0.0334* 0.4567** 0.4504** ( 0.0151 ) ( 0.0314 ) ( 0.0427 ) Log initial credit limit -0.0916** -0.5372** -0.5549** ( 0.0213 ) ( 0.0323 ) ( 0.0424 ) Poverty 0.1203 0.1402* 0.2866** ( 0.0747 ) ( 0.0755 ) ( 0.1037 ) Log Zip price/price in 1/2000 0.1108** ( 0.0211 ) Adj. R-sq. 0.11 0.11 0.09 degrees of freedom 316,287 207,646 129,631 Exclude value capped loans? N Y Y Table 5: Mean Zip Code fraction of black or Hispanic homeowners and median home value as of 2000 across all HECM originations by year Year Mean Minority Mean Log 2000 Median Value 2000 0.19 11.90 2001 0.18 11.93 2002 0.19 11.91 2003 0.20 11.92 2004 0.23 11.89 2005 0.23 11.86 2006 0.23 11.81 2007 0.25 11.72 2008 0.27 11.73 2009 0.21 11.88 2010 0.21 11.87 2011 0.20 11.88 29

Table 6: Log ratio of Zillow Zip Code home price in January, 2006, to January, 2011 regressed on Zip Code characteristics as of 2000 Census and ratio of HECM originations 2004-2007 to estimated eligible homeowning households, 2010. Note a larger value for the dependent value means a more severe price crash. All specifications include metropolitan area dummy variables (which subsume the Sand State main effect). Standard errors clustered at the metropolitan level ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) constant 0.0177 0.5722** 0.1474-0.1913 0.9556* -0.0135 ( 0.0395 ) ( 0.2174 ) ( 0.1911 ) ( 0.209 ) ( 0.4663 ) ( 0.1861 ) Originations 2004-2007 Eligible HH 2010 2.9648** 1.5044** 0.8006* 0.6361 0.4246 0.1956 ( 0.446 ) ( 0.5602 ) ( 0.4466 ) ( 0.5385 ) ( 0.68 ) ( 0.4945 ) Single Family % 0.0148 0.0053 0.0074 0.0081-0.0253 0.0152 ( 0.0348 ) ( 0.0335 ) ( 0.0323 ) ( 0.02 ) ( 0.0275 ) ( 0.0305 ) Log owners 65+, 2000 0.0077-0.0163* -0.0051 0.0229 0.0478-0.0021 ( 0.0095 ) ( 0.0099 ) ( 0.0101 ) ( 0.0188 ) ( 0.036 ) ( 0.0094 ) Log owners 65+, 2010-0.047** -0.0017-0.0076-0.018 0.011-0.0103 ( 0.0119 ) ( 0.0121 ) ( 0.0121 ) ( 0.0203 ) ( 0.0251 ) ( 0.0115 ) Log owners 2000 0.0154 0.0269** 0.0224** 0.0274** ( 0.0105 ) ( 0.0097 ) ( 0.0094 ) ( 0.0094 ) Log owners 2010 0.0321** -6e-04-0.0015-0.0044 ( 0.0098 ) ( 0.0099 ) ( 0.0101 ) ( 0.0085 ) minority 0.193** 0.1798** 0.6614 0.1169* 0.2071** ( 0.0374 ) ( 0.0464 ) ( 0.4785 ) ( 0.0563 ) ( 0.0447 ) Log 25th%ile home value -0.0447* -0.0063 0.02-0.0748* 0.0011 ( 0.0193 ) ( 0.0179 ) ( 0.0167 ) ( 0.0417 ) ( 0.0179 ) Sand State * minority 0.0873 0.0243-0.0388 0.0476 ( 0.0645 ) ( 0.0397 ) ( 0.0697 ) ( 0.0647 ) Sand State * log 25th%ile -0.1542** -0.0028-0.0102-0.1279** ( 0.0301 ) ( 0.0128 ) ( 0.0198 ) ( 0.0294 ) Log Zillow Zip price 2006/2002 0.3877** ( 0.0454 ) Adj. R-sq. 0.84 0.85 0.86 0.83 0.86 0.88 degrees of freedom 6537 6535 6533 1186 1187 6532 Minority subsample No No No Low High No 30