How Do Mortgage Subsidies Affect Home Ownership? Evidence from the Mid-century GI Bills

Fetter, Daniel K.

doi:10.3386/w17166

Cited by 22 publications

(30 citation statements)

References 8 publications

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“…Numerous economic studies found GI Bill benefits from World War II (WWII), the Korean War, and the Vietnam War increased aspects of socio-economic status (SES) such as education [5-8], income [9-11], occupational status [9,11], and homeownership [12]. Past work found men from disadvantaged backgrounds disproportionately benefit [8-11], while others argue most benefits accrued to men from advantaged backgrounds [5].…”

Section: Purposementioning

confidence: 99%

Can social policy influence socioeconomic disparities? Korean War GI Bill eligibility and markers of depression

Vable

Canning

Glymour

et al. 2016

Annals of Epidemiology

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Background The Korean War GI Bill provided socio-economic benefits to veterans, however its association with health is unclear; we hypothesize GI Bill eligibility is associated with fewer depressive symptoms and smaller disparities. Methods Data from 246 Korean War GI Bill eligible veterans and 240 non-veterans from the Health and Retirement Study were matched on birth year, southern birth, race, height, and childhood health using coarsened exact matching. Number of depressive symptoms in 2010 (average age=78) were assessed using a modified, validated Center for Epidemiologic Studies-Depression Scale, dichotomized to reflect elevated depressive symptoms. Regression analyses were stratified into low (at least one parent < 8 years schooling / missing data, N=167) or high (both parents ≥ 8 years schooling, N=319) childhood socio-economic status (cSES) groups. Results Korean War GI Bill eligibility predicted fewer depressive symptoms among individuals from low cSES backgrounds [β=-0.64, 95% Confidence Interval (CI):(-1.18, -0.09), p=0.022]. Socio-economic disparities were smaller among veterans than non-veterans for number of depressive symptoms [β=-0.76, 95% (CI):(-1.33, -0.18), P = 0.010] and elevated depressive symptoms [β=-11.7, 95%CI:(-8.2, -22.6), P = 0.035]. Conclusions Korean War GI Bill eligibility predicted smaller socio-economic disparities in depression markers.

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Section: Purposementioning

confidence: 99%

Can social policy influence socioeconomic disparities? Korean War GI Bill eligibility and markers of depression

Vable

Canning

Glymour

et al. 2016

Annals of Epidemiology

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“….8 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 198519871989199119931995199719992001200320052007200920112013 .4…”

Section: A2 Robustness Checks For Debt-income Regressionsmentioning

confidence: 99%

Technological Innovation in Mortgage Underwriting and the Growth in Credit: 1985–2015

Foote¹,

Loewenstein²,

Willen³

2019

Federal Reserve Bank of Boston Research Department Working Papers

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The application of information technology to finance, or "fintech," is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loanprocessing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-toincome ratios than previous underwriting guidelines would have allowed. In this way, technology brought about an exogenous change in lending standards that was especially relevant for borrowers with low current incomes relative to their expected future incomes-in particular, young college graduates. By contrast, the data suggest that the credit expansion during the 2000s housing boom was an endogenous response to widespread expectations of higher future house prices, as average mortgage sizes rose for borrowers across the entire income distribution.

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“…This estimated difference is interpreted as the a point in time. See, for example, Fetter (2013) and Hausman and Rapson (2017) who discuss RD using a time running variable more generally. 10 See Imberman and Lovenheim (2015) for a related design that uses a difference-in-differences (DD) approach to estimate the effect of a sudden news release of school ratings on home prices.…”

Section: Baseline Rd Approachmentioning

confidence: 99%

Who Benefits from Targeted Property Tax Relief? Evidence from Virginia Elections

Moulton¹,

Waller²,

Wentland³

2018

J Policy Anal Manage

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This study examines the market impact of targeted property tax relief, which is critical for understanding who exactly benefits from a widely used local policy. Specifically, we investigate this in the context of two statewide ballot measures in Virginia that provided property tax relief or heightened expectations for future relief intended to aid disabled veterans and seniors, respectively. Using residential multiple listing service microdata from Virginia, results from a regression discontinuity analysis show that once the 2010 tax relief measures passed on Election Day, property values rose sharply in response to the sudden increase in demand for homeownership among the targeted groups. We find that senior preferred housing and properties within areas with higher proportions of seniors and veterans experienced the highest price appreciation, while areas with fewer veterans or seniors saw little impact. The findings suggest that this type of policy provides an immediate benefit to current homeowners, thereby offsetting benefits for subsequent homeowners within the targeted groups. This effect represents an unintended consequence of targeted property tax relief as a policy tool more generally, as immediate capitalization into home prices subsequently increases the cost of housing for many individuals the relief was intended to help.

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How Do Mortgage Subsidies Affect Home Ownership? Evidence from the Mid-century GI Bills

Cited by 22 publications

References 8 publications

Can social policy influence socioeconomic disparities? Korean War GI Bill eligibility and markers of depression

Can social policy influence socioeconomic disparities? Korean War GI Bill eligibility and markers of depression

Technological Innovation in Mortgage Underwriting and the Growth in Credit: 1985–2015

Who Benefits from Targeted Property Tax Relief? Evidence from Virginia Elections

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