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This report sheds light on the mortgage readiness, i.e., future homeownership potential, of young adults aged 18-45 based on their credit characteristics. Since the population's rapidly changing racial and ethnic composition has profound implications for future homeownership sustainability, we also investigate the racial gap in homeownership potential. Additionally, we examine the extent to which future borrowers can afford homeownership in their areas and how long it could take to save for a down payment.
Homeownership is critical for building long-term wealth, and most first time homebuyers rely on obtaining a mortgage to become homeowners. After the financial crisis, mortgage ownership rates have declined substantially by all races/ethnicity1. The fastest-growing populations of potential homebuyers, the millennials, have been slow to obtain home mortgages partly due to differences in socio-demographic characteristics and preferences from earlier generations (Goodman, Pendall and Zhu, 2015, Choi et al., 2018). With rising house prices, lack of inventory, and tight lending standards following the COVID-19 pandemic crisis, the path to homeownership has become even more challenging for potential first-time homebuyers, especially for minorities. Blacks and Latinos have been particularly struck by the pandemic crisis since they are overrepresented in low-wage sectors or jobs that cannot transition to remote work. Despite a generally weak economy, many future homebuyers could qualify for a mortgage in today's marketplace, they are "Mortgage Ready."
This report sheds light on the mortgage readiness, i.e., future homeownership potential, of young adults of aged 18-45 based on their credit characteristics. Since the population’s rapidly changing racial and ethnic composition has profound implications for future homeownership sustainability, we also investigate the racial gap in homeownership potential. Lastly, we investigate the extent to which future borrowers can afford homeownership in their areas and how long it could take to save for a down payment. By evaluating the future borrowers based on their credit, income, and housing cost constraints, we can identify strategies that can better serve them in overcoming their challenges and realizing their dream of homeownership.
For our analysis, we use a uniquely constructed consumer credit data set that combines anonymized individual credit bureau records with credit bureau's marketing data for January 2021. The core credit bureau data—onto which the anonymized marketing data are merged—spans the entire universe of credit visible population in the United States in January 2021. The dataset contains detailed debt and credit information and a wealth of demographic information, such as race and ethnicity, age, gender, and geographic location. Please see Appendix B for more detail on the data set.
We define future borrowers using basic underwriting standards. We choose reasonable cut-points based on credit score, debt-to-income, foreclosure, bankruptcy, and delinquency status. The cut-points are chosen to reflect the lending behavior in a normal state of the economy. It is not our intention to propose the "correct" cut points, which are highly influenced by complex business/policy decisions and macroeconomic conditions. Instead, our objective is to investigate how the future homeownership potential varies by race/ethnicity for a set of credit characteristics acceptable by reasonable lending standards. Hence, our classifications are research-based assessments and do not relate to our Guide or underwriting criteria.
To identify the future borrowers in our dataset, we start with credit visible population of ages 45 and younger. Our starting population is approximately 115.2 million, as given in Exhibit 1. We focus on people of ages 45 and younger because our data does not allow us to observe tenure status of the consumers. Older homeowners are more likely to own their houses free and clear. Census data suggests that the homeownership rate increases with age but the mortgage ownership rate peaks at around age 45 and declines thereafter (see Dey and Brown, 2020). Therefore, we use age 45 as a judgmental cut-point to exclude non-mortgage owners who are potentially homeowners.
We broadly group our starting population into four mutually exclusive categories. Our first category consists of "Mortgage Owners," i.e., those who had a non-zero number of open mortgage-type tradelines reported in the previous six months as of January 2021. According to Exhibit 1, this group constitutes around 22% of the overall credit visible population ages 45 and younger. Next, we define a consumer as “Mortgage Ready” if he or she does not have a mortgage, is 45 or younger, has a credit score of 661 or above2, has a back-end debt-to-in-come ratio not exceeding 25 percent3, has no foreclosures or bankrupt-cies in the past 84 months, and has no severe delinquencies in the past 12 months. Exhibit 1 reports that 36% (approx. 41 million) of our starting population are "Mortgage Ready" as of January 2021.
If a consumer meets all the criteria to be a "Mortgage Ready" but has a lower credit score between 600 and 660, we consider him or her as "Near Mortgage Ready." In other words, consumers who are “Near Mortgage Ready” are reasonably close in time to being “Mortgage Ready.” According to Exhibit 1, 12 percent of credit visibles of ages 45 and younger are “Near Mortgage Ready” in the overall population. Lastly, if a consumer does not fall in any of the above categories, we consider him or her as "Not Currently Mortgage Ready." According to the Exhibit, 31% of the overall credit visible population below age 45 falls in this category.
Exhibit 1: Credit visible population (45 and younger) by their mortgage ownership/readiness status and race/ethnicity.
Exhibit 1 also reports the distributions of credit visible populations over their mortgage ownership/readiness status by various races/ethnicity. As the exhibit suggests, there is a wide racial gap in mortgage ownership status between Non-Hispanic Whites and all minority groups. In particular, the Black-White gap is striking (27% vs. 11%). Furthermore, the racial gap in homeownership potential persists as well. While 36% of Non-Hispanic Whites are "Mortgage Ready," only 22% (approx. 3.4 million) of Black Americans are "Mortgage Ready," the lowest among all racial groups. While the share of "Mortgage Ready" Hispanic Americans is slightly lower than Non-Hispanic Whites (36% vs. 34%), the share of Asian American "Mortgage Ready" is much higher than Non-Hispanic Whites (36% vs. 61%).
To better understand the "Mortgage Ready" consumers, we next explore their debt characteristics. We first investigate how many of them are in debt by race/ethnicity. For simplicity, we group the non-mortgage type debts by installment loans or revolving trades. While installment loans are paid off over time and in fixed amounts each month, revolving account payments vary by how much credit one uses. An example of revolving trades is credit card transactions. We further divide the installment loans into the auto loan, student loan, and other installments. Exhibit 2 displays the share of the "Mortgage Ready" population with a positive unpaid balance of each debt type by race/ethnicity.
Exhibit 2 Share of "Mortgage Ready" population with positive unpaid balance by race/ethnicity
As Exhibit 2 suggests, around two-third of the "Mortgage Ready" population have revolving trades. Moreover, the share of "Mortgage Ready" consumers with auto loans is higher than those with a student loan for all races/ethnicity. In addition, proportionally, more Black Americans have student loan debt compared to other racial/ethnic groups.
How much debt amount does the "Mortgage Ready" population carry? Exhibit 3 gives each debt-type’s median value of unpaid balance for the "Mortgage Ready" consumers. For calculating the medians, we restrict the population of those who have the non-zero amount by each debt type. While the "Mortgage Ready" Blacks have the highest student loan debt (around $24,000), "Mortgage Ready" Hispanics have the highest auto debt (approx. $11,500).
Exhibit 3 Median unpaid balance by race/ethnicity
Over the years, student loan debt has grown to be the second highest consumer debt, falling short of mortgage debt4. Overall, we do see from Exhibit 3 that the unpaid balance for student loan debt exceeds the unpaid balance for all other types of loans for all races/ethnicity. However, student loan debt typically has a longer-term for payment. To evaluate how debt-burdened are “Mortgage Ready” consumers in their day-to-day life, we examine the amortization of each loan by looking at the monthly required payment in Exhibit 4.
Exhibit 4. Median monthly required payment by each debt-type and by race/ethnicity.
As the exhibit suggests, the median required monthly payment for student loan debt is less than $200, almost half of the median required monthly payment for an auto loan. Even though the auto loan terms are shorter, the monthly payment may persist longer, considering that many consumers change their cars every couple of years. Compared to the Non-Hispanic Whites, we find that Black and Hispanic consumers are paying more for cars and less for education each month. Getting an auto loan to buy a car is a consumption debt while getting a student loan to acquire more education is an investment debt. A luxury car will still depreciate over time, but the accumulated human capital will more likely appreciate in terms of expected future income. Therefore, a good consumption habit can help future borrowers be less burdened by consumption debt and transition into homeownership faster.
Renters often struggle to save for a down payment and perceive it as one of the leading obstacles to homeownership. That said, many potential first-time homebuyers overestimate the amount of down payment needed to qualify for a mortgage. According to Goodman et al. (2017), 65% of today’s renters believe they need a down payment of at least 15% to qualify. For low-to-moderate and minority households living in high-cost areas, saving for a down payment that large can be particularly constraining. To this end, we calculate the number of years it can take a "Mortgage Ready" consumer to save for 20%, 5%, and 3% down payment. We do so by dividing the down payment amount needed on a median-priced single-family home at the county-level by his or her annual saving amount5. According to the statistics published by the U.S. Bureau of Economic Analysis, the average annual personal savings rate in 2019 is 7.5% of an individual’s disposable personal income6. For simplicity, we measure disposable income by after-tax income, which is calculated by subtracting both federal and state taxes from the estimated pre-tax personal income we obtained from the credit bureau data7. Individual tax amounts are calculated based on both estimated income level and state of residence using IRS tax brackets for 2020. For single-family house price, we obtained the median value by county from the Freddie Mac Home Value Explorer Data®.
Exhibit 5 gives the median “time to save” in years for 20%, 5%, and 3% down payment by race/ethnicity. In Appendix C, we also plot the heat map of "time to save" for a 3% down payment by race/ethnicity. Overall, Non-Hispanic Whites have the shortest "time to save" compared to all other races/ethnicity. Hispanics have the longest "time to save" not only because they have the lowest disposable income among all races/ethnicity, but also because they are highly concentrated in the high-cost areas, with the median house price being approximately $400,000. Asians have the highest disposable income, but they still have the second-longest “time to save” because they live in the most expensive areas.
In contrast, "Mortgage Ready" Black Americans are less concentrated in those high-cost areas. Though their median disposable income is much lower (approx. $3,500), their median "time to save" is very similar to “Mortgage Ready” Non-Hispanic Whites. This finding is encouraging because it implies that Black neighborhoods typically do not suffer from high housing costs, making it easier for potential home buyers to meet down payment requirements and transition into homeownership. Existing research shows that lack of intergenerational wealth transfers negatively impacts Black young adults' inability to meet required down payment and likelihood of owning a home8. According to Dey and Brown (2020), Blacks rely more than Whites on savings, assistance, or loan from a nonprofit or government agency, and seller contribution to meet their down payment requirements. Low down payment, no down payment, and savings match programs can certainly help "Mortgage ready" Blacks and Hispanics to meet the required down payment and transition to home mortgages faster.
Exhibit 5. Estimated “Time to Save” for “Mortgage Ready” population by race/ethnicity.
With housing prices soaring post-COVID-19 crisis and lack of inventory, affordability has become a major concern for low-to-moderate-income and minority renters, especially since their incomes are not able to keep up with rising housing costs9. Are the "Mortgage Ready" able to afford homes in their areas? To answer that we plot the percent of "Mortgage Ready” that can afford a home in their metro areas alongside a sizable local "Mortgage Ready" population to capitalize on that affordability in Exhibit 6.
The blue-purple areas in Exhibit 6 give the share of "Mortgage Ready" that can afford a median-priced single-family home in their area. Our affordability indicator is roughly based on NAR's methodology, i.e., if a consumer's quarterly household income is greater than or equal to the annual mortgage payment on a median-priced house (under the assumption of 3% down payment, 2.9% mortgage rate, 30-year contract), then that house is affordable for him or her. A dark blue metro is one where 80% or more "Mortgage Ready" consumers could afford the median-priced house, while a dark purple metro is where less than 20% could afford it.
The color of the dots, meanwhile, represents the share of "Mortgage Ready" consumers in that metro. A dark green dot means the "Mortgage Ready" share is above 30%, and red indicates less than 15%. As the exhibit suggests, the coastal areas and other large metros tend to have a larger "Mortgage Ready" share of their population, but a smaller percentage of that group can afford a median-priced single-family house in their area. In much of the rest of the country, the opposite is the case. Households in nearly every metro face barrier to homeownership, but the best strategies and approaches can sometimes vary based on the locality in question.
While the key objective of this report is to understand the barriers and opportunities for the "Mortgage Ready" population with higher credit, it is also worthwhile to take a closer look at the other two categories of non-mortgage owners we defined in section 2, so we can identify multiple strategies and outreach efforts to better prepare them for homeownership in medium and long-term.
We start looking at the "Near Mortgage Ready" population. Based on our methodology, these consumers mimic the credit profiles of "Mortgage Ready" consumers except that their credit scores are lower. Exhibit 7 gives the distributions of credit visible population over their mortgage readiness status by generation cohorts. We divide the credit visible population of ages 45 and younger into four- generation cohorts: Gen Z (ages 18-23 years), Young Millennials (ages 24-31 years), Old Millennials (ages 31-40 years), and Gen X (ages 41-45 years). As the table suggests, 13.3 million of the total starting population are in this category. Compared to all generations, Gen Z has the highest share "Near Mortgage Ready" population, around 19%.
Since the population's rapidly changing racial and ethnic composition has profound implications for future homeownership sustainability, we also investigate the racial gap in homeownership potential. Lastly, we investigate the extent to which future borrowers can afford homeownership in their areas and how long it could take to save for a down payment. By evaluating the future borrowers based on their credit, income, and housing cost constraints, we can identify strategies that can better serve them in overcoming their challenges and realizing their dream of homeownership.
Exhibit 7: Credit visible population (45 and younger) by their mortgage ownership/readiness status and generation cohorts.
The heat map in Exhibit 8 gives the share of "Near Mortgage Ready" that can afford a home in their area. As shown in the map, the affordability of "Near Mortgage Ready" is worse than "Mortgage Ready" for most parts of the country since they have lower incomes. They also take longer to save for down payment compared to "Mortgage Ready" (available upon request). The dots in the exhibit give the share of the "Near Mortgage Ready" population by MSA and indicate that they are more concentrated in the affordable area Mid-West and South parts, such as Minnesota, South Dakota, and New Mexico.
Overall, there are greater housing challenges for "Near Mortgage Ready." Education and outreach efforts should start early in educating these consumers about financial literary, credit restoration, budget management, and other principles that can empower them to be more confident and knowledgeable in their home purchase decisions in the near future.
Exhibit 8. Share of "Near Mortgage Ready" that can afford a home in their area.
The last group of non-mortgage owners discussed in section 2 are those who are "Not Currently Mortgage Ready." They either have a debt-to-income ratio that exceeds 25, foreclosure in 84 months, bankruptcies in 84 months and severe delinquencies in 12 months, or a low credit score. Understanding this population will give us more insights into our long-term outreach strategy. Exhibit 1 reports that 31% (35.8 million) of credit visibles below the age of 45 are "Not Currently Mortgage Ready." Further, disproportionately a higher share of Blacks (54%) and Hispanics (37%) are "Not Currently Mortgage Ready". In Exhibit 9, we construct a waterfall by ranking the "Not Currently Mortgage Ready" population by the severity of their credit concerns.
Exhibit 9. Breakdown of "Not Currently Mortgage Ready" population by their credit concerns.
As the exhibit suggests, around 80% of the overall "Not Currently Mortgage ready" population have some sort of delinquencies. Another 5% of this population has no delinquencies but has a debt-to-income ratio that exceeds 25%. From the remaining population, we find 14 percent of “Not Currently Mortgage ready” have thin files, i.e., and they have few trade lines reported in their credit records (less than or equal to 2). We call them “Clean” thin files because they have no delinquencies in the last 180 days, no foreclosures in the last 84 months, no bankruptcies in the last 84 months, and their debt-to-income ratio does not exceed 25. Hence, the consumers with "Clean" thin files do not have bad credit but have credit records that are considered "unscorable," that is, they contain insufficient credit histories to generate a credit score. Access to alternative credit data such as, telecom, utility, and rental information may be worthwhile in exploring credit worthiness of consumers with missing scores or thin files. Goodman and Zhu (2018) make a case for the inclusion of rental payments in assessing mortgage applications. They compare rental payments to mortgage payments by income level while demonstrating that past mortgage payment history helps predict future loan performance.
To conclude, this report provides insights into challenges faced by future borrowers, as well as the options available to help them overcome the challenges. Here are a few takeaways on how we could address their barriers to homeownership:
A combination of strategies supporting counseling and credit education opportunities, income and wealth creation, and debt rehabilitation will likely be the most effective in bridging the homeownership gap between Whites and minorities over time.
Begley, Jaclene. Forthcoming. "Parent Housing Wealth, Credit Constraints, and Homeownership Transitions." Journal of Housing Research.
Brevoort, Kenneth P., Philipp Grimm, and Michelle Kambara, 2015, "Data Point: Credit Invisibles", The CFPB Office of Research.
Charles, Kerwin Kofi, and Erik Hurst. 2002. "The Transition to Home Ownership and the Black-White Wealth Gap." Review of Economics and Statistics 84 (2): 281–97.
Choi, J. H., J. Zhu, L. Goodman, B. Ganesh, and S. Strochak. 2018. "TMillennial Homeownership: Why Is It So Low, and How Can We Increase It"T Urban Institute research report.
Choi, Jung Hyun, Jun Zhu, and Laurie Goodman. 2018. "Intergenerational Homeownership: The Impact of Parental Homeownership and Wealth on Young Adults' Tenure Choices." Washington, DC: Urban Institute.
Dey J. and L. Brown, 2020. "The Role of Credit Attributes in Explaining the Homeownership Gap Between Whites and Minorities Since the Financial Crisis, 2012-2018", Housing Policy Debate https://www.tandfonline.com/doi/full/10.1080/10511482.2020.1818599
Goodman, L., R. Pendall, and J. Zhu. 2015. "Headship and Homeownership: What Does the Future Hold?" Urban Institute Report.
Goodman, McCargo, Bai, Golding, Strochak, "Barriers to Accessing Homeownership Down Payment, Credit, and Affordability" – 2018. Urban Institute Brief. https://www.urban.org/research/publication/barriers-accessing-homeownership-down-payment-credit-and-affordability-2018
Goodman, L. and J. Zhu. 2018. “Rental Pay History Should Be Used to Assess the Creditworthiness of Mortgage Borrowers,” Urban Wire (blog), Urban Institute, April 17, 2018. https://www.urban.org/urban-wire/rental-pay-history-should-be-used-assess-creditworthiness-mortgage-borrowers
Li, W. and L. S. Goodman, "Comparing Credit Profiles of American Renters and Owners", Urban Institute Research Report, March 2016.
2 To qualify for a mortgage, the consumer needs a relatively high credit score along with ability to fund down payment and stable income. According to Li and Goodman (2015), consumer needs a minimum of 650 credit score to get a mortgage. We assume the minimum credit score of 661 to qualify for mortgage, which is well within the range of possibility and close to Li and Goodman’s cut point.
3 Back-end DTI ratio indicates what percentage of consumer’s gross monthly income goes into paying recurring monthly debt including mortgage payments (principal, interest, taxes, and insurance)
4 According to New York Fed's quarterly report on household debt and credit, as of 2021 Q1, total balances of mortgage, student, auto and credit card were $10.16 trillion, $1.58 trillion, $1.38 trillion and $0.77 trillion respectively
5 We assume everyone has zero savings today and will save a flat rate of their disposable income for down payments. In the real world, people have different amounts of savings as well as different ability to save. Other than personal savings, down payment sources may also include gift money from family and friends, seller contributions, and assistance from the government or non-government organizations. Our methodology provides an upper bound and shows that with the aid of low-down payment products, many areas of the country can save for a down payment within 1 to 2 years or less by utilizing other sources opening up additional opportunities for "Mortgage Ready" consumers to become homeowners.
7 The modeled measure of tax-reported income is based on the credit bureau's proprietary model, which includes consumer’s credit-based attributes as model inputs. It is inclusive of all income sources such as wages, investment income, alimony, rental income, and so on. In most cases, the income source also includes spousal income if taxes are filed jointly.
8 See Choi, Zhu and Goodman, 2018; Charles and Hurst, 2002, Begley, forthcoming.
9 See www.jchs.harvard.edu/press-releases/soaring-home-prices-tight-supply-and-millions-facerisk-eviction-or-foreclosure
10 For additional discussion, check out the Freddie Mac article: "Raising Down Payment Cash: What Your Borrowers Should Know".
11 We use the ACS PUMS to define individual-level estimates that only identifies the household head and spouse as the homeowners, excluding adult children and other adults living in the household. See Appendix A for detailed discussion on our methodology.
12 Using names and addresses to model race and ethnicity has been used by others such as the Consumer Financial Protection Bureau, for instance. See https://files.consumerfinance.gov/f/201409_cfpb_report_proxy-methodology.pdf
PREPARED BY HOUSING INSIGHTS AND SOLUTIONS GROUP
Jaya Dey, Quantitative Analytics Director
Sijie Li, Quantitative Analytics Senior
Robert Argento, Quantitative Analytics Senior
Jintao Huang, Quantitative Analytics Professional
Exhibit A1 plots the annual individual level ownership rate of houses with a mortgage or loan using 1-year American Community Survey public-use microdata (ACS PUMS), for Non-Hispanic Whites, Blacks, and Hispanics respectively11. As the figure suggests, Black and Hispanic mortgage ownership rates declined from 25 and 26 percent in 2008 to 20 and 21 percent in 2018, respectively. As of 2018, the White-Black and White-Hispanic mortgage ownership gaps stood at roughly 17 percentage points and 16 percentage points, respectively.
Figure A1. Annual Mortgage Ownership Rate at Individual Level, 2008-2018
We obtained de-identified consumer credit data from one of the three major credit bureaus. The credit data include age, various credit scores, and modeled measures of income and total debt-to-income. The modeled measure of tax-reported income is based on the credit bureau’s proprietary model, which includes consumer’s credit-based attributes as model inputs. It is inclusive of all income sources such as wages, investment income, alimony, rental income, and so on. In most cases, the income source also includes spousal income if taxes are filed jointly. We also have the number, dollar amounts, and payment status of mortgage, auto, credit card, student loan, and other bank or retail debt. The credit file identifies credit inquiries, public-record bankruptcies, and foreclosures. The data include some geographic information, including zip code, county, and state.
Because the credit records data do not have information on consumers' race or ethnicity and other demographic information, besides those mentioned, the credit bureau matched each consumer in the data set to their marketing data to get additional household-level data. The match rate was close to 100 percent. The marketing data consists of information on race, ethnicity, education level, gender, and marital status for the individuals living in the housing unit. These socio-demographic characteristics are based on the credit bureau's proprietary models, which include consumer's first and last name as well as geographic location amongst others as model inputs12. To protect consumer privacy, all personal identification information such as name and address were removed, leaving only information on select attributes and those with match success.
Note that our data represents the universe of consumers with available credit data, not the U.S. population. The credit bureaus have data only for those U.S. residents who have applied for or taken out a loan (auto, credit card, student loan, mortgage, or home equity line of credit). The data may also include individuals with public records such as bankruptcies and collections. Our data is likely to understate the percentage of those who have no credit history, no items in collections, including recent immigrants with little or no credit history in the United States, because of credit invisibles with no records at any of the three major credit bureaus. Nonetheless, the data include individuals with so-called thin files. To make sure the thin files are legitimate individuals with primary credit records, we identified and removed “fragment files” from the entire credit bureau archive. "Fragment files" are some consumers having multiple credit records containing a portion of consumer's credit histories that exist outside their primary file (see Brevoort, Grimm and Kambara, 2015).
Exhibit C1 plots "time to save" for a 3% down payment at the county-level. Dark green represents the shortest "time to save" while dark red represents the longest "time to save". From the map, it is evident that saving for a down payment is relatively challenging in many of the coastal counties along both coasts. These counties include the metro areas of Seattle, Portland, San Francisco, Los Angeles, San Diego, Miami, Washington D.C., New York, and Boston. Estimated "time to save" is also longer in the counties located around the Rocky Mountains, including the areas around Denver, Salt Lake City, and Jackson Hole. By contrast, the estimated "time to save" is relatively short in the center of the country. The area includes the Mid-West, the South, and the counties along the Appalachian Mountains.
Exhibit C1: Time to Save (3% Down) for "Mortgage Ready" Population by County
We also compare the "time to save" at the county level by various races/ethnicity in Exhibit C2. Many of the same regional trends from Exhibit C1 hold for all races/ethnicity. However, there are a few differences. For example, Black and Hispanic "Mortgage Ready" consumers often take longer "time to save" in many areas of the country relative to Non-Hispanic White consumers due to lower median income.
Exhibit C2: Time to Save (3% Down) for "Mortgage Ready" Population by County and Race/Ethnicity
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