By way of summary, the data set is built from a nationally representative random sample of credit bureau records provided by TransUnion for a cohort of 34,891 young individuals who were between the ages of 23 and 31 in 2004 and spans the period 19972014
The average marginal effect of student loan debt on homeownership for any given population will depend on the density of individuals near the relevant mortgage underwriting thresholds. This paper investigates a population of individuals who were mostly making their home-buying choices http://getbadcreditloan.com/payday-loans-tn prior to the housing s. Mortgage credit tightened considerably in the following years and has subsequently been (slowly) relaxing. The average marginal effect of student loan debt may therefore be different in years with considerably different levels of credit availability, an important point to keep in mind when extrapolating our results to other time periods.
The mechanisms discussed in this section are not specific to student loan debt-auto loans and credit card debt could impose similar burdens on debtors in the housing market. Student loan debt is particularly interesting to study, however, because of the ease of availability of student loads. Young people without incomes or collateral are able to take on tens of thousands of dollars of debt to pay for their education without any underwriting of the loans. In contrast, a borrower without a credit history or source of income would face very tight limits in markets for privately provided credit. Student loans therefore present a unique channel for individuals to become heavily indebted at a young age. See section IV.D for an empirical treatment of the effects of total nonhousing consumer debts.
III. Data
Our data are pooled from several sources. 9 Mezza and Sommer (2016) discuss the details of the data, check the representativeness of the merged data set against alternative data sources, and provide caveats relevant for the analysis.
Individuals are followed biennially between , and and . The data contain all major credit bureau variables, including credit scores, tradeline debt levels, and delinquency and severe derogatory records. 10
Since the credit bureau data do not contain information on individuals’ education, historical records on postsecondary enrollment spells and the institutional-level characteristics associated with each spell were merged on the TransUnion sample from the DegreeVerify and Student Tracker programs of the NSC. Additionally, individual-level information on the amount of federal student loans disbursed-our main measure of student loan debt-was sourced from the NSLDS. The NSLDS also provides information on Pell Grant receipts and enrollment spells funded by federal student loans, including the identity of each postsecondary institutions associated with the aid, which we use to augment the NSC data.
Information on individuals’ state of permanent residence at the time they took the SAT standardized test-sourced from the College Board-was merged for the subset of individuals who took this test between 1994 and 1999, a time when most of the individuals in our sample were exiting high school. Finally, we merged in institutional records, such as school sector (i.e., whether public or private, for profit or not for profit, and 4 or 2 year), from the Integrated Postsecondary Education Data System.
In what follows, we describe the construction of key variables used in our analysis: homeownership status, student loan balances, and subjects’ home state. A discussion of the remaining variables used in the analysis is available in the appendix.
We are not able to directly observe the individual’s homeownership status. Rather, the credit bureau data contain opening and closing dates for all mortgage tradelines that occurred prior to , which we use to infer homeownership by the presence of an open mortgage account. The obvious limitation of using mortgage tradeline information to infer the individual’s homeownership status is that we will not be able to identify homeowners who are cash buyers. However, because our analysis is restricted to home-buying decisions made between the ages of 22 and 32, the population of cash buyers is likely to be small, particularly among the subpopulation that required student loans to fund their education. Furthermore, the credit-rationing mechanisms discussed in section II.B would not bind on a buyer with enough liquid assets to purchase a house outright, so there is less scope for student loan debts to affect purchase decisions for any such individuals. In our analysis, we treat the individual’s homeownership status as an absorbing state, so that if an individual is observed to be a homeowner by a given month, the individual will be treated as a homeowner at all future dates.