Does getting a mortgage affect consumer credit use?

Abstract

Buying a house changes a household’s balance sheet by simultaneously reducing liquidity and introducing mortgage payments, which may leave the household more exposed to other shocks. We examine how this change impacts consumer credit beyond the mortgage. Using a large panel, we show that on acquiring a mortgage, credit card debt increases by about $1500 in the short term, severe delinquencies increase by 2.2 percentage points, and credit card utilization—the fraction of a consumer’s credit card limit that is used—increases by 11 percentage points. In the long term, credit card balances increase by $3900 and delinquencies by 9.1 percentage points. In our sample period before the 2008 financial crisis, credit limits increased faster than debt in the long run, pushing down long-term utilization. After the financial crisis, debt increased faster than credit limits in the long run, and credit card utilization rates rose upon the acquisition of a new mortgage, consistent with larger down payments leaving households more constrained.

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Notes

  1. 1.

    Although mortgage payments may not be higher than the rent payments a household used to make, there are typically high fixed costs of buying a house, including but not limited to the down payment.

  2. 2.

    We define liquidity as cash and other liquid assets available to a consumer, such as bank account deposits.

  3. 3.

    Calculations from the Survey of Consumer Finances (SCF) suggest that the median household has more available liquidity on credit cards (the total credit card limit minus the debt) than in liquid savings. The Report on the Economic Well-Being of U.S. Households (Board of Governors of the Federal Reserve System, 2018) similarly suggests that credit cards are either the most or second-most (after cash) frequently used source of liquidity to cover shocks.

  4. 4.

    The measure includes all of a given consumer’s cards, so both the credit limit and credit card balances are at an individual level. Credit card debt includes convenience debt, which is paid at the end of each month, as well as revolving debt, which is carried over from month to month. The data do not allow us to distinguish between these two types of credit card debt. Based on the Survey of Consumer Payment Choice (SCPC), almost 60% of credit card holders carried some unpaid balances during the previous 12 months (Greene & Stavins, 2018).

  5. 5.

    For example, see the Bank of England Prudential Regulation Authority’s rules on loan-to-income ratios (see: https://www.bankofengland.co.uk/prudential-regulation/publication/2014/implementing-the-fpcs-recommendation-on-loan-to-income-ratios-in-mortgage-lending, accessed November 1, 2019) or the US Consumer Financial Protection Bureau’s ability-to-repay rule (see: https://www.consumerfinance.gov/ask-cfpb/what-is-the-ability-to-repay-rule-why-is-it-important-to-me-en-1787/, accessed November 1, 2019).

  6. 6.

    See, for example, “4 Steps to Getting Your Credit Mortgage-Ready” on the Experian website (by Ismat Mangla, March 19, 2019, https://www.experian.com/blogs/ask-experian/how-to-get-your-credit-ready-for-a-mortgage/) or “How to Improve Your Credit Before Applying for a Mortgage” from USAA (https://www.usaa.com/inet/wc/advice-real-estate-improve-your-credit-before-applying-for-a-mortgage?akredirect=true) or the “How to deal with “bad credit”—or no credit—when you want to buy a home” from the CFPB (by Megan Thibos, March 17, 2017, https://www.consumerfinance.gov/about-us/blog/bad-credit-or-no-credit-when-you-want-buy-home/).

  7. 7.

    See “How to Lower Debt and Boost Your Credit Score In One Shot” (by Jim Akin, April 2, 2018, https://www.experian.com/blogs/ask-experian/how-to-lower-debt-and-boost-your-credit-score-in-one-shot/).

  8. 8.

    A description of the data can be found at https://www.newyorkfed.org/research/staff_reports/sr479.html.

  9. 9.

    The reporting of a joint mortgage appears to have changed somewhat over time. Figure 1 shows the combined percentage of mortgages with codes for “Joint” and “Shared.” The Joint or Shared status of a mortgage is determined by the Equal Credit Opportunity Act (ECOA) code appended to the mortgage. ECOA codes appear to be reported frequently with a lag, so we label a new mortgage Joint or Shared if it gets a Joint or Shared ECOA code within four quarters of acquisition. With this definition, we match 138,000 out of 146,000 first mortgage acquisitions. Reporting “Shared” became much more important for several years from 2004 to 2006, replacing “Joint” codes. Other than this period, the share of joint and shared mortgages that are “Joint” was relatively constant at approximately 65%. The fall in joint and shared mortgages in Fig. 1 is similar to the fall in co-borrowing mortgage applications in HMDA documented by Jakucionyte & Singh (2020).

  10. 10.

    For example, if a consumer has three open credit cards for the entire 1999–2017 period except for two quarters where the value is missing, we impute a value for those three cards for each of the two missing quarters. In a more complex example, a consumer has two open credit cards at the beginning of a string of missing values, and then the consumer reappears in the data with three credit cards. For such a situation, we fill in missing data “linearly,” meaning we allow the value to rise or fall with a constant slope from the old value to the new value. If in any case the number of missing quarters is greater than 12, we leave the data missing. In addition, we do not impute unless there are observations both before and after the imputation. The imputation reduces the number of—but does not eliminate—missing observations in the data. For example, observations with bankcards missing dropped from 12% of the sample to 7%. The number of individuals with at least one missing value dropped from 51% of the sample to 27%.

  11. 11.

    If a person obtained a mortgage prior to the beginning of our sample, we cannot determine if it is a first-time mortgage acquisition.

  12. 12.

    https://www.blackknightinc.com/what-we-do/data-services/.

  13. 13.

    This measure of debt is exclusively mortgage debt. The data come from HMDA, and the information was provided by loan originators.

  14. 14.

    Mortgages that were paid off, sold, or defaulted on before 1999 are not observed in the data. However, all mortgages held as of 1999 are in the data, and we can see the date of their acquisition even if they were acquired before 1999.

  15. 15.

    According to the Equifax definition, a mortgage is in default if payment is more than 120 days past due. The default rate is the percentage of all mortgage holders who have a mortgage in default. A mortgage is delinquent if the payment is at least 30 days past due, excluding those with payments that are more than 120 days past due.

  16. 16.

    Credit card debt includes convenience debt as well as revolving debt.

  17. 17.

    See Fulford (2015) for a model that links credit card limits and income explicitly.

  18. 18.

    It is also possible that demand for buying a first home was lower before the crisis, and people with good credit scores opted to rent rather than own.

  19. 19.

    In addition to estimating the specification with the interaction terms, we estimate pooled regressions and separate regressions for the time period before the financial crisis, from Q1 1999 through Q3 2007, and the time period after the financial crisis, from Q3 2009 through Q4 2017. The coefficients from the separate regressions are in Appendix Tables A2–A6 and are qualitatively very similar to the interaction term results.

  20. 20.

    Age and age-squared (not first differenced) are regressors in Eq. (1). Because the dependent variable is in changes, the age coefficients measure whether changes are faster or slower. The coefficients on age in many regressions are negative, indicating that the rate of change of the dependent variable decreases with age. Note that our data are quarterly and the credit bureau reports only year of birth, not the birth date, so age is updated only annually.

  21. 21.

    To test for robustness, we tried alternative numbers of quarters, and the results were qualitatively similar.

References

  1. Agarwal, S., & Qian, W. (2017). Access to home equity and consumption: Evidence from a policy experiment. Review of Economics and Statistics, 99(1), 40–52. http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00606.

  2. Andersson, F., Chomsisengphet, S., Glennon, D., & Li, F. (2013). The changing pecking order of consumer defaults. Journal of Money, Credit and Banking, 45(2-3), 251–275. http://onlinelibrary.wiley.com/doi/10.1111/jmcb.12001/full.

  3. Bertaut, C. C., Haliassos, M., & Reiter, M. (2009). Credit card debt puzzles and debt revolvers for self control. Review of Finance, 13(4), 657–692. https://doi.org/10.1093/rof/rfn033.

    Article  Google Scholar 

  4. Brown, M., Stein, S., & Zafar, B. (2015). The impact of housing markets on consumer debt: credit report evidence from 1999 to 2012. Journal of Money, Credit and Banking, 47(S1), 175–213. http://onlinelibrary.wiley.com/doi/10.1111/jmcb.12198/full.

  5. Browning, M., & Ejrnæs, M. (2009). Consumption and children. The Review of Economics and Statistics, 91(1), 93–111.

    Article  Google Scholar 

  6. Board of Governors of the Federal Reserve System. 2018. Report on the economic well-being of U.S. households in 2017. Available at https://www.federalreserve.gov/publications/files/2017-report-economic-well-being-us-households-201805.pdf.

  7. Calem, P. S., Lambie-Hanson, L., & Nakamura, L. I. (2017). Appraising home purchase appraisals. Federal Reserve Bank of Philadelphia Working Paper No. 17-23. Available at https://www.philadelphiafed.org/-/media/research-and-data/publications/working-papers/2017/wp17-23.pdf.

  8. Campbell, J. Y., & Cocco, J. F. (2007). How do house prices affect consumption? Evidence from micro data. Journal of Monetary Economics, 54(3), 591–621.

    Article  Google Scholar 

  9. Clark, W. A. V., Deurloo, M. C., & Dieleman, F. M. (1994). Tenure changes in the context of micro-level family and macro-level economic shifts. Urban Studies, 31(1), 137–154. https://doi.org/10.1080/00420989420080081.

    Article  Google Scholar 

  10. Cohen-Cole, E., & Morse, J. (2010). Your house or your credit card, which would you choose? Personal delinquency tradeoffs and precautionary liquidity motives. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1619929.

  11. Coulibaly, B., & Li, G. (2006). Do homeowners increase consumption after the last mortgage payment? An alternative test of the permanent income hypothesis. Review of Economics and Statistics, 88(1), 10–19. http://www.mitpressjournals.org/doi/abs/10.1162/rest.2006.88.1.10.

  12. Ferreira, F., Gyourko, J., & Tracy, J. (2010). Housing busts and household mobility. Journal of Urban Economics, 68(1), 34–45. https://doi.org/10.1016/j.jue.2009.10.007.

    Article  Google Scholar 

  13. Fulford, S. (2015). How important is variability in consumer credit limits? Journal of Monetary Economics, 72, 42–63.

    Article  Google Scholar 

  14. Greene, C. & Stavins, J. 2018. The 2016 and 2017 surveys of consumer payment choice: Summary results. Federal Reserve Bank of Boston Research Data Report No. 18–3. Available at https://www.bostonfed.org/publications/research-data-report/2018/the-2016-and-2017-surveys-of-consumer-payment-choice-summary-results.aspx.

  15. Jakucionyte, E., & Singh, S. (2020). Bowling alone, buying alone: The decline of co-borrowers in the US mortgage market. SSRN Working Paper: 3577436. Available at https://doi.org/10.2139/ssrn.3577436

  16. Kaplan, G., Violante, G. L., & Weidner., J. (2014). The wealthy hand-to-mouth. Brookings Papers on Economic Activity, 2014(1), 77–138. https://www.brookings.edu/wp-content/uploads/2016/07/2014a_Kaplan.pdf.

  17. Kaplan, G., & Violante, G. L. (2014). A model of the consumption response to fiscal stimulus payments. Econometrica, 82(4), 1199–1239.

    Article  Google Scholar 

  18. Kartashova, K., & Tomlin, B. (2017). House prices, consumption, and the role of non-mortgage debt. Journal of Banking and Finance, 83, 121–134.

    Article  Google Scholar 

  19. Keys, B. J., Piskorski, T., Seru, A., & Yao, V. (2014). Mortgage Rates, Household Balance Sheets, and the Real Economy. NBER Working Paper No. 20561. Cambridge, MA: National Bureau of Economic Research. http://www.nber.org/papers/w20561.pdf.

  20. Li, W. & Goodman, L. (2014). Measuring mortgage credit availability using ex-ante probability of default. November. Urban Institute Research Report. https://www.urban.org/sites/default/files/publication/33581/2000018-Measuring-Mortgage-Credit-Availability-Using-Ex-Ante-Probability-of-Default.pdf.

  21. Mann, R. J. (2009). Patterns of credit card use among low and moderate income households. In: M. S. Barr & R. M. Blank (Eds.), Insufficient funds: savings, assets, credit, and banking among low-income households. (pp. 257–284). New York, NY: Russell Sage Foundation.

  22. Mian, A., Rao, K., & Sufi., A. (2013). Household balance sheets, consumption, and the economic slump. The Quarterly Journal of Economics, 128(4), 1687–1726. https://www.ucl.ac.uk/economics/non-seminar/macrodynamics/mian.pdf.

  23. Mian, A., & Sufi, A. (2011). House prices, home equity-based borrowing, and the US household leverage crisis. American Economic Review, 101(5), 2132–2156. https://www.aeaweb.org/articles?id=10.1257/aer.101.5.2132.

  24. Miller, J. J., & Park, K. A. (2018). Same-sex marriage laws and demand for mortgage credit. Review of Economics of the Household, 16, 229–254. https://doi.org/10.1007/s11150-016-9356-7. Available at.

    Article  Google Scholar 

  25. Mulder, C. H. (2006). Home-ownership and family formation. Journal of Housing and the Built Environment, 21(3), 281–298. http://www.jstor.org/stable/41107349.

  26. Nakamura, L. (2010). How much is that home really worth? Appraisal bias and house-price uncertainty. Federal Reserve Bank of Philadelphia Business Review, 2010 Q1. Available at https://philadelphiafed.org/-/media/research-and-data/publications/business-review/2010/q1/brq110_home-worth-appraisal-bias.pdf.

  27. Ortalo-Magné, F., & Rady, S. (2006). Housing market dynamics: On the contribution of income shocks and credit constraints. The Review of Economic Studies, 73(2 Apr), 459–485. https://doi.org/10.1111/j.1467-937X.2006.383_1.x.

    Article  Google Scholar 

  28. Parker, J. A., Souleles, N. S., Johnson, D. S., & McClelland, R. (2013). Consumer spending and the economic stimulus payments of 2008. American Economic Review, 103(6), 2530–53.

    Article  Google Scholar 

  29. Rottke, S., & Klos, A. (2016). Savings and consumption when children move out. Review of Finance, 20(6), 2349–2377. https://doi.org/10.1093/rof/rfv064.

    Article  Google Scholar 

  30. Scanlon, K., Lunde, J., & Whitehead., C. M. E. (2011). Responding to the housing and financial crises: mortgage lending, mortgage products and government policies. International Journal of Housing Policy, 11(1), 23–49. https://doi.org/10.1080/14616718.2011.548585.

    Article  Google Scholar 

  31. Scholnick, B. (2009). Credit card use after the final mortgage payment: Does the magnitude of income shocks matter? ECB Working Paper 1142. Available at https://www.econstor.eu/bitstream/10419/153576/1/ecbwp1142.pdf.

  32. Sinai, T., & Souleles, N. S. (2005). Owner-occupied housing as a hedge against rent risk. The Quarterly Journal of Economics, 120(2), 763–789. https://doi.org/10.1093/qje/120.2.763.

    Article  Google Scholar 

  33. Telyukova, I. (2013). Household need for liquidity and the credit card debt puzzle. Review of Economic Studies, 80(3), 1148–1177. https://academic.oup.com/restud/article-lookup/doi/10.1093/restud/rdt001.

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Acknowledgements

The authors thank José Fillat, Joe Peek, participants at the Bank of Canada Retail Payments Conference, seminar participants at the University of St. Gallen, and the Swiss National Bank for providing helpful comments. They also thank Allison Cole, Jason Premo, and Liang Zhang for their excellent research assistance. This article can be accessed at the Federal Reserve Bank of Boston website at https://www.bostonfed.org/publications/research-department-working-paper/2019/does-getting-a-mortgage-affect-credit-card-use.aspx. This article is not published nor is under publication elsewhere. The views expressed herein are those of the authors and do not indicate concurrence by the Federal Reserve Bank of Boston, the Bureau of Consumer Financial Protection, the principals of the Board of Governors, or the Federal Reserve System.

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Correspondence to Joanna Stavins.

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Fulford, S., Stavins, J. Does getting a mortgage affect consumer credit use?. Rev Econ Household (2021). https://doi.org/10.1007/s11150-021-09550-1

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Keywords

  • Credit cards
  • Mortgage
  • Credit card utilization
  • Debt

JEL classifications

  • D14
  • D15
  • E21