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Pricing Credit Risk for Mortgages: Credit Risk Spreads and Heterogeneity across Housing Markets

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  • Robert M. Dunsky
  • James R. Follain
  • Seth H. Giertz

Abstract

We develop a Monte Carlo procedure to project MSA‐level house‐price paths from 2013 to 2023. These price paths are applied to a fixed portfolio of synthetic mortgages in order to estimate credit risk spreads (CRS) for each MSA. Like the well‐known annual percentage rate (APR)–which converts an array of fees into an all‐encompassing annual measure of costs to borrowers–the CRS is a holistic measure that encompasses both expected losses from default plus the cost of capital (or unexpected credit losses) needed to cover losses in a stress scenario. We find variation in the CRS across MSAs, with the range spanning 37 basis points. This range spans 86 basis points for those carrying first‐loss positions, such as private mortgage insurers. We conclude that, in order to accurately price credit risk, it is necessary to monitor more than borrower characteristics, but also local economic conditions.

Suggested Citation

  • Robert M. Dunsky & James R. Follain & Seth H. Giertz, 2021. "Pricing Credit Risk for Mortgages: Credit Risk Spreads and Heterogeneity across Housing Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(3), pages 997-1032, September.
    • Handle: RePEc:bla:reesec:v:49:y:2021:i:3:p:997-1032
    DOI: 10.1111/1540-6229.12264
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    References listed on IDEAS

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    1. James R. Follain & Seth H. Giertz, 2016. "US House Price Bubbles and Busts," Public Finance Review, , vol. 44(1), pages 132-159, January.
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    6. Paul Calem & James Follain, 2007. "Regulatory Capital Arbitrage and the Potential Competitive Impact of Basel II in the Market for Residential Mortgages," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 197-219, August.
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    8. Congressional Budget Office, 2014. "Taxing Capital Income: Effective Marginal Tax Rates Under 2014 Law and Selected Policy Options," Reports 49817, Congressional Budget Office.
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    Cited by:

    1. Zhengyuan Chai & Yi Yang & Yangyang Zhao & Yonghu Fu & Ling Hao, 2021. "Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model," Land, MDPI, vol. 10(11), pages 1-20, October.
    2. Zengzheng Wang & Fuhao Zhang & Yangyang Zhao, 2023. "Exploring the Spatial Discrete Heterogeneity of Housing Prices in Beijing, China, Based on Regionally Geographically Weighted Regression Affected by Education," Land, MDPI, vol. 12(1), pages 1-24, January.

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