1 / 16

House Prices and Mortgage Lending Patterns Across MSAs

House Prices and Mortgage Lending Patterns Across MSAs. Laura Berlinghieri UW – Eau Claire. House Price Appreciation in Miami. Data source: Freddie Mac’s Conventional Mortgage House Price Index (CMHPI). House Price Appreciation in Miami. Data sources: Freddie Mac; BLS.

Download Presentation

House Prices and Mortgage Lending Patterns Across MSAs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. House Prices and Mortgage Lending Patterns Across MSAs Laura Berlinghieri UW – Eau Claire

  2. House Price Appreciation in Miami Data source: Freddie Mac’s Conventional Mortgage House Price Index (CMHPI)

  3. House Price Appreciation in Miami Data sources: Freddie Mac; BLS

  4. Real House Price Appreciation in Milwaukee, Minneapolis Data sources: Freddie Mac; BLS

  5. Real Per Capita IncomeAverage Across Major MSAs Data sources: FRED database; BLS

  6. Real Interest Rate on Conventional MortgagesAverage Across Major MSAs Data sources: FNMA’s Monthly Interest Rate Survey (MIRS); BLS

  7. Housing Opportunity Index Share of homes sold that would be affordable to family earning median income; 30-yr FRM; 10% downpayment Data source: National Association of Home Builders (NAHB)

  8. Mortgage Lending ActivityAverage Percentage Change Across Major MSAs Data sources: Home Mortgage Disclosure Act (HMDA); BLS

  9. Fraction of Originated Mortgages Sold to Non-Governmental Agency InvestorsAverage Across Major MSAs Data source: Home Mortgage Disclosure Act (HMDA)

  10. Literature • Linneman and Wachter (1989) • Mortgage market innovations (e.g. ARMs) reduce borrowing constraints, reducing barriers to homeownership • Mian and Sufi (2008) • Rapid increase in supply of credit to areas with high latent demand for mortgages was primary cause of house price boom • Lamont and Stein (1999) • In cities with large fraction of highly leveraged (e.g. high LTV) households, house prices are more sensitive to income shocks

  11. Expectations • Households in MSAs with low housing affordability will be more sensitive to changes in income, availability of mortgages • House prices will be more sensitive to these demand shocks

  12. Housing Affordability • By design, a larger number for NAHB’s Housing Opportunity Index (HOI) represents a high availability of affordable housing • Define: Constrained = 1 – HOI • An increase in Constrained represents a larger percentage of homes sold that can’t be afforded by family with median income and the “typical” mortgage

  13. Measuring Mortgage LendingActivity • Alternative measures of ΔLending variable: • Volume of single-family, purchase-only mortgages originated • Measured by number of loans: NumLoans • Measured by real dollar volume of loans: VolLoans • Share of originated mortgages sold to non-governmental agency investors: NGShare • All three calculated from HMDA data

  14. Basic Regression • Panel data (N=26; T=9) estimated with fixed effects • Regression specification: with three possible measures of ΔLending: ΔNumLoan, ΔVolLoan, orΔNGShare

  15. Empirical Results Notes: Robust standard errors in parentheses; Adjusted R2 is 0.69, 0.71, 0.72, respectively.

  16. Next Steps • Improve current regression setup • Should “constrained”/affordability measure be a dummy variable? • Alternative measure of mortgage market activity: % of mortgage originations that are high APR • Incorporate land share information from Davis and Palumbo (2008) • MSAs with high land share are likely to experience more house price volatility in response to demand shocks

More Related