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House Price Forecasts and Their Complex Relationship with Interest Rates February 15, 2006

House Price Forecasts and Their Complex Relationship with Interest Rates February 15, 2006. Fidelity Hansen Quality’s Analytics Group Mike Sklarz (Head); Jim Follain (SVP for Mortgage Valuation); Carl Bonham and Norm Miller (Consultants). Motivation and Purpose of Presentation.

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House Price Forecasts and Their Complex Relationship with Interest Rates February 15, 2006

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  1. House Price Forecasts and Their Complex Relationship with Interest Rates February 15, 2006 Fidelity Hansen Quality’s Analytics Group Mike Sklarz (Head); Jim Follain (SVP for Mortgage Valuation); Carl Bonham and Norm Miller (Consultants)

  2. Motivation and Purpose of Presentation • Motivation: We sense great interest in disciplined, third-party, and transparent processes to forecast house prices at local levels • Investors seek insight about their exposure • Experts acknowledge the critical role of local factors • IT and data innovations make more readily available • Purpose: Discuss Fidelity’s approach • Provide examples of both MSA and zip code forecasts • Highlight the complex relationship between interest rates and house price forecasts • Changes in core rates impact house price forecasts • Nature of house price forecasts affect risk-based pricing of credit risk

  3. Key Themes • Positive house price appreciation is still the best estimate for many markets • Flat nominal growth is the typical aftermath of an unusually large run up in house prices • Stress scenarios with double-digit price declines over three years are common • The responses of house prices to interest rate shocks vary widely among MSAs • Risk-based pricing of MSA credit risk is a viable alternative to the “nuclear” option and its many manifestations

  4. Fidelity’s Approach to Forecasting • MSA prices driven by mortgage rates and local fundamentals • Local employment • Affordable house price => median income • Zip code forecasts incorporate local sales activity and persistent relationships with movements in the MSA average • Functional forms driven by local conditions and history • Scenarios for the fundamental drivers are transparent and based upon local experience • The stress test for LA reflects its experience • Subjective adjustments are incorporated • Forecasts available for 375 CBSAs and thousands of zip codes

  5. Key results for Average of CBSAs • We construct a weighted average of the forecasts for 375 MSAs using recent sales as weights • Most scenarios show declines in house price appreciation • Expected annual growth rate on base path is 1.6 percent per year over the next four years for weighted average of CBSAs • The most stressful scenario reaches a trough at the end of 2009 and averages a -1.50 percent per year decline

  6. Fidelity National’s Analytic Group Index

  7. Most scenarios call for a substantial slowdown in house price growth • About one-third show modest declines

  8. The base case scenario calls for annual growth of about 1.5 percent • The most stressful path shows annual declines of about -1.5 percent thru 2009

  9. The Case of Los Angeles: Flat growth in base case but considerable downside risk

  10. Atlanta is expected to show continued growth and a less severe stress scenario

  11. Flat growth is the expectation for a wide diversity of places

  12. Four MSAs Face Serious Stress Scenarios

  13. 90680 is NW of Garden Grove and 92620 is further south and east

  14. We focus upon the differential impact of two interest rate scenarios on house prices

  15. San Francisco: Substantial Impact of an Interest Rate Increase on House Prices

  16. Providence: Substantial impact of an interest rate hike on base case house price forecast

  17. Smaller impacts Flagstaff Ft. Lauderdale Washington DC Detroit New Haven San Diego Dallas Larger impacts San Jose Honolulu New York City Miami West Palm Beach Las Vegas St. Louis Examples of large and small impacts of an interest rate hike

  18. A Risk-Based Pricing Approach to House Price Uncertainty • Credit Risk Spread: Measures the additional interest rate differential a lender should charge to earn the same rate of return losses on a mortgage due to default • Incorporates expected losses and a capital charge • Uses MSA specific house price forecasts by Fidelity Hansen Quality’s Analytics Group • Computes the credit risk spread appropriate to each MSA for a prime 95 percent LTV mortgage to a borrower with a 680 FICO score • Article published in the Mortgage Banking Magazine, October 2005 by Jim Follain and Mike Sklarz.

  19. Credit Spreads by CBSA

  20. Next Steps • Market House Price Forecasting Package • Includes data and forecasts • Contact: jfollain@hanqual.com or mike.sklarz@fnf.com • Continue development • Focus attention on larger CBSAs • Investigate the impact of affordable mortgage products • Incorporate the impact of external demand • Deepen investigation of zip code information • Create more stable indexes • Explain variations in “BETAS” among zip codes

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