Author:
John P. Harding, Professor of Finance and Real Estate
Eric Rosenblatt, Fannie Mae
Vincent W. Yao, Fannie Mae
Author:
John P. Harding, Professor of Finance and Real Estate
Eric Rosenblatt, Fannie Mae
Vincent W. Yao, Fannie Mae
Author:
Ronel Elul, Federal Reserve Bank of Philadelphia
Author:
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, S. Ghon Rhee
This paper investigates Japanese banks’ lending and earnings management behavior during three distinct periods of capital demand: (1) high-growth era (1985-1989); (2) financial distress period (1990-1994); and (3) banking crisis period (1995-1999).
There has been considerable public discussion of the roles Fannie Mae and Freddie Mac (the Enterprises) may have played in the financial crisis that began in the third quarter of 2007.
Author:
Anthony Pennington-Cross, Senior Economist
Using data from Fannie Mae and Freddie Mac, this paper estimates a competing risks proportional hazard model popularized by McCall (1996). The analysis examines the performance 30-year fixed rate mortgages from February 1995 to the end of 1999 and compares nonprime and prime loan default and prepayment behavior. Nonprime loans are identified by relatively higher mortgage interest rates.
Author:
Anthony Pennington-Cross, Senior Economist
This paper links the probabilities of default and prepayments to the distribution of losses associated with a synthetic portfolio of Fannie Mae and Freddie Mac mortgages randomly samples from 30-year fixed rate prime and subprime mortgages. The simulations exploit historical relationships found between mortgage characteristics and economic conditions in time and space as estimated in a competing risk conditional default and prepayment hazard model and a loss given default model.
Author:
Anthony Pennington-Cross, Senior Economist
This paper examines whether any consistent bias can be found in the creation of a repeat sales price index at the state level. This is done by comparing a transaction-based index with a housing-stock-based index. The housing-stock-based index weights each observed repeat transaction by the amount of housing it represents. Therefore, the aggregate or regional index should reflect the true appreciation of house prices.