The Effects of Purchases of Mortgages and Securitization by Government Sponsored Enterprises on Mortgage Yield Spreads and Volatility

In this paper, we investigate the effects of GSE (government sponsored enterprise) activities on mortgage yield spreads and volatility. Using various regression procedures (i.e., vector error correction (VEC) and GARCH models) and controlling for default and prepayment risk, we find that securitizations and purchases of mortgages by GSEs reduce mortgage yield spreads and volatility. In particular, we find that the yield spread between conforming and 10-year constant maturity treasury (CMT) rates decreases by 8.0 bp per $1billion increase in the level of GSE securitizations. Similarly, if GSEs increase mortgage purchases, the yield spread decreases 10.5 bp per $1billion increase of purchases. In addition, we hypothesize and find that GSE activities have a spillover effect to the non-conforming mortgage market; via investor substitutions, GSE purchases and securitizations of conforming loans reduce non-conforming loan rates. Thus, the measured influence of GSE activities is biased downward when measured using the spread of non-conforming loans over conforming loan rates. We also find that purchases of mortgages by GSEs significantly reduce mortgage yield volatility. In sum, our findings show that GSE activities reduce and stabilize mortgage market rates.     

GSE Activity and Mortgage Supply in Lower-Income and Minority Neighborhoods: The Effect of the Affordable Housing Goals

I estimate the credit supply effect of the Underserved Areas Goal (UAG), which establishes GSE purchase goals for mortgages to lower-income and minority neighborhoods. Taking advantage of discontinuous census tract eligibility rules and abrupt changes in tract eligibility, I find some evidence of a small UAG effect on GSE purchases and mortgage originations, without crowding-out of FHA and subprime lending. The results also suggest that the GSEs exploit the law??s lack of precision-targeting, yielding effects that might diverge from the law??s intent.     

GSEs,Mortgage Rates,and the Long-Run Effects of Mortgage Securitization

Our paper compares mortgage securitization undertaken by government-sponsored enterprises (GSEs) with that undertaken by private firms, with an emphasis on how each type of mortgage securitization affects mortgage rates. We build a model illustrating that market structure, government sponsorship, and the characteristics of the mortgages securitized are all important determinants of mortgage rates. We find that GSEs generally—but not always—lower mortgage rates, particularly when the GSEs behave competitively, because the GSEs implicit government backing allows them to sell securities without the credit enhancements needed in the private sector. Using our simulation model, we demonstrate that when mortgages eligible for purchase by the GSEs have characteristics similar to other mortgages, the GSEs implicit government-backing generates differences in mortgage rates similar to those currently observed in the mortgage market (which range between zero and fifty basis points). However, if the mortgages purchased by GSEs are less costly to originate and securitize, and if the GSEs behave competitively, then the simulated spread in mortgage rates can be much larger than that observed in the data.     

From new deal institutions to capital markets: Commercial consumer risk scores and the making of subprime mortgage finance

The investment fueled US mortgage market has traditionally been sustained by New Deal institutions called government sponsored enterprises (GSEs). Known as Freddie Mac and Fannie Mae, the GSEs once dominated mortgage backed securities underwriting. The recent subprime mortgage crisis has drawn attention to the fact that during the real estate boom, these agencies were temporarily overtaken by risk tolerant channels of lending, securitization, and investment, driven by investment banks and private capital players. This research traces the movement of a specific brand of commercial consumer credit analytics into mortgage underwriting. It demonstrates that what might look like the spontaneous rise (and fall) of a ‘free’ market divested of direct government intervention has been thoroughly embedded in the concerted movement of calculative risk management technologies. The transformations began with a sequence of GSE decisions taken in the mid-1990’s to implement a consumer risk score called a FICO^® into automated underwriting systems. Having been endorsed by the GSEs, this scoring tool was gradually hardwired throughout the industry to become a distributed and collective ‘market device’. As the paper will show, once modified by specific GSE interpretations the calculative properties generated by these credit bureau scores reconfigured mortgage finance into two parts: the conventional, risk-adverse, GSE conforming ‘prime’ and an infrastructurally distinct, risk-avaricious, investment grade ‘subprime’.     

Did the Federal Reserve's MBS purchase program lower mortgage rates?

On November 25, 2008, the Federal Reserve announced it would purchase mortgage-backed securities (MBS). This program affected mortgage rates through three channels: (1) improved market functioning in both primary and secondary mortgage markets, (2) clearer government backing for Fannie Mae and Freddie Mac, and (3) anticipation of portfolio rebalancing effects. We use empirical pricing models for MBS yields and for mortgage rates to measure relative importance of channels: The first two were important during the height of the financial crisis, but the effects of the third depended on market conditions. Overall, the program put significant downward pressure on mortgage rates.     

The refinancing threshold pricing model: An economic approach to valuing MBS

Investigation of MBS prepayment data indicates that mortgagors have different interest rate levels, or thresholds, at which they exercise their option to prepay their mortgage. In order to properly value an MBS with heterogeneous mortgagors, Merrill Lynch has developed the Refinancing Threshold Pricing Model (RTP). The RTP model focuses on the refinancing decision of the mortgagor when pricing the mortgage pool. The model divides each pool into groups of mortgagors who share similar refinancing costs. Using market data, the RTP model endogenously determines both the implied costs that mortgagors face, as well as the proportion of the MBS pool in each refinancing cost group. In addition to determining pool value, the RTP model also calculates MBS duration, dP/dY and convexity. Comparison between RTP model values and actual market data reveals a strong correlation. The RTP has a wide range of applications, including valuing 15-year and 30-year conventional MBS; pricing interest-only (IO)/principal-only (PO) derivative MBS; determining new versus seasoned MBS price spreads; and valuing specific MBS pools.The information set forth was obtained from sources we believe reliable, but we do not guarantee its accuracy. Neither the information, nor any opinion expressed constitutes a solicitation by us for the purchase or sale of any securities or commodities. Merrill Lynch, Pierce, Fenner & Smith, Inc. or its affiliates may have either a long or short position in, and may buy and sell for its own account or the accounts of others, these securities.     

The use of credit scoring in the mortgage industry

In times of increased focus on risk management, acquiring or growing comparatively low risk mortgage portfolios has become an attractive value proposition. Banks that pursue an aggressive growth strategy in this sector, do, however, require risk control mechanisms that enable them to make a clear judgment on how great a growth appetite they can afford to have in order to still grow profitably. Moreover, under Basel II, the proper quantification of mortgage portfolio risk tends to help the release of own capital, because the mortgage portfolio is one of those portfolios where the relative benefits of internal ratings-based approaches compared with the standardised approach are greatest. Credit scoring models in general, and credit scorecards in particular, are suitable methods for quantifying the risk of an individual mortgage applicant or mortgage customer. In addition to score card development, this paper reviews alternative scoring model types that could be used for mortgage scoring. It presents reasons why it is beneficial to build such models in-house, before focusing on the steps necessary for building a mortgage scorecard. Finally, it discusses the important topics of creating segments, deploying models and eventually monitoring models.     

The effect of large investors on asset quality: Evidence from subprime mortgage securities

Fannie Mae and Freddie Mac (the GSEs), the dominant investors in subprime mortgage-backed securities before the 2008 crisis, substantively affected collateral composition in this market. Mortgages included in securities designed for the GSEs performed better than those backing other securities in the same deals, holding observable risk constant. Consistent with the transmission of private information, these effects are concentrated in low-documentation loans and for issuers that were highly dependent on the GSEs and were corporate affiliates of the mortgage originators. Additional analysis of yield spreads shows that these performance differences were not reflected in prices.     

Markowitz efficiency and size effect: evidence from the UK stock market

Academics and practitioners have frequently debated the relationship between market capitalization and expected return. We apply the Markowitz efficient frontier approach to develop a portfolio performance measure that compares the return of a portfolio to its optimal return, using data from the UK stock market over the period 1985–2012. Our results show that there is a negative relationship between portfolio size and portfolio return during the period under study. When comparing actual portfolio return with achievable return for the same level of risk, we find that as the portfolio size expands, underperformance of the portfolio increases, i.e. the larger the portfolio size, the greater the underperformance. This indicates that Markowitz efficiency is difficult to achieve, particularly in large portfolios. Changing model parameters leads to alternative efficient frontiers that impact upon the measurement of performance. However, the use of alternative efficient frontiers does not affect our result of the size effect on the relative performance of portfolios. Our study shows that the size effect is present over the full period. Our findings also suggest that the excess returns found in small portfolios are likely to be associated with higher levels of diversifiable risk in comparison with larger portfolios. Furthermore, in contrast to other studies, we find no evidence to support the size reversal effect in the data.     

Analysis of REIT IPOs Using a Market Microstructure Approach: Anomalous Behavior or Asset Structure

In this research, we examine and present new evidence on the market activity following the initial public offering (IPO) of a real estate investment trust (REIT) using microstructure data. We analyze the bid-ask spread differences for REIT securities compared to common stocks and closed-end funds for all IPOs between 1985 and 1988. Our results show that REITs, as a whole sample, experience significantly greater bid-ask spreads immediately following the IPO compared to common stocks and funds. However, this outcome is driven by the equity REIT sample, with the mortgage REIT sample having significantly smaller bid-ask spreads. This is in contrast to the evidence documented by Nelling, Mahoney, Hildebrand, and Goldstein (1995). We attribute our result to the underlying asset structure (such as equity, hybrid, and mortgage portfolios) of the various REITs. Overall, however, we find that bid-ask spreads for REITs are similar to those of common stock when both asset structure and the traditional determinants of the spread (share price, trade volume, and returns variance) are considered.