Throwing Good Money After Bad? Cash Infusions and Distressed Real Estate

When a leveraged real estate project experience cash-flow problems, the owner must either inject additional cash or default on the mortgage. We show that it is not optimal for the owner to default as soon as net cash flow becomes negative. Surprisingly, the owner can expropriate some of the mortgage lender's wealth by injecting cash and continuing to pay interest. When the owner has cash constraints, outside investors may be able to extract significant economic rents by financing distressed real estate projects. These results have interesting implications for mortgage lending and the pattern of real estate transaction volume. Copyright American Real Estate and Urban Economics Association.

The Performance of Commercial Mortgages

This study examines the return characteristics of a large, well-diversified commercial mortgage portfolio. Mortgage-specific cash-flow histories are constructed for 2,480 loans originated over the period 1974 through 1990, and a contingent-claims approach to pricing risky debt is used to estimate inter-temporal market values. Quarterly holding-period returns are compared across selected mortgage groups and to alternate asset classes. Our findings suggest that both mortgage returns and volatility of return are comparable to those of other forms of fixed-income assets over the study period. Implied property price volatility is found to average 17%, a result significantly higher than reported in earlier studies. While mortgage returns are found to vary by property type and region of origin, cross correlation of returns is found to be high, illustrating the systematic effect of interest rates on the performance of commercial mortgages over the period 1974 through 1990. However, an increase in credit risk in the latter years of the study suggests that diversification may be a worthwhile objective for holders of these assets. We do not find evidence to suggest that abnormal returns were earned on commercial mortgage portfolios over the study period. Copyright American Real Estate and Urban Economics Association.

The influence of sponsor, servicer, and underwriter characteristics on RMBS performance

Securitization, Information asymmetries, Reputation, G21, G24, G32,

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants