A numerical model suggests that among loans which would have passed the standard ratio tests requiring loans to have values of LTV less than .8 and DSCR greater than 1.25, a Cap Rate Spread criteria requiring loans to have a value greater than 1% would have prevented the origination of an additional 1,798 CMBS loans reducing the rate of Non-performance from 14.9% with only the LTV and DSCR criteria to just 11.6% by adding the Cap Rate Spread criteria. Of course, adding additional criteria will also lead to errors of rejecting loans which would have performed well. Back testing with the same sample of CMBS loans, this Type I error rate rises from 19% with only the LTV and DSCR criteria to 34% with the addition of the Cap Rate Spread.

Ultimately, CMBS loan underwriters must individually determine an acceptable level of Non-performance appropriate to their business model and tolerance for risk. Using intuition, experience, tools, and rules, each underwriter must choose a balance between the competing risks of rejecting potentially profitable loans and accepting loans which will fail. This research result is important because it helps deepen our understanding of the relationships between property income and loan performance and provides an additional tool that underwriters may employ in assessing CMBS loan risk.

I re-examine the proportion of idiosyncratic risk in REITs with Carhart’s (1997) momentum factor, which is largely applied on the FF3 to control for the persistency of stock returns as supplemental risk in the finance literature. Next, I conduct cross-sectional regression and test the significance of the relationship between idiosyncratic risk and expected returns. I further analyze the role of property sector on idiosyncratic risk as well as on its relationship with expected returns.

I argue three conclusions. First, momentum has a relatively minor effect on the idiosyncratic risk consistent with the financial literature. Second, the effect of momentum is not strong enough to cause a significant change in the relationship between idiosyncratic risk and expected returns. Third, a REIT portfolio diversified across property sectors neutralizes the relationship between idiosyncratic risk and expected returns, though the contribution of each property sector is not statistically significant.

This study investigates the impact of transaction price feedback on residential appraisal judgment in a changed appraisal task environment, characterized by a depressed housing market, market pessimism, conservative lenders and a changed residential appraisal industry. As Hansz and Diaz (2001) find an upward appraisal bias in an upward-trending market, I expect market feedback to introduce a downward bias into residential appraisal judgments in a depressed market. Compared to a “no feedback” control group, residential appraisers receiving the feedback that their previous value estimates were too high, compared to the realized transaction price, are expected to make significantly lower subsequent value judgments for an unrelated property. The “too low” feedback is not expected to have an impact on subsequent value judgments.

I test the hypotheses with a controlled experiment using a pre-posttest design. The experimental design has one factor (transaction price feedback) fixed at three different levels (“too low”, “too high”, “no feedback”). A posttest-only validity control group is added to test for a potential testing bias in the pre-posttest design. This study uses residential expert appraisers, defined as active Oregon State certified residential appraisers, from the Portland metropolitan statistical area (MSA) as subjects. Experimental subjects are randomly selected from a list of all certified residential appraisers in the Portland MSA. Experimental subjects are randomly assigned to the control and treatment groups (10 subjects per group; N=40).

Subjects in the treatment groups and pre-posttest “no feedback” control group are asked to value a lot of vacant residential land in the geographically unfamiliar Roswell, Georgia. After they provide their value estimates for this first valuation case, subjects in the treatment groups are given a note from a seller’s broker stating the transaction price for the previously valued property. Subjects in the “too high” feedback group receive a transaction price that is 15% below their estimates and subjects in the “too low” feedback group receive a transaction price that is 15% above their value estimates. The control group receives no feedback. All treatment and control groups are then given a second (unrelated) valuation case of vacant residential land in Newnan, Georgia and asked for their value estimate. The experiment is concluded with an exit questionnaire containing demographic and professional questions as well as manipulation checks.

The experimental data are analyzed using the parametric independent samples t-test. The assumptions of normality and equal variances are not violated by the dataset. A one-way ANOVA and the non-parametric Mann-Whitney U test are used as robustness checks. All statistical tests conclude that neither the mean of the “too high” feedback group nor the mean of the “too low” feedback group are statistically different at the 5% level from the mean of the “no feedback” control group. Thus, no evidence is found that transaction price feedback biases residential appraisal judgments in a depressed market.

The insignificant results are further analyzed to assess whether they are due to a non-reception of the treatment by subjects, low statistical power or a non-existing relationship: The explanation that subjects did not read the treatment note can be excluded. A power analysis reveals low statistical power and very small effect sizes for both treatments. An alternative explanation for the insignificant results is the absence of the hypothesized relationship. The main client group of experimental subjects is appraisal management companies, which due to legislation passed after 2007, work with appraisers on behalf of lenders. As a consequence, residential appraisers do not receive direct client feedback anymore (compared to Hansz and Diaz, 2001) and may not respond subconsciously to the “too high” feedback.

In general, agricultural commodity futures contracts are a poor predictor of changes in farmland market values. When examining relationships with quarterly percentage change regression models of the included variables, I find the Mountain region provides the most reliable pricing model where both live cattle and Minnesota wheat futures contracts has a positive statistically significant relationships with farmland market values. Also, wheat futures prices have a significant relationship with farmland values in the Corn Belt region. Interest rate futures contracts, farmland capitalization rates and anticipated inflation are not statistically significant in the majority of the regions.

As a robustness check, I model the price levels of the variables using Johansen’s cointegration procedure. This time-series econometric methodology provides results in regards to long-run equilibrium relationships between the variables. The results are only slightly better. Corn, orange juice and sugar futures contracts have positive statistically significant relationships with farmland market values in multiple regions. Again, wheat has a statistically significant positive relationship with farmland values in the Corn Belt region. The Mountain region and interest rate futures contracts are not applicable for the cointegration tests as they are not integrated to the order of one.

Using three different specifications, I find that even though the announcement of FFO leads to abnormal trading, there is no association between the level of abnormal trading volume and the size of the surprise contained in the FFO announcement. I also find, using abnormal returns as a measure of investor response, that FFO explains significantly more variance in abnormal returns than net income suggesting that FFO provides more useful information than net income.

Lastly, I use the proportion of institutional holdings as a proxy for the number of informed traders to predict the amount of abnormal trading volume. I find no significant relation between abnormal trading volume and the proportion of institutional holdings. However, when I break down institutional ownership into two broad classifications, I find that the level of abnormal trading volume is significantly positively related to the holdings by mutual funds and investment advisors but negatively related to the holdings of other institutions (pension funds &.endowments, banks and insurance companies). This raises questions of whether the use of an aggregate measure of institutional ownership is appropriate in studies that examine the effect of institutional holdings.