This research improves our comprehension of mortgage default settlements by examining how borrowers make their decisions in contract negotiations. We discuss two major study questions: (1) Are borrowers likely to have a different experience between legal and psychological contracts? and (2) Do issues with equity fear, or the fear of being a victim, influence the choice of a borrower from the mortgage default settlement agreement? Our research suggests that borrowers don’t confuse legal and contractual psychological contracts in this situation, as it contradicts prior research findings. Moreover, as defaulting borrowers appear to place relatively little value on a clean credit report, they do not differentially enter/withdraw from contract negotiations based on a lender’s unwillingness/inability to clear their credit report. The findings against the established beliefs could signify the growing division within the U.S. between individuals and large institutions. Particularly, borrowers don’t believe in lending institutions and do not appear concerned with conventional risk-sensing and underwriting indicators like credit reports. Therefore, to efficiently and effectively deal with the massive amount of mortgages that are toxic and indebted to debt, a radical method of negotiations with borrowers that is reflective of the new standards must be taken into consideration. The analysis of another dataset indicates that these findings were based upon the culture and legal framework within the U.S. and, therefore, could not apply to other nations.

This is a glimpse of subscription content you can access through your work institution.

Notes

1. Inequity aversion refers to people’s dislike of being unfairly treated and how they will do anything to harm their financial situation to create trouble for the person who is inflicting the wrong. This is an important concept to consider in the mortgage market due to the significant distrust created between lenders and borrowers through the Financial Crisis. One of the objectives of this research is to look at the specifics of the default negotiation procedure and to examine inequity aversion using a fresh lens.
2. Legal versus psychological contracts are analyzed in a variety of complicated processes of decision-making, including purchasing a car, acquiring insurance, deciding on an agent, paying for extended warranties, and more. We focus on the mortgage default contract as this type of environment is distinct from all others, and, as such, it should be analyzed in a specific manner.
3. India’s Power Distance Index is 77, much higher than the Power Distance Index of 40 reported for the U.S. https://geert-hofstede.com.
4. http://economictimes.indiatimes.com/wealth/personal-finance-news/74-of-indian-consumers-check-credit-scores-atleast-twice-a-year-cibil-survey/articleshow/59234145.cms
5. Psychological contracts can also be found if arguments are not put in writing.
6. Ackert and Co. (2011) give an argument for a rational explanation. Ackert et al. (2011) suggest this may be due to a money illusion focusing on nominal rather than real wages.
7. A (2 2) design identifies the combination of two primary effects of treatment, while the “+2” describes two additional options we’d like to think about.
8. The “between” subjects design describes how each participant can see only one treatment. A different approach, an “within” subjects design, explains how the participant is guided through multiple, and perhaps all, medicines in the test.
9. As detailed in the following section, The role played by lenders played a significant part in contributing to the current housing crisis. As a result, the defaulting borrowers frequently view the lenders as mistreating them.
10. By construction, the legal and psychological contracts are identical because they allow the defaulting borrower to get from the loan. Ceteris Paribus will enable us to concentrate solely on the possible distinctions between a legal and psychological contract.
11. In a pre-experimental environment, we tested various numbers and discovered that our results did not respond to the amount of $10,000. Instead, proving that the defaulting borrower considers the number offered as an acceptable settlement amount is crucial. In stating explicitly the assumption that the terms of the agreement are suitable for the loanee, we remove all other considerations. To clarify things, the $10,000 amount doesn’t necessarily reflect the amount that is the difference between the outstanding balance of the loan and the value of your home. It is instead an all-encompassing number that makes the borrower accept the settlement terms. In addition, to ensure that any potential effects of scale on the proposed settlement of $10,000 do not influence our findings, Our analyses were re-evaluated concerning subsamples of loanees from different geographic regions divided by median splits of residential valuations and income levels. In line with previous research, our results remain valid across all these different groupings.
12. Collins, Harrison, and Seiler (2015) discuss why the modification of loans is less common and provide a reason for the present study.
13. In line with Seiler (2015 and 2017), We compensate the participants by charging a simple amount (of $1.09) because there are no correct or incorrect answers.
14. 2013 American Housing Survey, http://www.census.gov/programs-surveys/ahs/data/2013/ahs-2013-summary-tables/national-summary-report-and-tables%2D%2D-ahs-2013.html
15. A Kolmogorov-Smirnoff test with two samples is an instrument to determine if the values range across two treatments is equally dispersed. Independent Samples T-tests are a simple way to compare the average difference in the willingness to withdraw from two treatments.
16. See, for example, Seiler (2015a).
17. In the broader field of economics, much debate has been waged about the potential difference between a willingness-to-pay (WTP) versus a willingness-to-accept (WTA). We have found no significant differences in the present study.
18. Seiler et al. (2012) report that consumers ignore their credit score because before defaulting on their mortgages and credit cards, they made colossal cash or credit purchases for a second vehicle, home for college expenses, or even a trip. When these big-ticket purchases are being taken care of, they will likely wait sometime before they have to enter the market for credit again significantly. In this way, borrowers who are in default might rationally assign less value or importance to their credit scores.
19. We also incorporated bankruptcy laws (e.g., exemption levels for homestead and personal) and state laws on real estate (e.g., recourse states versus non-recourse ones, the right to redeem under statute power-of-sale, and legal foreclosure). Seiler et al. (2012) found that homeowners are unaware of the legal complexities involved, and we believe that these variables shouldn’t significantly impact our research results; this assumption was confirmed empirically by Multivariate Regression Analysis. Incorporating controls for state median income and the state’s home price index makes our findings qualitatively unaltered. The results of these specifications can be obtained by contacting the authors.
20. Additional multivariate analysis reveals that our results are valid to take into account whether the individual is concerned about his credit score. Unexpectedly, we discover that those concerned about their credit scores are much less likely to pull out of the contract than those who don’t. The results of the other tests are qualitatively the same as those presented in Table 6. These additional results can be obtained by contacting the authors on inquiry.
21. www.geert-hofstede.com.
22. Power Distance Index values are retrieved from www.geert-hofstede.com.
23. The instruments used in the India study are the same as the ones used in the U.S. sample, with only minor adjustments to accommodate currency differentials.

Real estate returns usually exhibit a high degree of positive autocorrelation. However, we have found two peculiarities in the Hong Kong housing market data. One is that the autocorrelations of submarket returns at the district level are generally negative. In contrast, even with minor or no submarket autocorrelations, the autocorrelation of total market returns is highly positive. This study provides a clear explanation for the two distinct patterns. We first show analytically how the observed autocorrelation noise from transactions and rate of return adjustment are interrelated. This model indicates that even if returns adjust instantly in response to changes, noise from transactions in prices observed will cause us to notice a negative autocorrelation. A methodological approach is developed to determine the speed of return adjustment by removing the autocorrelation bias. The autocorrelation of returns from an individual market results from more than just the autocorrelations between its submarkets and the cross-leap-lag relationships among the submarkets. Strong cross-lead-lag relationships increase the autocorrelation of total returns of the market. Two hypotheses are analyzed to explain the cross-leap-lag relationship between submarkets, specifically space-based information diffusion and transaction cost. Tests based on empirical data derived from Hong Kong housing market data confirm the hypothesis of transaction costs against the spatial diffusion hypothesis.