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The purpose of this paper is to investigate the lender-borrower relationship as it relates to Sannong loans for agricultural and rural financial markets by Rural Credit Cooperatives (RCCs) and other rural lenders. This paper is motivated by recent reforms to the rural credit market designed to encourage increased lending, particularly to farmers. Little is understood about the lender-borrower relationship in rural China. This paper fills that gap.

The paper investigates relational attitudes between 120 loan officers at RCCs in China's costal Shandong province, paired with a field survey using matched questions to 394 farm households in the same region. Pairing lenders’ perception toward borrowers regarding RCC microcredit lending mechanism, against borrowers’ perception toward lenders and how themselves were perceived by lenders in the same regards, the paper investigates the degree of disconnect between lenders and with distinct cluster groupings based on their perceptions, the paper analyzes the influence of demographics on the borrower and lender cluster memberships.

The paper identifies four borrower clusters and two lender clusters. Borrower clusters are segmented on credit access and satisfaction with their rural lender. The paper also identifies two lender clusters, segmented principally on financial incentives and lending activities. While all lenders view farming with higher regard than farmers believe they do, one cluster is clearly pro-farmer while the second is somewhat indifferent. Indifference is more related to current portfolio activities. The paper draws conclusions that policy initiatives should be put in place at RCCs that close the gap between lender and borrower in their credit relationship. Rural lenders should concentrate on advocating RCCs’ care and trust toward agriculture and farm households. At the institutional level, effort should be extended to train a dedicated team of loan officers that specialize in servicing farm households with standardized lending practices. This research provides financial institutions with outreach mechanisms to borrowers, while also training lenders to borrowers’ sensitivities.

Management studies of RCCs are few. This is the first paper that the authors are aware of that studies farmer and lender attitudes on the same scale.

The questions of loan availability and pricing were considered from the perspectives of financial economic theory and practice as well as a survey of lenders capable of financing a one-year bridge loan to determine the market's willingness to make such a loan and what rate of interest would be charged. Utilizing the sources above, in conjunction with professional knowledge and industry contacts, 101 lenders were selected as representative of the universe of lenders who had the capacity to make directly or otherwise to arrange, a $192 million bridge loan. The survey of lenders involved interviews with 67 of the 86 selected lenders from 59 firms. The paper aims to discuss these issues.

Loan availability and pricing were considered from perspectives of financial economic theory and practice plus a survey to determine market's willingness to make a loan at what price. Utilizing professional knowledge and industry contacts, 101 lenders were selected as representative of those which had the capacity to make a $192 million bridge loan. When lenders were evaluated against criteria of size, product type, geographic territory, and willingness/capability to provide nonstandard loans, list selected for telephone interviews was narrowed, then subsequently expanded with referrals that led to identification of new potential lenders to be contacted.

Nine lenders offered conceptualized deal structures to provide the required financing. Though the price may be expensive, especially relative to what borrowers may wish to pay, financing is available. Developers’ and deal-makers’ protestations that “it's impossible,” should be discounted and rejected. Because the subject property is characterized by high-risk, it is logical conclusion that the lenders expressing a desire to provide the bridge loan would expect to earn a high return, meaning that the interest rate would approach, if not exceed, 20 percent.

Because the nature of the research required that the specific identities of the building and the parties were not revealed, some lenders might decline to consider this financing opportunity. And, real world negotiation of financing terms could result in higher rates than quoted and/or disinclination of lenders to proceed. Because of very specialized circumstances surrounding this proprietary research, conducted subject to nondisclosure agreement, publication had to be deferred until those constraints no longer applied. Though the data are more than a decade old, this consideration does not compromise the relevance, validity, or generalizability of the findings.

Markets can accommodate transactions that might be perceived as improbable. Investors which approach opportunities with creativity and open mind, can make deals that would not be possible, were strict, rigid, unbending eligible deal preference parameters to be employed. Strategists establishing policies for real estate enterprises should insist on progressive, expansive thinking in turning the scope of their potential venture involvements. Real estate education and training should address more attention to financial economic theory, strategic initiative, and creative deal making, which priority topics are too seldom prioritized, with the consequence that too many in real estate think narrowly rather than expansively.

This research substantiates a fundamental theory of financial economics and refutes conventional applied wisdom. Seldom do researchers and investors have the opportunity to “get inside” the lending decision process for a large scale commercial property, especially one characterized by daunting circumstances and considerable complexity, such as studied here. A unique real world date set – not normally accessible to property scholars – enables study of the proposition that every commodity has a price, no matter how severe or difficult the circumstances, in a manner fully congruent with the new AACSB Business School Deans policy emphasis on relevance in addition to rigor.

As commercial mortgages much less studied than residential mortgages, this paper is significant addition to undeveloped segment of literature. As the majority of mortgage finance research, estimated to be in the range of 90 percent, has been limited to single family residential financing, the study of commercial mortgage financing is relatively under-researched. Further, the studies of commercial mortgage finance tend to be illustrative case studies with stylized facts rather than explorations of empiricism-based investigations. As most researchers engaged in exploring real estate topics limit themselves to public information, research that provides access to real world private transactions is especially important.

Despite the emergence of peer-to-peer (P2P) lending in Malaysia, there is a knowledge gap on what drives the lending decision of P2P lending in the emerging Malaysian market. This research investigates how borrower's loan tenure, funding purpose, verified documents, accumulated transaction and repayment history, age, trustworthy and geographical resemblance affect likelihood of lending decision in P2P platform.

Using snowball sampling, survey data was collected from 300 online banking users who were willing to invest in online P2P platform from different states in Malaysia (i.e. Selangor, Malacca, Johor and Negeri Sembilan). For estimation, regression analyses were estimated.

The findings suggest that borrower's loan tenure and borrower's age increase the probability of lending in online P2P platform, while funding purpose of credit card reduces the likelihood of lending in the P2P platform. The findings contribute to the signalling theory.

The findings imply that borrowers need to concentrate on loan tenure and clearly indicate their age in the listing in order to increase the funding probability. Moreover, they are suggested not to submit listing for credit card as funding purpose.

This study is first in its nature about P2P lending in Malaysia and the possible factors that influence lending decisions in this new financing platform.

This paper examines the effect of bank expansion on credit access and terms of credit in early America. The bank records from Plymouth Bank, Massachusetts and the Census records provide detailed information on borrowers, endorser, types and terms of loans, and borrower characteristics. The results show that the introduction of new banks did broaden credit access. However, after competition was introduced, the Bank focused more on short-term bills of exchange. In other words, the Bank shifted its emphasis from long-term accommodation paper to short-term bills of exchange.

The purpose of this paper is to model credit rationing among farmers in rural developing areas, based on micro level data of Kano State, Nigeria.

A total of 835 households and 45 microfinance banks were utilized as the samples of the study which were selected using multi-stage stratified sampling technique. Multinomial logit model was used to estimate the factors that determine credit rationing among the rural farmers in Nigeria.

The result of the discrete choice model shows that farmers who are either being engaged in subsistence farming or trading have a significant effect on credit rationing with the greatest impacts found on the farm profit and farmers’ location.

This study failed to carry out a dynamic analysis regarding agricultural credit rationing. Also, it is well known that formal credit interacts with informal credit sector; nevertheless, this interaction was unaccounted for in this study. Therefore, future studies can expand the scope of this research to account for this interaction. In fact, investigating heterogeneity among credit providers will be an important topic in the future.

Clear and sound policies are required for the establishment of new agencies and financial institutions devoted to agricultural sector. Similarly, an integrated system of forward-looking policies based on tax and subsidy-regimes to augment desired incentives for private financial sector and NGOs to lend money to the farmers are needed.

Consistent with risk-balancing theory, the good story for farmers is that profit making farmers are less likely to be among the constrained borrowers. It turned out from the credit rationing model that urban farmers had a greater chance of being successful applicants in the Nigerian agricultural credit market. In comparison to farmers at periphery, urban residents are less likely to be associated with being constrained borrowers.

Owing to the dramatic rise of FinTech credit in the financial sector, this study describes its knowledge and intellectual structure and paves the way for future research.

The study employs citation analysis, keyword analysis, co-author analysis, co-citation analysis and bibliographic coupling on 268 peer-reviewed articles published during 2010–2021 and extracted from the Web of Science database.

Research on FinTech credit has picked up momentum from 2016, with majority contributions from China, followed by UK and USA. International Journal of Bank Marketing is found to be the most productive journal. Co-citation analysis reveals that past studies have focused on three dominant themes, viz. (a) factors that influence user intention to adopt technological products and services (b) borrowers' and lenders' characteristics that impact fund-raising in FinTech credit platforms and (c) evolution of FinTech market over the years. Bibliographic coupling reveals that recent trends in FinTech credit include (a) impact of emerging technologies like blockchain, artificial intelligence, big data on financial system, (b) factors that encourage consumers to adopt the FinTech products and services, (c) mechanisms by which FinTechs have transformed formal credit markets, (d) factors that lead to successful fundraising in FinTech platforms and (e) critical perspectives on digital lending platforms.

To the best of the authors' knowledge, this is a pioneering study undertaking an exhaustive analysis of FinTech credit as a research area. The study offers valuable insights on potential topics of research in FinTech credit domain like investigating Balance Sheet Lending Model, investigating the impact of FinTechs on financial system, and new markets by collaborating with scholars of other regions.

The purpose of this paper is to discuss the case-based reasoning (CBR) approach to improve microcredit initiatives by means of providing a borrower risk rating system.

The CBR approach has been used to consider the Kiva microcredit system, which provides a characterization (rating) of the risk associated with the field partner supporting the loan, but not of the specific borrower which would benefit from it. The authors discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated with a loan request can be used to provide the end users with an indication of the risk rating associated with a loan request based on similar past situations.

The adopted approach is applied and evaluated employing a selection of cases from individual loans. From this perspective, the case base and the codified knowledge about how to evaluate risks associated with a loan represent two examples of knowledge IT artifacts.

The originality of the work lies in borrower risk rating in online indirect peer-to-peer microcredit lending platforms. The case base and the codified knowledge are the two contributions in knowledge IT artifacts.

This study investigates important factors that should be used by lenders in risk‐rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.

This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models.

Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples.

The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation.

The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.

This chapter investigates the nature of the transformation of macroeconomics by focusing on the impact of the Great Depression on economic doctrines. There is no doubt that the Great Depression exerted an enormous influence on economic thought, but the exact nature of its impact should be examined more carefully. In this chapter, I examine the transformation from a perspective which emphasizes the interaction between economic ideas and economic events, and the interaction between theory and policy rather than the development of economic theory. More specifically, I examine the evolution of what became known as macroeconomics after the Depression in terms of an ongoing debate among the “stabilizers” and their critics. I further suggest using four perspectives, or schools of thought, as measures to locate the evolution and transformation; the gold standard mentality, liquidationism, the Treasury view, and the real-bills doctrine. By highlighting these four economic ideas, I argue that what happened during the Great Depression was the retreat of the gold standard mentality, the complete demise of liquidationism and the Treasury view, and the strange survival of the real-bills doctrine. Each of those transformations happened not in response to internal debates in the discipline, but in response to government policies and real-world events.