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The United States Department of Agriculture Farm Balance Sheet forecasts provide important, timely information on the financial assets and debt in the U.S. farm sector. Despite their prominent role in policy and decision making, the forecasts have not been rigorously evaluated. This research examines the degree to which the USDA’s Farm Balance Forecasts are optimal predictors of subsequent official estimates.

Following prior studies of USDA’s farm income forecasts, archived asset and debt forecasts from 1986 through 2021 are used in regression-based tests of bias and efficiency.

Forecasts from 1986–2021 are found to be unbiased but inefficient. The forecasts have a tendency to over-react to new information early in the revision process.

These findings can be helpful for forecast users in adjusting their expectations and for forecasters in adjusting the current forecasting methods.

This study examines agricultural lending by commercial banks and the competition they face from the Farm Credit System (FCS) and non-traditional lenders, including merchants, dealers and other input suppliers.

We construct a measure of commercial banks' perceived competition with FCS or non-traditional lenders using the individual responses to the Federal Reserve Bank of Chicago's Land Values and Credit Conditions Survey between 1999 and 2019. Through regression analysis of an unbalanced panel of survey responses, we present a number of stylized facts on the relationship between perceived competition and farm loan rate spreads, collateral requirements, loan delinquencies and expected lending volumes.

Our analysis shows that the two sources of competition have very different effects on commercial bank lending terms, loan portfolio riskiness and expected loan volumes. With these results in mind, we offer a number of suggestions for future research.

We leverage the unique characteristics of the Land Values and Credit Conditions Survey to examine the competition with non-traditional lenders that cannot be observed using administrative data.

This study examines the relationship between economic fluctuations and financial distress in the US agricultural sector, which is associated with a large degree of financial instability.

The authors developed a parsimonious model of economic fluctuations in the US agricultural sector. The authors used statistical filter methods to identify the co-movement in cyclical fluctuations in real, cumulative growth rates in farm real estate values, farm sector debt and leverage.

The proposed model closely approximated the financial evolution of the US agricultural sector between 1960 and 2018. In addition, the authors proved that the proposed model is an early warning indicator of farm loan delinquencies and farm bankruptcies.

This study exploits recent advances in economic theory and empirical macroeconomic modeling to develop a model that is a robust predictor of financial distress in the agricultural sector. Further, the authors demonstrate that the policy interventions following the 1980s farm financial crisis demonstrate the likely long-run economic response to the policies enacted following the 2008 financial crisis.

Agricultural producers rely on debt capital to support many functions of their enterprise, yet private credit markets are frequently characterized by an imbalance between supply and demand. As a result, a number of public lending programs exist to mitigate the perceived market failures of private credit markets that serve agricultural producers. The paper aims to discuss these issues.

This study uses a structural disequilibrium model to examine the potential for excess demand or supply in the private market for non-real estate farm loans between 1978 and 2014.

The model demonstrates that the market is frequently characterized by disequilibrium, fluctuating between periods of excess demand and excess supply. These disequilibrium periods motivate the discussion of public intervention as a policy proposal within the agricultural sector.

This study uses traditional disequilibrium modeling to evaluate the private credit market for agriculture lending in a manner that has not been attempted previously in the literature. The model uses maximum likelihood methods with non-linear solution algorithms to investigate excess supply and demand in the sector.

The purpose of this paper is to compare the characteristics of farms who participate in farm management associations to the wider population of farms at the state level.

Farm-level records obtained from the USDA's Agricultural Resource Management Survey (ARMS) are compared to similar data obtained from farm management associations in three states: Illinois, Kansas, and Kentucky.

Data collected through farm management associations tend to represent larger farms and a greater share of crop producers as compared to livestock producers. Association data, however, capture a greater share of younger farm operators.

This is the first study to compare farm statistics from several farm management associations to ARMS, and the study confirms the findings of existing studies of prior USDA surveys.

The purpose of this paper is to consider how the federal crop insurance (FCI) program influences farm debt use, one of the key financial decisions made by farm operators.

Using data from the nationally representative Agricultural Resource Management Survey, the paper implements a propensity score matching model of the impact of FCI participation on various measures of farm business debt use. To account for the simultaneity of financial decisions, the paper further tests this relationship using a seemingly unrelated regression model.

FCI participation is associated with an increase in use of short-term farm debt, but not long-term debt, consistent with risk balancing behavior and current trends in the farm sector.

In addition to risk balancing, the results are also consistent with credit constraints or lender preferences. The paper cannot fully establish causality between crop insurance participation and short-term debt levels. Future research should address these limitations.

Agricultural lending standards are generally conservative and the farm sector as a whole currently has historically low leverage, which implies that an increase in debt use may not be a threat to the financial health of the farm sector.

The results indicate that the reduction in total risk facing the farm sector is significantly less than the decline in risk provided by FCI, which is an important consideration for policymakers.

This is the first paper to use an econometric model to analyze the relationship between FCI and farm debt use decisions. This paper can inform future research on the FCI program and farm financial decisions.

The purpose of this paper is to explore the consequences of appraisal smoothing in the estimation of the risks and returns of farm real estate. It examines the degree to which the risk and return characteristics of farm real estate are an artifact of the methods used to measure aggregate property values.

A multifactor asset pricing model is estimated using farm real estate returns in a manner consistent with prior research, as well as using farm real estate returns calculated using two synthetic unsmoothing procedures developed in the real estate finance literature.

The model suggests that unsmoothed farm real estate returns exhibit characteristics that differ from those suggested by prior research. The unsmoothed returns suggest a stronger correlation with economy wide investment risks.

This is the first study to evaluate the impacts of appraisal smoothing in a farm real estate context. It provides a simple framework for addressing many of the pricing anomalies associated with farmland.

Farmland plays a critical role in the financial health of the agricultural sector. As a result, a number of institutions closely monitor farm real estate markets and publicly report estimated farmland values. This study aims to compare the information content of reported farmland values from three institutions.

A state space model is formulated to link observed price estimates to the unobservable value of farmland. The model considers reported values over the period 1965‐2010 for Iowa from three surveys: Iowa State Extension Service, the Federal Reserve, and the USDA.

The values reported by Iowa State receive the greatest weight in estimating the unobservable market value, yet the appreciation rates implied by the USDA estimates most closely track those of the unobservable value.

This study is the first to estimate the unobservable value of farm real estate based on observed estimates. The empirical procedure offers a number of unique advantages. It combines information from data reported at both annual and quarterly intervals and addresses potential problems related to cointegration, nonstationarity, and nonlinearity.

The purpose of this article is to offer an introduction of the Agricultural Resource Management Survey (ARMS) for applied research in agricultural finance and farm management.

This article provides a brief overview of the history, design, use, and accessibility of the ARMS in government reporting and applied research.

The ARMS provides a number of unique advantages for addressing critical issues of the agricultural sector.

The paper provides an access point for researchers who are unfamiliar with the basic features of ARMS.