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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.

Over the past four decades, real values of farm real estate and the share of assets on farmers’ balance sheets attributed to farm real estate have increased. The purpose of this paper is to examine the factors that explain the concentration of the US agricultural balance sheet around a particular asset, farm real estate, and the extent to which the degree of asset concentration varies across United States Department of Agriculture production regions.

State-level data from 48 states and entropy-based inequality measures are used to examine changes in asset distributions (real estate vs non-real estate assets) both within and between regions over time.

The agricultural balance sheet is found to concentrate into real estate in the USA over the period 1960-2003 with the rate of concentration varying across production regions. In some regions, the concentration is mainly due to changes in real estate prices, while in other regions concentration is also driven by changes in real estate holdings or changes in total factor productivity.

This study formally estimates the degree to which the concentration of balance sheet items can be explained by the observed changes in farm real estate prices relative to observed changes in agricultural factor productivity or changes in farm real estate holdings. The computed regional differences in asset concentration and its main drivers have implications for changes in equity and solvency positions of farmers as well as agricultural lenders’ risk exposure.

The purpose of this paper is to investigate the market microstructure related to the Farm Credit System (FCS), Commercial Banks (CB) and Farm Services Administration (FSA). The commercial banks frequently call out the FCS as having an unfair advantage in the agricultural finance market place due to tax exempt bonds, and an implied guarantee of those bonds. This paper addresses the issue by examining the interrelationships since 1939, while addressing the historically distinctive roles that the FCS, CB and FSA have played in the US agricultural credit market.

There are two components to our model. The first is the estimation of short and long run credit demand elasticities, as well as land elasticities. These are estimated from a dynamic duality model using seemingly unrelated regression. The point elasticity measures are then used as independent variables in least square regressions, combined with farm specific and related macro variables, for the Cornbelt states. The dependent variable is the year-over-year changes in paired FCS, CB and FSA loans.

The genesis of the FCS was to provide credit to farmers in good and bad years. Therefore, we expected to see a countercyclical relationship between FCS and CB. This is found for the farm crisis years in the 1980s but is not a continuous characteristic of FCS lending. In good times the FCS and CB appear to compete, albeit with differentiated market segmentation into short- and long-term credit. The FSA, which was established to provide tertiary support to both the FCS and CB, appears to be responding as designed, with greater activity in bad years. The authors find the elasticity measures to be economically significant.

The authors conclude that the market microstructure of the agricultural credit market in the US is important. Our analysis applies a broader definition of market microstructure for institutions and intermediaries and reveals that further research examining the economic frictions caused by comparative bond vs deposit funding of agricultural credit is important.

The authors believe that this is the first paper to examine agricultural finance through the market microstructure lens. In addition our long-term data measures allow us to examine the economics through various sub-periods. Finally, we believe that our introduction of credit and land demand elasticities into a comparative credit model is also a first.

The purpose of this paper is to examine the effect of falling commodity prices on farm debt usage of corn and soybean farms, and how this debt usage differs based on the financial leverage of the farm.

Using panel data on farms surveyed at least twice in the Agricultural Resource Management Survey (ARMS) from 1996 to 2015, this paper uses a difference-in-differences approach to measure the effect of low commodity price shocks on financially vulnerable farms. To account for the correlation in the error structure between the three dependent variables (real estate debt, non-real estate debt, and interest payments) we use a seemingly unrelated regression approach.

Following a commodity price shock, financially vulnerable farms (debt-to-asset ratio greater than 40 percent) were found to increase their non-real estate debt when compared with non-financially vulnerable farms. Off-farm business income was found to help farms reduce real estate debt and interest payments in the face of these shocks.

Data consist of corn and soybean farms surveyed more than once in the ARMS from 1996 to 2015 and are not representative of all US farms, but have similar characteristics to US commercial farms.

The results indicate that financially vulnerable commercial crop farms respond to lower prices by taking on non-real estate debt, increasing financial stress. Well-targeted federal programs could prevent further financial stress for this group.

This is the first paper to use unbalanced panel data from ARMS to examine how farm debt use responds to commodity prices. This paper can inform policymakers about the financial risks to farms resulting from the current low-price environment.

The authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans.

The authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models.

The authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds.

The contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios.

The purpose of this paper is to provide evidence of the positive impact of the FCS lending on farm incomes which should be useful to policymakers as they consider reforms and further support for this 100-year-old major agricultural lender.

The authors construct a panel for the 1991-2010 period from the FCS financial statements and evaluate how lending by the FCS institutions has affected farm incomes and farm output. The authors use fixed effects estimations and control for credit by other agricultural lenders as well as the stock of capital, prices, and interest rates. Since previous work suggests that rural financial markets are segmented and the FCS serves larger full-time farmers with mostly real-estate backed loans, the authors evaluate the impacts of farm real-estate backed loans and of short-term agricultural loans separately for a shorter period for which the data is available. The authors also perform robustness checks with alternative estimation techniques.

The authors found a positive association between credit by the FCS institutions and farm income and output. The magnitude of the estimated impact is larger during the 1990s than in the 2000s.

The positive link between the FCS institutions’ credit and farm incomes and output supports the notion that the FCS lending was beneficial to farmers. The evidence also supports the segmentation hypothesis of rural financial markets. The financial reports data for 1991-2010 are from the ACAs and FLCAs aggregated on the regional level because there is no clear way to classify FCS lending to a more disaggregate level like the state. The authors also assemble and analyze a state-level data set that contains state-level balance sheet data for the period 1991-2003.

The authors are not aware of another work that directly links (real estate and non-real estate) credit by FCS institutions to agricultural output and farm incomes.

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.

Oklahoma has seen rapid growth in the development of wind energy over the past decade. Residents are concerned about the negative impacts of turbines such as noise or their appearance. This has raised concerns about property values. Thus, this paper aims to examine and quantify the overall impact of wind turbines upon real estate prices in Western Oklahoma.

Sales prices and the history of approximately 23,000 residential real estate records for both platted and unplatted properties in five counties were examined prior to the announcement of construction, after announcement and after construction. A hedonic analysis was undertaken to examine the real estate prices of the properties near wind farms.

While there may be isolated instances of lower property values for homes near wind turbines, results show no significant decreases in property values over homes near wind farms in the study area. Similar results are found for the unplatted properties.

This paper highlights that in spite of mixed attitudes toward wind farms and misconceptions regarding the link between turbines and property values, Oklahoma’s growing wind industry can continue to thrive without negatively impacting nearby home and land values and prices.

Although there have been numerous studies examining the relationship between wind turbine locations and real estate prices, no study has combined the large quantity of records (over 23,000) as well as both platted and unplatted locations.

This paper seeks to examine the benefits of further diversifying a global portfolio of financial assets with New Zealand farm real estate (FRE).

The paper compares efficient sets generated with and without FRE using portfolio theory.

The results show that given the predominantly negative correlation between FRE and financial assets, the risk‐return tradeoffs of portfolios of financial assets can be improved significantly. The diversification benefits measured in terms of risk reduction, return enhancement, and improvement in the Sharpe performance ratios are robust under a number of FRE risk‐return scenarios as well as under high and low inflationary periods. Using five and ten‐year rolling periods it also finds that FRE is a consistent part of risk efficient portfolios. Consistent with the results reported in Lee and Stevenson, for the UK real estate the risk reduction benefits of diversifying with FRE are larger than the risk enhancement benefits.

The results suggest that FRE takes on a consistent role of risk‐reducer rather than a return‐enhancer in a globally diversified portfolio. FRE appears to deserve more serious consideration by investment practitioners that it has been accorded in the past.

The study examines the role of direct real estate in a globally diversified portfolio of financial assets.

This paper identifies factors that affect entry and exit of beginning, young and women farmers and ranchers.

The empirical framework is fixed effects regression analysis that uses county level data to evaluate how barriers to entry, access to and use of credit, local economic environment, and climate affect entry and exit of Beginning Farmers and Ranchers (BFRs). The dataset is assembled from several sources matching the Census of Agriculture years for the period of 1997–2017.

Results show that new farmers are more likely to enter in counties with more and smaller farms and with lower farm productivity, indicating that BFRs have the potential to improve the overall productivity in such counties if able to grow and succeed. The results also indicate that the high capital intensity nature of farming is an effective barrier to entry. BFRs are more likely to do better in counties where agriculture is more important to the economy and with more off-farm work opportunities. The net entry is positively associated with higher input/output price index and the use of insurance but is unaffected by government payments and farm and off-farm income. The authors observe substitutability between farming and alternative self-employment for more entrepreneurial young people. Net entry increases with availability of non-real-estate loans but decreases with real estate credit. Thus, for BFRs to acquire the assets needed to reach optimal scale, access to credit remains essential.

The authors are not aware of other work that estimates how barriers to entry and other economic factors including access to credit affect entry and exit of BFRs of various ages and young and women farmers using the Census of Agriculture data up to 2017.