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This paper aims to quantify the impact of environmental contamination on farmland valuation. It applies data fusion and hedonic pricing approaches to quantify the contribution of nitrogen and phosphorus loading on farmland sales transactions. It further suggests approaches to improve internalization of environmental cost in valuation approaches using shadow pricing. The work informs the field of environmental, social and governance (ESG) investing by fusing environmental data with financial transactions.

This paper is an empirical study implementing hedonic pricing of farmland in the Lake Huron major drainage area. Data sources and fusion were derived from AcreValue, the United States Department of Agriculture's Gridded Soil Survey Geographic database (gSSURGO) and the United States Geological Survey's Spatially Referenced Regression on Watershed Attributes database (SPARROW).

The results suggest that environmental contamination has statistically significant positive determination power on farmland prices such that prices increase with contamination. Conventional metrics such as percentage of cultivated land in the parcel, root zone depth, whether the parcel is designated by the Natural Resource Conservation Service as prime farmland, and the size of the farmland parcel contribution to farmland value as well. The results indicate that environmental impacts are not accurately accounted for in farmland transactions.

This paper points to inaccurate valuation of environmental contamination in farmland value. While geocoding allowed for positioning of farmland sales transactions relative to modeled areas of contaminant loading in the Lake Huron drainage area, the interpretation indicates that value is driven by cultivation. Hence, generalization to other areas needs a cautious approach. Empirical testing across locations and drainage areas with diverse farmland features will serve to verify the modeled data used in this study.

The lack of integration of externalities in land valuation has implications on lending and disclosure practices, as financial service providers increasingly seek to account for ESG risk on their loan books and broader investment portfolios. The impact of farmland accounting practices for contamination such as shadow pricing may impact land valuation based on future cash flows, and may serve to inform sustainability-linked lending practices to farm operations.

This is the first paper to fuse data from AcreValue, gSSURGO and SPARROW to discover the explanatory power of nutrient contamination in farmland value in the Lake Huron major drainage area.

The first theme addressed in this paper is agricultural asset performance. The low rate of return on agricultural assets has been of particular interest to policy makers. From a market portfolio perspective, several studies have analyzed the relationship between farm asset returns and systematic market factors, concluding that farmland adds little systematic risk to a well‐diversified portfolio. Because asset values adjust so that the return to each asset is in equilibrium with its relative risk, any persistent low return on agricultural assets may be due to differences in relative risk. The paper’s second theme is the valuation of farmland in the United States. Numerous studies have examined the factors affecting farmland values. Most have used the standard present value capitalization formula relating land values to land rents, although these models have been rejected by empirical data. Several studies have reformulated and improved the performance of the present value models. Since changes in rates of return of agricultural assets and land values can have drastic consequences for farmers’ wealth and sector solvency, future research needs in this area will continue.

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 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.

This study identifies key strategies employed by Illinois grain farms to prevent the erosion of their equity positions due to significant downturns in commodity prices during the implementation of the 1996 farm bill. The econometric results emphasize the collective importance of revenue enhancement, cost reduction, and capital management strategies. Nonfarm‐related strategies aimed at minimizing equity withdrawals through regulated family living expenditures, as well as supplementing low farm incomes with receipts from nonfarm employment and investments, significantly affect cost value equity growth rates. Moreover, significant financial and asset management strategies include those that minimize the costs of borrowing and maintain high asset productivity levels through elimination of excess farm capacity.

Prior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques.

The deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance.

The results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry.

Although deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.

The purpose of this paper is to review the life of the famous mathematician Kiyosi Itô and discuss his influence on the study of agricultural finance and agricultural economics.

This paper is a qualitative historical review.

The paper provides a biographical stretch of Itô's life. It is shown that his influence started to infiltrate the agricultural economics profession at around 1985 and is currently a major influence of a range of economic issues from farm policy to agricultural investments.

The biography is limited to a review of Itô's academic life and influence.

The paper offers a historical perspective on how probability emerged as a critical piece of the economic puzzle. For scholars and practitioners of agricultural finance, the paper provides an in depth review of how Itô processes have, and can, be used.

This paper provides a historical perspective on Itô that is of use to students and scholars of rural credit. This is the first “biography” of Itô to discuss his influence on agricultural finance and agricultural economics.

The main task when valuing land is to identify the variables affecting its value. This is critical when a large number of variables is involved. Furthermore, collinearity and other econometric disturbances frequently occur in this type of research. Against this background, and in an effort to surmount these difficulties, this paper proposes and then tests some statistical techniques based on multivariate analysis. Multiple correspondence analysis helps to ensure rigour, simplicity and accuracy in the process of identifying the variables involved in the valuation of farmland. Finally, once the relevant variables have been identified, a model for the valuation of farmland plots is then specified.

Lease contracts at the time of sale influence buyers' expectations about future returns of farmland ownership and may thus contribute to price dispersion. This paper investigates the conjecture that existing land lease contracts influence buyers' and sellers' costs of being information deficient and thus their bargaining position, their expectation formation about future returns, and thus ultimately the farmland price.

The authors link different levels of information, search, and bargaining costs to three buyer types and their land use intentions. Relying on a rich dataset of farmland transactions in the German Federal State of Saxony-Anhalt from 2014 to 2019, the authors use a hedonic pricing model to investigate five hypotheses applying multivariate one-sided tests.

The authors find buyer-specific effects related to lease status and lease term of a lot. Tenant buyers pay less than non-farmer buyers for leased lots, whereas non-tenant farmers pay a markup. While prices decrease for all buyer groups with an increasing lease term, this effect is the strongest for non-tenant farmer buyers. This study’s results suggest that an existing lease contract impacts buyers' costs of being information deficient, their bargaining positions and expectation formation, and ultimately the price discovery process.

To the authors’ knowledge, this is the first study that decomposes the effects of tenancy on farmland prices by buyer type and lease term. The study provides insights into price dispersion for identical characteristics of farmland and explains why empirical studies have found mixed or no empirical evidence that lease contracts influence the price discovery process.

The purpose of this paper is to investigate the relationship between dairy farmland prices and farmland rental incomes in New Zealand from 1982 to 2009.

Using the net cash income received under a 50/50 share‐milking agreement to proxy the net cash rent, the paper attempts to explore the prices and rental incomes relationship using the present value model and then apply them in a pool regression model to show how farmers formulate their price bids.

Results show that over the long‐term dairy farmland price growth tends to be in line with rental growth. However, there is substantially higher growth in land prices in relation to the rental growth since 2002. Moreover, the risk premium placed by farmland owners on future rental cash flows since 2002 appears substantially below the historical average. The research further shows that farmers nowadays place more emphasis on the current season's payout than historical incomes in their price bids.

As a consequence the recent high land prices will be extremely sensitive to a permanent change to the low interest rate environment and future growth of dairy income. A policy recommendation is also highlighted.

The results of this paper indicates that the rapid price appreciation for New Zealand dairy farmland since 2000s might give rise to bubbles.