For this research, Farm Appraiser was trained and tested with 155 real farmland sales data in Mason County, Illinois. Introduction Typically agricultural appraisals are completed using the traditional methodology of correlating values derived by using a market and an income approach. These methodologies are an industry standard but they require a considerable amount of time and effort. Neural networks have been successfully applied to urban residential appraisal. However, very little if any application of this technique has been applied to the agricultural market.
Neural networks have potential adaptability for agricultural appraisal analysis.
The objective of this paper is to describe the research and development of a neural network, Farm Appraiser, that could conceivably reduce the amount of time required for an agricultural appraisal and could also significantly reduce the cost. In farmland appraisal, two or three approaches are typically used to correlate a final appraised value. These include the market approach, income approach, and cost approach. It is also very common to put significant emphasis on the market approach since actual sales are those that reflect the current market conditions. Given the amount of time and costs involved for ascertaining an appraised price for agricultural properties some institutions are considering alternative methods of valuation, such as regression analysis.