The purpose of this paper is to describe the applicability of a neural network for agricultural appraisal analysis. 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. It is the objective of this paper to successfully construct a neural network that could conceivably reduce the amount of time required for an agricultural appraisal and could also significantly reduce the cost.

Typically in farmland appraisal methodology two or three approaches are used to correlate a final appraised value. These include the market approach, income approach, and cost approach[4]. It is somewhat common for a appraiser to employ a market and an income approach in an appraisal analysis. It is also very common to put significant emphasis on the market approach since actual sales are those that reflect the state of the market.

Agricultural appraisals can be quite time consuming and costly. This is especially true if the individual is not familiar with the particular area or locality that includes a subject property. The appraiser must familiarize himself (herself) with a subject property and must do a significant amount of research concerning market forces in the general area.

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. According to Do and Grudnitski[2], multiple regression alleviates some of the shortcomings of traditional appraisals, but often its assessments results in significant appraisal errors. Further, several methodological problems are associated with the use of multiple regression for appraisals, including function form misspecification, non-linearity, and heteroskedasticity.[9][10][11][12][13]

Neural networks have been successfully applied to the residential housing market and to the commercial real estate market[1] [2]. Agricultural real estate is a special subset of the overall real estate market. However, very little if any application of this technique has been applied to the agricultural market. This may be due to the lack of the readily available data and lack of expertise in the area of this emerging technology.

The job of a rural appraiser is usually difficult. This is especially true if the individual is not familiar with the particular area or locality that includes a subject property. The appraiser must familiarize himself (herself) with a subject property and must do a significant amount of research concerning market forces in the general area. This research can be time consuming and costly. In the meantime the client waits for the "magic number" so that he or she can complete their task.

In the past few years neural architecture models have been applied in a variety of disciplines. These models learn by interacting with their environment in a process that can be described as a recursive statistical estimation[2]. These models are data driven and learn by the reasoning process of induction. They have also been demonstrated to be particularly well suited for problems involving complex, incomplete, noisy or non-linear data. Such data is characteristic of that used in the market approach for farmland appraisal.

Data Acquisition:

The acquisition of data is a very important first step in the design and implementation of a neural network. A database was constructed for this project containing information relating to 85 sales of commercial agricultural properties located in Mason County, Illinois, from January of 1990 to May of 1995. Although the characteristics of these properties are quite variable they represent a reasonable number of learning facts for a neural network [2, p. 41].

Typically in conventional farmland appraisal methodology a relatively small number of comparable sales properties are compared to a subject property. A neural network approach using a significantly larger number of comparable sales properties can add more objectivity to the analysis and can offer a more statistically correct appraisal [3, p. 101].

Mathematical Representation of the Neural Network:

A mathematical representation for this project is Pi = f(Xij) where Pi is the selling price per acre of an agricultural property i and Xij represents a set of explanatory variables with j being the explanatory variable for property i. This model is similar in design to those used for urban residential appraisal [2, p. 39]. Table 1 defines each variable and its associated neuron for the initial neural network.

Training and Testing:

The neural network was created by Brainmaker 3.1 from California Scientific Software. The chosen learning algorithm was back-propagation. Ten percent of the input data was used for testing. The model contained fifteen input neurons, nine hidden neurons, and one output neurons. The number of hidden neurons was determined based on the formula (# input neurons + # output neurons)/2 = # hidden neurons [5, p.2-12].

The final model was created by preprocessing the output data as a square root function. Training and testing were performed in the same manner as the logarithmic function. After adding noise when the training error reached 0.12 the error levels were sequentially decreased until the network converged at a 0.08 level with 92 percent of the test data falling in the acceptable range. Testing the network at a 0.14 level found 3 out of 7 of the test items falling in the acceptable range. Table 2 contains the results of the test data in final form.

The final model was trained and tested relative to 85 farmland sales. The current neural network predicted the price of farmland averaging 80% of actual selling price. We are currently adding more input decision factors to further improve the network performance. It should be pointed out that very little or no research and/or application has been evidenced in the area of applying neural networks to agricultural appraisal analysis. This project, if not the first of its kind, is probably one of the first for this type of application.

1. Lawrence, Jeannette, Introduction to Neural Networks, 6th ed., Nevada City: California Scientific Software Press, July, 1994.

1. Do, A. Quang and Gary Grudnitski, "A Neural Network Approach to Residential Property Appraisal," The Real Estate Appraiser, Vol. 58, December, 1992.

1. Bylinsky, Gene, "Computers That Learn," Fortune, Vol. 128, September 6, 1993.

1. Murray, William G., Farm Appraisal and Valuation, 5th ed., Ames: The Iowa State University Press, 1969.

1. Lawrence, Jeannette and Janell Fredrickson, The Brainmaker's Users Guide Reference Manual, 7th ed., Nevada City: California Scientific Software Press, August , 1993.

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1. Larsen, J.E. and M.L. Peterson, "Correcting for Errors in Statistical Appraisal Equations," The Real Estate Appraiser & Analyst, Fall 1988, pp. 45-49.

1. Mark, J., "Multiple Regression Analysis and Mass Assessment: A review of the Issues," The Appraisal Journal, January, 1988, pp.23-27.

1. Coleman, J.W. and Larsen, J.E., "Alternative Estimation Techniques for Linear Appraisal Models," The Real Estate Appraiser & Analyst, Winter 1989, pp. 526-532.

1. Murphy, L. T. III, "Determining Appropriate Equation in Multiple Regression Analysis," The Appraisal Journal, October, 1989, pp.498-517.

1. Newsome, B.A. and J. Zeitz, "Adjusting Comparable Sales Using Multiple Regression Analysis-The Need for Segmentation," The Appraisal Journal, January, 1992, pp.129-135.
   

NOTE: Due to space limitations, a large number of tables for data preprocessing and all the details of the methodology used have been omitted. Please contact the authors for a full version of the paper.