An analysis of the use of mass appraisal methods for agricultural properties

Abstract

There are numerous factors that influence the price of a farm and some of these factors are not monetary related. This makes the task of the valuer complex and increases the possibility of large differences in the estimated market value determined and the actual selling price. This article reports the results of a study that analysed the unique and distinctive attributes of farms, in order to determine whether it is possible to develop a linear multiple regression model for the valuation of farms (which satisfies accuracy requirements) with reasonably available data. The improvement of accuracy levels of Multiple Regression Analysis (MRA) models as well as the limitations of using these MRA models during farm evaluations was also studied. By following a stepwise regression approach, 60 farms, primarily located in the eco-zone “mixed bushveld” western area of the Limpopo province, were analysed using ten independent variables. Three models have been developed. The results showed that a fairly accurate regression model could be developed. However, a model that achieves a high level of accuracy could not be developed, due to multifaceted reasons, including non-farm factors and the size of the geographical areas. Accurate MRA valuation estimates will be to the advantage of individual farm owners regarding their municipal tax assessments. It will lead to a wider use of MRAs for the valuation of farms, but great circumspect should be taken when using MRA models in farm valuations. This is due to the possibility that the MRA models do not satisfy minimum accuracy requirements. It is difficult, but possible, to develop a fairly accurate MRA model for the valuation of farms. Therefore, if currently used MRA models are not fairly accurate for municipal valuation purposes, it should be possible to improve the accuracy. Further research is recommended in the use of other regression techniques such as non-linear, geographic weighted regression and quantile regression. These other techniques would, however, require a larger data sample, in order to provide meaningful results.