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.
