A model for the valuation of farmland in Spain. The case for the use of multivariate analysis

Published date01 April 2003
DOIhttps://doi.org/10.1108/14635780310469111
Date01 April 2003
Pages136-153
AuthorTeresa Garcia,Ildefonso Grande
Subject MatterProperty management & built environment
JPIF
21,2
136
Journal of Property Investment &
Finance
Vol. 21 No. 2, 2003
pp. 136-153
#MCB UP Limited
1463-578X
DOI 10.1108/14635780310469111
ACADEMIC PAPERS
A model for the valuation of
farmland in Spain
The case for the use of
multivariate analysis
Teresa Garcia and Ildefonso Grande
Department of Business Administration, Public University of Navarra,
Pamplona, Spain
Keywords Valuation, Multivariate analysis, Models, Spain
Abstract 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.
1. Introduction
Spain is currently registering a surge of renewed interest in the procedure
involved in the valuation of farmland, largely as a consequence of
administrative requirements arising in relation to tax adjustments and the
compulsory purchase of land for public building and social purposes. Some of
this interest is no doubt also due to the fact that land needs to be valued when
its owners wish to claim EU grants.
Land valuation is, without doubt, a complex process. Accuracy in estimating
the value of farmland may be compromised by a whole range of factors of a
methodological, technical and legal nature, which must be considered
collectively in order to ensure an accurate estimation.
The main problems faced by analysts relate to methodological objectivity,
and the difficulties involved in selecting a comprehensive set of variables to
reflect the value and heterogeneity of the plots to be valued.
A method can be said to be objective or scientific when it leads to the same
results, no matter who applies it. There is no doubt, then, that statistical models
are in this sense objective. The use of certain tools, such as regression
modelling, for quantifying a given set of data gives the same set of results.
Statistical methods can be said to be scientific instruments in that they
guarantee the objectivity required of a valuation process.
The Spanish Administration has recognised the need to introduce objective
procedures for the valuation of farmland and has opted for those based on
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Academic papers:
Spanish farmland
137
statistical modelling. This endows the process with a degree of objectivity and
efficiency that was lacking in the valuation techniques used in Spain to date,
since it enables a great number of plots to be processed at once, while keeping
the land database permanently updated.
Statistical modelling is certainly necessary if valuations are to be accurate.
This in itself will not be sufficient to solve the entire valuation problem,
however. Even with heterogeneous data, the models constructed may be
adequate from the quantitative point of view, in terms of the percentage of
variance explained, for example. The results obtained by applying the models
may also prove satisfactory, from the quantitative viewpoint, even when
different independent variables are used.
This, however, is one of the risks involved in using statistical or econometric
modelling and has given rise to some criticism. Statistical and econometric
methods are not causal, they simply measure the relationships that exist
between variables previously selected by the analyst.
Though statistics based on dependence models provide a quantitative tool to
ensure the objectivity of results when the model is constructed, they do not
generally simplify the task of selecting the causal or independent variables. In
other words, the objectivity of the method is no guarantee against a biased
choice of explanatory variables.
This is a case for the application of techniques based on interdependent
multivariate statistics, which are extremely useful when selecting and isolating
independent variables to be subsequently introduced into statistical or
econometric models which can then be used in the valuation of farmland or any
other kind of property.
The need to ensure a comprehensive selection has already been mentioned.
This can be achieved by applying a multi correspondence factorial analysis in
the valuation process, in order to highlight all the potential variables that are
really worth including in an econometric model.
The heterogeneity of farmland plots in Spain is such that over a distance of
just a few kilometres, features such as soil composition, land relief, rainfall,
hours of sunshine or temperature range can vary widely. In more homogeneous
areas, there are certain to be fewer factors determining the value of land.
When this is the case, the construct used to model the valuation needs to
include a wide range of variables, but by doing so, the econometrician clashes
headlong with whole purpose of modelling, which is to find simple, stable
constructs to represent real situations and link land value to a known, limited
and permanent set of price-determining factors.
The purpose of this study, therefore, is to offer a proposal for an econometric
model to be used in valuing farmland. This will be based on factorial methods
that will enhance the objectivity of the choice of variables initially thought to
affect the value of the land. By combining both methods, it will be possible to
carry out the valuation procedures, while ensuring that the variables selected
will, as required, be objective, simple and comprehensive.

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