Brasilia, Brazil, 29 March - 3 April 1999
When Downtown Moves: Quantifying, representing and modelling the spatial variable in office rents
Jake Desyllas
The Bartlett School of Graduate Studies
(Torrington Place Site)
University College London
Gower Street
London WC1E 6BT
England
tel (44) (0)171 813 4364
fax (44) (0)171 813 4363
email j.desyllas@ucl.ac.uk
www http://www.spacesyntax.com
The location and time variables are together by far the most significant
determinants of differences in office rents in any given city. Whereas
techniques for modelling and predicting time series changes in office
rents are quite advanced, current techniques for quantifying, representing
and predicting changes in the spatial differences of rents are relatively
crude. In stable markets, experience and intuition can usually be depended
on to guess differences in value owing to location quite well. However,
in situations of urban change, experience and intuition can fail.
This paper presents a methodology for quantifying, representing and modelling
the spatial variable in rents that has been applied to a database of over
400 office rental contracts from Berlin for the period 1991 to 1997. Berlin
since reunification provides a dramatic example of spatial restructuring
of the property market because the location of prime rents has moved from
West Berlin to the East. Differences in rents in the database that can
be attributed to location are quantified by calculation of comparable
'effective' rents (where other contract variables that affect price are
controlled for). The representation of the rent surface at plot level
resolution is achieved through GIS data visualisation and animation techniques.
Axial maps are used to model Berlin's changing configurational structure
since the fall of the wall and it is demonstrated that they correlate
strongly to the changes in rent differences that have taken place there.
It is argued that the street level differences in rent are related to
the configurational structure of the city and that changes in distribution
of rent patterns can be tracked and predicted with configurational models.
Examples of rent surfaces in other cities are provided to demonstrate
the potential use of such modelling techniques in situations of dynamic
urban change, such as rapid urban expansion in developing cities or major
planning intervention in older cities.
This methodology could also be applied to other sectors, such as retail
or housing, where changes in the spatial structure of the city also have
an influence on rent surfaces.
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