Keywords: Inner city, Retail, Housing, Discrete choice experiment, SDSS
The retail vacancy rates in Dutch city centers pose a significant challenge to municipalities seeking to
maintain vibrant and economically viable urban areas. Retail vacancies can trigger a downward spiral
of deterioration that affects the business and investment climate. To address this problem,
municipalities are pursuing policies to reduce retail vacancy by transforming vacant retail space,
especially at the edge of the shopping area, into housing. However, little research has been done on
the residential preferences of people in a complex location such as the inner city. Knowing which
vacant retail properties are of interest to inner city target groups will support the municipality in
pursuing its transformation policy and keeping control over the infill of the area. Therefore, this
research aims to create a tool that can explore which vacant inner city retail properties are of interest
to be transformed into houses based on target group preferences. The tool is developed as a spatial
decision support system (SDSS) by integrating a multi criteria decision analysis (MCDA) in a geographic
information system (GIS). The criteria used in the MCDA are based upon the weights retrieved from
the discrete choice experiment (DCE). In the DCE, nine different attributes have been included that
relate to retail properties in the inner city. The results show that the total floor area and monthly rent
price were the most important factors in explaining people’s preferences, followed by accessibility
related attributes, the crowdedness of the street, and floor level of the potential house. The student
target group showed results quite similar, but students seem to have a higher preference for price,
accessibility, and parks. All significant DCE attributes are integrated into the tool to estimate the utility
of retail properties in Dutch inner cities. In combination with the exclusion criteria identified in this
research, this tool that can support municipalities in making decisions regarding the transformation
of vacant retail properties into housing by identifying properties that can be considered for
transformation and by analyzing which retail properties have the highest residential utility.