Statistical Models to Predict Commercial‐ and Parking‐Space Occupancy

Abstract

This paper examines the use of simple analytical models to estimate quantitatively the demand for site‐specific commercial and parking space. The influence of locational and other site characteristics on demand is captured and quantified through regression equations for occupancy, based on data from the Pittsburgh, Pennsylvania, central business district. In addition to site and locational considerations, the use and condition of the building and type of tenant are included in the models. The predicted occupancy is then used to analyze demand. The models are discussed in terms of price impacts, economic influences, and the effects of competing markets. These models are applied to the development of the site of the existing Greyhound bus terminal in Pittsburgh. Development options are identified and costs estimated, which are then compared with revenues based on market rates and predicted occupancy. A net present value analysis is used. Limitations and other applications of the models are also discussed.