The location of a shopping center is important for its future development. However, specific models for shopping center location selection were rarely examined in previous studies. This paper proposes a shopper preference–based competitive location model (SPCLM) to solve the location problem for shopping centers. Five attractive attributes are identified for model construction: size, diversity of the tenant inside the shopping center, retail agglomeration near the shopping center, distance to metro stations, and distance between consumers and shopping center. The parameters of the five variables are calibrated using four sample areas of Hong Kong. To benchmark the improvement of the model, an existing model from the literature is also calibrated for comparison. Furthermore, an onsite survey is carried out to validate the resulted optimal location of both SPCLM and the model from the literature. The results show that the optimal locations predicted by SPCLM have higher pedestrian flow rates than do the optimal location predicted by the model from the literature and the real locations of the existing shopping center. As a result, the proposed model significantly improves the prediction accuracy. Thus, it is a useful tool for both retail and community development.

In recent years, environmentally-friendly, sustainable, and compact development has become increasingly popular with governments. An extensive body of literature has focused on the influence on housing prices from an economic perspective. Although residential urban planning from the perspective of individual needs must be considered, little attention has been paid to residents’ demands in high-density and compact urban areas. In this study, we selected Hong Kong as the case and adopted a reliability interval method to rank residential attitude metrics, which indicated residents’ neighborhood needs in densely populated cities. The influences of location attributes on residents’ demands and residential value were compared. A hedonic price model was used to estimate the impacts of the attributes on housing prices. The results showed that both access to metro stations and median household income had important influences on residents’ preferences and housing prices. However, access to the central business district contributed largely to housing prices but not to residents’ attitudes. These findings support urban planners and policy makers during sustainable residential planning and policy formation by understanding residents’ needs in compact urban areas, help them to optimize the match between housing attributes and residents’ expectations, and balance the relationship between residents’ needs and economic interest.