When opening a new restaurant, geographical placement is of prime importance in determining whether it will thrive. Although some methods have been developed to assess the attractiveness of candidate locations for a restaurant, the accuracy is limited as they mainly rely on traditional data sources, such as demographic studies or consumer surveys. With the advent of abundant user-generated restaurant reviews, there is a potential to leverage these reviews to gain some insights into users' preferences for restaurants. In this paper, we particularly take advantage of user-generated reviews to construct predictive features for assessing the attractiveness of candidate locations to expand a restaurant. Specifically, we investigate three types of features: review-based market attractiveness, review-based market competitiveness and geographic characteristics of a location under consideration for a prospective restaurant. We devise the three sets of features and incorporate them into a regression model to predict the number of check-ins that a prospective restaurant at a candidate location would be likely to attract. We then conduct an experiment with real-world restaurant data, which demonstrates the predictive power of features we constructed in this paper. Moreover, our experimental results suggest that market attractiveness and market competitiveness features mined solely from user-generated restaurant reviews are more predictive than geographic features.