Well-being is a multifaceted concept, having intellectual origins in philosophy, psychology, economics, political science, and other disciplines. Its presence is correlated with a variety of institutional and business critical indicators. To date, methods to assess well-being are performed infrequently and superficially; resulting in highly aggregated observations. In this paper, we present well-being as a predictive entity for the management of a smart community. Our vision is a low latency method for the observation and measurement of well-being within a community or institution that enables different resolutions of data, e.g. at the level of an individual, a social or demographic group, or an institution. Using well-being in this manner enables realistic, faster and less expensive data collection in a smart system. However, as the data needed for assessing well-being is highly sensitive personal information, constituents require incentives and familiar settings to reveal this information, which we establish with Facebook and gamification. To evaluate the predictive value of well-being, we conducted a series of surveys to observe different self-reported psychological aspects of participants. Our key findings were that neuroticism and extroversion seem to have the highest predictive value of self-reported well-being levels. This information can be used to create expected trends of well-being for smart community management.