Predictive analytics can facilitate proactive property vacancy policies for cities

Abstract

Is it possible for a city to understand, analyze, predict, and therefore prevent vacant properties? In this paper, we demonstrate the feasibility of using techniques from machine learning and data mining to determine the future vacancy risks for individual properties and for neighborhoods using a variety of structural, demographic, socioeconomic, and city activity features with high accuracy. Within a larger systems-of-systems framework that we develop, these predictive analytics will allow a city to move from decision-making based on 'educated anecdotes' and reactive strategies aimed at the most urgent need, to policy development based on informed, holistic insight and proactive interventions that prevent and reverse decline. A demonstration of the use of predictive analytics within the sociotechnical system is provided using data from Syracuse, New York.