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ABSTRACT
Abandoned properties are a significant problem facing legacy cities. Given historic and ongoing population losses, many legacy cities turn to demolitions as one solution to their surplus property problems. Unfortunately, cities lack the resources needed to demolish all of the buildings that should arguably come down. Determining which properties should receive highest priority is a difficult task. Therefore, this paper presents an empirical method, based on basic suitability analysis, for prioritizing demolitions city-wide. Using Youngstown, Ohio as an example, every vacant property in the city was assigned a demolition score based on four factors: property characteristics, vacancy, neighborhood potential, and crime. Properties with higher scores were deemed stronger candidates for timely demolition. In addition to prioritizing demolitions, the proposed method can facilitate the creation of hotspot maps of proposed demolitions, and a per se strategic demolition plan.
Acknowledgments
The concept of assigning each property a demolition score based on a weighted average came from the work of Eric Shehadi (unpublished), a Youngstown State University student and intern with the Youngstown Neighborhood Development Corporation. I thank John Bralich, Thomas Hetrick, Matthew Honer, Julie Orto, and Alvin Ware for providing data for this paper. I would also like to thank Troy Rosencrants for his assistance with GIS.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. “Blight” in this paper is defined as “unwanted property conditions that stem from the presence of vacant properties.” This definition comes from the city of Flint, Michigan’s blight elimination framework (Pruett, Citation2015, p.11). Using this definition, blight includes, “…the presence of tall grass, accumulated waste, and the continuous challenges associated with dilapidated and vacant houses and buildings” (Pruett, Citation2015, p.11).
2. For a historical overview of demolition activities, see Hackworth (working paper).
3. In most cases, Moran’s I should be statistically significant. By considering a set radius from each property for several of the input variables, the construction of the model creates some degree of spatial autocorrelation amongst the scores. Spatial autocorrelation in the demolition priority scores is desirable because it means that demolitions are more likely to be concentrated, which results in better outcomes.
