Preprint Open Access
Baltimore Housing Prices Disparity for Comparable Neighborhoods: A Case for Enabling Interactive,Visual Exploration of Neighborhoods
Peshave, Akshay; Memon, Siraj; Chavan, Vedmurtty; Oates, Tim
As government agencies increasingly make public data available online, it provides opportunities to leverage such data for descriptive, predictive and prescriptive analytics. One domain where these technological capabilities are applicable is real-estate development and housing market domain. This domain is of interest to home buyers, investors and policy makers. Diverse and varying preferences of residents of a geography are latent behavioral factors that affect residential property prices. This paper describes a geographical area agnostic housing typology classifier for Baltimore City communities or neighborhoods. Further, it discussed correlation analysis and composite Vital Signs scores to characterize city population perceptions of different community development categories. These scores enable community clustering to investigate price disparity in comparable communities based on configurable categories and year-on-year trend analysis. Various visualization possibilities are discussed in conjunction with these approaches to make a case for interactive, visual exploration of geographical communities which may be extended to comparative analysis across geographies.
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- Publication date:
- September 4, 2017
- DOI:
-
- Keyword(s):
- correlation analysis clustering data visualisation Baltimore city housing and community development
- Meeting:
- Data for Policy 2017: Government by Algorithm? (Data for Policy), London, 6-7 September 2015
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International