Using Business Analytics and Data Visualization to Motivate Community Engagement in Support of Long-Term Real Estate Values

Reid Cummings and Jana Stupavsky

ERES from European Real Estate Society (ERES)

Abstract: Descriptive and diagnostic analysts more frequently utilize data visualization techniques to help frame and answer two key questions: “what happened?” and “why did it happen?” To comprehensively assess the quality of life in two neighboring Coastal Alabama counties, regional civic leaders identified 17 metrics that could help them interpret how the community is performing as a whole (the “what happened” question) and offer insights into areas needing improvement (the “why it happened” question), all with an eye toward long-term support of the community and its real estate values. A few examples of included metrics are crime, housing, poverty, high school graduation, obesity, infant and childhood mortality, and water and air quality. Our business research and services center, the South Alabama Center for Business Analytics, Real Estate, and Economic Development, joined the effort by developing and hosting a series of web-based, interactive, dynamic dashboards. We created multiple visualizations to address each metric using publicly available data sources to answer the “what happened” question. More importantly, because the community initiative’s goal is to spur conversations about addressing areas needing improvement, we took great care to design all dashboard visualizations to motivate and inform community improvement discussions that focus on “why it happened.” We did so by streamlining data comprehension and minimizing misinterpretation, offering a single question as the title of each visualization. Additionally, considering carefully the core visualization component techniques of spatialization and pre-attentive attributes, we worked to ensure that each visualization within the dashboard “family” had the same look and feel so that community leaders could spend their time working on discussing and promoting real community-based solutions rather than trying to interpret and explain differing visualization interfaces. We contend that better solutions to community problems help to ensure better community outcomes and help to ensure the values of real estate within a community.

Keywords: Community metrics; Community real estate values; Data analytics; Data Visualization (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2022-01-01
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