Poster Paper: Using Assessment Data to Characterize Housing Diversity

Saturday, November 5, 2016
Columbia Ballroom (Washington Hilton)

*Names in bold indicate Presenter

Gizem Korkmaz, Emily Molfino, Sallie Ann Keller, Aaron Schroeder and Daniel H Weinberg, Virginia Polytechnic Institute and State University

Diversity can be defined as the inclusion of individuals representing more than one group. In the context of communities where we live, learn, work, and play, diversity can reflect resilience or other aspects that might make the neighborhood more (or less) desirable to live in, such as housing prices, quality of life, and safety. In the literature, diversity measures are classified broadly as either socio-economic diversity (e.g., racial and ethnic diversity, variation in income, education, age, etc.) or diversity in housing stock such as variability in housing and lot size, the age of structures, the mix or single family and multiple family residences, and housing value. Moreover, housing value is an indicator of wealth. It is important to characterize the distribution of housing characteristics in the area of interest to identify patterns and to conduct analyses that will inform policies targeted at these areas.

In this paper, we examine housing diversity within local neighborhoods. Typically diversity metrics are developed using data from federal surveys such as the American Community Survey (ACS). The geographic resolution in these studies is at either the Census tract level or greater. In contrast, this study uses the Arlington County, Virginia real estate assessment data. The exact locations (latitude-longitude) of residential homes in the county real estate data provide the opportunity to study the spatial diversity within a census tract, at the census block-group level or even smaller neighborhoods. This provides a more refined characterization of diversity than can be done with ACS data alone.

Simpson indices of diversity are estimated for housing data using value, year built, property type, and number of bedrooms for each census tract and block group for 2013. Comparing the census tract and census block group distributions illustrate that there is heterogeneity within census tracts. Homogeneity, defined as similar diversity levels, is observed in census tracts that are geographically closer. We associate these diversity scores with policy relevant issues of access to public goods such as affordable housing, distance to parks and transportation, and availability of food and health care services. These findings demonstrate the opportunity to develop more geographically refined diversity measures using local data.