2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 319
governments, and community development practitioners better tailor
and target their investments and interventions in neighborhoods.
The project was conducted by RW Ventures, LLC, 1 in four cities (Chicago,
Cleveland, Dallas, and Seattle) in partnership with numerous local
and national organizations. The work was structured in four components:
data collection on a wide variety of indicators, capturing the key dimensions
of neighborhood change for every neighborhood in the four sample
cities over at least 10 years; a descriptive analysis of how neighborhoods
changed over this period; a series of regression models investigating the
drivers of neighborhood change; and a typology of neighborhoods to
enable investigating how patterns and drivers of change vary by neighborhood
type.
In order to measure neighborhood change, the DNT project sought to
develop a metric that would capture how current and potential residents
value a community. In economic terms, this evaluation is reflected in
the demand for housing in a neighborhood, and it can be measured in
changes in housing values once we control for changes in the quality of
the housing stock. Controlling for quality is important because the price
of a house reflects the qualities of the structure itself (size, construction
quality, number of bathrooms, and so forth) in addition to the desirability
of its location. By holding quality constant, it is possible to estimate
the change in price that can be attributed to a change in the desirability
of the neighborhood and its amenities, rather than to changes in the
characteristics of the house.
To measure quality-adjusted change in housing values, the DNT project
developed a repeat sales index (which measures appreciation based on
the repeated sales of the same house) at the census tract level. This index,
which proved a powerful tool in and of itself, used a cutting-edge methodology
to obtain reliable estimates of property appreciation at the census
tract level while mirroring closely the reality of the housing market. 2
In addition to changes in this repeat sales index, the project analyzed
trends in median house prices as well as in the quantity of housing available
in each census tract, in order to get a complete picture of the ways
neighborhoods changed between 1990 and 2006.
These three metrics (the DNT Repeat Sales Index, change in median
housing values, and change in housing quantity) were also used as dependent
variables in a series of regression models designed to estimate the
effect of various types of neighborhood amenities (including such things
as retail and services, access to transit, and development interventions