2000115-Strengthening-Communities-with-Neighborhood-Data

160 Strengthening Communities with Neighborhood Data
first round of grants to states and localities on greatest need, as measured
by a variety of housing distress indicators. Recognizing that concentrating
housing investment could result in greater impact, the department
issued the second round of grants competitively and required grantees
to use neighborhood indicators to identify the target neighborhoods.
Limited budgets prevent city- and community-based organizations
from dealing with all problem properties, and both neighborhood market
conditions and conditions of individual properties should influence
their choices. In neighborhoods where market conditions are weak, for
example, funding could be wasted by acquiring and rehabilitating too
many properties that turn out not to be sustainable in market terms
(a higher proportion of demolitions there might have made more sense).
Where market conditions are stronger, a less costly strategy (well-focused
code enforcement) might be enough to catalyze a trend of self-reinforcing
reinvestment. Matching properties and actions in reasonable proportions
in a politically difficult and budget-constrained environment represents
an extraordinary challenge.
A major problem is that a substantial amount of parcel-specific data is
needed to support effective decisionmaking at this level. The Center on
Urban Poverty and Community Development at Case Western Reserve
University expanded the Cleveland data system discussed at the beginning
of this chapter into the most comprehensive information system
available for these purposes. Nelson’s essay at the end of this chapter presents
a case study of this system, named Northeast Ohio Community and
Neighborhood Data for Organizing (NEO CANDO), and its many uses. 11
This system is noteworthy for two reasons. First is the breadth of its
content. In addition to more common parcel-level data from assessors and
recorders of deeds (e.g., ownership, physical characteristics, and sales prices),
the extensive system integrates data from many other sources, including
housing code violations, building permits, vacancy status, and foreclosure
status. Many of these data are updated weekly. Second is the wide variety
of neighborhood and citywide applications supported by NEO CANDO.
Since the mid 2000s, the data have been used as the basis for decisions about
individual properties within neighborhoods. Various stakeholders, including
city officials, representatives of community groups, and NEO CANDO
staff, meet to examine parcel-level maps and tables, paying attention to the
spatial clustering of conditions as well as the circumstances of individual
buildings (see figure 5.1). Discussions of the data in these meetings inform
decisions by all participants.