- To better allocate neighborhood development funds, cities are using programs
such as The Reinvestment Fund’s Market Value Analysis system to create
neighborhood typologies based on local indicators.
- Typology systems can target strategies such as code enforcement, rehabilitation,
and demolition to local needs as well as anticipate areas and parcels
at risk of future vacancy.
- The Neighborhoods in Bloom program in Richmond, Virginia successfully
raised property values in distressed neighborhoods by coordinating and
concentrating government and nonprofit resources in seven neighborhoods.
A critical component of efforts to
combat vacancy and redevelop
cities is determining how best to allocate
limited funds. Many managers
and researchers agree that simply distributing
dollars evenly among a city’s
neighborhoods or focusing only on its
very worst neighborhoods will usually
yield only small improvements that do
not spur enough private investment
to improve overall conditions. Some
form of targeting is necessary, ranging
from custom-tailored solutions at the
neighborhood (or even block) level to
extensive assistance focused on just a
However, the process of targeting
neighborhoods within a city for additional
investment — or for managed
decline, in more extreme cases — will
always be controversial. In many cities,
failed urban renewal policies of past
decades have left a legacy of mistrust.
And every neighborhood, no matter
how blighted or sparsely populated, is
History and research show that each
city’s redevelopment effort is unique,
both in terms of the relative needs
and challenges of its neighborhoods
and the political, economic, and social
pressures that influence how resources
are targeted. Despite this variation,
however, some strategies have emerged
for evaluating degrees of neighborhood
distress and creating categories
for how to focus response. Many cities
and organizations are developing datadriven
tools to respond to vacancy and
the community problems that vacant
properties can create (see “Vacant and
Abandoned Properties: Turning Liabilities
Into Assets” p. 1). As these systems
evolve — provided that the political
partnerships necessary to effect policy
change are maintained — they also can
help cities zero in on at-risk neighborhoods
and prevent further problems.
The approach to redeveloping a distressed property like the one above depends not only on the condition of the
structure itself, but also on the condition of the neighborhood in which it is located.
The Reinvestment Fund (TRF), a Philadelphia-
based community development
financial institution, has developed a
tool that some major cities have used
to help match neighborhood needs
to investment strategies. TRF’s Market
Value Analysis (MVA) system combines
available local administrative data with
relevant proprietary data to generate
a typology of neighborhoods at the
census-block-group level. Although the
data used in each city’s MVA may vary,
indicators consistently used include the
Median and variability of housing sale
- Housing and land vacancy.
- Mortgage foreclosures as a percentage
of units (or sales).
- Rate of owner occupancy.
- Presence of commercial land uses.
- Share of the rental stock that receives
The MVA system evaluates these indicators
with cluster analysis, resulting in a
neighborhood typology; a 2007–2008 analysis of Philadelphia, for example, categorized block groups as “regional choice/high value,” “steady,” “transitional,” or “distressed.”2 In 2010, TRF argued for focusing the large infusion of Philadelphia’s Neighborhood Stabilization Program (NSP) funds on the city’s more transitional markets or on distressed markets with steadier markets surrounding them. As Ira Goldstein, president for policy solutions at TRF, writes, “NSP funds will make the most impact when invested in areas where objective and systematic data show the housing market is functioning reasonably well.”3 This statement does not mean that larger distressed areas should not receive assistance — rather, it argues that large, one-time infusions of capital may be more effectively applied to areas where other funding sources can be
Baltimore’s Vacants to Value initiative includes strategies such as whole-block redevelopment in distressed areas like this street in the city’s Westport neighborhood.
In addition to Philadelphia, TRF has generated MVAs for Pittsburgh, Newark, San Antonio, Baltimore, Washington, Detroit, and many other cities, often funded by a combination of government and philanthropic funds.4 These analyses have helped cities reach varied goals; in Baltimore, for example, the creation of a neighborhood typology underpinned the city’s Vacants to Value initiative, which has six strategies targeted to neighborhood types, including using targeted code enforcement in stronger markets to penalize negligent property owners and trigger rehabilitation while supporting larger-scale redevelopment in more distressed areas and selectively holding or demolishing properties where short-term redevelopment is unlikely.5 As of January 2014, Vacants to Value has resulted in more than 1,500 rehabilitated properties, more than 1,100 receivership cases filed, more than 100 demolitions, and nearly $90 million in private investment.6
The MVA model, which focuses on housing market metrics, is only one of many approaches to data-based targeting. Depending on data availability and a city’s needs, other systems may emphasize crime statistics, educational data, or other social and demographic factors. Further, data-focused targeting systems can help cities not only combat existing vacancy but also forecast areas at risk for future vacancy. Through the company’s Smarter Cities Challenge, IBM helped the local government of Syracuse, New York, move from reactive to proactive interventions. IBM developed a data clearinghouse to normalize data from various city and external sources, including property features, neighborhood indicators, police call information, and census data.7 The company’s team then used an algorithm to determine key indicators that suggested that a property was vacant — principally, the number of code violations, “the full value assessment of the parcel, whether the property owner of record lives in Syracuse, and the year built.”8 These indicators were used to generate a parcel-level score for the vacancy risk of residential properties.
IBM applied a parallel process to determine which features predict
increased neighborhood-level vacancy rates, finding that “[m]ale unemployment emerged as the most dominant factor. Average family size, percentage of median family income, percentage of controlled substance calls to a neighborhood, percentage of disturbance calls, and percentage of local law violation codes also added significance to the model.”9 With this analysis, IBM categorized neighborhoods as “distressed,” “transitional,” “bubble,” or “stable.” Through a combination of parcel-level risk scores and neighborhood typologies, Syracuse is better positioned to anticipate properties at risk for vacancy and take preventative measures.
Regardless of the specific methodology employed, the city’s political context is an important factor in determining how these typologies are applied. One city often held up as a success story is Richmond, Virginia and its Neighborhoods in Bloom (NiB) program. Because of a strong partnership among the city manager, the director of the Richmond office of the Local Initiatives Support Coalition (LISC), and community
development corporation (CDC) leadership, along with an open and inclusive process that continually engaged city council members and neighborhood leaders, the city agreed to concentrate federal housing funds on seven neighborhoods, most of them identified as significantly distressed according to a typology developed by city planners.10 From 1999 to 2004, Richmond allocated $13.9 million, around two-thirds of its Community Development Block Grant (CDBG) and HOME program funds, to these neighborhoods; LISC earmarked about the same percentage of its housing funds to the areas and also helped build capacity by providing training funds to their CDC partners, who in turn also targeted the neighborhoods.11 The city also provided improved services to most of the neighborhoods, including enhanced code enforcement, public safety, and homeowner counseling.12
The NiB program showed positive results after five years. Using an adjusted interrupted time series methodology, Galster et al. found that home values in NiB neighborhoods went from being 35.5 percent lower than the citywide average in 1999 to slightly higher than average by 2004.13 Neighborhoods throughout the city with similar challenges to those participating in NiB — de facto “control” neighborhoods — did not experience significant gains over the same time period. Galster et al. further calculated that “‘NiB’ produced such a robust fiscal return on the city’s initial investment that it will likely pay for itself in 20 years through enhanced tax revenues.”14
The NiB program faded in prominence after 2004, however, because of the kind of broader political shifts and personnel transitions that can alter policy priorities in any city. In Richmond’s case, a structural change from a council-manager system to an at-large mayor, as well as the departure of key city and nonprofit staff, eventually led to decreased emphasis on (and funding for) the program. As Accordino and Fasulo explain, “Between 2002 and 2012, the city experienced a 35% decline in CDBG and HOME funds. Over the same period, its expenditures in the Neighborhoods in Bloom areas declined by 68%.”15 A lack of clear metrics defining success for the program and of a neighborhood exit strategy also contributed to NiB’s decline, and Richmond has largely returned to a less-targeted distribution approach for housing funds.16
NiB’s success and eventual decline presents an instructive example for other cities wishing to garner support for targeted investment. Although NiB was a strategy proven to effect real change to neighborhood conditions, it also proved that such change can be difficult to sustain politically. And Richmond, of course, is a medium-sized city; officials in larger cities, such as Detroit, often experience much more difficulty building coalitions and support for targeting efforts.
- Ira Goldstein. 2011. “Market Value Analysis: A Data-Based Approach to Understanding Urban Housing Markets,” in Putting Data to Work: Data-Driven Approaches to Strengthening Neighborhoods, eds. Matt Lambert and Jane Humphreys,Washington, DC: Board of Governors of the Federal Reserve System,52.
- Ira Goldstein. 2010. “Maximizing the Impact of Federal NSP Investments Through the Strategic Use of Local Market Data,” in REO & Vacant Properties: Strategies for Neighborhood Stabilization, eds. Prabal Chakrabarti, Matthew Lambert, and Mary Helen Petrus. Federal Reserve Banks of Boston and Cleveland and the Federal Reserve Board, 69–70.
- Ibid., 73.
- Goldstein 2011, 51.
- Ellen Janes and Sandra Davis. 2011. “Vacants to Value: Baltimore’s Market-Based Approach to Vacant Property Redevelopment,” in Putting Data to Work: Data-Driven Approaches to Strengthening Neighborhoods, 86.
- “Vacants to Value Code Enforcement: Track Our Progress,” Baltimore Housing website (cels.baltimorehousing.org/V2V_CodeProgress.aspx). Accessed 12 February 2014.
- Sheila U. Appel, Derek Botti, James Jamison, Leslie Plant, Jing Y. Shyr, and Lav R. Varshney. Forthcoming. “Predictive Analytics can facilitate proactive property vacancy policies for cities,” Technological Forecasting and Social Change, 8. Corrected proof available at www.sciencedirect.com/science/article/pii/S0040162513002138. Accessed 1 February 2014.
- Ibid., 9.
- Ibid., 10.
- John Accordino and Fabrizio Fasulo. 2013. “Fusing Technical and Political Rationality in Community Development: A Prescriptive Model of Efficiency-Based Strategic Geographic Targeting,” Housing Policy Debate 23:4, 623.
- Ibid., 626.
- Ibid., 622; George Galster, Peter Tatian, and John Accordino. 2006. “Targeting Investments for Neighborhood Revitalization,” Journal of the American Planning Association 72:4, 463–5.
- Galster et al., 466–7.
- Accordino and Fasulo, 627.
- Ibid., 627–8.
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