GIS for Housing and Urban Development (2003) / Chapter Skim
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4. Research and Policy Development
4. Research and Policy Development
Pages 69-92
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From page 69... ...
Using GIS locally and building relationships to gather and make available data on housing and other urban conditions could inform policies that affect public housing. Data showing areas of growth in employment opportunities, public transit stops, school district data, prevalence of crime, and other themes relevant to the targeting of HUD resources could improve the agency's efficiency and effectiveness in meeting mission goals.
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From page 70... ...
HUD can expand its research at the regional and metropolitan level to include geographic analysis of the spatial dimensions of urban poverty, the dynamics of neighborhood change, and market trends that affect the U.S. housing markets.
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From page 71... ...
Examples of broader or multi-scale processes that impact urban housing markets include middle-class flight to suburbs, patterns of service and high-tech industry siting, and the administrative structures of local, urban, and state government. Regional spatial analysis provides a more comprehensive account of the problems of poor localities.
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From page 72... ...
The examination of the application of GIS to Section 8 housing issues illustrates the importance of research on the spatial dimensions of urban poverty, the role of data intermediaries, and data issues including privacy concerns and the determination of causality. Using GIS for Section 8 Housing Policy Analysis encompasses understanding the needs and concerns of the residents that can be addressed using GIS, responding to those needs, keeping data on what worked and what did not and exercising judgment about what should be done next.
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From page 73... ...
It may take many maps to explore a topic before an analyst may gain insight into relationships among housing variables. A robust data management approach is fundamental to having access to data for mapping purposes because multiple maps are often necessary to develop an "analysis scenario." Figure 4.3 depicts HUD-assisted housing relative to employment concentrations mapped by traffic analysis zones (TAZs~the units most often used to compile employment data for transportation purposes.
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From page 74... ...
· How axe expectations of neighborhood residents and outsiders about the future of a neighborhood formed and changed? · How effective are different forms of intervention, under varying market conditions, at stemming or reversing neighborhood decline?
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From page 75... ...
~ ~ ~~ f~ ~~ . 0 10 Miles Home Purchase Nonreporting Rate, 1999 FIGURE 4.1 Share of home mortgage applications in Atlanta, Georgia without race-ethnicity information, 1999.
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From page 76... ...
A recent study presented a prototype application, the Housing Relocation Assistant (HRA) , which uses GIS to display neighborhood characteristics for the selection of Section 8 rentals based on user preferences.
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From page 77... ...
Data Bom the 2000 Census may stimulate new research to document trends in poverty concentration and racial segregation. Much research has been done on patterns of poverty concentration and racial segregation, but many questions remain unanswered or inconclusive, for example, the role of socio-economic class versus race in determining segregation patterns.
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From page 78... ...
SOURCE: Thompson and Sherwood, 1999. Neighborhood Effects Literature is extensive on the effects of neighborhood conditions in poverty-ridden, segregated communities on individual social outcomes.
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From page 79... ...
MSA Assisted housing includes all types of FIUD assisted housing; Employment concentrations were demarcated from maps of employment distribution by traffic analysis zones. SOURCE: Thompson and Sherwood, 1999.
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From page 80... ...
Examples of innovations in statistical spatial analysis5 that could augment and support a broad research agenda on housing arid urban issues and promote HUD's mission and goals include: · Spatial pattern analysis add interpolation using statistics, · Spatial econometric modeling, · Multi-level, or hierarchical linear modeling, across-scale analysis, · Bayesian statistical models of spatial and temporal patterns and processes, · Spatial metrics, · Spatially weighted regression, · Data mining and knowledge discovery techniques, and · Analysis of housing and labor market interactions arid spatial forecasting. Conclusion: As a federal agency with a strategic goal to probe a decent, safe, and sanitary home and equal opportunity for every American' creating a vision for the future of urban America is an appropriate endleavor for HUD.
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From page 81... ...
Neighborhood conditions are influenced heavily by the metropolitan context in which they exist; for example, housing market conditions in newer suburban communities interact with conditions in older inner-city and inner-ring suburbs. Without accounting adequately for these types of interactions, investments and policies may be ineffective or counterproductive.
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From page 82... ...
The purpose of the physical inspection was to establish statistical models to predict renovation costs for the full sample and its expansion to the city's housing stock. Costs to bring substandard housing up to standard condition were estimated via regression analysis.
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From page 83... ...
By using the predictive models estimated as part of the study, administrative records can be systematically monitored to provide leading indicators of emerging potential problems with the housing stock, in a timely and cost-effect~ve way that facilitates timely intervention. SOURCE: Waddell, 1994.
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From page 84... ...
Real estate tracking of market conditions for the metropolitan areas and for sub-markets within the area can be defined by housing type and geographic area. These firms keep up-to-date detailed information ore vacancy rates, rents, sales prices, and new construction; and issue regular reports on these conditions through local media and/or private reports available to
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From page 85... ...
HUD could use GIS to integrate local, metropolitan/ regional-level, and national-level data to analyze the effect of ordinances such as minimum lot size, bans on multi-family housing, and other zoning ordinances on the cost and supply of housing in suburban areas. Key Indicators of Local Housing Market Conditions A monitoring system relies on key indicators that efficiently describe the most salient characteristics of the local housing markets.
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From page 86... ...
GIS can be used to carry out analysis of this nature. Analyzing Housing Market Conditions and Trends Although it is clear that monitoring housing market conditions and trends is important to improving the ability to make informed and effective policy choices, there remains a significant gap between useful market information and inferences about the influence of the market on specific policy choices.
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From page 87... ...
Recommendation: To monitor and analyze metropolitan housing market conditions and trends, HUD should: · Identify and adopt means and formats for routine collection of housing-related data relevant to user needs and agency mission goals at regular intervals, along with development and adoption of a standardized method for data analysis; and · Perform research towards the development of spatial analytic tools to address quality-controlled price indices and variations in local context, and for time-series and comparative analyses between and among places. PRIORITIES FOR GEOGRAPHIC ANALYSIS OF URBAN AND HOUSING ISSUES Spatial analysis of urban poverty, neighborhood dynamics, and market trends affecting the housing market will provide data and information that HUD and its clients need to address housing and urban issues.
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From page 88... ...
Recommendation: HUD should incorporate into their research agenda and prioritize spatial analysis of the following urban issues at the regional and metropolitan-level: · Housing market conditions and trends, · Effects of these conditions on HUD program design and implementation, · HUD program effectiveness and effects on communities, · Interactions among communities in metropolitan areas, · Dynamics of neighborhood change including poverty concentration, racial segregation, and neighborhood effects, and · Housing and labor market interactions including regional and cross-border analyses. Urban data are useful for multiple applications.
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From page 89... ...
Effect of Technological Innovations on Housing and Urban Development Technological innovations, especially in telecommunication, play crucial roles in determining the trajectories of metropolitan development. Despite the growing interest among scholars in this topic, there have been no systematic federal initiatives focusing on the issue since the publication of The Technological Reshaping of Metropolitan America by the now defunct Congressional Office of Technological Assessment in 1995.
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From page 90... ...
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From page 92... ...
Programs and tools such as an online clearinghouse for spatial data research and urban simulation models using GIS and spatial analysis will promote analysis of complex urban issues that span geographic scales of neighborhood, community, region, state and the nation. Addressing these recommendations will necessitate resources including expertise in GIS, spatial analysis, geographic research, algorithm development, and spatial data manipulation.
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