4
Research and Policy Development

PD&R is directly involved in formulating potential urban policy solutions and monitoring and evaluating policy once it is in place. These policy issues include homelessness, fair housing, housing assistance programs, mortgages and lending, and assisting in community and economic development. PD&R is beginning to realize the potential of GIS in these areas. GIS offers multiple benefits to PD&R in terms of thinking about housing and urban issues and developing coherent public policy responses. Two of the most significant benefits are the ability to layer data from multiple sources and look at data at different scales or geographies.

HUD can use GIS to produce maps that show the distribution of public and federally assisted housing. Information about the spatial distribution of housing could show public housing authorities and other HUD field offices where their clients are and where public housing should be. Public Housing Authorities normally operate their facilities as specialized enterprises, concentrating primarily on the housing units themselves and rarely considering the surrounding framework of neighborhoods, cities, or metropolitan areas. 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. Currently, HUD’s field offices often lack both adequate data and staff who are proficient in GIS. GIS is a tool for



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4 Research and Policy Development PD&R is directly involved in formulating potential urban policy solutions and monitoring and evaluating policy once it is in place. These policy issues include homelessness, fair housing, housing assistance programs, mortgages and lending, and assisting in community and economic development. PD&R is beginning to realize the potential of GIS in these areas. GIS offers multiple benefits to PD&R in terms of thinking about housing and urban issues and developing coherent public policy responses. Two of the most significant benefits are the ability to layer data from multiple sources and look at data at different scales or geographies. HUD can use GIS to produce maps that show the distribution of public and federally assisted housing. Information about the spatial distribution of housing could show public housing authorities and other HUD field offices where their clients are and where public housing should be. Public Housing Authorities normally operate their facilities as specialized enterprises, concentrating primarily on the housing units themselves and rarely considering the surrounding framework of neighborhoods, cities, or metropolitan areas. 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. Currently, HUD’s field offices often lack both adequate data and staff who are proficient in GIS. GIS is a tool for

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data management and spatial analysis but the information derived from GIS is only as accurate as the data that went into the system in the first place, and as relevant as the questions posed. Understanding housing markets and the demand and supply of different types of housing is important. These gaps in data and staffing leave local HUD agencies with inadequate information for making decisions about how and where they should allocate their resources for maximum effectiveness. Using GIS to collect, store and deliver data, and ensuring the quality of the data are important, but the application of these data to policy analysis and planning depends on the relevance of the research questions posed. In addition, the relevant data (e.g., Census of Population and Housing; American Housing Survey, Department of Labor employment data, satellite imagery, EPA air quality data, DOT traffic and accident data, airport noise exposure data) have been collected for many different applications and must be adapted if HUD’s clients and partners are to use them. Data have meaning only within the context of an argument or hypothesis about how something works. This report adopts a regional/metropolitan-level focus for addressing urban and housing issues, as described in Chapter 1. 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. This chapter discusses the potential of an expanded urban research agenda that is appropriate for HUD as a federal agency and identifies priorities for geographic analysis of urban and housing issues. THE SPATIAL DIMENSIONS OF URBAN POVERTY Understanding urban poverty requires attention to processes at the regional and metropolitan levels that result in inner-city poverty. GIS can help integrate data from multiple levels to facilitate regional analyses. The dynamics of neighborhood change and the factors that concentrate poverty in urban areas can also be analyzed using geographic data and tools. The poor are often spatially segregated from the middle class and physically removed from basic services, such as health care, childcare, and retail, and from cultural amenities, such as libraries and museums. Both the percentage of inner city neighborhoods that are poor and the percentage of poor people living in those neighborhoods have risen in recent decades (Jargowsky, 1997). Similarly, although poverty rates have declined for many groups, the income gap between the rich and the poor is widening (Lichter and Crowley, 2002). Understanding the spatial dimensions of urban poverty and neighborhood change is essential to carrying out HUD’s mission.

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Regional Analyses There is growing scholarly and political recognition of the importance of regional analysis in dealing with the problems of low-income localities. For example, Orfield (1997) and Jargowsky (1997) identify processes that shape the conditions within which housing programs are situated. These processes operate at multiple geographic scales. In the past, poverty and segregation in urban housing markets were often explained in terms of locally specific analyses of individual behavior or neighborhood characteristics, without examining the processes operating at broader scales. 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. Efforts toward community empowerment should address the regional processes that create the problems confronting communities and localities. Khadduri and Martin (1997) suggest that data on the positive factors affecting families should be included in analysis in addition to the negative neighborhood factors such as crime and homelessness that are often the focus of analysis. Positive factors may include accessibility, services, formal and informal support networks, and income diversity. GIS can address these multi-scalar questions relevant to urban poverty in terms of these broader forces and processes that shape neighborhoods. Regional spatial analyses of this kind are not simple or user-friendly (Luc Anselin, University of Illinois, Urbana-Champaign, personal communication, 2002); rather they require a trained workforce. Box 4.1 presents an example of sophisticated statistical analysis that supports anti-discrimination efforts. The committee suggests the following research questions that lend themselves to spatial analysis and may illuminate the relationships between urban and suburban processes, and housing market conditions and trends within low-income communities. Addressing these issues using spatial data requires experience and expertise in geographic analysis and research. To what extent do policies that promote or allow rapid urban decentralization contribute to decline in inner-city neighborhoods? How effective are place-based investments in inner-city neighborhoods if rapid decentralization undermines inner-city housing markets? What are the alternative policies? How effective are growth management policies such as urban growth boundaries and smart growth initiatives in curbing the rate of urban expansion?

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Do growth management policies contribute significantly to housing affordability problems? Is there a threshold on the size of the region for metropolitan governance, beyond which regionalism may not work? How can we integrate “things-regionalism” with “people-regionalism”1 in urban and housing development? What is known about urban processes such as sprawl, economic polarization, and population growth; and the interactions among these and other processes at global, national, state, regional, metropolitan, and local scales? GIS and Section 8 Housing Policy Analysis This section describes the uses of GIS in analyzing Section 8 housing policy (Box 4.2). 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. Policy research that can be addressed using GIS include questions about the concentration of people and assisted housing; and the concentration of HUD Section 8 tenant-based assistance program households and employment opportunities (Thompson and Sherwood, 1999). Many internal HUD policy analysts, as well as external HUD research partners, are interested in population and income distribution and in the need for housing and services at neighborhood to regional scales. Thompson and Sherwood (1999) developed a guidebook for GIS use in which many of the examples focus on data for Section 8 housing. The guidebook presents a method for using micro-data (individual households) and a corresponding method for addressing confidentiality concerns. Confidentiality may be a concern when collecting and disseminating data on the spatial distribution of low-income housing. Methods should be devised 1   “Things-regionalism” refers to the most common local government attempts at regional initiatives that focus on a single function such as transportation, watersheds, sewers, and emergency management. The most extreme poverty in America is typically geographically concentrated, suggesting a need for “people-regionalism” to promote diversity, balance, and stability in every area of a region (Cisneros, 1996).

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to avoid identification of individual households but still provide high-resolution data that addresses policy concerns. Methods involve deletions and geo-coding procedures; restricted access to data; rounding numbers into larger groups, aggregation units, and scale; and random displacement of certain point data. Determining causality is another difficulty. GIS data analysis, like other data analyses, cannot compute causality. 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. Figure 4.4 shows the TAZ employment data aggregated to concentrations. Although maps can be used to answer many questions, they can also prompt questions such as: Do people in Section 8 housing get higher paying jobs first, then move? Or, do people move and then get higher paying jobs? Is this pattern a result of household mobility? GIS can inform questions and guide next steps for research on the distribution of Section 8 housing allocation in relation to employment. Dynamics of Neighborhood Change The process of neighborhood change has been a subject of academic research in several disciplines for many decades, and yet significant gaps persist in our understanding of the dynamic processes that produce decline, revitalization, gentrification, and other urban processes. Since the early twentieth century, researchers in various disciplines have studied neighborhoods. Early examples include application of the ecological lens of invasion and succession to neighborhood studies (Park, 1926), analysis of economic and social factors that contribute to neighborhood decline and revitalization (Downs, 1981), and theories of tipping behavior2 (Schelling, 1978). Sociologists have explored neighborhood patterns of racial segregation (Farley and Frey, 1994; Massey, 1990) and the emergence of concentrated poverty 2   A model explaining that the collective action of individuals may produce segregation even when the individuals prefer integration. Tipping behavior is the racial make-up of a neighborhood that prompts flight from the neighborhood (Schelling, 1971, 1972, 1978).

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(Jargowsky, 1997; Wilson, 1987); geographers have studied spatial patterns of gentrification (Smith, 1996). In spite of this sustained and broad spectrum of social science research on neighborhoods, a coherent synthesis and policy responsive to the dynamics of neighborhoods—especially poor neighborhoods—remains elusive. Among the pressing questions that remain are: What internal and external factors, in varying combinations, influence the decline and revitalization of neighborhoods? How do the mechanisms of decline and revitalization work, and how susceptible are they to intervention? How are 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? Is gentrification becoming more widespread; if so, under what conditions, and how can displacement of low-income residents be mitigated in gentrifying neighborhoods? BOX 4.1 Race and Mortgage Lending Race reporting is required from mortgage lenders as a result of the Home Mortgage Disclosure Act,1 which monitors lending practices in minority communities, however, racial disclosure to lenders when borrowing by phone or Internet is not required. Subsequently, the second largest racial/ethnic group of those seeking mortgage credit in the United States is listed as “Not Reported.” A recent study set out to analyze the geographic expression and causes of the “Not Reported” racial/ethnic designation. To this end, a GIS-enabled spatial analysis of nondisclosure reporting in Atlanta, Georgia, was conducted using the following three econometric models demonstrating: The differences in individual characteristics between disclosing and non-disclosing loan applicants; The degree to which non-reporting of racial/ethnic identity results from institutional factors rather than deficiencies of loan applications; and 1   The Home Mortgage Disclosure Act (HMDA), enacted by Congress in 1975 and implemented by the Federal Reserve Board’s Regulation C, requires lending institutions to report public loan data.

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The influence of neighborhood-level characteristics on nondisclosure rates after controlling for both individual and institutional measures. The study found that the geography of nondisclosure coincided with the areas that lacked accurate data for lending research and had a high proportion of African-American neighborhoods. Based on these findings, the study concludes that there is a need for coordinating outreach efforts to publicize the importance of reporting race/ethnicity data to enforce civil rights. SOURCE: Wyly and Holloway, 2002. FIGURE 4.1 Share of home mortgage applications in Atlanta, Georgia without race-ethnicity information, 1999. Pattern confirms that nondisclosure rates are highest in predominantly African-American neighborhoods. SOURCE: Wyly and Holloway, 2002.

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BOX 4.2 GIS and Section 8 Housing Choice The Section 8 tenant-based housing assistance program provides subsidies that allow low-income families to live in higher-quality private-market rental housing. This approach aims to more closely match housing preference to provision, and to increase opportunity beyond what is typically available near public housing, which is frequently located in high-poverty neighborhoods. Current research shows that some Section 8 families live in poorer and racially segregated neighborhoods (Turner et al., 1999). One possible cause of this concentration is the lack of sufficient counseling about rental housing. 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. The prototype uses seven categories of objective indicators across the metropolitan area of Pittsburgh, Pennsylvania, for multi-criteria analysis. These include: Availability of high-level entry-level employment Availability of affordable housing Public transit accessibility to jobs ocial services support Quality of education Public safety Local amenities and demographic characteristics Quality of life The HRA prototype contains more specific indicators of these seven broad categories. Most of these data were already widely available from local, state, and federal sources. The role of the housing counselor would be key to bringing such a prototype into practice and to assisting the Section 8 family to determine the best indicators for their decision. When the criteria are entered into this system, the local areas are correlated to these criteria based on user preference, and alternative destinations can be ranked by these preferences. GIS allows the information to be displayed visually and functions as a decision-support tool. (See Figure 4.2.) SOURCE: Johnson, 2002.

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FIGURE 4.2 Map of Allegheny County, Pennsylvania municipalities. Relocation areas that meet the hypothetical user criteria are marked in gray. SOURCE: Johnson, 2002. Poverty Concentration and Racial Segregation It is said that rising tides lift all boats, but there are clear winners and losers in the economic boom of the late 1990s. Research is needed to address the underlying causal mechanisms and consequences of the economic and social factors that result in poverty, segregation, homelessness and other urban ills. Box 4.2 provides an example of how GIS can be used to analyze poverty concentration. Data from 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. New multiple-scale segregation measures using GIS (Wu and Sui, 2001), as well as theoretical perspectives such as Sen’s entitlement theory (Sen, 1976), can be applied to these efforts to explain the housing situation of the urban poor. In addition, the growing urban digital divide—the existence of the information-rich and the information-poor— should be taken into account.

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FIGURE 4.3 HUD-assisted housing relative to the location of employment, 1996, Portland (OR-WA) MSA. Assisted housing includes all types of HUD-assisted housing. 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. Research sheds light on the effects of neighborhood concentration of poverty on teenage pregnancy and dropping out of school (Crane, 1991), and on a range of other outcomes, such as crime, cognitive skills, and labor market success (see Jencks and Mayer, 1990 for a review). In large part, this research has helped to motivate and shape HUD’s Moving to Opportunity Program,3 a 10-year research demonstration project providing tenant-based rental assistance and housing counseling to assist very low-income families to relocate from poverty-stricken urban areas to less poor neighborhoods. It has also aided the 3   The final report of this study, Families in Transition: A Qualitative Analysis of the MTO Experience, is available on-line at <http://www.huduser.org/publications/pdf/mtoqualf.pdf>.

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FIGURE 4.4 HUD-assisted housing relative to major employment concentrations, Portland (OR-WA) MSA Assisted housing includes all types of HUD assisted housing; Employment concentrations were demarcated from maps of employment distribution by traffic analysis zones. SOURCE: Thompson and Sherwood, 1999. Gatreaux project, which provided portable vouchers, mobility counseling, and housing location assistance to 7,100 families residing in Chicago’s public housing to assist their moving to private housing, mostly in Chicago’s suburbs. Moving to Opportunity and the Gatreaux project have been closely studied in an attempt to document the effects of moving public housing residents into suburban environments (Rubinowitz and Rosenbaum, 2000). Isolating neighborhood effects statistically from individual and broader economic and social context effects has proven difficult. Although the Panel Survey of Income Dynamics4 provides a valuable national source of information for longitudinal analysis of the socioeconomic conditions of persons and households, the sampling frame for the survey was not designed for the analysis of neighborhood effects, and geographic clustering within the survey is insufficient to identify neighborhood effects. A major new data initiative 4   <http://www.isr.umich.edu/src/psid/>.

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Variations in inter-regional migration streams that influence demand for housing; and Local policies, for example, zoning and land use regulations that affect the responsiveness of local housing supply to changes in market. BOX 4.3 Application of GIS to the Assessment of Housing Conditions in Dallas, Texas In 1993, the city of Dallas Department of Housing and Neighborhood Services commissioned a study of housing conditions within the city. The objectives were to assess the physical condition of the city’s housing stock; to estimate the costs required to bring all substandard housing to standard condition; to describe the demographic characteristics of the population; and to provide reliable, empirical data upon which housing programs and strategies could be designed. The city wanted to be able to identify the geographic areas in which substandard housing was concentrated and in which substandard housing was likely to increase. The data collection effort included surveys (a reconnaissance survey; a household survey; and mail, telephone, and in-person surveys) and physical inspections by trained building inspectors of a sub-sample of housing units. 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. The final set of independent variables used in the development of the cost estimates included: deterioration ratings, a neighborhood deterioration score, a dummy variable for frame construction, age of building, and number of violent crimes. The adjusted R2 was 0.59, and the coefficients were statistically significant at the 5 percent confidence level. Using the regression model, and applying the model to the full sample and then to the full housing stock of the city, at a parcel level, cost estimates for renovation were generated. The predicted cost for bringing the entire housing stock of the city of Dallas up to the HUD standard was estimated as just over $900 million, representing slightly less than 4 percent of the total residential property value of the city in 1993. The costs were disproportionately represented by single-family structures for which renovation costs were estimated as $783 million, or 4.4 percent of single-family appraised value for the city as a whole. Multi-family housing renovation would cost $124 million, or 2.4 percent of the appraised value of multi-family structures in Dallas.

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By integrating spatial analysis and visualization using GIS techniques, this analysis of housing conditions was carried out at a parcel level of detail. Figures 4.5 and 4.6 depict the predicted conditions of the single family and multi-family housing stocks, at a parcel level, and reveal spatial clustering that warrants closer attention by the city. 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-effective way that facilitates timely intervention. SOURCE: Waddell, 1994. FIGURE 4.5 Estimated renovation cost as a percent of property value for single-family housing, City of Dallas, 1993. SOURCE: Waddell, 1994.

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FIGURE 4.6 Estimated renovation cost as a percentage of property value for multi-family housing, City of Dallas, 1993. SOURCE: Waddell, 1994. A systematic effort on the part of PD&R to monitor metropolitan housing market conditions and trends would, in part, duplicate monitoring activities by private real estate market research firms, at least in many metropolitan areas. 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 on 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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subscribers. HUD could develop cost-effective and reasonably accurate monitoring systems by partnering with market research firms, or in some cases non-profit agencies or university research centers that deal with real estate and community planning. 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. At a minimum, these metropolitan housing market indicators would include: Vacancy rates by type of housing, Sales prices and rents by type of housing, New construction, conversion, and demolition of units by type, Drivers of housing market conditions and trends including: Employment change by sector, Changes in wages and their distribution by sector and occupation, Population change by type, Interest rates, costs of construction, tax policies. Cost burdens for renters and owners at various income levels. Sub-metropolitan spatial detail (resolution) is vital to understanding the dynamics of metropolitan housing markets. Some form of sub-market definition that differentiates by type of housing and by geographic market area will significantly enhance the usefulness of a monitoring system for housing conditions and trends. Hedonic Analysis of Quality-Controlled Prices In monitoring sales prices or rents, the typical approach is to report median prices and rents from recent transactions, and compare them with the preceding month or with the same month in the previous year. This approach provides useful information, but does not adequately control for the changing composition of the housing stock reflected in the transactions, which might account in large part for observed changes in prices. In other words, median

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prices may rise because larger houses (or better quality houses, or those in more desirable locations) are being built or are sold as market conditions change, rendering the median transaction price a potentially misleading indicator of housing prices. A more effective indicator of prices or rents is the use of a hedonic, or quality-controlled, housing price index (Waddell et al., 1993). Hedonic analysis examines prices and demand for individual sources of pleasure so that it can used to understand the relationship between quality and price. Such analysis can be used to analyze housing markets and to understand the relationships among rental prices and the characteristics of structures, such as size, number of bedrooms, lot size, and quality of construction, in addition to the locational characteristics that so influence housing values. Using hedonic analysis, trends in price over time can be estimated in a way that holds constant the other factors in the analysis. Many of the factors that affect housing markets are spatial. It is said of real estate that what matters most is location. A significant advantage of hedonic analysis is that it can readily incorporate substantial spatial detail, allowing analysis of how various locational factors influence the housing market. Spatial factors include location of public housing, concentration of poverty, crime patterns, access to transportation, and amenities (e.g., parks, supermarkets, libraries) and disamenities (e.g., waste sites, heavy industry, abandoned parcels) that vary spatially. 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. Valid analytical methods are needed to make such inferences and carry out such analyses. HUD can use GIS-supported analysis agency-wide for program monitoring and oversight including self-assessment and continuous improvement, internal quality assurance procedures, customer satisfaction, community and resident involvement, and the cost-effectiveness and affordability of programs as recommended in the NAPA report on HUD-assisted public housing programs (McDowell, 2001). Specifically, research is needed to develop analytical methods to allow HUD and its constituents to:

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Use results from monitoring of housing conditions and trends within particular metropolitan areas to infer potential effects on low income communities and residents; Assess the effects of these conditions and trends on the viability of specific HUD or local community investments and policies; Assess, in turn, the effects of HUD or community investments and policies on these conditions and trends; and Evaluate the effects of HUD or community investments on specific outcome measures of interest (e.g., segregation, crime, or community involvement such as voluntary associates per capita or per capita hours of participation in community-based activities [NRC, 2002c]), holding constant the effects of background market conditions and trends. Conclusion: HUD needs internal spatial analysis capabilities and a systematic approach to monitoring metropolitan housing market conditions and trends in order to help local governments and nongovernmental groups develop their policy analysis capabilities. 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. In this section, the committee outlines research priorities for HUD in the agency’s efforts to use geographic information to address urban and housing issues. The committee also offers ideas for research in support of national security needs and trends in communications and other technology development that are not among the research priorities but are issues that may affect urban

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development. To provide strategic guidance for the agency, the committee underscores the importance of an agency-wide GIS and identifies research areas that will contribute to the HUD’s internal management and program assessment, and promote the agency’s broader mission goals. Conclusion: An agency-wide GIS can be used to examine urban issues and housing trends across multiple geographic scales from neighborhood to region, and at different levels of spatial resolution in a metropolitan, regional, or international context. HUD can work with internal datasets and with those produced by partners; investigate the spatial structures and social processes at work in a metropolitan or regional context that underpin many community concerns with housing and investment; and engender participation among partners with interests in policy analysis, research, and community building. 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. Table 4.1 presents examples of current work and opportunities for the application of geographic information using GIS at HUD. The entries in the table are potential GIS applications for HUD that require GIS tools for support. HUD and Homeland Security Since HUD has custody of much valuable detailed information related to communities across the country, the committee believes that the agency can play an important role in various new federal initiatives dealing with homeland security. Issues in which HUD can play an important role using GIS include efforts to:

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Understand the particular vulnerability of highly populated urban areas with important government, commercial, military, or historic buildings; develop better methods of identifying the most vulnerable communities; and prepare plans to respond to the potential attacks; Understand the role of GIS in the context of homeland security issues, for example, research on regional processes including border issues and trans-border process such as those at work in the colonias along the U.S.–Mexico border; and Ensure that local-level data are available for integration in homeland security’s information base. 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. Yet, new technological innovations in telecommunication (mobile phone, wireless communication) and computer technology during the past 5 years were unprecedented and their effects on society in general, and housing and urban development in particular are still unknown. The committee offers the following suggestions for research questions: How will mobile phone and wireless communication shape metropolitan development in the United States? Although there is a huge literature on telecommuting, we know little of the consequences of telecommuting. What are the effects of telecommuting on urban development? To what extent is technological innovation responsible for the growing gap between information-rich and the information-poor? What role should HUD play in narrowing the growing digital divide? Are technological innovations moving us closer to the goals of urban sustainable development or further away from them? How can we make future urban and housing development more energy efficient and less material intensive?

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TABLE 4.1 Cross-Reference HUD Responsibilities and Potential Application Categories Potential GIS Application Categories Conduct research for policy development Use research to support program offices Collect and Distribute Data to HUD and Partners Home ownership for low-income and minorities Study impact of housing policies; home mortgage discrimination; mortgage distribution; home ownership and demographic information for determination of fair-market rent and purchase price. Support research needs for: Implementing the Blueprint for American Dream; Distributing FHA and other HUD-insured lending programs; and Identifying potential Home Ownership Zones. Provide data on neighborhood characteristics including schools, transportation, access to health care to allow more informed choice for disadvantaged groups. Colonias on Southwest U. S. border Determine the extent of Colonias’ development and associated lack of infrastructure Inform decisions addressing: Allocation of CDBGs; Affordable housing needs in the Southwest region; and Needs of Colonias field offices. Link state and local data to info from aerial photographs. The program needs to include data from other agencies such as DOT, HHS, EPA and Census. Growth management Promote regional and metropolitan land supply GIS database development Assist communities with fiscal policy based on multi- scale analysis; Guide CDBGs to counter sprawl; and Support state-wide programs with geographic information to evaluate opportunities for affordable housing developers. Promote inter-organizational and public participation in land use decision making, collect and maintain new building permit data.

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Data integration Create comparable data for comparative analysis Bring practitioners, policy makers, and citizens to same page through maps when planning Urban Empowerment Zones. Provide Web-based GIS as data integration engine Analysis of Neighborhood Change Identify spatial statistical relationship between and among variables Support the research needs of fair housing assistance programs; Determine spatial concentration of Section 8 voucher recipients; and Evaluate Hope VI effectiveness. Integrate parcel-level data from multiple metropolitan areas for comparative analysis Public Housing (Low income and minority) Perform multi- scalar analysis to understand neighborhood change Inform decisions about siting subsidized housing; Identify Section 8 housing options within vicinity; and Enhance PHA performance assessments. Make data available at finest resolution Web site as Data Warehouse Provide information to broaden the range of choice in residential location for low- income groups Provide common data formats; and Accommodate vertical and horizontal data sharing. Make it easier to maintain and provide data as needed Partnership Building Broaden the agencies research agenda through collaboration with other agencies Identify spatial barriers to employment for residents of subsidized housing. Share data among federal, state, local groups, promote data interoperability Cell entries represent potential GIS application topics.

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SUMMARY To better understand regional- and metropolitan-level urban processes and the urban housing market, HUD should spatially enable their research portfolio by incorporating GIS into HUD research across the agency. Support is essential for local governments and other users at the local level to develop capability in spatial analysis. 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.