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Summer 2013   

    HIGHLIGHTS IN THIS ISSUE:

        Preserving Affordable Rental Housing: A Snapshot of Growing Need, Current Threats, and Innovative Solutions
        How Research Tools Are Assisting Communities To Preserve, Plan Affordable Housing
        Models for Affordable Housing Preservation


How Research Tools Are Assisting Communities To Preserve, Plan Affordable Housing

Highlights

      • Spatial mapping programs can help policymakers get a more comprehensive view of an area's affordable housing stock to better target preservation efforts.
      • The NYU Furman Center's Subsidized Housing Information Project combines 50 disparate government and public datasets to catalog privately owned, publicly subsidized affordable rental properti es developed in New York City.
      • The Shimberg Center at the University of Florida's Housing Suitability Model scores each land parcel in a county or metropolitan area using various physical, land use, transportation, and neighborhood characteristics to identify preferable locations for affordable housing development and preservation.


In recent years, research advances have greatly enhanced public and private efforts to preserve affordable housing. Several housing and urban policy research centers have used multilayered databases and sophisticated mapping tools to better understand the nation's current stock of privately owned and publicly subsidized housing.1 Integrated datasets, which layer property-specific subsidy data as well as other housing and neighborhood level indicators, are clarifying the nature of units subsidized by local, state, and federal programs. In addition, data layering has improved the ability of policymakers, planners, and nonprofit organizations to assess challenges to the existing subsidized housing stock, enabled more effective tracking of how many units have left affordability programs, and identified which properties will soon be eligible to withdraw from their affordability programs.2 Researchers are also using integrated databases of affordable rental stock at the local level to build geographic information system (GIS) models, which enable spatial analysis of affordable housing's proximity to employment, transit, and other community amenities. Identifying the spatial relationship of affordable housing to locations of opportunity for low-income households is critical for local communities, planners, and policymakers attempting to implement sustainable community initiatives.

This article examines the Subsidized Housing Information Project (SHIP) developed at New York University and the Housing Suitability Model (HSM) built at the University of Florida, situates these projects within the academic literature about spatial mismatch theory, and explores the implications of publicly accessible layered databases and mapping models for the future of affordable housing preservation.

The Need for the Subsidized Housing Information Project

SHIP is a comprehensive, publicly accessible database developed by New York University's Furman Center for Real Estate and Urban Policy. The database combines 50 disparate government and public datasets to catalogue privately owned, publicly subsidized affordable rental properties in New York City.3 SHIP includes information on nearly 235,000 subsidized units financed through HUD's financing and insurance programs, HUD's Project-Based Rental Assistance (PBRA) program, the city- or state-sponsored Mitchell-Lama program, and the federal Low-Income Housing Tax Credit Program. SHIP was created in response to a preservation capacity assessment funded in 2007 by the MacArthur Foundation. The assessment found that a major challenge to preserving the affordability of New York City's subsidized properties was the absence of a single independent and objective source of data on all properties. Before SHIP, subsidy information was housed in individual agency databases — often in multiple databases — which made it difficult "for agencies, tenants, and community organizations and leaders to obtain the comprehensive and up-to-date information about subsidized properties they need to identify properties that may leave affordability programs."4 Standardizing these data was difficult because properties subsidized by local, state, and federal programs were not only housed in separate agencies but also subject to disparate forms of property identification.5 SHIP's developers had to standardize the spelling and punctuation of all property addresses, merge properties across portfolios, and standardize the data that each agency attached to its subsidies.6 An advantage of this effort is that SHIP can track multiple funding sources associated with a single property.7

Why Layered Data Matters

The ability to layer local, state, and federal subsidy data about individual properties is invaluable for those working to preserve affordable housing. Layered datasets such as SHIP reveal how rental subsidies interact to keep properties affordable, allowing researchers and practitioners to better understand what properties are at risk of leaving affordability programs. For example, the Furman Center's first comprehensive analysis using SHIP revealed that 15 percent of New York City's currently affordable properties are subsidized through multiple programs, and more than 50 percent of PBRA properties have an additional subsidy.8 In fact, every affordable property in SHIP receiving multiple subsidies has either PBRA or HUD financing and insurance that requires affordability.9 This finding illustrates a key conclusion about privately owned, publicly subsidized rental housing in New York City: state and local financing programs consistently leverage HUD resources.10 Deriving such a conclusion would have been nearly impossible before SHIP; manually sifting through all of the financing layers on any given property would have been too onerous a task.11


The Data Search Tool from the Furman Center. Image courtesy: Furman Center for Real Estate & Urban Policy, New York University
Multilayered databases such as SHIP are producing critical findings about the nature of subsidized units that have already left affordability programs and units that are at the greatest risk of opting out. Subsidy layering, for example, helps researchers accurately assess opt-out risk simply by counting the units that have exited one of the subsidy programs. According to SHIP, a single-layer analysis would have concluded that 108,402 units were in developments that no longer receive a subsidy. SHIP's multilayered analysis, however, reveals that only 62,000 units are in developments that no longer receive a subsidy.12 By compiling which properties are operating under renewable or nonrenewable contracts into a single database, SHIP has the potential to target units that will soon be eligible to exit affordability restrictions and prioritize them for preservation. Although some units will permanently leave the affordable housing stock, others will remain affordable through another program. Since 2000, for example, 106 properties containing 24,173 units expired from one subsidy program tracked by the SHIP but were required to remain affordable through another program.13

In addition to collecting property-specific information about subsidy layers, the Furman Center integrated more than 360 neighborhood-based indicators into SHIP. These metrics of neighborhood health include detailed information from the physical and financial condition of properties to changes in local market and neighborhood characteristics. In addition to the maps generated for its published studies, the Furman Center offers a Data Search Tool on its website to view the relationships between neighborhood indicators and affordable housing locations. The Data Search Tool allows visitors to select a range of variables to create customized maps and downloadable tables (including xy coordinates for exporting to GIS) as well as track trends over time.14 Users can overlay SHIP data onto their generated maps, allowing government agencies, community organizations, and housing developers to analyze the effects of affordable housing properties. Along with identifying preservation opportunities across the various subsidy programs, SHIP's comprehensive overview of New York City's subsidized housing is informing research about the spatial relationships connecting the locations of subsidized housing with a broad array of community indicators.15

Putting Affordable Housing Into a Spatial Context

By situating subsidized properties in the context of other neighborhood-level indicators, SHIP allows users to compare different types of subsidized housing and their distribution throughout the city. Visualizing this comparison yields greater insight into affordable housing's role as a platform for improving the well-being of residents and achieving other community goals.16 Analyzing the location of affordable housing units relative to locations of interest, especially access to transit, employment, and other neighborhood amenities, has been a major research focus ever since the spatial mismatch hypothesis was first advanced by John Kain in 1968.
SHIP allows users to analyze the location of affordable units relative to transit access, employment, and other neighborhood amenities.
17 According to this hypothesis, the negative effect of segregating low-income residents in the city center is magnified by the decentralization of jobs.18 The spatial mismatch of housing and jobs means that low-income workers, because of their constrained mobility, have trouble finding affordable housing near locations with employment opportunities.19 Lower-income workers also face higher search costs because their commuting costs are high relative to their wages and because of the inefficiency of a long-distance job search.20 Empirical evidence has largely supported this hypothesis.21 According to a May 2011 report from the Brookings Institution, "about one-quarter of jobs in low- and middle-skill industries are accessible via transit within 90 minutes for the typical metropolitan commuter, compared to one-third of jobs in high-skill industries."22

The spatial mismatch hypothesis has helped researchers expand the definition of affordable housing to encompass the combined cost of housing and transportation relative to household income. The Center for Neighborhood Technology, for example, advanced this more comprehensive understanding of affordability through its Housing + Transportation Affordability Index, which calculates the transportation costs associated with a home's location in 337 metropolitan areas.23 Policymakers have also cited the critical connections among affordable housing sites, job locations, transportation systems, and environmental goals when making the case for building sustainable communities.24 Current research takes advantage of specialized mapping software such as GIS to analyze the best locations for developing and preserving affordable housing in relation to these other social variables.25

Housing Suitability Model


Source: Shimberg Center for Housing Studies, University of Florida.
The Shimberg Center for Housing Studies at the University of Florida specializes in geospatial modeling of suitable locations for affordable housing.26 Like the Furman Center, the Shimberg Center maintains a multilayered database known as the Assisted Housing Inventory (AHI), which tracks 252,000 affordable rental units in Florida subsidized by local, state, and federal sources. In addition to AHI, the Shimberg Center manages the Lost Properties Inventory (LPI), a complete dataset of affordable multifamily rental units in Florida formerly subsidized by different federal, state, or local programs. LPI reveals that between 1993 and 2012 Florida has lost approximately 525 multifamily rental properties with 70,774 units, including over 52,000 affordable units.27 Like the Furman Center, the Shimberg Center received a MacArthur Foundation grant to use AHI to expand its analysis of Florida's privately owned, publicly subsidized rental stock. This funding, along with support from Wells Fargo, led to the development of the Housing Suitability Model (HSM), a GIS-based tool for identifying locations suitable for affordable housing development and preservation. HSM, which was developed in partnership with the University of Florida's Department of Urban and Regional Planning and GeoPlan Center, scores each land parcel in a county or metropolitan area using various physical, land use, transportation, and neighborhood characteristics.28 The model is based on "suitability analysis," which is "the process of determining the fitness, or the appropriateness, of a given tract of land for a specified use."29 Suitability analysis, which can be used to evaluate the siting of both current and planned affordable housing locations, relies on multilayered mapping and is based on the following premise:

"Fitness" depend on the combination of different determinants that are represented on individual maps that are then overlaid. While each layer provides key information, visualizing their synthesis through the superimposition allowed by suitability analysis produces entirely new knowledge that is difficult to figure out just by analyzing each individual factor.30

HSM's strength lies in its ability to show locations where positive attributes overlap and conflicting characteristics coincide, enabling comparisons and identifications of tradeoffs.31 HSM layers spatial characteristics related to four major components: residential suitability, rental housing costs, driving costs, and transit accessibility. Residential suitability includes three subcomponents: physical infrastructure and environment, neighborhood characteristics, and neighborhood accessibility. Data sources include parcel-level property and sales characteristics from county property appraisers, transit system maps, the U.S. Department of Transportation's National Household Travel Survey, the U.S. Census Bureau's Longitudinal Employer-Household Dynamics program, and other parcel- and neighborhood-level data made available through the Florida Geographic Data Library.

Each parcel in the geographic area covered by the model receives a suitability score for each component. For example, an affordable housing location might score well on rental housing costs based on the ratio of rent to income on its block; it might also earn a high transit accessibility score if there are nearby transit stops providing access to employment opportunities. However, that same location might score poorly on residential suitability if the surrounding neighborhood has concentrated poverty, a high risk of crime, and residents with lower education levels. The Shimberg Center studies that use HSM have important implications for the affordable housing rental stock in the Florida counties analyzed to date: Orange, Duval, Pinellas, DeSoto, Glades, Hardee, Hendry, Highlands, Okeechobee, Polk, Lake, Osceola, Seminole, and Volusia.32

The Suitability Model in Practice

The goal of HSM is to evaluate new and existing affordable housing sites according to their access to jobs and transit, driving costs, physical environment, and neighborhood socioeconomic characteristics.33 To that end, Shimberg Center researchers have used the model to generate a series of papers about the suitability of affordable housing locations in Florida. Some major highlights from the research include the following:

  • In the five counties of central Florida serviced by the LYNX bus transit system (Orange, Osceola, Seminole, Volusia, and Lake), 75 percent of assisted housing developments have poor access to major employment centers (those employing 100 or more) that are within walking distance to the bus system (defined as an 800-meter walking network buffer).34

  • In Orange County, Florida, a spatial mismatch exists between available employment opportunities and the location of affordable housing. Using housing demand and supply analysis, Shimberg found that most AHI units are located more than a half-mile from employment locations. This finding is especially important because assisted housing residents must bear higher commuting costs to reach employment locations that are 4 miles or more from their homes. According to researchers, this analysis provides tools "that can be used by the housing finance corporations to evaluate the locations of funded properties as well as evaluating the suitability of new sites suggested by developers."35

  • Assisted housing units in Orange County, Florida, score higher than the average of all parcels in the county, including residential, nonresidential, and vacant, on factors related to accessibility: infrastructure and environment, neighborhood accessibility, residential suitability, driving cost, and transit accessibility. These units score worse, however, than the average of all parcels in the county on neighborhood characteristics, reflecting difficult socioeconomic conditions. According to Shimberg researchers, this difference is related to the "urban trade off: central locations are more accessible and present better infrastructure but at the same time they tend to concentrate poverty, crime, and low education attainment."36 This assertion may only represent local conditions, and its relevance to other U.S. urban areas deserves greater exploration. Recent research on the changing geography of U.S. poverty, particularly the growing suburbanization of poverty, reflects a new understanding of the links between poverty and place in metropolitan areas.37

  • In Orange County, Florida, affordable housing units subsidized by HUD perform better in components related to infrastructure, neighborhood accessibility, and transit than do units subsidized by other programs because of their central locations. On the other hand, units funded by the Florida Housing Financing Corporation (FHFC), which relies on low-income housing tax credits, have high scores in neighborhood characteristics but only medium scores for both accessibility and infrastructure and environmental conditions, reflecting their dispersed spatial pattern oriented to second-ring suburbs.38 Shimberg researchers suggest that this difference in spatial pattern is the result of the "relative composition of the assisted stock according to decade."39 HUD properties were developed largely in 1970s and 1980s, reflecting "more central development patterns for the Orlando area," whereas FHFC properties were developed primarily in the 1990s and 2000s as population growth moved outward from the city center."40 However, FHFC properties developed later in the 2000s, as the agency began providing more transit accessibility incentives in its funding process, do show an increasing tendency toward transit access.

  • Properties that have left affordability programs in Orange County, Florida, display better suitability conditions than do properties that remain subsidized. This finding poses a challenge to public policy, according to Shimberg researchers, because "improving the accessibility and socio-economic conditions of the assisted stock can represent its failure in the long term as more properties become at risk to opt-out from contracts and income and rent requirements."41 This phenomenon, also explored by the Furman Center in its analysis of the factors leading owners to opt-out rather than renew subsidies, represents a potential debacle for affordable housing preservation efforts.42 These findings underscore how important it is for policymakers to add a "preservation dimension... to the necessary efforts to improve the stock."43

The research findings generated through HSM are valuable for crafting affordable housing policy. State housing finance corporations, for example, can better evaluate the locations of funded properties as well as the suitability of new sites proposed by developers.44 Transit planners can more efficiently locate transit stops and improve route and service delivery to maximize housing and employment opportunities.45 HSM can also inform community-driven planning processes around long-term sustainable development. The Shimberg Center is currently using this model to help planning councils in Florida anticipate future affordable housing needs as well as analyze existing affordable properties as part of two HUD Sustainable Communities Regional Planning Grant initiatives.46 The Shimberg Center is employing similar GIS-based, parcel-level tools to help Neighborhood Housing Services of South Florida identify concentrations of distressed, small, and midsized rental properties for possible stabilization and preservation. This model holds great potential for practitioners of affordable housing preservation — both for those seeking to stabilize and improve the current stock of assisted housing and those planning future sites for affordable properties.

Looking Ahead


Map of suitability per component of the HSM (Transit Accessibility). Image courtesy: Shimberg Center for Housing Studies, University of Florida
Innovative affordable housing preservation tools developed at the local level by two leading academic research centers are providing new opportunities for those with a stake in affordable housing preservation. The implications of this work are profound for both the communities that are the objects of these research efforts and other communities that could benefit from their use. In New York City, government agencies and nonprofit organizations interested in preserving affordability can use SHIP's nuanced analysis to determine where to focus their preservation efforts.47 Publicly accessible, multilayered databases such as SHIP could enable other communities to track at-risk subsidized housing units and identify new opportunities for preserving affordability. Such targeted efforts are essential for working within the resource constraints facing all levels of government. Layered datasets such as SHIP and AHI will become even more critical as older subsidies expire and new ones are utilized to finance future affordable housing. Although this article has focused only on the development of layered databases at the local level, efforts are underway at the national level to collect comprehensive data and create national preservation databases. The National Low Income Housing Coalition partnered with the Public and Affordable Housing Research Corporation to launch a national preservation database in November 2012 that includes data on 4.5 million units in more than 75,000 federally assisted properties.48

Research efforts aimed at incorporating layered datasets into mapping tools are being used to evaluate the location of affordable housing in relation to other sustainability priorities, such as employment centers, transit stops, and other neighborhood amenities. Spatially representing the linkages among these opportunities through GIS-based mapping tools is enhancing community-driven planning efforts. Communities can use a tool such as HSM to ensure that policies related to housing, community development, energy efficiency, and transportation are well coordinated. By visualizing the tradeoffs between these policy goals, communities can better understand the ways some of their priorities conflict and others coincide. Geospatial models are also critical for the development of new affordable housing, especially because many states promote sustainability by emphasizing proximity to public transit when assessing which assisted housing developments to fund.49 In addition, GIS tools such as HSM can improve the siting of new units by identifying and mapping underutilized parcels that may be suitable for housing development in addition to mapping expiring use properties close to transit.50 Great potential exists for broadening HSM's impact beyond the Florida counties that have already been assessed. Enhanced mapping tools such as HSM will improve local decisionmaking processes about affordable housing by providing communities with more useful, accessible information.



  1. This article focuses on privately owned, publicly subsidized properties, not on other affordable housing development strategies such as public housing or tenant-based vouchers.
  2. Vincent Reina and Michael Williams. 2012. “Importance of Using Layered Data To Analyze Housing: The Case of the Subsidized Housing Information Project,” Cityscape: A Journal of Policy Development and Research 14:1, 221.
  3. For more information about the Furman Center, please visit www.furmancenter.org.
  4. Jaclene Begley, Caitlyn Brazill, Vincent Reina, and Max Weselcouch. 2011. “State of New York City’s Subsidized Housing: 2011,” Furman Center for Real Estate and Urban Policy.
  5. The local challenges of standardizing parcel-level information about subsidized properties into a single database are similar to the data collection barriers of building a national database of standardized parcel-level data collected from local counties. See: Department of Housing and Urban Development. 2013. “The Feasibility of Developing a National Parcel Database: County Data Records Project Final Report.”
  6. Reina and Williams, 217.
  7. Ibid.
  8. Begley et al.
  9. Ibid.
  10. Reina and Williams, 218.
  11. Ibid.
  12. Ibid., 219.
  13. Ibid.
  14. For more information about the Furman Center’s Data Search Tool, please visit www.furmancenter.org/data.
  15. The Furman Center produces an annual report, “The State of New York City’s Housing and Neighborhoods,” which examines statistics on housing, demographics, and quality of life in New York City, its 5 boroughs, and 59 community districts. For more information, please visit furmancenter.org/research/sonychan/.
  16. See HUD’s 2010–2015 Strategic Plan, Goal 3: Utilize Housing as a Platform for Improving Quality of Life (portal.hud.gov/hudportal/documents/huddoc?id=HUDStrategicPE_goal3.pdf) and remarks by HUD Secretary Shaun Donovan to the Local Initiatives Support Corporation 2009 Conference, 4 November 2009: “It comes down to a fundamental belief that I know we all share: That when you choose a home, you don’t just choose a home. You also choose transportation to work — schools for your children, public safety. You choose a community — and the choices available in that community. A belief that our futures should never be determined — or our choices limited — by our zip code.” (portal.hud.gov/hudportal/HUD?src=/press/speeches_remarks_statements/2009/speech_11042009). Accessed 18 July 2013.
  17. Laurent Gobillon, Harris Selod, and Yves Zenou. 2007. “The Mechanisms of Spatial Mismatch,” Urban Studies 44:12, 2401.
  18. Keith Ihlanfeldt. 1993. “The Spatial Mismatch Between Jobs and Residential Locations Within Urban Areas,” Cityscape: A Journal of Policy Development and Research 1:1, 219–44.
  19. Ibid., 229.
  20. Gobillon et al., 2408.
  21. Ihlanfeldt, 224.
  22. Adie Tomer, Elizabeth Kneebone, Robert Puentes, and Alan Berube. 2011. “Missed Opportunity: Transit and Jobs in Metropolitan America,” Metropolitan Policy Program at Brookings Institution. (www.brookings.edu/research/reports/2011/05/12-jobs-and-transit). Accessed 18 July 2013.
  23. Center for Neighborhood Technology. 2010. “Penny Wise Pound Fuelish: New Measures of Housing + Transportation Affordability” (www.cnt.org/repository/pwpf.pdf). Accessed 19 July 2013.
  24. See HUD’s 2010–2015 Strategic Plan, Goal 4: Build Inclusive and Sustainable Communities Free From Discrimination, Subgoal 4B: Promote energy-efficient buildings and location efficient communities that are healthy, affordable, and diverse. Walkable, transit-oriented, mixed income, and mixed-use communities — coupled with a strong commitment to energy-efficient and affordable green building — substantially reduce transportation costs, create energy savings, reduce greenhouse gas emissions, and enhance the health and well-being of all residents.” (portal.hud.gov/hudportal/documents/huddoc?id=HUDStrategicPF_goal4.pdf).
  25. Amy S. Bogdon and Ayse Can. 1997. “Indicators of Local Housing Affordability: Comparative and Spatial Approaches,” Real Estate Economics 25:1, 45.
  26. For more information about the Shimberg Center, please visit www.shimberg.ufl.edu.
  27. “Lost Properties Inventory,” Shimberg Center for Housing Studies website (www.flhousingdata.shimberg.ufl.edu/a/lpi). Accessed 1 August 2013.
  28. For a fuller description of the data used by HSM to develop scoring and weighting measures, see: Ruoniu Wang, Andres Blanco, Jeongseob Kim, Hyungchul Chung, Anne Ray, Abdulnaser Arafat, William O’Dell, and Elizabeth Thompson. 2012. “Evaluating Suitable Locations for the Development and Preservation of Affordable Housing in Florida: The AHS Model,” Shimberg Center for Housing Studies.
  29. Frederick Steiner. 1983. “Resource Suitability: Methods for Analysis,” Environmental Management 7:5, 401–20.
  30. Wang et al., 3.
  31. Ibid., 2.
  32. Correspondence with Anne Ray, June 2013. The Shimberg Center constructs the HSM on a county or regional basis, and the comparisons embedded in the model refer to the other places in the region under study rather than an external standard. The model was first piloted in three counties — Orange, Duval, and Pinellas — and has been further developed during the course of assisting two HUD Sustainable Communities Regional Planning initiatives in Florida.
  33. “Connecting Housing, Transportation and Jobs,” Shimberg Center for Housing Studies website (www.shimberg.ufl.edu/index.html). Accessed 18 July 2013.
  34. Elizabeth Thompson, Abdulnaser Arafat, William O’Dell, Ruth Steiner, and Paul Zwick. 2012. “Helping Put Theory Into Practice for Planning Sustainable Communities: A GIS Tool for Measuring Transit Accessibility,” Shimberg Center for Housing Studies.
  35. Abdulnaser Arafat, Yuyang Zou, Andres Blanco, and Ruoniu Wang. 2012. “Allocation and Preservation of Affordable Housing: A Spatially Discriminated Supply-Demand Analysis Based on Parcel Level Employment Assignment,” Shimberg Center for Housing Studies. This study estimates the demand for affordable housing based on a method that uses a road-network-based distance shed around each residential parcel to capture the parcel-level employment. Geospatial modeling is used to estimate the supply based on the current locations of assisted and affordable housing.
  36. Wang et al., 10.
  37. Elizabeth Kneebone and Alan Berube. 2013. Confronting Suburban Poverty in America. Washington, DC: Brookings Institution Press.
  38. Ibid., 12–3.
  39. Ibid., 17.
  40. Correspondence with Anne Ray, June 2013.
  41. Wang et al., 18.
  42. See Begley et al., 19: “In economic booms, property owners have greater incentive to leave subsidy programs because they (or new owners to whom they sell the property) may be able to command higher rents than the subsidy programs allow. High real estate appreciation in the late 1990s and mid-2000s is associated with spikes in program exits in those time periods.”
  43. Wang et al., 18.
  44. Arafat et al.
  45. Thompson et al.
  46. “Affordable Housing Suitability Model,” Shimberg Center for Housing Studies website (www.shimberg.ufl.edu/fl_housingSuitableModel.html). Accessed 18 July 2013.
  47. Interview with Max Weselcouch, June 2013.
  48. National Low Income Housing Coalition. 2012. “NLIHC Launches National Database of Federally Assisted Rental Properties,” press release (nlihc.org/article/nlihc-launches-national-database-federally-assisted-rental-properties). Accessed 18 July 2013. For more information, please visit www.preservationdatabase.com.
  49. Thompson et al., 3. See also: Reconnecting America. 2012. “Locating Affordable Housing Near Transit: A Strategic Economic Decision,” policy brief. The brief explores how state housing finance agencies are using the Low-Income Housing Tax Credit Program to provide developers with incentives to build affordable housing near transit.
  50. U.S. Department of Transportation and U.S. Department of Housing and Urban Development. 2008. “Better Coordination of Transportation and Housing Programs To Promote Affordable Housing Near Transit.”

 

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