Objective 5.3:

Identify priority data needs around affordable housing, infrastructure, and green spaces across varying levels of flood risk at the regional, parish, and municipal levels.

The Need

Policymakers and planners should identify priority data needs to craft credible and useful solutions for affordable housing, infrastructure, and green spaces. Currently, more data are critical to inform legal, planning, policy, and project decisions at the regional, parish, and municipal levels. Some priority issues most relevant to the Regional Vision include the need for more: (1) social and environmental vulnerability assessments; (2) forward-looking and comprehensive data; (3) inclusive data; and (4) resources to translate and make data available for policymakers and the public to support resilience efforts.

As one general note, a lot of this objective features affordable housing more prominently than infrastructure and green spaces. This is not intended to diminish the latter as important priorities for Region Seven. Rather, as illustrated below, there is more history, research, and data in the United States around affordable housing. Therefore, although much of the background and existing challenges presented in this objective focus on affordable housing, it is important to recognize the potential for infrastructure and green spaces to play a similar, but distinct role to build resilience in Region Seven.  

^{Credit: The City of Norfolk, Virginia, Missing Middle Pattern Book 8 (2021), available at https://www.norfolk.gov/DocumentCenter/View/66555/MissingMiddlePatternBook.} 

Social and Environmental Vulnerability Assessments

There is currently a need for more mapping of housing using geographical information systems (GIS) and other platforms to support planning, land-use, and zoning decisions. Currently, many tools only have aggregated data around affordable housing considerations which includes demographic and economic statistics (e.g., what percentage of income people spend on their homes). For example, the Louisiana Housing Corporation created and maintains an interactive mapping tool of the state of Louisiana to assist housing advocates, developers, policymakers, and state and federal elected officials “leverage a wide range of data to make more informed decisions related to affordable housing.”^{See footnote 1} The tool provides data about housing, demographics, environment and social vulnerability (which includes information about FEMA Disaster Declarations of floods, hurricanes and severe storms), and other economic factors.^{See footnote 2} Another example are the Louisiana Housing Fact Sheets created by HousingLouisiana, which includes housing data (e.g., cost-burdened by tenure, median rents and home values, and tenure percentages) about regions in Louisiana. These resources are good starting points for decisionmakers to evaluate housing affordability and demographics. However, more data is needed to support planning, land-use, and project decisions aimed at “greauxing” or growing community resilience. 

However, asset and social vulnerability to flooding and population changes and how these can be determinants of individual, social, and environmental resilience are additional necessary analyses. These types of datasets are currently not available, or if they are, they are not always specific or tailored enough to address priority community needs. For example, a coalition of multiple parishes and municipalities in Region Seven could follow the lead of the Manufactured Home Community Coalition of Virginia and project:HOMES, a nonprofit affordable housing provider in central Virginia. In 2016, the two organizations commissioned An Assessment of Central Virginia’s Manufactured Housing Communities: Understanding the Conditions, Challenges, and Opportunities, a first-of-its-kind report that evaluates the existing conditions of manufactured home parks in the central Virginia region. The report includes an analysis of the socioeconomic status and demographic trends for manufactured home residents. In addition, the report includes a detailed quality survey of more than 50 manufactured home parks across the region. New flood-risk data from Headwaters Economics could be laid on top of base housing assessments like the one from central Virginia to give regional and local policymakers a better picture of how to prioritize resilience actions for existing and new manufactured homes and communities in Region Seven based on social and environmental vulnerability.

Forward-Looking Data

There are publicly available data sources on flood risk, but the data is often not presented in a forward-looking manner that analyzes and predicts future trends and impacts let alone on a scale appropriate for making specific decisions around housing and infrastructure. For example, the Federal Emergency Management Agency (FEMA)’s National Risk Index (NRI) is a mapping tool that can help communities across the United States understand their current relative risk and resilience related to natural hazards. The NRI identifies the relative vulnerability of all counties and Census tracts within the United States to 18 natural hazards. The relative vulnerability is based on detailed data and estimates of expected annual economic losses, social vulnerability, and community resilience. 

The NRI is intended to help governments and other stakeholders set resilience priorities. Policymakers can use the NRI to identify where more refined risk assessments or improvements in building standards or codes may be needed. However, the NRI is backward looking and does not consider discrete land-use factors for housing and infrastructure, among others, that are necessary to greauxing resilience through housing and nature-based solutions. For example, the NRI does not account for future hazard forecasts or climate change and its projected impacts. 

Where forward-looking data is available and connected to housing, it may not be sufficient to answer questions about regional and local resilience. Two examples illustrate this point. In 2020, Climate Central published an article and companion tool assessing affordable housing at risk of coastal flooding due to sea-level rise out to the year 2050.^{See footnote 3} In what Climate Central asserts is “the first nationwide assessment of [sea-level rise] risk to the country’s affordable housing supply,” it is estimated that “By 2050, virtually every coastal state is expected to have at least some affordable housing exposed to more than one ‘coastal flood risk event’ per year, on average — up from about half of coastal states in the year 2000.”^{See footnote 4} In Louisiana, the anticipated number of affordable housing units at risk between 2000 and 2050 increases from 214 to 685, respectively.^{See footnote 5} 

In 2018, another study by the Union of Concerned Scientists evaluated the combined cost of residential and commercial property losses in coastal areas from sea-level rise and flooding.^{See footnote 6} The Union of Concerned Scientists found that an estimated 150,000 existing homes and commercial properties, worth $63 billion, could be exposed to recurrent flooding in the next 15 years.^{See footnote 7} Notably, that risk could double by 2045.^{See footnote 8} Here, the Union of Concerned Scientists focused on properties at risk of chronic flooding, which it defines as flooding at least 26 times a year.^{See footnote 9} 

Among other findings a part of the larger work, the Union of Concerned Scientists pays particular attention to the disproportionate impacts chronic flooding will have on communities with less resources to recover after these events including those with a high poverty rate and/or percentage of Black, Hispanic, and tribal populations.^{See footnote 10} Specific to Louisiana: 

Nearly 175 communities nationwide can expect significant chronic flooding by 2045, with 10 percent or more of their housing stock at risk. Of those, nearly 40 percent — or 67 communities — currently have poverty levels above the national average. The largest share of these is in Louisiana, where there are 25 communities with above-average poverty rates and with 10 percent or more of the homes at risk by 2045.^{See footnote 11}

These recent studies provide forward-looking data that draws attention to the impacts of sea-level rise on coastal homes, including affordable units, and properties. This work marks an important contribution nationally. However, there are several gaps that prevent these and many other similar resources from meeting the ongoing needs of regional and local policymakers in pursuit of the goals contained in the Regional Vision. For instance, these reports only look at one type of flooding in coastal areas to the exclusion of others affecting both coastal and inland communities. Further, they were not prepared at a scale that can be used to inform more granular planning, land-use, and project-specific decisions at the regional and local levels in Louisiana or elsewhere. In addition, this work does not look at the intersecting impacts of sea-level rise and chronic flooding on infrastructure or green spaces, which are critical to comprehensively greaux resilience. 

As the above elaborations suggest, generally, there is a lack of and/or a mismatch between what data on housing, infrastructure, green spaces, flooding, and population changes exist and what parishes and municipalities require to take a data-driven approach to fulfill the Regional Vision. Accordingly, parishes and municipalities in Region Seven will need to develop public-private-community-nonprofit partnerships and invest in generating the right types and scale of data to develop laws, plans, policies, and projects that better prepare communities to adapt to changing conditions. 

Inclusive Data 

Beyond a lack of sufficient forward-looking data, there are a few other priority trends that are worth noting. First, the data presented in tools and reports is more often quantitative rather than qualitative. Quantitative data is quantifiable, numerical data. Conversely, qualitative data is data that can be observed (e.g., through interviews) but necessarily cannot be measured on a numeric scale.^{See footnote 12} A lack of collecting and analyzing quantitative and qualitative data together leads to an absence of informed and holistic decisions around flood resilience. Second, data disproportionately leaves out Black, Indigenous, and People of Color (BIPOC), and low-income communities.^{See footnote 13} These deficiencies fail to paint a full picture of the actual flood risk facing a community and concurrently lead to incomplete decisions. 

Resources to Translate and Make Data Available to End Users

In addition to challenges with risk assessment data, currently, there are not enough tools or examples available for policymakers to translate this data into decisionmaking. Specifically, policymakers and communities require accessible and user-friendly tools, resources, and technical assistance to identify and assess the benefits and tradeoffs associated with different housing and infrastructure options and adaptation tools and strategies to build resilience. 

Beyond parish and municipal governments, public access to data about flood and housing risk is also limited. At present, not enough data is accessible. Creating data tools can also be useful for accountability and transparency purposes as well as monitoring the implementation of plans, laws, policies, or projects. 

Given the foregoing, policymakers in Region Seven and beyond should consider how to better identify priority data needs around affordable housing, infrastructure, and green spaces options to inform legal, planning, and policy decisions at the regional, parish, and municipal levels, especially in BIPOC and low-income communities.

How to Make Progress on This Objective

This part presents four separate, but related elements that regional and local governments can consider to make progress on this objective: 

• Quantitative and qualitative data; 
• Existing sources of data; 
• Assessing future data needs; and 
• Using data to build resilience. 

These elements can be explored separately or together. However, they are all well connected and can build on one another to ideally support more complete and comprehensive decisionmaking efforts. Regardless, at the start of every process, parish and municipal governments should first identify their priority data needs. These priority needs should be guided by the goals and parameters of the relevant decisionmaking effort and informed by community input. This initial step can more effectively shape a landscape assessment of what types and sources of data already exist and where new data is required. When looking at this objective, decisionmakers should also look to Objective 5.2, which looks at flood mitigation and drainage more broadly.

Quantitative and Qualitative Data 

Data-driven decisions around affordable housing, infrastructure, and green spaces should ideally use a mix of quantitative and qualitative data sets. When identifying priority data needs and utilizing data to inform legal, planning, policy, and project decisions at the regional, parish, and municipal levels, quantitative data should be integrated and aligned with qualitative data to enable more comprehensive approaches to greaxing resilience. 

In regards to housing, quantitative data may include data about land use, economic statistics, and demographics. Quantitative data sets are produced by government (e.g., United States Census Bureau), nongovernmental (e.g., Headwaters Economics), and private entities. 

Moreover, qualitative data may include information about people’s cultural and emotional ties to a community and land, perceptions of affordable housing, and stories about the personal impacts of flooding. To gather qualitative data, residents can provide important data points based on their local knowledge and lived experiences to inform vulnerability assessments and identify priority goals and needs. Parish and municipal governments should evaluate strategies and partnerships to work directly with residents and community-based organizations to learn from them in ways that honor and respect their histories and insights (for recommendations and best practices for community engagement generally, see Objective 5.1). 

Data sets may present quantitative and qualitative separately. However, rather than having siloed analyses, quantitative and qualitative data should be analyzed together. This will lead to more informed and holistic decisions around flood resilience. For example, plans and land-use decisions are more credible and effective when they are driven by the best available data for housing, land use, and social and environmental vulnerabilities. To illustrate, manufactured housing is one of the largest sources of unsubsidized affordable housing in the United States.^{See footnote 14} Local plans and ordinances should promote the equitable integration of manufactured and modular homes throughout the flood-safe, residential and mixed-use portions of a rural community. To determine where to build and integrate manufactured and modular homes will require the collection of quantitative data that includes demographic and economic statistics. However, leading with a comprehensive data-driven approach that includes qualitative data can help overcome potential barriers (e.g., misconceptions about affordable housing, maintaining the historical character of a community, or “Not in My Backyard” or “NIMBYISM” bias) to building and integrating manufactured and modular homes, especially in rural areas. 

In 2016, the Oak Leaf Mobile Home Park in the Cully neighborhood of Portland, Oregon was threatened with closure and sale to a residential developer that planned to evict all residents.^{See footnote 15} In order to preserve MHC across the city, a campaign to change Portland’s comprehensive plan and zoning laws, led by the community-based organization Living Cully, resulted in amendments to the City of Portland’s comprehensive plan and the creation of the Manufactured Dwelling Park Zone in 2018. One common misconception held by city officials broadly was that manufactured housing communities (MHC) are simply an undesirable housing option of last resort for most people. Public testimonies from MHC residents worked to show the city otherwise. Specifically, many residents spoke about how they choose to and enjoy living in MHC, as they are an affordable housing option that offers strong community ties, autonomy over personal space and property, and options for aging in place. In Portland, the qualitative data of personal stories and perspectives was critical to combating these negative stereotypes and perspectives that cannot be obtained from just analyzing quantitative data. 

This is one example where qualitative data is particularly necessary, but any discussion regarding affordable housing, infrastructure, and green spaces should be supplemented with qualitative data. Quantitative data may not paint the whole picture, so there are benefits of supplementing quantitative data with qualitative data. As such, governments should take a comprehensive approach to collecting data. 

In pursuing quantitative and qualitative data jointly, policymakers will have to evaluate how to bring both sets and types of information together in ways that accurately and meaningfully illustrate the bigger picture of affordable housing, infrastructure, and green spaces in communities changing over time and simultaneously support legal, planning, policy, and project decisions. 

In pursuit of this objective specifically, regional and local governments will have to find ways to access, maintain, and update these types of data, in addition to combining them into one or a few usable platforms. Integrated or coordinated data mechanisms can draw more attention to the relationship between these sectors to better support holistic decisionmaking efforts.

Existing Sources of Data 

There is currently a lack of sufficient data surrounding affordable housing, infrastructure, and green spaces for decisionmakers to make holistic, forward-looking resilience decisions around these issues. This is somewhat in contrast to available data for flooding (see Objective 5.2) where planners and policymakers in Louisiana may have more to draw from initially, especially given the ongoing data and modeling work through the Louisiana Watershed Initiative. Unlike some public and private sources that exist for flooding, there are not as many national, state, or regional tools or databases for housing, infrastructure, and green spaces. One possible reason for this disparity is that data about housing, infrastructure, and green spaces are inherently local matters. For example, in 2019, the Rappahannock-Rapidan Regional Commission received a community impact grant from Virginia Housing to conduct a regional housing study for multiple Virginia counties.^{See footnote 16} The goals of the study were to: (1) provide data to understand housing challenges in the region; (2) analyze regional land-use practices and zoning ordinances; and (3) provide recommendations.^{See footnote 17} 

Additionally, as a jurisdiction experiencing significant population growth, the City of Austin, Texas has begun preparing anti-displacement measures to ensure that current renters and homeowners are not priced out of the area. To inform the city’s anti-displacement efforts, agencies leading Project Connect partnered with the city’s Department of Housing and Planning to create a series of anti-displacement maps that outline the displacement risk of various neighborhoods along new planned transit routes.^{See footnote 18} The maps help show displacement risk geographically and are designed to inform community conversations and investment of anti-displacement funding.^{See footnote 19} The anti-displacement maps can also serve as a decisionmaking tool for other agencies involved in building out the city’s transit and amenities to support expected population growth. As such, national-level studies and data sets are not typically going to fit the bill to support planning and land-use decisions. 

Thus, while parishes and municipalities may be able to draw from these types of tools and databases to inform siting and design decisions for housing, infrastructure, and green spaces when contemplating overall flood risk and social vulnerability, they will have to be supplemented by other regional and locally specific data sets about factors like the number, location, condition, and types of housing, infrastructure, and green spaces in their communities.

At the start of decisionmaking processes, policymakers should spend time assessing what existing sources of data align with their priority data needs and can inform legal, planning, and policy decisions. Understanding what kind of data already exists can potentially save time and resources since certain analyses may have already been conducted. When gathering data from publicly available data sources, jurisdictions should make sure they can verify the authenticity, quality, and accuracy of the data, which can include using reliable sources and checking the methodology of data collection. 

Decisionmakers also need to be mindful of learning about the different types of data included in each tool and any potential shortcomings to be able to weave them together in one picture to support comprehensive decisionmaking efforts (e.g., is social equity data available, what types of flooding are included, are residential land use and road network overlays available). Where existing information is not available that fits the bill, policymakers may then have to consider investing in new sources.

Assessing Future Data Needs 

As previously described, parishes and municipalities in Region Seven will likely require significant investments in data and resources that are better aligned with meeting this objective. More work needs to be done across the board including to elevate interdisciplinary and cross-jurisdictional thinking on Objectives 5.2 and 5.3 collectively. To build equitable and meaningful resilience, flood mitigation and watershed management should be viewed as interdependent concepts that influence and are impacted by future housing and development patterns and environmental protection in the context of population shifts and transitions. 

When evaluating potential investments in data, parishes and municipalities should consider the following nonexhaustive list of factors:

• Evaluate the definition for “affordable housing” used in laws, plans, policies, and projects against the one used in new or existing data sets. The Regional Vision puts forward a broad and inclusive description for housing that promotes the idea of affordable housing for all. This scope exceeds the definition set by the U.S. Department of Housing and Urban Development that says housing is unaffordable if the total cost of people’s mortgage or rental payments exceeds 30 percent of their annual incomes. Therefore, planners and policymakers should ensure that the two definitions match. 
• Acquire current and forward-looking data about regional and local land use and development including the number, types, location, and condition of housing, infrastructure, and green spaces, in addition to trends for flooding, environmental, and population changes (e.g., current and future demographics predictions, current and future flood trends and risk for both coastal and inland communities, and access to employment, public transportation, education, and a healthy environment). Where possible, combine all of these different data points into one tool or resource. This can support more resilient outcomes that evaluate regional and local land use comprehensively instead of excluding or siloing any one sector. 
• Evaluate social and cultural ties to the land and anti-displacement considerations.
• Work on both local and regional scales. As stated elsewhere, housing, flooding, and population changes are simultaneously local and regional, if not statewide and national issues. Therefore, parish and municipal governments and communities have to invest in data that looks beyond jurisdictional boundaries to make the most cost-effective decisions that maximize social and environmental benefits (e.g., Rappahannock-Rapidan Regional Commission).

Future investments to collect and analyze data will likely include collaborating with other expert entities like universities, consultants, and community-based organizations. Other considerations for assessing future data needs may include determining how data will be collected, analyzed, managed, and updated; where the data will be housed; if the data will be publicly available and if so, how it will be presented to the public. 

Using Data to Build Resilience 

Parishes and municipalities should clearly determine how they will use any data in the context of building resilience. In other words, decisionmakers should be certain that the data collected directly supports their legal, planning, policy, and project decisions around affordable housing, infrastructure, and green spaces. This can result in greater administrative efficiencies and cost savings for Region Seven jurisdictions, in addition to ensuring that data needs are aligned with achieving community-driven outcomes for housing and nature-based solutions. 

One example of a tool that combines some of these categories of data is Asheville, North Carolina’s Climate Justice Screening Tool. In January 2020, Asheville’s City Council called for the city to create a Climate Justice Plan. The city is working to create a Climate Justice Screening Tool that will help prioritize climate equity in Asheville’s future projects and initiatives. As a preliminary step to creating the Climate Justice Screening Tool, Asheville built a Climate Justice Data Map, an online geographic information system (GIS) map of the city that overlays climate impacts with social and economic vulnerabilities. The map gives a quantitative “Climate Justice Index Score” showing how these compounding threats are facing each part of the city and in particular, Black, Indigenous, and People of Color (BIPOC) residents. The aim of this tool is to help decisionmakers better identify the disproportionate impacts facing different neighborhoods and groups of residents in Asheville to guide more resilient city planning and investments. 

Across all of these sectors, data should be provided in ways that are accessible and can allow both policymakers and communities to track and adaptively manage implementation progress, as necessary. For example, the New York City Rezoning Commitments Tracker (Tracker) is an online tool that enables city residents to monitor the city’s progress in implementing several neighborhood-level comprehensive plans. The neighborhood plans, referred to generally as “rezonings,” include zoning code changes as well as city commitments to specific capital and programmatic investments. The tool can be used to both inform the city’s internal coordination and project management as well as provide external transparency for community members. The Tracker also serves to help users understand how zoning changes will manifest in tangible projects, translating the technical information from neighborhood rezoning plans into specific initiatives. Using and maintaining data in this manner is another way to support and implement community-led decisionmaking processes. 

^{Credit: NYC Rezoning Commitments Tracker, City of New York, New York, https://www1.nyc.gov/site/operations/performance/neighborhood-rezoning-commitments-tracker.page (last visited June 14, 2022).} 

Regarding green infrastructure, in 2017, the city council in Austin, Texas passed a resolution to develop an integrated green infrastructure plan that would align relevant agencies and programs toward a shared goal of maintaining and increasing Austin’s green infrastructure as the city grows.^{See footnote 20} To this end, Austin has created an online resource that consolidates information on Austin’s plans and programs related to green infrastructure in one location.^{See footnote 21} The webpage functions to educate the public on the city’s green infrastructure initiatives and includes tabs for urban forest, water resources, parks, green streets, and environmental habitats.^{See footnote 22} The city is also enhancing public transparency by publishing green infrastructure indicators online, including performance metrics related to community gardens, park access, stream water quality, permanently preserved land, and tree canopy coverage.^{See footnote 23} Using and maintaining data in this manner is another way to support and implement community-led decisionmaking processes for affordable housing, infrastructure, and green spaces. 

Crosscutting Considerations and Practice Tips

When identifying priority data needs around affordable housing, infrastructure, and green spaces across varying levels of flood risk at the regional, parish, and municipal levels, decisionmakers may consider the following practice tips: 

• Develop locally and culturally appropriate and respectful processes to engage communities in data collection
• Assess what scale of data is appropriate
• Dedicate enough time and resources to collect, analyze, manage, and update data
• Build public-private-nonprofit-community partnerships

These tips are based on priority implementation best practices and considerations most relevant to this specific objective and do not present an exhaustive list for regional and local planners and policymakers.

It is important to acknowledge that every jurisdiction will be starting from a different place and have a unique local context and needs, among other factors. Therefore, these practice tips could be adopted individually, collectively, or not at all. It will be up to policymakers to work directly with their communities and other key stakeholders and partners to assess and determine potential tools and approaches to implement this goal and objective.

• Develop locally and culturally appropriate and respectful processes to engage communities in data collection: Data collection and analysis must equitably represent BIPOC and low-income communities.^{See footnote 24} For example, policymakers must ensure that the data does not have gaps, biases, and lack representation.^{See footnote 25} If acquiring their own data, policymakers must apply shared lived experiences of community members as a qualitative form of data to inform legal, planning, policy, and project updates for affordable housing, infrastructure, and green spaces solutions. There is no one-size-fits-all approach to engage with communities; however, to work towards effective solutions, planners and policymakers must create meaningful and equitable opportunities for community engagement and data collection. Additionally, decisionmakers must acknowledge people’s contributions to data collection processes and should compensate and honor them for their time and knowledge. For more recommendations and practice tips on equitable community engagement, refer to Objective 5.1. 

• Assess what scale of data is appropriate: Scale will be an important consideration for parishes and municipalities pursuing resilience strategies in Region Seven. First, the geographic scale of decisionmaking (e.g., regional, parish, or municipality) is a key factor when identifying priority data needs around affordable housing, infrastructure, and green space options. Regional and local governments will need data at various geographic scales to support decisions at different scales from the watershed and regional (to support cross-jurisdictional efforts e.g., flooding, population movements) down to the parcel scale (e.g., individual property adaptations) and everything in between. 
  
  Second, scale will come into play at different decisionmaking phases. For example, the level of detail required at a parish- or city-wide ordinance or planning stage will change when siting and designing an onsite project in a neighborhood.
  
  Third, questions around scale will also have a temporal component as flood risk, development, and population change in a jurisdiction over time.
  
  Therefore, it is important that governments have data at the relevant geographic, decisionmaking, and temporal scales to best support legal, planning, policy, and project decisions around housing, infrastructure, and green spaces.
  
  For example, the Louisiana Housing Corporation created an interactive mapping tool of the state of Louisiana to assist housing advocates, developers, policymakers, and state and federal elected officials “leverage a wide range of data to make more informed decisions related to affordable housing.”^{See footnote 26} Asheville, North Carolina is working to create a Climate Justice Screening Tool that will help prioritize climate equity in Asheville’s future projects and initiatives.^{See footnote 27} Louisiana Housing Corporation’s mapping tool and Asheville’s Climate Justice Data Map and Story Circles represent data presented at different scales. Louisiana’s program is intended to help inform the development of Louisiana laws and policies from the local to federal level. Thus, the data is presented at an all-encompassing state level. Conversely, the Climate Justice Data Map is intended to inform planning and investments at the city level, so the data is limited to Asheville. Assessing the scale of the data will help tailor the information to be more specific and appropriate for the relevant level of decisionmaking and avoid dedicating time and resources to collecting data that is not relevant for the decisionmaking process.

• Dedicate enough time and resources to collect, analyze, manage, and update data: Given the foregoing as presented in this objective, data-related efforts may require significant investments in staffing, time, and money, especially where publicly available data does not exist or at least not on a type or scale that is relevant to a given decisionmaking effort. This will potentially necessitate significant investments for determining priority data needs, assessing if data is accurate and reliable, collecting and analyzing data, creating partnerships, and applying for external sources of funding. These considerations should be factored into the timing and expectations for legal and planning processes relying on this data because they will take longer. For government-created or owned data sets or platforms, additional investments may have to be made in managing, maintaining, and updating these over time.

• Build public-private-nonprofit-community partnerships: Regional capacity-building and partnership opportunities between government and nongovernmental stakeholders and community members are an important part of creating long-lasting housing and flood risk solutions in communities. A lot of the expertise and resources needed to collect and analyze data to greaux resilience will come from outside government. Interacting with nonprofits (e.g., The Data Center), academic institutions (e.g., Louisiana State University), and community-based organizations (e.g., Healthy Community Services) will be especially important for gathering qualitative data and developing decision-support tools and trackers, like the examples provided above. 

For more information on building public-private-nonprofit-community partnerships, Objective 5.4 provides more information on regional governance structures and Objective 5.5 addresses partnership opportunities between government and nongovernmental stakeholders and community members. Both of these collaborative efforts are relevant to consider when collecting and analyzing data.

The summaries below highlight resources and case studies available in Georgetown Climate Center’s Adaptation Clearinghouse that are relevant to this objective. They illustrate how many of the above benefits, practice tips, and planning, legal, and policy tools were or are being evaluated and used in practice in different jurisdictions. To learn more and navigate to the Adaptation Clearinghouse, click on the “View Resource” buttons.