Find suitable areas for new affordable housing

Affordable housing is an increasing problem, especially in urban areas. A lack of affordable housing can impact a city's growth, livability, and sense of economic mobility. As housing becomes more expensive, renter-occupied housing units increase, diminishing the percentage of residents able to build wealth through their home. Expensive aging homes also cause higher homeowner expenses for improvements. Ultimately, residents may leave a city that does not have enough affordable housing, causing population decline.

In 2023, the United States Census Bureau estimated that the population of Long Beach, California, had declined since 2020. This decline might be due to high housing prices compared to the city's average household income. In this tutorial, you'll use infographics in ArcGIS Business Analyst Web App to learn more about housing in Long Beach. Then, you'll perform a suitability analysis to determine which census block groups throughout the city would most benefit from the development of new affordable housing.

This tutorial's workflow can be applied to any other city in the United States, as well as to counties or states, to identify areas in need of more affordable housing and improved policy planning.

This tutorial was last tested on May 13, 2025.

View final result

Requirements

• ArcGIS organizational account with a Publisher, Facilitator, or Administrator role (see options for software access)
• ArcGIS Business Analyst Web App with an Advanced license

Understand the problem

To learn more about the affordable housing situation in Long Beach, you'll create infographics showing relevant demographic data for the city.

Create a project

First, you'll create a project in ArcGIS Business Analyst Web App.

1. Go to Business Analyst Web App.
2. If you are not signed in, click Sign In. Sign in with your ArcGIS organizational account.

   Note:

   If you don't have an organizational account, see options for software access.

   Your account must be licensed to use Business Analyst Web App. If you are the administrator of your organization, you can assign yourself a license. Otherwise, you'll need to contact your administrator for permission.

3. If necessary, on the ribbon, click Home. Click New project.

   

4. In the Create project window, for Project name, type Affordable Housing Analysis and add your name or initials. Click Create.

   A message explains that the project is being created. When it finishes, either the project opens automatically or a confirmation message appears.

5. If the confirmation message appears, check Open new projects as soon as they are created and click OK.

   The project opens.

   Tip:

   If your project does not open, you can open it from the Home page. Under Recently Created, confirm that the Projects tab is selected. Point to your Affordable Housing Analysis project and click Open project.

   

   Note:

   Depending on your organization's settings, the default extent and appearance of your project may differ from the example image.

   Before you continue, you'll confirm that you're using the latest data source for the United States.

6. On the ribbon, confirm that Data source is set to USA (Esri 2024) (or a more recent year if available).

   

   Note:

   If your data source is different, click the Data source drop-down button. In the list of data sources, click USA and select the most recent data source.

Map the area of interest

Next, you'll add a boundary on the map for your area of interest, Long Beach.

1. On the ribbon, next to the search bar, click the drop-down menu and choose Add a boundary.

   

2. In the search bar, type Long Beach and choose Long Beach City, CA.

   

   The map navigates to Long Beach, California. A polygon on the map shows the city's boundary and a pop-up provides additional options. You'll use the boundary to create a site, which will save the boundary as a layer in your project so you can use it for analysis.

3. In the pop-up, click Create site.

   

   You can change the style of the boundary polygon before you make a site from it. For this tutorial, you will keep the default style.

4. Leave the style parameters unchanged and click Create site.

   The site is saved. It is included in the My geographies layer, which you can find on the project pane.

   Before you continue, you'll change the basemap. The default basemap contains a lot of contextual information that isn't necessary for your analysis. You'll use a more simplified basemap to emphasize your analysis results.

5. Under the search bar, click the Basemaps button.

   

6. In the Basemaps pane, click Light Gray Canvas.

   

   The basemap changes.

7. Close the Basemaps pane.

   The city boundary polygon shows more clearly on the simplified basemap.

   

Create infographics

Now that you've mapped your study area, you're ready to understand more about its affordable housing situation. To get an idea of key demographic information at a glance, you'll run an infographic for the area.

1. On the site pop-up, click the Infographics button.

   Tip:

   If you closed the pop-up, you can reopen it by clicking the site on the map.

   

   After a few moments, an infographic is created. The information shown in the default infographic may vary depending on your organization settings. It's likely that it doesn't show information relevant to housing, so you'll change it to an infographic template that does.

2. On the ribbon, click the Infographic drop-down menu.

   

   Note:

   Your default infographic template may differ from the example image.

3. If the Explore gallery infographics window appears, click Dismiss.
4. In the search bar, type Community Change Snapshot. In the list of infographics, click Community Change Snapshot.

   

   Tip:

   To learn more about an infographic before you run it, point to the information button next to the infographic's name.

   After a few moments, the Community Change Snapshot infographic runs based on your site of Long Beach, California.

   

   Note:

   Demographic data is updated frequently. Your infographic may show more recent values than the example images.

   The Total Population section of the infographic indicates that population has declined by about 15,000 people from 2020 to 2024. Five-year forecasting predicts an additional decline of 10,000 people by 2029.

   The Owner vs Renter Occupied Units section shows that about 60 percent of households are renter occupied, while the Average Household Size section shows that household sizes are getting smaller, meaning there are fewer people per household.

   Next, you'll look at an infographic with information about the housing market.

5. On the ribbon, click the Infographic drop-down menu. Search for and run the Housing Market Characteristics infographic.

   After a few moments, the Housing Market Characteristics infographic runs for Long Beach.

   

   The first page of the infographic indicates that the average home value is 9 percent higher than the California average, while the Housing Affordability Index is 40. A Housing Affordability Index of 100 represents an area where the median income is sufficient to qualify for a loan on a home valued at the median home price and not be cost-burdened. Values under 100 indicate decreasing affordability.

   Over 8,500 households use over 50 percent of their salary toward mortgage. Additionally, the majority of houses were built before 1970. Housing over 50 years old is considered old infrastructure, leading to more expensive upkeep and less affordable housing.

   With a high population of renters and a lack of affordable housing, residents can't build wealth through their home, making economic mobility difficult. These infographics indicate a need for new affordable housing in the area.

   Note:

   The Housing Market Characteristics infographic has a second page with additional demographic information. To access it, click the Next page button to the upper right corner of the infographic.

6. On the ribbon, click the Close button.

   

   The infographic closes and you return to the map. Before you continue, you'll clear your site from the map. (You'll still be able to use the site for analysis.)

7. On the toolbar to the right of the map, click the Clear map button.

   

   Tip:

   You can expand the right toolbar by clicking the Expand button at the bottom of the toolbar. Expanding shows the name of each button on the toolbar.

8. In the Clear map window, check the All box. Click Clear.

   The map is cleared.

You've explored relevant demographic information for Long Beach using infographics. Now that you better understand the problem of housing affordability in the area, you'll perform a suitability analysis to determine the places that could benefit the most from new affordable housing.

Analyze the area

Suitability analysis identifies areas that best match a series of criteria. It can be used to answer a wide range of questions, such as finding the best location for a new business. You'll use it to find the best location for new affordable housing.

Choose suitability criteria

To perform a suitability analysis, you must determine the criteria you use to decide whether a location is suitable or not. It's subjective which criteria are best to use; different criteria will lead to different results. For this tutorial, your criteria will be based on demographic variables about housing affordability and financial vulnerability. Places where housing is too expensive and residents are struggling financially would likely benefit more from new affordable housing than places where housing is already affordable.

Note:

There are many other potential criteria that might be important for determining the best places for new affordable housing, such as zoning or available open space. When analyzing your own city or region, think about what other criteria might be appropriate to consider.

1. On the ribbon, click Run analysis and choose Suitability analysis.

   Note:

   Suitability analysis is only available with ArcGIS Business Analyst Advanced licensing. If you have Standard licensing, you won't be able to access the workflow.

   

   The Suitability analysis pane appears. First, you'll choose the locations to analyze. You'll analyze census block groups in Long Beach. Census block groups are the smallest geographic area for which the United States Census Bureau collects data.

2. Select Geographies or hexagons.

   

3. Click Next.
4. For Analysis extent, click the search bar and choose Your sites.

   

5. In the Add sites window, check the box for Long Beach City, CA and click Apply.

   The site you created earlier is added as the analysis extent.

6. For Level of detail, confirm that Block Groups is selected.

   

   The census block groups appear on the map within the Long Beach boundary.

7. Click Next.

   Next, you'll choose the criteria that will determine whether a census block group is suitable or not. You'll create a list of criteria using demographic variables about the population.

8. Under Create a list, click Select criteria and choose Add variables from data browser.

   

   You can add variables from the data browser or add a point layer. A point layer can be used if you want proximity to certain features to be a factor in the suitability analysis. For instance, if you were finding a suitable location for a business, you might add a point layer of competitor stores and ensure suitable locations aren't too close to one.

   You'll learn more about using a point layer for suitability analysis later in the tutorial. For now, you'll use the data browser to search for demographic variables.

9. Click Add variables from data browser.

   

   The Data browser window appears. This window contains a list of all the demographic variables available from your current data source.

   Note:

   If you're not using the USA (Esri 2024) or later data source, you'll see different variables than those in the workflow. You confirmed you were using the correct data source earlier in the tutorial when you created the project.

   First, you'll add variables that will determine housing affordability. You want the locations you find to be ones where affordability is low.

10. In the search bar, type Housing Affordability and press Enter. In the list of search results, click 2024 Housing Affordability Index (Esri) (or a more recent version, if available).

    

    The Housing Affordability Index measures the ability of a typical resident to purchase a home in a geographic area. Values over 100 indicate increasing affordability, while values under 100 indicate decreasing affordability.

    Tip:

    To learn more about a variable, point to the information button to the right of the variable.

    When you check the variable, the Selected variables button appears in the window, indicating one variable is selected.

    Next, you'll add a variable that indicates areas where households with mortgages have monthly owner costs over half of household income, another indicator of low affordability.

11. Click Explore.

    

12. Search for Monthly Mortgage. Next to 2022 HHs w/Mortgage: Monthly Owner Costs 50+% of HH Income (ACS 5-Yr), click the Percentage button.

    

    By choosing this variable as a percentage rather than a count, you'll ensure the results aren't skewed by population.

13. Check the box for the 2022 HHs w/Mortgage: Monthly Owner Costs 50+% of HH Income (ACS 5-Yr) variable.

    The variable is added to the Selected variables list.

    Note:

    You must click Percentage before checking the box for the variable. Otherwise, you'll select the default variable, which uses count instead of percentage. If you accidentally add the wrong variable, you can remove it by unchecking its box or by clicking the Selected variables button and removing it from the list.

    You'll add four more variables. One will indicate areas with high rent, one will indicate areas with a large percentage of residents below the poverty level, and the last two will indicate average home value, both currently and in future projections.

14. Search for and add the following variables:
    • 2022 HHs w/Gross Rent 50% + of Household Income ACS 5-Yr (change from Count to Percentage)
    • 2022 Households Below the Poverty Level (ACS 5-Yr) (change from Count to Percentage)
    • 2024 Average Home Value (Esri) (Average)
    • 2029 Average Home Value (Esri) (Average)

    You've selected six variables in total.

15. Click the Selected variables (6) button.

    

    You'll save this list in case you want to use it for subsequent analysis.

16. In the Selected variables window, click Save list.
17. In the Save criteria list window, for List name, type Housing Affordability Variables. Click Save.

    Your list is saved. You can access it later by clicking the Saved lists tab in the Data browser window.

18. At the bottom of the Data browser window, click Apply.

    Suitability analysis is run based on the variables you chose. On the map, census block groups are styled with darker colors if they have higher values for all or most criteria on average compared to other census block groups, and lighter colors if they have lower values.

    

Adjust influence and weights

Your suitability analysis isn't complete yet. By default, the six variables you chose are given a positive influence over the analysis results. This means that if a census block group has a higher value for a variable, it is considered more suitable.

However, not all of your variables should have a positive influence. For the Housing Affordability Index variable, higher values indicate higher affordability. To prioritize areas where affordability is low, which would be more suitable for new affordable housing, you have to change the variable's influence.

1. In the Suitability analysis pane, for 2024 Housing Affordability Index, change Influence to Inverse.

   

   Now, areas with low values for this variable are considered more suitable. The map changes to reflect the new results.

   Next, you'll change the way the results are presented in the Results pane so you can better understand the site rankings.

2. In the Suitability analysis pane, scroll down to Scoring method. For Final score scale, choose 0 to 100.

   

   The Results pane changes to show results on a scale from 0 to 100. This change particularly affects the Top 5 section, which shows the differences in suitability between the top five census block groups.

   

   The most suitable block group has a suitability score of 100, while the fifth most suitable has a suitability score of 96.8. Because these scores are all relatively high, these five census block groups are all potentially suitable locations for new affordable housing.

   Tip:

   To see any of the top five census block groups, point to their bar in the Results pane. The corresponding census block will become highlighted on the map.

   Next, you'll adjust the weights for the variables. By default, your six variables all have equal weight, meaning they are equally important to the final results. You can adjust weights so certain variables affect the results more or less than the others.

3. In the Suitability analysis pane, under Weighting, click Adjust weights.

   

   You can adjust weights as a percent or relative to the other variables. You can use the sliders or the input boxes to increase or decrease weights for specific variables. As you adjust weights, the suitability scores will be recalculated for each census block group.

   You'll increase the weight for the housing affordability index variable, as it is most directly tied to housing affordability.

4. For 2024 Housing Affordability Index, change the box to 20 percent and press Enter.

   

   Increasing this weight also decreases the weights for the other variables. All weights must add up to 100 percent. To prevent a weight from changing, you can lock it.

5. For 2024 Housing Affordability Index, click the lock button.

   

   Now, this weight won't change even if you adjust other weights. You'll also give the future average home value variable a higher weight to place more emphasis on future suitability over the present.

6. For 2029 Average Home Value, change the weight to 20 percent and lock the weight.

   Now, two variables are weighted at 20 percent and the other four are weighted at 15 percent.

   Weighting is a subjective process and can significantly affect results. You could continue to adjust weights, but for now, you'll keep these weights.

7. At the bottom of the Adjust weights window, click Done.

   You've finished adjusting influence and weights for your suitability criteria. The map now shows the most suitable locations for new affordable housing based on the criteria you chose, with darker red census block groups being more suitable and lighter yellow block groups being less suitable. Your results can be used in conjunction with further socioeconomic analysis for even more robust results.

   You'll save this layer to refer to later.

8. At the bottom of the Suitability analysis pane, click Save layer.
9. In the Save layer window, for Layer name, type Housing Suitability Analysis. Click OK.

Add more criteria

You've determined the best places for new affordable housing based on need alone, but when planning where to build new housing, there are other factors to consider. For instance, new housing without access to essential services could place additional financial burdens on residents, even if the housing itself is affordable.

For a more robust suitability analysis that considers a wider range of factors, you'll add the locations of grocery stores to the map. Then, you'll refine your suitability analysis to consider proximity to grocery stores as additional criteria.

1. On the ribbon, click Create maps and choose Points of interest (POI) search.

   

   A POI search adds features to the map based on categories you choose, such as specific types of businesses. When you choose the POI search option, your suitability analysis results are removed from the map. You'll be able to access them again later because you saved them.

2. If necessary, close the Updated search experience window.
3. In the Points of interest (POI) search pane, expand Geography. Change Analysis extent to your Long Beach City, CA boundary.

   You'll search for and add grocery stores to the map.

4. Under Search, click the search bar. In the search window, click Category.

   

5. In the list of categories, expand Food & Grocery Stores. Expand Grocery Stores and check the Grocers-Retail box.

   

6. Click Search.

   The search is run. Retail grocers within Long Beach are added to the map.

   

   Tip:

   The current results include both large grocers and small ones. Optionally, you can filter the results to only larger grocery stores that can handle a larger resident population. To do so, under Filters, click Show filters. In the Filters window, under Sales volume, change the minimum sales volume to a higher number. There are also options to filter stores by number of employees and other characteristics. For this tutorial, you won't apply any filters.

   You'll save this layer so you can add it as suitability analysis criteria.

7. At the bottom of the Points of interest (POI) search pane, click Save layer. Change Layer name to Grocers Long Beach and click OK.

   The layer is saved. Now you can add it as criteria for your suitability analysis.

   Note:

   Proximity to grocers is only one of the many criteria you could account for when considering the best places to build new affordable housing. Other important resources and infrastructure you might want near new affordable housing include parks, transit stops, medical facilities, and educational facilities. In this tutorial, you won't add features representing these resources, but you're encouraged to incorporate them when performing suitability analysis for your own city or region. You can add point features for these resources and more by using POI search and searching for different categories.

8. On the ribbon, click Affordable Housing Analysis.

   

   The project pane appears.

9. Under Suitability analysis layers, for Housing Suitability Analysis, click the options button and choose Edit layer.

   

   After a few moments, the suitability analysis results you previously created are added back to the map. They have a new style, with green meaning higher suitability and red meaning lower suitability, but the results themselves are unchanged.

10. In the Suitability analysis pane, under Analysis criteria, scroll to the bottom of the list of criteria. Click Add criteria and choose Add point layer.

    

11. In the Add point layer window, confirm that Grocers Long Beach is checked. Click Close.

    The grocers layer is added as criteria. By default, the layer has an inverse influence, meaning that if a census block group has more grocer points in it, the block group is considered less suitable. This is the opposite of what you want, because access to groceries will reduce the financial burden of residents.

12. In the Suitability analysis pane, under Grocers Long Beach, change Influence to Positive.

    

    On the map, block groups with grocers in them have increased suitability scores.

    

    You'll save this result as a new layer in case you want to compare it to the results without grocers.

13. At the bottom of the Suitability analysis pane, click Save layer. Change Layer name to Housing Suitability Analysis with Grocers and click OK.

Isolate the most suitable areas

Your suitability analysis is complete. Before you share the results, you'll create a layer showing only the most suitable census block groups. This layer will indicate areas for consideration at a glance and can be used for subsequent analysis if necessary.

1. Scroll to the bottom of the Suitability analysis pane. Under Legend, click Filter results.

   

   The Filter results window appears, showing a histogram of all census block groups organized by their final score. You'll filter the results to show the top 10 most suitable block groups.

2. Select Filter by rank. Change the rank to top 10.

   

3. Click Done.

   On the map, only the top 10 most suitable block groups are shown.

   

   You'll select these block groups and export them to a new site layer. To make it easier to select them, you'll clear the map of the other features, such as the grocery stores.

4. On the toolbar to the right of the map, click the Clear map button.
5. In the Clear map window, check the boxes for Grocers Long Beach and Long Beach City, CA. Click Clear.

   Now, the map only shows the block groups.

6. On the toolbar to the right of the map, click the Select button.

   

7. On the map, drag a box around all of the Long Beach census blocks.

   

   The 10 block groups are selected. A pop-up appears over one of the block groups with more options.

8. In the pop-up, click Create sites.

   

   Like when you created the site for the Long Beach boundary, you can change the style of the sites. You'll keep the default style.

9. Click Create 10 sites.

   The sites are created. They can be turned on or off individually or as a group. You can also create infographics for the sites, the same way you made them for Long Beach, if you wanted to better understand the demographic characteristics of these block groups.

Share the results

Now that your suitability analysis is complete, you'll share your results. You can share your results in many ways, including as an image or web map. In this tutorial, you'll share your results as a dashboard in ArcGIS Dashboards. A dashboard allows you to display both the sites on the map and associated infographics.

1. On the ribbon, click Share results and choose ArcGIS Dashboards.

   

2. In the ArcGIS Dashboards pane, click Get started.

   You can create a dashboard where you can view a selected infographic for many different sites or where you can view many infographics about one site. You have 10 sites, so you'll choose the former.

3. Confirm that Many sites > Same infographic is selected and click Next.

   Next, you'll choose the dashboard layout. The side layout includes a map, while the main layout does not, so you'll choose the former.

   Tip:

   To preview a layout before you choose it, point to the icon next to the layout name.

4. Click Side Layout.

   Next, you'll add the sites to use in the dashboard. You'll also include the site for Long Beach as a whole, so users can see infographics for the entire city if they want.

5. Click Add sites. In the Add sites window, check the Name box to select all 10 high suitability sites and the Long Beach boundary site.

   

6. Click Apply. In the ArcGIS Dashboards pane, click Next.

   Next, you'll choose the infographic to display in the dashboard. You'll use the infographic you looked at earlier that provided information about the housing market.

7. Click Select infographic. Search for and choose Housing Market Characteristics.

   

8. For Enter dashboard title, type Suitable Locations for New Affordable Housing.

   

   You can also share your dashboard with your ArcGIS organization or a group that you are a member of. In a real-world scenario, you'd likely want to share your dashboard so other members of your team could see it, but for this tutorial, it's not necessary.

9. At the bottom of the ArcGIS Dashboards pane, click Create.

   After a few minutes, the dashboard is created and the Dashboard ready window appears.

10. In the Dashboard ready window, click View dashboard.

    The dashboard opens in a new tab.

    

    The dashboard contains an infographic on the main screen, a list of sites that can be explored, and a map of the area.

11. Under Select from list, click any of the census block groups.

    The map pans to the selected block group and the infographic changes to show information for it.

    Note:

    To edit the dashboard further, return to the Business Analyst tab. In the Dashboard ready window, click Edit dashboard. This tutorial won't cover how to edit a dashboard. If you want to learn more about dashboards, check out the series Try ArcGIS Dashboards. For more advanced formatting options, try the tutorial Get started with advanced formatting in ArcGIS Dashboards.

In this tutorial, you investigated the issue of affordable housing in Long Beach using infographics. Then, you analyzed suitable locations for new affordable housing based on demographic variables and proximity to grocery stores. Lastly, you shared the most suitable sites in a dashboard that combines maps and infographics.

This workflow can be performed for any city or region in the United States. You can try it for your own city. The criteria you choose for suitability analysis and the weights you give them are subjective. Think about other criteria that would be important for determining suitability for new affordable housing and add that criteria to your analysis.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• Esri Demographics data accessed with ArcGIS Business Analyst. Terms of use.
• Topographic map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• Light Gray Canvas map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community