Category Archives: Income

Examining HMDA/CRA Census Tract Demographics

.. the ability to effectively analyze low, moderate, middle, and upper income population and households by small area geography is important to housing market stakeholders, lenders, investors, cities/neighborhoods and others. Low and moderate income data by block group and census tract are used for compliance, eligibility determination and program performance in many Federal programs and agencies. See the main Web page for more detail.

This section reviews the scope and use of the FFIEC 2019 HMDA/CRA census tract data (released September 2019). Use the interactive table to view, rank, compare selected items from these updated data for any/all tracts. Use GIS tools with these data to map and geospatially analyze these data as illustrated and further described as illustrated here. See more about banking, CRA and LMI tracts and more about these data.

Visual Analysis of Banks in Context Census Tract Demographics
Click graphic for larger view; expand browser window for best quality view.

– view developed using CV XE GIS and related GIS project.
– install this GIS tool and related GIS project on your computer to examines patterns, market share and more.

Low & Moderate Income Population by Census Tract
Low, moderate, middle, upper income classification by census tract is based on the median family income of a specific census tract relative to the metropolitan statistical area (MSA) or non-MSA area in which the tract is located. The FFIEC data include a “low and moderate income indicator”:
1 – Low — MFI is less than 50% of the MSA/parent area MFI
2 – Moderate — MFI is from 50% to 80% of the MSA/parent area MFI
3 – Middle — MFI is from 80% to 120% of the MSA/parent area MFI
4 – Upper — MFI is 120% or more of the MSA/parent area MFI
0 – NA — MFI is 0 or not available
where MFI is the Median Family Income

Low and moderate income designation is closely associated with implementation of the Home Mortgage Disclosure Act (HMDA) and the Community Reinvestment Act (CRA) and is a widely used in many other applications as a measure of economic prosperity.

Using the Interactive Table
Use the interactive table to examine individual tracts or sets of tracts as to their low and moderate income status and related demographics. The following view illustrates use of the table. Clicking buttons below table, this sequence of steps was used to obtain this view:
– click ShowAll
– click “Find CBSA; Low & Mod Tracts”
  >this selects tract in CBSA 26420 (Houston) that are low or mod
– click “Status Cols”
The table refreshes to show 470 tracts that are low/mod in this metro.
Finally, click the column header “Tract MFI %Region” to sort in descending order.

View your areas of interest. Start the steps over and use your CBSA code for a metro of interest.

Bankers Analytics Tools Web Sessions
Join me in a Bankers Analytic Tools Lab session (every Wednesday 3:00 pm ET) to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.
Topics:
• mapping and geospatially analyzing your data with FFIEC data
• tract demographic vintages and trends
• issues regarding MSA/MD vintage, change; about the 2018 vintage CBSAs
• defining and using assessment area geography
• examining the community & neighborhoods in context of assessment areas
• using the FDIC bank location/deposits data with FFIEC/ACS demographics
• using the FFIEC/ACS interactive table below
• alternative methods of accessing census tract ACS data

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Examining Economic Well-Being by Metro

.. examining the state of economic well-being: 2008-2017 .. expanding insights through data analytics .. tools and data to examine how and where the U.S. by metropolitan area real personal income is changing.  Real per capita personal income (RPCPI) is the best single measure of personal economic-well being. By examining RPCPI trends over a period, stakeholders can better determine if the area in improving, stable or declining. Still, RPCPI is but one important measure among many others, such as the Regional Price Parities (RPP). Use the interactive table, and related GIS tools, to examine these and other real personal income measures for the Nation’s 383 Metropolitan Statistical Areas (MSAs). See related Web section for more detail.

Based on data released by the Bureau of Economic Analysis in May 2019, see about in statistical release dates, real state personal income grew 2.6 percent in 2017, after increasing 1.5 percent in 2016. Real state personal income is a state’s current-dollar personal income adjusted by the state’s regional price parity and the national personal consumption expenditures price index. Across metropolitan areas, the percent change ranged from 14.8 percent in Midland, MI to -5.9 percent in Enid, OK

Visually Examining Patterns of Economic Well-Being
Geographic Information Systems (GIS) tools provide a powerful to explore these data. The following graphic shows a view of real per capita personal income (RPCPI), 2017, for Texas MSAs. Color patterns for levels of RPCPI are shown in the inset legend. MSAs are labeled with the 2017 RPCPI rank among all U.S. MSAs. Click graphic for larger view; expand browser window for best quality view. The larger view shows MSAs labeled with short name and a mini-profile for Harris County (pointer).

.. view developed with ProximityOne CV XE GIS and related GIS project.

Using the Interactive table
The interactive table (click link to view/use) includes a row for the U.S., U.S. nonmetro area and each Metropolitan Statistical Area (383). Each row provides a 2008-17 annual time series for four items (and derived items): real per capita personal income, regional prices parities-all items, total real personal income, implicit regional price deflator.

The following graphic shows these areas ranked on RPCPI in 2017. See rightmost column rank. Click graphic for larger view. Midland, TX MSA has the 4th highest 2017 RPCPI (shown in blue; also shown in blue in the map graphic above). But … that metro experienced a decrease of 12.1 percent over the period 2010-2017 .. and down from a high RPCPI of $115,069 in 2014. The dynamics of the oil industry!

Data Analytics Web Sessions
See these applications live/demoed. Run the applications on your own computer.
Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Block Group Demographic Data Analytics

.. use tools described here to access block group data from ACS 2016 (or ACS2017 in December 2018) using a no cost, menu driven tool accessing the data via API. Select from any of the summary statistic data. Save results as an Excel file or shapefile. Add the shapefile to a GIS project and create unlimited thematic pattern views. Add your own data. Join us in a Data Analytics Web session where use of the tool with the ACS 2017 data is reviewed.

See related Web section for more details.
– examine neighborhoods, market areas and sales territories.
– assess demographics of health service areas.
– create maps for visual/geospatial analysis of locations & demographics.

Illustration of Block Group Thematic Pattern Map – make for any area

– click graphic to view larger view
– pointer (top right) shows location of Amazon HQ2

Topics in this how-to guide (links open new sections/pages)
• 01 Objective Thematic Pattern Map View
• 02 Install the CV XE GIS software
• 03 Access/Download the Block Group Demographic-Economic Data
• 04 Download the State by Block Group Shapefile
• 05 Merge Extracted Data (from 03) into Shapefile (04)
• 06 Add Shapefile to the GIS Project; Set Intervals
• 07 Viewing Profile for Selected Block Group
• 08 BG Demographics Spreadsheet
• 09 Block Group Demographics GIS Project
• 10 Why Block Group Demographics are Important

Data Analytics Web Sessions
Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Low & Moderate Income Census Tracts; 2017 Update

..  data and tools to analyze characteristics and patterns of census tract geography with a focus on low and moderate income.   See related Web page for more detail.

Of the total 75,883 census tracts for which low and moderate income data were tabulated in the HMDA 2017 data, 6,023 (8.7%) were low income, 16,873 (24.5%) were moderate income, 32,509 (47.1%) were middle income and 19,159 (27.8%) were upper income. See more about these classifications. Find out about your tracts/neighborhoods of interest and how they compare to others using data and tools provided in this section.

Analysis of the low, moderate, middle, and upper income of the population and households by small area geography is important to housing market stakeholders, lenders, investors, cities/neighborhoods and others. Low and moderate income data by block group and census tract are used for compliance, eligibility determination and program performance in many Federal programs and agencies.

• Use the interactive table below to view, query, compare, sort census tracts.
• Use tract estimates & projections to examine changing characteristics.
– extended demographic-economic measures, annual 2010-2022

Low & Moderate Income by Census Tract
The following view shows census tracts designated as low and moderate income (orange fill pattern) in the the Houston, TX MSA (bold brown boundary) area. These are tracts having income level with codes 1 and 2 in the interactive table. A wide range of market insights can be created zoom-in views for counties, cities and neighborhoods and linking these with other data. Make variations of this view using ProximityOne data and tools described in this section.

– View developed using CV XE GIS and related GIS project.

View similar maps for these areas:
.. Atlanta metro
.. Chicago, IL metro
.. Dallas, TX metro
.. Knoxville, TN metro
.. with drill-down views for Knoxville city
.. Los Angeles, CA metro
.. San Francisco, CA metro

Using the Interactive Table
  – Examining LMI Tracts in Your Metro

Use the interactive table to view, query, sort compare tracts based on various demographic and LMI characteristitcs. The following graphic illustrates how the table can be used to view low and moderate income tracts for the Charlotte, NC-SC metro.
– click ShowAll button below table.
– enter a CBSA code in the edit box at right of Find CBSA LMI>.
– click the Find CBSA LMI button.
Resulting display of Charlotte metro LMI tracts only.

– click graphic for larger view.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

County 5-Year Trends: Income & Income Inequality

.. tools and data to examine how the U.S. by county household income and income inequality are changing … how is household income changing in counties of interest? What are the trends; what is causing the change? What are the characteristics of income inequality and how is it changing? How might this change impact your living environment and business?

This section provides access to tools and data to examine U.S. by county measures of household income and income inequality between two 5-year periods (2006-10 and 2011-2015). These data can provide insights into how household income and income inequality are changing for one county, a group of counties and the U.S. overall. Use the interactive table to view median household income and measures income inequality for all counties. See more detail about these topics here. Measures of income inequality can be estimates/examined using the Gini Index.

The Gini Index & Measuring Income Inequality
The Gini Index is a dimensionless statistic that can be used as a measure of income inequality. The Gini index varies from 0 to 1, with a 0 indicating perfect equality, where there is a proportional distribution of income. A Gini index of 1 indicates perfect inequality, where one household has all the income and all others have no income.

At the national level, the 2015 Gini index for U.S. was 0.482 (based on 2015 ACS 1-year estimates) was significantly higher than in the 2014 ACS Index of 0.480 (based on 2014 ACS 1-year estimates). This increase suggests that income inequality increased across the country.

Examining Household Income & Income Inequality Patterns & Change
The following two graphics show patterns of the GIni Index by county. The first view is based on the American Community Survey (ACS) 2010 5-year estimates and the second is based on the ACS 2015 5-year estimates. The ACS 2010 estimates are based on survey respondents during the period 2006 through 2010. The ACS 2015 estimates are based on survey respondents during the period 2011 through 2015. One view compared with the other show how patterns of income inequality has changed at the county/regional level between these two 5-year periods.

Following these Income Inequality views are two corresponding views of median household income; using data from ACS 2010 and ACS 2015. Use CV XE GIS software with the GIS project to create and examine alternative views.

Patterns of Income Inequality by County; ACS 2010
The following graphic shows the patterns of the Gini Index by county based on the American Community Survey 2010 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Income Inequality by County; ACS 2015
The following graphic shows the patterns of the Gini Index by county based on the American Community Survey 2015 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Economic Prosperity by County; ACS 2010
The following graphic shows the patterns of median household income ($MHI) by county based on the American Community Survey 2010 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Economic Prosperity by County; ACS 2015
The following graphic shows the patterns of median household income ($MHI) by county based on the American Community Survey 2015 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Tools to Analyze County Demographic-Economic Characteristics

.. demographic-economic characteristics of counties are essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine these data for all counties in the U.S. This annual update includes geographic area characteristics based on ACS 2015 data.  The tools/data are organized into four related sections summarized below.

1. General Demographics
View interactive table at http://proximityone.com/us155dp1.htm
Patterns of School Age Population by County
Use GIS tools to visually examine county general demographics as illustrated below. The following view shows patterns of percent population ages 5 to 17 years of age by county — item D001-D004-D018 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

2. Social Characteristics
View interactive table at http://proximityone.com/us155dp2.htm 
Patterns of Educational Attainment by County
– percent college graduate
Use GIS tools to visually examine county social characteristics as illustrated below. The following view shows patterns of percent college graduate by county — item S067 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

3. Economic Characteristics
View interactive table at http://proximityone.com/us155dp3.htm 
Patterns of Median Household Income by County
Use GIS tools to visually examine county economic characteristics as illustrated below. The following view shows patterns median household income by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

4. Housing Characteristics
View interactive table at http://proximityone.com/us155dp4.htm 
Patterns of Median Housing Value by County
Use GIS tools to visually examine county housing characteristics as illustrated below. The following view shows patterns median housing value by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

America’s Cities: Demographic-Economic Characteristics Annual Update

.. tools and data to interactively examine demographic-economic characteristics of America’s 29,321 cities/places .. understanding demographic-economic characteristics of cities and places is essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine characteristics of all cities/places in the U.S. This annual update includes data for 29,321 cities/places based on ACS 2015 data.

Accessing the Data; Using Interactive Tables
Each of the four links below opens a new page providing access to U.S. by city/place interactive tables — by type of subject matter. Use tools and usage notes below table to select operations to perform queries, sort and select columns.
General Demographics
Social Characteristics
Economic Characteristics
Housing Characteristics

How the the Tables/Data Can be Used
The following table shows data derived from the Economic Characteristics table. The top 10 cities/places having the highest median household income ($MHI) are shown. The table also shows population, median family income ($MFI) and per capita income ($PCI). The $250,000 value is a cap; the actual value is $250,000 or higher. Use the interactive tables to create similar views for states of interest. Use the button below the table to select/view cities within a selected metro. Compare attributes of cities of interest to a peer group based on population size.

Visual Analysis of City/Place Population Patterns
Use GIS resources to visually examine city/place demographic-economic patterns. The following view shows patterns of population percent change by city in the Charlotte, NC-SC metro area.

… view developed using the CV XE GIS software.
… click map for larger view and details.

Related Data
Cities/Places Main Section
Citie Population Estimates & Trends, 2010-15

More About Using These Data
Using ACS 1-year and 5-year data

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.