New Authorized Monthly Residential Construction by City

.. access/analyze monthly building permits data for any/all of 7,700 cities in a few minutes via a web browser. Building permits data, a leading economic indicator, are one of the few current indicators available at the city level to examine characteristics and trends. These data can provide insights into the type and value of housing that is being added. Use the VDA Web GIS to examine how the housing situation is changing for cities of interest.

The VDA Web GIS MetroDynamics city layer/dataset is updated with the most recent 6 months of the number of housing units by type and value. The current data provide monthly aggregated data for January 2022 through June 2022. The layer/dataset includes many other attributes (see details). Of the ~19,000 U.S. cities, ~7,700 issue monthly building permits and accessible via this resource.

Examining City Housing Characteristics & Trends
An example: cities in the Phoenix, AZ area labeled with the total building permits.

.. table under map view shows cities with largest number of building permits.
.. see steps to develop the above map and table.

Illustrative Profile for Surprise, AZ
.. selected items from MetroDynamics City layer/dataset
.. integrated subject matter from several sources
.. building permits most current

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About Building Permits Data
Building permits data are collected monthly by the Census Bureau from permit issuing agencies. Monthly data are reported for access within one month of the reporting date. Data are collected on the number of new housing units authorized by type of units in building by value.

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Majority-Minority Population by Census Tract

.. based on the Census Bureau’s assessment of Census 2020 results, the most prevalent racial or ethnic group for the United States was the White alone non-Hispanic population at 57.8%. This decreased from 63.7% in 2010. This trend will continue into the future. Over time, interest has grown in majority-minority areas ranging from congressional districts to neighborhoods. Of the 85,190 Census 2020 census tracts, 28,317 tracts were majority-minority based on Census 2020 demographics. Decision-making information using VDA Web GIS .. examining lending institution assessment areas .. see below.
.. of the 1,113 Census 2020 census tracts in Harris County, TX (Houston), 549 tracts are majority-minority tracts based on Census 2020 data. Minority-majority areas are those areas where the resident population is less than 50% non-Hispanic whites. Use tools and methods described in this section to analyze the majority-minority patterns in your census tracts, neigborhoods, areas of interest.
Patterns of Majority-Minority Population in Harris County, TX
The following view shows patterns of Census 2020 percent majority-minority population by Census 2020 census tract in the Houston area.
This view was developed using the VDA Web GIS. You can create a similar for areas of interest. Only a Web browser is needed. In this view, the tracts layer is selected as the “active layer” and a census tract is clicked in the map window. The tract highlights with a blue hatch pattern and a profile is shown in the lower left panel. See that this tract has a Census 2020 total population of 5,380 (TotPop) and White alone, non-Hispanic population (White1NH) of 131. Use the VDA Table/Query feature to examine tracts in a spreadsheet/grid.
Majority-Minority Population by Tract Interactive Table
The following view illustrates use of the VDA Table/Query feature to examine and query the majority-minority population by tract.

This SQL statement is used to select, compute and show the data in a tabular form by tract.
select uid, geoid, totpop, white1nh, 100*(totpop-white1nh)/totpop where totpop>0 and geoid like ‘48201%’
The percent majority-minority is computed “on the fly”.
The table is ranked by on this sort instruction:
100*(totpop-white1nh)/totpop desc
The resulting table shows tracts as rows with the highest percent majort-minority at the top. A larger population tract is selected by clicking on it in the grid. The tract 48-201-331700 is selected in the table; that tract is zoomed-to in the map, and the demographics are shown in the profile (TotPop 4,045, White1NH 29).
Examining Majority-Minority Tract Patterns in Context of Lending/Mortgages
Majority-minority census tracts relates to banks/lenders and the Community Reinvestment Act (CRA). The following graphic developed using VDA Web GIS, shows locations of a California bank in context of patterns of percent majority-minority tracts. It is easy to see how bank locations relate to patterns of majority-minority tracts. Lenders and stakeholders are enabled to analyze patterns and gain insights. See more about adding/using the national FDIC bank locations data to VDA Web GIS to perform more in-depth analysis.

Using VDA GeoSelect Tool to Examine Assessment/Service Areas
The following graphic illustrates use of the VDA GeoSelect tool to select tracts around a bank location to evaluate demographic characteristics of an area .. an assessment area or service area. As tracts are selected they are shown with a hatch pattern. The Profile panel, lower left, is dynamically updated to show aggregated demographics for the set of tracts selected including total population and race/origin details.

Using the Visual Data Analytics (VDA) Web GIS
Learn more about VDA.
Sign-in to to VDA using browser, nothing to install.
Select the “Base — Majority-Minority Tracts” GIS Project.
The opening view shows majority-minority tract patterns, similar to the above graphic/view.

Learn more — Join me in the Data Analytics Web Sessions
Join me in a Accessing & Using GeoDemographics Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Warren Glimpse/ProximityOne/Alexandria, VA USA and Takashi Hamilton/Tsukasa/Osaka, Japan are co-developers of VDA. Contact Warren. Join Warren on LinkedIn.

Census 2020 Residential Address Counts by Block

.. using Census 2020 residential address count data to examine change since 2010 .. the Census Bureau has released preliminary Census 2020 residential address counts by Census 2010 census block. These data, count of residential addresses and group quarters addresses, reflect updates as of October 2019 and do not represent final Census 2020 counts. The data will continue to be updated to support Census 2020. See related Web section with more detail and updates.

Importance and Use
These data are of immediate value in determining and analyzing how the number of housing units have changed, 2010 to 2019. Since the data are at the census block level, they may be aggregated to any other Census-defined summary level/type of geographic area such as block group, tract, ZIP code, city, county, school district, etc. These data are also important as they give us a “year in advance look” at how small area demographics are changing since 2010. Before this, the most recent census block data were from Census 2010. A lot has happened in many areas. These data provide insights into that change. The Census 2020 block level data will be released in early 2021 for Census 2020 census block geography. So, another important feature of these data is that they are summarized for Census 2010 census block boundaries. Census 2010 and 2020 block boundaries may differ, particularly in areas experiencing larger demographic growth/change. An important limitation is that they are counts, subject to change as the Census data are collected/tabulated.

Comparing Census 2010 Housing Units with Census 2020 Address Counts
The following graphic shows patterns of Census 2010 housing counts with the Census 2020 (late 2019 vintage) residential address counts by census block. This view is focused on census tract 3608100700 (tract 000700) in Queens County, NY (code shown near center of graphic). Individual blocks are labeled with block code (4 digits) with the Census 2010 housing units (yellow label) and Census 2020 residential address count (green label) shown below the block code. As an example, the block located at the pointer has block code 3006 (or full national scope unique block code 36-081-00700-3006) with a Census 2010 count 44 housing units and a Census 2020 (late 2019 count) of 232 residential addresses. Click graphic for larger view. Expand browser window to full screen for best quality view.

.. view created with ProximityOne CV XE GIS software and related GIS project.

More About Using these Data
We have summarized these data at the census tract level and are evaluating their use, in combination with other data, to develop current estimates and projections to 2025.

Data Analytics Web Sessions
Join me in a Demographics 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.

Housing Price Index Updates & Trends

.. this past week we have updated Housing Price Index data and tools to examine patterns and trends for the U.S., states, metros and counties .. the Housing Price Index (HPI) is one of many measures useful to gain insights into the housing market. The HPI provides information on how housing value appreciation is changing for areas of interest. Use the interactive table to view, compare, sort metros/CBSAs based on annual HPI 2010-2017 and housing value appreciation during the period. These annual data, with a 2000 base index value of 100, provide insights into longer term patterns.  The HPI is alos updated quarterly for U.S./state/metro areas quarterly for analyses requiring more recent data.  These data are new as of February 2018.

Visual Analysis of Housing Price Appreciation
The following graphic shows housing value appreciation as of 2017 based on the HPI with 2000 base of 100 by county in the Charlotte, NC-SC metro area. See more about by HPI by county for the Charlotte metro.

– view developed using CV XE GIS and related GIS project.
– Click graphic for larger view and details.

See similar HPI 2017 patterns view for the Houston, TX metro.

Housing Price Appreciation 2010-2017 — Largest 10 Metros
This table, derived from the  interactive table, shows the largest 10 metros based on total population. the HPI 2010, HPI 2017, housing price appreciation 2010-2017 and total population are presented in the table. Click the CBSA code link to view HPI by county component for the metro and an extended series.

Metro	CBSA	HPI2010	HPI2017	HPA1017	Pop2016
New York	35620	159.53	172.76	8.29	20,153,634
Los Angeles	31080	169.83	242.78	42.95	13,310,447
Chicago	16980	117.48	124.58	6.04	9,512,999
Dallas	19100	120.89	175.35	45.05	7,233,323
Houston	26420	134.02	183.52	36.93	6,772,470
Washington	47900	166.82	198.74	19.13	6,131,977
PhiladelphiaA	37980	157.26	162.91	3.59	6,070,500
Miami	33100	140.43	213.91	52.33	6,066,387
Atlanta	12060	103.95	129.24	24.33	5,789,700
Boston	14460	134.33	165.27	23.03	4,794,447

– Metro names abbreviated; use table to view full name and code.

Using the HPI Annual 2010-2017 Interactive Table
The following graphic illustrates use of the HPI Annual 2010-2017 interactive table. Click graphic for larger view. This view shows metros in the 250,000-300,000 population peer group. Set your own criteria using tools below the table. There are 23 metros in this group. The table has been sorted on housing price appreciation (HPA) from 2010-2017 (second column from right). It shows that the Merced, CA metro had the highest HPA — 82.13% di=uring this period.

Use the interactive table and examine areas of interest.

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.

 

 

State of the States: Demographic Economic Update

.. tools and resources to examine the demographic-economic state of the states .. in 2016, the U.S. median housing value was $205,000 while states ranged from $113,900 (Mississippi) to $592,000 (Hawaii). See item/column H089 in the interactive table to view, rank, compare, analyze state based on this measure … in context of related housing characteristics. These data uniquely provide insights into many of the most important housing characteristics.

Use new tools, data and methods to access, integrate and analyze demographic-economic conditions for the U.S. and states. These data will update in September 2018.

Approximately 600 subject matter items from the American Community Survey ACS 2016 database (released September 2017) are included in these four pages/tables:
• General Demographics
• Social Characteristics
• Economic Characteristics
• Housing Characteristics

GIS, Data Integration & Visual Data Analysis
Use data extracted from these tables in a ready-to-use GIS project. These ACS sourced data (from the four tables listed above) have been integrated with population estimates trend data, components of change and personal income quarterly trend data. See details in this section.

Examining Characteristics & Trends
Below are four thematic pattern maps extracted from the main sections listed above. Click a map graphic for a larger view. Use the GIS project to create variations of these views.

Patterns of Median Age by State
Yellow label shows the state USPS abbreviation; white label shows median age. Legend shows color patterns associated with percent population change 2010-2016.

– View developed using CV XE GIS software and associated GIS project.
– See item/column D017 in the interactive table to view, rank, compare, analyze state based on median age.

Patterns of Educational Attainment by State
Yellow label shows the state USPS abbreviation; white label shows % college graduates. Legend shows color patterns associated with percent population change 2010-2016.

– View developed using CV XE GIS software and associated GIS project.
– See item/column S067 in the interactive table to view, rank, compare, analyze state based on percent college graduates.

Patterns of Economic Prosperity by State
Yellow label shows the state USPS abbreviation; white label shows $MHI. Legend shows color patterns associated with percent population change 2010-2016.

– View developed using CV XE GIS software and associated GIS project.
– See item/column E062 in the interactive table to view, rank, compare, analyze state based on median household income.

Patterns of Median Housing Value by State
Yellow label shows the state USPS abbreviation; white label shows $MHV. Legend shows color patterns associated with percent population change 2010-2016.

– View developed using CV XE GIS software and associated GIS project.
– See item/column H089 in the interactive table to view, rank, compare, analyze state based on median housing value.

Examining Characteristics & Trends; Using Data Analytics
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.

Creating & Using Location Shapefiles

.. GIS tools and methods to develop and update location shapefiles .. location shapefiles are essential to most GIS applications. Location shapefiles, or point shapefiles, enable viewing/analyzing locations on a map and attributes of these locations such store or customer ID, street address, city, date updated, value, ZIP code and wide-ranging attributes about the location. This section reviews tools and methods to develop and use location shapefiles. See more detail about topics covered in this section in the related Web page.

Viewing/Analyzing Store Locations in the Dallas, TX Area
The following graphic illustrates how store locations can be shown in context of other geography and associated demographic-economic attributes. This view shows store locations (red markers) in context of Dallas city (blue cross-hatch pattern) and broader metro area. Markers shown in this view are based on a location shapefile created using steps described below. The identify tool is used to click on a location and show attributes in a mini-profile.

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

View the locations contextually with thematic patterns by tract or other geography. Combine views of store, customer, agent, competitor and other location shapefiles.
The following view shows patterns of median household income by census tract.

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

Development of location shapefiles often starts with a list of addresses. Locations are not always address-oriented; they might be geographically dispersed measurement or transaction locations — having no address assigned. In applications reviewed here, locations are organized as rows in a CSV file. Each CSV file contains like-structured attributes for each location. The example used in this section uses store locations located in the Dallas, TX area.

There are two basic methods used to create location shapefiles: 1) geocoding address-data contained in the source data file or 2) using the latitude-longitude of the location included in the source data file record. The focus here is on option 2 — using the latitude-longitude of the location already present in the source data file.

Creating a Location Shapefile
The process of creating a location shapefile uses the CV XE GIS Manage Location Shapefile feature. With CV running, the process is started with File>Tools>ManageLocationShapefile. The following form appears.

.. ManageLocationShapefile feature/operation in ProximityOne CV XE GIS.

CV XE GIS provides other ways to create location shapefiles:
• Tools>AddShapes>Points — click points on the map window canvas.
• Tools>FindAddress — creates a single point shapefile based on specified address.
• Tools>FindAddress (Batch) — creates a point shapefile based on specified file of address records.
See details in User Guide.

Steps to Create a Location Shapefile
The process of creating the shapefile “C:\cvxe\1\locations1pts.shp” can be viewed by clicking the Run button on the form (with CV running). Two input CSV structured files are required:
• data definition file
• source data file

There are two sets of illustration location input files included with the CV installer:
• locations1_dd.csv and locations1.csv (7 locations in Johnson County, KS)
• locations2_dd.csv and locations2.csv (252 locations in Dallas and Houston)
These files are located in the \1 (typically c:\cvxe\1) folder. The marker/location shapefile used in the map shown above was created using the lcoations2 input files.

Data Definition File
The Data Definition (DD) file is an ASCII/text file structured as a CSV file. It may created with any text editor. The DD file is specific to the source data file. But in the case of recurring source data files for different periods the same DD file might apply to many source data files. There are several rules and guidelines for development of the DD file:
• there is one line/record for each field in the source data file.
• each line/record must be structured in an exact form:
.. each line/record is comprised of exactly 4 elements separated by a comma:
.. 1 field name for subject matter item
– must consist of 1 to 10 characters and include no blanks or special characters
.. 2 field type: C for character, N for numeric
.. 3 field length: an integer specifying the maximum with of the field
.. 4 maximum number of decimals for field (value is 0 for character fields)
The DD File must include three final fields:
LATITUDE,n,12,6
LONGITUDE,n,12,6
GEOID,c,15,0
The structure of these three DD file records must be as shown above. The source data file, described below, must have the LATITUDE and LONGITUDE fields populated with accurate values. The GEOID field may populated with either an accurate value of placeholder value like 0.

Example. Data for each store for the default DD file name “C:\cvxe\1\locations1_dd.csv” include the following fields/attributes:
  NAME,C,45,0
STORE,c,15,0
ADDRESS,c,60,0
CITY,c,40,0
LATITUDE,n,12,6
LONGITUDE,n,12,6
GEOID,c,15,0

Optionally create a DD File using the Create DD File button on the form. Clicking this button will create a DD File containing attributes of the dBase file specified in the associated edit box. The DD File name is created from the dBase file name. If the dBase file name is “c:\cvxe\1\locations1pts.dbf”, the DD File will be named “c:\cvxe\1\locations1pts_dd.csv”.

About the GEOID
The GEOID is a 15 character code which defines the Census 2010 census block containing each location. The GEOID is generally assigned by the ManageLocationShapefile operation and is one of the important and distinctive features of this tool. The GEOID is used to uniquely determine, with the GIS application, any of the following: state, county, census tract, block group, or census block.

The GEOID, as used in this section, is the 15 character Census 2010 geocode for the census block. The GEOID value 481130002011012 (see in location profile in map at top of section) is structured as:
state FIPS code: 48 (2 chars)
county FIPS code: 113 (3 chars)
census tract code 000201 (6 chars)
census block code: 1012 (4 chars) (block group code: 1 — first of 4 characters)

About the Source Data File
The Source Data File is an ASCII/text file structured as a CSV file. It is typically developed by exporting/saving an Excel or dBase file in CSV structure. There are several rules and guidelines for development of the source data file:
• fields must be structured and arranged as defined in the DD File.
• character fields must not contain embedded commas.
• final items in record sequence must be:
.. LATITUDE – must have accurate decimal degree value; 6 digit precision suggested.
.. LONGITUDE- must have accurate decimal degree value; 6 digit precision suggested.
.. GEOID – this may be 0, not assigned or the accurately assigned GEOID value.
– optionally create/rewrite the GEOID used in the new shapefile.

Updates; Combining Vintages of Location Attributes
Location based data might update frequently, even daily. The recommended method to add, update and extend the scope of location-based data is to create new address shapefiles corresponding to different vintages or dates covered. The structure of the files must be the same so that they files can be used together or separately. Suppose there is one set of data covering year to date and a second set of data covering the following month. The ManagePointShapefile operation would be run once for each time period. Two shapefiles would be created. These shapefiles may be added to a GIS project and used separately or in combination to view/analyze patterns.

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.

Examining County Migration: 2010-2016

.. tools and data to examine U.S. by county migration 2010 to 2016 … is the population moving away or into your counties of interest? What are the trends; what is causing the change? What are the characteristics of the population moving in and out? How might this impact your living environment and business?

The total net international migration among all counties 7/1/2010 – 7/1/2016 was 5,641,260, an annual average of 940,432. The sum of net domestic migration among counties is zero by definition, but domestic migration among counties varies radically by size and direction. This section is focused on U.S. by county migration from 2010 to 2016. Migration is one component of change used to develop population estimates. See more about county population estimates and components of change in this related Web section.

Largest 10 Counties Based on 2016 Population
This table shows how domestic migration varies widely among the most populated counties. Use this interactive table to develop your own custom views for counties of interest.

Patterns of Population Change by County, 2010-2016
– the role and impact of migration
The following graphic shows how counties have gained population (blue and green) and lost population (orange and red) during the period 2010 to 2016. Click graphic for larger view; expand browser window for best quality view.

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

Examining Population Components of Change
– net migration and natural change
Population change can be examined in terms of components of change. There are three components of change: births, deaths, and migration. The change in the population from births and deaths is often combined and referred to as natural increase or natural change. Populations grow or shrink depending on if they gain people faster than they lose them. Examining a county’s unique combination of natural change and migration provides insights into why its population is changing and how quickly the change is occurring.

Using the Interactive Table
– examining migration by county
Use the interactive table to examine characters of counties by states, metro or peer group. The following graphic illustrates use of the interactive table to view net migration for the Houston metro by county. The net migration button was used to select only the net migration columns, FindCBSA button used to show only counties in this metro and the final step was to sort the resulting table on 2016 population. 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.

Housing Value Appreciation by 3-Digit ZIP Code: 2015Q3-2016Q3

.. tools, data and methods to examine housing value appreciation from 2015Q3 to 2016Q3 by 3-digit ZIP code based on the Housing Price Index (HPI).  How is the housing value changing in areas of interest?  How does housing value appreciation compare among a set of ZIP codes? Which ZIP code areas have the highest and lowest housing value appreciation in a state, region custom defined areas of interest? The HPI is calculated in part using home sales price information from Fannie Mae- and Freddie Mac-acquired mortgages. The U.S. all transactions HPI rose 5.6 percent from the 3rd quarter of 2015 to the 3rd quarter of 2016. Rank, compare, evaluate quarterly or annual housing value change for the approximate 900 3-digit ZIP code areas using the interactive table.

3-Digit ZIP Codes with Highest Housing Value Appreciation
Derived from the interactive table below this table shows the ten 3-digit ZIP codes having the highest housing value appreciation over the year 2015Q3-2016Q3. The areas are ranked on percent HPI change (rightmost column).

Gaining Insights in Housing Prices, Conditions & Markets
.. data, tools and methods to assess characteristics, patterns & trends
.. weekly Housing Data Analytics Lab sessions

Patterns of Housing Value Change by 3-Digit ZIP Code
The following graphic shows housing value appreciation 2015Q3-2016Q3 by 3-digit ZIP code based on the HPI. Use related GIS tools to zoom-in, assign labels, show in context with other geography.

– view developed using CVGIS and related GIS project.
– Click graphic for larger view and details;

Examining Housing Appreciation by 3-Digit ZIP Code
Use the interactive table below to view/rank/compare the non-seasonally adjusted “all transactions” HPI for the most recent 5 quarters for all 3-digit ZIP codes. The ranking table shows the latest quarterly HPI data and preceding quarters for one year earlier. This table will be updated on February 24, 2017, with 4th quarter 2016 data and related prior quarterly estimates and re-computed quarterly change values (last column).

Using the Interactive Table
The following graphic illustrates use of the HPI by 3-digit ZIP code interactive table. HPI data are shown for the quarterly period 2015Q3 through 2016Q3. The state selection below the table has been used to select only California ZIP codes. The Group1 button below the table has been used to select ZIP codes with a 2016Q3 HPI value of 175 ore more. The table is then sorted on the rightmost column. The resulting view shows that among all California 3-digit ZIPs having an HPI of 175 or more in 2016Q3, ZIP code 948/Richmond CA had the highest housing value appreciation — a 10.6% increase over the year.

Use the interactive table to examine states or ZIP code groups of interest.

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.

ZIP Codes with Highest & Lowest Economic Prosperity

.. the latest data for ZIP Code Areas show that eleven had a median household income of $250,000 or more during the period 2011-15. More than 20 ZIP code areas had a median housing value of $2,000,000 or more. Contrast these ZIP code areas with higher economic prosperity with the more than 150 ZIP codes that had a median housing value of less than $30,000.  Use the interactive table in this related Web section to see which ZIPs meet these and other criteria.

ZIP Codes with MHI $100,000 or More; Dallas, TX Metro
Analyzing economic prosperity patterns using combined types of small area geography … the following graphic shows ZIP code areas a red markers with the median household income or $100,000 or more in context of median household income by census tract thematic pattern. Click graphic for larger view with more detail. Expand browser window for best quality view. Use CV XE GIS software and associated GIS project to develop variations of this view for your areas of interest. .

– view developed with CV XE GIS software.

This section reviews measures of economic prosperity for all ZIP code areas. These data were released in December 2016. This section updates with new data December 2017. See the list of all ZIP ccdes showing population, housing and economic characteristics in the interactive table shown below. Use the interactive table to view, rank, compare and query ZIP code attributes.

Examining demographic-economic characteristics by ZIP code is important for several reasons. We are familiar with our own ZIP codes as a geographic location. We tend to be interested in our area compared to other areas. ZIP codes provide an easy way to do that. Also, many secondary data resources are tabulated by ZIP code area; some important data are only available by ZIP code. See more about ZIP Code areas.

Resources & Methods to Examine Small Area Demographics
• See related ZIP Code Demographic-Economic Interactive Tables
  .. extended subject matter
• See related Census Tract Code Demographic-Economic Interactive Tables
• Examine ZIP Code Urban/Rural Characteristics
• Examine ZIP Code Business Establishment patterns
• Examine ZIP Code Housing Price Index patterns
• Join us in the weekly Data Analytics Lab Sessions
  .. reviewing applications using these and related data.

ZIP Code Areas with $MHI $100,000 or More
The following graphic shows ZIP code areas as red markers having median household income or $100,000 or more. Click graphic for larger view with more detail. Expand browser window for best quality view. Use CV XE GIS software and associated GIS project to develop variations of this view; integrate other data; select alternative ACS 2015 subject matter.

– view developed with CV XE GIS software. Click graphic for larger view.

ZIP Code Areas with $MHV Less than $30,000
The following graphic shows ZIP code areas as orange markers having median housing value of less than $30,000. Click graphic for larger view with more detail. Expand browser window for best quality view. Use CV XE GIS software and associated GIS project to develop variations of this view; integrate other data; select alternative ACS 2015 subject matter.

– view developed with CV XE GIS software. Click graphic for larger view.

ZIP Code Areas: Population & Economic Prosperity
  — Interactive Table –
Use the interactive table to view, rank, compare, query ZIP codes based on a selection of demographic-economic measures. The following graphic illustrates how the table can be used to examine patterns of the three digit ZIP code area (San Diego) by 5-digit ZIP code. Table operations are used to select ZIP codes in the 921 3-digit area (containing 39 5-digit ZIP codes). These 39 ZIP code are then ranked in descending order on median household income. See results in the table shown below. ZIP code 92145 has the highest $MHI in this group with $228.036.

– click graphic for larger view.

Try it yourself. Use the table to examine a set of ZIP codes on your selected criteria in for a state/area of interest.

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.

State and Regional Decision-Making Information

Organized on a state-by-state basis, use tools and geographic, demographic and economic data resources in these sections to facilitate planning and analysis. Updated frequently, these sections provide a unique means to access to multi-sourced data to develop insights into patterns, characteristics and trends on wide-ranging issues. Bookmark the related main Web page; keep up-to-date.

Using these Resources
Knowing “where we are” and “how things have changed” are key factors in knowing about the where, when and how of future change — and how that change might impact you. There are many sources of this knowledge. Often the required data do not knit together in an ideal manner. Key data are available for different types of geography, become available at different points in time and are often not the perfect subject matter. These sections provide access to relevant data and a means to consume the data more effectively than might otherwise be possible. Use these data, tools and resources in combination with other data to perform wide-ranging data analytics. See examples.

Select a State/Area

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware D.C. Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas

Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina

North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Topics for each State — with drill-down to census block
• Visual pattern analysis tools … using GIS resources
• Digital Map Database
• Situation & Outlook
• Metropolitan Areas
• Congressional Districts
• Counties
• Cities/Places
• Census Tracts
• ZIP Code Areas
• K-12 Education, Schools & School Districts
• Block Groups
• Census Blocks

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.