Category Archives: real estate investment

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.

Housing Price Index by 5-Digit ZIP Code

.. tools to examine housing prices by 5-digit ZIP code and how they are changing .. of the 17,931 5-digit ZIP codes tabulated, 8,074 experienced a decrease in housing value during the period 2010 to 2015. At the same time, 8,672 ZIP code areas experienced an increase in housing value. Housing prices increased for most ZIP codes from 2014 to 2014.  Find out more about housing prices and trends for ZIP codes of interest using tools described here. These data are based on experimental estimates of the Housing Price Index (HPI) by 5-digit ZIP code based in part on home sales price data from Fannie Mae- and Freddie Mac-acquired mortgages. See more about these data.

• Use the interactive table to view, rank, compare the HPI for all 5-digit ZIP code areas tabulated.
• Use GIS tools described here to develop thematic pattern maps; add your own data & geography, select different HPI measures or criteria; zoom to different geographic extents, label and modify colors as desired.

Gaining Insights in Housing Prices, Conditions & Markets
  .. Characteristics, Patterns & Trends
  .. join in .. one hour web session — overview & connectivity details

Patterns of Housing Value Change by ZIP Code: 2010-15
The following graphic shows patterns of housing value appreciation by ZIP Code: 2010-15 for the Houston metro (bold brown boundary). The color patterns/intervals are shown in the inset legend. Data are not available, using the criteria applied (2000 base year), for areas not colored In the larger view (click graphic), ZIP codes are labeled with HPI percent change from 2010 to 2015. Click graphic for larger view. Expand browser to full window for best quality view. Use the GIS tools described here to develop thematic pattern maps for a range of data and criteria.

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

Additional views:
Atlanta area
New York City area
Washington, DC area
Los Angeles area

Examining Recent Trends; Current Estimates & Projections
The interactive table presents annual HPI data 2010 through 2015. A much larger set of these ZIP codes show a negative change between 2010 and 2015 compared to the one year change 2014-2015; The data generally show more ZIP codes experiencing housing value appreciation 2014-2015 compared to the longer period 2010 to 2015. These trends underscore the importance of having more recent data for use in analysis, planning and decision-making. The next update based on transaction data will be May 2017 or later.

ProximityOne uses the HPI transaction data with other data to develop HPI current estimates (2016) and annual projections to 2021 with quarterly updates as a part of the Regional Demographic-Economic Modeling System (RDEMS). Experimental county-up (metro, state, U.S.) and sub-county estimates and projections are planned for the fall 2016 quarterly update. The model based estimates and projections include the number of units by type and value that are added to the housing stock used to compute a variation of the HPI.

Housing Price Index by 5-Digit ZIP Code: 2010-2015
  — Interactive Table
Use the interactive table to examine the Housing Price Index (HPI) by 5-digit ZIP code. The following graphic illustrates use of the table to show the 10 ZIP codes experiencing the largest percentage increase in the HPI from 2014 to 2015. Click graphic for larger view. Examine cities or ZIP code ranges of interest using tools below the table.

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.

Developing & Using Map Graphics

.. steps to develop map graphics & visual analysis of market/study areas .. map graphics are an important part of most demographic-economic analyses and essential for many applications. Not only are are maps needed to show geographic boundaries and the relative location of geography within a broader area, they can come alive by showing patterns. A thematic pattern map of median household income by block group is a good example; higher and lower areas of economic prosperity by neighborhood can be immediately determined. Map graphics can improve our ability to communicate complex information. Convey information faster. Make more compelling presentations. Collaborate more effectively through the use of map graphics.  See related Web section for more details.

The focus of this section is on creating and using KML files to prepare map graphics for use in developing Market-Study Area Comparative Analysis Reports. These files and map graphics also have broader uses. Steps are reviewed to develop the KML circular area map graphics files, convert them to shapefile structure and integrate both files into mapping and GIS applications and put them into operational use. 

KML (Keyhole Markup Language) files are XML structured files useful for visualizing geographic objects (like circles) using Internet-based browsers, notably Google Earth. Why develop/use KML files? They are easy to create with precision, there is little to no learning curve, they can be used in many venues and they are free to develop. KML files can be used side-by-side with shapefiles. Shapefiles, structured very much unlike KML files, are the dominant vector-based file structure used in GIS applications involved in both viewing and geospatial analysis. 

Developing Circular KML Files & Map Graphics
An “objective view” of this section is shown in the following graphic. The graphic shows a study site location (red marker), 1-mile & 3-mile radius circles. The site location is a Starbucks located at 302 Nichols Road, Kansas City, MO 64112. The view shows a circular KML-based graphics in context of patterns of median household income by block group. Develop similar views for any area, any site circular configuration, using steps reviewed in this section.

– view developed using CV XE GIS

Creating the KML Circular Graphics File
Proceed through the next steps to develop a KML file used to create the graphic below on left — a Starbucks located at 302 Nichols Road, Kansas City, MO 64112. Graphic on the right is a Starbucks location in Paris, used to illustrate this process works globally. Both graphics include study area center point and 1-mile and 3-mile radius circles.

302 Nichols Road, Kansas City, MO 64112
23 Avenue de Wagram, 75001 Paris, France

Start the create KML file application
• Key in address 302 Nichols Road, Kansas City, MO 64112 to Google Maps
.. see the latitude-longitude (39.041548,-94.592965) in the URL bar.
• Open this web page to create the circles & save results as KML file.
• Refresh this page if making a new KML file.
• Set the colors and lines to medium, blank and clear.
• Enter coordinates — key in lat 39.041548 and lon -94.592965
.. these for for this example
.. enter the values for your location of interest
Add center point
• In the Radius Distance, key in 0.05 miles
• Click Draw Radius blue button (at right of longitude).
Add site 1 circle
• In the Radius Distance, key in 1.00 miles (use preferred radius for inner circle).
• Click Draw Radius blue button (at right of longitude).
Add site 2 circle
• In the Radius Distance, key in 3.00 miles (use preferred radius for inner circle).
• Click Draw Radius blue button (at right of longitude).
View study area geography
• Optionally navigate up to the map view and make the view similar to the graphic at the top of this page 
.. this step is not required but might be useful to verify the study area appearance.
Save KML file
• Navigate down the page to “Google Earth KML Output”. Click the blue button Generate KML.
• Click “Download KML file Here.” Save the file to a folder and make a note of the file path and name
.. save the file as c:\sitereport\302nichols.kml (this file and filename are used below).
Done
• The three part KML file has been created and saved to the local computer. 
• Finished using this browser application.

This same process may be used again to create similarly structured KML files of any radius about any point for any location in the world. 

Loading a KML file into Google Maps
Optionally create the objective map graphic using the following steps. Or, the KML file may be used with the CV XE GIS software (see below) enabling yet further analytical possibilities. 

 Click this link to start the Google MyMaps application.
• When the new page opens click create new map button
• Next click import button
• Enter the file path/name as created above (c:\sitereport\302nichols.kml), or any KML file.
• Edit the MyMaps rendering to achieve preferred view.
• Use preferred screen capture tool to save that part of the map view as a graphic for the study report.

Using the KML File with an Existing GIS Project and converting the KML file to shapefile structure

1. Add the KML file to an Existing CV XE GIS Project
• Start the CV XE GIS software and open the project file c:\cvxe\1\cvxe_us2.gis (distributed with installer).
.. uncheck Locations and $MHI x BG layers in legend panel.
• Click the AddLayer button (second button from left on toolbar)
• Select the KML file that was created above (c:\sitereport\302nichols.kml) .. circles appear in the map window.
• Use LayerEditor to adjust settings for KML layer (transparent, bold outline)
• Navigate to zoom-in view and smaller map window.
• Use Toolbar button Save to Image (button 7 from left) to save the map window view to a .jpg file.

Navigate to this view:

2. Converting a KML File to a Shapefile
This step requires the CV XE GIS Basic or higher level version. After the KML layer appears in the above sequence, proceed as follows:

• Click File>ExportShapefile.
• Select the KML layer name.
• Set Coordinate System edit box value to NAD83.
• Click OK button.
• On the Export Layer/FileSave dialog, select an output file path and name.
• The shapefile is generated and may be reused with any GIS project.

3. Editing attributes of the study area shapefile and project file
There are three shapes in the shapefile (center point, circle 1, circle 2). 
Modify the appearance of these shapes/objects by using the Select tool (mouse in Select mode).
• In legend panel click on circles layer; name turns blue indicating this is the active layer.
• In the map window, click in the circle; the profile/editor appears (pop-up)
• Initially all three shapes have the name “Polygon”.
.. change the object names successively to Point1 (the small circle), Circle1 and Circle2.
• The shapefile attributes have been permanently changed.
• When each shape/object has been renamed, use the LayerEditor to modify the appearance of each shape.
.. the changes modify the project file and not the shapefile.
.. optional save the project (overwriting the former version) or save the project with a new name.

Renaming a shape to “Point1”
Click for larger view

Using LayerEditor to set attributes of the study area layer
Click for larger view

View of final study area layer in context of broader project
Click for larger view

View as above with $MHI x BG checked on/visible
Click 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 Price Index Metro & State Trends

.. HPI quarterly update … use the Housing Price Index (HPI) to examine quarterly or annual housing value appreciation by metro or state. How is housing value appreciation changing among metros of interest? The HPI is calculated in part using home sales price information from Fannie Mae- and Freddie Mac-acquired mortgages. The U.S. HPI all transactions, non-seasonally adjusted, value increased by 5.25 percent from 2014Q2 to 2015Q2. During this period the Sebastian-Vero Beach, FL MSA experienced the highest rate of housing value appreciation among all metros.

Use the interactive table to compare and contrast HPI quarterly data for/among all metropolitan statistical areas, states and the U.S. Use the GIS tools and data resources reviewed below to visually examine and map these data. See related Web section for more detail.

Visual Analysis of 2014Q2-2015Q2 HPI Patterns
The following graphic shows housing value appreciation 2014Q2-2015Q2 by metro based on the HPI.

– click graphic for larger view and details; view developed using CV XE GIS.
Use the HPI GIS project with CV XE GIS software; create different views.
– view/analyze different HPI measures; zoom-in; add labels.
– apply queries; add other geography/data.
– create views/graphics for reports and stories.

HPI Metro & State Patterns Interactive Table
Use the interactive table to view/rank/compare the non-seasonally adjusted “all transactions” HPI for the most recent 5 quarters for all Metropolitan Statistical Areas (MSAs), states and the U.S. The graphic shown below illustrates the start-up view of the table. The blue highlighted value shows the U.S. national annual change 2014Q2 to 2015Q2.

– click graphic for larger view

Ranking Metros
The following graphic illustrates how the table can be used to 1) select only metros and 2) rank metros in descending order based on annual change 2014Q2-2015Q2. The Sebastian-Vero Beach, FL MSA experienced the highest rate of housing value appreciation among all metros.

– click graphic for larger view

Data Analytics Lab Web Sessions
The HPI provides one part of a larger picture of demographic-economic conditions and trends. Join me in Data Analytics Lab sessions where we explore the mechanics of making the HPI thematic pattern view shown above. Examine the process of integrating other data. Create variations of this view or entirely different visual analysis GIS projects.

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.

Housing Price Index 2013Q3-2014Q3

The Housing Price Index (HPI) is a popular economic measure available by state and metro in a timely manner with quarterly updates.  It fits into many decision-making applications where insights are needed into how housing prices are changing by state, region and metro. This section provides an update on the HPI 2014Q3 and quarterly data for the past year. See the related Web page for more detailed data and access to the HPI data via interactive table.

Visual Analysis of 2013Q3-2014Q3 HPI Patterns
The following graphic shows housing value appreciation 2013Q3-2014Q3 by metro based on the HPI.

Click graphic for larger view and details. This view developed using CV XE GIS and related GIS project. Members of the ProximityOne User Group (join now, no fee) may used the CV XE GIS software and GIS project to create similar views with different HPI measures. Zoom-in. Add labels. Add other geography/data. Create views/graphics for reports and stories.

The Larger Picture
By itself, the HPI provides limited insights into the broader picture of “the why” and “how otherwise” states and metros are changing. The MetroDynamics Metro Profiles provide an integrated view of the HPI measure in combination with other economic, demographic and business activity measure. View the HPI integrated with other subject matter in these examples … HoustonCharlotte. Metro Profiles are updated continuously and are available for each of the 917 metropolitan areas

About the HPI
The HPI is calculated using home sales price information from Fannie Mae- and Freddie Mac-acquired mortgages, continued upward momentum in U.S. house prices remained strong in the third quarter 2014, as prices rose 0.9 percent from the previous quarter. This is the thirteenth consecutive quarterly price increase in the purchase-only, seasonally adjusted index.

As measured with purchase-only indexes for the 100 most populated metropolitan areas in the U.S., third quarter price increases were greatest in the San Jose-Sunnyvale-Santa Clara, CA MSA where prices increased by 6.6 percent. Prices were weakest in the Greensboro-High Point, NC MSA, where they fell 4.4 percent. Eleven of the 20 metropolitan areas with the highest annual appreciation rates were in California.

HPI Interactive Table
Use the HPI interactive table to view/rank/compare the non-seasonally adjusted “all transactions” HPI for the most recent 5 quarters for all Metropolitan Statistical Areas (MSAs), states and the U.S. The graphic shown below illustrates use of the interactive table to rank all metros in descending order on the percent change over the past year.

Updates
Quarterly HPI measures are used to updated the interactive table, GIS project and Metro Profiles. Use the calender to view the year-ahead HPI release and integration schedule.

Support Using these Resources
Learn more about demographic economic data and related analytical tools. Join us in a Decision-Making Information Web session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants.