New Private Housing Units Authorized by Building Permits: 1-Unit Structures for Los Angeles-Long Beach-Anaheim, CA (MSA) (LOSA106BP1FHSA)
NOTES
| Title | Release Dates | |
|
|
||
| New Private Housing Units Authorized by Building Permits: 1-Unit Structures for Los Angeles-Long Beach-Santa Ana, CA (MSA) | 2012-02-27 | 2019-03-13 |
| New Private Housing Units Authorized by Building Permits: 1-Unit Structures for Los Angeles-Long Beach-Anaheim, CA (MSA) | 2019-03-14 | 2024-09-25 |
| Source | ||
|
|
||
| Federal Reserve Bank of St. Louis | 2012-02-27 | 2018-04-23 |
| U.S. Census Bureau | 2018-04-24 | 2024-09-25 |
| Release | ||
|
|
||
| Housing Units Authorized By Building Permits (Seasonally Adjusted) | 2012-02-27 | 2018-04-23 |
| Housing Units Authorized By Building Permits | 2018-04-24 | 2024-09-25 |
| Units | ||
|
|
||
| Units | 2012-02-27 | 2024-09-25 |
| Frequency | ||
|
|
||
| Monthly | 2012-02-27 | 2024-09-25 |
| Seasonal Adjustment | ||
|
|
||
| Seasonally Adjusted | 2012-02-27 | 2024-09-25 |
| Notes | ||
|
|
||
|
The data services of the Federal Reserve Bank of St. Louis include series that are seasonally adjusted. To make these adjustments, we use the X-12 Procedure of SAS to remove the seasonal component of the series so that non-seasonal trends can be analyzed. This procedure is based on the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on this program can be found at http://www.census.gov/srd/www/x12a/. The seasonal moving average function used is that of the Census Bureau's X-11-ARIMA program. This includes a 3x3 moving average for the initial seasonal factors and a 3x5 moving average to calculate the final seasonal factors. The D11 function is also used to output the entire seasonally adjusted series that is displayed. For specific information on the SAS X-12 procedure, please visit their website: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_x12_sect001.htm. |
2012-02-27 | 2018-04-23 |
|
The data services of the Federal Reserve Bank of St. Louis include series that are seasonally adjusted. To make these adjustments, we use the X-12 Procedure of SAS to remove the seasonal component of the series so that non-seasonal trends can be analyzed. This procedure is based on the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on this program can be found at https://www.census.gov/srd/www/x13as/. The seasonal moving average function used is that of the Census Bureau's X-11-ARIMA program. This includes a 3x3 moving average for the initial seasonal factors and a 3x5 moving average to calculate the final seasonal factors. The D11 function is also used to output the entire seasonally adjusted series that is displayed. For specific information on the SAS X-12 procedure, please visit their website: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_x12_sect001.htm. Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator. |
2018-04-24 | 2019-03-05 |
|
The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found at https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html. More information on X-13ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator. |
2019-03-06 | 2019-03-13 |
|
The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. In some cases, the NSA data will be updated but the SA data will not be updated. The reason is usually that the data series has not accumulated enough new seasonal factors to trigger an adjustment.The NSA series can be located here (https://fred.stlouisfed.org/series/LOSA106BP1FH) The FRED team is currently working on a new procedure to replace SA data that has not yet be updated with NSA data that has been updated. Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator. |
2019-03-14 | 2019-12-22 |
|
The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/LOSA106BP1FH). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." |
2019-12-23 | 2020-02-25 |
|
This series represents the total number of building permits for 1-unit structure types. The 1-unit structure type is a single-family home or house. A single-family home or house types include fully detached, semidetached (semidetached, side-by-side), row houses, and townhouses. For attached units, each must be separated from the adjacent unit by a ground-to-roof wall in order to be classified as a single-family structure. These units also must not share heating/air-conditioning systems or utilities. Units built on top of one another and those built side-by-side that do not have a ground-to-roof wall and/or have common facilities (i.e., attic, basement, heating plant, plumbing, etc.) are not included under single-family structures. The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/LOSA106BP1FH). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator. |
2020-02-26 | 2024-09-25 |
RELATED DATA AND CONTENT
Data Suggestions Based On Your Search
Content Suggestions
