VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2020 Census blocks nest within every other 2020 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

This blog post was posted on January 28, 2013.

After a year of pandemic and calls for social justice change, the U.S. Department of Health and Human Services (HHS) answered both by bringing on a cohort of fellows focused on data, innovative solutions, and equity. HHS’ InnovationX and the Office of the Chief Data Officer hosted four Civic Digital Fellows, and a Kinder Summer Scholar throughout the Summer of 2021.

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

The contents of the dataset relate to the population living in the province of Trento. The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Historical-Series/Demography Data are grouped by year and gender. Data are expressed in absolute values. The metadata ‘time coverage’ refers to the time interval taken into account by the Historical Series which is identified in the file name with the suffix _ST. Time coverage refers to 31 December of each year. The dataset is updated to 31 December each year with the addition of a new time series. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated with the Good Tables library. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html ATTRIBUTION: data processed by the Office for the Study of Policies and the Labour Market on ISTAT data.

Indonesia Import: Volume: HS: 82056000: Blow lamps data was reported at 0.001 kg mn in Jun 2019. This records a decrease from the previous number of 0.001 kg mn for May 2019. Indonesia Import: Volume: HS: 82056000: Blow lamps data is updated monthly, averaging 0.001 kg mn from Jan 2017 (Median) to Jun 2019, with 30 observations. The data reached an all-time high of 0.009 kg mn in Feb 2019 and a record low of 0.000 kg mn in Jan 2018. Indonesia Import: Volume: HS: 82056000: Blow lamps data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAC015: Foreign Trade: Indonesia Custom Fare 2012: by HS: 8 Digits: Base Metals and Articles Thereof.

The state of annual leave in the UK and beyond from 2022 to 2024. Survey data and user data.

Indonesia Import: Value: HS: 82056000: Blow lamps data was reported at 0.014 USD mn in Jun 2019. This records an increase from the previous number of 0.009 USD mn for May 2019. Indonesia Import: Value: HS: 82056000: Blow lamps data is updated monthly, averaging 0.007 USD mn from Jan 2017 (Median) to Jun 2019, with 30 observations. The data reached an all-time high of 0.031 USD mn in Jul 2018 and a record low of 0.000 USD mn in Jan 2018. Indonesia Import: Value: HS: 82056000: Blow lamps data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAC015: Foreign Trade: Indonesia Custom Fare 2012: by HS: 8 Digits: Base Metals and Articles Thereof.

Indonesia Export: Value: HS: 82056000: Blow lamps data was reported at 0.000 USD mn in May 2019. This records a decrease from the previous number of 0.005 USD mn for Apr 2019. Indonesia Export: Value: HS: 82056000: Blow lamps data is updated monthly, averaging 0.001 USD mn from Feb 2017 (Median) to May 2019, with 15 observations. The data reached an all-time high of 0.006 USD mn in Dec 2018 and a record low of 0.000 USD mn in Mar 2019. Indonesia Export: Value: HS: 82056000: Blow lamps data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAC015: Foreign Trade: Indonesia Custom Fare 2012: by HS: 8 Digits: Base Metals and Articles Thereof.

Indonesia Export: Volume: HS: 82056000: Blow lamps data was reported at 0.000 kg mn in May 2019. This records an increase from the previous number of 0.000 kg mn for Apr 2019. Indonesia Export: Volume: HS: 82056000: Blow lamps data is updated monthly, averaging 0.000 kg mn from Feb 2017 (Median) to May 2019, with 15 observations. The data reached an all-time high of 0.008 kg mn in Feb 2019 and a record low of 0.000 kg mn in Jun 2017. Indonesia Export: Volume: HS: 82056000: Blow lamps data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAC015: Foreign Trade: Indonesia Custom Fare 2012: by HS: 8 Digits: Base Metals and Articles Thereof.

Indonesia Import: Volume: HS: 84773000: Blow moulding machines data was reported at 0.074 kg mn in Jun 2019. This records a decrease from the previous number of 0.295 kg mn for May 2019. Indonesia Import: Volume: HS: 84773000: Blow moulding machines data is updated monthly, averaging 0.218 kg mn from Jan 2017 (Median) to Jun 2019, with 30 observations. The data reached an all-time high of 0.465 kg mn in Nov 2018 and a record low of 0.074 kg mn in Jun 2019. Indonesia Import: Volume: HS: 84773000: Blow moulding machines data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAC016: Foreign Trade: Indonesia Custom Fare 2012: by HS: 8 Digits: Machinery, Electric and Electronic Equipments.

Australia PPI: Articles: Mfg Industries: Rubber and Plastics: Plastic Products: Plastic Blow Moulded Prod data was reported at 90.600 1989-1990=100 in Jun 2009. This records a decrease from the previous number of 91.700 1989-1990=100 for Mar 2009. Australia PPI: Articles: Mfg Industries: Rubber and Plastics: Plastic Products: Plastic Blow Moulded Prod data is updated quarterly, averaging 99.100 1989-1990=100 from Mar 1983 (Median) to Jun 2009, with 106 observations. The data reached an all-time high of 117.400 1989-1990=100 in Dec 1996 and a record low of 70.300 1989-1990=100 in Mar 1983. Australia PPI: Articles: Mfg Industries: Rubber and Plastics: Plastic Products: Plastic Blow Moulded Prod data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.I022: Producer Price Index: 1989-90=100: ANZSIC 1993: Output of the Manufacturing Industry.