Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Note:- Only publicly available data can be worked upon

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Our dedication to offering high-quality real estate data extraction services is based on the utilization of Zillow Real Estate Data. APISCRAPY's integration of Zillow Real Estate Data sets it different from the competition, whether you're a seasoned real estate professional or a homeowner wanting to sell, buy, or invest.

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Searchable online database of homes for sale, rent, and not currently on the market, with value estimator, market report, and real-estate trend tool. Users search by _location (neighborhood, city, zip code, address) and parameters, such as property specifications, pricing, and keyword. Registration allows for favorite listing saving, customized property e-mail alerts, and other privileges. Users can also access real-estate listing data through an API.

This web service depicts nearly 17,000 neighborhood boundaries in over 650 U.S. cities. Zillow created the neighborhood boundaries and is sharing them with the public under a Creative Commons license. Users of the data must credit Zillow as the data source. Additional information regarding this dataset can be found at https://www.zillow.com/howto/api/neighborhood-boundaries.htm. Note that neighborhood boundaries are not formal geographic boundaries for legal or jurisdictional purposes and should not be interpreted as such.

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.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Charlie

Released under Other (specified in description)

Contents

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 List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents 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. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

The ZTRAX data is a national database of property sales collected by Zillow. The data is available to researchers who submit a research proposal to Zillow. Portions of this dataset are inaccessible because: Not publicly available. They can be accessed through the following means: Requires a data sharing agreement with Zillow. Format: National property sales database

https://www.zillow.com/research/ztrax/.

This dataset is associated with the following publication: Mamun, S., A. Castillo, K. Swedberg, J. Zhang, K.J. Boyle, D. Cardoso, C.L. King, C. Nolte, M. Papenfus, D. Phaneuf, and S. Polasky. Valuing water quality in the United States using a national dataset on property values. PNAS (PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES). National Academy of Sciences, WASHINGTON, DC, USA, 120(5): e2210417120, (2023).

Context

This dataset includes the median list price divided by the square footage of a 1-bedroom home for a select number of neighborhoods around the United States.

Content

When available, data includes median price per square foot on a monthly basis between January 2010 and September 2016.

Selected neighborhoods include:

• Upper East Side, New York, NY
• Spring Valley, Las Vegas, NV
• Hollywood, Los Angeles, CA
• Williamsburg, New York, NY
• Harlem, New York, NY
• Enterprise, Las Vegas,NV
• Downtown, San Jose, CA
• Sheepshead Bay, New York, NY
• Forest Hills, New York, NY
• Jackson Heights, New York, NY
• Gramercy, New York, NY
• Flagami, Miami, FL
• Downtown, Memphis, TN
• Chelsea, New York, NY
• Oak Lawn, Dallas, TX
• Greater Uptown, Houston, TX
• South Loop, Chicago, IL
• Makiki-Lower Punchbowl-Tantalus, Honolulu, HI
• Downtown, Los Angeles, CA
• Capitol Hill, Seattle, WA
• Clinton, New York, NY
• Alexandria West, Alexandria, VA
• Financial District, New York, NY
• Flatiron District, New York, NY
• Landmark-Van Dom, Alexandria, VA
• Flamingo Lummus, Miami Beach, FL
• Winchester, Las Vegas, NV
• Brickell, Miami, FL
• Waikiki, Honolulu, HI
• Back Bay, Boston, MA
• Sutton Place, New York, NY
• and several others

Inspiration

• What neighborhoods have the most expensive real estate per square foot? Least expensive?
• Which neighborhoods and/or cities have the fastest growth rates in price?
• Are there any neighborhoods that remain relatively steady in price?
• Given that this metric is listing price per square foot, is there a similar dataset that could help you compare median square footage in a 1-bedroom home across neighborhoods?

Acknowledgement

This dataset is part of Zillow Data, and the original source can be found here, under the Neighborhoods link.

This dataset is a snapshot from October 2022 of all 48 homes in a section of a neighborhood nearby a large university in Central Florida. All of the homes are single family homes featuring a garage, a driveway, and a fenced-in backyard. Data was gathered by hand (keyboard) via a collection of sites, including Zillow, Realtor, Redfin, Trulia, and Orange County Property Appraiser. All homes were built in the same year in the early 2000's and feature central air and all other utilities typical of contemporary suburban homes in the United States. The area is close to a university and a large portion of renters are college students and young professionals, as well as families and older adults.

There are 30 columns:

• HID: House ID, a unique identifier for each house (int from 1 to 48, not the actual address number) -Sqft: The Square Footage of the Interior of the house (int) -LandSqft: The Total Square Footage of the land (int) -Neighbors: The number of homes directly adjacent to each house (int) -Stories: The number of stories in each house (int) -Pool: Does the house have a pool (int, 0 for 'No', 1 for 'Yes') -Bedrooms: The number of bedrooms in each house (int) -Bathrooms: The number of bathrooms (full or half) in each house (int) -DateLastSold: The date on which the house was last sold (datetime) -PropertyTaxes2022: The annual property taxes for 2022 (float) -OwnedByBank: Is the house owned by a bank (int, 0 for 'No', 1 for 'Yes') -OuterPortion: Is the house on the Outer Portion of the Neighborhood (int, 0 for 'No', 1 for 'Yes') -NextToLoudRoad: Is the house directly adjacent to a loud road (int, 0 for 'No', 1 for 'Yes') -PriceLastSold: Price that the house was last sold for (float) -Zestimate: Zillow's Price Estimate for the house (float) -RentZestimate: Zillow's Estimate for the Monthly Price of rent for the house (float) -RealtorcomEstimate: Realtor dot com's Estimate for the house (float) -RedfinEstimate: Redfin's Estimate for the house (float) -TruliaEstimate: Trulia's Estimate for the house (float) -OCPALandValue2022: The Land Value on the county's 2022 records (float) -OCPABuildingValue2022: The Building Value on the county's 2022 records (float) -OCPAFeaturesValue2022: The Features Value on the county's 2022 records (float) -OCPAMarketValue2022: The Market Value on the county's 2022 records (float) -OCPAAssessedValue2022: The Assessed Value on the county's 2022 records (float), AKA what homeowners are taxed on -OCPALandValue2021: The Land Value on the county's 2021 records (float) -OCPABuildingValue2021: The Building Value on the county's 2021 records (float) -OCPAFeaturesValue2021: The Features Value on the county's 2021 records (float) -OCPAMarketValue2021: The Market Value on the county's 2021 records (float) -OCPAAssessedValue2021: The Assessed Value on the county's 2021 records (float), AKA what homeowners are taxed on -Notes: any notes on any of the homes (str)

Note that while the dataset is exhaustive in that it has all of the houses, some homes are missing some columns, typically because a home did not feature a estimate on a site or the one home not found on the property appraiser's site. This also is therefore not a randomized dataset, so the only population of homes that it can be used to infer on are those within this specific portion of the neighborhood. Personally, I am going to use the dataset to practice a couple of aspects of real-world data: Cleaning, Imputing, and Exploratory Data Analysis. Mainly, I want to compare different approaches to filling in the missing values of the dataset, then do some Model Building with some additional Dimensionality Reduction.

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

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: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. 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 can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that 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. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. 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 (CPI) 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 the CPI itself.

Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".

Please cite the original paper:

Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” Applied Geography 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.

WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).

All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.

Three WUI maps were generated using structures built on of before the years indicated below:

2000 - "CA_WUI_2000.tif"

2010 - "CA_WUI_2010.tif"

2020 - "CA_WUI_2020.tif"

Acknowledgments -

We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.

Across the Western United States, human development into the wildland urban interface (WUI) is contributing to increasing wildfire damage. Given that natural disasters often cause greater harm within socio-economically vulnerable groups, research is needed to explore the potential for disproportionate impacts associated with wildfire. Using Zillow Transaction and Assessment Database (ZTRAX), hereafter “Zillow”, real estate data, we explored whether lower-priced structures were more likely to be damaged during the most destructive, recent wildfires in Southern California. Within fire perimeters occurring from 2000–2019, we matched property price data to burned and unburned structures. To be included in the final dataset, fire perimeters had to surround at least 25 burned and 25 unburned structures and have been sold at most seven years before the fire; five fires fit these criteria. We found evidence to support our hypothesis that lower-priced properties were more likely to be damaged, however, the likelihood of damage and the influence of property value significantly varied across individual fire perimeters. When considering fires individually, properties within two 2003 fires–the Cedar and Grand Prix-Old Fires–had statistically significantly decreasing burn damage with increasing property value. Occurring in 2007 and later, the other three fires (Witch-Poomacha, Thomas, and Woolsey) showed no significant relationship between price and damage. Consistent with other studies, topographic position, slope, elevation, and vegetation were also significantly associated with the likelihood of a structure being damaged during the wildfire. Driving time to the nearest fire station and previously identified fire hazard were also significant. Our results suggest that further studies on the extent and reason for disproportionate impacts of wildfire are needed. In the meantime, decision makers should consider allocating wildfire risk mitigation resources–such as fire-fighting and wildfire structural preparedness resources–to more socioeconomically vulnerable neighborhoods.

Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing.

Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Keith A. Burghardt, USC Information Sciences Institute, USA

Codebook: these CBSA statistics are stratified by degree of aggregation. - CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015 - CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015 - CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. - Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015 - Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.

The statistics are the following:

msaid: CBSA code id: (if patch statistics) arbitrary int unique to each patch within the CBSA that year year: year of statistics pop: population within all CBSA counties patch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA) patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA) patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA) all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patches all_bupl: Same as above but for all data in 2015 regardless of whether properties were in patches all_bua: Same as above but for all data in 2015 regardless of whether properties were in patches num_nodes: number of nodes (intersections) num_edges: number of edges (roads between intersections) distance: total road length in km k_mean: mean number of undirected roads per intersection k1: fraction of nodes with degree 1 k4plus: fraction of nodes with degree 4+ bearing: histogram of different bearings between intersections entropy: entropy of bearing histogram mean_local_gridness: Griddedness used in text mean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles)

Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems 95: 101803.
doi: 10.1016/j.compenvurbsys.2022.101803

Context

This dataset contains shapefiles outlining 558 neighborhoods in 50 major cities in New York state, notably including Albany, Buffalo, Ithaca, New York City, Rochester, and Syracuse. This adds context to your datasets by identifying the neighborhood of any locations you have, as coordinates on their own don't carry a lot of information.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. What fields does it include? What's the time period of the data and how was it collected?

Four files are included containing data about the shapes: an SHX file, a DBF file, an SHP file, and a PRJ file. Including all of them in your input data are necessary, as they all contain pieces of the data; one file alone will not have everything that you need.

Seeing how none of these files are plaintext, it can be a little difficult to get set up with them. I highly recommend using mapshaper.org to get started- this site will show you the boundaries drawn on a plane, as well as allow you to export the files in a number of different formats (e.g. GeoJSON, CSV) if you are unable to use them in the format they are provided in. Personally, I have found it easier to work with the shapefile format though.

To get started with the shapefile in R, you can use the the rgdal and rgeos packages. To see an example of these being used, be sure to check out my kernel, "Incorporating neighborhoods into your model".

Acknowledgements

These files were provided by Zillow and are available under a Creative Commons license.

Inspiration

I'll be using these in the NYC Taxi Trip Duration competition to add context to the pickup and dropoff locations of the taxi rides and hopefully greatly improve my predictions.

An ESRI Shapfile containing spatially generalized built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the data processing.

Additionally, there is a CSV file (HISDAC-US_patch_statistics.csv) containing the counts of built-up property records (BUPR), and -locations (BUPL), as well as total building indoor area (BUI) and built-up area (BUA) per CBSA, year, and patch, extraced from the HISDAC-US data (Uhl & Leyk 2018, Uhl et al. 2021). This CSV can be joined to the shapefile (column uid2) by concatenating the columns msaid_year_Id.

Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175 Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States. Earth system science data, 13(1), 119-153. DOI: https://doi.org/10.5194/essd-13-119-2021