Rental data is essential for making informed decisions. Property managers streamline operations, investors find opportunities, and asset managers enhance valuation tools using this critical resource. With verified listings and broad market coverage, our rental data outperforms traditional sources.

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.

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.

A global database of Real Estate Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date urban planning data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial real estate dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Urban Planning Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Urban Planning Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Demographic data export methodology

Our location data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Real Estate databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

New Upload:

Added +32,000 more locations. For information on data calculations please refer to the methodology pdf document. Information on how to calculate the data your self is also provided as well as how to buy data for $1.29 dollars.

What you get:

The database contains 32,000 records on US Household Income Statistics & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 348,893 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to up vote. Up vote right now, please. Enjoy!

Household & Geographic Statistics:

• Mean Household Income (double)
• Median Household Income (double)
• Standard Deviation of Household Income (double)
• Number of Households (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The dataset originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

Sources, don't have 2 dollars? Get the full information yourself!

2011-2015 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved August 2, 2017, from https://www2.census.gov/programs-surveys/acs/summary_file/2015/data/5_year_by_state/

Found Errors?

Please tell us so we may provide you the most accurate data possible. You may reach us at: research_development@goldenoakresearch.com

for any questions you can reach me on at 585-626-2965

please note: it is my personal number and email is preferred

Check our data's accuracy: Census Fact Checker

Access all 348,893 location records and more:

Don't settle. Go big and win big. Optimize your potential. Overcome limitation and outperform expectation. Access all household income records on a scale roughly equivalent to a neighborhood, see link below:

Website: Golden Oak Research Kaggle Deals all databases $1.29 Limited time only

A small startup with big dreams, giving the every day, up and coming data scientist professional grade data at affordable prices It's what we do.

Doorda's UK Residential Building Characteristics Real Estate Data provides a comprehensive database of over 426K buildings aggregated from 15 data sources, offering unparalleled insights for insurance underwriting and analytics purposes.

Volume and stats: - 426K Buildings - Number of units in each Building - Building Height - Presence of Basements - Number of Floors

Our Residential Real Estate Data offers a multitude of use cases: - Cladding Risks - Insurance Underwriting - Compliance Checks - Flood Risk - Location Planning

The key benefits of leveraging our Residential Real Estate Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

US Postal Service ZIP Code boundaries in Wake County, NC. This dataset is updated as needed when property lines change and when source data from the US Postal Service is updated, and it is maintained by the Wake County GIS Addressing Team. GIS metadata is available here.

This data collection is a component of Summary Tape File (STF) 3, which consists of four sets of data files containing detailed tabulations of the nation's population and housing characteristics produced from the 1980 Census. The STF 3 files contain sample data inflated to represent the total United States population. The files also contain 100-percent counts and unweighted sample counts of persons and housing units. All files in the STF 3 series are identical, containing 321 substantive data variables organized in the form of 150 "tables," as well as standard geographic identification variables. Population items tabulated for each person include demographic data and information on schooling, Spanish origin, language spoken at home and ability to speak English, labor force status in 1979, residency in 1975, number of children ever born, means of transportation to work, current occupation, industry, and 1979 details on occupation, hours worked, and income. Housing items include size and condition of the housing unit as well as information on value, age, water, sewage and heating, number of vehicles, and monthly owner costs (e.g., sum of payments for real estate taxes, property insurance, utilities, and regular mortgage payments). Selected aggregates and medians are also provided. Each dataset in STF 3 provides different geographic coverage. Summary Tape File 3B provides summaries for each 5-digit ZIP-code area within a state, and for 5-digit ZIP-code areas within states that were contained within Standard Metropolitan Statistical Areas (SMSAs), portions of SMSAs, or within counties, county portions, or county equivalents. All persons and housing units in the United States were sampled. Population and housing items include household relationship, sex, race, age, marital status, Hispanic origin, number of units at address, complete plumbing facilities, number of rooms, whether owned or rented, vacancy status, and value for noncondominiums. The Census Bureau's machine-readable data dictionary for STF 3 is also available through CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: CENSUS SOFTWARE PACKAGE (CENSPAC) VERSION 3.2 WITH STF4 DATA DICTIONARIES (ICPSR 7789), the software package designed specifically by the Census Bureau for use with the 1980 Census data files. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR08318.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

The Canadian County Parcel Data Public View is a set of geospatial features representing the surface ownership of property in fee simple for property tax purposes as required by 68 O.S. § 2821 and other related data used to produce the parcels such as subdivision boundaries and subdivision lots. The data is created from source documentation filed with the Canadian County Clerk's Office including deeds, easements, and plats. Other data sources such as filed Certified Corner Records filed with the State of Oklahoma or highway plans produced by the Department of Transportation may be used to adjust parcel boundaries. Single legal descriptions may be split up into two or more parcels if the description crosses the boundaries of multiple taxing jurisdictions or crosses quarter section boundaries. Accuracy of parcel data can vary considerably due to a combination of factors. Most parcels and subdivision legal descriptions reference a quarter section or quarter section corner. The accuracy of the quarter section corners is discussed with Canadian County's Public Land Survey System Data. Accuracy is further enhanced or degraded by the quality of the legal description used to create the feature. Generally, legal descriptions created from surveys will have higher accuracy the newer they were created due to improvements in the field of surveying. However, it can be difficult to determine the age of a legal description as descriptions are generally reused on subsequent deeds after the description was first created. Legal descriptions can occasionally contain updated bearings and distances and may denote the updates. The Assessor's Office uses the latest available legal description for creating parcels. Legal descriptions may lack specificity such as the use of "North" instead of a measured bearing or have missing parameters such as missing bearings for curved boundaries. In these cases, parcel data accuracy can be degraded. Further, if a legal description contains a specific landmark or boundary, sometimes called a "bound", the boundary is drawn to that point or landmark regardless of whether the bearing and/or distance accurately arrive at that point. For instance, if a legal description reads "...to the south line of the southeast quarter", the boundary is drawn to the south line of the quarter section even if the bearing and distance are short of or extend beyond that point. Because parcel data must be created for the entire county regardless of the accuracy of the descriptions used to create those parcels, parcels may need to be "stretched" or "squeezed" to make them fit together. When possible, the Assessor's Office relies on the most accurate legal descriptions to set the boundaries and then fits older boundaries to them. Due to the large number of variables, parcel data accuracy cannot be guaranteed nor can the level of accuracy be described for the entire dataset. While Canadian County makes every reasonable effort to make sure parcel data is accurate, this data cannot be used in place of a survey performed by an Oklahoma Licensed Professional Land Surveyor.ParcelDataExternal - Polygons representing surface fee simple title. This parcel data formatted and prepared for public use. Some fields may be blank to comply with 22 O.S. § 60.14 & 68 O.S. § 2899.1Attributes:Account (account): The unique identifier for parcel data generated by the appraisal software used by the Assessor's Office"A" Number (a_number): An integer assigned in approximate chronological order to represent each parcel divided per quarter sectionParcel ID (parcel_id): Number used to identify parcels geographically, see Parcel Data Export Appendix A for an in-depth explanation. This identifier is not unique for all parcelsParcel Size (parcel_size): Size of the parcels, must be used in conjunction with following units fieldParcel Size Units (parcel_size_units): Units for the size of the parcel. Can be "Acres" or "Lots" for parcels within subdivisions that are valued per lotOwner's Name (owners_name): Name of the surface owner of the property in fee simple on recordMailing Information (mail_info): Extra space for the owners name if needed or trustee namesMailing Information 2 (mail_info2): Forwarded mail or "In care of" mailing informationMailing Address (mail_address): Mailing address for the owner or forwarding mailing addressMailing City (mail_city): Mailing or postal cityMailing State (mail_state): Mailing state abbreviated to standard United States Postal Service codesMailing ZIP Code (mail_zip): Mailing ZIP code as determined by the United States Postal ServiceTax Area Code (tax_area): Integer numeric code representing an area in which all the taxing jurisdictions are the same. See Parcel Data Appendix B for a more detailed description of each tax areaTax Area Description (tax_area_desc): Character string code representing the tax area. See Parcel Data Appendix B for a more detailed description of each tax areaProperty Class (prop_class): The Assessor's Office classification of each parcel by rural (no city taxes) or urban (subject to city taxes) and exempt, residential, commercial, or agriculture. This classification system is for property appraisal purposes and does not reflect zoning classifications in use by municipalities. See Parcel Data Appendix B for a more detailed description of each property classificationLegal Description (legal): A highly abbreviated version of the legal description for each parcel. This legal description may not match the most recent legal description for any given property due to administrative divisions as described above, or changes made to the property by way of recorded instruments dividing smaller parcels from the original description. This description may NOT be used in place of a true legal descriptionSubdivision Code (subdiv_code): A numeric code representing a recorded subdivision plat which contains the parcel. This value will be "0" for any parcel not part of a recorded subdivision plat.Subdivision Name (subdiv_name): The name of the recorded subdivision plat abbreviated as needed to adapt to appraisal software field limitationsSubdivision Block Number (subdiv_block): Numeric field representing the block number of a parcel. This value will be "0" if the parcel is not in a recorded subdivision plat or if the plat did not contain block numbersSubdivision Lot Number (subdiv_lot): Numeric field representing the lot number of a parcel. This value will be "0" if the parcel is not in a recorded subdivision platTownship Number (township): Numeric field representing the Public Land Survey System tier or township the parcel is located in. All townships or tiers in Canadian County are north of the base line of the Indian Meridian.Range Number (range): Numeric field representing the Public Land Survey System range the parcel is located in. All Ranges in Canadian County are west of the Indian MeridianSection Number (section): Numeric field representing the Public Land Survey System section number the parcel is located inQuarter Section Code (quarter_sec): Numeric field with a code representing the quarter section a majority of the parcel is located in, 1 = Northeast Quarter, 2 = Northwest Quarter, 3 = Southwest Quarter, 4 = Southeast QuarterSitus Address (situs): Address of the property itself if it is knownSitus City (situs_city): Name of the city the parcel is actually located in (regardless of the postal city) or "Unincorporated" if the parcel is outside any incorporated city limitsSitus ZIP Code (situs_zip): ZIP Code as determined by the United States Postal Service for the property itself if it is knownLand Value (land_val): Appraised value of the land encompassed by the parcel as determined by the Assessor's OfficeImprovement Value (impr_val): Appraised value of the improvements (house, commercial building, etc.) on the property as determined by the Assessor's OfficeManufactured Home Value (mh_val): Appraised value of any manufactured homes on the property and owned by the same owner of the land as determined by the Assessor's OfficeTotal Value (total_val): Total appraised value for the property as determined by the Assessor's OfficeTotal Capped Value (cap_val): The capped value as required by Article X, Section 8B of the Oklahoma ConstitutionTotal Assessed Value (total_assess): The capped value multiplied by the assessment ratio of Canadian County, which is 12% of the capped valueHomestead Exempt Amount (hs_ex_amount): The amount exempt from the assessed value if a homestead exemption is in placeOther Exempt Value (other_ex_amount): The amount exempt from the assessed value if other exemptions are in placeTaxable Value (taxable_val): The amount taxes are calculated on which is the total assessed value minus all exemptionsSubdivisions - Polygons representing a plat or subdivision filed with the County Clerk of Canadian County. Subdivision boundaries may be revised by vacations of the plat or subdivision or by replatting a portion or all of a subdivision. Therefore, subdivision boundaries may not match the boundaries as shown on the originally filed plat.Attributes:Subdivision Name (subdivision_name): The name of the plat or subdivisionSubdivision Number (subdivision_number): An ID for each subdivision created as a portion of the parcel ID discussed in Parcel Data Export Appendix APlat Book Number (book): The book number for the recorded documentPlat Book Page Number (page): The page number for the recorded documentRecorded Acres (acres): The number of acres within the subdivision if knownRecorded Date (recorded_date): The date the document creating the subdivision was recordedDocument URL (clerk_url): URL to download a copy of the document recorded by the Canadian County Clerk's OfficeBlocks - Polygons derived from subdivision lots representing the blocks

Real Estate Rentals in Ecuador

Analyzing Location, Price, and Amenities

By [source]

About this dataset

This dataset provides a comprehensive overview of rental properties in Ecuador. It contains a wealth of information about the properties, such as their titles and locations, as well as the number of bedrooms, bathrooms and garages within them. Furthermore, it also includes valuable data points like area size to aid informed decisions for those looking to rent or lease property within the country. The data can be used for various reasons such as analyzing trends in properties offered for rent and looking into pricing differences between regions or localities. It is an invaluable resource for anyone interested in real estate within Ecuador and beyond!

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For more datasets, click here.

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How to use the dataset

This dataset is an ideal starting point for anyone looking to dive into the rental market in Ecuador. With this data, you can explore the different rental properties, look at their prices and features and compare them with other properties in the same area. Additionally, it gives you insight into what type of property would best suit your needs and budget, as well as how many bedrooms and bathrooms are necessary to get your desired living space.

To use this dataset effectively, start by selecting specific columns that correspond to important information such as location (Provincia), price (Precio) or number of bedrooms & bathrooms (Num. dormitorios & Num. banos). With these columns selected, run some analysis on the data such as averages or mode/median values for each selection of parameters; this will give you a general idea on pricing within certain areas or specific types of houses/apartments available for rent in Ecuador. You may also wish to include all variables within your analysis; this will give more comprehensive insights about which variables are impacting price the most in a given area, allowing for further comparisons between different regions throughout Ecuador . With these tools at your disposal you'll have all the info needed to decipher which properties will fit your needs without sacrificing quality!

Research Ideas

• Use this dataset to determine the average rental costs in different provinces of Ecuador, which can be used to inform the user on how much they should expect to pay for rent when visiting or relocating.
• Analyze and compare rental prices within a certain city or neighborhood by using the data provided on rental properties in that area.
• Generate heat maps that show the variation in prices across different areas based on specific criteria such as size, number of bedrooms, etc., which could give users a better understanding of where it is most affordable and valuable to buy or rent property in Ecuador

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: real_state_ecuador_dataset.csv | Column name | Description | |:---------------------|:--------------------------------------------------------| | Titulo | Title of the rental property. (String) | | Precio | Price of the rental property. (Numeric) | | Provincia | Province where the rental property is located. (String) | | Lugar | Location of the rental property. (String) | | Num. dormitorios | Number of bedrooms in the rental property. (Numeric) | | Num. banos | Number of bathrooms in the rental property. (Numeric) | | Area | Area of the rental property. (Numeric) | | Num. garages | Number of garages in the rental property. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The historical building references define the exact position of over 4 million buildings in North Rhine-Westphalia. The data source is the real estate cadastre and thus the official directory of all parcels and buildings. The content of the real estate cadastre is usually based on an individual on-site survey and is continuously updated by the cadastral authorities. The historical building references do not contain any postal information (zip code, postal place name, addition to the postal place name, postal district) on the buildings, as these are not part of the real estate cadastre in North Rhine-Westphalia. At the end of a year, the databases of the building references are historicized and made available as time slices. The annual cuts are available from 2016.

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about the Low-Income Housing Tax Credit Program visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low Income Tax Credit Program

The rental housing developments listed below are among the thousands of affordable units that are supported by City of Chicago programs to maintain affordability in local neighborhoods. The list is updated periodically when construction is completed for new projects or when the compliance period for older projects expire, typically after 30 years. The list is provided as a courtesy to the public. It does not include every City-assisted affordable housing unit that may be available for rent, nor does it include the hundreds of thousands of naturally occurring affordable housing units located throughout Chicago without City subsidies. For information on rents, income requirements and availability for the projects listed, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

Data to illustrate our geographic RD methodological framework. Illustration is a a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries. We rely on two data sources. Our main source is the New Jersey voter file. This dataset has measures of party registration, gender and age directly from the voter file, and imputed values of education, income, poverty status, and employment status. The voter file also contains the address of each voter, which allows us to find each voter's geographic location and avoid the use of naive distances. Our second data source is property sales records. We acquired records for all houses sold in the appropriate zip codes in New Jersey from January 2006 to November 2008. In this time period, nearly 3,000 homes were sold in this area -- although we only used the 1,800 house sales inside one specific school district, see below. The housing sales data allow us to conduct a fine-grained analysis of the sales price differential along the boundary of interest.

This feature dataset contains a snapshot of all King County parcels from September 2020, with all of the "additional relevant criteria" data used in Method 2 of the LCI opportunity area determination described below.There are two methods by which a property may qualify as being in an opportunity area:Method 1. Property meets all three of the following "specified criteria" in King County code 26.12.003.(a) Areas "located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County; (b) "located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP codes in King County; and (c) "are within the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within one-quarter mile of a residence, or are outside the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within two miles of a residence." (King County Code 26.12.003)Data results related to Method 1 are shown in the LCI Opportunity Areas dataset on the King County GIS Open Data site. In this dataset, the parcels where the "CriteriaAllYN" column is equal to "Y" also represents those parcels.Method 2. If a property does not qualify under Method #1, a project may qualify if: "the project proponent or proponents can demonstrate, and the advisory committee determines, that residents living in the area, or populations the project is intended to serve, disproportionately experience limited access to public open spaces and experience demonstrated hardships including, but not limited to, low income, poor health and social and environmental factors that reflect a lack of one or more conditions for a fair and just society as defined as "determinants of equity" in KCC 2.10.210." (King County Code 26.12.003)Conservation Futures (CFT) values the use of multiple sources of data and information to demonstrate that a property is in an opportunity area. Applicants are welcome to provide additional criteria and data sources not identified in this report to demonstrate that a property is in an opportunity area. These sources are provided in the document here: Understanding the Data Report.

Dataset SummaryPlease note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.About this data:The operational purpose of the vacant land dataset is to facilitate the tracking and mapping of vacant land for the purposes of promoting redevelopment of lots to increase the City's tax base and spur increased economic activity. These properties are both City owned and privately owned. The vast majority of vacant lots are the result of a demolition of a structure that once stood on the property. Vacant lots are noted in the official tax parcel assessment records with a class code beginning with 3, which denotes the category vacant land.Related Resources:For a searchable interactive mapping application, please visit the City of Rochester's Property Information explorer tool. For further information about the city's property tax assessments, please contact the City of Rochester Assessment Bureau. To access the City's zoning code, please click here.Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Business and Zoning.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

The Geospatial Imagery Analytics Marketsize was valued at USD 11.88 USD Billion in 2023 and is projected to reach USD 83.39 USD Billion by 2032, exhibiting a CAGR of 32.1 % during the forecast period.Geospatial analytics gathers, manipulates, and displays geographic information system (GIS) data and imagery including GPS and satellite photographs. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. geospatial visualization enables businesses to better understand complex information and make informed decisions. They can quickly see patterns and trends and assess the impact of different variables by visualizing data in a spatial context. The field encompasses several techniques and algorithms, such as spatial interpolation, spatial regression, spatial clustering, and spatial autocorrelation analysis, which help extract insights from various geospatial data sources. The growing adoption of location-based services in various industries, including agriculture, defense, and urban planning, is driving the demand for geospatial imagery analytics. Recent developments include: August 2023: onX, a digital navigation company, partnered with Planet Labs PBC, a satellite imagery provider, to introduce a new feature called ‘Recent Imagery’. This feature offers onX app users updated satellite imagery maps every two weeks, enhancing the user experience across onX Hunt, onX Offroad, and onX Backcountry apps. This frequent data update helps outdoor enthusiasts access real-time information for safer and more informed outdoor activities., August 2023: Quant Data & Analytics, a provider of data products and enterprise solutions for real estate and retail, partnered with Satellogic Inc. to utilize Satellogic’s high-resolution satellite imagery to enhance property technology in Saudi Arabia and the Gulf region., April 2023: Astraea, a spatiotemporal data and analytics platform, introduced a new ordering service that grants customers scalable access to top-tier commercial satellite imagery from providers such as Planet Labs PBC and others., May 2022: Satellogic Inc. established a partnership with UP42. This geospatial developer platform enables direct access to Satellogic’s satellite tasking capabilities, including high-resolution multispectral and wide-area hyperspectral imagery, through the UP42 API-based platform., April 2022: TomTom International BV, a geolocation tech company, broadened its partnership with Maxar Technologies, a space solution provider. This expansion involves integrating high-resolution global satellite imagery from Maxar’s Vivid imagery base maps into TomTom’s product lineup, enhancing their visualization solutions for customers.. Key drivers for this market are: Growing Demand for Location-based Insights across Diverse Industries to Fuel Market Growth. Potential restraints include: Complexity and Cost Associated with Data Acquisition and Processing May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address.