Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

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

The most traditional way to examine land use change is to use a cross-tabulation matrix to identify the most important categorical land use transition from time 1 to time 2. However, such method does not necessarily capture or indicate the real changes on the landscape. For example, assuming that from 1986 to 2015, Utah’s total agricultural land loss (aka, net change) is 200 square miles, but this does not mean that only 200 square miles of agricultural land have experienced land use change in the last 30 years. It is highly possible that a given quantity of agricultural land loss at one location can be accompanied by another quantity of agricultural land gain at another location (aka, swapping). Thus, by purely using net change, we might fail to capture the swapping component of change, and fail to capture the intricate transitions of landscape. This dataset analyzed important categorical land use change while account for persistence and swaps. It provides additional information concerning what happened on the landscape.

This dataset includes a statistical table and a GIS raster file. The table summarizes the persistence and swaps, as well as gross gain and gross loss in the Wasatch Range Metropolitan Area (WRMA). The GIS file is the compiled spatial layer that represents the gain, loss, persistence, and swaps on the landscape. We used Water Related Land Use data of Year 1986 to Year 2015 for this analysis. Land use categories used in this dataset include urban (URB), irrigated agricultural land (IR), and non-irrigated agricultural land (NI), sub-irrigated agricultural land (SubIR), riparian (RIP), water, (WATER), and other (OTHER). We then examined the categorical land use changes with a transition matrix.

A categorical land use gain is determined as the conversion from other sources to this particular categorical land use, and a categorical land use loss is defined as conversion from this particular categorical land use to other uses. For example, the gain of irrigated agricultural (IR) land use will be the sum of areas of urban to IR, non-irrigated agricultural land to IR, sub-irrigated agricultural land to IR, riparian to IR, water to IR, and other to IR. The total change is calculated as the sum of gain and loss. The net change equals to |Gain|-|Loss|. The Swap =2* MIN(Gain,Loss).

WELDLCLUC.015 was decommissioned on December 2, 2019. The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 meter (m) land use land change product for the contiguous United States (CONUS). The data were generated from five years of consecutive growing season WELD weekly composite inputs from April 15, 2006, to November 17, 2010. WELD data are created using Landsat Thematic Mapper Plus (ETM+) Terrain Corrected data. This product includes data about tree cover loss and bare ground gain, which are composited over the five year period. WELD LCLUC is distributed in Hierarchical Data Format 4 (HDF4).The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.Improvements/Changes from Previous Version Version 1.5 is the original version.

About this dataset

Context

The dataset illustrates the median household income in De Land, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for De Land decreased by $18,869 (27.20%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 8 years and declined for 5 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for De Land median household income. You can refer the same here

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled land use classes for each year. See additional information about land use in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010). Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Hawaii TCC 2011 cartographic dataset is comprised of a single layer. The pixel values range from 0 to 99 percent. The background is represented by the value 255. The dataset has data gaps due to consistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 110.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

NY Property & Casualty Insurance Premiums Written 1998-2022

Benchmarking Insurer Performance

By State of New York [source]

About this dataset

This dataset contains total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Listings include essential financial security requirements that are required by Article 41 of the New York Insurance Law and provide insights into how the industry has evolved over time. This is an invaluable resource for researchers, analysts, policy makers, and insurance agents alike who wish to better understand the changing dynamics of the insurance market in New York. Download now and explore this unique dataset detailing net premiums written for insurers over a 20+ year period

More Datasets

For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset contains the total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Using this dataset, users can explore total property insurance premiums written over the course of twenty-four years in order to gain an understanding of the property insurance industry trends in New York State.

To use this dataset effectively, first download and read the Terms of Service before using the data. Once familiar with how to leverage data licenses effectively, you can analyze or visualize various facets of this large dataset. You may be interested in seeing changes over time and can compare these values with national averages or Gross Domestic Product (GDP) figures for periods analyzed.

Additionally, you could study any variation by geographic areas or other variables such as age groupings or type of policy written during a certain period. This dataset provides comprehensive insights that allow you to look at macro levels (loose overview) as well as more granular views depending on your questions and analysis methods. Regardless of your specific analysis goals; utilization of this open source data set should yield valuable insight into past trends which have potential impacts on future activities related to property and casualty insurance policies within New York State!

Research Ideas

• Identifying trends in Property & Casualty insurance rates over time in New York State to inform consumer decision making or policy strategies.
• Developing a risk management model by analyzing the financial security requirements of insurers in New York State and predicting potential premiums on different types of coverage areas.
• Comparing different insurers on their total net premiums written to compare their relative market size and influence within the state’s property & casualty insurance industry

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: total-property-insurance-premiums-written-annually-in-new-york-beginning-1998-1.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------| | Net Premiums Written | The total amount of premiums written by the insurer in thousands. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse The Coastal Alaska TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and line up. The USFS's GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the FS-Cartographic version of the 2011 and/or 2016 datasets for their cartographic applications.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available.

The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available.

The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.

These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

About this dataset

Context

The dataset illustrates the median household income in Land O'Lakes town, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Land O'Lakes town increased by $8,812 (17.24%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 9 years and declined for 4 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Land O'Lakes town median household income. You can refer the same here

The dataset ‘Heat stored in the Earth system: Where does the energy go?’ contains a consistent long-term Earth system heat gain over the past 58 years. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. This Earth Energy Imbalance (EEI) is a fundamental metric of climate change. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory, and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2018.

Single Family Home Prices in the United States decreased to 422400 USD in July from 432700 USD in June of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Exploring Potential Tax and Water Liens in NYC

Investigating Geographic Distribution and Effects of Local Policies

By data.world's Admin [source]

About this dataset

This dataset captures properties in New York City that have tax and/or water liens potentially eligible to be included in the next lien sale. Explore the city's fiscal landscape with information about borough, lot, tax class code, building classes, community board, council district, house number street name and zip code. This data is updated monthly with new liens being added from the most current month back to 12 months prior. By analyzing this data you can gain greater insight into New York City’s financial conditions over time as well as how this affects individual properties throughout the city. This data is provided by the New York City open data portal is subject to terms of use outlined on its website so please refer to it for any additional information regarding usage rights

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This data is sourced from New York City's Open Data portal. By exploring this dataset you can search for properties with possible tax and/or water liens located in a particular area or neighborhood or by a specific attribute such as the house number or street name. You can also take a look at particular subsections of potential eligible lien sale records over time – for example you could look at all potential water debt liens only during a certain month – to get some more specific insights into what tax and water liens may be available at certain points in time. To use this dataset please note the following important tips: • Start by familiarizing yourself with each column’s field meaning (using our table above); • When searching for records use quotation marks if you are looking up something which is two words (e.g “construction”) ;
• Use an underscore _for replacing spaces if necessary e.g “west_village”;
• Be aware that Boroughs are referenced by their full names (e.g Manhattan, Queens, etc);
• If using wild cards (*) make sure not to put them on either side of your query - e.g instead of Lastname * use Lastname*.
We hope this guide was helpful and good luck exploring!

Research Ideas

• Real Estate Investment Analysis: Create a platform or tool using this dataset to assist individuals looking to invest in properties with potential tax and water liens. The platform/tool should provide insights into the best locations for purchasing real estate based on location, tax class code, building class and council district data points from this dataset.
• Tax Foreclosure Notifications: Use this dataset to create an automated notification system which informs registered users when a property they are interested in is coming up for sale with a lien in the next sale date.
• Local Planning Solutions: Leverage data from this dataset to identify areas where there might be concentration of properties with tax liens that could potentially benefit from local planning solutions such as community grants, affordable housing initiatives etc.. This could help municipalities deploy resources more effectively towards restoring distressed properties instead of letting them slip out of their control via foreclosure at lien sales

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: tax-lien-sale-lists-1.csv | Column name | Description | |:---------------------|:-----------------------------------------------------------------| | Month | The month in which the lien sale is eligible. (String) | | Borough | The borough in which the property is located. (String) | | Lot | The lot number of the property. (Integer) | | Tax Class Code | The tax class code of the property. (Integer) | | Building Class | The building class of the property. (String) | | Community Board | The community board in which the property is located. (Integer) | | Council District | The council district in which the property is located. (Integer) | | House Number | The house number of the property. (Integer) | | Street Name | The street name of the property. (String) | | Zip Code | The zip code of the p...

Summary

This repository contains spatial datasets with metadata on land cover, tree canopy change, and estimated tree points and crown polygons for New York City (NYC; New York, USA) as of 2021, made available by The Nature Conservancy, New York Cities Program and developed under contract by the University of Vermont Spatial Analysis Lab. The datasets are provided herein with high-level background and information; additional analysis, particularly on tree canopy change and distribution across NYC considering various geogrpahic units are planned for release in a forthcoming report by The Nature Conservancy. For questions about these data, contact Michael Treglia, Lead Scientist with The Nature Conservancy, New York Cities Program, at michael.treglia@tnc.org.

Datasets included here are as follows (file names in italics):

Land cover as of 2021 (landcover_nyc_2021_6in.tif):

Raster dataset with six-inch (15.24 centimeter) pixel resolution, delineating land covers as: 1) tree canopy (with crowns greater than eight feet [2.44 meters] tall; 2) grass/shrub (including vegetation less than or equal to eight feet [2.44 feet] tall; 3) bare ground; 4) open water; 5) building; 6) road; 7) other impervious; and 8) railroad. This is intended to serve as an update to high-resolution land cover data for 2010 and 2017 made available by the City of New York.

Tree canopy change during 2017-2021 (treecanopychange_nyc_2017_2021_6in.tif):

Raster dataset with six-inch (15.24 centimeter) pixel resolution, with pixels that were estimated tree canopy in 2017 (based on 2017 land cover data) or 2021 delineated as: 1) canopy that did not change (“no change”); 2) canopy that was gained (“gain”); 3) canopy that was lost (“loss”). This is intended to be an updated tree canopy change dataset, analogous to a canopy change dataset for 2010-2017 made available by the City of New York.

Estimated tree points, crown polygons, and objects as of 2021 (Trees_Centroids_Crown_Objects_2021.gdb.zip):

The approximated locations (centroids) and approximated tree crowns as circles (shapes), and tree objects themselves based on canopy data (objects) for individual trees with crowns taller than eight feet (2.44 meters); in cases where there are trees with overlapping crowns, only the top trees are captured. These data are based on automated processing of the tree canopy class from the land cover data; additional methodological details are included in the metadata for this dataset. Given the height cutoff, that this dataset only captures the trees seen from above, and the large number of understory trees in some areas (e.g., forested natural areas), and limits in the automated processing this is not intended to be a robust census of trees in NYC, but may serve as useful for some purposes. Unlike the land cover and tree canopy change datasets, no directly comparable datasets for NYC from past years that we are aware of.

These datasets were based on object-based image analysis of a combination of 2021 Light Detection and Ranging (LiDAR; data available from the State of New York) for tree canopy and tree location/crown data in particular) along with high-resolution aerial imagery (from 2021 via the USDA National Agriculture Inventory Program and from 2022 via the New York State GIS Clearinghouse), followed by manual corrections. The general methods used to develop the land cover and tree canopy datasets are described in MacFaden et al. (2012). A per-pixel accuracy assessment of the land cover data with 1,999 points estimated an overall accuracy of 95.52% across all land cover classes, and 99.06% for tree canopy specifically (a critical focal area for this project). Iterative review of the data and subject matter expertise were contributed by from The Nature Conservancy and the NYC Department of Parks and Recreation.

While analyses of tree canopy and tree canopy change across NYC are pending, those interested can review a report that includes analyses of the most recent data (2010-2017) and a broad consideration of the NYC urban forest, The State of the Urban Forest in New York City (Treglia et al 2021).

References

MacFaden, S. W., J. P. M. O’Neil-Dunne, A. R. Royar, J. W. T. Lu, and A. G. Rundle. 2012. High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis. Journal of Applied Remote Sensing 6(1):063567.

Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., & Maxwell, E.N. (2021). The State of the Urban Forest in New York City. The Nature Conservancy. doi: 10.5281/zenodo.5532876

Terms of Use

© The Nature Conservancy. This material is provided as-is, without warranty under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 (CC BY-NC-SA 4.0) license.

The Nature Conservancy (TNC) oversaw development of these data and reserves all rights in the data provided.

TNC makes no guarantee of accuracy or completeness.

Data are for informational purposes and are not suitable for legal, engineering, or surveying purposes. Data do not represent an on-the-ground survey and represent only the approximate relative location of feature boundaries.

TNC is not obligated to update/maintain the data to reflect changing conditions.

Commercial use is not allowed.

Redistribution (sublicensing) is allowed, provided all accompanying metadata as well as these Terms of Use are provided, unaltered, alongside the data.

TNC should be credited as the data source in derivative works, following the recommended citation provided herein.

Users are advised to pay attention to the contents of this metadata document.

Recommended Citation

If using any of these datasets, please cite the work according to the following recommended citation:

The Nature Conservancy. 2024. New York City Land Cover (2021), Tree Canopy Change (2017-2021), and Estimated Tree Location and Crown Data (2021). Developed under contract by the University of Vermont Spatial Analysis Laboratory. doi: 10.5281/zenodo.14053441.

Technical Notes about the Spatial Data

All spatial data are provided in the New York State Plan Long Island Zone (US survey foot) coordinate reference system, EPSG 2263. The land cover and tree canopy change datasets are made available as raster data in Cloud Optimized GeoTIFF format (.tif), with associated metadata files as .xml files. The vector data of estimated tree locations and crown objects and shapes are made available in a zipped Esri File Geodatabase, with metadata stored within the File Geodatabase.

Success.ai’s Commercial Real Estate Data for Commercial Real Estate Professionals in Europe provides a highly detailed dataset tailored for businesses looking to engage with key decision-makers in the European commercial real estate market. Covering developers, property managers, brokers, and investors, this dataset includes verified contact data, decision-maker insights, and firmographic details to empower your outreach and strategic initiatives.

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Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and parcels nationally.

Over 60M parcels reflecting over 330M permits over the past 20 years.

This comprehensive dataset contains building permits issued in the United States, providing valuable insights into residential and commercial construction activities. With over millions of records covering millions of homes, this dataset offers a vast opportunity for analysis and business growth.

Includes permits from various states across the US

Covers residential and commercial construction activities

Insights:

Residential vs. Commercial: Analyze the distribution of permits by type (residential, commercial) to understand local market trends.

Construction Activity: Track permit issuance over time to identify patterns and fluctuations in construction activity.

Geographic Patterns: Examine the concentration of permits by state, county, or city to reveal regional development opportunities.

Potential Applications:

Contractors and Builders: Utilize this dataset to identify potential projects, estimate job values, and stay up-to-date on permit requirements.

Local Governments: Analyze building permit data to inform land-use planning, zoning regulations, and infrastructure development.

Investors and Developers: Explore the types of construction projects being undertaken in specific areas, enabling informed investment decisions.

Value Propositions:

Understand Current Home Condition: Gain insights into the current state of homes by analyzing building permit data, allowing you to:

Identify areas with high concentrations of permits

Determine the scope and type of work being performed

Infer the potential for improved home values

Lender Lead Generation: Use this dataset to identify potential refinance candidates based on improved homes, enabling lenders to:

Target homeowners who have invested in their properties

Offer tailored financial solutions to capitalize on increased property value

Contractor Lead Generation:

Solar installers can target neighbors of solar customers, increasing the chances of successful referrals and upselling opportunities.

Pool cleaners can target new pools, identifying potential customers for maintenance and cleaning services.

Roofing contractors can target homes with recent roofing permits, offering replacement or repair services to homeowners.

Home Service Providers:

Handyman services can target homes with permit records, offering a range of maintenance and repair services.

Appliance installers can target new kitchens and bathrooms, identifying potential customers for appliance installation and integration.

Real Estate Professionals:

Realtors can analyze permit data to understand local market trends, adjusting their sales strategies to capitalize on areas with high construction activity.

Property managers can identify potential investment opportunities, using permit data to evaluate the feasibility of investment projects.

Data Analysis Ideas:

Trend Analysis: Identify trends in permit issuance by type (residential, commercial), project size, or location to forecast future demand.

Geospatial Analysis: Visualize permit data on a map to analyze the concentration of construction activity and identify areas with high growth potential.

Correlation Analysis: Examine the relationship between permit issuance and local economic indicators (e.g., GDP, unemployment rates) to understand the impact of construction on the local economy.

Business Use Cases:

Market Research: Analyze permit data to inform business decisions about market trends, competition, and growth opportunities.

Risk Assessment: Identify areas with high concentrations of permits and potential risks (e.g., building code non-compliance) to adjust business strategies accordingly.

Investment Analysis: Use permit data to evaluate the feasibility of investment projects in specific regions or markets.

Data Visualization Ideas:

Interactive Maps: Create interactive maps to visualize permit concentration by location, type, and project size.

Permit Issuance Charts: Plot permit issuance over time to illustrate trends and fluctuations in construction activity.

Bar Charts by Category: Display the distribution of permits by category (e.g., residential, commercial) to highlight market trends.

Additional Ideas:

Combine with other datasets: Integrate building permit data with other sources (e.g., crime statistics, weather patterns) to gain a more comprehensive understanding of local conditions.

Analyze by demographic factors: Examine how permit issuance varies across different demographics (e.g., age, income level) to understand market preferences and behaviors.

Develop predictive models: Create statistical mo...