This dataset is an authoritative inventory of new housing units constructed in the City of Saint Paul from 2010 through the end of Q1 2025. The data originates from two sources: the City's permitting system, and from the City's records on housing affordability. The dataset helps provide a deeper understanding of trends in market rate and affordable housing production. This dataset is updated quarterly, generally by the 15th of the month following the end of each quarter.For the purposes of this dataset, the delineation of "affordable units" is tied to the construction of the new units: does the project — its development financing or the regulatory framework under which it was built — require units be affordable upon the completion of construction?
This definition of affordability does not include units that are affordable only because of a post-construction subsidy or other similar subsequent commitment to affordability, such as through the city's Rental Rehab Loan Program or 4d Affordable Housing Incentive Program. It does, however, include units that are affordable under the terms of zoning district-based density bonuses for affordability. Projects built under a zoning-based density bonus currently comprise a very small portion of the larger total, and are identified in the Notes column of the associated table.This dataset will be updated quarterly, given the manual work currently involved in bringing it up-to-date. It is the product of work over five years across three City departments.Field definitions are available below. In addition to being available for download through the Open Information website, this data is perhaps more easily accessible in an interactive Housing Production Dashboard.This data is designed under a methodology specific to the City of Saint Paul. Other government entities use the same originating permit data, but somewhat divergent methodologies, which can produce very different results. We believe this particular methodology gives the fullest and most timely depiction of housing production available. For specific details, see the "Methodologies Compared" tab at the bottom of the Housing Production Dashboard.Technical detailsThis dataset is generally designed to have one record (row) per building project that creates new units. A project may be the result of one or more building permits. In cases when a project contains both subsidized / affordable and unsubsidized / market rate units, the project is split across two records (rows).

Fields (Columns) Defined

PropertyRSN: An internal unique identifier for the address point with which the permit is associated.

Property Address: The street address at which the permit work took place.

ParcelID: The county-assigned unique identifier for the parcel on which the permit work took place.

Type of Work: The kind of work undertaken at the site. CHOICES: New · Addition · Remodel

Residence Type: What is the physical form of the dwelling units that were created under this building permit? CHOICES: 2-Family/Duplex · Mixed (Commercial/Residential) · Residential (Multi-Fam) · Single Family DwellingDwelling Unit Type: The type of financial structure tied to the new dwelling units created under this permit. CHOICES:Market Rate Unit: Units that did not receive some sort of direct public subsidy or assistance outside normal market sources.Affordable Unit: Units that contractually ensure affordability / access for those in need, at the level of 80% of Area Median Income (AMI) and below. This definition does include units that are affordable under the terms of zoning-based density bonuses, which comprise a very small portion of the overall total. This demarcation of affordable units does not include units that received financial assistance in preparing the site for redevelopment, for activities such as pollution remediation. Further, the affordability included here are only those contractually included at the closing of the development financing of the project, and does not include units restricted as affordable at a later date, such as through the City's 4(d) Affordable Housing Incentive Program, or the Rental Rehab Loan Program.

Commercial to Housing Conversion: The units shown were produced by converting formerly commercial space (including retail, commercial, institutional and industrial type uses) into residential space (including single family, duplex, 3-4 unit, multifamily and congregate-type residential uses). CHOICES:Yes: The housing units shown were converted from commercial space.No: The housing units shown were not converted from commercial space.Project Permit Issue Date: The date the first permit was issued for the project that created the new dwelling units.

Project Permit Issue Year: The year the first permit was issued for the project that created the new dwelling units.

Existing Dwelling Units: The number of dwelling units that existed just prior to the start of the project under the definition of "dwelling unit" in the International Building Code.

New Dwelling Units: The number of new dwelling units created under the building permit(s) under the definition of "dwelling unit" in the International Building Code.

Total Final Dwelling Units: The number of dwelling units existing upon completion of the associated building permit(s), under the definition of "dwelling unit" in the International Building Code.

Notes: This field contains notes on specific unique circumstances. In particular, a few building permits produced both subsidized / affordable and unsubsidized / market rate dwelling units. To make building permits in this scenario function as needed within data systems, we split such permits into two lines, one for each type of unit, and made a notation in this field to reflect that division.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

This layer shows housing units by year built by tenure (owner or renter). This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is modified every few years to change the top-end and bottom-end categories of the years.This layer is symbolized to show the predominant period that housing units were built in. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25034, B25036, B25035, B25037 (Not all lines of ACS table B25036 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

IMOPE is the reference database for buildings at national level. To date and on a daily basis, it supports nearly 20,000 public and private actors and more than 800 territories (in operational context: fight against unworthy housing, fight against vacancy, energy renovation, OPAH-RU, PIG, VOC,...) wanting to know and transform the French building sector.

Resulting from public research conducted at Mines Saint-Etienne (Institut Mines Télécom), this breakthrough innovation, the methods of which have been patented by the Ministry of the Economy, Industrial and Digital Sovereignty, brings together all the data of interest (+ 250 items of information) on each of the 20 million existing buildings.

⁇ Consult the news of the ONB and the national IMOPE database ⁇ ACTU ONB/IMOPE

IMOPE has been co-built, since its creation in 2016, with and for the actors of the territories (ALEC, operators ANAH, ADIL, DDT, ADEME, EPCI, urban planning agencies ...) in order to meet the multiple challenges of the building sector. Issues on which we can cite:energy renovation, combating vacancy, precariousness and unsanitary conditions, attrition of housing, home support, adaptation to climate change, etc.

The sourcing of merged and reprocessed data: A single and multiple sourcing to increase knowledge and merging in particular: - Open Data: BAN, BDTOPO, DVF, DPE (ADEME), consumption data (ENEDIS, GRDF), RPLS, QPV, Georisks, permanent equipment base, SITADEL, socio-economic data (RP, FiLoSoFi, INSEE), OPAH, ... - "Conventional" data: Land files enriched by Cerema (source DGFiP DGALN), LOVAC, non-anonymised data of owners, RNIC (ANAH) - Local or business data: devices, FSL, LHI, orders, procedures, reporting, planning permission, rental permit, ANAH aid, ... - "Enriched" data: Machine Learning and Deep Learning (DVF, DPE, power source and heating type predictions)

A strong commitment to the commons: U.R.B.S, spin-off of Mines Saint-Etienne, maintains, develops and improves on a clean background and since 2019 the IMOPE database. With a view to mutualisation and openness, U.R.B.S. invites the entire building community (architects, public decision-makers, insurers, artisans, diagnosticians, researchers, citizens, design offices, etc.) to disseminate and reuse widely internally as well as externally, natively or with post-processing, the data contained in the IMOPE database.

It is driven by this philosophy of sharing that we have deployed the**National Building Observatory** (ONB). The**ONB** is a citizen geo-common. As a decision-making tool providing knowledge of the building stock, it makes it easier for everyone to access the information contained in the national IMOPE database.

Convinced that together we will go further, the ONB and IMOPE are initiatives led by civil society. Civil society of which we are part and which, we are convinced, is the keystone for achieving the energy, climate and social objectives of the building sector.

⁇ For more information: https://www.urbs.fr ⁇ To contact us: contact@urbs.fr ⁇ To access the ONB: https://app.urbs.fr/onb/connection

⁇ To access the data catalogue, click here

A. SUMMARY

This dataset reports the number of new residential units made available for occupancy in San Francisco since January 2018. Each row in this dataset shows the change in the number of new units associated with a building permit application. Each row also includes the date those units were approved for occupancy, the type of document approving them, and their address.

Values in the column [Number of Units Certified] can be added together to produce a count of new units approved for occupancy since January 2018.

These records provide a preliminary count of new residential units. The San Francisco Planning Department issues a Housing Inventory Report each year that provides a more complete account of new residential units, and those results may vary slightly from records in this dataset. The Housing Inventory Report is an in-depth annual research project requiring extensive work to validate information about projects. By comparison, this dataset is meant to provide more timely updates about housing production based on available administrative data. The Department of Building Inspection and Planning Department will reconcile these records with future Housing Inventory Reports.

B. METHODOLOGY

At the end of each month, DBI staff manually calculate how many new units are available for occupancy for each building permit application and enters that information into this dataset. These records reflect counts for all types of residential units, including authorized accessory dwelling units. These records do not reflect units demolished or removed from the city’s available housing stock.

Multiple records may be associated with the same building permit application number, which means that new certifications or amendments were issued. Only changes to the net number of units associated with that permit application are recorded in subsequent records.

For example, Building Permit Application Number [201601010001] located at [123 1st Avenue] was issued an [Initial TCO] Temporary Certificate of Occupancy on [January 1, 2018] approving 10 units for occupancy. Then, an [Amended TCO] was issued on [June 1, 2018] approving [5] additional units for occupancy, for a total of 15 new units associated with that Building Permit Application Number. The building will appear as twice in the dataset, each row representing when new units were approved.

If additional or amended certifications are issued for a building permit application, but they do not change the number of units associated with that building permit application, those certifications are not recorded in this dataset. For example, if all new units associated with a project are certified for occupancy under an Initial TCO, then the Certificate of Final Completion (CFC) would not appear in the dataset because the CFC would not add new units to the housing stock. See data definitions for more details.

C. UPDATE FREQUENCY

This dataset is updated monthly.

D. DOCUMENT TYPES

Several documents issued near or at project completion can certify units for occupation. They are: Initial Temporary Certificate of Occupancy (TCO), Amended TCO, and Certificate of Final Completion (CFC).

• Initial TCO is a document that allows for occupancy of a unit before final project completion is certified, conditional on if the unit can be occupied safely. The TCO is meant to be temporary and has an expiration date. This field represents the number of units certified for occupancy when the TCO is issued. • Amended TCO is a document that is issued when the conditions of the project are changed before final project completion is certified. These records show additional new units that have become habitable since the issuance of the Initial TCO. • Certificate of Final Completion (CFC) is a document that is issued when all work is completed according to approved plans, and the building is ready for complete occupancy. These records show additional new units that were not accounted for in the Initial or Amended TCOs.

About this dataset

Context

The dataset tabulates the New Haven population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New Haven across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of New Haven was 15,974, a 0.78% increase year-by-year from 2022. Previously, in 2022, New Haven population was 15,850, an increase of 0.70% compared to a population of 15,740 in 2021. Over the last 20 plus years, between 2000 and 2023, population of New Haven increased by 2,695. In this period, the peak population was 15,974 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the New Haven is shown in this column.
• Year on Year Change: This column displays the change in New Haven population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 New Haven Population by Year. You can refer the same here

The City of Saint Paul's Department of Safety and Inspections requires homeowners or licensed contractors to obtain a building permit before the following changes are made on one or two-family residences, multi-family residences, or buildings for commercial, industrial, or institutional use:Building a new structureAdding an addition to current structureRemodeling or repairing a structureFor more information about the requirements and the application process, please visit: https://www.stpaul.gov/departments/safety-inspections/building-and-construction/construction-permits-and-inspections/building-permits-inspections Note: We have identified an issue with the time-related data in our datasets. The times are displayed correctly as Central time when viewing the data in the City’s open information portal. Upon downloading or exporting the data, any date/time columns are converted to Coordinated Universal Time (UTC). This results in the times getting converted to of either 5 hours (during Daylight savings time) or 6 hours (for Standard time) ahead of our Central time.

To correct this issue, determine if it is Standard time or Daylight Savings time. Central Daylight Time (CDT) runs from the second Sunday in March to the first Sunday in November. Central Standard Time (CST) is the remainder of the year. If it is CDT, subtract 5 hours from UTC time and if it is CST, then subtract 6 hours. This issue comes from the ESRI platform and is unable to be modified at this time.

Displacement risk indicator showing the number of new residential units through building construction permits filed with the Seattle Department of Construction and Inspections (SDCI). Summarized at the census tract level; available for every year from 2006 through the most recent year of available data.

Overview

This residential real estate data set was created by Redfin, an online real estate brokerage. Published on January 9th, 2022, this data summarize the monthly housing market for every State, Metro, and Zip code in the US from 2012 to 2021. Redfin aggregated this data across multiple listing services and has been gracious enough to include property type in their reporting. Please properly cite and link to RedFin if you end up using this data for your research or project.

Source: RedFin Data Center

Property Type

Property type defined by RedFin

• All Residential: All properties defined as single-family, condominium, co-operative, townhouses, and multi-family (2-4 units) homes with a county record.
• Single Family Home (SFH): are homes built on a single lot, with no shared walls. Sometimes there’s a garage, attached or detached.
• Condominium (Condo): Usually a single unit within a larger building or community. Generally come with homeowners’ associations (HOAs), which require the residents to pay monthly or yearly dues.
• Cooperatives (Co-op): Usually a single unit within a larger building or community, but with a different way of holding a title to a shared building. You join a community and everyone in the community owns the building together.
• Townhouse: a hybrid between a condo and a single-family home. They are often multiple floors, with one or two shared walls, and some have a small yard space or rooftop deck. They’re generally larger than a condo, but smaller than a single-family home.
• Multifamily (2-4 units): They are essentially a home that has been turned into two or more units but the units cannot be purchased individually. There is one owner for the whole building.
• Land: Just land, no home of any type for sale.

Source: Building Types

Property Type

For more definitions, please visit RedFin Data Center Metrics

• Average sale to list: The mean ratio of each home's sale price divided by their list price covering all homes with a sale date during a given time period. Excludes properties with a sale price of 50%.
• Home sales: Total number of homes with a sale date during a given time period.
• Inventory: Total number of active listings on the last day of a given time period.
• Median active list ppsf: The median list price per square foot of all active listings.
• Median active list price: The median list price of all active listings.
• Median active listings: The median of how many listings were active on each day within a given time period.
• Median days on market: The number of days between the date the home was listed for sale and when the home went off-market/pending sale covering all homes with an off-market date during a given time period where 50% of the off-market homes sat longer on the market and 50% went off the market faster. Excludes homes that sat on the market for more than 1 year.
• Median days to close: The median number of days a home takes to go from pending to sold.
• Median list price: The most recent listing price covering all homes with a listing date during a given time period where 50% of the active listings were above this price and 50% were below this price.
• Median list price per square foot: The most recent listing price divided by the total square feet of the property (not the lot) covering all homes with a listing date during a given time period where 50% of the active listings were above this price per sqft and 50% were below this price per sqft.
• Median listing with price drops: The median of how many listings were active on each day and whose current list price is less than the original list price within a given time period.
• Median sale price: The final home sale price covering all homes with a sale date during a given time period where 50% of the sales were above this price and 50% were below this price.
• Median sale price per square foot: The final home sale price divided by the total square feet of the property (not the lot) covering all homes with a sale date during a given time period where 50% of the sales were above this price per sqft and 50% were below this price per sqft.
• Months of supply: When data are monthly, it is inventory divided by home sales. This tells you how long it would take supply to be bought up if no new homes came on the market.
• New listings: Total number of homes with a listing added date during a given time period.
• Off market in two weeks: The total number of homes that went under contract within two weeks of their listing date.
• Pending home sales: Total homes that went under contract during the period. Excludes homes that were on the market longer than 90 ...

Graph and download economic data for Total Construction Spending: Residential in the United States (TLRESCONS) from Jan 2002 to Jul 2025 about residential, expenditures, construction, and USA.

Land use categories for every parcel in San Francisco. The land use categories are derived from a range of City and commercial databases. Where building square footages were missing from these databases they were derived from a LIDAR survey flown in 2007.

Land use categories are as follows (units are square feet):
CIE = Cultural, Institutional, Educational
MED = Medical
MIPS = Office (Management, Information, Professional Services)
MIXED = Mixed Uses (Without Residential)
MIXRES = Mixed Uses (With Residential)
PDR = Industrial (Production, Distribution, Repair)
RETAIL/ENT = Retail, Entertainment
RESIDENT = Residential
VISITOR = Hotels, Visitor Services
VACANT = Vacant
ROW = Right-of-Way
OPENSPACE = Open Space

Other attributes are:
RESUNITS = Residential Units
BLDGSQFT = Square footage data
YRBUILT = year built
TOTAL_USES = Business points from Dun & Bradstreet were spatially aggregated to the closest parcel, and this field is the sum of the square footage fields
The subsequent fields (CIE, MED, MIPS, RETAIL, PDER & VISITOR) were derived using the NAICS codes supplied in the Dun & Bradstreet dataset, and the previous TOTAL_USES column.

The determining factor for a parcel's LANDUSE is if the square footage of any non-residential use is 80% or more of its total uses. Otherwise it becomes MIXED.

In the case where RESIDENT use has some square footage of non-residential use, this is mainly accessory uses such as home businesses, freelancers, etc.

Last updated: March, 2016

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the _location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

A. SUMMARY This filtered view contains one record per building and provides basic characteristics (such as size and vintage). For commercial buildings, the table indicates when an energy audit or decarbonization plan is due.

The Existing Buildings Energy Performance Ordinance (Environment Code Chapter 20) requires that each non-residential building with at least 10,000 square feet of conditioned (heated or cooled) space and each residential building with at least 50,000 square feet of conditioned space must be benchmarked annually using Energy Star Portfolio Manager. Each non-residential building specified above is also required to undergo an energy audit, retrocommissioning, or develop a plan for decarbonization at least once every 5 years.

More information: San Francisco Existing Buildings Energy Ordinance Website

B. HOW THE DATASET IS CREATED The data is sourced from the benchmark and energy audit reports submitted for compliance with Environment Code Chapter 20. The dataset is presented in two tables which together provide basic characteristics, compliance status, and a public record of reported energy performance.

C. UPDATE PROCESS This dataset will be updated on a monthly basis.

D. HOW TO USE THIS DATASET Existing Buildings - Benchmark Reports -- Each row of this table presents one year of benchmarking data for one building – so there are multiple records per building. One year of data includes compliance status, and if the building complied it also presents energy use data including gas, electricity, steam and EPA-estimated operational carbon emissions.

Existing Buildings Energy Performance Ordinance Report -- The main dataset contains the information of the two views joined on Parcel Number.

Graph and download economic data for New Private Housing Units Authorized by Building Permits for Texas (TXBPPRIV) from Jan 1988 to Aug 2025 about permits, buildings, new, TX, private, housing, and USA.

New Private Housing Authorized by Building Permits for Iowa was 1131.49157 Units in August of 2025, according to the United States Federal Reserve. Historically, New Private Housing Authorized by Building Permits for Iowa reached a record high of 1923.74179 in August of 2016 and a record low of 365.65389 in March of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for New Private Housing Authorized by Building Permits for Iowa - last updated from the United States Federal Reserve on November of 2025.

About this dataset

Context

The dataset tabulates the Hadley town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hadley town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Hadley town was 1,956, a 0.86% decrease year-by-year from 2022. Previously, in 2022, Hadley town population was 1,973, a decline of 0.55% compared to a population of 1,984 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Hadley town decreased by 30. In this period, the peak population was 2,181 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Hadley town is shown in this column.
• Year on Year Change: This column displays the change in Hadley town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Hadley town Population by Year. You can refer the same here

Content

The Occupational Employment Statistics (OES) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OES estimates are constructed from a sample of about 51,000 establishments. Each year, forms are mailed to two semiannual panels of approximately 8,500 sampled establishments, one panel in May and the other in November.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Clem Onojeghuo on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This is a filtered view of the Building Permits dataset. This dataset only shows building permits where the 'Permit Creation Date' is after December 31, 2012 23:59:59.

A. SUMMARY The dataset includes details of all building permit applications filed with the Department of Building Inspection. Permit numbers and associated information may be repeated in multiple rows if there are multiple addresses associated with the permit. To view the deduplicated building permits dataset with just the primary address, click here. Data is uploaded nightly by DBI. Users can view associated addenda, routing, and inspection information for each permit in the dataset online through DBI’s Permit Tracking System here.

A full data dictionary for this dataset is available by clicking here.

B. HOW THE DATASET IS CREATED The dataset is created by extracting permit characteristics and key dates (including permit filing, issuance, and completion dates) from DBI’s Permit Tracking System.

C. UPDATE PROCESS The process that builds this dataset will run nightly and include all permits entered into the system up to the time of the refresh (see the “data as of” column in the dataset).

D. HOW TO USE THIS DATASET Users can use this dataset to search for all permits associated with a specific address or parcel number and view characteristics like addresses, permit descriptions of work, unit counts, valuations, etc. If the analytical objective is to measure permitting trends for all addresses, we recommend using the primary address building permits dataset.

Note: if you need to open this dataset in Excel, use one of the pre-filtered datasets. Opening the main dataset in Excel will cause some data to be lost as there are more than one million rows in the full dataset.

1. Building Permits filed on or after January 1, 2013
2. Building Permits filed before January 1, 2013

E. RELATED DATASETS Department of Building Inspections Permits Data Building Permits (Unique) Building Permit Addenda with Routing Building Permits Contacts Electrical Permits Plumbing Permits Boiler Permits Dwelling Unit Completion Counts by Building Permit

Planning Department Permits Planning Department Permits

Public Works Construction Permits Department of Public Works Street Use Permits Department of Public Works Large Utility Excavation Permits

Fire Department Permits Fire Department Permits

Other housing/ construction related datasets related to building permits Planning Department Housing Development Pipeline Quarterly datasets

Mayor’s Office of Housing and Community Development: Affordable Housing Pipeline Affordable Housing Pipeline

For even more permit datasets, click this link and search “Permits.”

About this dataset

Context

The dataset tabulates the Hurley town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hurley town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Hurley town was 6,127, a 0.29% decrease year-by-year from 2022. Previously, in 2022, Hurley town population was 6,145, a decline of 1.05% compared to a population of 6,210 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Hurley town decreased by 327. In this period, the peak population was 6,492 in the year 2004. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Hurley town is shown in this column.
• Year on Year Change: This column displays the change in Hurley town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Hurley town Population by Year. You can refer the same here