This project provides a national unified database of residential building retrofit measures and associated retail prices and end-user might experience. These data are accessible to software programs that evaluate most cost-effective retrofit measures to improve the energy efficiency of residential buildings and are used in the consumer-facing website https://remdb.nrel.gov/ This publicly accessible, centralized database of retrofit measures offers the following benefits: Provides information in a standardized format Improves the technical consistency and accuracy of the results of software programs Enables experts and stakeholders to view the retrofit information and provide comments to improve data quality Supports building science R&D Enhances transparency This database provides full price estimates for many different retrofit measures. For each measure, the database provides a range of prices, as the data for a measure can vary widely across regions, houses, and contractors. Climate, construction, home features, local economy, maturity of a market, and geographic _location are some of the factors that may affect the actual price of these measures. This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

Description:The City of Irving issues residential building permits as part of its regulatory process to ensure that all residential construction projects comply with local building codes, zoning laws, and safety standards. This dataset includes records of residential building permits issued between February 15, 2022 through present, featuring issue dates, permit numbers, types, locations (address, city, state, zip code), and project descriptions.Residents, developers, researchers, and other members of the public can use this dataset to gain insight into the City’s residential development scope and scale, making it an important tool for tracking urban development trends, facilitating strategic planning, and ensuring regulatory compliance. Potential Applications:Use by urban planners and city officials for planning services and infrastructure to meet residential growth.Reference for local community groups to understand housing trends and advocate for community needs.Public awareness of ongoing and future residential projects.Data Sources and Frequency of Updates:Data is sourced from the City's Inspections Department through MGO, the City's permitting software utilized to field permit applications and manage approvals. The dataset is updated monthly to reflect new permits issued.Data Quality:Prior to February 15, 2022, residential permits were managed using a different software, Trakit, resulting in distinct reporting structures compared to the current system, MGO. This variation has led to the publication of two separate datasets on the open data portal: "Residential Permits Issued: Feb 15 2022 Through Present" and "Residential Permits Issued: Jan 1 2018 Through Feb 14 2022" with differences in field labeling and report formats. Users should exercise caution when comparing historical data across these datasets due to these discrepancies in collection methods and field variations.Contact Information:For questions about the dataset or specific data points, users are encouraged to contact the owner of the dataset:City of Irving Inspections DepartmentEmail: irving-permits@cityofirving.orgWebsite: https://www.cityofirving.org/3888/Inspections Related Services:This dataset complements other development and planning datasets on the open data portal:Datasets:Residential Permits Issued: Jan 1 2018 Through Feb 14 2022Commercial Permits Issued: Feb 15 2022 Through PresentCommercial Permits Issued: Jan 1 2018 Through Feb 14 2022Maps & Apps:New Development DashboardZoning Applications

The data is tabular data containing information on residential history, neighborhood built environment, individual-level economic and demographic information, and measured serum metals. This dataset is not publicly accessible because: The data is not owned by the EPA and contains protected information in the form of residential history and thus cannot be uploaded into ScienceHub. It can be accessed through the following means: The data can be accessed by contacting Dr. Chantel Martin. Format: Data is tabular data containing information on residential history, neighborhood built environment, individual-level economic and demographic information, and measured serum metals concentrations. This dataset is associated with the following publication: Lodge, E., C. Martin, R.C. Fry, A. White, C. Ward-Caviness, S. Martin, and A. Aiello. Objectively measured external building quality, Census housing vacancies and age, and serum metals in an adult cohort in Detroit, Michigan. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 177-186, (2023).

Abstract

The liberalized economic system in Armenia has led to a sharp growth in individual housing construction by individuals for their own use. High rates of individual housing construction may be observed in some geographic (regional) locations. However a lack of accurate administrative registers of licences for construction, the prevalence of some constructions (built without any license), create particular difficulties in producing reliable and comprehensive statistical data collection on individual housing construction.

In general, problems faced in collecting information about house construction may be separated in the following main groups: • incompleteness of indicators on volumes of individual housing construction by marz (region) breakdown, • introduction of the instruments being used in the international practice, taking into consideration peculiarities of the sphere, • lack of precise mechanisms for monitoring the process of individual housing construction, • expanding and improvement of the existing indicators set, • necessity of forming and updating of the individual housing construction register.

In this context, in order to improve the statistical accounting of house construction, it is important to conduct periodical surveys and by so doing to improve the instruments available, through the development and use of state statistical reporting forms, and to obtain some broad indicators of levels of activity in at least some regions of the Country.

Taking into account the above-mentioned, the main purpose of this survey was to improve statistics on individual housing construction. In particular, • ensuring the comparability of the statistical data on house construction with the methodologies and standards used in the international practice, • ensuring the comprehensiveness of the indicators by regional breakdown, • use of the sampling methods and improvements of their methodology in construction.

The survey results provide: - complete and reliable information on individual housing construction in some key regions, particularly studying structure and volumes of the buildings, - and increase in the quality of information, - to complement the database on house construction within the official statistics with new indicators, - a model for a register for newly built houses which can be used to monitor periodically the level housing construction activity.

The derived results enable NSSRA to improve and update its database, to expand its list of published indicators, to improve methodology, and to support more informed policy making by providing state and local selfgovernment bodies with key information.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

There were two main approaches - entire and sampling - used for the conduct of the survey.

Lists of the licenses for individual housing construction, which had been given since 2005 by the state government body in the urban development, served as the main information source for the survey.

However there were, in some regions, serious inaccuracies and lack of availability of lists of licensed permits for individual house construction. These weaknesses, together with restrictions of available financial and human resources and the objective of receiving representative data, led to a concentration of survey resources in those regions where the individual housing construction is more prevalent and where reasonably up-to-date lists of licences are available. Yerevan and the following 4 marzes - Aragatsotn, Ararat, Armavir and Kotayk- were selected. The results of the survey therefore only apply to Yerevan and to these 4 marzes.

The licenses given for individual housing construction in Yerevan city were surveyed in their entirety, but in the other marzes - by the random sampling, considering the differences between the numbers of the mentioned licenses (from 100 to 640, meanwhile 100 - in Armavir, 136 - in Aragatsotn, 304 - in Ararat, 640 -in Kotayk), based on which the sample "steps" had been determined.

Overall there were 1330 licences granted, permitting individuals to construct a house for their own use. These were predominantly in Yerevan.

Although the survey was aimed at 1330 houses, it was foreseen to survey also those buildings under construction in the neighbourhood of the surveyed buildings, which were out of the list of the buildings to be surveyed.

Mode of data collection

Face-to-face [f2f]

The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
 ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
Null - Could not be geocoded (does not appear on the map) 

For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in eight separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Worker Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Ratio Verification, and Part 8, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: ICPSR, retrieved 06/28/2011)

Abstract: This digitized collection contains approximately 90,000 images of Residential Assessment Records (a.k.a. “field cards”) created by the Tax Assessors Department of the City of Providence, Rhode Island. These cards contain information on structural conditions of buildings (e.g., grades of condition for foundations, walls, roofs, building materials, etc.) organized by, street address, and plat & lot number within the City of Providence. The verso of these cards contains structural diagrams, land valuations, and cost computations. Finding aid: Providence City Field Assessment Records Series 1: 1940 Creator: Providence City Archives Language of materials: English Repository: Providence City Archives Record Group Number: RG 131.3 Scope and Content: The field cards were developed as a measure to accurately grade the value of a structure for tax purposes within the City of Providence. The strength of the collection, contemporarily, concerns the sections under “Land Valuation” on the verso of the digitized documents, particularly the status for which a building was zoned for (eg., house, apartment, industrial, or commercial structure, etc.). This information helps distinguish the maximum amount of tenants a residential building can have within code. Other areas of interest include quality assessments from building materials to structural condition (eg., cost computation of values before and after deductions of existing conditions and improvements of a structure). Arrangement: The field cards are digitized and arranged in the Socrata database system accessed by address + plat/lot. Access to the collection: There are no restrictions to access. Use of materials: The materials are within public _domain. However, researchers are kindly asked to cite the Providence City Archives if reproduced. Preferred citation: Residential Assessment Records, RG131.3, Providence City Archives.

The NYC Department of City Planning's (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP's annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

​Building permits are required for new buildings, additions or alterations to existing buildings, and for demolitions or salvage and abatement work.As permit application processing is a collaboration between the customer / applicant and the City, this dataset has been updated with the elapsed time from the date at which an application generated a permit number to when the permit was first issued. Specifically, the fields PermitNumberCreatedDate and PermitElapsedDays (includes weekends and holidays) are available in all views as well as through data exports and API access. In addition to existing fields that allow this data to be grouped (PropertyUse, TypeofWork, SpecificUseCategory) we have created a new group field, PermitCategory, that focuses on higher volume, lower complexity project scopes.This dataset includes information of all building permits issued by the City of Vancouver, starting in 2017. The data is based on permit issuance date and does not show current status of a permit or changes after a permit is originally issued. Data currency​The extract for the current year is updated daily but the extract for prior year is static. Data accuracyThere may be addresses that do not return coordinates through the geocoding process (using BC Address Geocoder API). These Issued Building Permits do not appear on the Map. Please consult the Table view for a complete list of Issued building permits​​There may be some loss of quality from data entry errors and omissions, in particular where the original application date was prior to May 2016 (when permit software changed). Websites for further information When you need a permit Building permit Chief Building Official and Vancouver Building By-law Statistics on Construction Activity Zoning and Development By-law Active ​and archived rezoning applications Active and achived development permit applications

Data were collected in 70 detached houses built in 2011-2017 in compliance with the mechanical ventilation requirements of California’s building energy efficiency standards. Each home was monitored for a one-week period with windows closed and the central mechanical ventilation system operating. Pollutant measurements included time-resolved fine particulate matter (PM2.5) indoors and outdoors and formaldehyde and carbon dioxide (CO2) indoors. Time-integrated measurements were made for formaldehyde, NO2 and nitrogen oxides (NOX) indoors and outdoors. Operation of the cooktop, range hood and other exhaust fans was continuously recorded during the monitoring period. One-time diagnostic measurements included mechanical airflows and envelope and duct system air leakage. All homes met or were very close to meeting the ventilation requirements. On average the dwelling unit ventilation fan moved 50% more airflow than the minimum requirement. Pollutant concentrations were similar or lower than those reported in a 2006-2007 study of California new homes built in 2002-2005. Mean and median indoor concentrations were lower by 44% and 38% for formaldehyde and 44% and 54% for PM2.5. Ventilation fans were operating in only 26% of homes when first visited and the control switches in many homes did not have informative labels as required by building standards.

Methods Overview of HENGH Study

The HENGH study was conceived, designed and implemented for the purpose of evaluating impacts of residential mechanical ventilation equipment requirements that have been part of the California’s Building Energy Efficiency Standards since 2008. Starting in 2009, these standards have required bath and kitchen exhaust fans and dwelling unit mechanical ventilation with sizing and performance levels based on the residential ventilation standard (62.2) of the ASHRAE organization. The ventilation standards are intended to help maintain indoor air quality as homes are constructed with tighter shells to reduce uncontrolled outdoor air infiltration for energy efficiency.

The study was led by Lawrence Berkeley National Laboratory (LBNL). All study protocols involving interactions and collection of data from private individuals and monitoring in occupied homes were reviewed and approved by the LBNL Human Subjects Committee. Research Funding and technical contributions of collaborators are noted below in the acknowledgements.

The field study included the following data collection elements:

Homeowner survey about household demographics, ventilation practices, activities that can impact indoor air quality, and satisfaction with environmental conditions in the home.
Compilation of basic data about the houses (location, size, number of bedrooms, etc.) and gas appliances and mechanical ventilation equipment (technology type, make, model, etc.)
Measurements of air leakiness of the building envelope and forced air system ductwork.
Measurements of the following parameters over a weeklong monitoring period:

  Airflows of all mechanical ventilation equipment;
  Air pollutants and environmental parameters indoors and outdoors;
  Cooktop and oven surface temperatures to identify burner use.


Participants were expected to complete a daily activity log. 

What is contained in this dataset?

The dataset contains the most relevant information collected about the 70 houses and their mechanical equipment, results of the participant survey, results of air leakage and airflow measurements at the homes, pollutant concentrations measured by time-integrated passive samplers inside and outside of the home, usage of cooktop and oven, external door open state, and time-series or air pollutants and environmental indicators measured within and outside of the houses.

Organization of Dataset

Airflow

  This folder contains time series data of monitored mechanical ventilation equipment, estimates of air infiltration rate, and overall air exchange rate. There is one csv file for each home. See HENGH_Airflow_ReadMe for more details. 


Ambient_PM

  This folder contains a summary of PM2.5 data reported by one or more ambient air monitoring stations nearest to each study home. There is one EXCEL file containing PM2.5 data reported from up to three closest regulatory monitoring sites. A composite estimate of ambient PM2.5 was calculated for each home using an inverse distance weighing method. 


Home_Equipment_Data

  This folder contains data about the house, including basic characteristics, air leakage test results, and measured airflow rates of mechanical ventilation equipment. There is one EXCEL file containing the data for all homes. The EXCEL file has ReadMe information about the data provided and a note about data quality issue concerning exhaust airflow measurements of over-the-range microwaves.  


IAQ_Monitoring

  This folder contains time-resolved air quality data, including estimated PM2.5 as measured by photometry (PM), carbon dioxide (CO2), nitrogen dioxide (NO2), formaldehyde (FRM), temperature (T), and relative humidity (RH). There is one csv file of 1-minute time-series data for each home. See HENGH_IAQ_Monitoring_ReadMe for data header definitions and data issues. 


IAQ_Sample

  This folder contains the results of time-integrated air quality samples, including passive measurements of formaldehyde, nitrogen dioxide and nitrogen oxides, and PM2.5 gravimetric filter measurements. There is one EXCEL file containing all data. Detail information about chemical analysis of air samples are provided elsewhere in the journal paper and report. 


Occupant_Activity

  This folder contains tabulated information provided by study participants from their daily activity logs. There is one EXCEL file containing data transcribed by a staff member, which was independently spot checked by another staff to confirm accuracy. The PDF file is the daily activity log used. 


Occupant_Survey

  This folder contains results of a survey about the occupants, their general activities related to ventilation and IAQ satisfaction, completed by study participants. There is one EXCEL file containing data transcribed by a staff member. Two homes did not complete surveys; these homes have "No survey" in each data file. Questions for the occupant surveys are provided in MS Word and PDF formats. 


State_Monitoring

  This folder contains time series data of cooking burners monitored with iButton temperature sensors and open/close status of external (mostly patio) doors monitored with state sensors. There is one csv file for each home. See HENGH_State_Monitoring_ReadMe for more details.

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO2, PM2.5 and radon; and time-integrated NO2, NOX and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2-2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO2, and NO (NOX-NO2) when operated with WHMV; and also had faster PM2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19 vs. 3%) felt their IAQ could be improved and more reported dryness as a problem (58 vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in nine separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Mover Group Record, Part 8, Recodes (One Record per Housing Unit), and Part 9, Weights. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02912.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

Abstract (en): This data collection provides information on the characteristics of a national sample of housing units. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who have recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of an exterminator service, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. Housing units in the United States. The basic sample of approximately 55,000 housing units was selected from the 1980 Census of Population and Housing records and updated by a sample of addresses from building permits to include new construction and conversions.

Uruguay Construction Value Index: New Housing: Tower Block: Medium Quality with Lift data was reported at 73,609.304 1990=100 in Dec 2013. This records a decrease from the previous number of 78,409.154 1990=100 for Jun 2013. Uruguay Construction Value Index: New Housing: Tower Block: Medium Quality with Lift data is updated semiannually, averaging 6,382.982 1990=100 from Jun 1991 (Median) to Dec 2013, with 46 observations. The data reached an all-time high of 78,409.154 1990=100 in Jun 2013 and a record low of 224.050 1990=100 in Jun 1991. Uruguay Construction Value Index: New Housing: Tower Block: Medium Quality with Lift data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Uruguay – Table UY.EA002: Construction Value Index.

The metropolitan survey is conducted in even-numbered years, cycling through a set of 41 metropolitan areas, surveying each one about once every 6 years. This data collection provides information on the characteristics of a metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. The data are presented in seven separate parts: Part 1, Work Done Record (Replacement or Addition to the House), Part 2, Journey to Work Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, and Part 7, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This dataset provides the location and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Development FAQs - IMS/PIC | HUD.gov / U.S. Department of Housing and Urban Development (HUD), for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Buildings Date Updated: Q1 2025

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

When buying a home in the country, people need to consider certain factors that usually do not confront the urban homebuyer, such as whether or not the water supply is adequate and if the means of disposing of wastewater is safe . Disappointed rural homeowners have sometimes found out too late that the well drilled on their new land does not yield enough water or that the water is of poor chemical quality. Also, foundations can become unstable from excess surface runoff or from high ground-water levels . Septic systems, if not located properly or if soil conditions are not properly considered, can fail . Wells can be contaminated by septic systems or barnyard wastes. Shallow or dug wells on farms or near older homes that served adequately in earlier years are often inadequate for modern uses. Preventing water problems or coping with them when buying or building a rural home can be either complex or relatively simple. Prospective homeowners need to know about the terrain, the proximity of the house to other structures, and the condition of the existing well and septic system . If building in an unpopulated area, drill a well first-or if buying an old house, find out if the water supply is adequate. This booklet describes the most common well problems encountered by rural homeowners, how to recognize them, solve them, or get help . But first, the characteristics and behavior of ground water and the relationship between ground water and the surrounding land are discussed briefly.