This dataset provides information about the number of properties, residents, and average property values for Residence Drive cross streets in Marysville, OH.

SSA provides a web service and downloadable file for SSA Resident Station locations, telephone numbers, and hours of operation. (Note: If you think an office might be closed due to an emergency, check www.ssa.gov/agency/emergency)

At the level of forecasting rooms, the proportion of residents who were reported at their address at their address for at least five years on 31.12.2018 is presented. On 31.12.2018, 63.7 % of Berlin’s residents had at least five years of residence at their address at that time. In seven forecast rooms, more than 70 % of the inhabitants lived at their home address for at least five years. The highest share was Heiligensee-Konradshöhe with 75.4 %, followed by Kaulsdorf/Mahlsdorf (75.1 %) and Frohnau-Hermsdorf (73.7 %) residents with at least five years of living. The lowest shares were Friedrichshain Ost (48.7 %), Forst Grunewald (52.8 %) and Mitte-Zentrum (54.5 %).

Living arrangement by age group for the Department of Development Services (DDS) population. Information is reported by the Regional Centers to the DDS and is extracted from the Client Master File (CMF). The Client Master File (CMF) is the primary source of demographic information on individuals receiving services funded by DDS.

This dataset provides information on 255 in Jiangsu, China as of March, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information about the number of properties, residents, and average property values for Waters Way cross streets in Mountain Home, ID.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

List of buildings with residential function within Stichtse Vecht including address and location coordinates. A map of this is available via download — HTML.

Detailed description: — The information provided here is a list of buildings with purpose “habit” supplemented with address, neighborhood, district and geographical location. This data comes from the Basic Registration Addresses and Buildings (BAG). Since buildings can change over time (new construction, demolition, refurbishment, change of address and purpose of use) the data on this data platform is automatically updated weekly. The municipality of Stichtse Vecht is the source of this information. Inaccuracies can be transmitted via the website of the Land Registry. These will, after examination, be changed by the BAG administrator in the BAG. This Data Platform is only a pass-through.

Terminology: The BAG uses the terms buildings, residence objects and purpose of use. Within a building, one or more residence objects can be housed and an address is 1:1 linked to a residence object. The purpose of use is also linked to a residence object. Sometimes a residence object even has multiple uses.

For example: An apartment building can be one building but can house dozens of residence objects. Most of this user purpose will have living, but it can also include residence objects with shopping function or even residence objects with residential and store function.

Source: Bag (Basic Registration Addresses and Buildings) Restrictions: This dataset is not suitable for legal purposes. Coordinate system: WGS84/RD

The Department of Health requires adult care facilities (ACFs) to complete an electronic filing of each facility's licensed adult home and enriched housing program bed census on an annual basis. These facilities include adult homes (AHs), enriched housing programs (EHPs), assisted living programs (ALPs), assisted living residences (ALRs), special needs assisted living residences (SNALR), and enhanced assisted living residences (EALR). Available bed and occupancy information in ACFs are self-reported and is not audited by the NYSDOH. This dataset is refreshed on a annual basis. For more information, check out http://www.health.ny.gov/facilities/adult_care/. The "About" tab contains additional details concerning this dataset.

This survey collected data for all institutions of higher education in the United States. Variables include number of students in enrollment classifications by residence status (in-state/out-of-state/foreign), by home state, and by sex.

Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Region: Residence in the West Census Region (CXU800721LB1105M) from 1984 to 2023 about West Census Region, owned, market value, information, financial, residents, housing, estimate, and USA.

The judgement on local infrastructure in selected cities. Satisfaction with residential surroundings, local politics and the job of city management.

Topics: 1. Residential surroundings: local residency and local ties; desire to move and preferred type of place of residence; spatial distance to relatives; detailed information on size of living space, number of rooms and heating of residence; rent costs and costs in addition to rent; details of a profile of demands for a new residence; description of population structure of current neighborhood; desire for more neighborhood contacts; satisfaction with residential surroundings and housing conditions locally; residential status; living together with foreigners in the building; contacts with foreigners; attitude to limitation on proportion of foreigners in the residential area; attitude to the right of foreigners to vote in municipal elections.

1. Judgement on public services and local politics: most important problems and tasks of the municipality and assumed preferences of municipal politicians; most important tasks in the area of housing conditions and environmental protection; attitude to privatization of selected public services; attitude to the welfare state; judgement on financial government expenditures for the individual types of school and further education facilities; most important problems in the school area; attitude to the integrated comprehensive school; satisfaction with local education facilities and public transportation; use of various means of transport in selected situations; satisfaction and priorities in local traffic policy and culture policies; satisfaction with local health facilities and social facilities; assumed consideration of group interests in traffic policy; preferred social political measures for selected groups in the city; desired measures to create jobs and satisfaction with job offerings in the city; attitude to support for selected energy sources and support for environmental protection measures given concurrent jeopardy for jobs.

2. Judgement on inner safety: assessment of the sense of security at place of residence; fear of becoming victim of a crime; particularly unsafe areas locally; personal impact from a traffic offense; personal victimization experience and attitude to selected measures to combat crime; point in time and type of crime; filing a report and its outcome; perceived threat to the state from terrorism; preferred measures to defend against terrorism.

3. Dealing with authorities and personal activities in local politics: judgement on contact with authorities; institutions and their influence; political participation and rank order of the effectiveness of political actions; personal opinion leadership or opinion allegiance in political questions; party preference.

4. Miscellaneous: type and length of leisure activities; desire for additional leisure activity; satisfaction with local leisure offerings; media usage; general contentment with life; importance of areas of life and degree of realization of important goals in life; distance to workplace; assessment of personal job security; year of birth of children; date of birth; car possession; possession of a telephone; possession of house and property; memberships; attending meetings and honorary offices; judgement on the effect of survey results on city management; attitude to a regular survey of citizens.

Demography: school education; occupational training; occupational position; household size.

Interviewer rating: type of building; number of contact attempts; willingness of respondent to cooperate; reliability of respondent; length of interview; date of interview; identification of interviewer; sex.

The dataset contains the details of the answers () to the questionnaire for measuring the satisfaction of users who use the online service for requesting residence, change of residence and address, activated in November 2020 in compliance with the indications of the Digital Administration Code (D Legislative Decree 82/2005, article 7, paragraph 3 - amended by Legislative Decree 179/16, article 8, paragraph 1). The questionnaire can be completed online by the user after making the request and allows him to express his satisfaction with the accessibility, simplicity and effectiveness of the service. The user accesses the questionnaire via a link received by email at the end of the request. The results of the survey are published monthly on the page https://www.comune.milano.it/comune/administration-trasparente/servizi-erogati/servizi-in-rete. () only closed questions

Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Region: Residence in the Midwest Census Region (CXU800721LB1103M) from 1984 to 2023 about Midwest Census Region, owned, market value, information, financial, residents, housing, estimate, and USA.

MT 1.5.1 Population by country of birth, age, sex and place of usual residence

Description of the change of housing situation of former residents of Cologne Nippes. Residence selection criteria, type and condition of residence, description of residential furnishings.

Topics: length of residence in building and part of town; time of to the quarter; reasons for the selection of this quarter; knowledge about the quarter before moving; comparison of old and new residence regarding housing situation, size of residence in qm, size of household, residence parties in the building, owner status of residence or the building, rent costs, heating costs, age of building and number of rooms; personal assessment of improvement in housing situation; criteria of improvement (scale); reasons for moving away; moving away voluntarily; insistence of landlord on moving and possible compensation offers; source of information for current residence; residence authorization; personal concepts of city life by means of frequency of visits of various facilities; residence selection criteria; renovations conducted since moving and their perceived extent; number of car in household; sources of supply of furniture; preferred characteristics of residential furnishings (preferred style); type of foods guests are served. Demography: age; sex; marital status; religious denomination; school education; number of further persons in household and their age; age of youngest child and his supervision on working days; occupational position; employment; professional career; employment and occupational position of partner; employment of father; number of persons in household with income; household income; party preference (Sunday question); citizenship.

Also encoded was: presence of other persons during interview; date of interview; length of interview; interview number; interview room within residence; type of floor; structuring of walls, ceilings and windows; type of cabinets; amount of space; general condition of housing areas; language of respondent.

Abstract

The household registration system known as ho khau has been a part of the fabric of life in Vietnam for over 50 years. The system was used as an instrument of public security, economic planning, and control of migration, at a time when the state played a stronger role in direct management of the economy and the life of its citizens. Although the system has become less rigid over time, concerns persist that ho khau limits the rights and access to public services of those who lack permanent registration in their place of residence. Due largely to data constraints, however, previous discussions about the system have relied largely on anecdotal or partial information.

Drawing from historical roots as well as the similar model of China’s hukou, the ho khau system was established in Vietnam in 1964. The 1964 law established the basic parameters of the system: every citizen was to be registered as a resident in one and only household at the place of permanent residence, and movements could take place only with the permission of authorities. Controlling migration to cities was part of the system’s early motivation, and the system’s ties to rationing, public services, and employment made it an effective check on unsanctioned migration. Transfer of one’s ho khau from one place to another was possible in principle but challenging in practice.

The force of the system has diminished since the launch of Doi Moi as well as a series of reforms starting in 2006. Most critically, it is no longer necessary to obtain permission from the local authorities in the place of departure to register in a new location. Additionally, obtaining temporary registration status in a new location is no longer difficult. However, in recent years the direction of policy changes regarding ho khau has been varied. A 2013 law explicitly recognized the authority of local authorities to set their own policies regarding registration, and some cities have tightened the requirements for obtaining permanent status.

Understanding of the system has been hampered by the fact that those without permanent registration have not appeared in most conventional sources of socioeconomic data. To gather data for this project, a survey of 5000 respondents in five provinces was done in June-July 2015. The samples are representative of the population in 5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong. Those five provinces/cities are among the provinces with the highest rate of migration as estimated using data from Population Census 2009.

Geographic coverage

5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling for the Household Registration Survey was conducted in two stages. The two stages were selection of 250 enumeration areas (50 EAs in each of 5 provinces) and then selection of 20 households in each selected EA, resulting in a total sample size of 5000 households. The EAs were selected using Probability Proportional to Size (PPS) method based on the square number of migrants in each EA, with the aim to increase the probability of being selected for EAs with higher number of migrants. “Migrants” were defined using the census data as those who lived in a different province five years previous to the census. The 2009 Population Census data was used as the sample frame for the selection of EAs. To make sure the sampling frame was accurate and up to date, EA leaders of the sampled EAs were asked to collection information of all households regardless of registration status at their ward a month before the actual fieldwork. Information collected include name of head of household, address, gender, age of household’s head, household phone number, residence registration status of household, and place of their registration 5 years ago. All households on the resulting lists were found to have either temporary or permanent registration in their current place of residence.

Using these lists, selection of survey households was stratified at the EA level to ensure a substantial surveyed population of households without permanent registration. In each EA random selection was conducted of 12 households with temporary registration status and 8 households with permanent registration status. For EAs where the number of temporary registration households was less than 12, all of the temporary registration households were selected and additional permanent registration households were selected to ensure that each EA had 20 survey households. Sampling weights were calculated taking into the account the selection rules for the first and second stages of the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was mostly adapted from the Vietnam Household Living Standard Survey (VHLSS), and the Urban Poverty Survey (UPS) with appropriate adjustment and supplement of a number of questions to follow closely the objectives of this survey. The household questionnaire consists of a set of questions on the following contents:

• Demographic characteristics of household members with emphasis on their residence status in terms of both administrative management (permanent/temporary residence book) and real residential situation. • Education of household members. Beside information on education level, the respondents are asked whether a household member attend school as “trai-tuyen” , how much “trai-tuyen” fee/enrolment fee, and difficulty in attending schools without permanent residence status. • Health and health care, collecting information on medical status and health insurance card of household members. • Labour and employment, asking household member’s employment status in the last 30 days; their most and second-most time-consuming employment during the last 30 days; and whether they had been asked about residence status when looking for job. • Assets and housing conditions. This section collects information on household’s living conditions such as assets, housing types and areas, electricity, water and energy. • Income and expenditure of households. • Social inclusion and protection. The respondents are asked whether their household members participate in social organizations, activities, services, contribution; whether they benefit from any social project/policy; do they have any loans within the last 12 months; and to provide information about five of their friends at their residential area. • Knowledge on the Law of Residence, current regulations on conditions for obtaining permanent residence, experience dealing with residence issues, and opinion on current household registration system of the respondents.

Cleaning operations

Managing and Cleaning the Data

Data were managed and cleaned each day immediately upon being received, which occurred at the same time as the fieldwork surveys. At the end of each workday, the survey teams were required to review all of the interviews conducted and transfer collected data to the server. The data received by the main server were downloaded and monitored by MDRI staff.

At this stage, MDRI assigned a technical team to work on the data. First, the team listened to interview records and used an application to detect enumerators’ errors. In this way, MDRI quickly identified and corrected the mistakes of the interviewers. Then the technical team proceeded with data cleaning by questionnaire, based on the following quantity and quality checking criteria.

• Quantity checking criteria: The number of questionnaires must be matched with the completed interviews and the questionnaires assigned to each individual in the field. According to the plan, each survey team conducted 20 household questionnaires in each village. All questionnaires were checked to ensure that they contained all essential information, and duplicated entries were eliminated. • Quality checking criteria: Our staff performed a thorough examination of the practicality and logic of the data. If there was any suspicious or inconsistent information, the data management team re – listened to the records or contacted the respondents and survey teams for clarification via phone call. Necessary revisions would then be made.

Data cleaning was implemented by the following stages: 1. Identification of illogical values; 2. Software – based detection of errors for clarification and revision; 3. Information re-checking with respondents and/or enumerators via phone or through looking at the records; 4. Development and implementation of errors correction algorithms; The list of detected and adjusted errors is attached in Annex 6.

Outlier detection methods The data team applied a popular non - parametric method for outlier detection, which can be done with the following procedure: 1. Identify the first quartile Q1 (the 25th percentile data point) 2. Identify the third quartile Q3 (the 75th percentile data point) 3. Identify the inter-quartile range(IQR): IQR=Q3-Q1 4. Calculate lower limits (L) and upper limits (U) by the following formulas: o L=Q1-1.5*IQR o U=Q3+1.5*IQR 5. Detect outliers by the rule: An observation is an outlier if it lies below the lower bound or beyond the upper bound (i.e. less than L or greater than U)

Data Structure The completed dataset for the “Household registration survey 2015” includes 9 files in STATA format (.dta): • hrs_maindata: Information on the households, including: assets, housing, income, expenditures, social inclusion and social protection issues, household registration procedures • hrs_muc1: Basic information on the

This dataset provides information about the number of properties, residents, and average property values for Deer Point Drive cross streets in White House, TN.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

Codebooks for analyzing property (house, condo, flat, etc.) listing data for each of the 10 select regions in the bay area megaregion of California, USA (SAN JOSE, MODESTO, FRESNO, TURLOCK, LIVINGSTON, ATWATER, MERCED, MADERA, MARIPOSA, OAKHURST) were obtained from Zillow Inc. on a monthly basis between March 2018 and May 2019 (denoted as the period before 2020) and May 2020 and September 2021 (after 2020). Combined, the total number of observations (unique listed properties) is N = 57,414. For each month, we obtained a set of unique listing identifiers (ZPID) by manually scanning across the entire Zillow.com directory for a given region and property type (“For Sale†and “Rent†). Read the enclosed document DataDryad_DataDescription_Petersen_Zillow.pdf for a description of the data and output of provided supporting code. Contact the corresponding author for the raw property-level data files, which are anonymized [property address and property identifier (ZPID) fields]., We used the open-access Zillow Inc. GetSearchResults API to sample house data for each ZPID in accordance with daily API call limits. For more information on the API see the official documentation page: https://www.zillow.com/howto/api/GetSearchResults.htm. We anonymized the property address and ZPID fields. , Programs required: Mathematica 11 (or later version) and STATA 13 (or later version). The workflow for executing Mathematica notebooks is simply Shift+Enter to execute commands contained in any given cell; the initial cells upload the data files, and from there the notebook cells should be executed from start to end in linear order., # Zillow property-level data panel for select California cities before and after 2020

Brief summary

Codebooks for analyzing property (house, condo, etc.) listing data for 10 select regions were obtained from Zillow Inc. on a monthly basis between March 2018 and May 2019 (denoted as the period before 2020) and May 2020 and September 2021 (after 2020). For each month, we obtained a set of unique listing identifiers (ZPID) by manually scanning across the entire Zillow.com directory for a given region and property type ("For Sale" and "Rent"). Combined, the total number of observations (unique listed properties) is N = 57,414.

Description of the Data and file structure

All data files are provided in CSV (comma separated value) format. See DataDryad_DataDescription_Petersen.pdf for data file structure details. Contact the corresponding author for the raw property-level data files, which are anonymized [property address and property identifier (ZPID) fields]. Â

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