Home Ownership Rate in the United States decreased to 65 percent in the second quarter of 2025 from 65.10 percent in the first quarter of 2025. This dataset provides the latest reported value for - United States Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Negative Equity Trends in US Housing Markets

Time Series Data Across Regions and Housing Types

By Zillow Data [source]

About this dataset

• This unique dataset explores the trends in negative equity within US housing markets from 2011 to 2017, allowing users to uncover the various factors and determinants that affected the outcome in each market. With data provided on all home types such as single-family homes, condominiums, and co-ops, as well as special metrics such as cash buyers and affordability analyses, you will be able to gain a comprehensive understanding of how these forces have interacted over time. Using this data you can not only learn more about historical behavior but also make predictions for future trends in these impacts.

• In addition to data collected by Zillow through their own internal resources, they have also partnered with TransUnion and other affiliate sources to give an even more precise look into what has been driving these changing dynamics across US housing markets. Such information includes negative equity metrics which allow us to track actual outstanding home-related debt amounts over time - a valuable resource when evaluating potential investments or relocations!

• And of course with any dataset there are a few guiding principles that one should take note of before delving in – this is especially true when it comes down to copyright issues or prohibited uses; though all data can be freely obtained here for public use - clear attribution of such information is legally required at all times (as stated on Zillow’s very own Terms & Conditions page). Furthermore additional resources such as Mortgage Rate Series or Jumbo Mortgages are also available through Zillow; again making sure that appropriate disclaimers are read before utilizing them.

Regardless this little treasure trove of knowledge is waiting at your fingertips – whether you’re trying your luck investing wise or just looking for an area where renting rates are equitable compared real estate values; it provides everything you need understand regional housing market fluctuations over the last half decade!

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How to use the dataset

This dataset provides historical and current trends in negative equity (the amount a mortgage is underwater) across the United States. It contains negative equity data from Zillow, one of the leading real estate data providers. The dataset covers all housing types (including single family, condominiums and co-ops). Additionally, it includes cash buyers share, mortgage affordability index, rental affordability index and other relative measures of affordability for US metro areas. This guide will help you understand how to use this data set for your own analysis.

Overview of Covered Data:

The dataset contains time series data that shows your current trend in negative equity rate as well as some associated metrics across different scales such as region, county, city and MSA level. To access this information you will need to take following columns into consideration while using this data set:

• RegionName: Name of the region (e.g., city/county/MSA)
• SizeRank: Ranking of the region by size
• RegionType: Type of region (e.g., city/county/state)
• StateName: Name of the state
• MSA: Metropolitan Statistical Area FORMAT_4C A4 RINFOX_ RTI Information Exchange File Format [multi value 9] FORMAT_3E A3 FITS Flexible Image Transport System VERSION 4C 3E 1 Language Indicator 0 0 1 1 DONTCOPY 536880031 FILEEXTN 3 Stream Type buffer 'USTD' file version 2 HNEED 8 FILETYPE 'UDIO' creation date 05 FEB 1985 Source FMT0025 APPLICAT TRAINFORM File Organization Spooled Files DF140520 Header Block Length in Words 682 with Header Offset 636 / ULQUACK INTLCHAN * ETBFMT(V7R2),D*RECORD ACCOUNT CRFTIME FT240187 batch process status continuous Availability Continuous Version number V03C02 LOADAT AT04

Research Ideas

• Analyzing which markets have been disproportionately affected by the housing crisis and utilizing this information to inform investment strategies and...

Home Ownership Rate in Canada decreased to 66.70 percent in 2023 from 69.30 percent in 2021. This dataset provides the latest reported value for - Canada Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Home Ownership Rate in the United Kingdom decreased to 64.50 percent in 2023 from 64.70 percent in 2022. This dataset provides the latest reported value for - United Kingdom Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

North Carolina Population and Housing Statistics

Demographic and Housing Trends in North Carolina

By Matthew Schnars [source]

About this dataset

This comprehensive dataset provides a well-detailed and robust statistical representation of various characteristics related to the population and housing conditions of North Carolina. The dataset originates from NC LINC (Log Into North Carolina), a critical data allocation platform that focuses on sharing information regarding diverse aspects of the state’s overall demographics, socio-economic conditions, education, and employment background.

The dataset highlights a variety of facets such as population estimates by age group, race or ethnic group encompassing multiple demographic groups across different geographic areas within the state including counties and municipalities. Utilizing this expansive set of data could prove instrumental for researchers looking into demographic trends, market estimation studies or any other analysis requiring population certifications.

Revolving around Housing Statistics in North Carolina, this dataset also gives a complete perspective about various ypes of residences available throughout the region. Availability types include both renter-occupied housing units along with owned homes, providing an encapsulating vision into the home ownership versus rental situation in North Carolina. In conjunction with providing insight into occupancy details for vacant homes.

An intriguing section included within these datasets is congregated ethnicity-based data spread across numerous age-groups which can assist research based out on diverse cultures dwelling within this area.

Overall, this dataset constitutes an essential resource for stakeholders interested in understanding demographic transformations over time or gaining insights into housing availability situations across different localities in North Carolina State to inform urban planning strategies and policies beneficially impacting residents’ lives directly

How to use the dataset

This dataset offers a broad range of demographic and housing data for North Carolina, making it an ideal resource for those interested in demographic trends, urban planning, social science research, real estate and economic studies. Here's how to get the most out of it:

• Interpretation: Determine what each column represents in terms of demographic and housing attributes. Familiarize yourself with the unique characteristics that each column represents such as population size, race categories, gender distributions etc.

• Comparison Studies: Analyze different locations within North Carolina by comparing figures across rows (geographic units). This can provide insight on socio-economic disparities or geographical preferences among residents.

• Temporal Analysis: Although the dataset doesn't contain specific dates or timeframes directly related to these statistics, you can cross-reference with external datasets from different years to conduct temporal analysis procedures such as observing the growth rates in population or changes in housing statistics.

• Joining Data: Combine this dataset with other relevant datasets like education levels or crime rates which may not be available here but could add multidimensional value when conducting thorough analyses.

• Correlation Studies: Perform correlation studies between different columns e.g., is there a strong correlation between population density and number of occupied houses? Such insights may be valuable for multiple sectors including real estate investment or policy-making purposes.

• Map Visualization: Use geographic tools to map data based on counties/townships providing visual perspectives over raw number comparisons which could potentially lead to more nuanced interpretations of demographic distributions across North Carolina

• Predictive Modelling/Forecasting: Based on historic figures available through this database develop models which predict future trends within demographics & housing sector

8: Presentation/Communication Tool: Whether you're delivering a presentation about social class disparities in NC regions or just curious about where populations are densest versus where there are more mobile homes vs homes owned freely -hamarize and display data in an easy-to-understand format.

Before diving deep, always remember to clean the dataset by eliminating duplicates, filling NA values aptly, and verifying the authenticity of the data. Furthermore, always respect privacy & comply with data regulation policies while handling demographic databases

Research Ideas

• Urban Planning: This dataset can be a val...

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

Georeferenced Amsterdam Rental Values

Exploring Urban Housing Markets Through Historical Patterns

By [source]

About this dataset

This dataset provides insightful and comprehensive information on the spatial distribution of rental values in Amsterdam throughout a period of time. In order to generate this data, the Verponding registration from Amsterdam City Archives was consulted, which collected a tax known as the Verpondings-quohieren van den 8sten penning on the rental value of immovable property. This data was attained through transcribing and geo-referencing registration books from the archives; thereby incorporating both transcribed rental values of all real estate properties listed therein as well as geo-referenced tax records plotted onto an historical map of Amsterdam.

The compilation and analysis of historic rental values may offer further insights into underlying social, economic, and cultural developments within Amsterdam over time. Therefore, the potential applications for this dataset are enormous; offering investigators an opportunity to gather useful information with relation to urban renewal efforts or even supporting archaeological research studies. Moreover, with various columns such as order number, wijk district where applicable property is located within respective street name as well as details on whether said property is available for rent/own disposition - researchers may also utilize these collected metrics for meaningful planning/management decisions simultaneously unfolding hidden patterns concerning disparities or trends that might be discerned when compared to current trends employed by residents today

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How to use the dataset

This dataset provides insight into the spatial distribution of rental values in Amsterdam between 1647 and 1652. The data provided is a valuable resource for researchers looking to study the economic, social, and cultural history of Amsterdam over this period in time. With this data set, users can explore hidden patterns, disparities, and trends that may inform decision-making or help with urban renewal projects. Moreover, this dataset can also be used to assess archaeological and cultural heritage research.

In order to understand the georeferenced rental values better and draw meaningful conclusions from the data set it is important to keep few things in mind: - Check out handy columns such as ‘wijk’ (district) which offers information about where each property is located;
- The ‘rent/own’ indicates whether a property was rented (huur) or owned (koop);
- The ‘value’ column contains information regarding the rental value of each property; - The ‘tax’ column shows how much tax was paid on each listed property;
- In addition to this additional notes have been provided in some cases offering more insights into particular properties;

By seeing all these details together one will get an excellent overview of individual households renting or owning their real estate properties along with their tax payment throughout Amsterdam during this period 1647-1652. Additionally by graphing this data one could compare rental value against geographic location or even track consecutive years on how they vary year after year! This can help trace any historical changes taking place how they affect individual households within Amsterdam as well as socio-economic changes occurring throughout the city over the years!

Research Ideas

• Creating a predictive heat map by analyzing correlation between rental values and various other factors such as geographic location, proximity to public transportation, availability of amenities/services etc.
• Comparing and contrasting current maps of real estate prices in Amsterdam with the maps from this dataset to analyze shifts in property prices over time and understand the evolution of urban housing markets in the city.
• Studying socio-economic differences between different geographical areas based on rental values from this dataset, which could help provide a better understanding of the social, economic, and cultural history of the city

Acknowledgements

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

License

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

Home Ownership Rate in Netherlands decreased to 68.80 percent in 2024 from 69.30 percent in 2023. This dataset provides the latest reported value for - Netherlands Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Home Ownership Rate in Sweden decreased to 64.80 percent in 2024 from 64.90 percent in 2023. This dataset provides the latest reported value for - Sweden Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Real Estate Breakeven Analysis for U.S. Home Types

Buy vs Rent Comparison Across Markets

By Zillow Data [source]

About this dataset

This dataset provides a comprehensive analysis of the current real estate situation in the United States. It includes breakeven analysis charts that compare buying vs renting across major U.S. markets. This dataset contains various metrics such as home types, housing stock, price-to-income ratio, cash buyers, mortgage affordability and rental affordability to name a few. This data has been compiled using Zillow's own data along with TransUnion financing survey data and the Freddie Mac Primary Mortgage Market Survey to provide an accurate understanding of each metro area’s market health and purchasing power for buyers and renters alike. By downloading this information you can compare different regions based on size rank and other factors to get full insights regarding their potential fit for your needs or investments strategies as well as any potential risks associated with each region's housing market health

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How to use the dataset

This dataset is for real estate professionals, owner-occupants, potential buyers and renters who are interested in understanding which U.S. markets offer the most favorable home buying or rental opportunities from a financial perspective over the long term.

The “Real Estate Breakeven Analysis for U.S Home Types” dataset contains data pulled from Zillow's current and forecasted housing market metrics across many different real estate regions in the United States including cities, counties, states, metro areas and combined statistical areas (CSAs). The data includes several measures of affordability such as median price-to-rent ratio (MedPR), median breakeven horizon (MedBE) - which refers to how long it takes to make up purchase costs when compared with renting; cash purchaser share; mortgage rate; mortgage affordability indices; rental affordability rates etc.

In order to analyze and compare buying vs renting decisions across various regions in the US this dataset provides breakeven analysis at various levels of geographies i.e., state names, region types (city/metro area/county) and show how long it will take homeowners to break even on their purchase costs when compared with renting in that region over a longer period of time using discounted cash flow methodology. This information helps people understand what type of transaction is a better fit for them by weighing short term vs long term goals accordingly by evaluating these different factors related to housing metrics carefully before making financial decisions about purchasing or renting properties in desired location(s).

To use this dataset one can use either basic filters like RegionType or RegionName or more detailed filter criteria like CountyName, City name , Metro area name , State Name etc . For example if someone wanted to look at properties available for rent only then they can apply filters based on Province Type =‘Rental’ Also one can further refine searches based on filtering them with defined SampleRate , Median Price – To – Rent Ratio …..etc . This could be useful if seekers would want only specific type of property like Condominium/Coop /Multifamily 5+ Units /Duplex Triplex listing etc …and then apply other parameters like Cash Buyers percent , Mortgage Affordability Rate….etc ..in order narrow down search results while looking at Breakeven scores /horizons in their target locations . One should take advantages of all relevant parameters while searching through data before making any decision related with owning rental properties so that they can make sure best possible investment decision given

Research Ideas

• Visualizing changes in real estate trends across regions by comparing price to rent ratios, mortgage affordability indices and cash buyers over time.
• Market segmentation analysis based on region-level market characteristics such as negative equity data, rental affordability, median house values and population size.
• Predicting housing demand within a particular region based on its breakeven horizon or price to rent ratio

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: BreakEven_2017-03.csv | Column name | Description | |:----------------|:----------------------------------------------------...

Home Ownership Rate in Spain decreased to 73.70 percent in 2024 from 75.30 percent in 2023. This dataset provides the latest reported value for - Spain Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Home Ownership Rate in Denmark increased to 60.90 percent in 2024 from 60 percent in 2023. This dataset provides the latest reported value for - Denmark Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Home Ownership Rate in Ireland decreased to 69.30 percent in 2024 from 69.40 percent in 2023. This dataset provides the latest reported value for - Ireland Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset offers a comprehensive record of international football matches from the very first game in 1872 to the present day in 2024. It covers a broad spectrum of football matches, including major tournaments like the FIFA World Cup and various friendly matches. With a total of 47,126 match records, this dataset is a valuable resource for analyzing historical trends, team performances, and match outcomes over more than a century of international football.

Key Features

• Extensive Coverage: The dataset includes detailed information on 47,126 international football matches, providing a rich historical archive of over 150 years.
• Diverse Match Types: Records include results from high-profile tournaments such as the FIFA World Cup and lesser-known events like the FIFI Wild Cup, as well as numerous friendly matches.
• In-depth Details: Each record features comprehensive match details, including scores, dates, locations, and tournament names.
• Player and Team Insights: The dataset includes information on goal scorers, penalty shootouts, and own goals, offering a deeper understanding of match events and player performances.

File Breakdown

1) Match_Results.csv - Date: The date when the match was played. - Home Team: The team playing at home. - Away Team: The team playing away. - Home Score: The score of the home team, including extra time but not penalty shootouts. - Away Score: The score of the away team, including extra time but not penalty shootouts. - Tournament: The name of the tournament or competition in which the match was played. - City: The city where the match was held. - Country: The country where the match took place. - Neutral: Indicates if the match was played at a neutral venue (TRUE/FALSE).

2) Penalty_Shootouts.csv - Date: The date of the match. - Home Team: The name of the home team. - Away Team: The name of the away team. - Winner: The team that won the penalty shootout. - First Shooter: The team that took the first shot in the penalty shootout.

3) Goal_Scorers.csv - Date: The date of the match. - Home Team: The name of the home team. - Away Team: The name of the away team. - Team: The team that scored the goal. - Scorer: The player who scored the goal. - Minute: The minute when the goal was scored. - Own Goal: Indicates if the goal was an own goal (TRUE/FALSE). - Penalty: Indicates if the goal was scored from a penalty (TRUE/FALSE).

Data Usage

• Historical Analysis: This dataset is ideal for exploring the history of international football, including changes in team performances and the evolution of the sport over the decades.
• Performance Trends: Analyze trends in team performance across different eras, including how teams have fared in various tournaments and friendlies.
• Geopolitical Insights: Investigate how geopolitical changes have impacted international football, including changes in team names and country borders over time.
• Match Statistics: Detailed statistics on scores, penalty shootouts, and goal scorers provide insights into match dynamics and player contributions.

Additional Notes

• Team and Country Names: The dataset uses current names for teams to simplify historical tracking. For example, teams that were known by different names in the past are referred to by their current names to maintain consistency.
• Geographic and Temporal Coverage: Covering matches played worldwide from 1872 to 2024, this dataset provides a global perspective on international football history.

Acknowledgement

Full credit goes to Mart Jürisoo for the original work on international football results. The dataset titled International Football Results from 1872 to 2017 provided the foundational data and inspiration for this comprehensive historical archive.

The purpose of sharing this dataset is to foster collaborative research and analysis within the football community. By making this extensive historical data available, we aim to support studies on historical trends, team performances, and the evolution of international football over more than 150 years. This dataset is intended to be a valuable resource for researchers, analysts, and enthusiasts who wish to explore the rich history of international football and gain deeper insights into the sport's development.

Demolitions in the Occupied Territories is a dataset that provides statistics on the demolition of Palestinian-owned homes and structures in the Occupied Territories.

The information is based on investigations conducted by B’Tselem – The Israeli Information Center for Human Rights in the Occupied Territories.

Dataset Details:

The dataset covers a period from January 2004 to August 2023 and includes information about the date of demolition, locality, district, area, housing units, people left homeless, minors left homeless, type of structure, and reason for demolition.

Interesting Task Ideas (for Data Analysts):

1. Analyze the trend of demolitions over time to identify any significant patterns or changes.
2. Investigate the distribution of demolitions across different localities, districts, and areas to understand the geographical impact.
3. Explore the relationship between the number of housing units demolished and the number of people, particularly minors, left homeless.
4. Examine the reasons for demolitions and assess their frequency and impact.
5. Visualize the data using maps and charts to highlight the magnitude and geographical distribution of demolitions.

The intention of using this data should be solely for objective analysis and understanding of the situation, without any political intent. Any analysis or interpretation should be approached with sensitivity and respect for human rights.

Related Datasets:

Fatalities in the Israeli-Palestinian Conflict

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Photo by Oleg Solodkov on Unsplash

Urbanisation is a form of social transformation from traditional rural societies to modern, industrial and urban communities. It is long term continuous process. It is progressive concentration of population in urban unit. Kingsley Davies has explained urbanisation as process of switch from spread out pattern of human settlements to one of concentration in urban centers. Migration is the key process underlying growth of urbanization.

Challenges in urban development--->;

Institutional challenges

Urban Governance 74th amendment act has been implemented half-heartedly by the states, which has not fully empowered the Urban local bodies (ULBs). ULBs comprise of municipal corporations, municipalities and nagar panchayats, which are to be supported by state governments to manage the urban development. For this , ULBs need clear delegation of functions, financial resources and autonomy. At present urban governance needs improvement for urban development, which can be done by enhancing technology, administrative and managerial capacity of ULBs.

Planning Planning is mainly centralized and till now the state planning boards and commissions have not come out with any specific planning strategies an depend on Planning commission for it. This is expected to change in present government, as planning commission has been abolished and now focus is on empowering the states and strengthening the federal structure.

In fact for big cities the plans have become outdated and do not reflect the concern of urban local dwellers, this needs to be take care by Metropolitan planning committee as per provisions of 74th amendment act. Now the planning needs to be decentralized and participatory to accommodate the needs of the urban dwellers.

Also there is lack of human resource for undertaking planning on full scale. State planning departments and national planning institutions lack qualified planning professional. Need is to expand the scope of planners from physical to integrated planning- Land use, infrastructure, environmental sustainability, social inclusion, risk reduction, economic productivity and financial diversity.

Finances Major challenge is of revenue generation with the ULBs. This problem can be analyzed form two perspectives. First, the states have not given enough autonomy to ULBs to generate revenues and Second in some case the ULBs have failed to utilize even those tax and fee powers that they have been vested with.

There are two sources of municipal revenue i.e. municipal own revenue and assigned revenue. Municipal own revenue are generated by municipal own revenue through taxes and fee levied by them. Assigned revenues are those which are assigned to local governments by higher tier of government.

There is growing trend of declining ratio of own revenue. There is poor collection property taxes. Use of geographical information system to map all the properties in a city can have a huge impact on the assessment rate of properties that are not in tax net.

There is need to broaden the user charge fee for water supply, sewerage and garbage disposal. Since these are the goods which have a private characteristics and no public spill over, so charging user fee will be feasible and will improve the revenue of ULBs , along with periodic revision. Once the own revenue generating capacity of the cities will improve, they can easily get loans from the banks. At present due to lack of revenue generation capabilities, banks don’t give loan to ULBs for further development. For financing urban projects, Municipal bonds are also famous, which work on the concept of pooled financing.

Regulator

There is exponential increase in the real estate, encroaching the agricultural lands. Also the rates are very high, which are not affordable and other irregularities are also in practice. For this, we need regulator, which can make level playing field and will be instrumental for affordable housing and checking corrupt practices in Real estate sector.

Infrastructural challenges

Housing Housing provision for the growing urban population will be the biggest challenge before the government. The growing cost of houses comparison to the income of the urban middle class, has made it impossible for majority of lower income groups and are residing in congested accommodation and many of those are devoid of proper ventilation, lighting, water supply, sewage system, etc. For instance in Delhi, the current estimate is of a shortage of 5,00,000 dwelling units the coming decades. The United Nations Centre for Human Settlements (UNCHS) introduced the concept of “Housing Poverty” which includes “Individuals and households who lack safe, secure and healthy shelter, with basic infrastructure such as piped water and adequate provision for sanitation, drainage and the removal of hou...

The empirical dataset is derived from a survey carried out on 25 estates in 14 cities in nine different European countries: France (Lyon), Germany (Berlin), Hungary (Budapest and Nyiregyha´za), Italy (Milan), the Netherlands (Amsterdam and Utrecht), Poland (Warsaw), Slovenia (Ljubljana and Koper), Spain (Barcelona and Madrid), and Sweden (Jo¨nko¨ping and Stockholm). The survey was part of the EU RESTATE project (Musterd & Van Kempen, 2005). A similar survey was constructed for all 25 estates.

The survey was carried out between February and June 2004. In each case, a random sample was drawn, usually from the whole estate. For some estates, address lists were used as the basis for the sample; in other cases, the researchers first had to take a complete inventory of addresses themselves (for some deviations from this general trend and for an overview of response rates, see Musterd & Van Kempen, 2005). In most cities, survey teams were hired to carry out the survey. They worked under the supervision of the RESTATE partners. Briefings were organised to instruct the survey teams. In some cases (for example, in Amsterdam and Utrecht), interviewers were recruited from specific ethnic groups in order to increase the response rate among, for example, the Turkish and Moroccan residents on the estates. In other cases, family members translated questions during a face-to-face interview. The interviewers with an immigrant background were hired in those estates where this made sense. In some estates it was not necessary to do this because the number of immigrants was (close to) zero (as in most cases in CE Europe).

The questionnaire could be completed by the respondents themselves, but also by the interviewers in a face-to-face interview.

Data and Representativeness

The data file contains 4756 respondents. Nearly all respondents indicated their satisfaction with the dwelling and the estate. Originally, the data file also contained cases from the UK.

However, UK respondents were excluded from the analyses because of doubts about the reliability of the answers to the ethnic minority questions. This left 25 estates in nine countries. In general, older people and original populations are somewhat over-represented, while younger people and immigrant populations are relatively under-represented, despite the fact that in estates with a large minority population surveyors were also employed from minority ethnic groups. For younger people, this discrepancy probably derives from the extent of their activities outside the home, making them more difficult to reach. The under-representation of the immigrant population is presumably related to language and cultural differences. For more detailed information on the representation of population in each case, reference is made to the reports of the researchers in the different countries which can be downloaded from the programme website. All country reports indicate that despite these over- and under-representations, the survey results are valuable for the analyses of their own individual situation.

This dataset is the result of a team effort lead by Professor Ronald van Kempen, Utrecht University with funding from the EU Fifth Framework.

Credit Card Offer Acceptance Trends in Banking

Exploring Credit Card Offer Response and Customer Behaviors

By Anshuman Gautam [source]

About this dataset

This dataset contains credit card offer acceptance information for customers of a bank. We seek to use this data to build models that will provide insights into why some bank customers choose to accept these offers. The data contains various customer attributes such as customer number, offer accepted, reward type, mailer type, income level and more. Additionally, the dataset includes quarterly balances across all accounts and whether or not the customer owns their home or holds any overdraft protection. By exploring this data we hope to gain better understanding of the factors that influence offer acceptances in order to improve future marketing campaigns and increase customer satisfaction levels

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How to use the dataset

This dataset provides a convenient way to explore customer acceptance trends of credit card offers from Banking. It contains customer information such as their income level, number of bank accounts open, overdraft protection, number of credit cards held, number of homes owned and household size. Additionally the dataset also tracks average balance across all accounts, quarterly balances on each account and rewards offered with the offer.

Using this dataset you can analyse consumer behaviour when presented with credit card offers from your bank and gain insight into customer preferences for rewards or other incentives among various market segments. You can use the data to better predict customer acceptance rates based on past responses and use marketing strategies tailored to specific customer segments in order to improve offer acceptance rates.

To analyse this data using the Unlocking Credit Card Offer Acceptance Trends in Banking dataset you will need basic knowledge in topics like Python programming language or Microsoft Excel spreadsheets etc. You may also need specialized statistical software packages such as R or SPSS depending on what kind of analysis you wish to perform. After obtaining this necessary skillset it's important that you familiarize yourself with exploration techniques like descriptive statistics as well as methods like linear & logistic regression if needed for more advanced models that can be used establish relationships between factors that affect whether customers accept an offer or not (income level vs reward type). While analysing it is important remember that variables should be treated consistently regardless of profile type because inconsistent variable treatment might lead skewed results & unreliable conclusions drawn from datasets created through collecting responses from people who come from different socioeconomic backgrounds& ages etc; which could mask any genuine trends found within equally segmented populations answering similar questions about products/ services - making them a biased source for informed decisions about population behaviour! Finally once you have explored your data & identified any notable characteristics worth drawing attention too; consider presenting your findings through visually engaging/ informative methods like graphs/ compelling narratives etc so that stakeholders may understand just how useful predictive modelling using machine learning could really be by developing valuable insights into customers’ preferences when they apply for new product offerings at banks!

Research Ideas

• Determining the optimum offer and reward structure for a bank's credit card offers based on customer income level, number of bank accounts open, and other factors.
• Predicting customer acceptance behavior using machine learning techniques and insights from the dataset such as household size, average balance, etc.
• Segmenting customers into different groups to better target offers based on their financial profile including Credit rating, Number of Credit cards held or Own Your Home and customize marketing message appropriate to each segment

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: creditcardmarketing-bbm.csv | Column name | Description ...