This dataset contains unemployment rates for the U.S.(1948 - Present) and California (1976 - Present). The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces. This rate is also defined as the U-3 measure of labor underutilization.

As of the second quarter of 2022, online agents had a market share of *** percent of exchanges in the United Kingdom. Yorkshire and The Humber had the higher share of online purchases at almost ** percent. Unlike other industries, the housing market has a relatively small online penetration rate as the overall cost and grandiosity of buying a home still encourages people into physical stores.

Average house prices

Average house prices are affected by several factors. Economic growth, unemployment, interest rates and mortgage availability can all drive them up or down. A shortage of supply means that the need for housing and the competitive market created will push house prices up. An excess of housing, on the other hand, means prices fall to stimulate buyers.

House price growth slowing down

After two years of a staggering house price growth, the UK housing market has started cooling down and in June 2022, the annual house price growth fell below ***** percent - the lowest since July 2021. In the five-year period until 2026, London is forecast to see the slowest house price growth.

The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.

This rate is also defined as the U-3 measure of labor underutilization.

The series comes from the 'Current Population Survey (Household Survey)'

The source code is: LNU04000000

This paper examines the association between the Great Recession and real assets among families with young children. Real assets such as homes and cars are key indicators of economic well-being that may be especially valuable to low-income families. Using longitudinal data from the Fragile Families and Child Wellbeing Study (N = 4,898), we investigate the association between the city unemployment rate and home and car ownership and how the relationship varies by family structure (married, cohabiting, and single parents) and by race/ethnicity (White, Black, and Hispanic mothers). Using mother fixed-effects models, we find that a one percentage point increase in the unemployment rate is associated with a -0.5 percentage point decline in the probability of home ownership and a -0.7 percentage point decline in the probability of car ownership. We also find that the recession was associated with lower levels of home ownership for cohabiting families and for Hispanic families, as well as lower car ownership among single mothers and among Black mothers, whereas no change was observed among married families or White households. Considering that homes and cars are the most important assets among middle and low-income households in the U.S., these results suggest that the rise in the unemployment rate during the Great Recession may have increased household asset inequality across family structures and race/ethnicities, limiting economic mobility, and exacerbating the cycle of poverty.

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

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

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

Includes permits from various states across the US

Covers residential and commercial construction activities

Insights:

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

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

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

Potential Applications:

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

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

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

Value Propositions:

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

Identify areas with high concentrations of permits

Determine the scope and type of work being performed

Infer the potential for improved home values

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

Target homeowners who have invested in their properties

Offer tailored financial solutions to capitalize on increased property value

Contractor Lead Generation:

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

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

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

Home Service Providers:

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

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

Real Estate Professionals:

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

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

Data Analysis Ideas:

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

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

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

Business Use Cases:

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

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

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

Data Visualization Ideas:

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

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

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

Additional Ideas:

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

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

Develop predictive models: Create statistical mo...

The employment rate and household consumption are two indicators that are directly related. In this statistic, the year-on-year employment and household consumption variation in Spain are compared across the fourth quarter from 2015 to 2023. In the last quarter of 2023, the YoY employment rate in Spain amounted to *** percent, while the household consumption rate indicated an inter-annual change of *** percent.

The operation Census of the Labour Market (CMT) aims, on the one hand — the supply side — to know the workforce constituted by the population over 16 years living in family homes. On the other hand, -the demand side-, aims to know the employment situation generated by economic establishments in each of the 11 employment basins defined for the Basque Country.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tri Pointe Homes: Navigating the Housing Market (TPH)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Landsea Homes' (LSEA) Future: Analysts Project Growth Amid Housing Market Shifts

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

During the COVID-19 pandemic, the number of house sales in the UK spiked, followed by a period of decline. In 2023 and 2024, the housing market slowed notably, and in January 2025, transaction volumes fell to 46,774. House sales volumes are impacted by a number of factors, including mortgage rates, house prices, supply, demand, as well as the overall health of the market. The economic uncertainty and rising unemployment rates has also affected the homebuyer sentiment of Brits. How have UK house prices developed over the past 10 years? House prices in the UK have increased year-on-year since 2015, except for a brief period of decline in the second half of 2023 and the beginning of 2024. That is based on the 12-month percentage change of the UK house price index. At the peak of the housing boom in 2022, prices soared by nearly 14 percent. The decline that followed was mild, at under three percent. The cooling in the market was more pronounced in England and Wales, where the average house price declined in 2023. Conversely, growth in Scotland and Northern Ireland continued. What is the impact of mortgage rates on house sales? For a long period, mortgage rates were at record-low, allowing prospective homebuyers to take out a 10-year loan at a mortgage rate of less than three percent. In the last quarter of 2021, this period came to an end as the Bank of England rose the bank lending rate to contain the spike in inflation. Naturally, the higher borrowing costs affected consumer sentiment, urging many homebuyers to place their plans on hold and leading to a decline in sales.

The US home loan market, a significant component of the broader mortgage industry, is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, a consistently low unemployment rate and rising disposable incomes are empowering more Americans to pursue homeownership. Secondly, historically low interest rates (though potentially fluctuating) throughout much of the forecast period are making mortgages more accessible and affordable. Thirdly, government initiatives aimed at boosting housing affordability, such as tax incentives and relaxed lending criteria (though subject to potential policy changes), contribute significantly to the market's expansion. Furthermore, the increasing preference for larger homes, particularly among millennials and Gen Z, further fuels demand. The market is segmented across various loan types (home purchase, refinance, home improvement), sources (banks, housing finance companies), interest rates (fixed, floating), and loan tenures. While fluctuating interest rates and economic uncertainties represent potential restraints, the long-term outlook for the US home loan market remains positive, driven by sustained demand and ongoing innovation within the financial technology sector. The competitive landscape is intensely dynamic, with major players like Rocket Mortgage, LoanDepot, Wells Fargo, and Bank of America dominating the market. However, smaller, regional lenders and online mortgage providers are also carving a niche for themselves by offering tailored services and competitive pricing. Market segmentation also presents opportunities for specialized lenders to focus on specific demographic groups or loan types, leveraging technology and data analytics to refine their offerings. The regional distribution of the market mirrors the US population density, with the Northeast, West Coast, and Southern regions demonstrating the highest activity. However, the market is becoming increasingly decentralized, with rising homeownership rates across previously less active areas. Overall, the US home loan market presents a compelling investment opportunity characterized by substantial growth potential, albeit with inherent risks tied to macroeconomic volatility and regulatory changes. Recent developments include: June 2023: Bank of America Corp has been adding consumer branches in four new U.S. states, it said on Tuesday, bringing its national footprint closer to rival JPMorgan Chase & Co. Bank of America will likely open new financial centers in Nebraska, Wisconsin, Alabama, and Louisiana as part of a four-year expansion across nine markets, including Louisville, Milwaukee, and New Orleans., July 2022: Rocket Mortgage entered the Canadian Market with the acquisition. The company expanded from offering home loans in Ontario at launch to now providing mortgages in every province, primarily from its headquarters in downtown Windsor. The Edison Financial team grew along with the company, starting with just four team members in early 2020 to more than 140 at present.. Key drivers for this market are: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Potential restraints include: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Notable trends are: Growth in Nonbank Lenders is Expected to Drive the Market.

Full edition for scientific use. The Microcensus special survey from September 2003 consisted of two parts: -> standard-questions of the extended housing survey (A-sheet) -> labour force survey (B-sheet) Since 1983 labour force surveys (LFS) are conducted annually in all European Union (EU) member states. The LFS serve as a basis for internationally compatible (in terms of definition and survey method) data on employment and unemployment for the European Commission. In Austria the LFS is conducted fully annually. The chosen month therefore is March because in this month the Microcensus-quarterly-survey which is most suitable in terms of scheduling for the LFS is performed. Central questions for the assessment of the number of employed and unemployed persons (and as a result for the calculation of the unemployment rate according to international standards) are in addition (since 1994) asked quarterly in the Microcensus standard survey. The survey conducted in March always relates to the week before the interview and includes the whole population, which means everybody who has their main residence in Austria. Data for persons not found have to be added via a substitution method so that results for the whole population can be provided. In Austria (as well as in several other states) the LFS is only conducted among the population in private households; people who live in institutions (retirement homes, boarding homes, and the like) are not included in the survey (the Microcensus special surveys are not conducted in institutional households due to organisational problems and problems with performing the surveys there). These are topics of the LFS: -> immigrants with and without the Austrian citizenship (4 questions) -> features of the first job (21 questions) -> statements on part-time jobs (6 questions) -> previous employments of unemployed persons (7 questions) -> job-seeking (13 questions) -> situation of unemployed persons (3 questions) -> school and professional education (9 questions) -> situation one year previous to the survey (7 questions) Furthermore, there are questions on the demographic background, providing information, evidence and the like. In the Microcensus, the annual special LFS survey contains 70 questions in addition to the questions of the standard survey which is concerned with standard LFS topics. The questions have remained more or less the same over the years. The only questions that have been changed slightly were those on education. Missing information is substituted with information from persons with similar socio-demographic variables (imputation), so that there are no unknown cases.

Full edition for scientific use. Since 1983 labour force surveys (LFS) are conducted annually in all European Union (EU) member states. The LFS serve as a basis for internationally compatible (in terms of definition and survey method) data on employment and unemployment for the European Commission. In Austria, the LFS is conducted in full annually. The chosen month therefore is March because in this month the Microcensus quarterly-survey which is most suitable in terms of scheduling for the LFS is performed. Central questions for the assessment of the number of employed and unemployed persons (and as a result for the calculation of the unemployment rate according to international standards) are in addition (since 1994) asked quarterly in the Microcensus standard survey. The survey conducted in March always relates to the week before the interview and includes the whole population, which means everybody who has their main residence in Austria. Data for persons not found have to be added via a substitution method so that results for the whole population can be provided. In Austria (as well as in several other states), the LFS is only conducted among the population in private households; people who live in institutions (retirement homes, boarding homes, etc.) are not included in the survey. These are topics of the LFS: -> immigrants with and without the Austrian citizenship (4 questions) -> features of the first job (21 questions) -> statements on part-time jobs (6 questions) -> previous employments of unemployed persons (7 questions) -> job-seeking (13 questions) -> situation of unemployed persons (3 questions) -> school and professional education (9 questions) -> situation one year previous to the survey (7 questions). Furthermore, there are questions on the socio-demographic background. The questions have remained more or less the same over the years. The only questions that have been changed slightly were those on education. Missing information is substituted with information from persons with similar socio-demographic variables (imputation), so that there are no unknown cases.

In 2023, the percentage of informal employment in Argentina stood at 50 percent of the total employed population. The share of employment informality has decreased slightly in comparison to the previous year. Argentina is among the countries with the lowest share of informal employment in Latin America.

Vulnerability of the population The main issues of informal employment are the lack of job security, social security, and the low quality of jobs. All factors heavily impact the vulnerability of the population under such conditions. During the last few years, Argentina increased the share of households under the poverty line, in fact, during the first half of 2024, the percentage exceeded 40 percent of homes, more than double the 2018th rate. Labor force and unemployment During the past four decades, the labor participation rate in Argentina has been significantly higher among the male population than their female counterparts. For males, around 70 percent of working age people were part of the workforce, while only around half of females did the same. Nevertheless, the unemployment rate has been decreasing considerably, reaching its lowest point in 2023 since at least 2004.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Invitation Homes: A Look at (INVH) Rental Portfolio Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Since 1983 labour force surveys (LFS) are conducted annually in all European Union (EU) member states. The LFS serve as a basis for internationally compatible (in terms of definition and survey method) data on employment and unemployment for the European Commission. In Austria the LFS is conducted fully annually. Central questions for the assessment of the number of employed and unemployed persons (and as a result for the calculation of the unemployment rate according to international standards) are in addition (since 1994) asked quarterly in the Mikrozensus standard survey. In Austria (as well as in several other states) the LFS is only conducted among the population in private households; people who live in institutions (retirement homes, boarding homes, and the like) are not included in the survey (the Mikrozensus special surveys are not conducted in institutional households due to organisational problems and problems with performing the surveys there). Because of a major reform of the Labour Force Survey the special survey program isn’t available 4.quarter 2003.

The Japan Mortgage/Loan Brokers Market, valued at ¥5.20 billion in 2025, is projected to experience steady growth with a Compound Annual Growth Rate (CAGR) of 3.92% from 2025 to 2033. This growth is driven primarily by increasing urbanization, a rising young population entering the housing market, and government initiatives aimed at boosting homeownership. Low interest rates in recent years have also stimulated mortgage demand. However, fluctuating economic conditions and potential regulatory changes pose challenges. The market is segmented by mortgage loan type (conventional, jumbo, government-insured, and others), loan terms (15, 20, and 30-year mortgages, and others), interest rates (fixed and adjustable), and provider (primary and secondary lenders). Major players include prominent Japanese financial institutions like the Bank of Japan, Bank of China (with significant operations in Japan), Suruga Bank, SMBC Trust Bank, Shinsei Bank, and several international banks with a presence in the Japanese market. The market's future trajectory will likely depend on the effectiveness of government policies supporting homeownership, the stability of the Japanese economy, and the adaptability of brokers to evolving technological advancements in financial services. Competition among brokers is expected to intensify, pushing for innovation in services and digital platforms to attract customers. The dominance of established financial institutions in the market highlights the need for smaller brokers to establish strong partnerships or differentiate themselves through specialized services. While the 30-year mortgage remains a significant segment, growing awareness of financial prudence and shorter-term financial goals could lead to increased demand for 15 and 20-year mortgage options. The increasing adoption of online platforms and fintech solutions is also anticipated to transform how mortgage brokerage services are delivered, potentially impacting the operational models of traditional players. Analyzing trends in interest rates and their correlation with overall market growth will be crucial for predicting future market performance. The impact of macroeconomic factors, such as inflation and unemployment, will also play a significant role in influencing mortgage demand and consequently, the growth of the brokerage market. Recent developments include: In March 2024, Leading Japanese online stocks broker Matsui Stocks Co., Ltd. established a partnership with global fintech firm Broadridge Financial Solutions, Inc. to boost its stock lending business via Broadridge's cloud-based SaaS post-trade processing technology., In July 2023, Mitsubishi UFJ Financial Group and Morgan Stanley expanded their 15-year-old partnership. At their joint brokerage operations, the Japanese and American institutions have decided to work together more closely on forex trading, as well as on researching and selling Japanese stocks to institutional investors.. Key drivers for this market are: Increase in demand for Financial Home Loan Solutions, Increased Accessibility to Loan Broker Services. Potential restraints include: Increase in demand for Financial Home Loan Solutions, Increased Accessibility to Loan Broker Services. Notable trends are: Consistent level of interest rate and Increasing Real Estate price affecting Japan's Mortgage/Loan Broker Market..

In April 2025, approximately ******* home construction projects started in the United States. The lowest point for housing starts over the past decade was in 2009, just after the 2007-2008 global financial crisis. Since 2010, the number of housing units started has been mostly increasing despite seasonal fluctuations. Statista also has a dedicated topic page on the U.S. housing market as a starting point for additional investigation on this topic. The impact of the global recession The same trend can be seen in home sales over the past two decades. The volume of U.S. home sales began to drop in 2005 and continued until 2010, after which home sales began to increase again. This dip in sales between 2005 and 2010 suggests that supply was outstripping demand, which led to decreased activity in the residential construction sector. Impact of recession on home buyers The financial crisis led to increased unemployment and pay cuts in most sectors, which meant that potential home buyers had less money to spend. The median income of home buyers in the U.S. fluctuated alongside the home sales and starts over the past decade.

This round of Eurobarometer surveys focused on health issues, public security, awareness of consumer protection legislation, sickness benefits allocation, and attitudes toward the police. In addition, respondents were queried on a few standard Eurobarometer measures, such as whether they attempted to persuade others close to them to share their views on subjects they held strong opinions about and whether they discussed political matters, and how they viewed the need for societal change. Respondents provided information about their personal health care, including their general state of health, number of hospital stays, types of examinations received, and whether they had been on a diet, as well as their perceptions of heart disease. Respondents also answered questions about the health care system in their countries and indicated how well health care was run, to whom the government should provide health care, whether the government should spend more money on health care, and if they were willing to pay more taxes or have the government spend less on other things in order to contribute to health care. A number of questions concentrated on sickness benefits allocation. Questions concerning public security included how safe respondents felt walking alone near their homes, how often they had witnessed drug-related problems near their homes, if their homes had been broken into, and whether they had been attacked or seriously threatened. Respondents were also asked about their awareness of consumer protection legislation adopted or introduced by the European Union (EU) in areas such as import, postal services, toy safety, pricing, packaging, advertising, contracts, holiday conditions, and court proceedings. Opinions were also elicited on the police, type of contact respondents had with the police, and the cooperation between the police forces from EU member states. In addition, respondents answered a series of questions concerning their views on employment, unemployment, and gender equality. These data are reported in a separate collection, EUROBAROMETER 44.3OVR: EMPLOYMENT, UNEMPLOYMENT, AND GENDER EQUALITY, FEBRUARY-APRIL 1996 (ICPSR 2443). Demographic and other background information provided includes respondent's age, gender, nationality, marital status, left-right political self-placement, occupation, age at completion of education, household income, size of household, car ownership, region of residence, and subjective size of community.

Listing of SONYMA target areas by US Census Bureau Census Tract or Block Numbering Area (BNA). The State of New York Mortgage Agency (SONYMA) targets specific areas designated as ‘areas of chronic economic distress’ for its homeownership lending programs. Each state designates ‘areas of chronic economic distress’ with the approval of the US Secretary of Housing and Urban Development (HUD). SONYMA identifies its target areas using US Census Bureau census tracts and block numbering areas. Both census tracts and block numbering areas subdivide individual counties. SONYMA also relates each of its single-family mortgages to a specific census tract or block numbering area. New York State identifies ‘areas of chronic economic distress’ using census tract numbers. 26 US Code § 143 (current through Pub. L. 114-38) defines the criteria that the Secretary of Housing and Urban Development uses in approving designations of ‘areas of chronic economic distress’ as: i) the condition of the housing stock, including the age of the housing and the number of abandoned and substandard residential units, (ii) the need of area residents for owner-financing under this section, as indicated by low per capita income, a high percentage of families in poverty, a high number of welfare recipients, and high unemployment rates, (iii) the potential for use of owner-financing under this section to improve housing conditions in the area, and (iv) the existence of a housing assistance plan which provides a displacement program and a public improvements and services program. The US Census Bureau’s decennial census last took place in 2010 and will take place again in 2020. While the state designates ‘areas of chronic economic distress,’ the US Department of Housing and Urban Development must approve the designation. The designation takes place after the decennial census.