About this dataset

Context

The dataset illustrates the median household income in Horizon City, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Horizon City increased by $3,480 (5.59%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 9 years and declined for 4 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Horizon City median household income. You can refer the same here

Much research studies US inflation history with a trend-cycle model with unobserved components, where the trend may be viewed as the Fed's evolving inflation target or long-horizon expected inflation. We provide a novel way to measure the slowly evolving trend and the cycle (or inflation gap), by combining inflation predictions from the Survey of Professional Forecasters (SPF) with realized inflation. The SPF forecasts may be treated either as rational expectations (RE) or updating according to a sticky information (SI) law of motion. We estimate RE and SI state-space models with stochastic volatility on samples of consumer price index and gross national product/gross domestic product deflator inflation and the associated SPF inflation predictions using a particle Metropolis-Markov chain Monte Carlo sampler. The trend converges to 2% and its volatility declines over time-two tendencies largely complete by the late 1990s.

Canada Consumer Expectations: Inflation: 5-Yr Ahead data was reported at 3.390 % in Mar 2025. This records an increase from the previous number of 2.990 % for Dec 2024. Canada Consumer Expectations: Inflation: 5-Yr Ahead data is updated quarterly, averaging 3.560 % from Jun 2015 (Median) to Mar 2025, with 40 observations. The data reached an all-time high of 4.260 % in Jun 2018 and a record low of 2.620 % in Dec 2023. Canada Consumer Expectations: Inflation: 5-Yr Ahead data remains active status in CEIC and is reported by Bank of Canada. The data is categorized under Global Database’s Canada – Table CA.H025: Consumer Expectations Survey. Consumer Expectations Survey Questionnaire: Inflation expectations for each horizon: What do you expect the rate of inflation (deflation) to be?

About this dataset

Context

The dataset presents the median household income across different racial categories in Horizon City. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Horizon City population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 44.35% of the total residents in Horizon City. Notably, the median household income for White households is $66,769. Interestingly, despite the White population being the most populous, it is worth noting that American Indian and Alaska Native households actually reports the highest median household income, with a median income of $120,399. This reveals that, while Whites may be the most numerous in Horizon City, American Indian and Alaska Native households experience greater economic prosperity in terms of median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Horizon City.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Horizon City median household income by race. You can refer the same here

South Korea NAIRU: Equilibrium Unemployment Rate data was reported at 3.542 % in 2022. This records an increase from the previous number of 3.535 % for 2021. South Korea NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 3.437 % from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 4.236 % in 1998 and a record low of 2.793 % in 1991. South Korea NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

ABSTRACTThe traps of the tripad of the Brazilian macroeconomic policy. This paper analyses the so-called tripod of the Brazilian macroeconomic policy, which since 1999 has been combining an inflation target regime, a floating exchange rate regime and targets for primary fiscal surplus. I argue that, unless its modus operandi is changed, the tripod will not be able to free the Brazilian economy from another "possible trinity": high real interest rates, real exchange rate appreciation and very low economic growth. After briefly analysing the theoretical base under the macroeconomic tripod, I will show why this macroeconomic policy regime, if it is evaluated in a medium or long-term perspective, has not been able to assure neither price stability nor economic growth. In addition to the suggestion of breaking with the Brazilian strategy of growing with foreign savings, the paper also suggests three main ways of changing the modus operandi of the Brazilian tripod: i) increase the time horizon for reaching the inflation target, as has been the experience of most countries that adopt this monetary policy regime; ii) restore the countercyclical role of the Brazilian fiscal policy; and iii) adopt a mix of instruments aiming at preventing the Brazilian currency from entering into a new cyclical trend of appreciation in real terms.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Horizon City. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Horizon City. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Horizon City, the median household income stands at $71,611 for householders within the 45 to 64 years age group, followed by $61,744 for the 65 years and over age group. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $59,660.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Horizon City median household income by age. You can refer the same here

Estimates of the slope of the Phillips curve reported in the literature cover a range from roughly ? 0.6 to zero depending on specification. Forward-looking specifications, favored by theory, produce the smallest slope estimates. This paper addresses this puzzle by studying the bivariate process of inflation and unemployment in a fairly general unobserved components framework allowing for stochastic trends and related cycles. Analysis reveals that the slope of the implied Phillips curve will depend critically on the horizon of the forward-looking inflation expectation provided the cyclical component of unemployment is highly persistent. Empirical analysis results show that is the case, suggesting that the choice of expectation horizon, generally set at one quarter in the New Keynesian literature, may play an important role in this debate.

Ireland IE: NAIRU: Equilibrium Unemployment Rate data was reported at 7.152 % in 2022. This records an increase from the previous number of 7.136 % for 2021. Ireland IE: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 8.407 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 12.280 % in 1990 and a record low of 7.136 % in 2021. Ireland IE: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Ireland – Table IE.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

We calculate the squeezed limit of the bispectrum produced by inflation with multiple light fields. To achieve this we allow for different horizon exit times for each mode and calculate the intrinsic field-space three-point function in the squeezed limit using soft-limit techniques. We then use the delta N formalism from the time the last mode exits the horizon to calculate the bispectrum of the primordial curvature perturbation.

Czech Republic CZ: NAIRU: Equilibrium Unemployment Rate data was reported at 3.891 % in 2022. This records an increase from the previous number of 3.831 % for 2021. Czech Republic CZ: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 6.107 % from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 7.608 % in 1999 and a record low of 3.831 % in 2020. Czech Republic CZ: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

Argentina’s production capabilities, characterized by its abundant natural capital assets and well-educated workforce, have the potential to drive sustained and inclusive economic growth. Argentina is home to diverse natural resources, including the world’s second-largest deposits of lithium, and the second-largest gas shale and fourth-largest shale oil reserves. Its fertile land makes it a major agricultural producer, ranking third in soybean production worldwide. Human capital is rooted in its historically high-quality education and health services, as well as notable achievements in knowledge-intensive sectors such as research and innovation. This report identifies three key constraints to sustaining growth in Argentina. First and foremost, macroeconomic volatility is largely responsible for poor growth outcomes: high policy uncertainty and fiscal procyclicality have contributed to a cycle of booms and crashes. Volatility is also driven by an increasing overreliance on primary commodities. Stubborn and high inflation in addition to abrupt changes in exchange rates reduce planning horizons for long-term investment and impede the development of capital markets. Second, restrictive trade policies, in place partly because of macroeconomic imbalances, prevent Argentina from leveraging its vast comparative advantages to reap the benefits of international trade. Third, while human capital is among Argentina’s greatest assets, its quality is gradually declining. Without corrective policies, the skills of the country’s workforce could fall rapidly behind those demanded by a dynamic, technology-driven, knowledge-intensive global economy.

Lithuania LT: NAIRU: Equilibrium Unemployment Rate data was reported at 6.680 % in 2022. This records a decrease from the previous number of 6.683 % for 2021. Lithuania LT: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 7.322 % from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 9.571 % in 2011 and a record low of 5.965 % in 2002. Lithuania LT: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

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.

Horizon Bancorp Inc. (HBNC): Ready to Reach New Heights?

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

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Horizon City. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Horizon City, the median income for all workers aged 15 years and older, regardless of work hours, was $34,348 for males and $26,213 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 24% between the median incomes of males and females in Horizon City. With women, regardless of work hours, earning 76 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Horizon City.

- Full-time workers, aged 15 years and older: In Horizon City, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,405, while females earned $34,670, leading to a 27% gender pay gap among full-time workers. This illustrates that women earn 73 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Horizon City, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Horizon City median household income by race. You can refer the same here

Israel IL: NAIRU: Equilibrium Unemployment Rate data was reported at 4.181 % in 2022. This records an increase from the previous number of 4.122 % for 2021. Israel IL: NAIRU: Equilibrium Unemployment Rate data is updated yearly, averaging 8.909 % from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 11.761 % in 2002 and a record low of 4.106 % in 2020. Israel IL: NAIRU: Equilibrium Unemployment Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Israel – Table IL.OECD.EO: Non-Accelerating Inflation Rate of Unemployment (NAIRU): Forecast: OECD Member: Annual. NAIRU - Equilibrium unemployment rate The equilibrium unemployment rate (code NAIRU) is estimated using a Kalman filter in a Phillips curve framework which assumes inflation expectations are anchored at the central bank’s inflation target . The NAIRU is then projected forward from the last estimated period using a simple autoregressive rule, exceptionally modified to account for recent labour market reforms, until the end of the forecasting horizon More details on methodology in Rusticelli E., Turner D. and M. C. Cavalleri (2015), Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment, OECD Economics Department Working Papers No.1231 OECD, Economics Department Working Papers: Incorporating anchored inflation expectations in the Phillips Curve and in the derivation of OECD measures of equilibrium unemployment:https://www.oecd-ilibrary.org/economics/incorporating-anchored-inflation-expectations-in-the-phillips-curve-and-in-the-derivation-of-oecd-measures-of-equilibrium-unemployment_5js1gmq551wd-en

We estimate a behavioral New Keynesian (NK) model in which households and firms plan over a finite horizon. The finite-horizon planning (FHP) model outperforms rational expectations versions of the NK model as well as other behavioral NK models. In the FHP model, households and firms are forward-looking in thinking about events over their planning horizon but are backward looking regarding events beyond that point. This gives rise to substantial aggregate persistence without resorting to additional features such as habit persistence and price contracts indexed to lagged inflation.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Horizon City, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 18,989, while the mean income for the highest quintile (20% of households with the highest income) is 175,682. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 274,901, which is 156.48% higher compared to the highest quintile, and 1447.69% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Horizon City median household income. You can refer the same here

Pension Real Estate Market Outlook

The global pension real estate market size was valued at approximately USD 1.2 trillion in 2023 and is projected to reach nearly USD 2.3 trillion by 2032, registering a compound annual growth rate (CAGR) of 7%. The significant growth factor for this market is the increasing need for stable, long-term investment returns within retirement portfolios, driven by the aging global population and the rising demand for diversified investment avenues.

One of the primary growth drivers for the pension real estate market is the steady shift towards alternative investments among pension funds. Traditional asset classes like stocks and bonds have exhibited higher volatility and lower returns in recent years, prompting fund managers to seek more stable and lucrative investment opportunities. Real estate, with its potential for steady income generation and capital appreciation, has emerged as a compelling option. The tangible nature of real estate assets also provides a level of security and risk mitigation that is attractive to both public and private pension funds.

Another key factor is the increasing urbanization and infrastructural development worldwide. As cities expand and economies grow, the demand for residential, commercial, and industrial properties rises correspondingly. Pension funds, with their long-term investment horizon, are well-positioned to capitalize on these trends. Moreover, the strategic allocation of capital into real estate helps in inflation hedging, as property values and rental incomes typically increase with inflation. This aspect is particularly relevant in the current economic climate, where inflationary pressures are a growing concern.

Technological advancements and the digital transformation of real estate management practices are also contributing to market growth. The adoption of PropTech solutions, such as blockchain for property transactions, AI-driven analytics for market forecasting, and IoT for building management, is enhancing the efficiency and transparency of real estate investments. These innovations are making real estate a more accessible and manageable asset class for pension funds, encouraging greater participation and investment.

In the realm of real estate investments, the adoption of an Investment Management Solution for Real Estate is becoming increasingly vital. This solution provides a comprehensive framework for managing diverse real estate portfolios, optimizing asset performance, and enhancing decision-making processes. By integrating advanced analytics and data-driven insights, these solutions enable pension funds to effectively navigate market complexities and capitalize on emerging opportunities. The ability to streamline operations, manage risks, and ensure compliance with regulatory standards makes investment management solutions indispensable tools for real estate investors aiming to achieve sustainable growth and long-term value creation.

Regionally, North America is expected to dominate the pension real estate market, driven by a robust economic landscape and well-established real estate sector. Europe follows closely, benefiting from strong institutional frameworks and favorable regulatory environments. The Asia-Pacific region is witnessing rapid growth, fueled by rising urbanization, economic expansion, and an increasing middle-class population. Latin America and the Middle East & Africa are also emerging as potential markets, albeit at a slower pace, due to economic and political variability.

Property Type Analysis

The pension real estate market can be segmented based on property type into residential, commercial, industrial, and others. Residential properties continue to be a major focus for pension real estate investments. The steady demand for housing, compounded by the global population growth and urban migration trends, makes residential real estate a lucrative and stable investment. Pension funds are increasingly investing in multifamily units, senior housing, and affordable housing projects to meet the diverse needs of the population.

Commercial properties, including office spaces, retail centers, and hospitality assets, also represent a significant portion of pension real estate investments. The commercial real estate sec

The economy was seen by 49 percent of people in the UK as one of the top three issues facing the country in June 2025. The ongoing cost of living crisis afflicting the UK, driven by high inflation, is still one of the main concerns of Britons. Immigration has generally been the second most important issue since the middle of 2024, just ahead of health, which was seen as the third-biggest issue in the most recent month. Labour's popularity continues to sink in 2025 Despite winning the 2024 general election with a strong majority, the new Labour government has had its share of struggles since coming to power. Shortly after taking office, the approval rating for Labour stood at -2 percent, but this fell throughout the second half of 2024, and by January 2025 had sunk to a new low of -47 percent. Although this was still higher than the previous government's last approval rating of -56 percent, it is nevertheless a severe review from the electorate. Among several decisions from the government, arguably the least popular was the government withdrawing winter fuel payments. This state benefit, previously paid to all pensioners, is now only paid to those on low incomes, with millions of pensioners not receiving this payment in winter 2024. Sunak's pledges fail to prevent defeat in 2024 With an election on the horizon, and the Labour Party consistently ahead in the polls, addressing voter concerns directly was one of the best chances the Conservatives had of staying in power in 2023. At the start of that year, Rishi Sunak attempted to do this by setting out his five pledges for the next twelve months; halve inflation, grow the economy, reduce national debt, cut NHS waiting times, and stop small boats. A year later, Sunak had at best only partial success in these aims. Although the inflation rate fell, economic growth was weak and even declined in the last two quarters of 2023, although it did return to growth in early 2024. National debt was only expected to fall in the mid to late 2020s, while the trend of increasing NHS waiting times did not reverse. Small boat crossings were down from 2022, but still higher than in 2021 or 2020. .