The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of ***** index points in 2006. In 2024, the housing affordability index measured **** index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached ******* U.S. dollars in 2023.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Graph and download economic data for Housing Affordability Index (Fixed) (FIXHAI) from Jul 2024 to Jul 2025 about fixed, housing, indexes, and USA.

Housing Index in Spain increased to 2094 EUR/SQ. METRE in the second quarter of 2025 from 2033 EUR/SQ. METRE in the first quarter of 2025. This dataset provides the latest reported value for - Spain House Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Price to Rent Ratio in the United States increased to 134.20 in the fourth quarter of 2024 from 133.60 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

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.

Euro Stoxx 50: Back on Track?

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

House prices in Ireland have been on an upward trend since 2013, with a brief period of decline in 2020 and 2023. In the fourth quarter of 2024, nominal prices rose by **** percent year-on-year. When adjusted for inflation, the increase was slightly slower, by **** percent. How expensive are homes in Ireland? The average list price of residential property in Ireland varied significantly between different counties. In the second quarter of 2024, Wicklow and Dublin were among the most expensive regions in the country, exceeding the national average of around ******* euros. Leitrim and Longford, on the other hand, offered the most affordable housing options, averaging below ******* euros. Has income kept up with the development of house prices? The house price-to-income ratio measures the development of housing affordability and is calculated by dividing the nominal house price by the nominal disposable income per head. Between 2015 and 2024, the house price-to-income ratio in Ireland grew by about ** index points, which means that house values increased in relation to earnings. This makes homeownership in Ireland more challenging due to the decreasing affordability of dwellings.

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.

Union Pacific (UNP): On Track for Growth or Derailed by Industry Headwinds?

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.

Is SoundHound (SOUNW) on the Right Track?

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

In August 2025, the Consumer Price Index including owner occupiers' housing costs (CPIH) inflation rate of the United Kingdom was *** percent, down from *** percent in the previous month. The inflation rate fell noticeably after the COVID-19 pandemic but rose sharply between Spring 2021 and Autumn 2022. After peaking at *** percent in October 2022, CPIH inflation declined throughout 2023 and into 2024, falling to *** percent by September of that year, before increasing again recently. Cost of living problems persist into 2025 Although it is likely that the worst of the recent inflation surge may have passed, the issues caused by it look set to linger into 2025 and beyond. While the share of households experiencing living cost rises has fallen from ** percent in August 2022, to ** percent in July 2024, this share rose towards the end of the year, with more than half of households reporting rising costs in December. Even with lower inflation, overall consumer prices have already increased by around ** percent in the last three years, rising to almost ** percent for food prices, which lower income households typically spend more of their income on. The significant increase in people relying on food banks across the UK, is evidence of the magnitude of this problem, with approximately **** million people using food banks in 2023/24. Other measures of inflation While the CPIH inflation rate displayed here is the preferred index of the UK's Office of National Statistics, the Consumer Price Index (CPI) is often more prominently featured in the media in general. An older index, the Retail Price Index (RPI) is also still used by the government to calculate certain taxes and rail fares. Other metrics include the core inflation rate, which measures price increases without the volatility of food and energy costs, while price increases in goods and services can also be tracked separately. The inflation rate of individual sectors can also be measured, and as of December 2024, prices were rising fastest in the communications sector, at *** percent, with costs falling in the transport and furniture sectors.

Retail properties had the highest capitalization rates in the United States in 2023, followed by offices. The cap rate for office real estate was **** percent in the fourth quarter of the year and was forecast to rise further to **** percent in 2024. Cap rates measure the expected rate of return on investment, and show the net operating income of a property as a percentage share of the current asset value. While a higher cap rate indicates a higher rate of return, it also suggests a higher risk. Why have cap rates increased? The increase in cap rates is a consequence of a repricing in the commercial real estate sector. According to the National NCREIF Property Return Index, prices for commercial real estate declined across all property types in 2023. Rental growth was slow during the same period, resulting in a negative annual return. The increase in cap rates reflects the increased risk in the investment environment. Pricing uncertainty in the commercial real estate sector Between 2014 and 2021, commercial property prices in the U.S. enjoyed steady growth. Access to credit with low interest rates facilitated economic growth and real estate investment. As inflation surged in the following two years, lending policy tightened. That had a significant effect on the sector. First, it worsened sentiment among occupiers. Second, it led to a decline in demand for commercial spaces and commercial real estate investment volumes. Uncertainty about the future development of interest rates and occupier demand further contributed to the repricing of real estate assets.

The construction output price in the United Kingdom has reached an annual growth rate of *** percent in June 2025 compared to the same month of the previous year. Construction costs had been increasing at a lower rate than in 2022 and 2023, but started rising again slowly in late 2024. The year-over-year growth rate was over ** percent in May and July 2022. Public and private housing was the construction segment with the highest output price increase. How have material costs developed over the years? Several factors influence construction material costs, including supply and demand, regulatory requirements, and transportation logistics. Manufacturing efficiency and global trade policies also play a big part, along with economic factors like inflation and currency fluctuations. In June 2022, the price of construction materials for new houses in the UK were ** percent higher than in 2015. What is the largest component of those costs? Labor costs are often one of the largest expenses in construction projects. That is due to the skilled nature of the work, which has a high demand for specialized trades. The construction sector's labor costs accounted for around ** percent of the sector's earnings in the United Kingdom in 2024. In the past years, the size of labor costs as a share of the construction sector have increased slightly, but they were still lower than in before 2014. As of June 2025, the construction output price growth rate has been revised to *** percent.

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over (LES1252881600Q) from Q1 1979 to Q2 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, employment, real, and USA.

India’s per capita net national income or NNI was around *** thousand rupees in financial year 2025. The annual growth rate was *** percent as compared to the previous year. National income indicators   While GNI (Gross National Income) and NNI are both indicators for a country’s economic performance and welfare, the GNI is related to the GDP plus the net receipts from abroad, including wages and salaries, property income, net taxes and subsidies receivable from abroad. On the other hand, the NNI of a country is equal to its GNI net of depreciation. In 2020, India ranked second amongst the Asia Pacific countries in terms of its gross national income. This has been possible due to a favorable GDP growth in India. Measuring wealth versus welfare   National income per person or per capita is often used as an indicator of people's standard of living and welfare. However, critics object to this by citing that since it is a mean value, it does not reflect the real income distribution. In other words, a small wealthy class of people in the country can skew the per capita income substantially, even though the average population has no change in income. This is exemplified by the fact that in India, the top one percent of people, control over 40 percent of the country’s wealth.

Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q2 2025 about disposable, payments, personal income, debt, percent, households, personal, income, services, and USA.