House Price Index YoY in the United States decreased to 2.30 percent in July from 2.70 percent in June of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

30 Year Mortgage Rate in the United States decreased to 6.19 percent in October 23 from 6.27 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

The property listings dataset contains information about real estate properties available for sale or rent in Brazil. It includes details such as property type (apartment, house, commercial property), location (city, neighborhood), size (square footage, number of rooms), price, amenities, and contact information for the property owner or real estate agent. This dataset can be used for market analysis, property valuation, and identifying trends in the real estate market.

Sales and Rental Prices Dataset: The sales and rental prices dataset provides information about the prices of real estate properties in Brazil. It includes data on property transactions, including sale prices and rental prices per square meter or per month. This dataset can be used to analyze price trends, compare property prices across different regions, and identify areas with high or low real estate market demand.

Property Characteristics Dataset: The property characteristics dataset contains detailed information about the features and attributes of real estate properties. It includes data such as the number of bedrooms, bathrooms, parking spaces, floor plan, construction year, building amenities, and property condition. This dataset can be used for property classification, identifying popular property features, and evaluating property quality.

Geographical Data: Geographical data includes information about the location and spatial features of real estate properties in Brazil. It can include data such as latitude and longitude coordinates, zoning information, proximity to amenities (schools, hospitals, parks), and neighborhood demographics. This dataset can be used for spatial analysis, identifying hotspots or desirable locations, and understanding the neighborhood characteristics.

Property Market Trends Dataset: The property market trends dataset provides information about market conditions and trends in the real estate sector in Brazil. It includes data such as the number of property listings, average time on the market, price fluctuations, mortgage interest rates, and economic indicators that impact the real estate market. This dataset can be used for market forecasting, understanding market dynamics, and making informed investment decisions.

Real Estate Regulatory Data: Real estate regulatory data includes information about legal and regulatory aspects of the real estate sector in Brazil. It can include data on property ownership, property taxes, zoning regulations, building permits, and legal restrictions on property transactions. This dataset can be used for legal compliance, understanding property ownership rights, and assessing the legal framework for real estate transactions.

Historical Data: Historical real estate data includes past records and trends of property prices, market conditions, and sales volumes in Brazil. This dataset can span several years and can be used to analyze long-term market trends, compare current market conditions with historical data, and assess the performance of the real estate market over time.

The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China’s actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China’s interest rates and CPI, over half of the years China’s actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China’s CPI. China’s CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents’ expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China’s real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China’s CPI calculation method should be adjusted to take into account the rise in housing prices, so China’s actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China’s financial system.

• Acquisition for the home of some good: car or motorbike, home furnishings, household appliances or computers and small household appliances.
• Monthly savings capacity at home.
• Retrospective assessment of the family's economic situation (6 months). Reason for the positive and negative assessment.
• Number of unemployed and job-seeking people in the surrounding area. Its evolution since 6 months.
• Retrospective and prospective assessment of the possibility of improving or finding employment in Spain (6 months).
• Retrospective and prospective assessment of the economic situation in Spain (6 months).
• Prospective assessment of the probability of acquiring durable goods for the household (1 year).
• Evolution of the saving capacity of the household (1 year).
• Prospective assessment of the family's economic situation (6 months). Reason for the positive and negative assessment.
• Prospective assessment of inflation in Spain (1 year).
• Evolution of interest rates and house prices in Spain (1 year). Reason for the opinion of the increase and decrease in the price of housing. Intention to buy a home in the next year.
• Ideology scale of the interviewee.
• Participation and remembrance of vote in the 2011 general election.
• Level of studies completed by obtaining a degree and in progress from the person interviewed.
• Time of acquisition of Spanish nationality. Level of knowledge of Spanish of the interviewee. Country of origin.

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.

Mortgage Rates: Hot Economic Conjecture Puts the Squeeze on Homebuyers

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

Average House Prices in Canada decreased to 686800 CAD in September from 687600 CAD in August of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

The Residential Property Price Index in Australia rose by 4.7 percent qoq in Q4 2021, above market consensus of 3.9 percent and after a 5.0 percent growth in Q3. This was the sixth straight quarter of growth in property prices, supported by record-low interest rates and strong demand. The strongest quarterly price increases were recorded in Brisbane (9.6 percent), followed by Adelaide (6.8 percent), Hobart (6.5 percent), and Canberra (6.4 percent). Through the year to Q4, the index jumped to a record high of 23.7 percent, with Hobart, Canberra, Brisbane, Sydney, and Adelaide having the largest annual rise since the commencement of the series; while Melbourne had the largest annual rise since Q2 2010. This dataset includes a chart with historical data for Australia House Price Index QoQ.

Mortgage Rate in Australia decreased to 5.52 percent in August from 5.76 percent in July of 2025. This dataset includes a chart with historical data for Australia Mortgage Rate.