House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May 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 6.50 percent in September 4 from 6.56 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.

This dataset contains an history of nearly all of the real estate transactions concerning a single house/appartment in France from 2014 to today. Some variables likely to have an impact on the price of real estate are also provided as time series: the households income levels per city, the average debt level of french peoples, the average amount of savings of french people, the interest rates of loans, the price of the rent per city, the number of houses and number of vacant houses per city.

This dataset is provided under a permissive licence, and is free to use for commercial uses. It has a vocation of helping research concerning the dynamics of real estate prices.

The dataset consist in extraction from several openly available datasets put together in a practical format: The DVF+ database of real estate transactions, the IRCOM dataset of household incomes and income taxes, average interest rates of real estate loans from the banque de france website, the LOVAC dataset of number of vacant and occupied housings per city, the OECD dataset of financial assets per capita, the "carte des loyers" dataset of 2018 and 2022 which list the average price of the rent per square meter, the Indice de Référence des Loyers (IRL) time series which is an index defining the maximum rent increase that can be applied to an already rented housing and is calculated every 3 months as the inflation adjusted buying power of 100€ in 1998, the TEC00104 eurostat dataset of debt levels.

Abstract This paper presents the more commonly used methods of hedonic estimation and applies them to a fiscal data base from Belo Horizonte, Brazil, 1995-2012. The results of the price indexes from the various estimations suggest a pronounced real estate appreciation from 2005 to 2012. Increasing real estate financing (after the statutory lien legislation of 2004), falling interest rates and growth in real family income contributed to the positive performance of the real estate market in the period. Results also indicate that real estate prices continued to increase despite the world financial crisis of 2008/2009 possibly due to countercyclical policies implemented by the Brazilian Government. These results help to shed light on the potential use of hedonic price models and a fiscal database to construct an official housing price index for Brazil in the future.

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.

AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment?

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

Dividend-Per-Share Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

Begin-Period-Cashflow Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

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.

Prologis () Stock: A Real Estate Titan on the Rise?

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.

Anywhere Real Estate Inc. (HOUS): Stock on the Rise?

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.

Machine Learning Predicts QQQ to Increase in Value by 5% in the Next 3 Months

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

Abstract copyright UK Data Service and data collection copyright owner.The 5% Sample Survey of Building Society Mortgage Completions (BSM) has been in existence since 1965. The Archive holds data from 1974. Monthly returns, giving detailed information on a nominal 5% sample of all mortgage completions, have been submitted on a voluntary basis by most building societies to the Department of Environment who process the data on a quarterly basis. The survey results have served as the offical source of statistics on the owner-occupied housing market, providing a wealth of information on mortgage advances, dwelling prices and the characteristics of borrowers and properties. An increased share of the mortgage market being accounted for by other lenders and a widening range of mortgage products during the 1980s have necessitated change, leading to the BSM being succeeded by the Survey of Mortgage Lenders (SML) in 1992 (see GN: 33254). An important consideration for users of the data is that the SML figures allow continuity with the BSM survey results to be maintained for a reasonable period. Main Topics: Building Society code, date mortgage completed, whether dwelling is wholly or partly occupied by borrower. Mortgage amount, whether solely for purchase of property, period of mortgage, gross rate of interest, repayment method. Purchase price and whether discounted in any way. Location of dwelling, whether new, age of dwelling, type, number of habitable rooms, whether garage, rateable value. Number and sex of borrowers, age of main borrower, basic income, other income, total income, whether applicant previously owner occupier, previous tenure, whether main borrower nominated by LA under support lending scheme. Building Societies are divided into four strata according to the size of their assets. All the largest societies are asked to complete questionnaires on a sample of 5 per cent of their new mortgage advances. Mortgages are included if their reference numbers end in specified digits chosen so that every twentieth mortgage is selected. Societies in the next stratum are arranged in order of size of assets and alternate societies chosen each of which are asked to complete questionnaires on 10 per cent of their mortgages. In the next stratum 20 per cent of the mortgages of every fourth society are obtained. The smallest societies are completely excluded.

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.

Fox (FOX): Can the Rise Continue in 2023?

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

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.

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.

Inflation on the Rise: What Does This Mean for You?

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 increased to 688700 CAD in July from 688500 CAD in June of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

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.

VICI Properties Inc. (VICI): Stock on the Rise or Not?

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