Single Family Home Prices in the United States decreased to 422600 USD in August from 425700 USD in July of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Real Estate Markets (EMVMACRORE) from Jan 1985 to Aug 2025 about volatility, uncertainty, equity, real estate, and USA.

From 2007 to 2016, the EU prefabricated buildings market showed a mixed trend pattern. A significant drop in 2008 (79% Y-o-Y) was followed by a gradual increase over the next three years until it plunged again in 2013 (91% Y-o-Y).

About Dataset

Source

The source of this dataset is REDFIN Data Center. To download the latest dataset available, please go to: https://www.redfin.com/news/data-center/

They also provide a page with the definitions for each metric used here: https://www.redfin.com/news/data-center-metrics-definitions/

For more informaton on Data and Data Quality, please visit: https://www.redfin.com/about/data-quality-on-redfin Reading the Data

The data is a .tsv format and can be imported using pandas as follows:

df = pd.read_csv("weekly_housing_market_data_most_recent.tsv000", sep='\t')

MOST RECENT DATAPOINT: 2022-07-11

Nahb Housing Market Index in the United States remained unchanged at 32 points in September. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Sep 2025 about median and USA.

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.

Understanding the Dynamics and Implications of a Housing Market Recession

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

Existing Home Sales in the United States decreased to 4000 Thousand in August from 4010 Thousand in July of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Index in China decreased by 2.50 percent in August from -2.80 percent in July of 2025. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A comprehensive latest dataset of Connecticut’S housing market. This dataset includes key metrics such as median sale price, number of homes sold, and inventory levels, updated monthly.

Housing Index in Saudi Arabia increased to 105 points in the second quarter of 2025 from 104.90 points in the first quarter of 2025. This dataset provides - Saudi Arabia Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage News Daily is a leading news and analysis provider of U.S. mortgage markets and publish Mortgage News Daily rate index which is published daily.

Introduction

The global AI in real estate market is experiencing remarkable growth, with projections indicating a substantial increase in value. By 2033, the market is anticipated to reach a staggering USD 41.5 billion, reflecting a notable compound annual growth rate (CAGR) of 30.5% during the forecast period from 2024 to 2033. This growth trajectory underscores the transformative impact of artificial intelligence (AI) on the real estate sector, revolutionizing various aspects of operations and decision-making processes.

The integration of Artificial Intelligence (AI) in real estate is transforming how the industry operates, from property management to sales. AI technologies enable more efficient data processing and interpretation, facilitating better decision-making. Key applications include automated valuation models, predictive analytics for market trends, and chatbots for customer service. This innovation leads to improved user experiences and operational efficiencies.

The AI in real estate market is experiencing significant growth. This expansion can be attributed to the increasing demand for smarter and more efficient real estate solutions, which AI provides. Real estate companies are investing in AI to enhance property search engines, implement smart home technologies, and improve transaction processes. These advancements are attracting both investors and companies looking to capitalize on the enhanced capabilities of AI to streamline operations and increase profitability.

Despite challenges such as data privacy concerns and the integration of AI with traditional systems, the momentum for AI adoption in real estate remains strong. AI has the potential to create significant value for the industry, ranging from cost reduction to operational improvement. According to surveys, AI could generate substantial value ranging from $110 billion to $180 billion and beyond, highlighting its transformative potential.

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 the Home Construction Index Signaling a Housing Market Shift?

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

Graph and download economic data for Housing Inventory: Median Days on Market Month-Over-Month in Newport News City, VA (MEDDAYONMARMM51700) from Jul 2017 to Aug 2025 about Newport News City, VA; Virginia Beach; VA; median; and USA.

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

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

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

The average Canadian house price declined slightly in 2023, after four years of consecutive growth. The average house price stood at ******* Canadian dollars in 2023 and was forecast to reach ******* Canadian dollars by 2026. Home sales on the rise The number of housing units sold is also set to increase over the two-year period. From ******* units sold, the annual number of home sales in the country is expected to rise to ******* in 2025. British Columbia and Ontario have traditionally been housing markets with prices above the Canadian average, and both are set to witness an increase in sales in 2025. How did Canadians feel about the future development of house prices? When it comes to consumer confidence in the performance of the real estate market in the next six months, Canadian consumers in 2024 mostly expected that the market would go up. A slightly lower share of the respondents believed real estate prices would remain the same.

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.

Exploring the Positive Correlation Between Interest Rates and Housing Market Recessions

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

According to Cognitive Market Research, The Global Property Management Service market was estimated at USD 14.5 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 7.8% from 2023 to 2030. Rising Demands for SaaS-based Property Management Software to Expand Market Penetration

Subscription-based SaaS solutions benefit companies of all sizes. Businesses increasingly use SaaS solutions to optimize operations by automating workflows and removing manual input. Businesses can also lower the cost and complexity of on-premises deployment by installing SaaS solutions. SaaS software assists large multifamily property management organizations integrate several technologies across their portfolio. In addition, the SaaS model is crucial for multi-vendor device compatibility with legacy systems.

For instance, Planon collaborated with AddOnn in March 2021 to combine AddOnn's SaaS solution with Planon's software platform for building and service digitalization to provide end-to-end solutions to end-users worldwide.

(Source:planonsoftware.com/uk/news/planon-and-addonn-launch-partnership-with-introduction-of-mobile-cleaning-solution/)

Employees in real estate organizations rely on up-to-date information to make vital decisions. SaaS systems allow users to access information from any location and device with internet connectivity. A SaaS platform can help property managers link their property solutions with sophisticated payment services for quick and easy transactions.

Evolving Trends of Workforce Mobility to Strengthen Market Share

Many employees nowadays prefer to work from home rather than in offices, corporate headquarters, or a global company branch. This contributes to the need for flexible access to office resources and data. Besides, organizations are using virtual workplaces to reduce their physical infrastructure requirements to a bare minimum, allowing them to be more flexible and use their office space better. Many businesses seek mobility, workplace, and other integrated facility management solutions. This enables property managers to retain productivity while working with a huge crew. These solutions can be used by associated real estate agents & property managers to maintain track of all the properties they manage and the routine maintenance that needs to be performed on them. As a result, the rising trend of workplace mobility is propelling the property management service industry forward.

For instance, Entrata Inc. reported the integration of Alexa with residential buildings in April 2021. This integration would enable property managers to monitor or set up Alexa-enabled devices in each unit, allowing them to create voice-controlled automated homes.

(Source:www.prnewswire.com/news-releases/entrata-enables-alexa-experience-at-scale-with-amazons-alexa-for-residential-301263114.html)

Market Dynamics of Property Management Service

Integration Complexity and Data Security Concerns to Limit Market Growth

One significant restraint property management software services face is the complexity of integrating with existing systems and databases. Many property management companies already have established tools for accounting, tenant communication, maintenance tracking, and more. Implementing new software solutions can lead to compatibility challenges and difficulties in transferring data seamlessly. Furthermore, as property management software handles sensitive information such as tenant details, financial records, and property documents, ensuring robust data security becomes critical. Any breaches or unauthorized access can lead to legal consequences, financial losses, and company reputation damage.

Impact of COVID-19 on the Property Management Service Market

The COVID-19 pandemic significantly impacted the property management service market, introducing shifts in tenant behavior, remote work trends, and economic uncertainties that prompted property managers to adapt their strategies. Lockdowns and travel restrictions decreased demand for short-term rentals, while remote work trends increased the significance of property amenities and flexible leasing options. Property managers incorporated virtual tours, contactless services, and enhanced sanitation measures to address safety concerns. Moreover, the pandemic accelerated the adoption of proptech solutions for remote property monitoring and digital communication, reshap...

In 2016, the global prefab housing imports stood at 5.3M tons, coming up by 3% against the previous year figure. Overall, prefab housing imports continue to indicate a relatively flat trend pattern....