The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.

In 2024, approximately 51,000 cash home sales took place in the United States. Despite the number of cash transactions declining since the peak in 2021, it remained elevated compared to the long-term average. This can be attributed to the substantial increase in mortgage rates following the COVID-19 pandemic. Despite cash purchases growing in popularity, the majority of home purchases were financed with a conventional mortgage in 2024.

The US residential real estate market, a significant component of the global market, is characterized by a moderate but steady growth trajectory. With a projected Compound Annual Growth Rate (CAGR) of 2.04% from 2025 to 2033, the market demonstrates resilience despite fluctuating economic conditions. The 2025 market size, while not explicitly provided, can be reasonably estimated based on available data and considering recent market trends. Assuming a continuation of the observed growth pattern in preceding years, a substantial market value in the trillions is plausible. Key drivers include sustained population growth, particularly in urban areas, increasing household formations among millennials and Gen Z, and ongoing demand for both rental properties (apartments and condominiums) and owner-occupied homes (landed houses and villas). However, challenges persist, including rising interest rates which impact affordability, supply chain constraints affecting new construction, and the potential for macroeconomic shifts to influence buyer confidence. Segmentation analysis highlights the varying performance across property types, with apartments and condominiums potentially experiencing higher demand in urban centers while landed houses and villas appeal to a different demographic profile and geographic distribution. The competitive landscape includes a mix of large publicly traded real estate investment trusts (REITs) like AvalonBay Communities and Equity Residential, regional developers like Mill Creek Residential, and established brokerage firms such as RE/MAX and Keller Williams Realty Inc., all vying for market share within distinct segments. The geographical distribution of the market shows significant concentration within North America, particularly in the US, reflecting established infrastructure, economic stability, and favorable regulatory environments. While other regions like Europe and Asia-Pacific contribute to the global market, the US continues to be a dominant force. The forecast period (2025-2033) suggests continued expansion, albeit at a moderate pace, indicating a relatively stable and mature market that remains attractive for investment and development. Future growth hinges upon addressing affordability concerns, navigating fluctuating interest rates, and managing supply-demand dynamics to ensure sustainable market expansion. Government policies influencing housing affordability and construction regulations will play a crucial role in shaping the future trajectory of the US residential real estate sector. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Notable trends are: Existing Home Sales Witnessing Strong Growth.

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.

Dow Jones U.S. Real Estate: A True Reflection of the Market?

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 number of new homes sold increased in 2024, but remained below the levels observed during the 2020-2021 housing boom. Conventional loans are the most popular financing option, accounting for 513,000 of the 686,000 home purchases in 2024. Despite comprising a small share of sales, cash purchases have risen notably over the past five years. This can be explained by the dramatic increase in mortgage interest rates, which makes cash purchases more attractive for those who can afford them. Development of house prices The U.S. housing market is suffering a supply shortage, which has contributed to a substantial increase in house prices. Over the past five years, construction costs risen notably, pushing the price of newly built homes up. Meanwhile, income growth has failed to keep up, resulting in a worsening housing affordability. According to the house price to income index, home prices outgrew income by nearly 32 percent between 2015 and 2024. Is the U.S. housing stock growing? There were approximately 187 million housing units in the U.S. in 2024, indicating an increase of one percent over the previous year. Apart from new-single family housing, the number of newly built multifamily units has also risen notably. Multifamily allows construction in denser urban areas with overheated housing markets, earning it increasing popularity among investors.

United States - Real Estate Investment Trusts and Closed-End Funds; Equity and Investment Fund Shares Excluding Mutual Fund Shares and Money Market Fund Shares; Asset, Transactions was -8.00000 Mil. of $ in January of 2025, according to the United States Federal Reserve. Historically, United States - Real Estate Investment Trusts and Closed-End Funds; Equity and Investment Fund Shares Excluding Mutual Fund Shares and Money Market Fund Shares; Asset, Transactions reached a record high of 52124.00000 in January of 2004 and a record low of -62648.00000 in July of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Real Estate Investment Trusts and Closed-End Funds; Equity and Investment Fund Shares Excluding Mutual Fund Shares and Money Market Fund Shares; Asset, Transactions - last updated from the United States Federal Reserve on July of 2025.

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.

Dow Jones U.S. Real Estate: Resilient in the Face of Uncertainty?

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 Real Estate Investment Trusts (REIT) market size will be USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
The industrial segment is the fastest-growing application in the REITs market, largely due to the rapid expansion of e-commerce and the demand for distribution centers and warehouses

Market Dynamics of Real Estate Investment Trusts (REIT) Market

Key Drivers for Real Estate Investment Trusts Reits Market

Growing Demand for Stable Income-Generating Assets to Boost Market Growth

The demand for stable income-generating assets is one of the key drivers of the Real Estate Investment Trusts (REITs) market. Investors increasingly seek predictable cash flows, especially in uncertain economic climates. REITs provide access to a diversified portfolio of income-producing properties, such as office buildings, shopping centers, and residential complexes, offering consistent dividends. This appeal is particularly strong among income-focused investors like retirees or those seeking to reduce risk. Additionally, REITs allow smaller investors to gain exposure to large-scale real estate investments without the need for substantial capital, further fueling market growth. For instance, in November 2023, 1031 Crowdfunding launched the Covenant Senior Housing REIT, Inc., which aims to create new ways for senior living investors to grow their holdings. The newly formed REIT stands as its own company, and 1031 is the REIT’s sponsor. With the launch, 1031 Crowdfunding focused on “exchange-type vehicles” and working with investors interested in “non-correlating assets who want to invest in senior housing”

Rise in Investor Interest for Diversification and Liquidity to Drive Market Growth

The growing desire for diversification and liquidity among investors has contributed to the expansion of the REITs market. Unlike direct property ownership, REITs provide liquidity as they can be traded on major stock exchanges, offering an attractive alternative for those looking for easier access to real estate investments without the complexities of managing properties. This liquidity makes REITs a highly attractive investment vehicle, especially in volatile markets. Furthermore, REITs enable investors to diversify their portfolios across different types of real estate assets, helping to mitigate risks and enhance returns in a well-balanced investment strategy.

Key Restraint for the Real Estate Investment Trusts Reits Market

Impact of Fluctuating Interest Rates to Hamper Market Growth

Fluctuating interest rates represent a significant restraint for the REITs market. When interest rates rise, the cost of borrowing increases, making it more expensive for REITs to finance property acquisitions or development projects. This can limit growth opportunities and reduce profitability. Additionally, higher interest rates tend to make fixed-income investments more attractive relative to REITs, which may cause a shift in investor preferences. The sensitivity of REITs to interest rate changes can lead to price volatility, which could deter some investors from entering or staying in the market, particularly those seeking stable returns.

Key Trends for Real Estate Investment Trusts Reits Market

The Rise of Thematic and Sector-Specific REITs to Draw Targeted Investments

A notable trend within the REIT...

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.

Will the Dow Jones U.S. Real Estate Index Weather the Storm?

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.

Dow Jones U.S. Real Estate Index Forecast: Moderate Growth Anticipated

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

Monaco was the leading city in terms of most expensive luxury real estate worldwide in 2024. One million dollars could only buy 19 square meters of luxury property there. In London, the same amount of money could purchase 34 square meters of luxury real estate. In Tokyo, one million dollars was enough to buy 58 square meters of prime real estate in 2024. Luxury real estate – additional information Real estate is considered one of the best long-term investments, and it certainly is one of the major investments one might make during a lifetime. As far as luxury real estate is concerned, though, only the most affluent individuals or prominent real estate companies can afford to invest in prime properties in the world’s most attractive locations.

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.

Dow Jones U.S. Real Estate: Ready for Lift-off?

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 the Dow Jones U.S. Real Estate Index a Reliable Indicator?

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

To investigate the issue of inflation-hedging to find appropriate hedging assets against inflation by using the VAR or VECM model. We have collected data encompassing housing price indices, stock indices, price indexes, and money supply from five countries: the United States, Hong Kong, South Korea, Singapore, and Taiwan. The housing price index focuses on the transaction prices of listed residential houses in the metropolitan area as the benchmark, the stock price index is the ordinary stock market index of various countries, the price index is the consumer price index (CPI), and the money supply is M2 aggregate. The time period for obtaining data on the housing price index and stock price index is not the same.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

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.

Dow Jones U.S. Real Estate Capped Index - Soaring High or Stumbling?

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 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

The Latin America E-commerce Logistics Market is projected to grow from USD 5.75 billion in 2025 to USD 11.44 billion by 2033, exhibiting a CAGR of 10.30% during the forecast period (2025-2033). The market growth is attributed to the increasing demand for e-commerce, the growth of the middle class, and the improving infrastructure in the region. The rising adoption of smartphones and the internet has led to a surge in online shopping, which has subsequently increased the demand for e-commerce logistics services. Additionally, the growing trend of cross-border e-commerce is expected to drive the market growth further. Key market drivers include the increasing demand for e-commerce logistics services, the growth of the middle class, the rising adoption of smartphones and the internet, and the improving infrastructure. Key trends include the adoption of advanced technologies, the development of new e-commerce platforms, and the increasing popularity of cross-border e-commerce. Key companies operating in the Latin America E-commerce Logistics Market include DB Schenker, Loggi, FedEx Corporation, DHL Express, Nippon Express, Gefco Logistics, CEVA Logistics, Kuehne Nagel, B2W Digital, Kerry Logistics, CH Robinson Worldwide Inc, and Bollore Logistics. Recent developments include: July 2023: DHL Supply Chain invested a substantial amount of money in Latin American markets, intending to continue these investments until 2028. These investments aim to bolster DHL's operations in Latin America. Their initiatives include decarbonizing their domestic fleet by adopting greener alternatives, constructing and renovating real estate and warehouses, and investing in new technologies such as robotics and automation solutions. These advancements are geared towards enhancing workplaces, improving operational efficiency, and providing greater flexibility for customers. This forms a pivotal part of DHL's strategic investment plan, intended to fortify logistics capabilities in key industries such as healthcare, automotive, technology, retail, and e-commerce., September 2022: AP Moller–Maersk extended its footprint in Latin America by inaugurating a new warehouse in Brazil. This facility offers comprehensive supply chain management services encompassing order fulfillment, receipt and storage of goods, inventory management, picking and packing of pallets or cases, loading, consolidation and deconsolidation, warehouse management systems, cross-docking, and other value-added services., March 2022: Cubbo, a company specializing in e-commerce fulfillment logistics, which manages warehousing, packaging, and order shipping, recently acquired Dedalog, a competitor headquartered in São Paulo, Brazil. Key drivers for this market are: Rise In Population, Rapid growth in Urbanization. Potential restraints include: Integration Complexities, Technical reliability issues can hinder entry into the region. Notable trends are: E-commerce Boom Spearheading Last-mile Delivery Demand.

The drastic need for apartments has led to an expansion for apartment and condominium construction contractors over the past five years. Still, changing interest rates have led to years of expansion and contractions for contractors. Overall, revenue has been increasing at a CAGR of 3.8% to total an estimated $91.8 billion through the end of 2025, including an estimated 2.2% increase in 2025. Low interest rates amid the pandemic led residential investment to swell, which included apartment complexes. As inflationary concerns and interest rate hikes lingered, many contractors delayed construction, leading to a contraction in 2023 as housing starts sank. Profit has risen slightly as materials price inflation has cooled and contractors have been able to adjust their rates, passing along higher prices to customers. This has also been a driver of revenue growth. Multifamily complexes are still very much needed as young professionals and immigrants move to major cities, leading to growth in 2025. Home prices are set to see slower growth in the coming years than in the previous five, causing a shift in the housing market back to homeownership. Also, continued rate cuts will incentivize home construction. Mortgage rates have remained stubbornly high in the face of cuts to the federal funds rate, however. Elevated mortgage rates will keep buying a house out of reach for many, pushing more people to rent. Apartment construction is set to continue to account for the growing population in the US. Affordable housing complexes remain crucial in many large cities and will be needed as more people enter. Rental vacancies will continue threatening contractors, as many consumers may split housing with roommates and fulfill current stock to save money. Overall, industry revenue is forecast to expand at a CAGR of 1.8% to total an estimated $100.5 billion through the end of 2030.

The Qatar residential construction market exhibits robust growth potential, driven by a burgeoning population, increasing urbanization, and significant government investments in infrastructure development. The market size, valued at $12.39 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 11.45% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, Qatar's hosting of the FIFA World Cup 2022 spurred substantial investments in residential properties to accommodate visitors and support the growing tourism sector. Secondly, the ongoing infrastructure projects related to the country's National Vision 2030, focusing on sustainable development and economic diversification, contribute significantly to the demand for new residential units. The market segments are diverse, encompassing apartments & condominiums, villas, and other residential types, with both new construction and renovation projects driving growth. The strong economy and government support for the real estate sector continue to underpin market expansion. Competition in the Qatar residential construction market is intense, with numerous local and international companies vying for projects. Key players include QD-SBG Construction, Midmac Contracting Co, Hamad Bin Contracting Company, and others. However, potential restraints include fluctuations in global commodity prices, particularly construction materials, and the availability of skilled labor. Furthermore, stringent building codes and regulations, aiming to ensure high-quality construction and sustainable practices, may impact project timelines and costs. Despite these challenges, the long-term outlook for the Qatar residential construction market remains positive, driven by consistent economic growth and the government’s commitment to long-term development plans. The focus on sustainable and technologically advanced construction methods will also shape the industry in the coming years. Recent developments include: July 2022: The Gateway Plaza building in Richmond, Virginia, USA, has been purchased by Qatar First Bank LLC (public) (QFB). A wonderful addition to the bank's investment portfolio, the new acquisition is a Class AA trophy asset with a 330,000-square-foot area that was built in 2015 as a build-to-suit building and will continue to ensure steady cash flows. With a goal to increase its presence and level of knowledge in the US real estate market, the new investment marks QFB's eleventh US real estate property and its fourteenth investment under its new Shari'a-compliant real estate investment strategy., August 2022: Ascott successfully acquired Oakwood Worldwide in July 2022, increasing its portfolio's total number of units by over 15,000 to over 153,000 across more than 900 locations. With the addition of freshly signed and opened properties across their brands, Ascott had substantial organic growth in the first half of 2022 after completing the acquisition of Oakwood. They have started the process of integrating Oakwood with Ascott, which will strengthen their ability to spur additional growth, provide higher returns to their property owners, and provide better services to their visitors.. Notable trends are: Qatar's Residential Market is Slightly Improving.