As of the first quarter of 2025, the sentiment of most homebuilders in the U.S. was negative. That index has remained stable since 2023. That was according to a monthly index that measures the sentiment among home builders in the United States. The index reflected a negative mood in the housing industry, as the sentiment was below ** percent in the past years.

Nahb Housing Market Index in the United States increased to 33 points in July from 32 points in June of 2025. 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.

The NAHB Housing Market Index is a monthly survey conducted by the National Association of Home Builders that measures homebuilders' sentiment regarding the U.S. housing market.

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

The number of home sales in the United States peaked in 2021 at almost ************* after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to ***********. Home sales remained muted in 2024, with a mild increase expected in 2025 and 2026. A major factor driving this trend is the unprecedented increase in mortgage interest rates due to high inflation. How have U.S. home prices developed over time? The average sales price of new homes has also been rising since 2011. Buyer confidence seems to have recovered after the property crash, which has increased demand for homes and also the prices sellers are demanding for homes. At the same time, the affordability of U.S. homes has decreased. Both the number of existing and newly built homes sold has declined since the housing market boom during the coronavirus pandemic. Challenges in housing supply The number of housing units in the U.S. rose steadily between 1975 and 2005 but has remained fairly stable since then. Construction increased notably in the 1990s and early 2000s, with the number of construction starts steadily rising, before plummeting amid the infamous housing market crash. Housing starts slowly started to pick up in 2011, mirroring the economic recovery. In 2022, the supply of newly built homes plummeted again, as supply chain challenges following the COVID-19 pandemic and tariffs on essential construction materials such as steel and lumber led to prices soaring.

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 Home Construction Continue to Build? Index

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 sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting ****** U.S. dollars per square foot in 2024. In 2024, the average sales price of a new home exceeded ******* U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly ** percent year-on-year, and in 2022, the increase was even higher, at close to ** percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under ***** percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

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 Home Construction Index Build a Bullish Future?

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. Select Home Construction: Riding the Housing Wave or Facing Headwinds?

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

Indonesia House Purchase/Construction Plan in the Next 12 Months: Number Desire data was reported at 71.665 % in Mar 2025. This records a decrease from the previous number of 71.858 % for Feb 2025. Indonesia House Purchase/Construction Plan in the Next 12 Months: Number Desire data is updated monthly, averaging 67.197 % from Jan 2017 (Median) to Mar 2025, with 77 observations. The data reached an all-time high of 78.410 % in Oct 2022 and a record low of 62.234 % in Dec 2017. Indonesia House Purchase/Construction Plan in the Next 12 Months: Number Desire data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HA007: Consumer Confidence Index: Respondent's First Choice Type of Investments in the Next 12 Months. [COVID-19-IMPACT]

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.

Tri Pointe Homes: Navigating the Housing Market (TPH)

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

Indonesia House Purchase/Construction Plan in the Next 12 Months: Very Possible data was reported at 4.834 % in Mar 2025. This records an increase from the previous number of 3.781 % for Feb 2025. Indonesia House Purchase/Construction Plan in the Next 12 Months: Very Possible data is updated monthly, averaging 5.671 % from Jan 2017 (Median) to Mar 2025, with 77 observations. The data reached an all-time high of 7.849 % in Feb 2018 and a record low of 2.840 % in Apr 2022. Indonesia House Purchase/Construction Plan in the Next 12 Months: Very Possible data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HA007: Consumer Confidence Index: Respondent's First Choice Type of Investments in the Next 12 Months. [COVID-19-IMPACT]

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.

Home Construction Outlook: Modest Gains Expected for the Dow Jones U.S. Select Home Construction index.

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 prospects of investment and development in the house building for sale real estate market in Europe since 2018 generally decreased, despite an uptick in 2025. In a 2024 survey among real estate industry experts, investment in house building received a prospect score for the next year amouting to 3.71 on a scale from 1 (poor) to 5 (excellent). The sectors with the highest prospect scores in 2025 were new energy infrastructures, healthcare, and data centers.

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

Purchase Or Construction of New House: Positive data was reported at 17.960 % in Sep 2018. This records an increase from the previous number of 17.170 % for Jul 2018. Purchase Or Construction of New House: Positive data is updated monthly, averaging 12.390 % from Jan 2012 (Median) to Sep 2018, with 41 observations. The data reached an all-time high of 20.560 % in May 2018 and a record low of 6.640 % in May 2013. Purchase Or Construction of New House: Positive data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.H003: Consumer Confidence Survey.

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.

Home Construction Index Projected to Rise Slightly

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

Manufactured Mobile Homes Market Outlook

The global manufactured mobile homes market size was valued at approximately $25 billion in 2023 and is expected to reach around $40 billion by 2032, growing at a CAGR of 5.5% during the forecast period. The demand for affordable housing solutions, along with advancements in manufacturing technologies, is driving substantial growth in this market. As housing prices continue to climb in urban areas, manufactured mobile homes offer an affordable and flexible alternative for many individuals and families. Additionally, the growing emphasis on sustainable and eco-friendly building materials further propels the market growth.

One of the significant growth factors of the manufactured mobile homes market is the rising demand for affordable housing solutions. With increasing housing costs in urban areas, many people are turning to manufactured homes as a cost-effective alternative. These homes offer a lower price point compared to traditional site-built homes, making homeownership more attainable for a broader segment of the population. Furthermore, modern manufactured homes are designed to comply with the same building codes and standards as traditional homes, ensuring safety and durability, which boosts consumer confidence and adoption.

Another driver for the market is the advancements in manufacturing technologies. The introduction of innovative materials and construction techniques has significantly improved the quality, design, and energy efficiency of manufactured mobile homes. These advancements allow for faster production times and reduced costs without compromising on quality. Additionally, the use of sustainable and eco-friendly materials in the construction of manufactured homes appeals to environmentally conscious consumers, further driving market demand. As manufacturers continue to invest in research and development, we can expect continued improvements in the quality and features of these homes.

The increasing focus on sustainable living and eco-friendly building practices is another factor contributing to the growth of the manufactured mobile homes market. Consumers are becoming more aware of the environmental impacts of their housing choices and are seeking alternatives that minimize their carbon footprint. Manufactured homes, which often use less material and generate less waste during construction compared to traditional homes, are viewed as a more sustainable option. Additionally, the integration of energy-efficient technologies, such as solar panels and advanced insulation, makes these homes more appealing to eco-conscious buyers.

Regionally, North America holds a significant share of the manufactured mobile homes market, driven by factors such as the high cost of traditional housing and the availability of expansive land for mobile home parks. The United States, in particular, is a major market due to favorable regulations and a strong culture of mobile home living. Europe and Asia Pacific are also witnessing growth in this market, driven by increasing housing demand and government initiatives to promote affordable and sustainable housing solutions. Meanwhile, Latin America and the Middle East & Africa regions are expected to see moderate growth due to economic and infrastructural challenges but have significant potential for future expansion.

Product Type Analysis

The manufactured mobile homes market is segmented by product type into single-wide, double-wide, and triple-wide homes. Single-wide homes represent the most basic and compact option, measuring approximately 18 feet wide and 90 feet long. These homes are popular among individuals and small families due to their affordability and ease of transport. Despite their smaller size, modern single-wide homes come with various amenities and can be customized to meet the needs of the buyer. The demand for single-wide homes remains robust, especially in areas with limited space or where land costs are high.

Double-wide homes, on the other hand, offer more space and a layout that is closer to that of a traditional site-built home. Typically measuring around 36 feet wide and 90 feet long, these homes appeal to larger families and those seeking more living space. The increased room allows for more luxurious features, such as larger kitchens, additional bedrooms, and spacious living areas. As a result, double-wide homes are becoming increasingly popular among middle-income families who desire the benefits of a manufactured home with the comfort and space of a traditional home.

Triple-wide homes are the largest and most l

Indonesia House Purchase/Construction Plan in the Next 12 Months: Possible data was reported at 23.502 % in Mar 2025. This records a decrease from the previous number of 24.361 % for Feb 2025. Indonesia House Purchase/Construction Plan in the Next 12 Months: Possible data is updated monthly, averaging 26.638 % from Jan 2017 (Median) to Mar 2025, with 77 observations. The data reached an all-time high of 31.337 % in Feb 2017 and a record low of 17.981 % in Oct 2022. Indonesia House Purchase/Construction Plan in the Next 12 Months: Possible data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HA007: Consumer Confidence Index: Respondent's First Choice Type of Investments in the Next 12 Months. [COVID-19-IMPACT]