Nahb Housing Market Index in the United States decreased to 32 points in June from 34 points in May 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.

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 50 percent in the past years.

Turkey Construction Confidence Index (CCI): Seasonally Adjusted (sa) data was reported at 88.884 Point in Mar 2025. This records a decrease from the previous number of 89.287 Point for Feb 2025. Turkey Construction Confidence Index (CCI): Seasonally Adjusted (sa) data is updated monthly, averaging 91.590 Point from Jan 2011 (Median) to Mar 2025, with 171 observations. The data reached an all-time high of 107.454 Point in Jul 2011 and a record low of 50.851 Point in Apr 2020. Turkey Construction Confidence Index (CCI): Seasonally Adjusted (sa) data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.S005: Sectoral Confidence Indices: Seasonally Adjusted. [COVID-19-IMPACT]

France - Construction confidence indicator was -12.10% in June of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Construction confidence indicator - last updated from the EUROSTAT on July of 2025. Historically, France - Construction confidence indicator reached a record high of 10.30% in December of 2021 and a record low of -14.60% in March of 2025.

The entrepreneurial confidence index in the Russian construction industry was strictly negative during the period under consideration. In the first quarter of 2025, it stood at *** percent, having increased from the previous three-month period. Over the observed period, the index was the most negative at *** percent in the second quarter of 2020.

Turkey Construction Confidence Index: Past 3 Mth: Building Activity data was reported at 58.580 Point in Oct 2018. This records a decrease from the previous number of 60.659 Point for Sep 2018. Turkey Construction Confidence Index: Past 3 Mth: Building Activity data is updated monthly, averaging 95.380 Point from Jan 2011 (Median) to Oct 2018, with 94 observations. The data reached an all-time high of 119.200 Point in Jul 2011 and a record low of 58.580 Point in Oct 2018. Turkey Construction Confidence Index: Past 3 Mth: Building Activity data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.S004: Sectoral Confidence Indices.

Euro Area - Construction confidence indicator was -2.80% in June of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Euro Area - Construction confidence indicator - last updated from the EUROSTAT on July of 2025. Historically, Euro Area - Construction confidence indicator reached a record high of 10.50% in December of 2021 and a record low of -5.80% in June of 2024.

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

Brazil Construction Confidence Index: IE-CST: CNAE 2.0: Expectations: sa data was reported at 94.800 Index in Apr 2025. This records a decrease from the previous number of 96.000 Index for Mar 2025. Brazil Construction Confidence Index: IE-CST: CNAE 2.0: Expectations: sa data is updated monthly, averaging 96.050 Index from Jul 2010 (Median) to Apr 2025, with 178 observations. The data reached an all-time high of 115.600 Index in Jul 2010 and a record low of 59.700 Index in Apr 2020. Brazil Construction Confidence Index: IE-CST: CNAE 2.0: Expectations: sa data remains active status in CEIC and is reported by Getulio Vargas Foundation. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SH005: Construction Survey.

Graph and download economic data for Business Tendency Surveys: Composite Business Confidence: Economic Activity: Construction for Finland (BCCICP02FIM460S) from Jan 1985 to May 2025 about Finland, business sentiment, composite, construction, and business.

In the second quarter of 2021, the business confidence index in the construction sector in Angola was a measure at minus 14 points. Although still negative, it recovered from the previous quarters. For instance, in Q2 2020, the indicator fell to minus 49 points, indicating a lower level of optimism among executives from the industry.

Graph and download economic data for Business Tendency Surveys: Composite Business Confidence: Economic Activity: Construction for Belgium (BCCICP02BEM460S) from Jan 1985 to May 2025 about Belgium, business sentiment, composite, construction, and business.

Germany - Construction confidence indicator was -13.80% in June of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Construction confidence indicator - last updated from the EUROSTAT on July of 2025. Historically, Germany - Construction confidence indicator reached a record high of 12.70% in November of 2021 and a record low of -18.80% in March of 2024.

Turkey Construction Confidence Index: Next 12 Mth Change: FCI data was reported at 94.986 Point in Jul 2018. This records a decrease from the previous number of 105.585 Point for Jan 2018. Turkey Construction Confidence Index: Next 12 Mth Change: FCI data is updated monthly, averaging 107.384 Point from Jan 2011 (Median) to Jul 2018, with 16 observations. The data reached an all-time high of 117.820 Point in Jan 2011 and a record low of 94.986 Point in Jul 2018. Turkey Construction Confidence Index: Next 12 Mth Change: FCI data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.S004: Sectoral Confidence Indices.

Turkey Construction Confidence Index: Next 3 Mth: Prices Expectation data was reported at 93.872 Point in Oct 2018. This records a decrease from the previous number of 99.081 Point for Sep 2018. Turkey Construction Confidence Index: Next 3 Mth: Prices Expectation data is updated monthly, averaging 108.422 Point from Jan 2011 (Median) to Oct 2018, with 94 observations. The data reached an all-time high of 126.580 Point in Apr 2012 and a record low of 93.055 Point in Nov 2016. Turkey Construction Confidence Index: Next 3 Mth: Prices Expectation data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.S004: Sectoral Confidence Indices.

According to Cognitive Market Research, the global stone flooring market size was valued at USD XX billion in 2024 and is expected to reach USD XX billion at a CAGR of XX% during the forecast period from 2024 to 2029

• The global stone flooring market will grow significantly by XX% CAGR between 2024 to 2029. • Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market. • The report includes an analysis of the regional as well as market trends, key players, application areas, and market growth strategies. • Detailed analysis of Market Drivers, Restraints and Opportunities • Asia Pacific dominated the market and accounted for the highest revenue of XX% in 2023 and it is projected that it will grow at a CAGR of XX% in the future. • The report consists size of the market. Market Dynamics of Stone Flooring Market

Key Drivers

Infrastructural developments are boosting the stone flooring market growth

New construction and home renovation projects are growing as the housing market improves, which encourages developers and homeowners to choose premium materials that raise the value and appeal of their properties. The market for stone flooring is largely driven by new construction, which generates a large demand for flooring options. Natural stone is preferred by both developers and homeowners because of its classic elegance, enduring quality, and capacity to raise the value of a property. Materials like marble, granite, and travertine are commonly used in new residential constructions, ranging from luxury homes to high-rise apartments, because of their ability to improve interior aesthetics and offer long-lasting performance. Stone flooring is preferred for commercial projects with high foot traffic, such as office buildings, hotels, and retail spaces, due to its durability and minimal upkeep needs. Stone flooring's adaptability to different architectural styles and its practicality make it a preferred option, which adds to its continued appeal in the building sector. The demand for stone flooring is anticipated to increase as new construction projects are undertaken worldwide. A common choice among homeowners looking to update and increase the value of their homes is natural stone flooring, such as marble, granite, and travertine. Renovation projects often entail replacing outdated or worn-out flooring with high-quality materials that not only enhance the visual appeal but also provide long-term advantages like easy maintenance and durability.

For Instance, the improvement in the housing market index from 44 in January 2024, to 54 in April 2024, as reported by the National Association of Home Builders, signifies a strengthening housing market, positively single-family sales for the next six months expected to rise from 57 to 60, and prospective buyer traffic, from 29 to 34, reflects growing confidence among builders and buyers. This combination of new construction and renovation, driven by a stronger housing market, significantly boosts demand for stone flooring, highlighting its role in creating luxurious, durable and visually appealing living spaces.

(source https://www.nahb.org/news-and-economics/press-releases/2024/01/builder-sentiment-surges-on-falling-interest-rates)

Rapid Urbanization and Increasing Disposable Income is leading to growth of Stone Flooring market

The trend of global urbanization has increased the need for both residential and non-residential infrastructure construction. Moreover, a number of studies forecast expansion in the US, China, and Indian construction industries. The majority of stone flooring is used in the building and construction sector. Emerging economies have seen a sharp increase in the consumption of these commodities due to rapid urbanization and industrialization.

The need for housing expands along with the population. In residential construction projects, this creates a need for flooring materials. The rising demand for various flooring materials benefits the flooring market, whether for new house constructions or renovations. Interior design, a larger selection of designs, and increased investment in home renovation projects in developing countries. Additionally, floors are a crucial part of the structure because they provide a level, smooth, and aesthetically pleasing surface that enhances the room's atmosphere. As a result, the increasing urban de...

The confidence of the construction sector in the European Union has remained negative since 2023. Nevertheless, the indicator values are not as low as during the start of the COVID-19 pandemic in 2020. Lower values signal that respondents expect construction activity to decline, if they are negative, that means that most of the respondents had negative expectations. Some of the leading construction companies in Europe are ***** (France), *** (Spain) and ******** (France).

Belgium - Construction confidence indicator was -8.40% in May of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Belgium - Construction confidence indicator - last updated from the EUROSTAT on June of 2025. Historically, Belgium - Construction confidence indicator reached a record high of 9.20% in March of 2022 and a record low of -11.80% in July of 2024.

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

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