Portugal, Italy, Ireland, Greece, and Spain were widely considered the Eurozone's weakest economies during the Great Recession and subsequent Eurozone debt crisis. These countries were grouped together due to the similarities in their economic crises, with much of them driven by house price bubbles which had inflated over the early 2000s, before bursting in 2007 due to the Global Financial Crisis. Entry into the Euro currency by 2002 had meant that banks could lend to house buyers in these countries at greatly reduced rates of interest.

This reduction in the cost of financing contributed to creating housing bubbles, which were further boosted by pro-cyclical housing policies among many of the countries' governments. In spite of these economies experiencing similar economic problems during the crisis, Italy and Portugal did not experience housing bubbles in the same way in which Greece, Ireland, and Spain did. In the latter countries, their real housing prices (which are adjusted for inflation) peaked in 2007, before quickly declining during the recession. In particular, house prices in Ireland dropped by over 40 percent from their peak in 2007 to 2011.

Home prices fell by 16.7 percent during the Great Recession of 2007 to 2009 in the United States. However, such a significant decrease in prices did not happen in the other four recessions which have occurred since 1980.

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

Understanding the Dynamics and Implications of a Housing Market Recession

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q1 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q1 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

This paper studies the role of the Federal Reserve's policy in the recent boom and bust of the housing market, and in the ensuing recession. By estimating a structural dynamic factor model on a panel of 109 US quarterly variables from 1982 to 2010, we find that, although the Federal Reserve's policy between 2002 and 2004 was slightly expansionary, its contribution to the recent housing cycle was negligible. We also show that a more restrictive policy would have smoothed the cycle but not prevented the recession. We thus find no role for the Federal Reserve in causing the recession.

In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the 50 largest metro areas in the United States. The Californian metro area received an overall score of 72.8 percent, which was compiled after factors such as home price volatility and average home loan-to-value ratio were examined.

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.

Looming Shadows: A Deep Dive into the Housing Market Recession

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

The year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.

We investigate the effect of house prices on household borrowing using administrative mortgage data from the United Kingdom and a new empirical approach. The data contain household-level information on house prices and borrowing in a panel of homeowners, who refinance at regular and quasi-exogenous intervals. The data and setting allow us to develop an empirical approach that exploits house price variation coming from the idiosyncratic and exogenous timing of refinance events around the Great Recession. We present two main results. First, there is a clear and robust effect of house prices on borrowing. Second, the effect of house prices on borrowing can be explained largely by collateral effects. We study the collateral channel through a multivariate and nonparametric heterogeneity analysis of proxies for collateral and wealth effects.

Graph and download economic data for All-Transactions House Price Index for Reno, NV (MSA) (ATNHPIUS39900Q) from Q2 1978 to Q1 2025 about Reno, NV, appraisers, HPI, housing, price index, indexes, price, and USA.

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US Residential Construction Market Size 2025-2029

The US residential construction market size is forecast to increase by USD 242.9 million at a CAGR of 4.5% between 2024 and 2029.

The Residential Construction Market in the US is experiencing significant growth driven by increasing household formation rates and a rising focus on sustainability in new projects. According to the latest data, household formation is projected to continue growing at a steady pace, fueling the demand for new residential units. This trend is particularly evident in urban areas, where population growth and limited space for new development are driving up demand. Meanwhile, the emphasis on sustainability in residential construction is transforming the market landscape. With consumers increasingly prioritizing energy efficiency and eco-friendly features in their homes, builders and developers are responding by incorporating green technologies and sustainable materials into their projects.
This shift not only appeals to environmentally-conscious consumers but also offers long-term cost savings and regulatory compliance benefits. However, the market is not without challenges. Skilled labor shortages continue to pose a significant hurdle for large-scale residential real estate projects. The ongoing shortage of skilled laborers, including carpenters, electricians, and plumbers, is driving up labor costs and delaying project timelines. To mitigate this challenge, some builders are exploring alternative solutions, such as modular construction and automation, to streamline their operations and reduce their reliance on traditional labor sources. The Residential Construction Market in the US presents significant opportunities for companies seeking to capitalize on the growing demand for new housing units and the shift towards sustainability.
However, navigating the challenges of labor shortages and rising costs will require innovative solutions and strategic planning. By staying informed of market trends and adapting to evolving consumer preferences, companies can effectively position themselves for success in this dynamic market.

What will be the size of the US Residential Construction Market during the forecast period?

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The residential construction market in the United States continues to exhibit dynamic activity, driven by various economic factors. Housing supply remains a key focus, with ongoing discussions surrounding the affordable housing trend and efforts to increase inventory, particularly for single-family homes and new constructions. Mortgage and federal funds rates have an impact on residential investment, with fluctuations influencing buyer decisions and construction costs. The labor market plays a crucial role, as workforce availability and wages affect both housing starts and cancellation rates. Inflation and interest rates, monitored closely by the Federal Reserve, also shape the market's direction. Recession risks and economic conditions influence construction spending across various sectors, including multifamily and single-family homes.
Federal programs, such as housing choice vouchers and fair housing initiatives, continue to support home buyers and promote equitable housing opportunities. Building permits and housing starts serve as essential indicators of market health and future growth, with some sectors experiencing double-digit growth. Overall, the residential construction market in the US remains a significant economic driver, shaped by a complex interplay of economic, demographic, and policy factors.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Apartments and condominiums
  Luxury Homes
  Other types


Type

  New construction
  Renovation


Application

  Single family
  Multi-family


Construction Material

  Wood-framed
  Concrete
  Steel
  Modular/Prefabricated


Geography

  US

By Product Insights

The apartments and condominiums segment is estimated to witness significant growth during the forecast period.

The residential construction market in the US is experiencing growth in both the apartment and condominium sectors, driven by the increasing trend toward urbanization and changing lifestyle preferences. Apartments, typically owned by property management companies, and condominiums, with individually owned units within a larger complex, contribute significantly to the market. The Federal Reserve's influence on the economy through the federal funds rate and mortgage rates impacts borrowing rates and home construction activity. The affordability of housing, particularly for younger generations, is a concern due to factors such as inflation, labor market conditions, and savings

The homeownership rate in the United States declined slightly in 2023 and remained stable in 2024. The U.S. homeownership rate was the highest in 2004 before the 2007-2009 recession hit and decimated the housing market. In 2024, the proportion of households occupied by owners stood at **** percent in 2024, *** percentage points below 2004 levels. Homeownership since the recession The rate of homeownership in the U.S. fell in the lead up to the recession and continued to do so until 2016. Despite this trend, the share of Americans who perceived homeownership as part of their personal American dream remained relatively stable. This suggests that the financial hardship caused by the recession led to the fall in homeownership, rather than a change in opinion about the importance of homeownership itself. What the future holds for homeownership Homeownership trends vary from generation to generation. Homeownership among Americans over 65 years old is declining, whereas most Millennial renters plan to buy a home in the near future. This suggests that homeownership will remain important in the future, as Millennials are forecast to head most households over the next two decades.

The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

Subprime and the collapse of the U.S. mortgage market

The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

Market Panic and The Great Recession

As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

This paper studies the impact of unemployment insurance (UI) on the housing market. Exploiting heterogeneity in UI generosity across US states and over time, we find that UI helps the unemployed avoid mortgage default. We estimate that UI expansions during the Great Recession prevented more than 1.3 million foreclosures and insulated home values from labor market shocks. The results suggest that policies that make mortgages more affordable can reduce foreclosures even when borrowers are severely underwater. An optimal UI policy during housing downturns would weigh, among other benefits and costs, the deadweight losses avoided from preventing mortgage defaults.

Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

It can be seen that the Northern Ireland house price index was 202.5 points in the third quarter of 2007, but during the global recession it decreased and never re-gained the pre-crash levels. As of the third quarter of 2023 the residential property price index in Northern Ireland was 161.8, up from 158.5 in the same period in the previous year.

The surge in credit and house prices that preceded the Great Recession was particularly pronounced in ZIP codes with a higher fraction of subprime borrowers (Mian and Sufi, 2009). We present a simple model with prime and subprime borrowers distributed across geographic locations, which can reproduce this stylized fact as a result of an expansion in the supply of credit. Due to their low income, subprime households are constrained in their ability to meet interest payments and hence sustain debt. As a result, when the supply of credit increases and interest rates fall, they take on disproportionately more debt than their prime counterparts, who are not subject to that constraint.