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.

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

In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the ** largest metro areas in the United States. The Californian metro area received an overall score of **** 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.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

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.

Chinese housing prices rose by over 10 percent per year in real terms between 2003 and 2014 and are now between two and ten times higher than the construction cost of apartments. At the same time, Chinese developers built 100 billion square feet of residential real estate. This boom has been accompanied by a large increase in the number of vacant homes, held by both developers and households. This boom may turn out to be a housing bubble followed by a crash, yet that future is far from certain. The demand for real estate in China is so strong that current prices might be sustainable, especially given the sparse alternative investments for Chinese households, so long as the level of new supply is radically curtailed. Whether that happens depends on the policies of the Chinese government, which must weigh the benefits of price stability against the costs of restricting urban growth.

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.

The consumer price of housing in urban areas of the United States increased by over four percent in 2024. 2022 and 2023 saw the largest price increases on a year-over-year basis since 2000. Meanwhile, 2010 was the only year in which housing prices decreased. One of the main reasons for that may have been the subprime mortgage crisis of 2007. During that period, the value of new residential construction put in place in the U.S. stagnated.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Is China's Property Market Heading toward Collapse?, PIIE Policy Brief 14-21. If you use the data, please cite as: Liu, Li-Gang. (2014). Is China's Property Market Heading toward Collapse?. PIIE Policy Brief 14-21. Peterson Institute for International Economics.

Graph and download economic data for All-Transactions House Price Index for California (CASTHPI) from Q1 1975 to Q2 2025 about appraisers, CA, HPI, housing, price index, indexes, price, and USA.

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 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.

Local interviews: policy and other actors engaged in planning and delivery of sustainable housing. This project will examine the challenges and tensions associated with new housing growth in South Central England. It is concerned with the complex relationships between local communities, government agencies and the house building industry, at a time of market and public policy uncertainty. Using the Milton Keynes/Northamptonshire area as the laboratory, the project investigates the tensions and debates about new housing developments in three periods: before the slow-down in the housing market, during the property crash period, and in the current period of slow growth, public expenditure reductions and radical changes in Government planning and housing policy. Using reports and documents (such as local newspapers and planning reports) and interviews with local authorities, developers and community leaders, it will explore how attitudes to new housing among local policymakers have changed, why some areas find it acceptable and some do not. The research will also examine changes in support for sustainable development. Sustainability was a much-publicized objective of the previous government, with the promise to create "sustainable communities" through better urban design (including low carbon buildings), community-based planning and improved public transport, and remains a core element of contemporary planning policy.

Lehman Brothers, the fourth largest investment bank on Wall Street, declared bankruptcy on the 15th of September 2008, becoming the largest bankruptcy in U.S. history. The investment house, which was founded in the mid-19th century, had become heavily involved in the U.S. housing bubble in the early 2000s, with its large holdings of toxic mortgage-backed securities (MBS) ultimately causing the bank's downfall. The bank had expanded rapidly following the repeal of the Glass-Steagall Act in 1999, which meant that investment banks could also engage in commercial banking activities. Lehman vertically integrated their mortgage business, buying smaller commercial enterprises that originated housing loans, which allowed the bank to expand its MBS holdings. The downfall of Lehman and the crash of '08 As the U.S. housing market began to slow down in 2006, the default rate on housing loans began to spike, triggering losses for Lehman from their MBS portfolio. Lehman's main competitor in mortgage financing, Bear Stearns, was bought by J.P. Morgan Chase in order to prevent bankruptcy in March 2008, leading investors and lenders to become increasingly concerned about the bank's financial health. As the bank relied on short-term funding on money markets in order to meet its obligations, the news of its huge losses in the third-quarter of 2008 further prevented it from funding itself on financial markets. By September, it was clear that without external assistance, the bank would fail. As its losses from credit default swaps mounted due to the deepening crash in the housing market, Lehman was forced to declare bankruptcy on September 15, as no buyer could be found to save the bank. The collapse of Lehman triggered panic in global financial markets, forcing the U.S. government to step in and bail-out the insurance giant AIG the next day on September 16. The effects of this financial crisis hit the non-financial economy hard, causing a global recession in 2009.

The best fit of log-periodic power law parameters.

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.

Skyline Champion (SKY) Riding the Housing Wave: Is This a Dip or a Crash?

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 Hartford-East Hartford-Middletown, CT (MSA) (ATNHPIUS25540Q) from Q4 1977 to Q2 2025 about Hartford, CT, appraisers, HPI, housing, price index, indexes, price, and USA.

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.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.