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.

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.

In 2024, Miami was the housing market most at risk, with a real estate bubble index score of 1.79. Tokyo and Zurich followed close behind with 1.67 and 1.51, respectively. Any market with an index score of 1.5 or higher was deemed to be a bubble risk zone.

Home prices in the U.S. reach new heights The American housing market continues to show remarkable resilience, with the S&P/Case Shiller U.S. National Home Price Index reaching an all-time high of 325.78 in July 2024. This figure represents a significant increase from the index value of 166.24 recorded in January 2015, highlighting the substantial growth in home prices over the past decade. The S&P Case Shiller National Home Price Index is based on the prices of single-family homes and is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The S&P Case Shiller National Home Price Index series also includes S&P/Case Shiller 20-City Composite Home Price Index and S&P/Case Shiller 10-City Composite Home Price Index – measuring the home price changes in the major U.S. metropolitan areas, as well as twenty composite indices for the leading U.S. cities. Market fluctuations and recovery Despite the overall upward trend, the housing market has experienced some fluctuations in recent years. During the housing boom in 2021, the number of existing home sales reached the highest level since 2006. However, transaction volumes quickly plummeted, as the soaring interest rates and out-of-reach prices led to housing sentiment deteriorating. Factors influencing home prices Several factors have contributed to the rise in home prices, including a chronic supply shortage, the gradual decline in interest rates, and the spike in demand during the COVID-19 pandemic. During the subprime mortgage crisis (2007-2010), the construction of new homes declined dramatically. Although it has gradually increased since then, the number of new building permits, home starts, and completions are still shy from the levels before the crisis. With demand outweighing supply, competition for homes can be fierce, leading to bidding wars and soaring prices. The supply of existing homes is further constrained, as homeowners are less likely to sell and move homes due to the worsened lending conditions.

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.

Borrowing against the increase in home equity by existing homeowners was responsible for a significant fraction of the rise in US household leverage from 2002 to 2006 and the increase in defaults from 2006 to 2008. Instrumental variables estimation shows that homeowners extracted 25 cents for every dollar increase in home equity. Home equity-based borrowing was stronger for younger households and households with low credit scores. The evidence suggests that borrowed funds were used for real outlays. Home equity-based borrowing added $1.25 trillion in household debt from 2002 to 2008, and accounts for at least 39 percent of new defaults from 2006 to 2008. JEL: D14, R31

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q4 2024 about sales, median, housing, and USA.

Zimmer (?The role of copulas in the housing crisis?, Review of Economics and Statistics 2012; 94: 607-620) provides an interesting case study of the pitfalls of using parametric copulas to understand the US housing crisis in the latter part of 2000s. The original study by Zimmer (2012) employs a finite-mixture copula to illustrate that the symmetry of the Gaussian copula may not be tenable, especially for US housing price data during the time period from 1975:Q2 to 2009:Q1. We undertake a replication of his study in a wide sense. First, we replicate the study by incorporating revised data and then extending the dataset to include the most recent data. Second, we implement a nonparametric copula estimator recently proposed by Racine (?Mixed data kernel copulas?, Empirical Economics forthcoming) to the parametrically filtered data used in Zimmer (2012). Our replication finds that the application of the nonparametric copula to the same and extended filtered data provides an alternative flexible specification for copulas. However, the overall cautionary message of the flexible-form copula espoused in Zimmer (2012) remains.

Graph and download economic data for All-Transactions House Price Index for Hartford-East Hartford-Middletown, CT (MSA) (ATNHPIUS25540Q) from Q4 1977 to Q4 2024 about Hartford, CT, appraisers, HPI, housing, price index, indexes, price, and USA.

Context

Data augmentation for housing prices

Content

US Housing Data for 2008-2009 (pre crisis and crisis year) to predict housing prices more accurate

Inspiration

Housing price prediction competition on Kaggle

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 ForKids Housing Crisis Hotline is the central point of contact for all persons experiencing a housing crisis throughout Southeastern Virginia. The Hotline connects individuals in crisis to the region’s extended network of resources for assistance and to prevent them from falling into homelessness whenever possible. This dataset shows, by month, the purpose of calls serviced by the Housing Crisis Hotline for callers from Norfolk. This dataset will be updated monthly.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q4 2024 about appraisers, HPI, housing, price index, indexes, price, and USA.

This data package includes the underlying data files to replicate the data and charts presented in Why China's housing policies have failed, PIIE Working Paper 23-5.

If you use the data, please cite as: Huang, Tianlei. 2023. Why China's housing policies have failed. PIIE Working Paper 23-5. Washington, DC: Peterson Institute for International Economics.

The number of home sales in the United States peaked in 2021 at almost seven million after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to 4.8 million. 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.

Moderate rising of house prices are beneficial to the economic development. However, over high house prices worsen the economic distortions and thus hinder the development of the real economy. We use the stochastic frontier models to calculate the fundamental value in the housing in Chinese large and medium cities, and then obtain indexes which could measure the house prices’ deviations from the fundamental value. With the macroeconomic data in the city-level, this paper empirically investigates the effects of the house prices’ deviations on macro-economic variables like consumption, investment and output. The study reveals that the housing bubble exists in most Chinese cities, and first-tier cities fare the worst. House prices over the fundamental value, which could increase the scale of real estate investment, bring adverse impacts on GDP, as it causes declining civilian consumption and discourages real economy’s investment and production. The encouragement and the discouragement on macroeconomy caused by house prices’ deviation from its basic value take turns to play a key role in the process of China’ eco-nomic growth. In the early stage of China’s economic growth, the encouragement effect predominates. As urbanization and industrialization gradually upgrade to a higher level, the discouragement effect takes charge.

Financial overview and grant giving statistics of Jackson House Crisis Intervention

Moderate rising of house prices are beneficial to the economic development. However, over high house prices worsen the economic distortions and thus hinder the development of the real economy. We use the stochastic frontier models to calculate the fundamental value in the housing in Chinese large and medium cities, and then obtain indexes which could measure the house prices’ deviations from the fundamental value. With the macroeconomic data in the city-level, this paper empirically investigates the effects of the house prices’ deviations on macro-economic variables like consumption, investment and output. The study reveals that the housing bubble exists in most Chinese cities, and first-tier cities fare the worst. House prices over the fundamental value, which could increase the scale of real estate investment, bring adverse impacts on GDP, as it causes declining civilian consumption and discourages real economy’s investment and production. The encouragement and the discouragement on macroeconomy caused by house prices’ deviation from its basic value take turns to play a key role in the process of China’ eco-nomic growth. In the early stage of China’s economic growth, the encouragement effect predominates. As urbanization and industrialization gradually upgrade to a higher level, the discouragement effect takes charge.

Replication material for Jackelyn Hwang & Bina Patel Shrimali (2022) Shared and Crowded Housing in the Bay Area: Where Gentrification and the Housing Crisis Meet COVID-19, Housing Policy Debate, DOI: 10.1080/10511482.2022.2099934

Paper Abstract: Amid the growing affordable housing crisis and widespread gentrification over the last decade, people have been moving less than before and increasingly live in shared and often crowded households across the U.S. Crowded housing has various negative health implications, including stress, sleep disorders, and infectious diseases. Difference-in- difference analysis of a unique, large-scale longitudinal consumer credit database of over 450,000 San Francisco Bay Area residents from 2002 to 2020 shows gentrification affects the probability of residents shifting to crowded households across the socioeconomic spectrum but in different ways than expected. Gentrification is negatively associated with low- socioeconomic status (SES) residents’ probability of entering crowded households, and this is largely explained by increased shifts to crowded households in neighborhoods outside of major cities showing early signs of gentrification. Conversely, gentrification is associated with increases in the probability that middle-SES residents enter crowded households, primarily in Silicon Valley. Lastly, crowding is positively associated with COVID-19 case rates, beyond density and socioeconomic and racial composition in neighborhoods, although the role of gentrification remains unclear. Housing policies that mitigate crowding can serve as early interventions in displacement prevention and reducing health inequities.

This dataset is about book subjects and is filtered where the books includes Garden villages : empowering localism to solve the housing crisis, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).