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 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.

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.

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

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.

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.

Using aggregate quarterly data for the period 1975:Q1-2010:Q4, I find that the US housing market changed from a stable regime with prices determined by fundamentals, to a highly unstable regime at the beginning of the previous decade. My results indicate that these imbalances could have been detected with the aid of real-time econometric modeling. With reference to Stiglitz's general conception of a bubble, I use the econometric results to construct two bubble indicators, which clearly demonstrate the transition to an unstable regime in the early 2000s. The indicators are shown to Granger cause a set of coincident indicators and financial (in)stability measures. Finally, it is shown that the increased subprime exposure during the 2000s can explain the econometric breakdown, i.e.?the housing bubble may be attributed to the increased borrowing to a more risky segment of the market.

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

Safe, reliable, and equitable water access is critical to human health and livelihoods. In this study, we present the first longitudinal analysis of household access to running water—a vital social infrastructure—in the 50 largest US cities since 1970. In the accompanying paper published in Nature Cities, results of the analysis indicate that water access has worsened in an increasing number and typology of US cities since the 2008 global financial crash, disproportionately affecting households of color. We provide evidence to suggest that a ‘reproductive squeeze’—systemic, compounding pressures on households’ capacity to reproduce themselves on a daily and societal basis—is forcing urban households into more precarious living arrangements, including housing without running water, with few signs of abating.This file—which is the supplementary data that underpins the paper—contains the microdata dataset for the manuscript "Urban Inequality, the Housing Crisis and Deteriorating Water Access in US Cities" (Nature Cities). Here, we present customized and improved Public Use Microdata Sample (PUMS) definitions used in our study that enable researchers to compare US Metropolitan Statistical Area (MSA) over time, while minimizing spatial error. The dataset also includes accompanying R code for statistical analysis of census microdata and the creation of static and dynamic spatial visualizations.Parties interested in collaborating on use of the full script may contact the corresponding author (K. Meehan).If you use this dataset or code, please cite as follows: Meehan, Katie, Jason R. Jurjevich, Lucy Everitt, Nicholas M.J.W. Chun, and Justin Sherrill. (2024). Metropolitan Geographic Definitions and Code for "Urban Inequality, the Housing Crisis and Deteriorating Water Access in US Cities.” Tucson, AZ: University of Arizona Research Data Repository. DOI: 10.25422/azu.data.25724286FUNDINGThis research and dataset were supported by a grant selected by the European Research Council and funded by UKRI Horizon Europe Guarantee (Grant No. EP/Y024265/1)For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Affordable Housing Market Analysis The global affordable housing market is projected to reach $1,983.52 billion by 2033, exhibiting a CAGR of 4.71% from 2025 to 2033. The rising population, urbanization, affordability crisis, and supportive government policies are the primary drivers fueling market growth. The increasing demand for affordable single-family homes, multi-family units, and townhouses, coupled with the adoption of innovative construction methods like prefabrication, 3D printing, and sustainable construction, are key trends shaping the market. The market faces restraints such as escalating land and construction costs, regulatory challenges, and the shortage of skilled labor. Nevertheless, the emergence of crowdfunding platforms and non-profit organizations providing financial assistance, as well as government subsidies and tax incentives, are expected to mitigate these constraints. The market is segmented based on housing type, funding source, construction method, and target demographics. D.R. Horton, Taylor Morrison, PulteGroup, Zillow, Hovnanian Enterprises, and Lennar Corporation are notable companies in the global affordable housing market, with operations in key regions like North America, Europe, and Asia Pacific. Recent developments include: Recent developments in the Affordable Housing Market have highlighted the urgent need for innovative housing solutions as governments and organizations strive to address the growing housing crisis exacerbated by economic challenges and population growth. Various nations are prioritizing policies that encourage public-private partnerships to stimulate investment in affordable housing initiatives. Additionally, the integration of sustainable building practices and smart technologies is gaining traction as stakeholders aim to improve energy efficiency while reducing construction costs. Recent collaborations among international entities and local governments focus on leveraging funding for housing projects, particularly in urban areas where demand is surging. Moreover, rising material costs and labor shortages are prompting stakeholders to explore alternative building materials and methods, including modular construction and 3D printing, to streamline processes. These trends underscore a collective commitment to creating equitable housing opportunities while navigating the complexities of market dynamics, aiming for significant progress by 2032. Overall, this evolving landscape reflects a concerted effort to promote affordability, sustainability, and accessibility in housing worldwide.. Key drivers for this market are: Green building technologies adoption Public-private partnerships expansion Innovative financing solutions development Urban regeneration projects implementation Digital platforms for housing access. Potential restraints include: rising urbanization, government initiatives; increasing housing demand; socioeconomic disparities; affordable financing options.

This repository contains the data used to create the county-level figures in the following paper:

Gourevitch, J.D., Kousky, C., Liao, Y., Nolte, C., Pollack, A.B., Porter, J.R., & Weill, J.A. (2023). Unpriced climate risk and the potential consequences of overvaluation in US housing markets. Nature climate change, 13(3), 250-257.

Abstract:

Climate change impacts threaten the stability of the US housing market. In response to growing concerns that increasing costs of flooding are not fully captured in property values, we quantify the magnitude of unpriced flood risk in the housing market by comparing the empirical and economically efficient prices for properties at risk. We find that residential properties exposed to flood risk are overvalued by $121 – $237 billion, depending on the discount rate. In general, highly overvalued properties are concentrated in counties along the coast with no flood risk disclosure laws and where there is less concern about climate change. Low-income households are disproportionately at risk of losing home equity from price deflation, and municipalities that are heavily reliant on property taxes for revenue are vulnerable to budgetary shortfalls. The consequences of these financial risks will depend on policy choices that influence who bears the costs of climate change.

The foreclosure rate in the United States has experienced significant fluctuations over the past two decades, reaching its peak in 2010 at 2.23 percent following the financial crisis. Since then, the rate has steadily declined, with a notable drop to 0.11 percent in 2021 due to government interventions during the COVID-19 pandemic. In 2024, the rate stood slightly higher at 0.23 percent but remained well below historical averages, indicating a relatively stable housing market. Impact of economic conditions on foreclosures The foreclosure rate is closely tied to broader economic trends and housing market conditions. During the aftermath of the 2008 financial crisis, the share of non-performing mortgage loans climbed significantly, with loans 90 to 180 days past due reaching 4.6 percent. Since then, the share of seriously delinquent loans has dropped notably, demonstrating a substantial improvement in mortgage performance. Among other things, the improved mortgage performance has to do with changes in the mortgage approval process. Homebuyers are subject to much stricter lending standards, such as higher credit score requirements. These changes ensure that borrowers can meet their payment obligations and are at a lower risk of defaulting and losing their home. Challenges for potential homebuyers Despite the low foreclosure rates, potential homebuyers face significant challenges in the current market. Homebuyer sentiment worsened substantially in 2021 and remained low across all age groups through 2024, with the 45 to 64 age group expressing the most negative outlook. Factors contributing to this sentiment include high housing costs and various financial obligations. For instance, in 2023, 52 percent of non-homeowners reported that student loan expenses hindered their ability to save for a down payment.

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

Despite a short period of decrease after the burst of the U.S. housing bubble and the global financial crisis, the total amount of mortgage debt in the United States has been on the rise in recent years. In 2024, the mortgage debt amounted to 20.83 trillion U.S. dollars, up from 13.5 trillion U.S. dollars a decade ago. Which factors impact the amount of mortgage debt? One of the most important factors responsible for the growth of mortgage debt is the number of home sales: The more home transactions, the more mortgages are sold, adding to the volume of debt outstanding. Additionally, as house prices increase, so does the gross lending and debt outstanding. On the other hand, high numbers of housing unit foreclosures and mortgage debt restructuring and short-sales can reduce mortgage debt. Which property type has the largest share of the mortgage market? The total mortgage debt includes different property types, such as one-to-four family residential, multifamily residential, commercial, and farm, but the overwhelming share of debt can be attributed to mortgage debt one-to-four family residences.

The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of ***** index points in 2006. In 2024, the housing affordability index measured **** index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached ******* U.S. dollars in 2023.

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 2022. 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.

The manufactured home wholesaling industry continues to provide practical solutions to the persisting housing affordability crisis, with manufactured homes delivering up to 50.0% cheaper housing per square foot compared to site-built residences. Factors such as efficient factory production, bulk purchasing of materials and streamlined labor processes contribute to the lower prices. Demand has significantly increased because of elevated mortgage rates and costly site-built homes, with manufactured home production rising 15.9% in 2024. Simultaneously, the shift in ownership from individual owners to larger institutional investors in Manufactured Housing Communities (MHCs) is modifying the customer dynamics for wholesalers. These investors demand higher quantities, standardization and sophisticated services, necessitating operational expansion and increased efficiency from wholesalers. Through the end of 2025, industry revenue has climbed at a CAGR of 4.2% to reach $45.3 billion in 2025, when revenue is set to gain 2.9%. As the elderly population continues to grow, so does demand for cost-effective housing appropriate for fixed-income households, like manufactured homes. The maintenance concerns associated with conventionally built homes are minimized in manufactured homes, attracting seniors who comprise about 40.0% of buyers. Critical industry reshaping regulatory changes introduced by the HUD and USDA, such as the postponed implementation of updated Manufactured Home Construction and Safety Standards (MHCSS) to September 2025, provide more extensive options for potential buyers, expand financing eligibility and enforce modernized safety and energy efficiency requirements. Through the five years to 2030, the industry will remain resilient and experience continued growth because of the persistent housing affordability crisis and the industry's adaptability to economic fluctuations. Product modernization and technological integration in manufactured homes will fuel expansion. These upgrades, in conformity with consumer preferences for comfort, connectivity, eco-conscious living and regulatory energy and safety standards, broaden the attractiveness to varied buyers. The accelerated demand for advanced glass products, a response to green building practices and stricter energy codes, necessitates that wholesalers ensure a consistent supply of innovative glass solutions. Overall, wholesalers should remain agile, cultivate relationships with institutional investors, maintain inventory quality and diversity and establish strategies to navigate the evolving regulatory landscape and consider buyer market trends. Industry revenue will gain at a CAGR of 2.7% to reach an estimated $51.6 billion in 2030.

Graph and download economic data for All-Transactions House Price Index for Hartford-East Hartford-Middletown, CT (MSA) (ATNHPIUS25540Q) from Q4 1977 to Q1 2025 about Hartford, CT, appraisers, HPI, housing, price index, indexes, price, and USA.

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.

VITAL SIGNS INDICATOR
Fatalities From Crashes (EN4)

FULL MEASURE NAME
Fatalities from Crashes (traffic collisions)

LAST UPDATED
October 2022

DESCRIPTION
Fatalities from crashes refers to deaths as a result of fatalities sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of fatalities sustained as part of this metric. This total fatalities dataset includes fatality counts for the region and counties, as well as individual collision data and metropolitan area data.

DATA SOURCE
National Highway Safety Administration: Fatality Analysis Reporting System - https://www.nhtsa.gov/file-downloads?p=nhtsa/downloads/FARS/
1990-2020

Caltrans: Highway Performance Monitoring System (HPMS) - https://dot.ca.gov/programs/research-innovation-system-information/highway-performance-monitoring-system
Annual Vehicle Miles Traveled (VMT)
2001-2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1990-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1990-2020

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Fatalities from crashes data is reported to the National Highway Traffic Safety Administration through the Fatality Analysis Reporting System (FARS) program. Data for individual collisions is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision and location/jurisdiction of collision (for more information refer to the SWITRS codebook - http://tims.berkeley.edu/help/files/switrs_codebook.doc). For case data, latitude and longitude information for each accident is geocoded by SafeTREC’s Transportation Injury Mapping System (TIMS). Fatalities were normalized over historic population data from the US Census Bureau’s population estimates and vehicle miles traveled (VMT) data from the Federal Highway Administration.

The crash data only include crashes that involved a motor vehicle. Bicyclist and pedestrian fatalities that did not involve a motor vehicle, such as a bicyclist and pedestrian collision or a bicycle crash due to a pothole, are not included in the data.

For more regarding reporting procedures and injury classification, refer to the CHP Manual - https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ca_chp555_manual_2_2003_ch1-13.pdf.