Housing Market Volatility Research Articles

Housing return volatility in large metropolitan areas in the United States across market cycles (2000–2022)

This study compares the inter- and intra-metropolitan patterns of housing return volatility in four market phases (boom, bust, recovery, and pandemic) in the United States between 2000 and 2022. The study finds a weak correlation between the physical characteristics of neighborhoods and housing market volatility. Notably, higher-density neighborhoods show greater market volatility throughout the boom, bust, and recovery phases but the trend is reversed during the pandemic period. However, the sociodemographic composition of neighborhoods exhibits a stronger correlation with market volatility. Specifically, housing markets in Black neighborhoods demonstrate persistently higher volatility, irrespective of market fluctuations. Similarly, low-income neighborhoods are consistently associated with greater volatility before the pandemic. The study does not find a consistent association between housing supply and market volatility across the four market phases. It shows that during the 2008 housing downturn, housing markets were less volatile in metropolitan areas with stricter land use regulations, less developable land, fewer home constructions, and better natural amenities.

HOUSING MARKET VOLATILITY, SHADOW BANKS AND MACROPRUDENTIAL REGULATION: A DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODEL ANALYSIS

Given the risks of shadow banks and their risk contagion with the housing market, this study investigates the necessity, policy transmission mechanism and impact of incorporating shadow banks into a macroprudential regulation framework. It constructs a dynamic stochastic general equilibrium (DSGE) model incorporating housing market volatility and heterogeneous financial intermediation, where housing market volatility transmits to the financial intermediation sector through the collateral channel. Meanwhile, regulatory asymmetry expands the shadow banking scale and leverage indicators driven by the housing market, increasing the shadow banking risk. Moreover, considering shadow banking credit size, the macroprudential regulation framework can mitigate the volatility of economic variables and maintain macro-financial and economic stability under the conditions of tightening monetary policy and strengthening regulation. However, policy effects are limited in curbing shadow banking risks from housing market fluctuations. Thus, it is necessary to crop the source of shadow banking risks from the transmission mechanism, formulate more targeted and specialized regulatory policies to complement housing market control measures, maximize cross-market effects among different policies, and incorporate shadow banking into the regulatory system.

Housing market volatility under COVID-19: Diverging response of demand in luxury and low-end housing markets

The ongoing pandemic has led to substantial volatility in residential housing markets. However, relatively little is known about whether the volatility is dominated by housing demand or supply, and how different priced markets contribute to the volatility. This article first examines the temporal effect of COVID-19 on house prices, housing demand, and supply in Los Angeles, and second explores the effect heterogeneity in luxury and low-end housing markets within the city. For identification, the article employs a revised difference-in-differences (DID) method that controls more rigorously for unobservables and improves on the traditional DID with smaller prior trends. Using individual level data, the result first shows that, in response to the outbreak, house prices, demand, and supply all decreased in March to May 2020 and increased in July and August 2020, with demand dominating the process. Second, the heterogeneity exploration identifies diverging COVID-19 impacts in higher- and lower- priced markets. Particularly, the decline in overall price and demand before June originates mainly from the lower-priced market while the higher-priced one experienced limited changes in demand. After July, higher-priced markets led housing market’s surge in price, demand, and supply, whereas the lower-priced market has not fully recovered from decreases in house prices and housing demand. Finally, a larger price decline in lower-priced markets is found to be associated with higher service shares and lower homeownership rates. The results not only facilitate market participants in their decision making but also aid local governments in formulating policies and allocating subsidies to mitigate the effects of the outbreak.

Spatial Analysis and Modeling of the Housing Value Changes in the U.S. during the COVID-19 Pandemic

COVID-19 has affected almost all sectors of the economy, including the real estate markets across different countries in the world. A rich body of literature has emerged in analyzing real estate market trends and revealing important information. However, few studies have used a spatial perspective to investigate the impact of COVID-19 on property values. The main purposes of this study are as follows: (1) to explore the spatial distribution and spatial patterns of housing price changes during the COVID-19 pandemic crisis in the U.S. real estate market and (2) to model the spatially nonstationary relationships between the housing price change and COVID-19 characteristics. We find that housing price changes differ across space and appear associated with the spatial distribution of the COVID-19 case rates. The housing market volatility is amplified by the uneven distribution of some socioeconomic factors. The spatially uneven housing price changes may bring an uneven spillover effect to the rest of the economy and lead to divergence in economic growth across different areas.

금융시장 변동성과 주택시장 연계성에 관한 연구

Using the VAR model, this paper analyzes empirically relationship among financial mrket - using interest rate, stock price, and exchange rate – volatility. Also this study investigates the relationship among financial market volatility, housing market volatility, and price volatility. To achieve this research purpose, interest rate volatility, stock price volatility and exchange rate volatility were used as variables of financial market volatility. The results of analysis are as follows. Firstly, exchange rate volatility was found to be the largest among in the financial market, followed by stock price, and interest rate. The volatility of the financial market before and after the IMF foreign exchange crisis was greater in the post period than in the pre-IMF foreign exchange crisis period. The volatility of the financial market are found to be rather greater in post period than in the pre-IMF foreign exchange crisis period. Secondly, the correlation among financial market volatilities was found to have the highest correlation between stock price volatility and exchange rate volatility. It is found to be strong the correlation between interest rate volatility and housing market volatility. The relationship between financial market volatility and housing market volatility was found to be statistically significant in the post-IMF foreign exchange crisis period. Third, the relationship among financial markets is revealed to be high, and among financial market volatilities, the impact of exchange rate volatility was found to have the greatest effect on the increase of stock price volatility. Increasing interest rate volatility was found to increase uncertainty in the housing market after the IMF foreign exchange crisis period. Therefore, it is necessary to manage interest rate volatility as a key indicator to stabilize the housing market.

Post-crisis developments in young adults’ housing wealth

How has housing wealth inequality changed for young-adult households in the post-financial crisis period, and what is driving such change? We chart a path for subsequent studies by analysing the previously unexamined post-crisis housing wealth profile of young adults via different angles and using multiple inequality measures. Using household micro-data for 11 European countries ( Household Finance and Consumption Survey, 2010–2017) and the United States ( Survey of Consumer Finances, 2010–2016), we find that the accumulation of housing assets for 22–44 year olds is unevenly concentrated among high-income homeowners, over and above what would be expected given the well-known decline in homeownership. We describe and assess several potential drivers for these wealth profile changes, finding that the current explanations offered in the literature do not adequately account for the unequal wealth profile of young people. We conclude that a mix of dynamics, including housing market volatility, housing market configurations leading to uneven capital gains and losses, and the increased social selectivity of homeownership intersect to shape the ways that young adults navigate the housing market in post-crisis times.

Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment

This paper examines the predictive ability of housing-related sentiment on housing market volatility for 50 states, District of Columbia, and the aggregate US economy, based on quarterly data covering 1975:3 and 2017:3. Given that existing studies have already shown housing sentiment to predict movements in aggregate and state-level housing returns, we use a k-th order causality-in-quantiles test for our purpose, since this methodology allows us to test for predictability for both housing returns and volatility simultaneously. In addition, this test being a data-driven approach accommodates the existing nonlinearity (as detected by formal tests) between volatility and sentiment, besides providing causality over the entire conditional distribution of (returns and) volatility. Our results show that barring 5 states (Connecticut, Georgia, Indiana, Iowa, and Nebraska), housing sentiment is observed to predict volatility barring the extreme ends of the conditional distribution. As far as returns are concerned, except for California, predictability is observed for all of the remaining 51 cases.

Spatial inequality of housing value changes since the financial crisis

The financial crisis has dramatically reshaped the map of inequality; in particular, wealth has been redistributed because of fluctuations in the prices of equities and housing. However, since the explanation of this issue has by default been seen as economists' responsibility, the spatial dimension of the financial crisis still remains unexplored, especially at the intra-urban level. This study examines the local geographies of the housing value bust (2008–2012) and boom (2012–2016) since the financial crisis, with an explicit emphasis on their impacts on urban inequality in Salt Lake County, Utah. We find that housing value changes differ across space and appear strongly associated with the spatial distribution of neighborhood conditions and urban amenities. Additional regressions confirm that a city's housing market volatility is amplified by uneven distribution of physical and service amenities and residential segregation. Moreover, the significance level of local attributes changes over the bust-boom cycle of the housing market. The comparison between bust and boom models suggests that the value of houses enjoying shade from trees and proximity to jobs, places of worship, and good public schools are more resilient, experiencing less value drop in an economic bust, but also less increase in a boom. Other amenities such as public transport, hospitals, parks, and restaurants could be interpreted as types of discretionary consumption, which positively contribute to housing value volatility. Neighborhood conditions, especially white-Hispanic segregation, significantly contribute to housing value fluctuation. Hispanic communities in Salt Lake County tend to experience more loss of property value in a bust and gain more in a boom. Thus, our study suggests that a more balanced urban distribution of employment, races, and amenities would significantly enhance local economic stability by smoothing fluctuations of business cycles at the local level.

Long-range dependence in the returns and volatility of the Finnish housing market

PurposeThe purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types.Design/methodology/approachFor each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d,q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns.FindingsA higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour.Research limitations/implicationsInvestigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.Originality/valueTo the best of the authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.

Skilled migration and business cycle dynamics

Shocks to net migration matter for the business cycle. Using a structural vector autoregression and an estimated dynamic stochastic general equilibrium (DSGE) model of a small open economy, we find that migration shocks make an important contribution to the volatility of per capita GDP. Migration shocks contribute to variability in per capita consumption and investment, and to residential investment and real house prices. Despite the role of migration, other shocks remain more important drivers of these expenditure components and of housing market volatility. In the DSGE model, the level of human capital possessed by migrants relative to that of locals materially affects the business cycle impact of migration. The impact of migration shocks is larger when migrants have substantially different levels of human capital relative to locals. When the average migrant has more human capital than locals, as seems to be common for migrants into developed economies, a migration shock has an expansionary effect on per capita GDP and its components.

A Study on the Asymmetric Effect to Housing Market Price Volatility

The objective of this paper empirically analyzes the relations between information and housing market volatility using the housing price index of Seoul, San Francisco and Los Angeles for the time period from January 1995 to July 2019. For the empirical test of the asymmetric effect of information on housing market volatility, this paper employs GJR-GARCH model which enable good information and bad information to have impact on volatility. The analysis results are as follows. First, it was found that the GJR-GARCH (1,1) model is suitable for analyzing the asymmetric reaction of housing price volatility for information types. Second, it was found that for information types, Seoul, San Francisco, and Los Angeles all displayed asymmetric housing price volatility. It was found that Seoul reacted greater to volatility for unexpected positive earnings rate information than unexpected negative earnings rate information, while on the contrary, San Francisco and Los Angeles showed that they reacted greater to unexpected negative earnings rate information than to unexpected positive earnings rate information. These findings support the hypothesis. Third, for sensitivity to volatility, Seoul was found to be about five times higher than San Francisco and Los Angeles. It is necessary to differentiate the housing price volatility prediction model and portfolio composition according to the information type.

Migration and Business Cycle Dynamics

Shocks to net migration matter for the business cycles of some countries. Using an estimated dynamic stochastic general equilibrium (DSGE) model of a small open economy and a structural vector autoregression, we find that migration shocks account for a considerable proportion of the variability of per capita GDP. Migration shocks matter for the capital investment and consumption components of per capita GDP, but they are not the most important driver. Migration shocks are also important for residential investment and real house prices, but other shocks play a larger role in driving housing market volatility. In the DSGE model, the level of human capital possessed by migrants relative to that of locals materially affects the business cycle impact of migration. The impact of migration shocks is larger when migrants have substantially different levels of human capital relative to locals. When the average migrant has higher levels of human capital than locals, as seems to be common in most OECD economies, a migration shock has an expansionary effect on per capita GDP and its components.

Expectation, Speculation and UK House Pricing Fluctuation

The research aims at understanding the housing price fluctuations to examine what measures can be taken to reduce the housing market volatility and control the persistent boom and bust cycles in the UK housing market. In past research, several models have been developed to measure housing price volatility, such as the Generalized Auto Regressive Conditional Heteroscedasticity. This research is of seminal importance for future policy makers, and the Government to understand how to regulate the volatility of the UK housing market.

Housing market volatility connectedness among G7 countries

ABSTRACTThis study investigates international linkages among housing markets in the G7 countries, using the connectedness methodology developed in Diebold and Yilmaz (2012, 2015). We find that volatility connectedness varies over the business cycle, with a surge during the global financial crisis. We also show that the United States and Italy were major net transmitters of housing market volatility shocks to other countries during the global financial crisis and the European debt crisis, respectively.

Legacies of homeownership: Housing wealth and bequests

This paper examines the effect of housing price shocks during the recent housing boom and bust on the bequest behavior of older homeowners. Prior studies find that illiquid assets have a strong expected influence on bequests, which suggests volatile housing markets will alter household bequest expectations, particularly since housing is generally the largest share of household wealth. I calculate shocks to housing wealth as the deviation in values from their growth trend over the last three years, and I exploit the geographic variation in housing market volatility to explore the relationship between housing value shocks and bequest expectations over the housing boom and bust. I find that housing price shocks, and especially negative shocks, have a consistent influence on bequest expectations for homeowners, particularly for older homeowners, and for those without stocks. A complementary analysis of elasticities also reveals a larger influence of housing values on bequests than other forms of wealth.

Global Housing Markets and Monetary Policy Spillovers: Evidence from OECD Countries

What are the driving forces of housing market volatilities across countries within the context of financial globalization? To address this broad question, we integrate the Campbell-Shiller decomposition with a dynamic factor model and apply this approach to the housing price-rent ratios in 17 OECD countries. Our novel approach allows us not only to assess geographically the relative importance of global and country-specific factors in explaining the housing market volatilities, but also to distinguish economically between those housing market volatilities attributable to different economic driving forces including the expected rent growth, the expected risk free rate, and the housing risk premium, within global and country-specific factors respectively. We find that the housing market volatility for an average country is mainly driven by the global factors, especially during the years leading up to the 2007-2008 financial crisis. Furthermore, among the global factors it is the global housing risk premium component that is primarily responsible for the housing market volatility. Using a Structural Vector-Autoregressive (SVAR) model identified through the instrumental variable method, we find that an unexpected U.S. monetary policy tightening is typically followed by a persistent and statistically significant rise in the global housing risk premium with some lag to a run-up of the U.S. housing risk premium. Moreover, the local factor in U.S. housing risk premium tends to go up sharply around 2 years before a U.S. recession, and leads the global housing risk premium. Our findings are broadly in line with the credit or risk-taking channel of the monetary policy spillovers from the United States to the global financial markets.

Understanding Housing Market Volatility

The Campbell–Shiller present value formula implies a factor structure for the price–rent ratio of housing market. Using a dynamic factor model, we decompose the price–rent ratios of 23 major housing markets into a national factor and independent local factors, and we link these factors to the economic fundamentals of the housing markets. We find that a large fraction of housing market volatility is local and that the national factor has become more important than local factors in driving housing market volatility since 1999, consistent with the findings in Del Negro and Otrok (2007). The local volatilities mostly are due to time variations of idiosyncratic housing market risk premia, not local growth. At the aggregate level, the growth and interest rate factors jointly account for less than half of the total variation in the price–rent ratio. The rest is due to the aggregate housing market risk premium and a pricing error. We find evidence that the pricing error is related to money illusion, especially at the onset of the recent housing market bubble. The rapid rise in housing prices prior to the 2008 financial crisis was accompanied by both a large increase in the pricing error and a large decrease in the housing market risk premium.

Housing market volatility in the OECD area: Evidence from VAR based return decompositions

Vector-autoregressive models are used to decompose housing returns in 18 OECD countries into cash flow (rent) news and discount rate (return) news over the period 1970–2011. For the majority of countries news about future returns is the main driver, and both real interest rates and risk-premia play an important role in accounting for housing market volatility. Bivariate cross-country correlations and principal components analyses indicate that part of the return movements have a common factor among the majority of countries. We explain the results in terms of global changes in credit constraints and transactions costs as well as changes in monetary policy over this period. Among other things, our results shed new light on whether excessively low interest rates by the monetary authorities was a major cause for the housing boom up to 2006.

Housing Wealth, Psychological Well-being, and Cognitive Functioning of Older Americans

Economic security around retirement age may be an important determinant of psychological and cognitive well-being of older adults. This study examines the impact of the dramatic increase in housing prices from the mid-1990s to the mid-2000s on psychological and cognitive outcomes among Americans born between 1924 and 1960. Our quasi-experimental empirical strategy exploits geographic differences in housing market price trends during the housing boom (from the mid-1990s until 2006). We use individual-level data from the Health and Retirement Study (HRS) and estimates of housing values from DataQuick, a California-based real estate consultancy firm, to estimate the association of housing price increases with psychological and cognitive outcomes at follow-up. Greater housing appreciation over the follow-up period was associated with a significantly lower risk of anxiety (for women) and an improved performance on some but not all cognitive tasks. Effects for depressive symptoms, positive and negative affect, and life satisfaction were all in the beneficial direction but not statistically significant. The effects of price run-ups were concentrated on homeowners, as opposed to renters, suggestive of wealth-driven effects. Housing market volatility may influence the psychological and cognitive health of older adults, highlighting potential health consequences of pro-home ownership policies, which may be especially important in light of recent dramatic housing price declines.

The Lead-Lag Relationship between Stock Market Volatility and Housing Market Volatility in the U.S.

The Lead-Lag Relationship between Stock Market Volatility and Housing Market Volatility in the U.S.