House Price Volatility Research Articles

How housing affects the association between low income and living conditions-deprivation across Europe

Abstract To explain an emerging trend towards deteriorating living conditions among low-income households across several (West-)European countries, it makes sense to investigate domains of socio-economic regulation that impact on expenditures, rather than incomes. I focus specifically on the domain of housing. Multilevel analyses for 28 countries (EU-SILC) demonstrate that redistributive housing policies such as rental market regulation and housing allowances weaken the cross-sectional (between-country) positive association between a low-income and living conditions-deprivation, while also benefiting living conditions across the broader population. Regarding changes over time, increased uptake of housing allowances throughout the Great Financial Crisis (2008/2009) seems to have shielded in particular renters from deteriorating living conditions, and might have compensated for declining availability of social housing. Higher house prices and price volatility, indicating housing market financialization, are associated with increased living conditions-deprivation for renters and low-income owners, both cross-sectionally and within countries over time. Anti-poverty policies should thus take a broader perspective, and take better account of provision for housing and other basic needs.

The collateral channel: Dynamic effects of housing market on entrepreneurship

This paper examines whether and how housing market prices affect entrepreneurship activities. To this end, we empirically test the hypothetical collateral channel, exploiting a large-scale household-level survey from China. We find that a higher growth rate of house prices is associated with a higher propensity of being self-employed of a family member. We further find that lower house price volatility and higher liquidity in the real estate market boost entrepreneurial choices by households. Overall, the results provide favorable empirical evidence for the collateral channel.

A Study of Factors Affecting Real House Price Volatility

A Study of Factors Affecting Real House Price Volatility

Stochastic volatility forecasting of the Finnish housing market

ABSTRACT The purpose of the article is to assess the in-sample fit and the out-of-sample forecasting performances of four stochastic volatility (SV) models in the Finnish housing market. The competing models are the vanilla SV, the SV model where the latent volatility follows a stationary AR(2) process, the heavy-tailed SV and the SV with leverage effects. The models are estimated using Bayesian technique, and the results reveal that the SV with leverage effects is the best model for modelling the Finnish house price volatility. The heavy-tailed SV model provides accurate out-of-sample volatility forecasts in most of the studied regions. Additionally, the models’ performances are noted to vary across almost all cities and sub-areas, and by apartment types. Moreover, the AR(2) component substantially improves the in-sample fit of the standard SV, but it is unimportant for the out-of-sample forecasting performance. The study outcomes have crucial implications, such as portfolio management and investment decision-making. To establish suitable time-series volatility forecasting models of this housing market, these study outcomes will be compared to the performances of their GARCH models counterparts.

금융시장지수의 한국주택시장 적용성에 관한 연구

The 2007 U.S. subprime mortgage crisis shows the housing market crisis could expand into the financial market. Thus, if there is an early indicator of the housing market crisis, the financial crisis can be predicted in advance. This study calculates a volatility index for housing prices and examines whether the volatility index can account for information in the housing market, such as housing prices and the rate of change. The calculated volatility index confirmed that an increase over a fall in domestic housing prices was the predominant factor, especially the continued upward pressure on housing prices since 2016. On the other hand, the housing market has shifted from an upward trend to a downward trend since the second half of 2018 with each volatile index rising. As such, the ability to explain the housing price volatility indicators calculated for changes in the housing market was identified and the volatility indicators produced by them could be used as evidence to predict the housing market.

Housing Risk and Its Influence on House Price: An Expected Utility Approach

House price is affected by households’ expectation of future house price trend and volatility, where the expected volatility of housing capital return, indicated by variance, is defined as the housing market risk. Theoretically, risk element cannot be directly inserted in the standard housing models because most of the models are built on the underlying assumption of certainty. Extending the life-cycle model to a two-asset expected utility case with uncertainty, we show house price is affected by housing market risk premium, which is a function of households’ risk-aversion coefficient, real housing wealth, and expected housing volatility. Empirical analysis relying on China’s 2001–2018 provincial housing panel data supports the theoretical innovation. Despite the empirical results show that the effect of housing market risk on house price is tiny, simulations suggest that the consideration of risk is quite helpful in analyzing and predicting the long-run house price equilibriums.

The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective

The first-hand house price in Beijing, the capital of China, has skyrocketed with 43 percent annual growth from 2005 to 2017, exerting tremendous adverse effects on people’s livelihood and the development of real estate. Thus, exploring the behavioral mechanism and accurate forecasts of house prices is a critical element in making decisions under uncertain conditions and is of great practical significance for both participants and policymakers in real estate. According to the complex features of house price, including nonlinear, nonstationary, and multiscale, and considering the remarkable time and frequency discrimination capability of multiscale analysis in dealing with house price problems, we develop an ensemble empirical mode decomposition- (EEMD-) based multiscale analysis paradigm to investigate the behavioral mechanism and then obtain accurate forecasts of house prices. Specifically, the monthly house price in Beijing over the period January 2005 to November 2018 is first decomposed into several different time-scale intrinsic-mode functions (IMFs) and a residual via EEMD, revealing some interesting characteristics in house price volatility. Then, we compose the IMFs and residual into three components caused by normal market disequilibrium, extreme events, and the economic environment using the fine-to-coarse reconstruction algorithm. Finally, we propose an improved hybrid prediction model for forecasting house prices. Our experimental results show that the proposed multiscale analysis paradigm is able to clearly reveal the behavioral mechanism hidden in the original house price. More importantly, the mean absolute percentage errors (MAPEs) of the proposed EEMD-based hybrid approach are 5.62%, 7.24%, and 8.63% for one-, three-, and six-step-ahead prediction, respectively, consistently lower than the MAPE of the three competitors.

Volatility clustering, risk-return relationship and asymmetric adjustment in the Finnish housing market

PurposeThe purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018: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 apartment types.Design/methodology/approachBoth Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas.FindingsResults reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types.Research limitations/implicationsThe existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of 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 author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

High-Frequency Volatility Forecasting of US Housing Markets

We propose a logistic smooth transition autoregressive fractionally integrated [STARFI (p, d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy.

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.

Valuing Urban Land with Land Residual and Option Value Methods: Applications to House Price Dynamics and Volatility

Separating urban land and structure values is important for national accounts and for analysis of house price dynamics. A large part of the literature on urban land valuation uses the land residual method, which relies on the assumption that structures are easily replaced. But urban land value depends on accessibility to nearby land uses, implying that infrastructure and the slowly changing built environment are the most important components of land value. Investments in structures are only slowly reversible, implying that land and structure function as a bundled good whereas land residual theory severs the connection between land value and structure value over time. We develop a simple theoretical model that includes risk – and therefore the option to delay – and compare our model to a nested land residual model before and after a shock to values. Cross-sectionally our model shows that land residual theory overestimates structure value; over time almost all of any change in property value is allocated to land residuals. Data from Maricopa county, AZ, 2012–2018 strongly support option value models when nested within a general model that also includes land residuals. FHFA estimates use entirely different cost estimation methods: our analysis of FHA data suggest that our conclusions generalize to the U.S. as a whole, and that high and rising land value ratios (the “hockey stick” pattern found in the literature) are likely an artifact of the residual model. We further show that construction costs are valued by the housing market, suggesting a new use of the construction cost variable.

Economic policy uncertainty, monetary policy, and housing price in China

ABSTRACT This paper analyzes the impact of macroeconomic variables on house price volatility under different regimes of policy uncertainty, incorporating the Economic Policy Uncertainty Index and several Chinese macroeconomic data sets for the period from 1999 to 2014. We adopt a logistic smooth transition vector autoregressive model and a generalized impulse response function. The results show that macroeconomic progress leads to house price growth, which is augmented by policy uncertainty. In addition, the effect of macroeconomic shocks on house price volatility varies under different regimes of policy uncertainty. We find that shocks are asymmetric under regimes of high and low policy uncertainty. Under a high policy uncertainty regime, expansionary quantitative monetary policy can facilitate house price growth, whereas a contractionary monetary policy gives rise to an enduring “Home Price Puzzle,” which makes it difficult to regulate house prices.

Regional Divergence and House Prices

This paper develops a model of the U.S. housing market that explains much of the time series of rents and house prices since World War II. House prices depend on expectations of future rents. We show that rents are tied to regional income inequality, and therefore, house prices are determined by how much faster incomes are growing in richer regions. This theory also matches many cross-sectional facts, including regional variation in rents and prices, differing house price sensitivities to national trends, patterns of inter-state migration, and surveys of income expectations. An industry shift-share instrument provides causal evidence for our channel. The model implies that while interest rates have an ambiguous effect on house price levels, low rates increase house price volatility.

A Study on the Volatility of Housing Prices based on Regional Decoupling Flow in Housing Market

Recently, housing market is experiencing the differential flow of housing prices by region. This study analyzed the volatility of the housing market using data on housing prices and transactions from January 2009 to June 2019 in Busan, Ulsan and Gyeongsangnam-do. As a result of the analysis, housing prices in Seoul were different from those in Ulsan and Gyeongsangnam-do, and there was no significant correlation with Busan. Busan, Ulsan and Gyeongsangnam-do are likely to become sympathetic in terms of fluctuations in housing prices due to their spatial proximity. This is an integrated result of regional factors such as supply and demand conditions, development situation and economic situation in the housing market and national factors such as policy regulation and activation policy. The various housing policies announced in 2009 to solve the problem of unsold housing in the local housing market have influenced the differentiation between housing prices in Seoul and Busan, Ulsan, Gyeongsangnam-do from 2009 to 2012. From 2013 to 2016, housing price volatility in Seoul, Busan and Ulsan moved in the same direction because the housing market has risen nationwide as the government s policy of easing real estate to revitalize the economy and low interest rates have combined. However, there was no correlation between housing price changes in Seoul and Busan, Ulsan, and Gyeongsangnam-do from 2017 to June 2019.

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.

Towards Understanding the Volatility of Housing Prices and Exploring the Tourism Demand Impact

Using an Exponential Generalized Autoregressive Conditional Heteroskedasticity model (EGARCH), the volatile nature of residential property prices in New Zealand is examined. The model investigates the extent to which macroeconomic fundamentals and a combined population shift/visitor factor called "tourism" impact residential property values and capture variations in volatility across different regions. We find that fundamentals have different impacts across regional property markets. In line with the literature, the bigger metropolitan regions like Auckland and Christchurch are more sensitive to macroeconomic factors than smaller regions. Additionally, we find house price volatility in southern regions is affected by macroeconomic fluctuations more than northern areas. Novel to the literature, we discover that "tourism" has significant impact on house price volatility as well, particularly in popular touristic areas. These results imply that to better understand the determinants of house price volatility and mitigate its effects, policy makers should consider a coordinated approach, taking a regional perspective and the interaction of several variables into account.

통화량 변동성과 주택가격 변동성 간 관계에 관한 연구

통화량 변동성과 주택가격 변동성 간 관계에 관한 연구

Personal Experiences and Expectations about Aggregate Outcomes

ABSTRACTUsing novel survey data, we document that individuals extrapolate from recent personal experiences when forming expectations about aggregate economic outcomes. Recent locally experienced house price movements affect expectations about future U.S. house price changes and higher experienced house price volatility causes respondents to report a wider distribution over expected U.S. house price movements. When we exploit within‐individual variation in employment status, we find that individuals who personally experience unemployment become more pessimistic about future nationwide unemployment. The extent of extrapolation is unrelated to how informative personal experiences are, is inconsistent with risk adjustment, and is more pronounced for less sophisticated individuals.

Hedonic pricing models and residual house price volatility

This paper develops a simple method for measuring the residual house price volatility, namely, the house price variation which cannot be attributed to the heterogeneous nature of real estate goods. By creating a quality index of housing characteristics, the method is able to detect “anomalous” situations, in which the sale price does not reflect (in whole or in part) the attributes or qualities that are possessed by the property. In standard market situations, indeed, a higher (lower) price should identify a better (worse) set of housing characteristics. Therefore, we use the hedonic approach by considering that distinction. Furthermore, we estimate a hedonic model where the quality index of housing characteristics is used instead of the housing characteristics. Eventually, this method refines the estimates of marginal prices of housing characteristics and also provides an estimate of the residual house price volatility.

SUPPLY OVERHANG OF AFFORDABLE HOMES: A SURVIVAL ANALYSIS ON HOUSING LOANS APPLICATION

This study aims to investigate the issue of supply overhang of affordable homes and financial exclusion in the Malaysian housing market. It seeks to decipher the issue of unaffordability of homeownership from the financing perspective as offered by the financial institutions in Malaysia. By employing survival analysis via Kaplan-Meier survival estimates for the period covering Q1:2009 to Q4:2014, our findings suggest that higher inflation rate and lower house price volatility may reduce the likelihood for home loans exclusion and thus allow banks to allocate higher loan disbursements. Meanwhile, higher interest rate excludes clients from having access to finance where the compounding effects of interest shows increased hazards of exclusion among the home loans clients. Understanding the determinants of financial exclusion is, therefore, a pertinent issue to be addressed for potential house buyers to be aware of in order to increase the probability of access to financial services. To the financial institutions, identifying the determinants of financial inclusion will assist them to design products that are more marketable. For the policymakers, the findings of the study will shed some light on the policy ramifications towards promoting affordable homeownership in achieving sustainable development goals as outlined by the United Nation.