Housing Returns Research Articles

Model Risk in Risk Analysis for No-Negative-Equity-Guarantees

Understanding the risk for No-Negative-Equity-Guarantees (NNEGs) requires the proper modeling of the housing return, interest rate, and mortality rate dynamics. This article investigates the model risk for the risk measures of NNEGs by calculating the Value-at-Risk (VaR) and Conditional-Tail-Expectation (CTE) from the provider perspective, with an emphasis on the housing price return model. Therefore, we propose a jump ARMA-GARCH model, according to nationwide house price return data in the UK. Interest rate and mortality rate dynamics are assumed to follow the CIR model (Cox et al. 1985) and the CBD model (Cairns et al. 2006) respectively. Our numerical analyses reveal that the housing price risk, interest-rate risk, and longevity risk can affect the VaR and CTE of NNEGs, with the impact being as significant as that for housing risk. The VaR and CTE of NNEGs will be greater for female borrowers than for male borrowers, essentially because females have a longer life expectancy. The proposed framework can help financial institutions manage the major three risk factors for NNEGs and assist in meeting the regulator’s concerns. TOPICS:Quantitative methods, real estate, VAR and use of alternative risk measures of trading risk Key Findings ▪ We investigate model risk in risk analysis for No-Negative-Equity-Guarantees. ▪ Our numerical analyses reveal that the housing price risk, interest-rate risk, and longevity risk can affect the VaR and CTE of NNEGs, with the impact being as significant as that for housing risk.

Effects of Criminogenic Risk–Needs Assessment Feedback During Prerelease Correctional Rehabilitation

This pre–post follow-up randomized trial investigated the receptiveness and responsiveness of 82 incarcerated men undergoing reentry to feedback (discussion-based, form-based, or none–minimal) regarding their criminogenic risk–needs assessment results. Both short-term outcomes (self-perceived risk–needs, motivation for change, treatment readiness, and feedback satisfaction) and longer-term outcomes (intuitional conduct, rearrest, or halfway house return) were examined. As hypothesized, among study completers ( n = 67), motivation for change was significantly higher following discussion feedback, and both feedback formats were rated favorably by participants. Contrary to hypotheses, feedback recipients, including those who showed gains at post, did not appear reliably distinct from others on longer-term outcomes; nor were most outcomes significantly associated with baseline risk scores. Feedback about risk and needs may be useful in correctional treatment for motivation enhancement and treatment orienting, but special attention to measurement, contextual, and intensity factors is warranted.

Importance of Proper Monetary Liquidity: Sustainable Development of the Housing and Stock Markets

In this paper, we infer that when no excess monetary liquidity exists, people tend to invest available capital in assets associated with a high return or low risk. However, when excess monetary liquidity occurs, capital may successively boost asset markets, and the stock market wealth is thus likely to spill into housing markets, resulting in bubbles in these two markets and therefore in the unsustainable development of both the housing and stock markets. This paper uses relevant data from the United Kingdom from January 1991 to March 2020 to verify whether excess monetary liquidity is a crucial factor determining the relationship between the housing and stock markets. Continuous and structural changes are found to exist between housing price and stock price returns. This paper employs the time-varying coefficient method for estimation and determines that the influence of stock price returns on housing returns is dynamic, and an asymmetrical effect can occur according to whether excess monetary liquidity exists. An excessively loose monetary policy increases asset prices and can thus easily result in a mutual rise in asset markets. By contrast, when excess monetary liquidity does not exist, capital transfer among markets can prevent autocorrelation during excessive market investment and thereby aggravate market imbalance.

Bubbling Away: Forecasting Real Estate Prices, Rents, and Bubbles in a Transition Economy

Real housing prices have both surged and swooned in formerly socialist countries. We use 2000–2017 aggregate housing sales and rental price data from Kazakhstan to explore price movements during boom and stagnation eras, investigating the rent–price ratio’s (RPR) capacity to predict housing returns and rent growth for an emerging post-Soviet economy. RPR predicts returns better during periods of price increases than declines, and its importance in predicting the bubble component diminishes with time. Short-run RPR changes are consistent with rational bubble behavior during the period of secular upswing but different predictive variables matter during price increases and declines.

Linking U.S. State-level housing market returns, and the consumption-(Dis)Aggregate wealth ratio

Using state-level data for the U.S. housing market over the period of 1975:Q1-2012:Q2, we show that the consumption-wealth ratios derived from aggregate wealth (cay) and disaggregate (i.e. financial and housing) wealth (cday) are strong predictors of real housing returns (and their volatility). Additionally, we find that, barring the extreme ends of their respective conditional distributions, such effect is stronger for housing return volatility than housing returns. All in all, our findings show that state-level regressions can recover a large degree of heterogeneity that country-level exercises typically ignore. Such heterogeneity is prominent not only in terms of consumption smoothing behavior, but also with regard to housing return predictability.

Interactions Between Housing Market and Stock Market in the United States: A Markov Switching Approach

This study uses the Markov switching vector autoregressive model (MSVAR) model to examine dynamic relationships between stock and housing market returns in the United States covering the period from 1987 to 2017. The results show significant regime-dependent auto-correlations in stock and housing returns in both the high volatility and low volatility regimes studied. The feedback effects are stronger in the housing market than the stock market. We observe significant positive cross-market spillovers, consistent with the wealth story. Increases in stock returns in low volatility regimes create positive spillover effects into housing markets; likewise, positive spillovers in the reverse direction from housing market to stock market occur in high volatility regimes. We also find significant negative correlations between lagged stock returns and current housing returns in the high volatility regime, which implies that capital switching occurs as investors move their investments out of the stock market and into the housing market. In this manner, the housing market becomes a hedge against a volatile stock market.

High-Frequency Predictability of Housing Market Movements of the United States: The Role of Economic Sentiment

We analyze the ability of a newspaper-based economic sentiment index of the United States to predict housing market movements using daily data from 2nd August, 2007 to 19th June, 2020. For this purpose, we use a nonparametric causality-in-quantiles test, which allows us to test for predictability over the entire conditional distribution of not only housing returns, but also volatility, by controlling for misspecification due to nonlinearity and structural breaks. Our results show that economic sentiment does predict housing returns (unlike the conditional mean-based Granger causality test) and volatility, barring the extreme upper ends of the respective conditional distributions.

FEAR Index, city characteristics, and housing returns

AbstractWe use Google search frequency to construct a measure of aggregate sentiment in housing markets—Financial, Economic, and Real Estate (FEAR) Index—and analyze its relationship to housing returns. We find that housing markets react inversely to changes in FEAR Index, which captures negative sentiment, and that market characteristics affect the strength of this relationship. More financially distressed markets, as measured by bankruptcy rates and mortgage default double trigger, are more responsive to changes in FEAR Index than less distressed markets, and cold markets (markets with slow price appreciation) are more responsive than hot markets (markets with rapid price appreciation). We also examine these characteristics jointly and find that cold markets with financial distress are the most responsive to negative sentiment. Finally, we show that home prices are more sensitive to negative sentiment during recessionary periods.

How Do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch

ABSTRACT In this study, we examine the response of housing returns in China, India and Russia to different oil shocks, generated from a more accurate estimation approach. Given the available data for the relevant variables, the MIDAS approach which helps circumvent aggregation problem in the estimation process is employed. We also extend the MIDAS framework to account for nonlinearities in the model. Expectedly, the housing returns of the countries considered respond differently to the variants of oil shocks. More importantly, the result indicates that housing returns in Russia serve as a good hedge against oil price risk while housing returns in China and India do not. We also find that modeling with the MIDAS framework offers better predictability than other variants with uniform frequency.

Alternative explanation of the money illusion: The effect of unexpected low inflation

In this study, a theoretical framework was constructed to verify whether the inflation level determines the presence of money illusion. The unexpected occurrence of low inflation is typically taken as an indication that the economy has entered or is entering a recession, and people expect the Federal Reserve to rapidly intervene to recover the economy. Because rental contracts facilitate rent price rigidity, rent prices may not decline immediately in the presence of unexpected low inflation, causing house owners in general to consider their rent returns as still lucrative. The excess profits from the rent returns of house owners can be used to compensate potential loss incurred from falling housing prices in the future, subsequently reducing their risk of property ownership. Therefore, when encountering unexpected low inflation, house owners tend to lower their expectations of housing return risk and overestimate the housing price, thus resulting in money illusion effects. A long-term data set of the US housing market (sample period: 1960 Q1 – 2016 Q1) was employed to comprehensively estimate the biases of the price–rent ratio. The estimation results revealed that money illusion effects only exist in conditions with minimal commodity price increases or decreases, which comply with the hypothesis of the present study. Finally, this study evaluated expected housing risk and housing premiums and confirmed that unexpected low inflation increases housing premiums, consequently leading to a higher rise in housing prices than in rent prices and contributing to the mispricing of price–rent ratios.

Loan-to-value and the price-rent ratio

PurposeThe purpose of this paper is to highlight the relation between the loan-to-value (LTV) ratio and the price-rent (PR) ratio. The paper intends to relate the PR-ratio to housing return and the potential for a leverage gain in housing investments by considering the funding structure of housing investments.Design/methodology/approachCombining a PR-ratio approach with the housing return in the case of mortgage-financed housing, as presented by Borgersen and Greibrokk (2012), this paper relates LTV to the PR-ratio.FindingsWhen formalising the relationship between leverage and housing return, as given by Muellbauer and Murphy (1997), the paper finds the effect of a higher LTV on the user cost of housing as the net effect of a higher borrowing cost and the associated leverage gain. The latter depends on the relationship between house price growth and the mortgage rate and, because the leverage gain has an ambiguous effect on the user cost of housing, the relation between the LTV-ratio and the PR-ratio is context-specific.Originality/valueThe paper aims to contribute to the literature on PR ratios in two ways. First, by explicitly including the LTV-ratio in the user cost of mortgage financed housing and, correspondingly, in the PR-ratio derived from the user cost. Second, by including the funding structure of housing investments the expression for the capital gain, which often is discussed in the PR-ratio literature, is related to the funding structure and includes both a price gain and a leverage gain.

Do Fund Investors Consider Asset Returns? Substitute Relation Among Investment Funds in Korea

This study aims to investigate whether fund investors change their investment fund type depending on the return change of assets such as stocks, bonds, and housing. For this purpose, we examine the substitution relation among the equity, bond, and real estate investment funds by employing the net inflow. In particular, investment funds are classified according to investment objective, investment region, and investor. The main findings are as follows: first, the stock return of change is not attributed to the substitution relation among equity, bond, and real estate investment funds, which remains consistent across investment region and investors. Second, the yield change of three-year treasury bonds is attributed to the substitution relation between equity and bond investment funds. That is, if the bond yield increases and the bond prices decrease in turn, then money will go to the ‘domestic’ equity funds and goes out of the ‘domestic and international’ bond funds. The substitution relation is revealed between ‘publicly placed’ equity and ‘publicly and privately placed’ bond investment funds. Third, housing return of change is attributed to the substitution relation between domestic and international real estate investment funds. This implies that if housing prices rise, then money will go into the ‘domestic’ real estate investment funds and go out of ‘international’ real estate investment funds. The substitution relations among investment funds suggest that fund investors consider the return change of assets when determining investment funds.

The Time-series Linkages between US Fiscal Policy and Asset Prices

This article studies the interplay of fiscal policy and asset price returns of the United States in a time-varying parameter vector autoregressive (VAR) model. Using annual data from 1890 to 2013, we study the effects of dynamic shocks to both fiscal policy on asset returns and asset returns on fiscal policy. Distinguishing between low-volatility (bull market) and high-volatility (bear market) regimes together with a time-varying parameter VAR model enables us to isolate the different sizes and signs of responses to shocks during different time periods. The results indicate that increases in the primary surplus-to-gross-domestic-product ratio decrease house returns over the entire sample and at each impulse horizon. Unlike the house return responses, stock returns only decrease in the first year after the fiscal shock but then increase for the following eight years. Furthermore, the findings show that asset return movements affect fiscal policy, whereby fiscal policy responds more to equity returns than to house returns. The response of fiscal policy to asset returns proves relatively stable and constant over time while controlling for various asset return regimes. Asset returns respond uniformly to fiscal policy shocks since the 1900s.

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.

Sentiment and its asymmetric effect on housing returns

AbstractWe use Google search frequency to construct sentiment indices (positive and negative) for the housing market. We find that future housing prices are negatively related to our measure of negative sentiment but not significantly related to that of positive sentiment. These relationships are robust to controls for macroeconomic variables, stock market return, and Housing Market Index, a survey‐based housing sentiment index. Furthermore, we find that an increase in negative sentiment results in a significant negative response in housing prices, while a decrease evokes little response. Thus, the housing market exhibits asymmetric responses to negative and positive sentiment and to increases versus decreases in negative sentiment. We attribute these asymmetric responses to the “negativity effect.” Finally, we find that home prices are more sensitive to sentiment during recessionary periods.

The Gender Gap in Housing Returns

Housing wealth represents the dominant form of savings for American households. Using detailed data on housing transactions across the United States since 1991, we find that single men earn 1.5 percentage points higher unlevered returns per year on housing relative to single women. The gender gap grows significantly larger after accounting for mortgage borrowing: men earn 7.9 percentage points higher levered returns per year relative to women. Approximately 45% of the gap in housing returns can be explained by gender differences in the location and timing of transactions. The remaining gap arises primarily from gender differences in execution prices: data on repeat sales reveal that women buy the same property for approximately 2% more and sell for 2% less. Women experience worse execution prices because of differences in the choice of initial list price and negotiated discount relative to the list price. Gender differences in upgrade and maintenance rates, and preferences for housing characteristics and listing agents appear to be less important factors. Overall, the gender gap in housing returns is economically large and can explain 30% of the gender gap in wealth accumulation at retirement.

The Gender Gap in Housing Returns

Housing wealth represents the dominant form of savings for American households. Using detailed data on housing transactions across the United States since 1991, we find that single men earn 1.5 percentage points higher unlevered returns per year on housing relative to single women. The gender gap grows significantly larger after accounting for mortgage borrowing: men earn 7.9 percentage points higher levered returns per year relative to women. Approximately 45% of the gap in housing returns can be explained by gender differences in the location and timing of transactions. The remaining gap arises primarily from gender differences in execution prices: data on repeat sales reveal that women buy the same property for approximately 2% more and sell for 2% less. Women experience worse execution prices because of differences in the choice of initial list price and negotiated discount relative to the list price. Gender differences in upgrade and maintenance rates, and preferences for housing characteristics and listing agents appear to be less important factors. Overall, the gender gap in housing returns is economically large and can explain 30% of the gender gap in wealth accumulation at retirement. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

What Can Fifty-Two Collateralizable Wealth Measures Tell Us About Future Housing Market Returns? Evidence from U.S. State-Level Data

We use a novel U.S. state-level database to evaluate the role of housing wealth as a provider of collateral services. First, we estimate the cointegrating relationship between housing wealth and labour income for all 50 states, as well as the District of Columbia (D.C.), and overall U.S. Then, we assess the predictive ability of the housing wealth-to-income ratios (labelled by hwy) for state-level future real housing returns. We uncover: (i) positive estimates for the elasticity of housing wealth with respect to labour income, which are also largely heterogeneous across U.S. states; and (ii) a negative link between the housing wealth-to-income ratios and future housing returns, albeit the forecasting power of hwy also varies considerably across states. We conclude that country-level regressions typically "mask" this diversity of features surrounding the usefulness of housing in collateral provision and unfavourable labour income shock smoothing that state-level frameworks are able to recover.

Regime switches and permanent changes in impacts of housing risk factors on MSA‐level housing returns

AbstractThis study assesses impacts of housing risk factors on metropolitan housing excess returns in the augmented housing pricing model. Credit and liquidity factors represent demand and supply sides, respectively, of housing markets. All metropolitan statistical areas have significant regime switches, and booms and busts are turbulent and influenced by the two factors. The explanatory powers of the liquidity factor shrivel for the boom‐bust and regime‐switching volatility specifications, and excess housing returns are less sensitive to nationwide housing dynamics after 2008. This study lends support to time‐varying exposures of housing excess returns to risk factors in terms of temporary switches and permanent changes.

Monetary Policy and Financial Stability: The Role of Inflation Targeting

AbstractThis article examines the relationship between Inflation targeting (IT) and financial instability from 1990 to 2015 for Asian economies. To measure financial instability, a multidimensional financial conditioning index is calculated following the ECB's approach. Using a fixed effects panel data model the study finds that adoption of IT policy in Asian economies has an adverse impact on financial stability, thus rejecting the ‘conventional wisdom’ hypothesis. Further, the Vector Autoregression (VAR) result shows that an IT regime increases housing returns and encourages investors to take higher risks.