Housing Returns Research Articles

Housing search activity and quantiles-based predictability of housing price movements in the USA

Purpose Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility. Design/methodology/approach Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data. Findings The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered. Originality/value To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.

Asset Returns and Economic Uncertainty: A Cross-Country Analysis

This paper examines the effects of economic uncertainty (idiosyncratic vis-à-vis common uncertainty) on equity, bond and housing returns across both developed and developing countries. Building on International/Intertemporal Capital Asset Pricing Model (ICAPM), we find that economic uncertainty exerts negative effects on equity, bond and housing returns. When we decompose economic uncertainty into two parts: idiosyncratic and common economic uncertainty, we find that ‘idiosyncratic uncertainty’ affects equity, bond and housing returns more negative and pronounced than ‘common’ uncertainty, where investors do not demonstrate differences in responses to the different dimensions of uncertainty. Moreover, there are weak lagged effects of economic uncertainty on asset returns. Additionally, we find negative uncertainty premium for equity more persistently in bullish rather than bearish market conditions. Our results are also robust for low frequency data and inclusion of covid period in the analysis. Our findings have implications for policy makers in both developed and emerging markets regarding clear communication of economic policies.

Exploring the spatialisation of the performance of residential REITs investment and direct investment in the housing market

ABSTRACT Despite numerous studies about the housing sector, the connection between the performance of residential real estate investment trusts (REITs) investment and direct investment in the housing market is still under-researched. To help fill this gap, we used quarterly data of the Australian Securities Exchange (ASX) residential REITs index and direct housing returns of Australia and its major cities, spanning March 1983 to June 2021, and adopted a suite of econometric methods to document the following findings. First, publicly listed housing (residential REITs) and private housing markets are not separated, but both are closely related in terms of their investment performance. Second, the performance of direct investment in the housing market of Adelaide, Brisbane, Melbourne and Sydney could lead to positive investment performances for residential REITs over time, while no comparable evidence is found for Canberra, Darwin, Hobart and Perth. Third, further analyses revealed a one-way direction of performance, flowing from the housing market of Brisbane, Melbourne and Sydney to residential REITs, while no similar evidence of performance flow was found for Adelaide. Our study has provided new evidence that could contribute to the discussion on the expansion of residential properties in the portfolio of institutional investors.

Estimating U.S. housing price network connectedness: Evidence from dynamic Elastic Net, Lasso, and ridge vector autoregressive models

This paper investigates the dynamic connectedness of random shocks to housing prices between the 50 U.S. states and the District of Columbia. The paper implements a standard vector autoregressive (VAR) model as well as three VAR models with shrinkage effects — Elastic Net, Lasso, and Ridge VAR models. The transmission of real housing return shocks on a regional basis flows from Southern states to the other three regions, whereas the Northeast receives those shocks. The West receives shocks from the South and transmits shocks to the Midwest and the Northeast. Finally, the Midwest transmits shocks to the Northeast and receives shocks from the South and the West. Our results have important implications for policymakers and investors. To the extent that the housing market affects the business cycle, the Federal Reserve can monitor housing market movements in the net transmitter states to gather information about the beginnings of the housing market cycle. Moreover, the determination of which states or regions function as the main transmitter of shocks provides information to investors on acquiring housing assets in these markets rather than the ones that are more susceptible to such shocks as net receivers.

Yield Curves for Main Street: Housing and financial capital returns in a developing economy

AbstractTotal housing real returns in large agglomerations in Kazakhstan are approximately 1% per year lower than in the rest of the country. Our results are also supportive of emerging literature, that housing real rent returns, adjusted for population, are generally lower in large agglomerations compared to the rest of the country. We indicate that within‐country heterogeneity of housing returns mainly result from the spatial concentration of economic activity in urban centers. Our findings also suggest that residential real estate in a developing economy produces higher returns compared to financial capital over the long‐run. At the same time, the volatility of holding housing investment is substantially higher than its “safe” alternative (i.e. deposit rate paid for savings deposits). As such, “Yield Curves for Main Street” concept refers to the general positive spread between total housing returns and financial capital returns in a developing economy.

주택은 인플레이션을 헷지 할 수 있는가?

This paper examines the inflation hedging capacity of Korea’s housing return from January 1987 to December 2022. The exponential generalized autoregressive conditiuonal heteroskedasticity(EGARCH) model and a dynamic ordinary least squares (DOLS) regression were employed to study the hedging capacity of Korea’s housing return against expected and unexpected inflation over the short run and long run. The results that could be obtained through this paper is as follows. First, The empirical results show a hedge effect on expected inflation only in housing return excluding gold over the short term, but not in unexpected inflation. Second, Over the long run, housing and gold returns provide a positive inflation hedge against expected inflation only. Third, it is found that the price volatility of housing and gold have both a positive asymmetric effect on news shocks. Over the long run, an increase in wages is found to increase housing prices by promoting housing demand, While no similar evidence is found in money supply.Therefore, there is a need to implement a stable wage policy to stabilize the housing market.

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.

Valuation of Mortgages by Using Lévy Models

In a mortgage valuation model, the early termination (i.e., prepayment and default) hazard rates and the recovery rate can be specified as multivariate affine functions that include the correlated stochastic state variables. For good capturing of the distributions for state variables, we specify that the state variables follow Lévy models. Accordingly, the early termination hazard rates and the recovery rate also follow Lévy models. Three popular Lévy models, the normal, Variance Gamma (VG), and Negative Inverse Normal (NIG), were used to obtain the closed-form pricing formula for a mortgage. We also conduct numerical applications. Our results reveal the following findings: first, VG model is better than the normal and NIG models in fitting the actual distributions of the interest rate and the housing return rate. Thus, mortgage valuation using a VG model should be better than that using the other two models. Second, the mortgage value estimated by the normal model is the lowest among the three Lévy models. Third, a prepayment hazard rate with a deterministic value (e.g., using the PSA prepayment model) could result in an unreasonable mortgage duration. Fourth, the mortgage convexity calculated by the normal model is higher than that in the other two Lévy models. Our general pricing formula for a mortgage as described in this study can help market participants accurately value mortgages and effectively manage their risks.

Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks

We analyze the ability of an index of mortgage default risks (MDRI) for 43 states and 20 metropolitan statistical areas (MSA) of the US derived from Google search queries, in predicting (in- and out-of-sample) housing returns of the corresponding states and MSAs, based on various panel data and time-series approaches. In general, our results tend to prefer the panel data model based on common correlated effects estimation. We highlight that growth in MDRI negatively impacts housing returns within-sample, with predictive gains primarily concentrated beyond a year. These results are robust to alternative out-of-sample periods and econometric frameworks. Given the role of house prices as a leading indicators, our results are of value to policymakers, especially at the longer-run.

The Relationship Between Geopolitical Risks and Housing Returns in Türkiye: Evidence from the Cross - Quantilogram

The aim of the study is to analyze whether geopolitical risks have an effect on housing returns in Türkiye based on the data for the period January 2010 – September 2021. Considering its geography, Türkiye is one of the countries most likely to be exposed to geopolitical risks in the world. For this reason, from the point of view of both domestic and foreign investors investing in housing market, it is important to know whether this situation affects real estate returns, especially in periods when the growth in geopolitical risks is high and very high. For this purpose, in this study, the Cross-Quantilogram method introduced in the paper of Han et al.(2016), which is a robust measure of quantile dependence of two variables, was used. As a result of the analysis, it is seen that median and high-level geopolitical risks do not have an effect on housing prices or housing returns, but very high geopolitical risks have a short-term negative effect on returns. Based on all the findings, it is concluded that the geopolitical risks in Türkiye do not have a significant and permanent effect on the housing market.

Rationality test in the housing market: Project‐level evidence from China

AbstractWe test whether investors rationally react to rent news when pricing houses. Previous literature on house pricing uses aggregated housing price and rent data to estimate the rent news. We analytically demonstrate that using aggregated data biases the rationality test because it forces researchers to estimate the rent news using the cross‐project average dynamic relationship among the housing return, rent growth, and macroeconomic dynamics and, hence, misses the heterogeneity in the dynamic relationship. Our empirical tests using proprietary project‐level housing data from China's megacities find that the bias from missing the heterogeneity is economically substantial.

Predicting US Housing Returns and Rent Growth Using the Loan-to-Value Ratio

Predicting US Housing Returns and Rent Growth Using the Loan-to-Value Ratio

Warmth of the Welcome: Immigration and Local Housing Returns

Warmth of the Welcome: Immigration and Local Housing Returns

Difference-in-differences with Economic Factors and the Case of Housing Returns

Difference-in-differences with Economic Factors and the Case of Housing Returns

Time-varying impacts of monetary policy uncertainty on China's housing market

We investigate how the monetary policy uncertainty (MPU) affects China's regional housing prices. We extend the empirical literature on the relationship between MPU and housing prices by incorporating both time-varying features and core-periphery city network structures. Using the housing returns of 70 major cities from 2007 to 2020 and a newspaper-based MPU index for China, we find that the dynamics of spatial interdependence are negatively correlated with MPU, but the total effects of MPU on housing prices are divergent across different cities. Moreover, speculators tend to purchase houses in dominant (non-dominant) cities as MPU decreases (increases), leading to the so-called “flight-to-safety” effects. Our findings can be accounted for by cities' outstanding loan balance and unemployment rates. We highlight the role of MPU in determining regional housing prices, and call for regulating dominant cities when MPU level is high. • We augment a novel time-varying parameter spatial model with dominant cities. • Data-driven technique is used to identify dominant cities within the core-periphery structure. • Monetary Policy Uncertainty (MPU) affects China's housing prices heterogeneously. • The heterogeneity can be explained by city's loan balance and unemployment. • The flight-to-safety effects can explain the transmission of MPU to housing markets.

Co‐movement among oil, stock, bond, and housing markets: An analysis of U.S., Asian, and European economies

AbstractThis study explores the co‐movement among oil and the stock, bond, and housing markets of the U.S. and major developed countries across Europe and Asia. The results indicate that oil is long‐run integrated with each asset class, and that the extent of convergence has increased after the onset of the 2007–2009 global financial crisis (GFC). Moreover, oil contributes most heavily toward the common trends, implying that oil is the “leader” sector that drives each asset class toward long‐run equilibrium relationships. Short‐run analyses indicate that oil shocks induce a negative response in stock and housing returns and a positive reaction in bond returns, showing a tendency to become more intense and persistent after the GFC. When oil shocks are disentangled, the results indicate that supply and demand have heterogeneous effects on the three global asset classes. Over the long‐run, demand shocks make the most significant contribution to the common trends and “lead” the other asset classes, whereas supply shocks have either a negligible or a weaker impact. Over the short‐run, demand shocks positively impact the stock and housing markets and negatively impact bonds, while supply shocks induce negative and weaker impacts on all three asset classes.

Local Housing Market Sentiments and Returns: Evidence from China.

This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from January 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use partial least squares (PLS) to extract a recursive look-ahead-bias-free local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.

Spreads and Volatility in House Returns

Underlying idiosyncratic and illiquidity risks are suppressed in infrequently reported indexes of house prices and rents. Idiosyncratic risks result from bid–ask spreads for prices and rents. Time series autocovariances generate a distribution of prices and rents. Capital gains and rent-price ratios are transforms of these distributions, generating cross-sectional idiosyncratic volatility. Housing data are infrequent and usually made available every month. The monthly–quarterly volatility ratios of house prices and rents and their spreads estimate unobserved daily fluctuations and illiquidity risks. Including idiosyncratic and illiquidity risks, a U.S. house has a standard deviation in returns of 8.7% annually for three decades after 1990. With a mean excess return of 3.7%, the Sharpe ratio of 0.42 is comparable to the S&P 500. Excluding spreads, the house Sharpe ratio is 0.69. House returns respond to liquidity. A 1% increase in volume raises returns by 0.8%.

Can housing investment hedge against inflation?

PurposeThis paper aims to test the hedging ability of housing investment against inflation in Japan and the USA during the period 2000–2020.Design/methodology/approachThis study applies the deep learning method and The exponential general autoregressive conditional heteroskedasticity in mean (1, 1) model with breaks.FindingsWithin the asymmetric framework, it is found that housing returns (HR) can hedge against inflation in both these markets, which mentions that when investing in the housing market in Japan and the USA, investors are compensated for bearing from inflation. This result is consistent with Fisher’s hypothesis. Especially, the empirical results show that the risk-return tradeoff is available in Japan’s housing market and not available in the US housing market. Any signal of a high inflation rate – referred to as “bad news” – may cause a drop in HR in Japan and a raise in the USA.Originality/valueTo the best of the author’s knowledge, this is one of the first studies using the deep learning method (long short-term memory model) to estimate the expected/unexpected inflation rates.

The role of investor sentiment in forecasting housing returns in China: A machine learning approach

AbstractThis paper analyzes the predictive ability of aggregate and disaggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. We find that our new aligned investor sentiment index has greater predictive power for housing returns than the principal component analysis (PCA)‐based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the disaggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the disaggregate proxies of investor sentiment. Furthermore, when we let the machine learning models to choose from all key control variables and the aligned sentiment index, the forecasting accuracy is improved at all forecasting horizons, rather than just the short‐run as witnessed under standard predictive regressions. This result suggests that machine learning methods are flexible enough to capture both structural change and time‐varying information in a set of predictors simultaneously to forecast housing returns of China in a precise manner. Given the role of the real estate market in China's economic growth, our result of accurate forecasting of housing returns has important implications for both investors and policymakers.