Return Relationships between Listed Banks and Real Estate Firms: Evidence from Seven Asian Economies

This paper studies the return relationships between listed banks and real estate firms in seven Asian economies before and after the Asian financial crisis. We find that listed banks were exposed to real estate risk both before and after the crisis, but that the exposure increased in the post-crisis period. After the crisis, the hidden risk of real estate collateral in the bank lending process was explicit, as was evidenced by the increased sensitivity and the structure break. In terms of causality, the returns of listed real estate firms are found to Granger-cause the returns of listed banks. However, there is mixed evidence as to whether listed bank returns Granger-cause the returns of listed real estate firms. The study is significant because it indicates the importance of lending policies in relation to the real estate market in establishing a healthy financial system.     

The sensitivity of bank stock returns to real estate

According to the Federal Reserve Board, banking firms have recently been shifting significantly larger portions of their loan portfolios into real estate. This increase in real estate lending has caused concern about the continuing economic health of banks on the part of state and federal regulators, since changes in real estate returns, evidenced by changes in property value, can potentially have a significant impact on bank default risk and profit-ability. However, concerned parties do not seem to have explicitly considered the relationship between mortgage default risk and the specific characteristics of real estate investments.This study examines the sensitivities of stock returns for different bank groups, based on the percentage of total loans in real estate and the percentage of loans in five different mortgage categories (construction and development loans, farmland loans, one- to four-family residential loans, multifamily residential loans, and nonresidential and nonfarm loans), to changes in real estate market returns. This is done by developing and using a three-index model.The results of this study indicate that bank stocks, overall, are very sensitive to changes in real estae returns. Banks, with a larger portion of their total loans invested in all types of real estate loans, except farmland loans, are most sensitive to changes in real estate returns.     

Bank risk and real estate: An asset pricing perspective

While a number of papers have investigated the time-series behavior ofex post bank stock returns and real estate returns, no study has comprehensively studied the relationship betweenex ante risk premiums on both assetsand the time-varying nature of such premiums in relationship to economic and real estate market conditions. In this study, we investigate how the changing nature of bank risk taking, especially in the real estate market, has affected theex ante pricing of risk in the market for bank stocks. We find that the time variation in bank risk premiums are partly determined by interest rate and real estate market conditions. We also discover that the real estate factor has been important for banks in the 1980s.     

On the real estate market efficiency

This reseach reexamines the efficiency hypothesis of the real estate market using monthly data and the vector autoregressive (VAR) modelling technique. The tests focus on the causal linkage between real estate returns and a number of relevant financial and economic variables. An eight-by-eight VAR model is estimated using the FPE and the specific gravity criteria, in conjunction with an extensive series of specification tests. The empirical results distilled from system estimations suggest that the real estate market is efficient with respect to available information on the industrial production, the risk premia, the term structure of interest rates, and the monetary base. Movements in these variables are quickly and fully utilized by market agents, perhaps owing to the intensity with which their relationship with stock returns has been discussed in the literature and the popular media. However, the results also suggest the presence of a significant lagged relationship between real estate returns and fiscal policy moves, even when the paths through other potential determinants of these returns are taken into account. Of course, our finding that the fiscal policy measure is useful in predicting stock returns does not necessarily imply that the real estate market is inefficient. At a minimum, inefficiency is revealed only if a careful analysis of the budgetary process can help design a profitable (exploitable) trading strategy.     

Time-varying correlation between stock market returns and real estate returns

Direct investment in commercial or residential real estate is found to provide valuable diversification benefits for Australian investors though this is not so evident for indirect real estate investment vehicles like listed Australian real estate investment trusts (A-REIT). Further, multivariate analysis of Australian real estate and share market quarterly returns, spanning the period from the 3rd quarter 1986 to the 3rd quarter 2009, suggest that the correlation between real estate returns and share market returns is time-varying. Finally, while all of the asset class correlation coefficients increased with the Global Financial Crisis period this broad movement in asset class correlation is not evident in during the Wall Street Crash of 1987.     

The Other Side of Value: The Effect of Quality on Price and Return in Real Estate

We examine the effect of quality on price and return in real estate. Quality real estate stocks generally trade at higher prices. However, similar to findings in the aggregate stock market (Asness et al. 2013), quality in listed real estate is not fully reflected in price. A long quality, short junk portfolio (QMJ) is found to produce average risk-adjusted returns of 3.6 % per quarter (14.4 % annually). QMJ has a significantly positive coefficient when added as a dependent variable to a 4-factor model explaining U.S. real estate excess returns, including market, size, value and momentum factors, and leads to a small improvement in the overall fit of the model. Further investigation of the components of quality shows that profitability has a consistently positive relationship with real estate excess returns, while higher dividend payout is consistently associated with lower returns, in contrast to findings for conventional firms.     

Real Estate Ownership, Leasing Intensity, and Value: Do Stock Returns Reflect a Firm’s Real Estate Holdings?

Little is known about the effects of real estate ownership and leasing on the stock return characteristics of public firms. In this study, we first examine the sensitivity of retail firm returns to a real estate factor over the period 1998?C2008. The retail industry is chosen because of the significant use of real estate in a typical retail firm??s production function. Consistent with our expectations, retail stocks exhibit positive real estate risk exposure, even after controlling for sensitivity to general market risk as well as other standard risk factors. The second part of our analysis examines whether the intensity of real estate ownership and the use of off-balance operating leases to finance real property holdings are reflected in the market and real estate betas of retail stocks. We find that greater use of off-balance sheet operating leases is associated with higher market betas. In fact, the use of operating leases appears to have a larger impact on sensitivity to market risk than does the use of on-balance sheet debt. Our findings also confirm our hypothesis that real estate intensive firms display significantly greater exposure to a real estate factor. Moreover, our results strongly suggest that investors are fully aware of the risk associated with off-balance sheet operating leases.     

Are REITs real estate? Evidence from international sector level data

The aim of this study is to examine whether securitized real estate returns reflect direct real estate returns or general stock market returns using international data for the U.S., U.K., and Australia. In contrast to previous research, which has generally relied on overall real estate market indices and neglected the potential long-term dynamics, our econometric evaluation is based on sector level data and caters for both the short-term and long-term dynamics of the assets as well as for the lack of leverage in the direct real estate indices. In addition to the real estate and stock market indices, the analysis includes a number of fundamental variables that are expected to influence real estate and stock returns significantly. We estimate vector error-correction models and investigate the forecast error variance decompositions and impulse responses of the assets. Both the variance decompositions and impulse responses suggest that the long-run REIT market performance is much more closely related to the direct real estate market than to the general stock market. Consequently, REITs and direct real estate should be relatively good substitutes in a long-horizon investment portfolio. The results are of relevance regarding the relationship between public and private markets in general, as the ‘duality’ of the real estate markets offers an opportunity to test whether and how closely securitized asset returns reflect the performance of underlying private assets. The study also includes implications concerning the recent financial crisis.     

限购政策是否降低了上市房地产企业价值？——基于强度双重差分法的经验研究

完善住房市场体系是国民经济中的重要议题,限购政策作为政府稳定和调节房地产市场的主要手段,对房地产企业以及住房市场体系建设均有重要影响。本文利用上市房地产企业2008—2013年以及2015—2019年的相关数据,通过构建强度双重差分模型实证分析了两轮限购对上市房地产企业价值的影响及其作用路径。实证结果表明:第一,两轮限购政策均显著降低了上市房地产企业市场价值,当企业在限购城市销售占比越大时,价值下降幅度越大;第二,从企业经营绩效来看,两轮限购对其实际盈利和营运能力并未产生显著影响,第一轮提高了企业偿债的经营风险,而第二轮只是影响了企业的资产增长能力;第三,两轮限购对房地产市场产生异质性影响,第一轮并未显著影响房价上涨,而第二轮则显著遏制了房价上涨;第四,从股票市场看,第一轮限购主要是通过企业经营风险影响投资者预期,而第二轮限购则是通过影响房价来改变投资者预期,这进一步凸显了“房住不炒”的政策作用。本文的研究意义主要体现在制定与推进政策时应关注预期的作用,这对于当前“房住不炒”政策的长期实施及其政策效果的长期稳定都具有一定启示。     

中国房地产企业间系统性风险溢出效应分析——基于尾部风险网络模型

本文以2007—2021年沪深A股上市房企为样本,首先基于SIM单指数分位数回归技术提出测量系统性风险的新指标SIM-CoVaR,并结合前沿的TENET网络模型,构造跨房地产企业风险动态传染的尾部风险网络,然后采用块模型探究房地产市场系统性风险溢出的聚类性、触发机制及传播路径,最后考察网络整体结构和宏观经济变量对房地产市场系统性风险溢出的影响。研究表明:(1)我国房地产企业间存在明显的系统性风险联动性和溢出效应,在市场动荡时期房地产部门是金融风险溢出的放大器;(2)评估系统重要性节点企业时,除考虑企业规模等内部属性,还应考虑房企间关联结构,利用系统性风险指数可有效捕捉网络中系统重要性节点;(3)跨房企的系统性风险溢出具有显著的聚类特征,尾部风险网络可被划分为4个不同的功能模块,各模块的成员及其角色呈现明显的时变特性,监管部门可据此从供给端“因企施策”;(4)网络聚集性、网络效率和网络匹配性的降低能显著降低房地产市场的系统性风险溢出效应。本文从企业微观层面探讨房地产市场风险的形成机制,为促进房地产业健康发展和防范化解宏观层面的系统性金融风险提供参考。     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Real estate returns,money and fiscal deficits: Is the real estate market efficient?

This research examines the causal relationship between several financial variables and a portfolio of real estate returns using monthly data from January 1965 to December 1986. The empirical analysis is based on multivariate Granger-causality tests in conjunction with Akaike's final prediction error criterion. The results indicate that measures approximating monetary policy and market returns play an important role in causing changes in real estate returns. In particular, our findings suggest that base money and market returns have had significant lagged effects on current real estate returns.     

The predictability of real estate returns and market timing

Recent evidence suggests that all asset returns are predictable to some extent with excess returns on real estate relatively easier to forecast. This raises the issue of whether we can successfully exploit this level of predictability using various market timing strategies to realize superior performance over a buy-and-hold strategy. We find that the level of predicability associated with real estate leads to moderate success in market timing, although this is not necessarily the case for the other asset classes examined in general. Besides this, real estate stocks typically have higher trading profits and higher mean risk-adjusted excess returns when compared to small stocks as well as large stocks and bonds even though most real estate stocks are small stocks.     

Market States and the Effect on Equity REIT Returns due to Changes in Monetary Policy Stance

This study investigates the effect of changes in monetary policy on US equity real estate investment trust (EREIT) returns in lower and higher return ranges during bull, bear, and volatile stock market states using quantile regression. Results show that EREIT returns are sensitive to changes in monetary policy at different EREIT return ranges in different market states. During bull markets, changes in monetary policy have a significant negative impact on EREIT when investors have lower expectations of real estate price increases, but are not effective when investors have higher expectations of real estate price increases. During volatile and bear markets, EREIT returns are not sensitive to changes in monetary policy stance. Results also show that EREIT returns respond positively to stock returns in various states and conditions.     

Economic Risk Factors and Commercial Real Estate Returns

A great deal of research has focused on the links between stock and bond market returns and macroeconomic events such as fluctuations in interest rates, inflation rates, and industrial production. Although the comovements of real estate and other asset prices suggests that these same systematic risk factors are likely to be priced in real estate markets, no study has formally addressed this issue. This study identifies the growth rate in real per capita consumption, the real T-bill rate, the term structure of interest rates, and unexpected inflation as fundamental drivers or state variables that systematically affect real estate returns. The finding of a consistently significant risk premium on consumption has important ramifications for the vast literature that has examined the (risk-adjusted) performance of real estate, for it suggests that prior findings of significant abnormal returns (either positive or negative) that have ignored consumption are potentially biased by an omitted variables problem. The results also have important implications for dynamic asset allocation strategies that involve the predictability of real estate returns using economic data.     

What Moves Retail Property Returns at the Metropolitan Level?

In this article the determinants of metropolitan-level appraisal-based retail property returns are examined by estimating a six-equation model of retail construction starts, retail sales, stock-market returns, commercial mortgage rates, inflation, and the logarithm of stock-market volatility. Residuals from these equations are then used to explain actual movements in retail real estate returns. Our empirical procedure looks at both unadjusted and unsmoothed appraisal-based retail real estate returns. The general finding is that unsmoothed appraisal-based retail real estate returns lag significantly behind market conditions. Furthermore, the results suggest that very little of the variation in metropolitan-level appraisal-based retail real estate returns can be explained by macroeconomic news events.     

Interactions Between Property and Equity Markets: An Investigation of Linkages in the United Kingdom 1972–1992

Two strands of real estate research—that concerned with the relationships between securitized real estate and the underlying market and that dealing with the role of property in the wider economy—rarely are considered together. The paper utilizes the U.K. equity market and property company share data to explore the relationships between real estate and the rest of the economy, using a two-sector analytic model. Causality analysis suggests that the wider economy leads the real estate market in the short term but that, with a longer lag structure, positive real estate returns may point to negative future returns in the rest of the economy. This provides weak confirmatory evidence for theories of capital switching between sectors.     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Is there a real estate factor premium?

In this paper, we study the variation of expected returns on five different asset portfolios in a multi-factor model. We found the presence of a real estate factor, in addition to both a stock factor and a bond factor in asset pricing. This suggests that mutual fund managers should seriously consider including real estate assets in their portfolios, since one cannot capture the real estate factor premium without having some kind of real estate exposure. Another result is that the market segmentation found in previous studies disappears in a more general model of asset pricing in which we allow for multi-factors other than the market factor to affect asset returns. This implies that real estate assets can be treated just like other assets as far as mean-variance efficient asset allocations are concerned. We also have some preliminary evidence that equity REITs and the Russell-NCREIF index are driven by the same underlying real estate factor.     

Dynamics of Asset Prices and Transaction Activity in Illiquid Markets: the Case of Private Commercial Real Estate

This paper investigates the relationship between capital flows, turnover and returns for the UK private real estate market. We examine a number of possible implications of capital flows and turnover on capital returns testing for evidence of a price pressure effect, ‘return chasing’ behaviour and information revelation. The main tool of analysis is a panel vector autoregressive (VAR) regression model in which institutional capital flows, turnover and returns are specified as endogenous variables in a two equation system in which we also control for macro-economic variables. Data on flows, turnover and returns are obtained for the ten market segments covering the main UK commercial real estate sectors. Our results do not support the widely-held belief among practitioners that capital flows have a ‘price pressure’ effect on property prices. However, we do find a significant positive relationship between lagged turnover and contemporaneous capital returns, suggesting that asset turnover provides increased price revelation which, in turn, reduces investment risk and increases property values.