Real Estate Indices Research Articles

미국 · 중국 · 한국 거시경제변수가 한국 주식수익률 및 변동성 지수 변화율에 미치는 영향 분석

Purpose - This article analyzes the impact of macroeconomic variables of the United States, China, and Korea on KOSPI and VKOSPI, in that United States and China have a great influence on Korea, having an export-driven economy. Design/methodology/approach - The influence of US, China, and Korea interest rates, industrial production index, consumer price index, US employment index, Chinese real estate index, and Korea's foreign exchange reserves on KOSPI and VKOSPI is analyzed on monthly basis from Jan 2012 to Aug 2023, using multifactor model. Findings - The KOSPI showed a positive relationship with the U.S. industrial production index and Korea's foreign exchange reserves, and a negative relationship with the U.S. employment index and Chinese real estate index. The VKOSPI showed a positive relationship with the Chinese consumer price index, and a negative relationship with the U.S. interest rates, and Korean foreign exchange reserves. Next, dividing the analysis into two periods with the Covid crisis and the analysis by country, the impact of US macroeconomic variables on KOSPI was greater than Chinese ones and the impact of Chinese macroeconomic variables on VKOSPI was greater than US ones. The result of the forward predictive failure test confirmed that it was appropriate to divide the period into two periods with economic event, the Covid Crisis. After the Covid crisis, the impact of macroeconomic variables on KOSPI and VKOSPI increased. This reflects the financial market co-movements due to governments' policy coordination and central bank liquidity supply to overcome the crisis in the pandemic situation. Research implications or Originality - This study is meaningful in that it analyzed the effects of macroeconomic variables on KOSPI and VKOSPI simultaneously. In addition, the leverage effect can also be confirmed through the relationship between macroeconomic variables and KOSPI and VKOSPI. This article examined the fundamental changes in the Korean and global financial markets following the shock of Corona by applying this research model before and after Covid crisis.

Health Care REIT Returns & Covid-19: A Note

This study tests for pricing efficiency in Healthcare Real Estate Investment Trusts (REITs) surrounding the Covid-19 pandemic. Fractal analysis is used to ascertain whether any persistence or anti-persistence in returns can be observed over the “pre-Covid” and “Covid/post-Covid” periods for the 15 healthcare equity REITs included in the FTSE NAREIT US Real Estate Index. The results point to weak form efficiency during the complete sample period with some exceptions. A relative stability in Hurst exponents exists across the sub-periods for several of the REITs. However, there is evidence that many other REITs series switch from being anti-persistent to persistent, a result consistent with possible herding behavior. Some very limited evidence is found of series switching from being persistent to anti-persistent, consistent with investor overreaction for these REITs. Overall, inefficient pricing is seen in a limited number of cases, but there is a strong suggestion of a possible change like the return dynamic in the latter part of the study period associated with the pandemic.

Covid-19 Impact to Retail, Hospitality, and Office Space in Malaysia

Abstract The COVID-19 pandemic has disrupted economies and industries worldwide, including the real estate sector. This study aims to assess the effects of the pandemic on commercial real estate prices in the Malaysian market. By examining variations in property types and considering key factors influencing pricing, the research contributes to a better understanding of the pandemic’s impact on the real estate market. To analyze the effects of the COVID-19 pandemic on commercial real estate prices, a mixed-method approach was employed. The study combines data from direct real estate indices, which provide insights into property prices based on transaction data, and listed real estate, which includes publicly traded real estate investment trusts (REITs). By utilizing both sources, a comprehensive analysis of the market is achieved. The sample for this study consists of commercial real estate properties in the Malaysian market. It includes properties from various sectors, such as retail, hospitality, and office buildings. The sample is representative of the overall market and captures the different property types affected by the pandemic. The analysis begins by comparing direct real estate indices to highlight the limitations and potential biases associated with using these indices. It then examines the variations in commercial real estate prices during the COVID-19 outbreak, focusing on the different property types. Statistical techniques, such as regression analysis and trend analysis, are employed to identify patterns and quantify the impact on commercial real estate prices. The study’s main findings reveal that the retail and hospitality sectors experienced the most significant impact on commercial real estate prices during the COVID-19 pandemic. These sectors witnessed a substantial decline in property values due to restrictions, lockdown measures, and reduced consumer demand. Office buildings, although moderately affected, also experienced some decline in prices. This research contributes to the existing literature on the effects of the COVID-19 pandemic on commercial real estate prices, specifically in the Malaysian market. By combining data from direct and listed real estate sources, the study provides a comprehensive understanding of the variations in property prices across different sectors. The findings offer valuable insights for real estate investors, policymakers, and industry professionals in adapting to the changing market conditions and making informed decisions regarding commercial real estate investments. In conclusion, this article sheds light on the effects of the COVID-19 pandemic on commercial real estate prices in the Malaysian market. The research methodology, which combines data from direct and listed real estate, allows for a comprehensive analysis of property variations among different sectors. The findings emphasize the significant impact on the retail and hospitality sectors, while showing office buildings to have been moderately affected. It also provides important insights for stakeholders in the real estate industry, enabling them to make informed decisions and develop appropriate strategies.

Asymptotic normality of Nadaraya–Waton kernel regression estimation for mixing high-frequency data

High-frequency data is widely used and studied in many fields, especially in the econometrics and statistics. In this paper, the asymptotic normality of Nadaraya–Waton (NW) kernel regression estimator under ρ-mixing high-frequency data is studied. We first derive some moment inequalities for ρ-mixing high-frequency data, and then use them to study the asymptotic normality of NW kernel regression estimator, and give Berry–Esseen upper bounds. The numerical simulations report that the kernel regression estimator of high-frequency data has good asymptotic normality. Our empirical analysis is to fit the correlation between the five minute interval price increment and the corresponding trading volume for the Shanghai Stock Exchange Index, Entrepreneurship Index and Real Estate Index. These kernel regression curves better reflect people's investment behaviour.

Uticaj inflacije i kamatnih stopa na indekse nekretnina u SAD i EU

This paper investigates the relationship between real estate indices, inflation and interest rates in both Europe and the USA. The main objective is to examine the impact of inflation and interest rates on the respective real estate indices and test specific hypotheses. The study utilizes cointegration analysis to assess the long-term equilibrium and the speed of adjustment between the variables. The analysis reveals the presence of cointegrating relationships in both regions, indicating a long-term equilibrium between the variables The findings support the hypothesis that inflation has a significant and positive impact on the real estate indices in both regions. However, the hypothesis stating a significant and negative impact of interest rates on the indices is not supported.

Influence of Ukraine invasion by Russia on Turkish markets

This paper aims to examine the influence of the Ukraine invasion by Russia on Turkish markets, namely the Istanbul stock market index, Turkish real estate market index, Turkish gold market and Turkish foreign exchange market. This study used daily frequency data between February 24 and June 14, 2022. The variables used are BIST100, Turkey real estate index (XGMYO), Turkish gold commodity (XAU/TRY), Turkish foreign currency such as EURO/TRY, GBP/TRY, USD/TRY, TRY/UAH, TRY/RUB, and macro-economic variable RFR/TRY. The study employed Johansen cointegration, Impulse Response Functions and Markov-regime switching for the analysis. The findings established a long-run co-integration relationship among the Turkish markets. The finding also indicated that the shock from the Ukraine invasion by Russia has a positive effect on developed foreign currencies and a negative effect on currencies from emerging countries such as Turkey. The finding revealed that BIST100, XGMYO, and XAU/TRY shifted to regime 2 during the Ukraine invasion by Russia. The lack of need for more commodities such as wheat, gas and oil from the Turkish market prevented focusing on them, which may attract global attention. Despite this, the significance of this finding remains relevant in Turkey. Therefore, future research may focus on other markets with sufficient trading data for wheat and gas in Russia or Ukraine and any other countries of their study. This study established that Ukraine's invasion by Russia has a worldwide impact on the global markets. The effect is felt globally as a consequence, has been experienced across different developed and emerging markets due to the large market share of Russia on essential commodities such as gas and oil. Turkish foreign exchange markets experienced more storms during the Ukraine invasion by Russia even more than it was during the COVID-19 pandemic.

Optimisation of the investment strategy of the Norwegian Sovereign Wealth Fund by adjusting the real estate quota

Purpose This paper compares the benefits of direct international real estate investments in a mixed asset portfolio from the perspective of a passive investor with high and low bond allocation.Design/methodology/approach Due to high data availability and its professionalism, the Norwegian sovereign wealth fund was used as a representative example. Real estate indices from 8 countries were used for the portfolio analysis. The data were desmoothed according to Geltners’s 1993 approach.Findings The optimal real estate ratio in the present case is around 20–55%. However, this is strongly dependent on the bond ratio of the multi-asset portfolio. Portfolios with a high equity ratio benefit more from the additional direct real estate investments than portfolios with high bond ratios.Research limitations/implications A rebalancing of individual stocks and bonds was not analysed. Only indexes from MSCI (Morgan Stanley Capital International) were available.Practical implications Concludes that the weighting of stocks and bonds has a strong influence on the optimal real estate ratio and therefore structural changes that affect this weighting.Originality/value The originality of the paper lies in the analysis with different weights of stocks and bonds, the consideration of 8 real estate markets and the observation period. The results of the work highlight areas of interest for further research.

Efficiency of investing in closed-end real estate mutual funds

Real estate investments enjoy a steady interest of private investors, meeting their requirements for risk and profitability. One of the mechanisms for investing in real estate is implemented by a closed real estate mutual investment fund (Real estate Mutual fund), the principle of which is as follows: the real estate mutual fund invests investors' funds in residential or commercial real estate; in return for their money, depositors receive shares – securities of the fund. Their cost depends on the price of the real estate managed by the Real Estate Fund.This article presents the state of the Russian real estate market in 2022, the place of real estate funds in the market is determined. As an approach to assessing the effectiveness of investing in the real estate investment Fund, the calculation of the efficiency indicator as an index calculated on the basis of a comparative assessment of the attractiveness factors of the investment object is proposed. As a base for comparison, it is proposed to use the Moscow real estate index calculated by the Moscow Stock Exchange.The authors have proposed an index of real estate mutual funds, which reflects the dynamics of the net asset value of the largest funds. Based on the dynamics of the index values for 2021-2022, an assessment of the risk and profitability of investing in Russian real estate funds was carried out.

The Effect of Inflation, Interest Rate, Exchange Rate and GDP Growth on Stock Price Index

The purpose of this research is to examine the forces that will shape the performance of the IDX property and real estate index from 2017 to 2021. Inflation, interest, and exchange rates, as well as GDP growth, are the variables examined. Information gathered from www.idx.co.id, www.bi.go.id, and www.bps.go.id. Stock price index data covering 60 months was gathered for this analysis. In this research, regression analysis using Eviews 11 was employed for data crunching. The Normality, Multicollinearity, Autocorrelation, and Heteroscedasticity tests from the Classical Assumption Test are used in this investigation. The Coefficient of Determination Test (R2), the Partial T Test, and the Simultaneous F Test are all examples of Hypothesis Tests. Inflation was found to have a significant value (0.04040.05), interest rates (0.00210.05), exchange rates (0.00220.05), and gross domestic product (0.00260.05) were all found to be significantly different from zero.

COMMERCIAL REAL ESTATE PRICES IN MALAYSIA AND COVID-19

The consequences of the coronavirus disease 2019 (COVID-19) pandemic on commercial real estate pricing are examined in this article, with a focus on the Malaysian market. It begins by pointing out certain cautions to keep in mind while using direct real estate indices. The authors then look at how commercial real estate prices changed during the epidemic, highlighting disparities between property kinds. This study uses data from both direct and listed real estate to achieve this goal and goes on to explore changes in the primary elements driving commercial real estate pricing. The essay then moves on to the expected future trajectory of commercial real estate values. Retail and hospitality assets, as well as, to a lesser extent, office buildings, were shown to be the most affected by the findings.

How the real estate indexes have performed during the COVID-19 crisis? Multifractal analysis revisited with wavelet

PurposeThe purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic.Design/methodology/approachFirstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method.FindingsThe results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes.Research limitations/implicationsThe inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period.Originality/valueTo the authors’ knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes.

The performance of non-listed opportunity real estate funds in China

PurposeOpportunity real estate funds are an important style of real estate investing for institutional investors seeking nonlisted real estate exposure. Importantly, institutional investors have sought exposure to the China real estate market, often via opportunity real estate funds. This has been by a pure China opportunity real estate fund (100% China opportunity real estate) or by a pan-Asia opportunity real estate fund where China opportunity real estate was part of this pan-Asia opportunity real estate portfolio. Using two bespoke China opportunity real estate indices developed by the authors, this paper aims to assess the risk-adjusted performance and portfolio diversification benefits of China opportunity real estate in a mixed-asset portfolio over 2008–2020. It also highlights critical issues for institutional investors going forward to factor into their real estate investment decision-making for effective China real estate exposure.Design/methodology/approachThis paper develops two bespoke China opportunity real estate fund performance indices to assess the risk-adjusted performance and portfolio diversification benefits of China opportunity real estate funds in a mixed-asset portfolio over 2008–2020. An asset allocation diagram is used to assess the role of China opportunity real estate in a mixed-asset portfolio via both the non-listed and listed real estate investment channels.FindingsOver 2008–2020, China opportunity real estate exposure via pan-Asia opportunity real estate funds were seen to outperform pure China opportunity real estate funds. In both formats, China opportunity real estate funds were seen to have a significant role in a China mixed-asset portfolio across most of the portfolio risk spectrum; particularly compared to listed real estate exposure in China. On-going issues regarding real estate risk management in China will take on increased importance for institutional investors seeking China real estate exposure.Practical implicationsOpportunity real estate funds are an important style of real estate investing, often used by institutional investors to gain non-listed real estate exposure in a developing real estate market. This style of real estate investing has been popular with institutional investors seeking exposure to China real estate as part of the China economic growth dynamic. The results of this research highlight the importance of opportunity real estate investing in China, both via a pure China opportunity real estate fund and via a pan-Asia opportunity real estate fund. Based on this empirical analysis, China opportunity real estate exposure is seen to be more effective via a pan-Asia opportunity real estate fund than a 100% China opportunity real estate fund. A range of practical China real estate investment issues are also highlighted for the effective delivery of China real estate exposure for institutional investors going forward; this particularly relates to the on-going risk management for real estate investment in China.Originality/valueThis paper is the first empirical research analysis of the risk-adjusted performance of China opportunity real estate and its role in a mixed-asset portfolio. Using bespoke China opportunity real estate fund indices developed by the authors, this research enables empirically-validated, more informed and practical opportunity real estate investment decision-making regarding the strategic role of China opportunity real estate in an institutional investor's portfolio. It also highlights the importance of various facets of real estate risk management in China going forward.

Visual recognition and prediction analysis of China's real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks.

Today, with the rapid growth of Internet technology, the changing trend of real estate finance has brought great an impact on the progress of the social economy. In order to explore the visual identification (VI) effect of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) algorithm based on neural network optimization on China's real estate index and stock trend, in this study, artificial neural network (ANN) algorithm is introduced to predict its trend. Firstly, LSTM algorithm can effectively solve the problem of vanishing gradient, which is suitable for dealing with the problems related to time series. Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. Through the performance verification of the model, the results reveal that the CNN-LSTM optimization algorithm has a more accurate prediction effect, the prediction accuracy is 90.55%, and the prediction time is only 52.05s. At the same time, the significance advantage of CNN-LSTM algorithm is verified by statistical method, which can provide experimental reference for intelligent VI and prediction of trend of China real estate index and property company stocks.

Where is the distribution tail threshold? A tale on tail and copulas in financial risk measurement

Estimating the market risk is conditioned by the fat tail of the distribution of returns. But the tail index depends on the threshold of this distribution fat tail. We propose a methodology based on the decomposition of the series into positive outliers, Gaussian central part and negative outliers and uses the latter to estimate this cutoff point. Additionally, from this decomposition, we estimate extreme dependence correlation matrix which is used in the measurement of portfolio risk. For a sample consisting of six assets (Bitcoin, Gold, Brent, Standard&Poor-500, Nasdaq and Real Estate index), we find that our methodology presents better results, in terms of normality and volatility of the tail index, than the Kolmogorov–Smirnov distance, and its unnecessary capital consumption is lower. Also, in the measurement of the risk of a portfolio, the results of our proposal improve those of a t-Student copula and allow us to estimate the extreme dependence and the corresponding indexes avoiding the implicit restrictions of the elliptic and Archimedean copulas.

Comparative Analysis: Influence of Interest Rates on Returns of Real Estate Private Equity Index and Real Estate Public Equity Index

Abstract In this paper, we studied the influence of interest rates on a US-based real estate private equity index as well US Wilshire public equity REIT Index. The interest rates that are chosen as independent variables include Monthly LIBOR, Yearly LIBOR and the Federal Cost of Funds Index. The dependent variables include US-based real estate private equity index that includes quarterly returns of 1,035 real estate funds, including liquidated funds formed between 1986 and 2018. The other dependent variable is the US Wilshire REIT Index. The variance of returns of interest rates considerably influences the variance of returns of the US PERE Index, whereas variance of returns of interest rates doesn’t influence the variance of returns of the US Wilshire REIT Index. Also, the real estate index is positively correlated to interest rates and so rising interest rates influence the returns of US PERE Index in a positive manner. The study shows that private equity real estate investors should expect higher return as the cost of funds increase.

High-order Gaussian RBF-FD methods for real estate index derivatives with stochastic volatility

The 2008 financial crisis showed the necessity for managing risk associated with the price movements of real estate assets. Appropriate financial instruments for hedging real estate portfolios include options on a real estate index. Based on empirical evidence that real estate returns exhibit time varying volatility, a real estate index derivative pricing model with stochastic volatility is proposed. An efficient procedure using Gaussian radial basis functions-finite differences is developed for the numerical solution of the two-dimensional pricing partial differential equation. Fourth-order convergence rates on uniform meshes for European options are numerically demonstrated and the algorithm is also shown to compute accurate American option prices. A numerical study of the stability of the scheme reveals that errors remain small.

The Capitalized Generalized Autoregressive Conditional Heteroskedasticity

The aim of this paper is to shed new light on hedging discrete volatilities, in particular when using the generalized autoregressive conditional heteroskedasticity (thereafter GARCH) model. Despite its elegance, GARCH does not account for (i) correlation coefficients of debt and equity, (ii) equity parameter, (iii) risk premium, (iv) interest rates, and (v) shocks-stock markets. The unaccounted listed parameters are included into the GARCH(1,1) and the paper inverts a new model, expanded GARCH, called the capitalized GARCH. The results show that the capitalized GARCH convergences in a similar manner to the GARCH(1,1) in modeling volatility of bonds, commodities, equities, and real estate indices.

A new ICEEMDAN-based transfer entropy quantifying information flow between real estate and policy uncertainty

We quantify the dependence between real estate indices and global economic policy uncertainty for 12 top-advanced countries. Generally, real estate investments are found to be highly risky to information flow from global economic policy uncertainty. amidst policy uncertainty, we find diversification, safe haven, and hedging prospects – based on the market conditions – at short term frequencies only, for the pairs between (a) Japan and the US; (b) Singapore and the US; (c) China and Canada; (d) China and Hong Kong. Our findings underscore market efficiency (inefficiency) at mid-and long-term (short-term) frequencies. In the presence of policy uncertainty, our findings underscore the operability of ingrained market dynamics between real estate investments in the mid-and long-term horizons. It is prudent for investors to combine real estate investments with other asset classes that are less risky to (or are positive recipients of) information flow from global EPU to hedge against adverse market shocks from any asset in the portfolio based on market conditions. Practically, not only should legislations be flexible to the changing market trends in the short term, but they should also be strategically crafted to retain the fundamental market dynamics between real estate investments in the mid-and long-term economic horizons.

Influence of Various Asset Classes on Equity as an Asset Class: A USA Based Study

The study investigated the influence of asset classes like bond, commodity, venture capital, and real estate on the performance of equity as an asset class. The bond index, commodity index, U.S. venture capital index, and real estate index data was taken only for the USA market. The study revealed that U.S. equity market doesn’t perform in isolation and is highly related to other asset classes except bond/debt asset class. Therefore, it can be concluded that equity market doesn’t work in isolation and it related to performance of other asset classes in the financial market.

Deep Learning Approach in Predicting Property and Real Estate Indices

Abstract The real estate market is one of the most impacted sectors from the Corona Virus Disease 2019 (COVID-19) pandemic that happened in early 2020 globally. Here, we tried to apply an extension of the Long Short-Term Memory (LSTM) deep learning method, known as the Bidirectional LSTM (Bi-LSTM) networks for stock price prediction. Our focus is on six stocks that were included in the LiQuid45 (LQ45) property and real estate sectors. A simple three-layers Bi-LSTM network is proposed for predicting the stocks’ closing prices. We found that the prediction results fall in the reasonable prediction category, except for Pembangunan Perumahan Tbk (PTPP). Bumi Serpong Damai Tbk (BSDE) got the highest accuracy result with more than 90% score, while PTPP got the lowest score with less than 8% score. The proposed Bi-LSTM network could provide a baseline result for developing a good trading strategy. Keywords: Bi-LSTM networks, deep learning, LQ45, property and real estate, stock price prediction.