Real Estate Investment Risk Research Articles

A model to define a real estate investment risk index for the administrative municipalities of Naples

Real estate redevelopment processes represent an important avenue for achieving sustainable development goals, but at the same time, they pose complex and opaque decision-making problems. Most existing risk assessment tools involve aggregate-scale analysis or require knowledge of extensive project financial data, which is often not yet available under ex-ante evaluation conditions. The aim of the work is to define a specific index through a spatial decision support system based on an innovative model that enables public and private entities to conduct effective ex-ante risk assessment at a sub-municipal territorial scale for public-private partnership risks. The proposed model adopts the flexibility of the Analytic Hierarchy Process (AHP) technique and the GIS system’s ability to clearly display the spatial distribution of real estate risk.

Real Estate Return Distributions with New NCREIF Data Series

The accuracy of real estate return distribution parameter estimation is affected by the tools used to do the work as well as the data sets employed. Consistent with previous studies, investment risk models with infinite variance describe distributions of individual property returns in the new NCREIF Indicators: Capital Performance and Property Operations individual property database over the period 1990–2021. Applying Maximum Likelihood Estimation (MLE) to historic data shows real estate investment risk to be heteroscedastic, but the Characteristic Exponent of the investment risk function varies more among property types than previously reported whether computed by MLE or other estimation techniques.

Understanding of Real Estate investment Risk and Opportunity (Study Business and Economic Management)

Understanding the risks and opportunities in real estate investing is an exciting study for academic purposes and business applications in the field. The author believes that this question is very relevant to be discussed to understand the opportunities and risks if someone wants to run a property business, which is a very lucrative business opportunity. To complete the discussion of this theme, we have conducted a series of electronic data searches on several publication databases, journals, books, and websites that actively discuss the risks and opportunities of investing in real estate, both nationally and internationally. We cannot describe the data validly and adequately if we do not do an in-depth study first, involving a data coding system and a thorough evaluation to get data findings that can answer. We carried out data published in the last ten years from 2010 to 2021, considering that today's data is so fast that it continues to change and evolve. Based on the phenomenological study we saw from the extensive data exploration. We found several points which in general were understanding the opportunities of the risk of investing in property, so based on this discussion, we can conclude that the opportunity is this considering that property is a business that never runs out and cannot be as easy as others are in motion and the sale and purchase, they only need to maintain, and the risk will usually occur if mismanagement and natural disasters such as floods and fires, the rest of the property business opportunities are indeed very promising. Thus, this finding should be a complement to the same studies.

Simulation analysis of regional real estate investment risk based on System dynamics

The high risk of real estate investment is the main obstacle to the orderly and healthy development of regional real estate market. To study the risk of real estate investment, it is necessary to analyze its influencing factors. This paper takes Tianjin as an example, based on six subsystems such as market factors and economic factors, analyzes the influencing factors of each subsystem, and synthetically determines the weight Coefficient of the index, constructing the risk System dynamics model of real estate investment. The result shows that the rise of interest rate and public emergency will bring great risk to the development of Tianjin real estate market. Finally, from the government’s point of view to establish a risk control mechanism for real estate enterprises to reduce the risk of investment to make reasonable recommendations.

Practical Applications of Real Estate Returns

Practical Applications In Real Estate Returns, from the Fall 2020 issue of The Journal of Alternative Investments, authors Jack Clark Francis (Baruch College) and Roger G. Ibbotson (Yale University) compare real estate investment returns to those from traditional investments like stocks and bonds. Their key focus is on real estate’s total returns—not just capital gains from rising land prices. The authors find that during the study period (1991–2018), farmland had an annualized total return of 11.4%—outperforming stocks—while residential real estate returned 9.0% and commercial real estate returned 8.5%. Most importantly, they find that rents provide the majority of investment returns from all real estate. They therefore warn investors and financial advisors not to base their investment decisions solely on how much real estate prices go up. However, they say real estate investment risk is hard to gauge because the methods used to track real estate price changes cause artificial smoothing. Real estate’s performance also shows a low correlation with that of stocks and bonds—so financial advisors and institutional investors may wish to consider adding real estate to traditional investment portfolios to help with diversification. TOPICS:Real estate, performance measurement

Asset-Level Risk and Return in Real Estate Investments

Abstract In stark contrast with liquid asset returns, commercial real estate idiosyncratic return means and variances do not scale with the holding period, even after accounting for all cash flow-relevant events. This puzzling phenomenon survives controlling for vintage effects, systematic risk heterogeneity, and a host of other explanations. To explain the findings, I derive an equilibrium search-based asset-pricing model that, when calibrated, provides an excellent fit to transactions data. A structural model of transaction risk seems crucial to understanding real estate price dynamics. These insights extend to other highly illiquid asset classes, such as private equity and residential real estate.

Is Consideration of Global Real Estate Investment Risks Worth?

Real estate investment acts as a collateral security and facilitates easy access to credit. Cross-border real estate investment has also been on the increase, investors are considering real estate as a global asset class. But there is huge uncertainty in the returns as high risk is associated with investment. To capture different levels of market risk, it is necessary to identify and measure the factors leading to this uncertainty, such as country risk, structural risk etc. this paper simply puts the focus on various global real estate investment risk associated while considering it as an investment option.

Liquidity and the drivers of search, due diligence and transaction times for UK commercial real estate investments

Trading commercial real estate involves a process of exchange that is costly and which occurs over an extended and uncertain period of time. This has consequences for the performance and risk of real estate investments. Most research on transaction times has occurred for residential rather than commercial real estate. We study the time taken to transact commercial real estate assets in the UK using a sample of 578 transactions over the period 2004–2013. We measure average time to transact from a buyer and seller perspective, distinguishing the search and due diligence phases of the process, and we conduct econometric analysis to explain variation in due diligence times between assets. The median time for purchase of real estate from introduction to completion was 104 days and the median time for sale from marketing to completion was 135 days. There is considerable variation around these times and results suggest that some of this variation is related to market state, type and quality of asset, and type of participants involved in the transaction. Our findings shed light on the drivers of liquidity at an individual asset level and can inform models that quantify the impact of uncertain time on market on real estate investment risk.

Real estate infrastructure financing in Ghana: Sources and constraints

Finance represents a major barrier to real estate development in Ghana and constraints responsible remain unknown. In order to gain insight into this important social/economic issue, a positivist tradition of thinking via the survey approach sought to uncover, explain and rank these constraints using the Relative Importance Index. A total of 53 questionnaires were distributed to real estate developers within the Kumasi Metropolis of Ghana. The survey reveals that real estate developers predominantly use debt financing including short-term bank loans, mortgages and hire purchase. Finance is constrained by five main factors: i) legislation; ii) macroeconomic barriers; iii) collateral barriers; iv) inadequate risk assessment and diversification mechanisms; and v) a dearth of financial mobilization mechanisms. This paper provides a practical reference for the targeting of policy interventions to overcome these constraints and to catalyse the development of finance for real estate infrastructure delivery. The work is also useful to the Government, banks and capital market regulators who seek to develop the capital market to provide appropriate financing options. These options may include real estate investment trusts (REITs) and risk reduction/diversification platform via derivatives. Future research is proposed which seeks to explore practical innovations in designing tailor-made real estate financing options that meet the needs of real estate developers.

Asset-Level Risk and Return in Real Estate Investments

Relatively little is known in the academic literature about the idiosyncratic returns of individual real estate investments, though quite a few commercial properties command prices commensurate with the market values of small publicly traded companies. I use purchase and sale data from the National Council of Real Estate Investment Fiduciaries (NCREIF) to compute holding period price appreciation returns for commercial properties. In stark contrast with liquid asset returns, idiosyncratic drift and volatility estimates diverge as the holding period shrinks. This puzzling phenomenon survives a variety of controls for vintage effects, systematic risk heterogeneity, and sample selection biases. I derive an equilibrium search-based illiquid asset pricing model which, when calibrated, fits the data very well. Thus a structural model of illiquidity seems crucial to a descriptive theory of real estate investment returns. These insights can potentially be extended to other illiquid asset classes such as private equity, mergers and acquisitions, large whole loans, and other real assets. The model can also be used to price derivatives such as debt claims.

Undivided Real Estate Shares: Appraisal and Interactions with Capital Markets

The appraisal of undivided and indivisible real estate shares represents a recurring and underestimated issue by professional appraisers. This problem requires a logical solution considering that the undivided real estate shares are more difficult to sell and, consequently, there is a decrease of their market value. It follows that issue can be referred, in theoretical and practical terms, to the real estate investment risk valuation and on how to convert this risk into an expected rate that can compensate it. In this paper theCapital Asset Pricing Modelhas been integrated withPenalized Spline Semiparametric Methodin order to obtain an algorithm that allows to rationalize the appraisal of undivided real estate shares using easily accessible data.

Common Stochastic Volatility in International Real Estate Market

This study examined the real estate markets of Europe, North America, and Asia using daily continental real estate indices. It applied a multivariate stochastic volatility model to analyze the behavior of volatility trends in these markets. The results showed comovements in volatilities, especially between Europe and North America, as indicated by high degrees of correlation of their respective stochastic trend components. However, the impact of this common trend varies in these markets, especially for the early period of the sample. For the later period of the sample, the derived volatility trend indicated volatility convergence among them. It might imply that the role of emerging market such as Asia in diversifying real estate investment risk was not as significant as showed in early studies and is diminishing overtime.

The Determinant Factors of Real Estate Investment Trust (REIT)’s Performance: Evidence from Asian REITs

Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper investigates the factors that can influence the Real Estate Investment Trust (REIT)’s performance, paying particular attention to the listed REIT’s in Asian.   Samples of 45 Asian listed REITs are selected from five different countries namely Taiwan, Thailand, Malaysia, Hong Kong, Japan and Singapore for 5 years basis from 2007 to 2011 with 225 observations. Study used Net Asset value (NAV) and Return as the proxy for REITs performance while risk, dividend yield, net income and size to represent the determinants variable. Applying correlations and multiple regression analysis, the results provide evidence on the association between NAV and return with risk, dividend yield, net income and size of REITs. Results of this study are hoped to help the investors and portfolio managers to deepen their understanding of the dependence factors that might influence the performance of REITs in Asian. Keywords : Net asset value (NAV), Real Estate Investment Trust (REITs), Risk and Dividend Yield.

Real Estate Investment Risk Assessment Based on GABP Algorithm of Neural Network

Real Estate Investment Risk Assessment Based on GABP Algorithm of Neural Network

Fuzzy Comprehensive Evaluation Method Applied in the Real Estate Investment Risks Research

Abstract Real estate investment is a high-risk and high returned of economic activity, the key of real estate analysis is the identification of their types of investment risk and the risk of different types of effective prevention. But, as the financial crisis sweeping the world, the real estate industry also faces enormous risks, how effective and correct evaluation of real estate investment risks becomes the multitudinous scholar concern[1]. In this paper, real estate investment risks were summarized and analyzed, and comparative analysis method is discussed and finally presented fuzzy comprehensive evaluation method, not only in theory has the advantages of science, in the application also has the reliability, for real estate investment risk assessment provides an effective means for investors in real estate investing guidance on risk factors and forecasts.

Real Estate Return Distributions Using Maximum Likelihood Estimation: New Technology, New Results

The estimation of parameters of real estate return distributions is affected by the tools used to do the work. Consistent with previous studies, investment risk models with infinite variance describe distributions of individual property returns in the National Council of Real Estate Investment Fiduciaries (NCREIF) database over the period 1980 to 2010. Applying Maximum Likelihood Estimation (MLE) to the historic data shows real estate investment risk to be heteroscedastic, but the characteristic exponent of the investment risk function varies more among property types than previously reported. Apartment properties introduced in this study evidence the same basic performance characteristics as office, retail, and industrial properties.

A GIS based system for minimizing acquisition risk in Real Estate property market

Many economists have identified the main cause of recent global real estate market crisis both in the poor diffusion of systems capable of monitoring the historical property values that in the lack of public awareness about the risks of real estate investments. This paper proposes a new approach to evaluate property acquisition’s risk that does not rely on a standardize appraisal indices analysis. The proposed Geographical Information System is able to investigate the historical variation of the main real estate market indicators and to let the final user appraise the general level of real estate investment risk. The subsequent sections of this paper will discuss about the real strength of the most common literature’s algorithms used to appraise risk in real estate, then it will be explained how inexpensive information technology applications can help to increase the diffusion of awareness related to real estate market conditions. Finally it will be discussed the case of study; conclusions and some considerations about future improvements will close the study.

不動産バブルの教訓

The advent and collapse of real estate bubbles, or the sharp rises and falls in real estate prices, have posed major economic issues in many contries. Real estate prices soared sharply in Japan and Sweden in the 1990s and recently in many Western countries （until the mid─2000s）, until plunging in the wake of the financial crisis in the United States. The rapid rise and fall of real estate prices in Japan from the mid-1980s to the 1990s is said to represent the most significant real estate bubble of the 20th century. Following the collapse of this bubble, Japan experienced a long period of economic stagnation, often cynically described as the“l ost decades.” What have we learned from these ups and downs in the real estate market? Have recent real estate investment risk management efforts incorporated these lessons? To answer these questions, this paper would like to pay attention to the severalty of each real asset. The lesson learned from last century's economic bubble and from the expansion and shrinkage of the real estate market in the wake of the latest financial crisis is that real estate investment risks cannot be dispersed by diversifying investments, and major risks will remain unless assets are carefully selected.

SEL versus SUL: managing seismic risk in commercial real estate investments

AbstractTwo common scenario loss ratios are used when calculating Probable Maximum Losses from earthquakes: Scenario Expected Loss (SEL) and Scenario Upper Loss (SUL). Analyses of seismic loss ratios prepared by five seismic consulting firms, four loan pools securitized in the capital markets, two very large loans with many properties, two large hospitality portfolios and a general account portfolio indicate that use of SUL rather than SEL would yield significantly larger numbers of loans with loss ratios in excess of 20%. When using SEL, the percentage of loans in the four large pools exceeding a 20% loss ratio was 3.8%. When SUL was used on this same data set, 47.8% of these properties had SUL values above 20%. Common industry practice has been to use SEL. Some of the implications of tightening seismic underwriting standards to apply a 20% threshold to the SUL, rather than SEL, may include: lower loan production, properties may lose value, properties may be costlier and more difficult to finance, existing loan portfolios may appear more seismically risky, and demand for insurance and seismic retrofit could go up. Equally undesirable effects could be that seismic consultants and lenders who do more rigorous analysis will be less competitive than those who do not. Copyright © 2010 John Wiley & Sons, Ltd.

Revisiting Non-normal Real Estate Return Distributions by Property Type in the U.S.

In this updated empirical analysis, investment risk models with infinite variance are more descriptive of distributions of individual property returns in the NCREIF database over the period 1980 to 2003 than Normally distributed risk models. Real estate investment risk is heteroskedastic, but the Characteristic Exponent of the investment risk function is nearly constant across time although differences among property types are evident. Accordingly, asset diversification is far less effective at reducing the impact of non-systematic investment risk on real estate portfolios than in the case of assets with Normally distributed investment risk. The patterns found in the U.S. are the same in Australia and the United Kingdom, and the Characteristic Exponents are virtually identical across all three countries.