Climate Risk Assessment Framework in Real Estate: A Focus on Flooding

Market Risk of Real Estate

Bank Risk Factors and Changing Risk Exposures of Banks: Capital Market Evidence Before and During the Financial Crisis

Purpose– The purpose of this paper is to present a practice briefing in the form of a user’s manual for Excel-based simulation of real estate risk. Based on a generic discounted cash flow model, the simulation incorporates the often ignored heavy tail behaviour of real estate investments, and consolidates Jensen’s inequality. The briefing attempts to explain a model that permits the user to decide whether to include extreme events in real estate risk modelling and how extreme these events may be. Practitioners can generate a variety of modelling outcomes and then choose risk comfort zones in which to contemplate a range of returns.Design/methodology/approach– The paper provides an overview of the underlying mathematical concepts and challenges, as well as on the perspectives on their application from the current academic literature. It offers a step-by-step walk-through of the Excel model (the model being downloadable at:www.mathestate.com).Findings– Existing models for real estate risk modelling fall short with respect to realistic simulation of the probability of extreme events due to challenges in the implementation of stable laws. These former barriers to the implementation of stable laws have been overcome by providing a unique combination of Excel-resident functionalities with a stable pseudo random number generator.Research limitations/implications– Investment advisers no longer need expensive add-ins to estimate risk. The presented Excel model is more robust than common approaches as it considers distribution shapes that are not otherwise easily available. The only apparent limitation is that users need to be familiar with the most basic functionality of Excel.Practical implications– Practitioners are provided with an easy-to-use Excel model that does not require further software add-ins. The model simulates real estate investment returns, based on a more realistic inclusion of risk behaviour. It allows specifying how much extreme value behaviour characterizes the volatility in future projections modelled to guide investment decisions.Social implications– Risk is a cost to society. Many recent news events demonstrate the importance of including extreme values in modelling. The paper attempts to contribute to more realistic risk estimation in real estate investment.Originality/value– This briefing introduces a real estate risk simulation model that includes using stable laws, using Excel, a familiar and widely used platform. Such a model has not previously been reported in the academic or practitioners’ literature.

Executive Summary. Real estate accounts for a significant proportion of the corporate assets of publicly listed companies. The real estate exposure of publicly traded companies in land scare economies such as Hong Kong and Singapore is particularly high. This study explores the real estate exposure of listed companies in Singapore where non-real estate companies are classified by their real estate asset intensity. A six-factor Arbitrage Pricing Theory (APT) model, capturing changes in real estate prices, industrial production, expected inflation, unanticipated inflation, risk premium and term structure, shows that real estate exposure is priced. In addition, the real estate risk premium is found to vary across companies with different real estate asset intensity. A close linkage is also established between real estate exposure and various industries. Other than the real estate-intensive industries such as real estate, hotel and construction, conglomerates and financial companies also have a high exposure to real estate. Interestingly, our analysis shows that real estate exposure, which is computationally tedious to determine, can be proxied by real estate asset intensity, a simple accounting measure. The implications for portfolio management in a land-scare market are examined.

Building for a better hospital. Value-adding management & design of healthcare real estate

Abstract. There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ~ 1 km2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

Spatial decision support systems for effective ex-ante risk evaluation: An innovative model for improving the real estate redevelopment processes

Impact of Institutional Investors on Real Estate Risk

This paper is an exploratory study into the performance effects of corporate real estate (CRE) ownership for retail companies in the US. In the recent years, many brick-and-mortar retail stores have announced massive closings (as announced in 2018, 142 Sears locations will be closed, while Toys “R” Us filed for bankruptcy and announced shutting down all 800 locations). These struggles, along with the rise of e-commerce, signal significant changes in the CRE strategies for retail firms going forward. According to Gibson and Barkham (2001), CRE is closely linked to the business strategy of companies in the retail sector. As retail companies seek to assemble valuable CRE portfolios that can generate sustainable competitive advantages, inferior or inefficient locations can significantly undermine their long-term financial performance. On one hand, portfolio theory suggests that if real estate assets have a different risk profile than that of the operating business, then CRE ownership has the potential to provide diversification benefit to firms with high levels of property holdings. On the other hand, Tuzel (2010) has asserted that real estate risk is systematic, and CRE investment is riskier than investment in other types of capital for the firm due to slow depreciation of real estate. Therefore, firms with high real estate holdings are hurt more during recessions (Tuzel, 2010). For these reasons, CRE ownership has the potential to significantly impact the performance of retail companies.

The continued growth of the equity REIT market depends critically on investors' belief that the stock market provides fair and accurate valuations of real estate. An index of real estate stocks traded on the New York and American stock exchanges reflects changes in real estate market fundamentals in a more timely fashion than a widely used appraisal-based system. The index includes equity real estate investment trusts (REITs), real estate operating companies not organized in trust form, land subdividers and commercial developers, and general contractors. A statistical examination of the relation between the stock-based and appraisal-based series finds that the lagged returns of the real estate stocks help explain the behavior of the current-period appraisal series. The stock market apparently impounds information about changes in real estate values in a more timely manner than appraisal-based series constrained by infrequent property appraisals. oth practitioners and academics have become increasingly interested in the risks and returns of real estate ownership. In the absence of a centralized exchange to record sales, appraisal-based data such as the Russell-NCREIF Property Index (RNPI) are often used to analyze real estate returns. Such appraisal-based series are imperfect proxies for actual market conditions, however, largely because property valuations occur infrequently (quarterly at best), so appraisal-based series contain stale prices.1 This shortcoming has been highlighted recently by the failure of appraisal-based return indexes to capture either the timing or the magnitude of the recent dramatic downturn in commercial real estate markets. This article presents and analyzes the return behavior of a stock-market-based index of real estate performance. The index is composed of real-estate-related equities traded on the New York and American stock exchanges (NYSE and Amex). The firms in the index include owner-operators of existing properties as well as developers. Previous real estate research has primarily examined the returns of equity real estate investment trusts (REITs). That work generated two findings of particular relevance to this article. First, there is no significant contemporaneous correlation between equity REIT and appraisal series returns. Second, equity REIT returns are significantly positively correlated with broader stock market returns such as the SP real estate stock prices more accurately captured the industry downturn in the late 1980s than the well-known RNPI series. Despite the lack of a contemporaneous relation between appraisal and stock series, we conclude that there is an important connection between the two. This conclusion is based on a statistically and economically significant relation between lagged real estate stock returns and current-period returns on the appraisalbased RNPI. The evidence indicates that the two series incorporate similar information about real estate fundamentals, but the stock-based index does so in a more timely manner.

Real estate has always been an important asset class. Massive investment in real estate since the financial crisis 2008 has pushed the demand for integrated and micro-based risk modeling. The data for such risk management is available to banks and other investors, but is not structured and therefore not model-ready. Appraisals are available as printouts as well as pdf files, but the relevant data is only available in semi-structured form and differs by appraisal. A central issue in integrated real estate risk management consists of transferring the relevant information from appraisals into a risk management environment without too much unnecessary human effort. The goal of this article is to describe an approach to identifying and extracting existing semi-structured appraisal information into structured information ready to be consumed by risk models and other applications.

Although it is well known that there are many sources of risks in real estate investments, particularly in commercial real estate investments, the works are frequently commenced without consideration of risk resources and uncertainties in the project evaluation and investment decision making processes. The nature and impact on investment value of environmental, economic, real estate sector, and project risks are observed to vary from the beginning of the project development process until the end of it. Dynamic methods are often used in commonplace feasibility studies and integration of risks to calculations and periodically updating feasibility analyses are found to be usually neglected. However, project-specific risks such as the ownership of land, land development rights, and demands of the owners of neighboring parcels and economic risks directly affect the feasibility of the investment and can be major sources of failure.In this study, risk perceptions of upper middle managers of large-scale real estate development companies were evaluated and risk management approaches implemented within the companies were discussed. The scope of the study was limited to companies that are members of the Turkish Contractors Association (TCA), to which only the biggest real estate firms of Turkey can be members, and members of other important associations. In the construction and real estate market, two of the most important sectors in the Turkish economy, members of the TCA realize 70%of construction works within the country and 90%of the works undertaken by Turkish contractors abroad and have the ability to represent the industry. In this study, the impact of risk according to its resources on the preparation and implementation of real estate projects and risk perceptions of managers were examined based on the responses received to the questions electronically sent to the members of the TCA and other important associations. The study results revealed that that developers carry out risk management and decision support system activities, use intuitive risk analysis methods, utilize risks in integrated decision-making processes and the risk items that the developers consider are identified. According to the study results, sales forecasts, and the changes in foreign exchange and interest rates are significant in risk management in line with academic studies.

Flood maps are commonly used for flood risk communication. Misinterpretation of these maps can lead to over-heightened flood risk perception, with adverse risks for real estate markets. We, therefore, aim to identify an appropriate approach for framing these maps to mitigate such risks. To do this, we carry out five randomised control trials (RCT) in the UK and Pakistan, with each RCT asking a different group of 40 residents in each country to specify their willingness-to-pay (WTP) for coastal residential properties. In the first RCT, we provide no flood risk map. In each subsequent RCT, we provide a flood risk map framed differently. Our flood risk map frames include those with technical language, colour variations, and more accessible risk information. Our findings indicate that real estate demand uncertainty increases in response to flood risk maps, regardless of the framing approach used. We find that there is less WTP for properties in any zones with flood risk information, regardless of whether the risk is very low. This finding indicates that risk aversion takes precedence in real estate decisions under flood risk maps, leading to property devaluation in all flood risk zone categories. We discuss the wider implications of these findings. • Flood risk maps over-heighten flood risk perception. • Public access to these maps increases residential real estate demand uncertainty. • Flood risk maps have implications for property devaluation. • Risk-averse behaviours outweigh framing of these maps.

The socioeconomic impacts of flooding are huge. Between 1980 and 2013, flood losses exceeded $1 trillion globally, and resulted in approximately 220,000 fatalities. To reduce these negative impacts of floods, effective flood risk management is required. Reducing risk globally is at the heart of two recent international agreements: the Sendai Framework for Disaster Risk Reduction and the Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts. Prerequisites for effective risk reduction are accurate methods to assess hazard and risk, based on a thorough understanding of underlying processes. Due to the paucity of local scale hazard and risk data in many regions, several global flood hazard and flood risk models have been developed in recent years. More and more, these global models are being used in practice by an ever‐increasing range of users and practitioners. In this chapter, we provide an overview of recent advances in global flood hazard and risk modeling. We then discuss applications of the models in high‐level advocacy in disaster risk management activities, international development organizations, the reinsurance industry, and flood forecasting and early warning. The chapter concludes with several remarks on limitations in global flood risk models and the way forward for the future.

Chapter 6 - Uncertainty: Risk in real estate