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

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.

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

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.

Real Estate Allocation Puzzle in the Mixed-Asset Portfolio: Fact or Fiction?

Market Risk of Real Estate

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

Shenzhen is one of cities that is real estate market-oriented, and the real estate is an important industry in the economic development. Real estate price statistics have drawn more and more attention and is being questioned due to distortion of announced real estate price in recent years. For example, the new commercial house price statistics is measured by using a simple average of price statistics, but the sample's heterogeneity leads to the house prices by statistics inconsistent with the real house prices. Meanwhile, in the process of second-hand housing transactions, the transaction prices is distortion and real house prices are out of control, caused by fake contracts. In order to grasp changes and trends of the price in the real estate market accurately, this research designs a house price indexes model, which covers new and secondary house in Shenzhen based on the real estate database. The Shenzhen house price indexes system employs B/S and three-layer architecture. It is based on the Skyline platform, and utilizes the JavaScript language for secondary development. The Shenzhen house price indexes system can effectively integrate real estate data, spatial data, 3D simulation model and house price indexes model, which achieves to monitor, manage, and analyze real estate data, and house price indexes. The system prepares and publishes periodic house price indexes, which could provide the latest, timeliest, and most accurate real estate price information for the parties in the real estate market.

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.

Climate change exacerbates flood hazards, resulting in risks to real estate values that should be considered by long-term investors. Flood risk presents two major financial risks: market risk and credit risk. Market risk refers to potential property value loss, and credit risk increases the likelihood of mortgage defaults. However, methods and comprehensive data for quantifying global real estate flood risks are lacking. To address this problem, this paper proposes two flood risk assessment frameworks: the local-oriented approach (LOA) and global-oriented approach (GOA). Two hazard and three vulnerability assessment methods are also introduced to support these frameworks. The LOA vulnerability estimates of regions with complete records are required to support the GOA. Taiwan was selected as an example for the LOA assessment, and the results were used to estimate vulnerability overseas in GOA assessments. The results of case studies for buildings located in four cities in different countries were compared. The proposed framework enables investors and asset owners to globally quantify climate risks in real estate, even when the available data are incomplete. Users can choose the most appropriate approach on the basis of the available data and their tolerance for uncertainty.

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.

Impact of Institutional Investors on Real Estate Risk

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.

In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value.

An efficient analysis of the real estate data is critical for buyers to understand the real estate market and seek appropriate properties to live in or rent. In this paper, we first collect data from different channels which are not provided in existing commercial real estate systems, and integrate them to build a location-centred comprehensive real estate dataset, including information other than the house itself, such as education profile, transportation profile and regional profile. Then we develop HouseSeeker, a visualization-aided system for buyers to explore the real estate data, find appropriate properties based on their individual requirements, and compare properties/suburbs from different aspects to discover the strengths and weaknesses of each property/suburb. We demonstrate the effectiveness of our system based on a real-world dataset in Melbourne metropolitan area: it is able to help zero-knowledge users better understand local real estate market and find preferred properties based on their individual requirements. A preliminary implementation of the system is available at http://115.146.89.158/.

The researcher combines his personal case study with a literature review to present his real estate investment model, exploring commercial and residential real estate investment opportunities and risks in the current market economy. The main premise of the model is that using financial acumen and systems models as the approach to investing in real estate provides the opportunity for high rates of return due to a variety of innate factors. The model explores the risks and opportunities of investing by: a) The use of leverage in property acquisitions; b) Accessing significant IRS tax advantages for investors, including the use of depreciation schedules, cost segregation models, 1031 tax-deferred exchanges, and tax-free cash-out refinancing. Risks in residential and commercial real estate can result from internal factors within the owner’s control or be caused by external factors beyond the investor’s control. AI, machine learning, and dynamic factor models for real estate investment are explored.