Search results for: real estate valuation model

Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

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Authors: Linda Kauškale, Ineta Geipele

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Due to the rapidly changing external environment, portfolio management strategies became closely interconnected with real estate industry development and macroeconomic development tendencies. The aim of the research is to analyze sustainable real estate market development influencing factors, with particular focus on its economic and management aspects that influences real estate investment decisions as well. Scientific literature and article analysis, data analysis, expert evaluation, and other quantitative and qualitative research methods were used in the research. Developed real estate market sustainability model and index analysis approach can be applied by investors and real estate companies in real estate asset management and can help in risk minimization activities in international entrepreneurship. Future research directions have been identified in the research as well.

Keywords: indexes, investment decisions, real estate market, sustainability

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Authors: Li Li, Kai-Hsuan Chu

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It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference.

Keywords: real estate price, least-square, grey correlation, macroeconomics

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Authors: Deng Tingting

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There is a very close interaction between the rapid development of the real estate industry and regional urbanization. The real estate problem can be boiled down to the problem of urbanization, in essence. The growth of hundreds of millions of people in the future will determine the development of low-level demand in the real estate market. At the same time, the practical problems of urbanization also seriously restrict the healthy development of real estate itself. The latter two interact with each other by adjusting the industrial structure, economic aggregate, regional population flow, and many other linkage factors. Through the case analysis of Chongqing, this paper finds that the urbanization of Chongqing and the overall development level of the real estate industry are still in the stage of development and upgrading, and its development potential and future development and application space are still very large. Therefore, from the perspective of the regional economy, studying the interaction between the two is of great significance to accelerate the process of urbanization in Chongqing, promote the healthy development of the real estate industry, and promote the rapid growth of the regional economy.

Keywords: urbanization, demographics, real estate, interrelationships

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Authors: Andrey Matyukhin

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Russian official statistics have been showing a steady decline in residential real estate prices for several consecutive years. Price risk in real estate markets is thus affecting various groups of economic agents, namely, individuals, construction companies and financial institutions. Potential use of property derivatives might help mitigate adverse consequences of negative price dynamics. Unless a sustainable price indicator is developed, settlement of such instruments imposes constraints on counterparties involved while imposing restrictions on real estate market development. The study addresses geographical and classification heterogeneity in real estate prices by means of variance analysis in various groups of real estate properties. In conclusion, we determine optimal sample structure of representative real estate assets with sufficient level of price homogeneity. The composite price indicator based on the sample would have a higher level of robustness and reliability and hence improving liquidity in the market for property derivatives through underlying standardization. Unlike the majority of existing real estate price indices, calculated on country-wide basis, the optimal indices for Russian market shall be constructed on the city-level.

Keywords: price homogeneity, property derivatives, real estate price index, real estate price risk

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Authors: Rajat Kapoor

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Indian Real Estate sector is the second largest employer after agriculture and is slated to grow at 30 percent over the next decade. Indian cities have shown tumultuous growth since last two decades. With the growing need of infrastructure, it has become inevitable for real estate sector to adopt more organized and transparent system of investment. SPVs such as REITs ensure transparency facilitating accessibility to invest in real estate for those who find it difficult to purchase real estate as an investment option with a realistic income expectation from their investment. RIETs or real estate investment trusts is an instrument of pooling funds similar to that of mutual funds. In a simpler term REIT is an Investment Vehicle in the form a trust which holds & manages large commercial rent¬ earning properties on behalf of investors and distributes most of its profit as dividends. REIT enables individual investors to invest their money in commercial real estate assets in a diversified portfolio and on the other hand provides fiscal liquidity to developers as easy exit option and channel funds for new projects. However, the success REIT is very much dependent on the taxation structure making such models attractive and adaptive enough for both developers and investors to opt for such investment option. This paper is intended to capture an overview of REITs with context to Indian real estate scenario.

Keywords: Indian real estate, real estate infrastructure trusts, urban finance, infrastructure investment trusts

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Authors: Osaretin Rosemary Uyi, A. O. Ogungbemi

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Female involvement in policy making and partnership in a man-driven-world is fast gaining international recognition. The Nigeria commercial real estate is one of the sectors of the economy that has a significant number of the male in the business. This study was conducted to assess the participation of females in estate management in Lagos state, Nigeria. Lagos is the commercial nerve center of Nigeria having the highest number of real estate practitioners and investors. The population due to the daily influx of people has made real estate business to continue to grow in this part of Nigeria. A structured questionnaire duly pre-tested and validated was used to elicit information from the respondents. The data collected were presented using tables and charts and were analyzed using descriptive statistical tools such as frequency counts, percentages, were used to test the hypothesis. The results also indicated that most females that participated in commercial real estate business are educated (80%), fell within 31-40 years of age (75%) and of high income status (88%) earn above ₦800,000 per year, while 10% are real estate investors and 82% of the female in the sector are employee. The study concluded that the number of female participating in various aspect of commercial real estate business in the study area was moderate while the numbers of female investors are low when compared to male. This might be due to the problems associated with rent collection, land disputes and other issues that are associated with property management in Nigeria. It is therefore recommended that females in real estate should be empowered and encouraged to match with their male counterpart.

Keywords: commercial real estate, empowerment, female, participation, property management

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Authors: Lyka Selene Magnayi, Marcos Vinas, Roseanne Ramos

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As the real estate industry continually grows in the Philippines, Geographic Information Systems (GIS) provide advantages in generating spatial databases for efficient delivery of information and services. The real estate sector is not only providing qualitative data about real estate properties but also utilizes various spatial aspects of these properties for different applications such as hazard mapping and assessment. In this study, a three-dimensional (3D) model and a spatial database of real estate properties in Fort Bonifacio, Taguig City are developed using GIS and SketchUp. Spatial datasets include political boundaries, buildings, road network, digital terrain model (DTM) derived from Interferometric Synthetic Aperture Radar (IFSAR) image, Google Earth satellite imageries, and hazard maps. Multiple model layers were created based on property listings by a partner real estate company, including existing and future property buildings. Actual building dimensions, building facade, and building floorplans are incorporated in these 3D models for geovisualization. Hazard model layers are determined through spatial overlays, and different scenarios of hazards are also presented in the models. Animated maps and walkthrough videos were created for company presentation and evaluation. Model evaluation is conducted through client surveys requiring scores in terms of the appropriateness, information content, and design of the 3D models. Survey results show very satisfactory ratings, with the highest average evaluation score equivalent to 9.21 out of 10. The output maps and videos obtained passing rates based on the criteria and standards set by the intended users of the partner real estate company. The methodologies presented in this study were found useful and have remarkable advantages in the real estate industry. This work may be extended to automated mapping and creation of online spatial databases for better storage, access of real property listings and interactive platform using web-based GIS.

Keywords: geovisualization, geographic information systems, GIS, real estate, spatial database, three-dimensional model

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Authors: Haytham Yassine

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This research seeks to identify how money laundering activities are executed through the real estate sector. This article provides academics with literature on the topic and provides scholars, and practitioners with a better understanding of the risks and challenges involved. Data are gathered through survey in the Middle East and North Africa region and review of the available research. The results of the analysis will help identifying the factors attracting criminals to the real estate sector and develop an understanding of the methods used to launder illicit funds through this sector and the indicators of suspicion for reporting entities. Further analysis reveals the risks posed by money laundering and terrorist financing on the real estate sector and challenges facing states in this regard.

Keywords: money laundering, terrorism financing, real estate sector, Middle East and North Africa

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Authors: Shai Sarussi

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Suppose F is a field with valuation v and valuation domain Oᵥ, and R is an Oᵥ-algebra. It is known that there exists a filter quasi-valuation on R; the existence of a quasi-valuation yields several important connections between Oᵥ and R, in particular with respect to their prime spectra. In this paper, the notion of a module valuation is introduced. It is shown that any torsion-free module over Oᵥ has an induced module valuation. Moreover, several results connecting the filter quasi-valuation and module valuations are presented.

Keywords: valuations, quasi-valuations, prime spectrum, algebras over valuation domains

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Authors: Tuti Haryati Jasimin, Hishamuddin Mohd Ali

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Malaysia’s green building development is gaining momentum and green buildings have become a key focus area especially within the commercial sector with the encouragement of government legislation and policy. Due to the emerging awareness among the market players’ views of the benefits associated with the ownership of green buildings in Malaysia, there is a need for valuers to incorporate consideration of sustainability into their assessments of property market value to ensure the green buildings continue to increase in the market. This paper analyses the valuers’ current perception on the valuation practices with regard to the green issues in Malaysia. The study was based on a survey of registered real estate valuers and the experts whose work related to valuation in the Klang Valley area to rate their view regarding the perception on valuation of green building. The findings present evidence that even though Malaysian valuers have limited knowledge of green buildings, they recognize the importance of incorporating the green features in the valuation process. The inclusion of incorporating the green features in valuations in practice was hindered by the inadequacy of sufficient transactional data in the market. Furthermore, valuers experienced difficulty in identifying what are the various input parameters of green building and how to adjust it in order to reflect the benefit of sustainability features correctly in the valuation process. This paper focuses on the present challenges confronted by Malaysian valuers with regards to incorporating the green features in their valuation.

Keywords: green commercial office building, Malaysia, valuers’ perception, valuation, commercial sector

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Authors: Sol Girouard, Zona Kostic

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A home is often the largest and most expensive purchase a person makes. Whether the decision leads to a successful outcome will be determined by a combination of critical factors. In this paper, we propose a method that efficiently handles all the factors in residential real estate and performs predictions given a feature space with high dimensionality while controlling for overfitting. The proposed method was built on gradient descent and boosting algorithms and uses a mixed optimizing technique to improve the prediction power. Usually, a single model cannot handle all the cases thus our approach builds multiple models based on different subsets of the predictors. The algorithm was tested on 3 million homes across the U.S., and the experimental results demonstrate the efficiency of this approach by outperforming techniques currently used in forecasting prices. With everyday changes on the real estate market, our proposed algorithm capitalizes from new events allowing more efficient predictions.

Keywords: real estate prediction, gradient descent, boosting, ensemble methods, active learning, training

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Authors: Dishant Shahi, Aradhya Shandilya, Nand Kumar

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We study here the impact of the black component referred to as X component in the text on Real estate transactions. The X component involved not only acts as friction in transaction but also leads to dysfunctionality in the capital market of real estate. The effect of the component is presented by using a model of economy which seeks resemblance with that of India involving property deals. The rigidities which hinder smooth transactions in property or land deals are depicted and their impact on the economy as a whole has been modelled. The effect of subprime crisis (2007) on Indian housing capital market and the role which the X component played during it, is also included in one of the sections. In the entire text, we have utilised 4 Quadrant graphs to study supply and demand causalities involved in commercial real estate. At the end we have included the impact of demonetisation as a move to counter the problem of overvaluation in the property assets arising due to the X component. The case of Demonetisation which has been the latest move by the Indian Government to control huge amount of black money in circulation has been included along with its impact on the housing and rent as well as the capital market.

Keywords: X-component, 4Q graph, real estate, capital markets, demonetisation, consumer sentiments

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Authors: Patel Parul, Harsh Ganvit

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The construction industry is the second highest industry after agriculture provides employment in India. In developing countries like India, many construction projects are coming up to meet the demand. On the one hand, construction projects are increasing; on the other hand still, construction companies are struggling with many problems. One of the major problems is to ensure safe working conditions at the construction site. Due to a lack of safety awareness and ignorance of safety aspects, many fatal accidents are very common at the construction site in India. One of the key success factors for construction projects is “Accident-Free Construction Projects”. The construction projects can be divided into various categories like Infrastructure projects, industrial construction and real estate construction. Real estate projects are mainly comprised of commercial and residential projects. In the construction industry, private sectors play a huge role in urban and rural development and also contribute significantly to the growth of the nation. Infrastructure and Industrial projects are mainly executed by well-qualified construction contractors. For such projects, ensuring safety at construction projects is inevitable and probably one of the major clauses of contract documents as well. These projects are monitored from time to time by national agencies and researchers, too. However, Real estate projects are rarely monitored for safety aspects. No systematic contract system is followed for these projects. Safety is the most neglected aspect of these projects. In the current research projects, an attempt is made to carry out safety auditing for about 75 real estate projects. The objective of this work is to collect the activity-based safety survey of real estate projects in western India. The analysis of activity-based safety implementation for real estate projects is discussed in the present work. The activities are divided into three categories based on the data collected. The findings of this work will help local monitoring authorities to implement a safety management plan for real estate projects.

Keywords: construction safety, safety assessment, activity-based safety, real estate projects

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Authors: Tahmina Akhter

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In the present work, we make a study of the repercussions of the pandemic generated by Covid-19 in the real estate market, this disease has affected almost all sectors of the economy across different countries in the world, including the real estate markets. This documentary research, basically focused on the years 2021 and 2022, as we seek to focus on the strongest time of the pandemic. We carried out the study trying to take into account the repercussions throughout the world and that is why the data we analyze takes into account information from all continents as possible. Particularly in the US, Europe and China where the Covid-19 impact has been of such proportions that it has fundamentally affected the housing market for middle-class housing. In addition, a risk has been generated, the investment of this market, due to the fact that companies in the sector have generated losses in certain cases; in the Chinese case, Evergrande, one of the largest companies in the sector, fell into default.

Keywords: COVID-19, real estate market, statistics, pandemic

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Authors: Kathy Pain, Nalumino Akakandelwa

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The study explores the potential to fill a gap in health and wellbeing evidence for purposeful urban real estate asset management to make investment a powerful force for societal good. Part of a five-year programme investigating the root causes of unhealthy urban development funded by the United Kingdom Prevention Research Partnership (UKPRP), the study pilots the use of a systems mapping approach to identify drivers and barriers to the incorporation of health and wellbeing evidence in urban brownfield asset management decision-making. Urban real estate not only provides space for economic production but also contributes to the quality of life in the local community. Yet market approaches to urban land use have, until recently, insisted that neo-classical technology-driven efficient allocation of economic resources should inform acquisition, operational, and disposal decisions. Buildings in locations with declining economic performance have thus been abandoned, leading to urban decay. Property investors are recognising the inextricable connection between sustainable urban production and quality of life in local communities. The redevelopment and operation of brownfield assets recycle existing buildings, minimising embodied carbon emissions. It also retains established urban spaces with which local communities identify and regenerate places to create a sense of security, economic opportunity, social interaction, and quality of life. Social implications of urban real estate on health and wellbeing and increased adoption of benign sustainability guidance in urban production are driving the need to consider how they affect brownfield real estate asset management decisions. Interviews with real estate upstream decision-makers in the study, find that local social needs and impact analysis is becoming a commercial priority for large-scale urban real estate development projects. Evidence of the social value-added of proposed developments is increasingly considered essential to secure local community support and planning permissions, and to attract sustained inward long-term investment capital flows for urban projects. However, little is known about the contribution of population health and wellbeing to socially sustainable urban projects and the monetary value of the opportunity this presents to improve the urban environment for local communities. We report early findings from collaborations with two leading property companies managing major investments in brownfield urban assets in the UK to consider how the inclusion of health and wellbeing evidence in social valuation can inform perceptions of brownfield development social benefit for asset managers, local communities, public authorities and investors for the benefit of all parties. Using holistic case studies and systems mapping approaches, we explore complex relationships between public health considerations and asset management decisions in urban production. Findings indicate a strong real estate investment industry appetite and potential to include health as a vital component of sustainable real estate social value creation in asset management strategies.

Keywords: brownfield urban assets, health and wellbeing, social needs and impact, social valuation, sustainable real estate, systems mapping

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Simon Huston, Ebraheim Lahbash, Ali Parsa

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The development of the United Arab Emirates (UAE) into a regional trade, tourism, finance and logistics hub has transformed its real estate markets. However, speculative activity and price volatility remain concerns. UAE residential market values (MV) are exposed to fluctuations in capital flows and migration which in turn are affected by geopolitical uncertainty, oil price volatility, and global investment market sentiment. Internally, a complex interplay between administrative boundaries, land tenure, building quality and evolving location characteristics fragments UAE residential property markets. In short, the UAE Residential Valuation System (UAE-RVS) confronts multiple challenges to collect, filter and analyze relevant information in complex and dynamic spatial and capital markets. A robust (RVS) can mitigate the risk of unhelpful volatility, speculative excess or investment mistakes. The research outlines the institutional, ontological, dynamic, and epistemological issues at play. We highlight the importance of system capabilities, valuation standard salience and stakeholders trust.

Keywords: valuation, property rights, information, institutions, trust, salience

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Authors: Elona Saliaj

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The registration of real estate in Albania after the 90's has been a long process in time and with high cost for the country. Passing the registration system from a centralized system to a free market private system, it’s accompanied by legal uncertainties that have led to economic instability. The reforms that have been undertaken in terms of property rights have been numerous and continuous throughout the years. But despite the reforms, the system of registration of real estate, has failed to be standards requirements established by the European Union. The completion of initial registration of real estate, legal treatment of previous owners or legalization of illegal constructions remain among the main problems that prevent the development of the country in its economic sector. The performance of the registration of real estate system and dealing with issues that have appeared in the Court of First Instance, the civil section of the Albanian constitute the core of handling this analysis. This paper presents a detailed analysis on the registration system that is chosen to be applied in our country for real estate. In its content it is also determined the institution that administrates these properties, the management technique and the law that determinate its functionality. The strategy is determined for creating a modern and functional registration system and for the country remains a challenge to achieve. Identifying practical problems and providing their solutions are also the focus of reference in order to improve and modernize this important system to a state law that aims to become a member of the European Union.

Keywords: real estates registration system, comparative aspects, cadastral area, property certificate, legal reform

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Authors: H. D. Ibrahim, H. C. Chinwenyi, T. Danjuma

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An option is defined as a financial contract that provides the holder the right but not the obligation to buy or sell a specified quantity of an underlying asset in the future at a fixed price (called a strike price) on or before the expiration date of the option. This paper examined two approaches for derivation of Partial Differential Equation (PDE) options price valuation formula for the Heston stochastic volatility model. We obtained various PDE option price valuation formulas using the riskless portfolio method and the application of Feynman-Kac theorem respectively. From the results obtained, we see that the two derived PDEs for Heston model are distinct and non-unique. This establishes the fact of incompleteness in the model for option price valuation.

Keywords: Black-Scholes partial differential equations, Ito process, option price valuation, partial differential equations

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Authors: Julianto Agung Saputro, Jogiyanto Hartono

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Multiple valuation is widely used by investors and practitioners but its accuracy is questionable. Multiple valuation inaccuracies are due to the unreliability of information used in valuation, inaccuracies comparison group selection, and use of individual multiple values. This study investigated the accuracy of valuation to examine factors that can increase the accuracy of the valuation of multiple ratios, that are discretionary accruals, the comparison group, and the composite of multiple valuation. These results indicate that multiple value adjustment method with discretionary accruals provides better accuracy, the industry comparator group method combined with the size and growth of companies also provide better accuracy. Composite of individual multiple valuation gives the best accuracy. If all of these factors combined, the accuracy of valuation of multiple ratios will give the best results.

Keywords: multiple, valuation, composite, accuracy

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Authors: Carmen Cantuarias-Villessuzanne, Jeffrey Blain, Radmila Pineau

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Our research project aims at analyzing the sensitiveness of French households to urban biodiversity and urban ecosystem services (UES). Opinion surveys show that the French population is sensitive to biodiversity and ecosystem services loss, but the value given to these issues within urban fabric and real estate market lacks evidence. Using GIS data and economic evaluation, by hedonic price methods, weassess the isolated contribution of the explanatory variables of biodiversityand UES on the price of residential real estate. We analyze the variation of the valuefor three urban ecosystem services - flood control, proximity to green spaces, and refreshment - on the price of real estate whena property changes ownership. Our modeling and mapping focus on the price at theIRIS scale (statistical information unit) from 2014 to 2019. The main variables are internal characteristics of housing (area, kind of housing, heating), external characteristics(accessibility and infrastructure, economic, social, and physical environmentsuch as air pollution, noise), and biodiversity indicators and urban ecosystemservices for the Ile-de-France region. Moreover, we compare environmental values on the enhancement of greenspaces and their impact on residential choices. These studies are very useful for real estate developers because they enable them to promote green spaces, and municipalities to become more attractive.

Keywords: urban ecosystem services, sustainable real estate, urban biodiversity perception, hedonic price, environmental values

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Authors: Sergey Ermolin, Olga Ermolin

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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.

Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking

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Authors: Mark E. Lokanan, Liz Lee

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This paper aims to examine the role of accountants as gatekeepers in anti-money laundering compliance in real estate transactions. The paper seeks to answer questions on ways in which accountants are involved in real estate transactions and mandatory compliance with regulatory authorities in Canada. The data for the study came from semi-structured interviews with accountants, lawyers, and government officials. Preliminary results reveal that there is a conflict between accountants’ obligation to disclose and loyalty to their clients. Accountants often do not see why they are obligated to disclose their clients' information to government agencies. The importance of the client in terms of the amount of revenue contributed to the accounting firm also plays a significant role in accountants' reporting decision-making process. Although the involvement of accountants in real estate purchase and sale transactions is limited to lawyers or notaries, they are often involved in designing financing schemes, which may involve money laundering activities. The paper is of wider public policy interests to both accountants and regulators. It is hard not to see Chartered Professional Accountant (CPA) Canada and government regulators using the findings to better understand the decision-making processes of accountants in their reporting practices to regulatory authorities.

Keywords: money laundering, real estate, disclosure, legislation, compliance

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Authors: I. Inthanongsone, C. Drebenstedt, J. C. Bongaerts, P. Sontamino

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The major concern in evaluating the value of mining projects related to the deficiency of the traditional discounted cash flow (DCF) method. This method does not take uncertainties into account and, hence it does not allow for an economic assessment of managerial flexibility and operational adaptability, which are increasingly determining long-term corporate success. Such an assessment can be performed with the real options valuation (ROV) approach, since it allows for a comparative evaluation of unforeseen uncertainties in a project life cycle. This paper presents an economic evaluation model for open pit mining projects based on real options valuation approach. Uncertainties in the model are caused by metal prices and cost uncertainties and the system dynamics (SD) modeling method is used to structure and solve the real options model. The model is applied to a case study. It can be shown that that managerial flexibility reacting to uncertainties may create additional value to a mining project in comparison to the outcomes of a DCF method. One important insight for management dealing with uncertainty is seen in choosing the optimal time to exercise strategic options.

Keywords: DCF methods, ROV approach, system dynamics modeling methods, uncertainty

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Authors: Matthias Haerri, Tobias Huettche, Clemens Kustner

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This article discusses the impact of digitization on business valuation. In order to become and remain ‘digital’, investments are necessary whose return on investment (ROI) often remains vague. This uncertainty is contradictory for a valuation, that rely on predictable cash flows, fixed capital structures and the steady state. However digitisation does not make a company valuation impossible, but traditional approaches must be reconsidered. The authors identify four areas that are to be changing: (1) Tools instead of intuition - In the future, company valuation will neither be art nor science, but craft. This does not require intuition, but experience and good tools. Digital evaluation tools beyond Excel will therefore gain in importance. (2) Real-time instead of deadline - At present, company valuations are always carried out on a case-by-case basis and on a specific key date. This will change with the digitalization and the introduction of web-based valuation tools. Company valuations can thus not only be carried out faster and more efficiently, but can also be offered more frequently. Instead of calculating the value for a previous key date, current and real-time valuations can be carried out. (3) Predictive planning instead of analysis of the past - Past data will also be needed in the future, but its use will not be limited to monovalent time series or key figure analyses. With pictures of ‘black swans’ and the ‘turkey illusion’ it was made clear to us that we build forecasts on too few data points of the past and underestimate the power of chance. Predictive planning can help here. (4) Convergence instead of residual value - Digital transformation shortens the lifespan of viable business models. If companies want to live forever, they have to change forever. For the company valuation, this means that the business model valid on the valuation date only has a limited service life.

Keywords: business valuation, corporate finance, digitisation, disruption

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Authors: Hina Munir Abbasi

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The current paper examines the relationship and diversification ability of Islamic stock indices /conventional stocks indices and Real Estate Investment Trust (REITs).To represent conditional dependency between stocks and REITs in a more realistic way, new modeling technique, time-varying copula with switching dependence is used. It represents reliance structure more accurately and realistically than a single copula regime as dependence may alter between positive and negative correlation regimes with time. The fluctuating behavior of markets has significant impact on economic variables; especially the downward trend during crisis. Overall addition of Real Estate Investment Trust in stocks portfolio reduces risks and provide better diversification benefit. Results varied depending upon the circumstances of the country. REITs provides better diversification benefits for Islamic Stocks, when both markets are bearish and can provide hedging benefit for conventional stocks portfolio.

Keywords: conventional stocks, real estate investment trust, copula, diversification, risk spillover, safe heaven

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Authors: Mounir Azzam, Valerie Graw, Andreas Rienow

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The Syrian war, which commenced in 2011, has resulted in significant changes in the real estate market in the Damascus metropolitan area, with rising levels of insecurity and disputes over tenure rights. The quest for spatial justice is, therefore, imperative, and this study performs a spatiotemporal analysis to investigate the impact of the war on real estate differentiation. Using the hedonic price models including 2,411 housing transactions over the period 2010-2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation and depreciation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus city resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. We monitored 250 samples from hedonic datasets within three different years of the Syrian war (2015, 2019, and 2022). Our findings show that correlation values were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. The findings have valuable implications, not only for investment prospects in setting up a successful reconstruction strategy but also for spatial justice of property rights in strongly encouraging sustainable real estate development.

Keywords: hedonic price, real estate differentiation, reconstruction strategy, spatial justice, urban texture analysis

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Authors: Vanessa Kummer, Maik Meusel

Abstract:

Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Matteo Manera, Mariateresa Torchia, Gregory Moscato

Abstract:

Forensic accounting is a hot subject of research in accounting. Valuation remains one of the major topics for practitioners. Valuation standards are a powerful instrument that can contribute to a fair process: their use aims at reducing subjectivity and arbitrary decisions in courts. In most jurisdictions, valuation standards are not the law: forensic accountants are not obliged to use valuation standards when they perform valuation works for judges. To date, as far as we know, no literature work has investigated adoption and diffusion of valuation standards in the forensic accounting space. In this paper, we analyze the spread of valuation standards through the lenses of isomorphism and -as corollaries- of Agency Theory and Signaling Theory. Because of lack of research in the particular area of valuation standards adoption, the present work relies on qualitative, exploratory research, based on semi-structured interviews conducted (up to saturation) with expert forensic accountants. Our work digs into motivations behind adoption and diffusion, as well into perceptions of forensic accountants around benefits of valuation standards and into barriers to their diffusion: the result is that, while the vast majority of forensic accountants praise the great work of the standards setters in introducing valuation standards, it might be that less than 50% of forensic accountants actually use valuation standards, in courts. Our preliminary findings, to be supported or refuted by future research, lead us to address a “trilogy” of recommendations to the stakeholders involved in the process of adoption and diffusion of valuation standards in courts.

Keywords: forensic accounting, valuation standards, adoption of standards, motivations, benefits, barriers, Isomorphism

Procedia PDF Downloads 144