Traditional Valuation Research Articles

Machine Learning Valuation in Dual Market Dynamics: A Case Study of the Formal and Informal Real Estate Market in Dar es Salaam

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.

Is “Open” ready for investment? A venture capitalist’s dilemma

Learning outcomes Upon completion of the case study, student will be able to discuss valuation of new-age ventures and understand how it is different from the valuation of organisations with a longer history; analyse the considerations (quantitative and qualitative) while evaluating investments in new-age ventures; and develop a framework involving the various dimensions of investment readiness. Case overview/synopsis The fintech space in India has seen an upsurge of activities since 2016. The growth of Paytm, RazorPay and many such ventures and the drastic improvements in this ecosystem have been significant catalysts for this segment of new-age tech companies. Funding and valuations have seen a sharp increase, especially when businesses worldwide felt the after-effects of the pandemic, with India being home to a large number of unicorns, second only to the USA. Open Financial Technologies Ltd (OPEN TECH) is one such venture that claimed its spot as the 100th unicorn of India within a span of five years since inception. With a strong focus on disrupting the banking sector in India, this neo-bank aspires to be the equivalent of Stripe in India and eventually be a strong competitor in the international market. Richard O’Neil is an active investor in the fintech space, based out of the UK, and he is currently looking to expand the market by considering investment options. In the process, Richard and his team have identified India as a viable and competitive market, as new venture support and funding are increasingly emphasized through policies such as Startup India, Make in India and many such more to sustain and propel its benefits. As the team was exploring ventures worth investing, Open Financial Technologies caught their attention. However, Richard, given his experience across fields and being a seasoned private equity investor, realised that valuing new-age companies is as much an art as it is a science. Multiple quantitative and qualitative aspects need to be considered while relevance of traditional valuation techniques to put a value on such entrepreneurial ventures is questioned. At this juncture, he finds it crucial to evaluate the investment readiness of OPEN TECH. This case allows students to understand how valuation of new ventures is different from that of established companies and analyse the crucial factors worth considering while evaluating an investment proposal as a venture capitalist, which eventually helps shape the funding pitch of an entrepreneur in the space. Complexity academic level This case study can be useful for students undertaking graduate- and executive-level courses on business valuation and strategy and entrepreneurship, as well as entrepreneurial finance elective at the undergraduate level. One could use this case in courses on entrepreneurship and innovation, such as an introductory course on entrepreneurial finance and a course on venture capital and private equity. It also allows discussion on fintech and neobanking and the valuation of privately held companies. Supplementary materials Teaching notes are available for educators only. Subject code CSS 1: Accounting and finance.

Practice Briefing: The implications of a move towards explicit discounted cash flow (DCF) models for property investment valuations

PurposeThe primary purpose of this Practice Briefing is to examine the implications of the recommendations of the Royal Institution of Chartered Surveyors (RICS) Review of Investment Valuations to shift towards (explicit) discounted cash flow (DCF) valuations on valuers, clients, risk management and market data.Design/methodology/approachThis Practice Briefing will develop a conceptual framework to articulate the impact of the shift towards explicit modelling assumptions in valuations. A comparative analysis is conducted to contrast traditional valuation approaches with an explicit DCF approach.FindingsThe proposed shift to DCF valuations in the real estate industry carries both subtle and profound implications. While on the surface, the move appears to bring minimal change since current valuations are already DCF-based, a deeper exploration reveals a potential transformation to the understanding, analysis and measurement of pricing and risks in commercial real estate.Practical implicationsThe traditional techniques adopted in valuations have become pervasive in the industry, hindering more sophisticated modelling and analysis of individual assets and portfolios. Explicitly modelling assumptions in both the initial years of cash flow and perpetuity calculations would unveil fundamental relationships and rationales that have previously been buried within, and perhaps ignored by, an All Risks Yield (ARY).Originality/valueThis briefing identifies the cause of confusion when interpreting ARY differentials on implied target returns and risk and demonstrates how a change to explicit assumptions, such as growth and letting periods, would enhance transparency, enabling more informed client–valuer discussions, which in-turn will boost confidence in valuation accuracy, lead to more sophisticated asset modelling, market data, forecasting and market analysis.

Real Estate Insights: Establishing transparency – setting AI standards in property valuation

PurposeThis real estate insight scrutinises the emerging role of Artificial Intelligence (AI), particularly Large Language Models (LLMs) like ChatGPT, in property valuation, advocating for establishing standardised reporting guidelines in AI-enabled property valuation.Design/methodology/approachThrough a conceptual exploration, this piece examines the shift towards AI integration in property valuation and the critical role of Explainable Artificial Intelligence (XAI) in this transition. It discusses the CANGARU framework for developing inclusive and universally applicable reporting guidelines and the importance of human oversight in validating AI-enabled valuations.FindingsIntegrating LLMs into property valuation signifies potential efficiency gains and task automation but also introduces risks related to accuracy, bias, and ethical dilemmas. Standardised reporting guidelines are identified as essential for responsibly harnessing AI’s benefits.Practical implicationsThe article underscores the need for the real estate industry to adopt transparent reporting practices, with valuers acting as expert interpreters of AI outputs. Emphasising error reporting in XAI not only aids in understanding AI-generated insights but also builds trust among stakeholders, ensuring AI’s ethical and effective application in property valuation.Originality/valueThis commentary contributes to the discourse on AI’s role in property valuation by focusing on the need for standard reporting guidelines that align with professional standards and legal frameworks. It advocates for a balanced approach to AI integration, where technological advancements complement traditional valuation expertise, ensuring accurate, fair, and transparent property valuations.

Improvement of DEVA Model in Valuation Research of Internet Financial Information Service Companies

With the development of the digital economy and the internet, the internet financial information service industry has shown strong momentum. Different from traditional enterprises, internet financial information service companies possess characteristics of low assets, high risks, and difficulties in measuring non-financial indicators. As a result, traditional valuation methods exhibit inaccuracies in application and require the introduction of non-financial valuation models for validation. Therefore, considering the industry's characteristics from a user perspective, we introduce the Metcalfe valuation modelthe DEVA valuation model. Simultaneously, we improve this model and, using Hithink Royalflush Information Network Co., Ltd. as a case study, estimate the value at the evaluation point using both the original DEVA model and the revised new model. Finally, we analyze and compare the differences between the various valuations and Royalflush's total market value at the evaluation point, concluding that the improved DEVA model yields more fair and accurate valuation results.

Valuation analysis of China national petroleum corporation

Oil is considered the lifeblood of several countries due to the political need to offer energy security. Moreover, world economies depend on the state’s supervision and control of oil prices. Thus, the relevant policies of oil companies are carefully formulated in conjunction with government policymakers. Hence, the valuation of oil enterprises is of great significance to global security and economic development. As traditional valuation models tend to underestimate petroleum enterprises, this study adopts the economic value added (EVA) model and incorporates environmental, social, and governance (ESG) indicators to ascertain a new absolute valuation indicator for China National Petroleum Corporationbased on the weighted average adjusted enterprise value, providing a new evaluation indicator for the petrochemical industry.

On the importance of asset pricing factors in the relative valuation

In this study, we examine the implications of multi-factor asset pricing models in corporate valuation, focusing on the relative valuation approach. Our investigation centers around an eight-factor model, which we compare against traditional valuation methods that rely on industry or risk grouping and cross-sectional averages. The purpose of this comparison is to assess the efficacy of incorporating pricing factor variables into the valuation process and to understand their impact on valuation accuracy. The findings of our analysis suggest that the use of the eight-factor model leads to a reduction in estimation errors of valuation multiples, indicating an important improvement in precision compared to conventional valuation methods. Additionally, our research offers insights into the practicality of applying multi-factor models. It contributes to the intersection between empirical asset pricing and corporate finance by providing a detailed evaluation of advanced valuation models and their relevance in improving the accuracy of relative valuation.

Valuation, Accounting Principles, and Classification of Assets in the Metaverse

This study explores the valuation methods, accounting principles, and asset classification systems needed for accurate financial reporting in the Metaverse. The unique characteristics of virtual assets in the Metaverse pose challenges for traditional valuation methods and require the development of comprehensive and adaptable approaches. This study utilizes qualitative research methods, including in-depth interviews with accounting professionals in Bath and Bristol, UK. Fourteen accountants were selected based on their expertise in accounting and their understanding of the Metaverse. The findings reveal that effective governance, regulatory mechanisms, and community-driven protocols influence asset values in the Metaverse. It is shaped by factors such as engagement, scarcity, and competition. Tailored accounting principles should address legal recognition, accurate tracking, jurisdictional restrictions, and risk management. Compliance with regulations, transparent reporting, and collaboration with regulatory authorities are crucial, alongside integrating innovative technologies like blockchain for robust accounting practices.

Unleashing the Power of Artificial Intelligence in Real Estate Valuation: Opportunities and Challenges Ahead

The convergence of real estate valuation and artificial intelligence (AI) represents a transformative frontier in the real estate industry. This paper explores the strategic importance of real estate valuation and the integration of AI technologies, shedding light on their multifaceted contributions to decision-making, value enhancement, and market insights. Through a comprehensive review of literature, theoretical frameworks, and industry insights, the study examines the potential of AI-driven valuation models, predictive analytics, and Explainable AI (XAI) techniques in revolutionizing traditional valuation practices. Key areas of future research, including data integration, ethical considerations, and the development of hybrid AI systems, are identified to advance the field and address emerging challenges. By leveraging AI technologies responsibly, stakeholders can unlock new opportunities for efficiency, accuracy, and transparency in real estate valuation, ultimately shaping a more resilient and sustainable future for the industry.

Property Value Visionary

Abstract: In the world of dynamic and complex real estate markets, estimating the value of a property has become a crucial yet challenging task. Property valuation systems have emerged as useful tools, helping users assess the worth of their properties based on various factors. However, many traditional valuation systems rely on static and simplistic methods that may not capture the full range of what a property might be worth. These traditional systems might miss out on considering the diverse and specific features of each property and the changing market conditions. The aim of this project is to explore and propose a property value visionary approach that goes beyond these standard methods. Instead of using fixed and generic formulas, the proposed system leverages advanced data-driven techniques that can capture the spatial, temporal, and textual aspects of each property and the market environment, offering property value estimates that align more closely with the reality and the expectations of each user. It enhances the potential for users to discover the true value of their properties and make informed decisions. This project aims to demonstrate the effectiveness and efficiency of innovative machine learning approaches that promise a more accurate and reliable property valuation experience

The Study of Equity Valuation Based on Modified Ohlson Model: Case of BYD

New energy vehicle companies are at an early stage of development and have large differences in terms of technological development, market maturity and R&D costs. In this context, traditional valuation methods do not meet the requirements for valuing new energy vehicle companies. This study modified the traditional Ohlson model according to the characteristics of new energy vehicle companies, adopts DuPont's analysis system to decompose and measure several financial indicators, addresses the problem that residual returns are difficult to predict directly, and provides a reasonable and accurate valuation of new energy vehicle companies to provide investors with basis. The actual valuation will also be demonstrated with the example of BYD.

Enhancing Property Valuation in Ruwa, Harare, Zimbabwe, using Kobo Collect and GIS

The escalating urbanization of Ruwa, a burgeoning suburb on the outskirts of Harare, Zimbabwe, has catalyzed an exigent demand for precise property valuation. Traditional valuation methods have struggled amidst the challenges of manual data collection, data inaccuracies, and protracted analyses. To address these constraints, a pioneering solution emerged at the crossroads of modern data collection and geospatial analysis. The fusion of Kobo Collect and Geographic Information Systems (GIS) presents a transformative avenue to surmount these challenges. The conventional practices faced hurdles marked by manual data collection's limitations. Inherent subjectivity and potential errors from pen-and-paper surveys posed challenges to data integrity. Moreover, as Ruwa underwent rapid changes, the inability to update data in real-time diminished valuation accuracy. Slow analysis processes also constrained timely access to property valuation insights. The integration of Kobo Collect and GIS provides a solution aligned with Ruwa's urban evolution. Kobo Collect's agile data collection forms, accessible on mobile devices, amplify data accuracy and practicality. The assimilation of GIS adds a spatial layer, and geotagging properties, and facilitates spatial analyses, unraveling intricate interplays of property values and geographic context. The outcomes encompass heightened data accuracy, as digital data entry minimizes errors. Efficiency gains result from real-time data entry and expedited analyses. Spatial insights from GIS analysis divulge patterns influencing property values. Stakeholders, empowered with informed decision-making capabilities, envision strategic urban planning and property development. The endeavor was not without challenges. Technical training ensured optimal tool utilization, while data privacy measures secured sensitive information.

Does high-frequency trading cause stock prices to deviate from fundamental values?

We examine whether high-frequency trading (HFT) is associated with greater deviations of stock prices from firms’ fundamental, intrinsic values. Prior studies show that HFT can improve market liquidity and price discovery in the short-term, but countervailing effects can discourage new information acquisition, reduce the amount of fundamental news reflected in stock prices, and reduce institutional investors’ desire to invest and trade in stocks subject to high levels of HFT. We find that greater HFT leads to a greater deviation of stock prices from accounting-based valuation estimates. The results hold across univariate, multivariate, and cross-sectional tests, as well as a natural experiment that induces an exogenous shock to HFT for a small sample of firms. Our findings contribute to the understanding of the longer-term valuation implications of HFT, as well as the traditional valuation role of accounting variables.

Non-Fungible Token valuation: State of the art and future insight

Purpose: This paper aims to provide an exploratory analysis of Non-Fungible Tokens (NFTs) valuation. NFTs are a new kind of digital asset born out of the dis-ruptive technologies' introduction (i.e., blockchain). A lot of small and medium en-terprises (SMEs), as innovative start-ups, are involved in this domain. Nowadays, several issues in the evaluation field remain unclear. To fill this gap, this research adopts a holistic approach is crucial to draw a clear picture of the first-time ac-counting treatment of these new digital assets. Design/methodology/approach: Using a structured approach, this research considers some of the state-of-the-art international practices and reviews some major scholars' opinions on the matter. Particularly, the study analyses the main contributions provided by international entities (e.g., European Financial Reporting Advisory Group - EFRAG, Chartered Business Valuators institute - CBV, PriceWa-terhouseCoopers - PWC), digital operators, and academia. Findings: Based on the two main strands defined for the NFT's nature, NFTs valuation issues can be resumed in twofold. The first one that considers NFTs as intangible assets suggests following the traditional valuation approaches (cost, in-come, or market) that is already a part of international accounting standards. The second strand that considers NFTs as financial assets proposes a different valua-tion approach based on quantitative methods coming mainly from finance fun-damentals. Originality/value: The originality of this study includes the different NFT val-uation approaches, which enrich the literature and can help SMEs in managing and accounting for this new kind of digital asset.

Unlocking value: The impact of traditional valuation practices on the number of vacant retail units in the built environment

Across the social science and humanities disciplines, there has been an increased interest in questions of value and valuation. This article responds to the new emphasis by scrutinising the evolution of real estate valuation techniques over time, their embedded nature, and the implication of this for the (re)production of the built environment. This is achieved through the lens of two conceptual positions which are rarely used in the realm of real estate valuation. These are (a) path dependency which in this case charts the evolution of the discipline of valuation as it relates to retail related real estate assets; and (b) lock – in, which seeks to understand how valuation techniques and attitudes have become embedded during this evolution. This position sits alongside a series of investigatory interviews with professional valuers in international real estate organisations specialising in the commercial real estate market. This is often a missing voice in recent research into real estate and the wider subject of valuation. Findings suggest that valuation practice is reliant on confirmatory market practices that do not necessarily capture the contemporary affordability-based requirements of tenants. Instead, practice is locked into reinforcing the nature of zone-based market values for landlords and investors. The geographical implications of this situation at the micro-level can be vacant or poorly performing properties that undermine local areas in the interest of maintaining the headline value of properties in macro-level global capital markets. This new conceptual analysis provides understanding of how the physical built environment is developed and reproduced over time through notions of value, helps to connect often hidden practices of real estate valuation into existing academic debates in urban studies and geography, and in conclusion sheds some light on how practices of real estate valuation can be improved in the disrupted world of retail.

Evaluating the efficiency of factors influencing valuation of residential buildings: A priority weight-based model

Valuation of buildings is an utmost complication for different socio-economic stakeholders in the building's buying and selling process. The traditional valuation approaches can vary because it depends on the evaluator's opinions. Also, existing approaches have not considered all the parameters and the interrelation between these parameters for their models. The objective of this study is to develop a priority weights-based rating model for the valuation of existing and new buildings. In this paper, the Fuzzy Analytical Hierarchy Process (FAHP) is used to develop a rating system to use in the valuation process of buildings. The complementary novelty of this paper is the inclusion of the primary principles of engineering, sustainability, and adaptability into the valuation process of a building. Further, the subjectivity of this model is eliminated by using numerical descriptors. The opinions of 100 experts were collected and averaged to develop this model, and the consistency ratio of the experts’ opinions was maintained at less than 10 %. Finally, the valuation of a residential building is performed as a case study to show the numerical possibilities of the proposed model. The proposed model can be used to evaluate existing and new residential buildings in property transactions, insurance purposes, taxation, and making investment decisions.

The Impact of Riders on the Valuation of "Unicorn" Companies

With the development of the times, industry terms such as big data and artificial intelligence have become familiar, and with them the emergence of "unicorn" companies have also been given the opportunity to develop and the corresponding challenges, while the proliferation of "unicorn" companies has brought about a rigorous the proliferation of 'unicorn' companies brings with it the issue of rigorous valuation. In reports on unicorn companies published by major research institutions, Ant Group has led the way for the last five years, jumping from a valuation of US$75 billion to US$150 billion, making it a super unicorn. Whether its value actually matches its valuation, and which factors play a key role in this, and how, needs to be further explored: this paper takes Ant Group as the subject of this study, starting with a brief overview of 'unicorn' companies and Ant Group companies, and then introducing traditional valuation methods --The paper also examines the different types of riders in different financing rounds in the financing process of Unicorn companies, and finally analyses the impact mechanism and optimization of different riders on the valuation of Unicorn companies.

Stock price valuation analysis of the digital technology industry based on the HP-ARIMA method - with 18 listed companies of different volume sizes in its 3 segments as an example

The world securities market has formed a more complete system since its origin in the 17th century, with a considerable number of small and medium-sized investors and institutional investors, and proposing innovative new methods for securities valuation models can assist investors in making more accurate investment decisions. This paper proposes for the first time to use an interdisciplinary analysis method, ARIMA + HP filter analysis, to analyze the valuation of the securities market, by taking the opening prices of individual stocks in digital technology industry segments: blockchain, artificial intelligence, and Beidou navigation as examples for valuation analysis, firstly, the initial data of the opening prices of individual stocks from 2019-2021 are analyzed by HP filter analysis, and secondly, the data are analyzed while doing Tobit model test. By analyzing the long-term trend of the digital technology industry after noise reduction, its corresponding valuation forecast and value judgment results show that the head effect of the digital technology industry is more significant, and the opening price volatility shows a U-shaped relationship with company size and profitability, and the opening price volatility of individual stocks is less, while the shorter the company's establishment time, the greater the stock price volatility. While the past literature focused on the traditional valuation of financial conditions and historical data analysis of a single variable, this paper analyzes the data of individual stocks in the digital technology industry through an interdisciplinary analysis method, which allows for a more comprehensive use of historical data and prediction of possible stochastic fluctuations, providing a more reliable valuation method for securities investors and opening up new ideas for research related to the field of valuation analysis.

Predicting the Startup Valuation: A deep learning approach

ABSTRACT The investment and funding decisions of a new venture are based on the startup valuation, which remains an inconclusive and disputable subject matter. For this purpose, well-established strategic management theories such as resource-based view (RBV), industrial structure effect, and network-based theory have been leveraged as inputs. This study uses 757 Indian startup deals dataset during the period from January 2012 to December 2019 to develop a predictive model based on the Artificial Neural Network (ANN) technique, which is a deep learning approach to predict the startup valuation. The ANN-based model predicts the startup pre-money valuation, and we also compares the ANN model to a linear classifier, linear regression, in this study. The result shows that the application of the ANN model can be used as a supplementary method to predict the pre-money valuation, if not an alternative to the traditional valuation models depending on its adaptability and accuracy. This model provides a competitive advantage by building a strong foundation during the negotiation between VCs and entrepreneurs. This study provides managerial and theoretical implications to VCs, entrepreneurs, and policy-makers for upgrading the startup ecosystem.

Decoupled net present value: protecting assets against climate change risk by consistently capturing the value of resilient and adaptable investments

ABSTRACT There is growing awareness that traditional valuation methods based on discounted cash flows using constant risk-adjusted discount rates struggle to account for climate-related risks when assessing long-term investments in physical assets and infrastructure. Worst yet, such methods fail to consider numerous financial benefits accruing from investment in resilience and adaptation, categorizing such expenditures as sunk costs that reduce investors’ returns. Such traditional valuation methods encourage investors to postpone or forgo entirely investing in resilience and adaptation. The decoupled net present value (DNPV) method incorporates risk and risk-reduction measures into project valuations in clear and compelling financial terms. By quantifying both (i) risk exposures of assets to hazards and (ii) the reduction of such exposure through up-front investments, DNPV recasts the financial impact of risk-reduction measures. Thus, the benefits of risk-reducing investments such as adaptation and resilience can be fully valorized in project-level accounting, removing a significant barrier facing such investments today.