Purpose The aim of this study is to analyze the impact of real estate risks on the dynamics of financial sector stock returns, using a sample of countries across Asia and Oceania, Europe and North America, from January 2000 to December 2024, a period marked by significant crises including the Global Financial Crisis and the COVID-19 pandemic. Design/methodology/approach The wavelet quantile correlation (WQC) is implemented to shed new light on the dynamic real estate risk exposure in the financial sector (including the risk frequency, scale and location) during periods of turmoil and exuberance. With this metric, we can also analyze tail dependence and explore its consequences and implications across different investment horizons, for investors, real estate developers, bankers, policymakers and other stakeholders. Considering the connectedness between the real estate and financial sectors, we propose two factors to measure real estate risk along two complementary dimensions: U.S. real estate risk and domestic real estate risk. Findings Based on the WQC metric, our results separately report U.S. and domestic real estate risk exposures, which significantly affect global financial sector returns across three investment horizons: short-term (4–8 months), mid-term (32–64 months) and long-term (64–128 months). Globally, real estate risk exposure increases with investment horizon, while tail risk declines. Domestic real estate risk exposure is prevalent and higher in Asia, Europe and North America for all investment horizons. As revealed by the quantile analysis, U.S. real estate risk exposure in the financial sector is more significant in U.S. and European countries, whereas Asian countries (particularly Japan) are more affected by domestic real estate risk exposure. Originality/value We develop an international analysis of real estate risk exposure in the financial sector for a sample of 14 countries. We consider two complementary dimensions: U.S. real estate risk and domestic real estate risk, and we use a recently developed econometric methodology, based on WQC. Our results highlight the relative importance of real estate risk exposures in the global financial sector. These findings may inform the calculation of risk-weighted assets by banks, as required under the new 2025 Basel IV framework.
Purpose This paper aims to investigate the role of county-level income and population density effects in explaining spatial variation in German land prices from 2014 to 2018. Motivated by recent evidence that land values account for a growing share of housing price dynamics, our analysis pursues three core objectives. First, we quantify the direct and indirect effects of county-level agglomeration variables on land prices. Second, we evaluate the extent to which spillovers in land prices contribute to clustering patterns in major German cities and their suburban counties. Third, we assess whether these effects differ by land type – comparing residential land with vacant land designated for development. Design/methodology/approach Our main empirical model of interest is the Spatial Durbin Model (SDM), which allows for both endogenous spatial dependence in the dependent variable (prices) and exogenous interactions in agglomeration-related covariates (e.g. median income, population density). Findings We find that spatial agglomeration effects, especially in income and density, exert significant local and neighboring effects on land prices. Specifically, a 1% increase in median income leads to a 1.5–2.5% increase in local land prices and an additional 0.5–0.9% increase in neighboring counties. Similarly, a 1% increase in population density raises land prices by 2–3% locally and 1.5–2% indirectly. These effects display greater intensity in suburban counties near large metropolitan areas. Finally, the effects are more pronounced for residential land than vacant land, consistent with the idea that realized use and regulatory constraints magnify spatial externalities in housing markets. Originality/value We contribute to the literature on agglomeration and housing price dynamics by explicitly modeling income- and density-induced spatial spillovers in land prices using a spatial panel framework. This paper thereby provides a link between the literature on spatial housing dynamics, agglomeration variables and land valuation. Our findings suggest that policy interventions targeting housing affordability or regional price disparities should account not only for housing demand and supply but also for the spatial diffusion of land price shocks, especially in high-productivity regions experiencing density-driven growth.
PurposeThis study investigates how leasehold status affects cooperative apartment prices at the neighborhood level in Stockholm and whether housing cooperatives acted rationally when offered to purchase their leasehold land in 2022.Design/methodology/approachThe analysis is based on more than 20,000 cooperative apartment sales in Stockholm during 2021. Price effects of leasehold status are estimated at both citywide and neighborhood levels while also controlling for the impact of remaining lease duration. In addition, counterfactual apartment values are calculated under scenarios where cooperatives purchase their land.FindingsLeasehold apartments sell at an average discount of 3.6% in central Stockholm and 6.8% in suburban areas, with neighborhood-level effects ranging from negligible to over 15%. This heterogeneity reflects the importance of local market conditions. A potential explanation for the smaller units in the city center is that leasehold tenure is more common, and substitutes in the form of freehold apartments are less available. The counterfactual analysis shows that land purchases were typically unprofitable for cooperatives, even under the City of Stockholm's discounted terms.Originality/valueThe study contributes by highlighting the heterogeneity of leasehold capitalization within a single metropolitan area and by extending the analysis beyond buyer valuation to the cooperative's perspective on land acquisition. The findings suggest that the City of Stockholm likely mispriced leasehold land, raising questions about the efficiency of current pricing and policy frameworks.
Purpose This study investigates the anchoring effect in property valuation among students from different educational backgrounds, aiming to determine whether an engineering education mitigates cognitive bias compared to a social science education. Design/methodology/approach An experimental design was implemented during the final examinations in property valuation courses at two Swedish universities. Students from three educational programs – civil engineering in surveying, real estate brokerage, and property management – were randomly assigned either a low or high anchor value and tasked with appraising a property using the comparable sales method. Statistical analyses, including ANOVA and Scheffé’s multiple comparison tests, were conducted to assess the significance of anchoring effects across groups. Findings The results confirm a statistically significant anchoring effect among students with a social science background, while no significant effect was found among engineering students. These findings support the hypothesis that engineering education, with its emphasis on model-driven thinking, reduces vulnerability to anchoring bias. Practical implications The study highlights the importance of educational framing in valuation training. It suggests that incorporating model-thinking and quantitative reasoning into curricula may help mitigate cognitive biases in professional practice. Originality/value By comparing students from distinct educational traditions within a controlled experimental setting, this study contributes novel insights into how cognitive biases manifest in property valuation and how educational background influences susceptibility to anchoring.
Purpose This study examines the valuation of European-listed real estate companies by assessing the value relevance of accounting-based performance metrics – earnings per share (EPS), return on equity (ROE) and dividend per share (DPS) – as complementary or alternative indicators to the traditionally dominant net asset value (NAV). Design/methodology/approach Using a panel dataset of 102 firms from 2005 to 2024, the study applies three regression models – share price, price change and share return – alongside a difference-in-differences (DiD) approach to capture structural shifts during the 2008–09 financial crisis and the 2021–23 COVID-19 and interest rate hike period. Findings The findings reveal that EPS and DPS are the most consistent and significant predictors of share prices, with DPS showing the highest explanatory power, particularly during the COVID-19 and interest rate hike period. ROE is the strongest predictor of share returns, especially in times of economic stress. Sectoral effects are generally weak, indicating that firm-level financial performance outweighs industry classification in explaining market valuation. Research limitations/implications The study is subject to potential biases in sample selection such as firm size, geographic and market classification, language. The sample is representative of large, liquid and internationally oriented firms, the findings may not generalize to smaller, less liquid or emerging market companies. Practical implications For analysts, integrating accounting metrics alongside NAV enhances valuation accuracy and comparability. For generalist investors, understanding which factors consistently influence prices can inform long-term valuation models, portfolio construction and risk assessment. For practitioners, it provides a robust, multi-metric valuation framework that enhances decision-making by integrating familiar financial indicators with traditional asset-based measure. Originality/value The DiD framework is employed to capture how investor responds to financial metrics shift across crisis and COVID-19 and interest rate hike periods (2008–2009 and 2021–2023). Use of three regression models – share price, share price change and share return – isolates the explanatory power of each financial metric under varying market conditions. Sector-level analysis offers insights into performance heterogeneity. For academic research, it fills a gap in European real estate literature by empirically testing the relevance of accounting metrics in stock valuation, an area previously dominated by NAV-based approaches.
Purpose This study examines whether digitally expressed interest, measured through Google search activity for terms such as “Spain villas” between 2014 and 2024, is associated with residential property purchases in Spain by British nationals. It also explores whether this relationship remains stable in the presence of major institutional shocks, notably Brexit and the COVID-19 pandemic. Design/methodology/approach Using quarterly data for 2014–2024, the paper estimates autoregressive (AR) and AR models augmented with search intensity (ARX). Model specifications with and without Google Trends indicators are compared. The Brexit referendum and the COVID-19 pandemic are treated as structural breaks to assess changes in the relationship between search activity and realized transactions. Several alternative specifications and lag structures are used as robustness checks. Findings Prior to the 2016 Brexit referendum, Google search intensity is positively associated with residential purchases by British nationals and improves short-term forecasting performance. After 2016, this association weakens substantially: search activity remains elevated, while transactions do not follow pre-referendum patterns. The evidence suggests a structural change in the relationship between digitally expressed interest and realized housing transactions, particularly during periods of heightened uncertainty. Practical implications Online search data may complement traditional indicators in forecasting cross-border housing demand. However, their usefulness appears to depend on institutional stability, as predictive performance may decline during periods of political and macroeconomic disruption. Originality/value The paper contributes to the literature on digital behavioral indicators and international housing markets by documenting time variation in the association between online search activity and cross-border housing transactions, highlighting the importance of institutional context in predictive applications.
PurposeThe increasing complexities of real estate market forecasting, in combination with the accelerated evolution of Machine Learning (ML) algorithms, necessitates the optimisation of algorithm selection to reduce computational demands and enhance model accuracy. While numerous studies have examined the performance of individual algorithms, a significant research gap remains concerning the impact of dataset characteristics on algorithmic performance within this specific domain.Design/methodology/approachThe present study aims to address this research gap by undertaking a model-based meta-learning approach, in which a Random Forest classifier is trained on prior dataset characteristics and associated ML performances. In a final step, these results are illustrated using empirical data.FindingsThe findings suggest that, in this proof-of-concept setting, mapping dataset characteristics to an algorithm recommendation is feasible and yields encouraging predictive performance. The evaluation achieved an average AUC of 0.85 and an accuracy of 0.88, exceeding the No Information Rate of 0.38. However, results should be interpreted as exploratory given the limited meta-sample size.Originality/valueThis study is the first to apply meta-learning within a domain where datasets are heterogeneous and not publicly shared. It was shown that there is a systematic relationship between data structure and model performances which confirms the "no free lunch" theorem. This study may be considered as the initial attempt that can be developed further through subsequent studies.
PurposeThis paper broadens the range of macroeconomic factors influencing the real estate investment trusts (REITs) index of the Euro area (EU-REIT Index). In addition to traditional determinants, we incorporate new variables, including the business cycle, bond yields, producer price index (PPI), industrial production, private sector credit, hourly earnings in manufacturing, construction volumes and residential building permits.Design/methodology/approachUsing quarterly data on the EU-REIT Index from Q3 2006 to Q4 2019, the study applies the Granger causality test within a vector error correction model framework, based on the autoregressive distributed lag model, to examine both the short- and long-run relationships between the EU-REIT Index and an extended set of macroeconomic drivers. Furthermore, the robustness of the forecasting model is evaluated using root mean squared error, mean absolute error, mean absolute percentage error and Theil's U2 statistic.FindingsMacroeconomic factors such as the business cycle exhibit cyclical effects, while the PPI, industrial production, private sector credit and hourly earnings in manufacturing show a negative causal impact on the REIT index. In contrast, construction volumes and residential building permits demonstrate a positive causal relationship. Overall, the inclusion of these new variables significantly enhances the forecasting accuracy of REIT models.Practical implicationsThis study provides a more comprehensive understanding of REIT performance by expanding the set of macroeconomic factors considered in REIT dynamics, improving forecasting and investment decision-making.Originality/valueThe analysis validates the relevance of these newly proposed macroeconomic variables for performance assessment and forecasting in the context of REITs, offering fresh insights into their market behavior.
PurposeThis paper examines how digital disintermediation and perceptions of broker usefulness influence homebuyers' satisfaction in real estate transactions. It introduces the concept of broker redundancy beliefs (BRB) to understand the psychological impact of opportunity costs when buyers choose to work with a broker instead of pursuing a direct transaction (FSBO).Design/methodology/approachA survey of 1,964 French homebuyers was conducted to compare outcomes between brokered and direct transactions. Using moderated mediation models, the study assesses how transaction type influences price satisfaction and recommendation intentions, and how these effects are conditioned by BRB.FindingsResults show that buyers using brokers report significantly lower price satisfaction and are less likely to recommend this transaction mode, despite similar purchase prices and shorter search durations. The negative impact is entirely moderated by BRB: when BRB is low, the effect disappears. These results suggest that the perception of opportunity cost-rather than actual outcomes-drives dissatisfaction.Research limitations/implicationsThis study highlights how opportunity cost perceptions and broker redundancy beliefs shape buyer satisfaction, offering a behavioral explanation for persistent skepticism toward brokers. Although based on French data, the mechanisms identified are relevant across markets where digital platforms increase the visibility of FSBO alternatives. The findings imply that brokers must actively demonstrate skills, effort, and ethical conduct to counter redundancy perceptions, while policymakers and platform designers should consider how intermediation is positioned in increasingly digital ecosystems. Limitations such as data timing, response rate uncertainty, and lack of seller perspectives point to fruitful avenues for future research.Practical implicationsIn an AI-enhanced real estate environment, brokers must visibly demonstrate skill and effort to counteract perceptions of redundancy. Younger, digital-native buyers with high BRB represent a critical target for repositioning brokerage value. Emphasizing ethical conduct, transparency, and personalized insight may help rebuild trust.Social implicationsThis research shows that buyer dissatisfaction with brokers is not purely economic but rooted in perceived opportunity costs and redundancy beliefs. Such perceptions may erode trust in professional intermediation and accelerate a shift toward peer-to-peer transactions, reinforcing broader patterns of digital disintermediation in society. The findings highlight the importance of transparency, ethical conduct, and visible value creation in maintaining consumer confidence in intermediaries. They also suggest that without renewed credibility, brokerage risks losing its social legitimacy, particularly among younger and more digitally literate generations, with potential consequences for fairness, accountability, and professionalism in housing markets.Originality/valueThis study provides a novel psychological explanation for declining buyer satisfaction in brokered transactions, despite equivalent outcomes. It highlights how perceived opportunity cost and cognitive bias influence evaluations of intermediation in real estate.
PurposeThe adoption of digital technology among valuers globally remains low, limiting the profession's ability to modernise and meet evolving market demands. The aim of this study is to investigate the factors influencing technology adoption among valuers in New Zealand.Design/methodology/approachGuided by existing literature and established technology acceptance theories, this study developed a survey instrument that was distributed to registered valuers in New Zealand. Responses from 131 participants were analysed using descriptive statistics and logistic regression.FindingsThe results reveal that a significant majority of valuers (60.3%) reported the non-adoption of digital technologies, indicating a substantial gap in the profession. The strongest driver of adoption was the valuers' perception of opportunities, such as improved efficiency, valuation quality and professional transformation, with each unit increase in opportunity perception associated with a more than fourfold increase in adoption likelihood (AOR = 4.58, p = 0.001). While concerns about accuracy and data protection were common, their influence was weaker when opportunity perception was considered. Mid-career valuers (5-10 years of experience) showed lower adoption rates in unadjusted models, although this effect was not significant after controlling for other factors. Educational attainment showed some association, with master's degree holders more likely to adopt, but this was limited by the sample size.Practical implicationsEfforts to increase adoption should focus on enhancing valuers' understanding of the benefits of digital technologies through targeted communication, peer-led training and case-based demonstrations. Regulatory bodies should support this transition by addressing data protection concerns and promoting clear guidelines.Originality/valueThis study contributes novel insights into the underexplored area of technology adoption in New Zealand's valuation profession. It highlights the primacy of individual opportunity perception over demographic or organisational factors and offers a foundation for future research and policy development.
PurposeThis paper analyzes the evolution of digital transformation in the real estate sector over the past decade and classifies emerging digital technologies into a structured framework. By developing a comprehensive and actionable model, the study aims to guide future academic research and support practitioners in strategically allocating resources to critical components of digital transformation.Design/methodology/approachThe research began with a traditional literature review, followed by a systematic search complemented by snowball and citation techniques. This multi-step process traced the progression of digital transformation in real estate, identified the current state of digital technologies and clustered them into an integrative framework. The study also includes extended sources to bridge academic inquiry and industry practice, encouraging adoption of advanced technologies to maintain competitive advantage.FindingsThe review identified 54 relevant sources, including 28 core academic articles, which were categorized into four dominant thematic areas: digital value creation, digital systems and infrastructure, digital business models and dynamic capabilities. A conceptual framework was derived, mapping how these cross-cutting themes shape the process of digital transformation in real estate. The findings also reveal a pronounced gap between academic research and industry innovation, with strategic transformation initiatives underrepresented in current scholarly work.Practical implicationsThe study introduces a sector-specific definition, phased model and digital technology framework tailored to real estate. It also proposes a conceptual research model that captures key focal points for future inquiry and practice, including value creation, systems integration, business model innovation and dynamic capabilities.Originality/valueThis study contributes to the emerging discourse on real estate digitalization by offering both theoretical insights and practical guidance. It highlights how evolving technologies are likely to reshape the sector and emphasizes the urgency for academia and industry to align more closely in response to rapid technological change.
PurposeThis study aims to explore key enablers affecting the adoption of Blockchain Technology (BCT) in the construction industry (CI) and to rank them based on their importance as well as to investigate their differences in opinions based on the gender as well as based on the job profile to find effective ways of successful adoption and implementation of BCT in Jordan.Design/methodology/approachAn exploratory research perspective is applied to identify the critical enablers of BCT in the CI through an extensive literature review by conducting a bibliometric analysis. A questionnaire was constructed based on a comprehensive literature review to rank BCT enablers that were clustered based on the Political, Economic, Social, Technological, Legal and Environmental (PESTLE) framework by the relative importance index (RII) index. Moreover, the exploratory factor analysis (EFA) and T-test were the statistical tools to test the first hypothesis, while Kruskal-Wallis test to test the second hypothesis.FindingsThis study revealed the followings: (1) The total number of key enablers are 12 enablers clustered within the framework. (2) The PESTEL framework becomes Environmental, Political, Legal, Technological and Sociocultural (EEPLTS) based on the RII values ranked in the enablers. (3) There are no statistical differences based on gender toward the adoption of the BCT in the Jordanian CI except for the Legal enablers based on EFA and T-test. (4) There are no differences between the opinions based on the job profile regarding the ranking of the importance of the enablers.Originality/valueThis study identifies key enablers for blockchain adoption in construction, analyzes their importance by gender and job profile, and proposes a modified PESTLE framework based on their ranking.
PurposeThe aim is to investigate the impact of building condition classifications derived from images on the accuracy of real estate pricing models. It explores whether manual and computer vision-based classifications of three property condition classes can enhance the predictive accuracy of a basic hedonic pricing model for real estate valuation. Additionally, the study sheds light on potential challenges encountered in applying computer vision to real estate valuation.Design/methodology/approachThis study explores the influence of building condition classes, derived from images, on a basic real estate hedonic pricing model, using data from online brokerage platforms. The three condition classes are based on a standardized classification and assessed by real estate experts. To examine the potential of computer vision in automating property assessment, the study employs convolutional neural networks (CNNs) to replicate expert condition classifications within the pricing model.FindingsThe findings of this study indicate that human-based classifications significantly improve the model, while CNNs also enhance accuracy but less effectively. The model's predictive accuracy decreases when excluding the construction year in CNN-based assessments. CNNs show promise in automating property evaluations with around 60% accuracy for the three predicted classes.Research limitations/implicationsThe research faces limitations due to the subjective nature of manually classifying property conditions and the variability in image quality across real estate platforms. It also notes the challenge of standardizing the use of images for valuation purposes, specifically condition assessment, given the diverse presentation styles.Practical implicationsIt indicates that a shift toward more objective and standardized image assessments could improve the reliability of valuations. This advancement offers practical benefits for real estate professionals and platforms by streamlining appraisal processes and reducing costs.Originality/valueIts originality lies in the application of computer vision using real-word data and non-standardized images to automate property evaluations. The value of the research demonstrates the potential for enhancing pricing model accuracy through visual data analysis using data from online brokerage platforms.
Purpose Communication with end users involved in real estate (RE) projects can be challenging. End-users often have a limited ability to imagine spaces/designs from traditional 2D plans and 3D images and the use of Virtual Reality (VR) might be helpful. This study aims to present the development of a VR tool that can be applied in the context of workplace projects to facilitate end-user engagement. Design/methodology/approach A Design Science Research (DSR) methodology is used to develop the VR-tool prototype. This is done through the definition of understanding difficulties, a review of the potentials of VR, a draft research design to evaluate the VR tool and the tool-development itself. Findings Because of its immersive experience, VR has a huge potential to improve understanding and learning. A VR tool to learn about Activity-Based Working is developed, which can be used during workplace projects as an innovative change intervention. How the development can be successful regarding tool requirements, functionality, technology and VR content is presented. Evaluating the tool is part of further research. Practical implications VR can extend beyond design review and marketing tasks. The development of VR tools that are applicable for learning in workplace project settings, as well as other use cases and asset classes, not only supports change management but is also practically implementable and scalable. Originality/value The research broadens the understanding of the applicability of VR as an emerging technology in RE, which is currently mostly limited to design review and marketing. The DSR methodology shows, how ready-to-use tools can be developed for specific applications. The VR tool can be a basis for further research that involves end-users.
PurposeChina has taken on increased importance as an emerging real estate market in recent years. This article assesses the dynamic relationship between development sites and commercial real estate in China over 2007-Q3:2024. It also highlights critical issues for investors as this dynamic between these two sectors plays out more fully in the real estate space in China.Design/methodology/approachTo assess the dynamics of this relationship between development sites and commercial real estate in China, the MSCI/Real Capital Analytics database of global real estate transactions over 2007-Q3:2024 is used to drill-out critical details on development sites and commercial real estate transactions in China and globally. MSCI real estate performance data is also used to validate the risk-adjusted performance and portfolio diversification benefits of commercial real estate in China.FindingsThe dynamics of the relationship between development sites and commercial real estate in China are starting to evolve. This has seen an increased focus on commercial real estate transactions in recent years, particularly in the industrial, hotel and retail spaces, with all real estate sub-sectors increasing their percentage allocation to transactions in China in recent years. A reduced percentage contribution to real estate transactions by development sites has therefore been evident over the recent time period of 2020-Q3:2024. Development site transactions remain the dominant sector, but the commercial real estate sector is taking on an increasing role in China, as completed commercial real estate projects emerge from these development site transactions. The superior performance of commercial real estate compared to the other major asset classes in China over 2007-2023 is further validation of this increased focus on commercial real estate in China. Risk remains a critical issue for China commercial real estate, particularly with a lesser economic environment in China going forward and increased geopolitical risk.Practical implicationsAs well as the local real estate players in China, the major international real estate investors have shown an active interest in capturing the China economic growth story in recent years, with China commercial real estate being a key component in this institutional real estate investment strategy. This is seeing an increased focus on commercial real estate in China, compared to the traditional development sites transaction focus. The resulting role of commercial real estate in facilitating this economic growth story in China is clearly evident in the evidence-based performance analysis, with this China real estate performance analysis also being a strong validation of this strategic real estate decision-making. Whilst China has experienced a lesser economic environment in recent years and changing risk factors for real estate, China commercial real estate remains an important focus for real estate investors seeking emerging real estate market exposure.Originality/valueThis article is the first analysis of the dynamics between development sites and commercial real estate in China. Using real estate market transactions from China over 18 years, it provides an evidence-based analysis of this dynamic for a deeper understanding of the relationship between development site transactions and commercial real estate transactions in China. Clear evidence is seen of an increased focus on commercial real estate transactions compared to development site transactions in China.
Purpose The purpose of this research is to study the impact of g (growth) on the real estate discount rate in real estate valuation and to understand how professional valuers estimate this variable in practice, including how they incorporate future price change expectations into present value estimates, with particular focus on the Portuguese residential market. Design/methodology/approach The archival research method (with data from the EUROSTAT) is followed, combined with a self-completed questionnaire survey of real estate valuers in Portugal for explanatory research. Findings The findings suggest that most professional valuers interpret g as the implicit growth in the numerator of the Gordon Growth Model (GGM) formula and use market comparable data when they have to use/estimate it. However, this study also shows that valuers seem to apply comparable cap rates without determining what growth rate is being implied, which can potentially lead to results that are not accurate and harmful, particularly in implicit valuations and when estimating the residual value (which generally represents the majority of the value of the asset). Research limitations/implications One limitation of our study is the size of the sample, namely the number of complete answers to the questionnaire. Also, it is not clear how valuers collect comparable data from the market and estimate growth regarding sources of information and models used. Practical implications This paper revisits the literature on the GGM used in real estate valuation with a special focus on the residential market; it isolates the impact of g and its definition in the discount rate; it makes suggestions on how to deal with g in practice in discounted cash flow (DCF) analysis and in the yield method for residential valuation, framing it under the EVS 2025, RICS 2025 and IVS 2025 valuation standards. Originality/value While the GGM has traditionally been applied to commercial property valuation, it is increasingly being examined within residential valuation; this paper looks at the discount rate used in this context from the perspective of the GGM combined with a build-up approach, providing practical insights and guidelines to professional residential valuers who act in accordance with the EVS 2025, RICS 2025 and IVS 2025 valuation standards and identifies a clear need for standard adaptation and training to residential valuation.
