Private capital & investment governance · Original source
Christin Eckerle; Orestis Terzidis
Impact-driven startups face major challenges in measuring and reporting their social and environmental impacts. In a related manner, impact investors face challenges when attempting to assess the impact of startups. This is primarily due to a lack of comparability and transparency. Despite the proliferation of tools and frameworks, a misfit persists between the capabilities of startups and the expectations of investors. Resource constraints have a significant impact on the capacity of startups to engage in complex impact measurement. Current impact assessment tools are often ill-suited to the needs of startups, as they require extensive data that smaller companies may not have readily available. The present study proposes a design science research approach to derive design knowledge for the impact assessment of startups and subsequently develop a new tool tailored to the needs of startups. The purpose of this tool is to enable startups to measure and report their potential impact efficiently. The objective is to enhance transparency, comparability, and legitimacy, thereby helping startups attract investment while aligning their business activities with their social and environmental missions.
Private capital & investment governance · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Impact reporting can fail when investor expectations exceed a young company's capacity to measure. Eckerle and Terzidis develop design principles and a tool for startup impact assessment. This companion proposes a limited pilot that joins a theory of change, a small indicator set, explicit data-quality labels, and structured investor feedback. It treats the source's design-science contribution as a basis for testing rather than as proof that a scorecard improves impact or returns.
Real estate & resilience · Original source
Marcelo Cajias; Philipp Freudenreich; Anna Freudenreich; Wolfgang Schäfers
This paper analyses the highly under-researched German residential real estate market. Quality- and spatial-adjusted price and liquidity indices are calculated separately for the investment and rental market on a regional basis. Applying the “Partitioning Around Medoids (PAM)” clustering algorithm, the regions are clustered with respect to their price and liquidity development after the average silhouette method is applied to find the optimal number of clusters. The dataset underlying this analysis comprises more than 4.5 million observations in 380 German regions from 2013 Q1 to 2018 Q4. The clusters are then analysed by means of further economic and socioeconomic data in order to identify similarities. Furthermore, the clusters are interpreted from a geographic perspective. We find that the allocation to cluster 1 is always supported by higher growth rates in the variables, population, working population and real GDP, implying higher demand for space. Moreover, in each of the analysed categories cluster 1 reveals a lower unemployment rate as well as a higher disposable income. One of the most interesting implications is, that apparently a large part of the German population has developed into professional real estate investors. In Germany the largest share of landlords is the one of the so-called non-professional landlords. As the regions assigned to cluster 1, displaying the most significant price increase, seem to be chosen based on a very sophisticated market analysis by identifying the regions with the strongest fundamental data, it seems like the dominating market players have significantly increased their knowledge and approach for investing in residential real estate.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Regional price growth and the speed of letting or selling describe different dimensions of property performance. This companion proposes a regional evidence review that keeps those dimensions separate, tests the dependence of a family portfolio on common local drivers, and translates the result into financing and monitoring decisions without treating a historical cluster as a buy recommendation.
Real estate & resilience · Original source
Marcelo Cajias; Philipp Freudenreich; Anna Freudenreich
In this paper, the liquidity (inverse of time on market) of rental dwellings and its determinants for different liquidity quantiles are examined for the seven largest German cities. The determinants are estimated using censored quantile regressions in order to investigate the impact on very liquid to very illiquid dwellings. As market heterogeneity is not only observed between cities but also within a city, each of the seven cities is considered individually. Micro data for almost 500,000 observations from 2013 to 2017 is used to examine the time on market. Substantial differences in the magnitude and direction of the regression coefficients for the different liquidity quantiles are found. Furthermore, both the magnitude and direction of the impact of an explanatory variable on the liquidity, differ between the cities. To the best of the authors’ knowledge this is the first paper, to apply censored quantile regressions to liquidity analysis of the real estate rental market. The model reveals that the proportionality assumption underlying the Cox proportional hazards model cannot be confirmed for all variables across all cities, but for most of them.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
An average marketing period can conceal the properties that create the largest cash-flow interruptions. Building on censored-quantile research in German rental markets, this companion proposes a lease-up review that preserves unresolved listings, distinguishes local markets, and tests the upper tail of vacancy duration before capital or distributions are committed.
Real estate & resilience · Original source
Thomas Paul; Thomas Walther; André Küster-Simic
In this study, we analyze illiquidity premia and their effect on the expected returns of German real estate securities. To this end, we use a unique data set that includes real estate stocks, real estate investment trusts (REITs), and open- and closed-end real estate funds for 2003–2017. We follow Amihud’s (JFM 5:31–56, 2002) structural approach; specifically, we estimate Amihud’s illiquidity factors, investigate the relationships between expected returns and illiquidity, and analyze the effects of expected and unexpected market illiquidity on future returns. We show that illiquidity plays an important role in expected returns for real estate stocks and investment trusts (REITs); however, it has less clear effects on open- and closed-end funds. We find that the adjusted ILLIQ includes appropriate correction factors for securities with low trading activity and is a useful improvement. We also find evidence of structural breaks in the relationship between returns and illiquidity.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Listed property securities and property funds provide different routes to real-estate exposure, but their liquidity cannot be inferred from the asset-class label alone. This companion turns German evidence on illiquidity premia into a wrapper-specific review of tradability, redemption terms, valuation and the family’s actual cash obligations.
Real estate & resilience · Original source
Gianluca Marcato; Anupam Nanda
As expectations change, we may observe asymmetry in responses of economic agents over various phases of the economic cycles. In this paper, we analyze both demand and supply side information to understand the dynamics of price determination in the real estate market and examine the relationship between expectation parameters and demand-supply mismatch. Our hypothesis builds on the possibility that investors’ call for action in terms of their buy/sell decision and adjustment in reservation prices may provide valuable insights into impending demand-supply imbalances in the market. We study several real estate sectors to inform our analysis. The timeframe of our analysis (1995–2010) allows us to observe market dynamics over several economic cycles. We test our hypothesis variously using several measures of market activity within a structural panel VAR framework. Our analysis suggests that investors’ attitude may have substantial and statistically significant feedback effects in price determination. These results indicate noticeable asymmetry in responses during the boom, normal and recessionary periods.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
A property's last valuation cannot describe the current willingness of buyers and sellers to transact. This companion proposes a negotiation review that separates reservation prices, observable market activity and family financing constraints, using research on asymmetric demand-supply adjustment to challenge mechanical extrapolation across property cycles.
Real estate & resilience · Original source
Nam Bui; Le Wen; Basil Sharp
The impact of climate change is devastating in developing countries where flood protection and insurance schemes are limited. Certain parts of Ho Chi Minh City, Vietnam are under the constant threat of inundation due to sea-level rise. We integrate the hedonic property model in a difference-in-differences framework and spatial econometric analysis into a single analytical framework to estimate the economic effect of pluvial flooding. We find prices for affected houses were discounted by 9% after a large flood event on 30 September 2017. This research contributes to the existing literature as follows. First, we study the economic impact of pluvial floods, which has received less attention in existing studies where large and irregular floods are their focus. Second, the inclusion of legal status as a control variable accounts for the unique character of the Vietnamese housing market. Third, we also identify the recovery of house prices after the flood event.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Observed market repricing after a flood and the physical costs of future flooding are different quantities. This companion translates evidence from Ho Chi Minh City into a property-level dossier that separates hazard, damage, legal status, market response and recovery obligations before a family office treats a discounted property as an opportunity.
Real estate & resilience · Original source
Xi Chen
This paper examines how climate risk transparency influences house prices and credit supply. I contend that inadequate property-level climate risk disclosures induce homebuyers to demand a risk aversion discount on house prices, creating a potential market for lemons. Employing a stacked difference-in-differences design, I find that flood risk disclosure laws, by enhancing dwelling-specific flood risk transparency, raise house prices on average by 7.6%. Results strengthen in states with stricter disclosure requirements. Exploiting within-state heterogeneity and controlling for housing market trends, I find that the effects persist and intensify in regions with higher aggregate exposure to flood risk, greater information frictions, and more attention to climate risks. Conversely, the effectiveness of flood risk disclosure laws attenuates in regions where households are less concerned about climate change. Additionally, less sophisticated lenders extend credit to financially constrained borrowers in response to the laws but experience lower profitability.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Better information can change housing prices and credit supply in ways that differ from a simple risk-discount narrative. This companion proposes a disclosure review that treats transparency as an obligation and a decision input, while resisting the unsupported use of an average research estimate as a property-level valuation uplift.
Real estate & resilience · Original source
Henri Gruhl; Johannes Brehm; Arijit Ghosh; Philipp Breidenbach; Nils aus dem Moore
Do individuals learn from salient river floods and update their risk perceptions accordingly? We exploit the Western European flood in 2021 to estimate changes in property values across German flood risk regions. Flood-prone areas in proximity to the flood or with higher climate change beliefs experience housing price declines, while we observe no statistically significant effects in other flood risk zones. Our results imply that flood risk discounts in the housing market will not adjust uniformly, which may result in suboptimal capital allocation in areas at risk of future flooding.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
A market that shows little reaction to a major flood elsewhere may still contain material physical exposure. This companion develops a regional flood-risk review that separates hazard, recent experience and observed price response, drawing on evidence that German housing markets updated unevenly after the 2021 Western European flood.
Real estate & resilience · Original source
Carolin Pommeranz; Bertram I. Steininger
We analyze whether lower rents for energy-inefficient apartments reflect tenants’ willingness to pay due to a higher green awareness, purchasing power, or energy consumption costs. Based on a German rental apartment dataset from Q1 2007 to Q1 2019, we use interaction terms for socioeconomic characteristics in a hedonic regression model. We find that rents are lower for apartments with higher energy consumption, even in neighborhoods with lower levels of green awareness. This relationship is stronger in neighborhoods with higher purchasing power, such that communities with low levels of green awareness and high purchasing power show the steepest negative slope for increasing energy consumption (−8.6% from the highest to lowest rating). Thus, the rent-decreasing effect of purchasing power is higher than that of green awareness. Splitting the entire period into smaller windows, we find that the interaction effect of green awareness has emerged in the most recent years (2017–2019). This may be driven by changes in regulation, which have made it easier for tenants to assess the energy consumption before they rent, or by a general increase in green awareness over this period.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Energy efficiency, tenant purchasing power and environmental preferences can interact in rental pricing, but an observed rent association is not a retrofit business case. This companion proposes a decision process that separates building performance, resident costs, lawful rent assumptions and capital recovery before a family commits to an energy project.
Real estate & resilience · Original source
Song Zhang; Mark van Duijn; Arno J. van der Vlist
Urban areas have long had shopping districts in which retail properties are concentrated. It is argued that the diversity of retail tenants, often called tenant mix, contributes to the image and attractiveness of the high street shopping district. These districts are characterized by fragmented ownership and therefore the tenant mix is outside the control of individual property investors. We aim to explore the relationship between tenant mix and retail rents by studying high street shopping districts in the Netherlands. We start with delineating high street shopping districts using granular data on the spatial distribution of retail jobs. Within each shopping district, tenant mix is measured using detailed information on SBI sector classification and the number of retail properties. We find shopping districts with greater tenant mix show higher retail rents compared to districts with lower tenant mix.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
The economic setting around a retail property can matter as much as its own lease. Drawing on Dutch evidence linking tenant mix with high-street rents, this companion proposes a district-level diligence and stewardship process that examines complementary uses, ownership fragmentation and tenant viability without assuming that greater diversity always produces higher returns.
Real estate & resilience · Original source
Juergen Deppner; Benedict von Ahlefeldt-Dehn; Eli Beracha; Wolfgang Schaefers
In this article, we examine the accuracy and bias of market valuations in the U.S. commercial real estate sector using properties included in the NCREIF Property Index (NPI) between 1997 and 2021 and assess the potential of machine learning algorithms (i.e., boosting trees) to shrink the deviations between market values and subsequent transaction prices. Under consideration of 50 covariates, we find that these deviations exhibit structured variation that boosting trees can capture and further explain, thereby increasing appraisal accuracy and eliminating structural bias. The understanding of the models is greatest for apartments and industrial properties, followed by office and retail buildings. This study is the first in the literature to extend the application of machine learning in the context of property pricing and valuation from residential use types and commercial multifamily to office, retail, and industrial assets. In addition, this article contributes to the existing literature by providing an indication of the room for improvement in state-of-the-art valuation practices in the U.S. commercial real estate sector that can be exploited by using the guidance of supervised machine learning methods. The contributions of this study are, thus, timely and important to many parties in the real estate sector, including authorities, banks, insurers and pension and sovereign wealth funds.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Machine learning can reveal structured appraisal errors without acquiring the authority to determine value or approve a transaction. This companion proposes a human-led validation protocol for family offices that want to use model-assisted appraisal challenges while controlling data leakage, weak comparability, misleading explanations and unreviewed overrides.
Real estate & resilience · Original source
Mohammad B. Hamida; Angela Greco; Hilde Remøy; Vincent Gruis; Brian van Laar
Circular and adaptable strategies in building reuse are key to achieving a resource-efficient and future-proof built environment. Despite significant advances in circular building research, this field is affected by a significant theory–practice gap. To bridge this gap, we applied an action design research methodology, implementing a circular building design framework over a five-month period in the context of a Dutch monumental office building reuse. The objective of these interventions was to observe practitioners engaging with the framework and identify the barriers they encountered when considering and applying circular building strategies. We observed that the framework primarily functioned as a descriptive tool. Enhancing its usability and effectiveness required several refinements, including simplifying its self-description, clarifying its strategies through practical solutions and connections to related models, providing robust assessment tools, and improving its accessibility. Through iterative action research conducted during the observation and intervention period, we addressed these issues and advanced the framework. Our design-oriented approach led to the development of key design artifacts: a prescriptive guiding, assessment, and reporting tool; a stepwise approach to streamline application; and a hands-on worksheet for practitioners. These artifacts were integrated into a user-friendly platform, transforming the framework into a practical tool for real-world implementation. For theory, this study incorporates a circular perspective into a usable framework and demonstrates how an action design research approach can co-develop and improve frameworks and their usability and relevance. For practice, the produced artifacts represent boundary objects tailored to practitioners’ needs; thereby paving the way for future circular adaptive reuse. Clinical trial number: not applicable.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
A circular-building framework becomes useful when a project team can turn its concepts into choices, evidence and responsibilities. This companion draws on action design research in a Dutch historic office building to propose a practical workshop process for family owners considering reuse, with explicit treatment of technical constraints, regulatory uncertainty and future adaptation.
Real estate & resilience · Original source
Jens Kolbe; Rainer Schulz; Martin Wersing; Axel Werwatz
Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.
Real estate & resilience · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
Property listings are timely and accessible, but they are not equivalent to completed transactions. This companion proposes a data and decision firewall that lets family offices use listings for market monitoring while preventing advertised prices, duplicate advertisements and unverified outcomes from silently becoming portfolio values.
Intelligence, trust & financial decisions · Original source
Patrick Weber; K. Valerie Carl; Oliver Hinz
Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances—especially Artificial Intelligence—regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter.
Intelligence, trust & financial decisions · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
An explanation is useful only when it answers a consequential question for an accountable decision maker. This companion translates a systematic review of explainable AI in finance into an evidence contract for vendor selection, acceptance testing and ongoing review. The proposed contract separates predictive performance, explanation fidelity and operational usefulness; it has not been validated as a new intervention.
Intelligence, trust & financial decisions · Original source
Achiel Fenneman; Joern Sickmann; Thomas Pitz; Alan G. Sanfey
Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant’s degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms.
Intelligence, trust & financial decisions · Solutions companion
Family Office Private Wealth Journal · Editorial research desk
People may reject an algorithm because they expect weak performance or because they demand a different threshold before trusting it. This companion turns that distinction into an onboarding and evaluation protocol that seeks warranted reliance rather than higher adoption. The protocol preserves human responsibility and evaluates expectations and reliance separately.