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This paper analyses the choice of the optimal lease term of office property in relation to expected lag vacancy, periods of rent‐free and expected rental income growth. The optimal lease term is the one that minimizes the expected costs of contract negotiation from the perspective of landlords. Specifically, it presents an analysis of how to estimate effective rents based on the lease term, expected lag vacancy and rent‐free periods. This study shows that the optimal lease term tends to increase under the following conditions: when the expected rate of rental growth decreases, the discount rate increases, or the expected lag vacancy increases. A longer contract duration is less costly when future rentals are discounted at a higher rate. Altering the lease length will change the risks taken by the landlord. Thus, contract length can be used as a device for insuring vacancy risk. Moreover, we find that the optimal lease length is more sensitive to the lag vacancy when the discount rate is at a relatively high level.

This study hypothesises that sale and leaseback (SLB) cap rate is lower than the market cap rate in emerging economies, and the difference is due to institutional cost and vacancy risk. This study aims to provide a novel SLB-Cap-Rate Model to assess the performance of SLB transaction (SLBT).

SLBT data are generally not publicly available in developing countries. This study collected data from 31 SLBTs by conducting semi-structured interviews with stakeholders in Vietnam in 2019. The market cap rates were collected from consultants' reports. The hypotheses are tested by three regression models.

The results show that the SLBT cap rate is significantly less than the market cap rate in Vietnam, and most of the cap rate discount can be explained by institutional and risk factors. This suggests that SLBT helps to reduce search costs for tenants and vacancy risks. It explains why SLBTs are becoming more common in emerging countries.

The study has a strong practical implication for assessing the performance of SLBT for both buyers and sellers. It introduces a novel model for analysing the cap rates and potential risks of SLBT to facilitate property investment decisions.

This paper is one of the studies that contains new knowledge on SLBs in a developing country specifically Vietnam.

The purpose of this paper is to demonstrate the impact of lease duration and lease break options on the optimal holding period for a real estate asset or portfolio.

The authors use a Monte Carlo simulation framework to simulate a real estate asset’s cash flows in which lease structures (rent, indexation pattern, overall lease duration and break options) are explicitly taken into account. The authors assume that a tenant exercises his/her option to break a lease if the rent paid is higher than the market rental value (MRV) of similar properties. The authors also model vacancy duration stochastically. Finally, capital values and MRVs, assumed to be correlated, are simulated using specific stochastic processes. The authors derive the optimal holding period for the asset as the value that maximizes its discounted value.

The authors demonstrate that, consistent with existing capital markets literature and real estate business practice, break options in leases can dramatically alter optimal holding periods for real estate assets and, by extension, portfolios. The paper shows that, everything else being equal, shorter lease durations, higher MRV volatility, increasing negative rental reversion, higher vacancy duration, more break options, all tend to decrease the optimal holding period of a real estate asset. The converse is also true.

Practitioners are offered insights as well as a practical methodology for determining the ex-ante optimal holding period for an asset or a portfolio based on a number of market and asset-specific parameters including the lease structure.

The originality of the paper derives from its taking an explicit modelling approach to lease duration and lease breaks as additional sources of asset-specific risk alongside market risk. This is critical in real estate portfolio management because such specific risk is usually difficult to diversify.

Scoring is a widely used, long-established, and universally applicable method of measuring risks, especially those that are difficult to quantify. Unfortunately, the scoring method is often misused in real estate practice and underestimated in academia. The purpose of this paper is to supplement the literature with general rules under which scoring systems should be designed and validated, so that they can become reliable risk instruments.

The paper combines the rules, or axioms, for coherent risk measures known from the literature with those for scoring instruments. The result is a system of rules that a risk scoring system should fulfil. The approach is theoretical, based on a literature survey and reasoning.

At first, the paper clarifies that a risk score should express the variation of a property’s yield and not of its quality, as it is often done in practice. Then the axioms for a coherent risk scoring are derived, e.g. the independence of the risk factors. Finally, the paper proposes procedures for valid and reliable risk scoring systems, e.g. the out-of-time validation.

Although it is a theoretical work, the paper also focuses on practical applicability. The findings are illustrated with examples of scoring systems.

Rules for risk measures and for scoring systems have been established long ago, but the combination is a first. In this way, the paper contributes to real estate risk research and risk management practice.

The paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely “contacts per listing” as explained by hedonic, geographic and socioeconomic variables.

The authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.

The authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.

To the best of the authors’ knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.

This paper aims to show that the accuracy of real estate portfolio valuations and of real estate risk management can be improved through the simultaneous use of Monte Carlo simulations and options theory.

The authors' method considers the options embedded in Continental European lease contracts drawn up with tenants who may move before the end of the contract. The authors combine Monte Carlo simulations for both market prices and rental values with an optional model that takes into account a rational tenant's behaviour. They analyze how the options significantly affect the owner's income.

The authors' main findings are that simulated cash flows which take account of such options are more reliable that those usually computed by the traditional method of discounted cash flow.

Some limitations are inherent to the authors' model: these include the assumption of the rationality of tenant's decisions and the difficulty of calibrating the model given the lack of data in many markets.

The main contribution of the paper is both by accounting for market risk (Monte Carlo simulations for the prices and market rental values) and for accounting for the idiosyncratic risk (the leasing risk).

When current accommodation is unsatisfactory, office organisations consider relocating to new accommodation that optimally facilitates their main processes and supports image and financial yield. However, due to high vacancy levels, public opinion and governmental awareness oppose new office construction. Reusing existing buildings could be the egg of Columbus. This paper aims at answering the questions: which property characteristics are important push and pull factors for relocation? What does this mean for the decision: stay or go?

A literature review of factors determining organisations' accommodation choices was conducted. Interviews were held with large-scale office organisations and creative organisations, discussing relocation drivers. Henceforth, a survey was held among creative organisations, collecting data about property characteristics important for their preferences. Finally, office user preferences were compared with characteristics of structurally vacant buildings.

Traditional push factors like car accessibility, extension need, and location and building image remain important. Nowadays sustainability issues like reducing energy consumption and better public transportation accessibility are highly prioritised pull factors as well. Regarding the creative industries, bike- and public transportation accessibility, multi-tenancy, and ICT and meeting facilities are most important.

Knowing office users' preferences is important to attract and retain stable tenants. If office space supply is highly aligned to end-users' demands and easily adaptable to changing needs, probably more organisations will decide to stay instead of go, leaving behind empty offices.

This study combines data about push and pull factors with relocation decision-making, innovatively focussing on the creative industries. The data can be used to explore opportunities and risks of adaptive re-use of the existing building stock.

The purpose of this paper is to define an approach useful to evaluate real estate funds on the specific characteristics of the Italian market and on the basis of international best practices.

The first step is to identify specific factors and portfolio construction choices that could impact directly on the variability of inflows and outflows related to real estate fund. The analysis is realised constructing standard measures of financial and downside risk and identifying a panel model that allows to explain risk measure dynamics on the basis of some investments and portfolio characteristics. Results obtained are tested with an out of sample procedure in order to evaluate the type of misclassification risk related to each model. The second step is to evaluate the impact of debt policy on the risk assumed by a real estate funds. After an analysis of debt sustainability for each real estate unit on the basis of deadlines and amount of flows related to each investment, the study proposed looks directly at the debt policy of listed real estate funds: the analysis is aimed to evaluate the relationship between leverage choice and inflows/outflows variability and the coherence between declared results and expected results for high‐leveraged funds respect to the others.

The results stemming from the use of a real estate database supplied by Beni Stabili Gestioni Società di Gestione del Risparmio showed that the portfolio's construction choice impacts strongly on the variability of results of a real estate fund. The strict linkage between characteristics of debt and type of property makes difficult to evaluate the additional risk related to debt choice but on the basis of Italian market data are possible to point out the higher difficulties for high‐leveraged funds to achieve the result communicated to the market (the so‐called target IRR).

The value added of the paper is to study the relevance of specific risk factors respect to portfolio's ones in the evaluation of risk exposure for a real estate portfolio and the impact of the leverage choices on the variability of inflows and outflows related to the real estate investments.

This paper investigates the dynamic nature of risk in pre-, during- and post-COVID duration. It investigates how commercial office portfolio stakeholders in India perceived risk during the COVID pandemic, their risk response and mitigation strategies, and emerging structural changes that would impact the commercial office portfolio (COP) in the post-COVID period.

A qualitative and applied research method is adopted for the study. Through purposive sampling, commercial office portfolio stakeholders were selected and interviewed using a semi-structured questionnaire having two parts. In the first part, risk attributes were accessed on the Likert scale and in the second part there were open-ended questions.

The uncertainty during the COVID period increased the risk perception significantly. There was a sense of urgency to retain the tenants, preserve the headline rentals and keep the properties operational. COP managers were forthcoming to offer rent deferments, common area maintenance discounts and upgrades in the physical office in form of touchless equipment, better air filters, etc. Post-pandemic there would be extensive use of technology and data for facility management and space utilization analytics; mainstreaming of hybrid working and flexible office spaces; increased certification of buildings; adoption of ESG and sustainability norms; and better-designed buildings with a focus on EHS and wellbeing.

Identifying structural changes in the post-pandemic period will help the COP managers to align their portfolios to the emerging office market requirements.

This study helps in developing an understanding of the dynamic nature of the risk across pre-, during- and post-COVID periods. And risk responses and mitigation strategies adopted during the COVID period in an emerging market.

The purpose of this study is to introduce a new perspective on determinants of cross-border investments in commercial real estate, namely, the relative attractiveness of a target market. So far, the literature has analyzed only absolute measures of investment attractiveness as determinants of cross-border investment flows.

The empirical study uses a classic ordinary least squares estimation for a European panel data set containing 28 cities in 18 countries, with quarterly observations from Q1/2008 to Q3/2018. After controlling for empirically proven explanatory covariates, the model is extended by the new relative measurement based on relative yields/cap rates and relative risk premia. Additionally, the study applies a generalized additive mixed model (GAMM) to investigate a potentially nonlinear relationship.

The study finds on average a ceteris paribus, statistically significant lagged influence of the proxy for relative attractiveness. Nonetheless, a differentiation is needed; relative risk premia are statistically significant, whereas relative yields are not. Moreover, the GAMM confirms a nonlinear relationship for relative risk premia and cross-border transaction volumes.

The results are of interest for both academia and market participants as a means of explaining cross-border capital flows. The existing knowledge on determinants is expanded by relative market attractiveness, as well as an awareness of nonlinear relationships. Both insights help to comprehend the underlying transaction dynamics in commercial real estate markets.

Whereas the existing body of literature focuses on absolute attractiveness to explain cross-border transaction activity, this study introduces relative attractiveness as an explanatory variable.