Property development activities: value creation or distraction for REITs?

PurposeThis paper examines the evolution and impact of property development activities on REIT performance. The paper provides insights on whether REITs should venture into property development in addition to their core-business of holding income producing properties.Design/methodology/approachThis paper charts and highlights the evolution of development activities of US REITs from 1992 to 2020. The Tobin's Q of property developing REITs and non-property developing REITs are compared using univariate analysis.FindingsDevelopment activities of US REITs grew dramatically during the run up to global financial crisis (GFC) in 2008. The level of development activities has dropped since the GFC and it has not return to its pre-crisis peak. In comparison, development activities of listed property investment companies and homebuilders are less volatile over the same period. The data reveals that property developing REITs enjoy significantly higher Tobin's Q as compared to their non-developing counterparts.Practical implicationsOur graphical evidence from a market without development restriction suggests that development restriction in other REIT regimes has it value in limit REITs' excessive risk-taking tendency during a booming property market. The positive relationship between Tobin's Q and the existence of property development activity support the value creation of this business activity to REITs.Originality/valueThis paper raises overbuilding as a potential cause of the underperformance of the REIT sector during the GFC.

Grand ideas or delusions of grandeur? Placing big thinkers and essential theories in property economics research

PurposeThis article is looking to reflect on the various important touchstones of “grand theory” and “big thinkers” that can be framed when engaging empirical evidence in property economics research.Design/methodology/approachThe paper is reflexive in nature, using experiential reflection to consider theory in property economics. The importance of “methodology” is emphasised rather than “method”.FindingsUsing reflexive mode, the paper does not have “findings” as such: if the views expressed are accepted, then a research agenda to better understand property economics research is implied.Research limitations/implicationsThe nature of reflection is that it follows from the writer's experiential processes and interpretations. The reader may come from a different stance. Broadly accepting the propositions, there is a call for property economics research to be formulated in reason and logic, particularly as humans do not reason from facts alone. Such reasoned thinking could for example be in the property economic concepts of space and place, contracts and justice, capital and financialisation.Practical implicationsTo engage with such theory would provide some depth of philosophical roots for property as a discipline. Elevating property as a “real-world” discipline rather than simply an applied mathematics discipline.Social implicationsThe paper enables an understanding of how property economics research can benefit from more ontology and more inductive reasoning.Originality/valueThe paper reflects the views and experience of the author based on over 15 years of research in property economics.

Data intelligence and real estate – machines are the real game changer

PurposeThe paper shows with two concrete examples about how algorithms are used in active real estate management. The paper also highlights that the discussion about the adoption of new technologies is crucial for market players.Design/methodology/approach The authors review the current status quo about new technologies in real estate and provide two examples of how algorithms can be used to understand locations and the value drivers of rents.FindingsLocation, location, location is nowadays data, data, data coupled with the knowledge of how to create life out of data. Algorithm can help to understand the value drivers of rents and can also help to evaluate the attractiveness of a location.Practical implicationsReal estate management will adapt to new technologies fast. This change has the potential to disrupt exiting strategies due to the increase in efficiency, insights, transparency and location knowledge. Investment managers walking this talk will definitely benefit in future.Originality/valueThe paper makes usage of the latest machine learning technologies applied to real estate investment cases. This is a unique opportunity on bringing light on the discussion about transparency in real estate.

Commercial real estate finance and the lending cap rate

PurposeThe aim of this study is to report on a simple derivation that results in what the authors refer to as the lending cap rate. The lending cap rate is a unique cap rate resulting in a property valuation that perfectly aligns the maximum loan amount for the financing of commercial real estate.Design/methodology/approachThe derivation is the result of simple algebra relating the two most common underwriting ratios: debt service coverage and loan-to-value with the formula for the present value of an annuity. Numerical examples are presented to demonstrate the calculation of the lending cap rate, property valuation and maximum loan amount. The authors also present comparative statics results.FindingsThe main finding of this research is that once a lender knows the debt service coverage ratio, loan-to-value ratio and lending terms for a specific property financing request, a simple calculation reveals the lending cap rate and the property valuation that aligns the maximum loan amount implied by the two underwriting ratios.Practical implicationsOne practical implication of the research is that a simple calculation reveals the lending cap rate which facilitates timely property evaluations for lending purposes. The methods demonstrated also offer real estate finance educators a practical means of connecting the loan underwriting process with property appraisal thereby facilitating conceptual understanding.Originality/valueThe key finding is original, and the importance of the finding is that the determination of the lending cap rate is simple and has the ability to make commercial real estate lending faster and cheaper, especially in lending situations where an evaluation rather than an appraisal is appropriate.

The human factor: the “unknown unknowns” in the real estate development process

PurposeVolatility, Uncertainty, Complexity and Ambiguity (VUCA) are terms the military have coined to describe the environment they often operate in. This paper examines how this decision-making framework can be used to better inform real estate investment and development. In celebration of this journal's 40th anniversary, we also explore how VUCA can be related to and expand on the teachings of Dr. James A. Graaskamp who published his seminal piece on the Fundamentals of Real Estate Development (1981) the same year. In that piece, he highlights the importance of paying attention to the human factor, the consumers of real estate.Design/methodology/approachThis is a thought piece on an alternative decision-making framework that can help capture the dynamic environment that commercial real estate investors and developers are currently working in. VUCA captures the difficulty of predicting the future in a world of accelerating, unpredictable change. This is particularly important in today's rapidly changing world caused not only by the current COVID-19 pandemic but also the exponential growth of the proptech industry as well as the increasing risks and opportunities associated with climate change that continues to impact the built environment.FindingsThis is not a traditional research project with empirical findings. We are presenting an alternative framework for thinking about making investment decisions in these current volatile, uncertain, complex and ambiguous times today and in the future. In addition, the importance of multidisciplinary training and the human factor are stressed.Research limitations/implicationsThere are no limitations to this research as it is the ideas of the authors. Implications are to help real estate investors, developers and educators better understand the environment that they are working in.Practical implicationsVUCA captures better the dynamic nature of real estate investments compared to traditional analysis. It helps one better analyze the risks and returns but also to acknowledge that there is a lot you cannot predict and there are many exogenous variables that can, at times, completely change the rules of the game. Flexibility and adaptability are essential tools for working in a VUCA environment. In addition, the human factor plays an increasingly important role and real estate investors and developers that clearly understand this and focus on the consumer will likely be more successful.Originality/valueWe believe that this is the first time that VUCA has been used in the real estate academic literature.

Risk management processes used in determining project feasibility in the property development process early stages by Australia/New Zealand property developers

PurposeThe purpose is to examine the risk management processes and methods used in determining project feasibility in the early stages of the property development process by Australia/New Zealand property developers, including Monte Carlo simulation, Bayesian models and real option theory embedded in long-term property development and investment decision-making as instruments for providing flexibility and managing risk, uncertainty and change.Design/methodology/approachA questionnaire survey of 225 Australian and New Zealand trader developers, development managers, investors, valuers, fund managers and government/charities/other relating to Australia/New Zealand property development companies' decision-making processes in the early stages of the development process prior to site acquisition or project commencement – the methods used and confidence in their organisations' ability to both identify and manage the risks involved.FindingsFew of the organisations sampled use sophisticated methods; those organisations that are more likely to use such methods for conducting risk analysis include development organisations that undertake large projects, use more risk analysis methods and have more layers in their project approval process. Decision-makers have a high level of confidence in their organisation's ability to both identify and manage the risks involved, although this is not mirrored in their actual risk management processes. Although the majority of property developers have a risk management plan, less than half have implemented it, and a third need improvement.Practical implicationsProperty development organisations should incorporate more modern and sophisticated models of risk analysis to determine the uncertainty of, and risk in, a change of input variables in their financial viability appraisals. Practical application includes using such multiple techniques as what-if scenarios and probability analysis into feasibility processes and utilise these specific techniques in the pre-acquisition stages of the property development process and, specifically, in the site acquisition process to support decision-making, including a live risk register and catalogue of risks, including identification of and plans for mitigation of project risks, as a form of risk management.Originality/valueFirst study to examine the extent of the decision-making methods used by property developers in the pre-acquisition stage of the development process.

Long lease real estate – a revised role for real estate in pension fund portfolios

PurposeLong lease real estate funds (over £15bn in Q3 2020) have emerged as an increasingly important part of UK pension fund real estate portfolios. This paper explores the reasons for their dramatic growth, their characteristics and performance.Design/methodology/approachThis study uses data for the period 2004–2020 collected directly from fund managers and from AREF/MSCI and empirical analysis to explore their characteristics and performance.FindingsPension fund de-risking and regulatory guidance have supported the dramatic growth of long lease real estate funds. Long lease real estate funds have delivered strong risk-adjusted returns relative to both balanced property funds (with shorter lease terms) and the wider property market. This relative performance has been particularly strong when wider property market performance has been weak. Long lease funds have objectives aligned with liability matching and their performance suggests they are lower risk (more bond-like) investments. In addition, our analysis highlights they are far less responsive to the wider property market than balanced funds. However, they are not significantly different from balanced property funds in terms of their short-term relationship with gilt yield movements.Practical implicationsFor pension funds and other investors the paper highlights that long lease real estate funds offer a different exposure than balanced property funds. Long lease funds have objectives more closely aligned to the overall objectives for pension fund investment but are not significantly more reliable than balanced property funds in the short-term as a liability hedge. For real estate fund managers, occupiers, developers and others active in the real estate market, the paper highlights why these funds have been (and are likely to remain) attractive to investors leading to substantial demand for long lease real estate investments.Originality/valueThis is the first study to review this increasingly important part of the UK real estate fund universe.

Artificial intelligence algorithms to predict Italian real estate market prices

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

Money supply, exchange rates and office market dynamics: comparative evidence from the UK and Germany

PurposeThe aim of this study is to shed light on the relative importance of money supply and exchange rates variations on office markets prices dynamics.Design/methodology/approachUsing a parsimonious real estate asset pricing model, the authors focus on the two biggest European office markets; namely the United Kingdom and Germany. The authors use a panel approach based on a robust econometric methodology (GMM with correction errors-in-variables). The authors take into account the variations of exchange rates and money supplies for the most important currencies.FindingsThe results highlight the impact of money supplies and exchange rates on office prices after the Global Financial Crisis. The authors report that the monetary policies in the UK and in Germany (Euro zone) have had significant influences in the real estate sector after the Global Financial Crisis. However, the authors identified significant differences between British and German office markets for the 2009–2019 period regarding the impact of money supply and exchange rates variations on the office prices dynamics.Practical implicationsThe results highlight the impact of money supplies and exchange rates on office prices after the Global Financial Crisis. The detailed and exclusive database (composed of the main office markets in the United Kingdom and in Germany) allows the authors to identify significant differences and opportunities for investors.Originality/valueThe authors use a parsimonious model and apply a panel approach based on a robust econometric methodology to analyse the impact of exchange rates and money supply variations on the office prices dynamics. The detailed and exclusive database (composed of the main office markets in the United Kingdom and in Germany) allows the authors to identify significant differences for investors.