Editorial − 2020 Real Estate Finance & Investment Symposium

In September 2019 in Cambridge, as the 4th edition of the symposium was ending, we were delighted to be heading to Geneva in September 2020 for the 5th edition of the symposium. This was to mark the fact that the University of Geneva had joined the University of Cambridge, the University of Florida, and the National University of Singapore in organizing the Real Estate Finance and Investment Symposium. Little did we know of course that 2020 would be such an eventful year and that in-person events would become impossible.

The COVID-19 pandemic has had devastating impacts on people’s lives and on the economy. The effects on the real estate sector have also been important, with several papers having been published on the topic (see, e.g., Ling et al., 2020; Duca et al., 2021; Milcheva, 2021; Hoesli & Malle, 2022). The crisis has brought many positive changes as well. For one thing, we have become much more agile with technology. As a result, the 2020 edition of the Symposium was held online in September and October. There were five sessions with two papers each, for a total of 10 papers. We would like to take the opportunity to express our thanks to Louis Johner from the University of Geneva for his valuable help in the process of organizing the 2020 symposium.

Although we missed the interactions with colleagues during breaks and meals, the sessions were very lively with much feedback given to the presenters. Among the positive aspects of the online events is the fact that many more colleagues from around the world were able to join the sessions. Despite this clear advantage of the online format, we resumed the in-person events from 2022, as we believe that face-to-face contacts are paramount to creating lively and enjoyable discussions. Such discussions are particularly important for young researchers who can thus create a network of colleagues. Five papers from the symposium are included in this special issue. Three papers focus on commercial real estate, one on housing, and one on the two types of assets. The remainder of this introduction briefly describes the papers included in this issue.

In “Are Online-Only Real Estate Marketplaces Viable? Evidence from China”, Mandi Xu, Hefan Zheng, & Jing Wu manually collect data on online judicial housing auctions in China, which is currently the largest online real estate market globally, and investigate how information disclosure facilitates real estate transactions. Their results suggest that disclosing better quality information online can attract more potential buyers. In particular, providing more comprehensive information such as professional appraisal reports or videos of the property can help to convert buyers’ initial interests into completed transactions and higher sales proceeds. The positive effects of information are particularly strong when combined with offline services, in a more mature online market, and for low-value properties.

In “A Machine Learning Approach to Price Indices: Applications in Commercial Real Estate”, Felipe Calainho, Alex van de Minne, & Marc Francke present a model agnostic methodology for producing property price indices. The motivation to develop this methodology is to include non-linear and non-parametric models, such as Machine Learning (ML), in the pool of algorithms to produce price indices. The key innovation is the use of individual out-of-time prediction errors to measure price changes. The data consist of about 30,000 commercial real estate transactions in New York. The results indicate that the prediction accuracy is higher for the ML models compared to linear models. On the other hand, ML algorithms depend more on the data used for calibration; they produce less stable results when applied to small samples and may exhibit estimation bias. Hence, measures to reduce or eliminate bias need to be implemented, taking into consideration the bias and variance trade-off.

In “The Effect of Expected Losses on the Hong Kong Property Market”, Ling Li & Wayne Xinwei Wan show that expected losses anchored to purchase prices can affect actual transactions in different property sectors. Utilizing the data of over a million commercial and residential property transactions in Hong Kong, the authors report that sellers facing nominal losses relative to their prior purchase prices attained higher selling prices than their counterparts. They suggest two market factors to account for the extent of the loss effect on the market transaction prices. First, the loss effect is only prominent when comparable transaction information is not readily accessible, such as in the less-transacted commercial property market. Second, their results suggest the relevance of the loss effect to the boom-bust property cycle in both the residential and commercial markets. These results have implications for understanding the market adjustment of the loss effect in the property market and its association with the aggregate market dynamics in a boom-bust property cycle.

In “Risk Retention Rules and the Issuance of Commercial Mortgage Backed Securities,” Sumit Agarwal, Brent Ambrose, Yildiray Yildirim, & Jian Zhang study the impact of requiring 5% of underlying credit risk associated with commercial mortgage backed securities to be “retained” by the issuer. These risk-retention rules, which were motivated by the belief that creators of securitization deals should have interests aligned with investors, was included in the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 (Dodd-Frank Act) and was one of the most important legislative and regulatory actions in response to the Great Financial Crisis (GFS). The Act’s risk retention rules apply to commercial mortgage backed securities (CMBS) although there was a carve out for multifamily securitizations that provided a comparison sample for the authors. Because the primary objective of these rules is for deal sponsors to have skin in the game, the authors posit that underwriting standards should have tightened following the implementation of the rule. In addition, the reform should lead to a decrease in the probability of rating shopping by the sponsors as well as longer time-to-securitization and lower default probabilities. The authors’ empirical analysis reveals that the Dodd-Frank risk retention rules had a significant impact on commercial real estate mortgage originations. Following the rule’s implementation, they find that conduit loans saw a significant increase in the time-to-securitization relative to agency (i.e., multifamily) deals, issuers were less likely to engage in ratings shopping on conduit deals, conduit loans had a decline in their risk premium relative to agency loans, and the probability of default on conduit loans declined. Thus, these results are consistent with lenders imposing tighter underwriting standards on conduit loans following the risk retention rule implementation. Furthermore, the authors find that loan growth significantly declined for lenders who engaged in high conduit volume loan activity prior to the rule implementation. As a result, they confirm that the risk retention rule did curtail credit growth in the commercial real estate market.