Real Estate Economics and Finance

Housing research is one of the hot topics in many countries. This paper provides a quick review of the housing economics research in the US, Sweden, Latvia, China, Corsica, and Italy published in this special issue. Bao and Shah studied the effects of home-sharing platforms in general and the effects of the US’ Airbnb on neighbourhood rent. Wilhelmsson’s results showed that interest rates directly affected house prices and indirectly affected bank loans in Sweden. Caudill and Mixon threw light on the relative negotiating power of the buyer and seller as a key element of real estate price models. Čirjevskis presented a real application of “step-by-step” valuation options for real estate development projects as a managerial risk management tool for similar real estate development projects in the EU to make investment decisions during COVID-19 and in the post-COVID-19 era. Pelizza and Schenk-Hoppé used an exponential Ornstein–Uhlenbeck process to model price dynamics provincially and regionally to estimate the liquidation value. Full article

In the aftermath of the COVID-19 pandemic, non-core investments are gaining traction amongst institutional investors due to the shifting preference towards investment vehicles that position higher on the risk–return curve. Non-listed value-add real estate funds in Japan are one such vehicle. This research develops a comprehensive bespoke benchmark total return index using the ANREV database to reflect the performance of Japan-focussed non-listed value-add real estate funds. We compare the performance of such funds with that of other asset classes and perform portfolio and regression analyses. We conclude that there are several advantages to investing in those funds, including: (1) strong absolute total return performance, (2) competitive risk-adjusted performance, and (3) significant portfolio diversification potential in a mixed-asset portfolio context. The strategic implications for real estate investors are also assessed. Full article

In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a “black box”. An important example is hedonic property valuation modeling, where machine learning techniques typically improve predictive accuracy, but are too opaque for some practical applications that require greater interpretability. This problem can be resolved by certain structured additive regression (STAR) models, which are a rich class of regression models that include the generalized linear model (GLM) and the generalized additive model (GAM). Typically, STAR models are fitted by penalized least-squares approaches. We explain how one can benefit from the excellent predictive capabilities of two advanced machine learning techniques: deep learning and gradient boosting. Furthermore, we show how STAR models can be used for supervised dimension reduction and explain under what circumstances their covariate effects can be described in a transparent way. We apply the methodology to residential land and structure valuation, with very encouraging results regarding both interpretability and predictive performance. Full article

Price discovery is an important research topic in real estate due to the heterogeneous nature of housing attributes and relatively thin trading activities compared to other assets. In Commonwealth countries, including New Zealand, governments usually conduct periodic appraisals for the purpose of collecting rates and levies. Such official appraisal values of properties, also known as capital values (CVs), are considered a price anchor for market participants in their negotiation processes. Real estate agents often use these appraisal values to advertise their listings and negotiate transaction prices. In this study, we aim to make an initial attempt to study the influence of CV on market prices using Granger causality tests and a hedonic pricing model. To test the lead-lag relationships, three million housing transactions from 1990 to 2020 in New Zealand are used to construct the capital values (CVs) and transacted prices (TPs) indices in both primary and secondary housing markets. The Granger causality test suggests that the indices of TPs and CVs have a bi-directional lead-lag relationship in the secondary housing market, whereas the relationship does not follow in the primary market where the information on CVs is unavailable. The results imply the existence of a CV anchoring effect. Such anchoring effects are also contingent on the timeliness of price anchors, which is consistent with the availability heuristic from behavioural economics. Full article

This paper examines whether urban leverage, defined by the bank loan-to-deposit ratio in a city, affects housing prices in China. Using a panel dataset of 236 cities and hedonic models, we find a depressing effect of urban leverage on housing price in first- and second-tier cities while leaving third- and fourth-tier cities unaffected. Urban leverage negatively affects housing prices by influencing credit supply. Moreover, the difference-in-differences analysis indicates that purchase restriction policies amplify the depressing effect of urban leverage on housing prices. Overall, we show that urban leverage is an important determinant of housing prices in China. Full article

The real estate market of EU countries has undergone a severe global financial crisis 2008–2009, recovered successfully later, and now experiencing significant uncertainty due to the COVID-19 pandemic event. Significant volatility of the real estate business is once again evident, just as it was following the global financial crisis. The paper aims to provide a case study of a real estate project by giving insight into the Latvian real estate project that had been experiencing similar economic uncertainty, to demonstrate hybrid real options valuation (ROV) method to adapt real estate investments to changing circumstances and to develop the decision-making solution to similar EU real estate problems during the pandemic. The paper provides the “step-by-step” ROV application’s methodology in real estate development projects. The presented methodology is a powerful managerial risk management tool for the executives of similar real estate development projects in the EU countries struggling to make investment decisions in the pandemic and post-pandemic period. Since any estimation includes assumptions, ROV results should be interpreted and perceived as approximations only. The future works can provide robust ROV analyses and interpretations regarding the demand for real estate, showing quantitatively how competition can impact strategic investment decisions. Full article

This paper explores the effects of home-sharing platforms in general and Airbnb in particular on rental rates at a neighbourhood level. Using consumer-facing Airbnb data from ten neighbourhoods located within large metropolitan areas in the U.S. between 2013–2017, as well as rental data from the American online real estate database company, Zillow, this paper examines the relationship between Airbnb penetration and rental rates. The results indicate that the relationship is not as unanimous as once thought. Viewing the relationship at an aggregate level, an approach used by many researchers in the past, hides the complexities of the underlying effects. Instead, Airbnb’s impact on rental rates depends on a neighbourhood’s individual characteristics. This study also urges policy makers to create tailor-made solutions that help curb the negative impacts associated with the platform whilst still harnessing its economic benefits. Full article

Presently, one of the most critical challenges for e-government and e-banking is the accurate and correct realization of factors that have a significant impact on customer behavior. Without appropriate knowledge of these factors, it would be impossible to predict the level of welcoming toward new services, acquire a competitive advantage, and coordinate marketing programs with the needs of customers. On the other hand, in today’s competitive world, banks are obliged to implement new services to retain current customers and attract new ones. This research has been conducted with the goal of identifying influential factors that have an impact on the development of user intentions. The theoretical research model has been designed based on the technology acceptance model (TAM), as well as technology adoption theory, technology dissemination theory, and planned behavior theory. This study adopted an empirical approach to investigate key acceptance factors in a case organization. The statistical population of this research consists of customers and employees in different branches of a financial institution called Mehr bank in Iran. The data was collected by means of questionnaires that were completed by 200 customers and employees who work at Mehr bank or have business relationships with it. Data analysis in descriptive and inferential statistics domains had been done in SPSS and AMOS software, respectively. This paper presents first-hand data analysis of a case study on technology adoption in banking systems in Iran. In addition, structural equations have been used for inferential analysis. The findings of this study confirm the direct impact of “perceived usefulness” and “perceived ease of use” towards user attitudes. In addition, results show that “attitude” and “perceived usefulness” have a direct impact on the development of usage intention in customers. However, the results do not confirm the role of subjective norms on the development of user intent. This study is limited to a selected organization, and the proposed model should be examined by applying it in different contexts. Full article

The main objective is to answer the question: What role does the housing market play for the transmission mechanism and (in particular) is the impact constant over time? The research question also includes analyzing the importance of the housing market for the transmission mechanism. We estimate an eight-variable structural vector autoregression (SVAR) model of the Swedish economy over the period 1993 and 2018 using quarterly data, covering both the internet bubble in 2000 and the financial crises in 2008. The results indicate that interest rates have both a direct effect on housing prices and an indirect impact through the bank lending channel. Over time, the traditional interest rate channel importance has been stable. On the other hand, the role of the bank lending channel has increased over time. Household debt has increased substantially in Sweden and elsewhere. That means that the interest rate sensitivity in society has increased. Based on the results, it is possible to evaluate and forecast potential house price effects (both direct and indirect) when the interest rate changes. Full article

The relative bargaining power of the buyer and seller is a key feature of real estate pricing models. Classic real estate studies have sought to address bargaining effects in hedonic regression models. Prior research proposes a procedure to estimate bargaining effects in hedonic regression models that depends critically on a substitution to eliminate omitted variables bias. This study shows that the proposed solution that is often cited in the real estate economics literature does not solve the omitted variables problem given that both models are merely different parameterizations of the same model, and thus produces biased estimates of bargaining power when certain property characteristics are omitted. A classic hedonic regression model of real estate prices using Corsican apartment data supports our contention, even when the assumption of bargaining power symmetry is relaxed. Full article

Our paper forecasts the expected recovery rates of defaulted Italian mortgage loans backed by either residential or commercial real estate. We apply an exponential Ornstein–Uhlenbeck process to model the price dynamics at the provincial and regional level, and two haircut models to estimate the liquidation value. Compared to our findings, rating agencies such as Moody’s, which use geometric Brownian motion to model the price dynamics, paint a rosier picture with higher recovery rates. As a consequence, non-performing mortgage loans held by Italian banks might be overvalued. Full article