Despite having abundant literature blaming a faulty financial system and exuberant‎ price expectations as the primary causes of housing bubbles, there is a lack of research that looks at the impact of house price instability on the economy. This study aims to fill this gap by thoroughly examining the connection between house prices and economic output, and the effect of house price volatility on economic stability. Drawing from long-spanning quarterly data from 17 OECD countries from 1970 to 2019, the study develops and tests economic growth and volatility models to uncover significant insights. The empirical results show that house price returns have a significant asymmetric impact on economic growth, with negative returns having twice the effect of positive ones. Moreover, the results indicate that house price volatility significantly contributes to economic instability. In light of these findings, the paper concludes with valuable policy recommendations to enhance the housing market and improve overall economic stability. This study provides a compelling argument for the importance of closely monitoring and regulating the real estate market in order to maintain a healthy and stable economy.

We investigate the impact of governmental restrictions on the short-term risk perception, as proxied by the going-in cap rate, of investors in regional and neighborhood shopping centers. We use the COVID-19 pandemic as a natural experiment and proxy for the length and severity of COVID-19 restrictions with the political affiliation of state governors. Using a sample of 40 metropolitan statistical areas (MSAs) across 27 states over the period of 2018 to 2021, we find that for states with Republican governors, which proxy for shorter and fewer COVID-19 restrictions, investors in regional malls required a lower going-in cap rate in the pandemic period than for states with Democratic governors. This effect does not exist for neighborhood shopping centers, whose tenants were not as affected by COVID-19 restrictions. Robustness checks suggest that our findings can be explained with mask mandates as one type of governmental restrictions, and that COVID-19 related restrictions do not impact the long-term risk perception of retail real estate investors. We furthermore find that the political attitudes of an MSA have an impact on investor risk perception.

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.

This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from January 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use to extract a recursive local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.

Online businesses have been surging worldwide during the past decade, especially during the recent COVID-19 epidemic. However, the market share of online real estate transactions is still limited, mainly due to the information-asymmetry problem. In this study, we 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. The empirical 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. We also provide preliminary analysis of factors affecting online-information-disclosure quality from both the macro and micro perspectives. We also provide preliminary analysis of factors affecting online-information-disclosure quality from both the macro and micro perspectives.

Housing policy, as well as academic research, are increasingly concerned with the role of bias in subjective dwelling valuations as a proximate measure of households' house price expectations and their relationship with housing demand. This paper contributes to this area of study by exploring the possibility of simultaneous relationships between households' price expectations and incentive to maximise the size of housing services demanded also accounting for the supply side factors and regional perspective. The empirical estimation takes the form of a system of a two simultaneous equations model applying two stage least squares estimation technique. Cross sectional estimations utilise data extracted from the Israeli Longitudinal Panel Survey (LPS) data. Applying the best available proxy for households' price expectations, calculated as the ratio between subjective dwelling valuations (LPS) and the estimated market value of the same properties, research has identified the interrelated factors that simultaneously influence householders' price expectations and housing demand. Results offer conceptual and empirical advantages, highlighting the imperfect nature of the housing market, as reflected by the inseparability of bias in subjective valuations and housing decisions.

Using a large, non-student sample, we assess and differentiate between borrowers' Risk Aversion and Ambiguity Aversion levels and their willingness to pay to resolve a mortgage default settlement negotiation. Ambiguity Aversion is found to be negatively associated with willingness to pay for borrowers with high financial literacy in both the gain and loss domains, whereas personality traits matter more for borrowers with low financial literacy. This finding is important to policymakers in that they should adopt differential resolution strategies for defaulting borrowers based on these intervening variables.

Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender 2016; Demiroglu and James (4), 1747-1760 2018). The extreme events on the tails are usually known as "black swans" (Taleb 2010) in finance and their existence complicates financial risk management, assessment, and regulation. We show via theory, Monte Carlo experiments, and an empirical example that a direct relation exists between non-normality of the pricing errors and goodness-of-fit of the house pricing models. Specifically, we provide an empirical example using US housing prices where we demonstrate an almost perfect linear relation between the estimated degrees-of-freedom for a Student's distribution and the goodness-of-fit of sophisticated evaluation models with spatial and spatialtemporal dependence.

This paper uses the global systemic shock associated with the outbreak of the novel coronavirus Covid-19 to assess the risk-return relationship in the cross-section of real estate equities in the US and in selected Asian countries. I construct regional Covid-19 Risk Factors (CRFs) to assess how the risk exposure of stocks to the pandemic affects their performance. I find substantial differences between stocks in Asia and the US as a result of the pandemic. During the early stages of the pandemic, the sensitivity of Asian real estate companies to the market becomes negative, while it remains positive and increases in the US. Real estate sectors experience strong divergence in performance in the US while little sectoral difference is observed in Asia. The most affected sectors in the US are retail and hotels, while in Asia it is office. A Fama-MacBeth regression shows evidence for a low-risk effect during the Covid period: while insignificant prior to the pandemic, the return-risk relationship becomes significantly negative during the Covid period, with valuation effects driving the results in both regions. Firms in the US perform significantly worse if their exposure to the pandemic is higher, which is not the case in Asia. The results point towards strong divergence of expectations between US and Asian real estate companies in the onset of Covid-19, which may be associated with the level of prior experience to similar pandemics.

The Coronavirus Aid, Relief, and Economic Security (CARES) Act was passed in response to both the global pandemic's immediate negative and expected long-lasting impacts on the economy. Under the Act, mortgage borrowers are allowed to cease making payments if their income was negatively impacted by Covid-19. Importantly, borrowers were not required to demonstrate proof of impaction, either currently or retrospectively. Exploring the economic implications of this policy, this study uses an experimental design to first identify strategic forbearance incidence, and then to quantify where the forborne mortgage payment dollars were spent. Our results suggest strategic mortgage forbearance can be significantly reduced, saving taxpayers billions of dollars in potential losses, simply by requiring a 1-page attestation with lender recourse for borrowers wishing to engage in COVID-19 related mortgage payment cessation programs. Additionally, we demonstrate the use of these forborne mortgage payments range from enhancing the financial safety net for distressed borrowers by increasing precautionary savings, to buying necessities, to equity investing and debt consolidation.

This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%.

Location spillovers are a common theme in real estate and urban economics research, but this is the first test on the relationship between hospital service quality and the demand for proximate medical office space. We hypothesize that hospitals with reputations for high quality service represent an opportunity for physicians, and other service providers, to benefit from reputation spillovers. Further, the reputation benefit is capitalized into the practices' willingness to pay for proximate office locations, thereby driving up the rental rates for nearby space. We find that distance from, and overall quality ranking of the hospital, both independent and in concert, are significantly linked to the base rents. The degradation in rent with distance is significantly greater when the hospital is ranked high in overall service quality, supporting the notion that a rent premium is linked to the high-quality hospital rather than simply an artifact of the neighborhood.

This article reviews research on the economics of information in real estate. It covers equity investment in private and public markets and intermediation by brokers. The review shows how, by examining the nature and extent of information frictions in these important markets, research has improved our understanding of potential market failures and corrections which can improve market functioning.

The efficiency of the real estate market is a major concern for homeowners, investors, lenders, policymakers, and researchers. Modern academic literature has mostly moved beyond an early emphasis on formal tests of informational efficiency. The Grossman and Stiglitz (The American Economic Review 70:393-408, 1980) paradox holds that perfect informational efficiency is impossible and the joint hypothesis problem implies that market efficiency is not even testable. Instead, researchers now commonly examine the speed, accuracy, and persistence of price movements in response to new information, as the allocative efficiency of a market ultimately depends on its degree of informational (and operational) efficiency. This special issue is devoted to exploring these issues.

Accurate and efficient valuation of property is of utmost importance in a variety of settings, such as when securing mortgage finance to purchase a property, or where residential property taxes are set as a percentage of a property's resale value. Internationally, resale based property taxes are most common due to ease of implementation and the difficulty of establishing site values. In an Irish context, property valuations are currently based on comparison to recently sold neighbouring properties, however, this approach is limited by low property turnover. National property taxes based on property value, as opposed to site value, also act as a disincentive to improvement works due to the ensuing increased tax burden. In this article we develop a spatial hedonic regression model to separate the spatial and non-spatial contributions of property features to resale value. We mitigate the issue of low property turnover through geographic correlation, borrowing information across multiple property types and finishes. We investigate the impact of address mislabelling on predictive performance, where vendors erroneously supply a more affluent postcode, and evaluate the contribution of improvement works to increased values. Our flexible geo-spatial model outperforms all competitors across a number of different evaluation metrics, including the accuracy of both price prediction and associated uncertainty intervals. While our models are applied in an Irish context, the ability to accurately value properties in markets with low property turnover and to quantify the value contributions of specific property features has widespread application. The ability to separate spatial and non-spatial contributions to a property's value also provides an avenue to site-value based property taxes.