Journal of Real Estate Research最新文献

{"title":"Assessing the Explanatory Power of Dwelling Condition in Automated Valuation Models","authors":"A. Oust, Sjur Westgaard, Jens Erik Waage, Nahome Kidane Yemane","doi":"10.1080/08965803.2023.2280280","DOIUrl":"https://doi.org/10.1080/08965803.2023.2280280","url":null,"abstract":"","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"17 9","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Asymmetric Effects of Real Estate Uncertainty Shock","authors":"Seulki Lee, Young Min Kim","doi":"10.1080/08965803.2023.2280320","DOIUrl":"https://doi.org/10.1080/08965803.2023.2280320","url":null,"abstract":"","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"7 9","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time Varying Dependences Between Real Estate Crypto, Real Estate and Crypto Returns","authors":"Cathrine Nagl, Maximilian Nagl, Daniel Rösch, Wolfgang Schäfers, J. Freybote","doi":"10.1080/08965803.2023.2277479","DOIUrl":"https://doi.org/10.1080/08965803.2023.2277479","url":null,"abstract":"","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"61 5","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138595549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Sustainability, Certification, and Price Premiums: Evidence from Europe","authors":"Chinmoy Ghosh, Milena T. Petrova","doi":"10.1080/08965803.2023.2267717","DOIUrl":"https://doi.org/10.1080/08965803.2023.2267717","url":null,"abstract":"","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"49 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Does the COVID-19 Pandemic Affect Housing Market? Evidence from Shanghai, China","authors":"Xuejun Du, Zhonghua Huang, Junhua Chen","doi":"10.1080/08965803.2023.2281770","DOIUrl":"https://doi.org/10.1080/08965803.2023.2281770","url":null,"abstract":"","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"12 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139210018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Market Asset Returns, Economic Conditions, and Firm Fundamentals on Net Lease Capitalization Rates","authors":"Stace Sirmans, Stacy Sirmans, Greg Smersh, Daniel Winkler","doi":"10.1080/08965803.2023.2266282","DOIUrl":"https://doi.org/10.1080/08965803.2023.2266282","url":null,"abstract":"AbstractThis study fills a void in the literature by examining real estate capitalization rates for single-tenant net lease (STNL) properties. First, we examine cap rate variation in relation to market and firm-level fundamentals using individual transaction data in a multistage regression approach. Second, our single-tenant dataset, which allows us to control for characteristics such as industry and tenant credit ratings, gives us unique insight into not only the pricing of cap rates, but also their underlying drivers and their relationship to market fundamentals and returns on alternative assets. Using this unique dataset of more than 8,000 single-tenant net lease retail property transactions, we develop a quarterly cap rate index controlling for Metropolitan Statistical Area (MSA) and industry fixed effects, property and lease characteristics, and localized influences such as population density and household income. Third, we examine the effect of excess corporate bond spreads, excess stock returns, stock market indicators, firm financials, and economic and demographic indicators. Finally, we examine the effect on cap rates of MSA characteristics such as size, wealth, poverty, crime, gross domestic product, and growth. The findings show that, besides the systematic risk from stock and bond returns, national and metropolitan economic forces and firm fundamental factors explain variation in cap rates.Keywords: cap ratesnet leaseexcess returnmacroeconomic factors AcknowledgmentsWe thank the editor and the anonymous referees for their insightful feedback, which has substantially improved this article. We also thank the participants at the 2023 ARES Conference for their helpful comments.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 As a percentage of total investible wealth, real estate is a significant percentage of domestic publicly traded equities. According to Siblis Research Ltd, the total market capitalization of the U.S. stock market was $53.4 trillion as of December 31, 2021 (https://siblisresearch.com/data/us-stock-market-value/) while, according to the National Association of Real Estate Investment Trusts (NAREIT), the total value of U.S. commercial real estate was $20.7 trillion as of June 2021 (https://www.reit.com/data-research/research/nareit-research/estimating-size-commercial-real-estate-market-us-2021).2 The cap rate is defined as the ratio of a property’s net operating income to its current market value; most often the cap rate is based on expected net operating income.3 Common multifactor models in the finance literature include the Fama-French three-factor model (1992) and the arbitrage pricing theory (APT) model (Ross, Citation1976).4 All the studies discussed in this section use either the full cap rate or the excess cap rate (typically defined as the full cap rate minus the three-month TB yield) as the dependent variable in their analysis.5 To examine the relationship between real ","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using REIT Follow-on Equity Offerings to Pay down Credit Line Balances: Bank Certification or Monitored Financial Flexibility?","authors":"Vladimir A. Gatchev, Nandkumar Nayar, S. McKay Price, Ajai Singh","doi":"10.1080/08965803.2023.2263246","DOIUrl":"https://doi.org/10.1080/08965803.2023.2263246","url":null,"abstract":"AbstractUsing hand collected data from offering prospectuses and other corporate filings, we examine the market response to real estate investment trust (REIT) follow-on stock offerings’ stated uses of proceeds. We also track REIT banking relationships over time. Consistent with the idea of bank certification, we show that markets react relatively favorably to REIT equity offers where issuers have lending relationships with affiliates of the underwriters. However, we also find that reactions are most favorable where REIT issuers intend to repay their bank’s line of credit, regardless of the bank’s affiliation with the underwriters. This pattern is particularly strong among smaller firms with lower institutional ownership. We posit that credit line repayments preserve benefits of bank monitoring while enhancing financial flexibility. Further examination reveals that this monitored financial flexibility is the dominant effect.Keywords: REITsequity offeringscredit linesbanking relationshipscertificationmonitored financial flexibility AcknowledgmentsWe thank Nevin Boparai, Paul Brockman, John Cobb, Sandeep Dahiya, Chitru Fernando, Ioannis Floros, Kathleen Weiss Hanley, Bill Hardin, David Harrison, Masaki Mori, Christo Pirinsky, Victoria Rostow, Calvin Schnure, Paul Schultz, Qinghai Wang, Ke Yang, two anonymous reviewers, and participants at the American Real Estate Society Conference and European Real Estate Society Conference for helpful discussions and comments. Natalya Bikmetova, Debanjana Dey, Xin Fang, and Sulei Han provided invaluable research assistance. We remain responsible for any errors. Gatchev and Singh are grateful for the financial support provided by the SunTrust Endowment; Nayar appreciates support from the Hans Julius Bär Endowed Chair; Price acknowledges support from the Collins-Goodman Endowed Chair.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 Indeed, the increase of REITs’ commercial bank borrowings, such as lines of credit and term loans, in recent years has attracted the attention of investors, credit rating agencies, and the press. See, for example, https://www.wealthmanagement.com/reits/are-reits-maxing-out-bank-borrowing.2 Consistent with Puri (Citation1996), we also use the term investment houses interchangeably with investment bankers (or underwriters) to distinguish them from pure commercial banks. To denote the dual underwriting and commercial banking relations, we use the term “underwriting-banking relations” through the rest of the paper.3 The general conclusion across prior studies is that the certification benefits, net of any conflicts of interest costs, stem from information advantages gained through the lending channeland are most, and sometimes only, evident for junior, information sensitive securities. For example, Duarte-Silva (Citation2010) finds that announcement returns to equity offers are less negative when underwriters also have prior lending relationships wit","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REIT Long-Term Returns and Wealth Creation","authors":"Gow-Cheng Huang, Kartono Liano, Ming-Shiun Pan","doi":"10.1080/08965803.2023.2254587","DOIUrl":"https://doi.org/10.1080/08965803.2023.2254587","url":null,"abstract":"AbstractThis study examined the performance of 371 equity real estate investment trusts (REITs) over the period 1972–2020. Unlike stocks, we found that the majority of the 371 REITs outperformed one-month T-bills, particularly over longer holding periods and in the modern REIT era. While most REITs outperformed the T-bills, only a minority of them outperformed the overall equity REIT market. REITs that outperformed the overall equity REIT market concentrated in the health care, industrial, residential, and other specialty REIT sectors. In terms of wealth creation, REITs in aggregate created a total net wealth of $0.89 trillion to their shareholders, but the wealth creation was highly concentrated in relatively few top-performing REITs. Specifically, the top five (20) REITs together accounted for almost 30% (60%) of the total net wealth creation. Overall, our results suggest that relative to the T-bills, REITs performed better than stocks.Keywords: Real estate investment trustsREIT return performanceshareholder wealth AcknowledgmentsThe authors thank three anonymous referees for helpful comments.Disclosure StatementNo potential conflict of interest was reported by the authors.Notes1 The maximum lifetime return was 37,628% for REITs, while it was 244.3 million % for stocks.2 For example, Bessembinder (Citation2018) reported that the mean and median monthly returns for stocks were 1.13% and 0.00%, respectively. Our results showed that the mean and median monthly returns for REITs were 1.06% and 0.95%, respectively.3 For the period from 1972 to 2020, the average monthly return for the CRSP value-weighted market index was 0.94%, which was slightly higher than 0.88% for the FTSE NAREIT ALL REIT index. However, the average monthly income return (i.e., dividend yield) for stocks was 0.24%, which was less than 0.63% for REITs.4 We grouped equity REITs into eight property types, including diversified, health care, hotel, industrial, office, residential, retail, and other equity REITs. Other equity REITs included casino, self-storage, and specialty REITs.5 There are two REITs with the same company identification number in CRSP. We treated these two REITs as one company and calculated their lifetime wealth creation as the sum of dollar wealth creations from these two REITs. Also, see note 19.6 Our sample began in January 1972 because return data for the FTSE NAREIT All Equity REIT index are available since then. Eight equity REITs were listed in CRSP prior to January 1972. The sample also excluded four equity REITs with an initial listing date in 2020.7 The CRSP database does not contain the offering price of an IPO and, hence, the return in the IPO month is not available. Consequently, our analysis did not capture the return from the IPO month of a REIT. Furthermore, some firms changed their status from a non-REIT to a REIT after operating for some time. For these REITs, the first monthly return used was the month when their REIT status was established. We ","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changing the Location Game – Improving Location Analytics with the Help of Explainable AI","authors":"Moritz Stang, Bastian Krämer, Marcelo Cajias, Wolfgang Schäfers","doi":"10.1080/08965803.2023.2258012","DOIUrl":"https://doi.org/10.1080/08965803.2023.2258012","url":null,"abstract":"AbstractBesides its structural and economic characteristics, the location of a property is probably one of the most important determinants of its underlying value. In contrast to property valuations, there are hardly any approaches to date that evaluate the quality of a real estate location in an automated manner. The reasons are the complexity, the number of interactions and the non-linearities underlying the quality specifications of a certain location. By combining a state-of-the-art machine learning algorithm and the local post-hoc model agnostic method of Shapley Additive Explanations, this paper introduces a newly developed approach – called SHAP location score – that is able to detect these complexities and enables assessing real estate locations in a data-based manner. The SHAP location score represents an intuitive and flexible approach based on econometric modeling techniques and the basic assumptions of hedonic pricing theory. The approach can be applied post-hoc to any common machine learning method and can be flexibly adapted to the respective needs. This constitutes a significant extension of traditional urban models and offers many advantages for a wide range of real estate players.Keywords: Location AnalyticsExplainable AIMachine LearningShapley ValuesAutomated LocationValuation Model Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 This term describes the fact that this technique is applied after the actual training of an algorithm (= post-hoc) and can be applied for different algorithms (= model-agnostic).2 In the context of the SHAP-LS methodology, it is in principle possible to use both purchase or rental prices. Both reflect the observable willingness to pay for a property with certain characteristics and a certain location and can thus be used in this logic in an arbitrary manner.3 An example of the identification of aggregated results would be the Permutation Feature Importance (see e.g., Krämer, Nagl, et al., Citation2023).4 Theoretically, the SHAP-LS of single features could be used for this kind of analysis. However, this is not recommended, as one is exposed to the capriciousness of the algorithms and data providers. Often, locational features such as the distance to the next bus stop and to the next subway station correlate highly. Consequently, the algorithm cannot distinguish perfectly between these correlated features, which can lead to a blurring of the individual SHAP values. Another reason that should not be neglected is the dependence on the categorization of the location characteristics of the data providers. In some cases, individual amenities overlap considerably, e.g., the classification of restaurants, pubs or bars. Combining several individual characteristics into categories can counteract this blurring. As a rule of thumb, it can be stated that the more data available, the smaller the categories that can be used.5 It should be noted that the overall and categorica","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts on REIT Stock Capital Structures, Equity Costs, and Market Liquidities of Being Included in ETF Managed Portfolios","authors":"Long Ma, Ronald W. Spahr, Mark A. Sunderman","doi":"10.1080/08965803.2023.2254039","DOIUrl":"https://doi.org/10.1080/08965803.2023.2254039","url":null,"abstract":"AbstractIn recent years, especially as compared to mutual funds, exchange traded fund (ETF) markets have grown and advanced significantly compared to other financial asset classes because of relative advantages. We found that inclusion of real estate investment trusts (REITs) in ETF assets under management (AUM) positively impacts REIT capital structure (financial leverage), cost of equity capital, and stock market liquidity. As percentages of REIT outstanding shares included in ETF AUM increased, we found corresponding reductions in financial leverage (both book and market leverage), reduced costs of equity capital, and greater market liquidity. This should be of particular interest to REIT and ETF managers as well as REIT and ETF investors. Partially because regulatory statutes incentivize REITs to rely more heavily on external equity financing, REIT stocks included as ETF AUM showed greater reductions in leverage compared to non-REIT stocks also held as ETF AUM. Our results, including applying difference-in-differences models, were robust with respect to these findings, REIT type, and firm fixed effects.Keywords: REITETFAUMmarket liquiditycost of equity capitalfinancial leverage Disclosure StatementNo potential conflict of interest was reported by the authors.Notes1 We suggest that the reason REITs experience a significant increase in institutional ownership and in stock turnover on ETF inclusion as AUM is that ETF provide another and possibly stochastically superior way to own real estate assets and their associated advantages.2 The Investment Company Act of 1940 is an act of Congress that regulates investment funds, investment companies, and pass-through companies that include REITs and ETFs. It was passed as a United States Public Law (Pub.L. 76–768) on August 22, 1940, and is codified at 15 U.S.C. §§ 80a-1 – 80a-64. The act is enforced and regulated by the Securities and Exchange Commission (SEC), and defines the responsibilities and requirements of investment companies, including ETFs, and the requirements for any publicly traded investment product offerings such as open-end mutual funds, closed-end mutual funds, and unit investment trusts. The act primarily targets publicly traded retail investment products.3 Typically, ETFs hold assets in trust in their portfolios, technically not holding title to assets. ETFs are formed by an ETF manager (sponsor) filing a plan with the U.S. SEC to create an ETF. When approved, the sponsor forms an agreement with an authorized participant (AP), generally a market maker, specialist, or large institutional investor that is empowered to create and redeem ETF shares. Often, the AP and the sponsor are the same. The AP then borrows REIT stock shares from an institutional investor, often a pension fund, places those shares in a trust, and uses them to form ETF creation units (CU). CUs bundle stock, commonly 50,000 shares (one creation unit) of an ETF. Then, the trust provides fractionalized shares of the ETF ","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135194086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}