Commercial Real Estate Assets Research Articles

The Impact of Real Estate Investment Trusts (REITs) on the Indian Commercial Real Estate Market: A Study of Investor Perception and Market Performance

The introduction of Real Estate Investment Trusts (REITs) in India has marked a significant evolution in the country's commercial real estate landscape. This paper examines the multifaceted impact of REITs on the Indian commercial real estate market, focusing on key dimensions such as liquidity, transparency, investor access, and market development. Firstly, REITs have enhanced liquidity in the commercial real estate sector by providing investors with a liquid avenue to invest in income-generating properties, thereby reducing the traditionally illiquid nature of real estate investments. This increased liquidity has facilitated capital flow into the market and stimulated transaction activity. Secondly, REITs have contributed to greater transparency in the Indian real estate market. By mandating regular disclosures and adherence to stringent governance standards, REITs have improved information symmetry between investors and property developers, leading to a more efficient allocation of capital and reduced investment risk. Thirdly, the introduction of REITs has widened investor access to commercial real estate assets. Individual investors, institutional funds, and foreign investors now have the opportunity to participate in the market through REIT investments, thereby diversifying their portfolios and potentially earning stable returns from rental income and capital appreciation. Furthermore, REITs have played a pivotal role in the development of the Indian commercial real estate market. Their presence has spurred the professionalization of property management practices, encouraged the adoption of international standards, and catalyzed the growth of ancillary industries such as real estate services and financial advisory firms.

Risk Perception and Loan Underwriting in Securitized Commercial Mortgages

We use model-implied volatility to proxy for property risk perceptions in the commercial real estate lending market. Although loan-to-value ratios (LTVs) unconditionally decreased following the Global Financial Crisis, LTVs conditioned on implied volatility and other theoretically motivated fundamental determinants of optimal leverage show no conclusive trend before or after the crisis. Taking reported property and loan attributes at face value, we find no clear pattern of unwarranted credit being extended to commercial real estate assets. We conclude that systematically higher LTV decisions pre-crisis would have primarily stemmed from risk misperceptions rather than imprudent practices. Our findings suggest that the aggregate LTV level should be interpreted as a proxy for lending standards only after controlling for aggregate risk perceptions, among a host of asset and lending market factors. Our findings also highlight the importance of measuring and tracking aggregate risk perceptions in informing regulators and policymakers.

Combining machine learning and econometrics: Application to commercial real estate prices

AbstractIn this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.

Commercial real estate and air pollution

AbstractWe analyze the causal effect of air pollution (acute fine particulate matter) exposure on the commercial real estate (CRE) market. We instrument for air pollution using changes in local wind direction to find that an increase in fine particulate matter exposure leads to a contemporaneous decrease in CRE market values and (net) income as well as an increase in capital expenditures. Heterogeneous treatment analysis within a building‐level fixed effects framework uncovers that the negative effect on market values is concentrated in the office sector, consistent with the notion that air pollution‐induced decreases in CRE values are driven by a reduction in CRE assets’ productive capacity. Additionally, we document that the negative impact on (net) income is concentrated in the apartment sector, which is consistent with a broad set of local disamenity mechanisms identified in previous residential real estate literature.

Benchmarking Hotel Investment Risk: Differences Based on Types of Hotels

Commercial real estate (CRE) investment involves risk, and hotels are perceived as the riskiest CRE assets because of the high turnover of guest room occupants and are the most operation-intensive of all types of CRE properties. Furthermore, that risk may vary significantly across types of hotels based on different dimensions. The existing academic literature regarding CRE investment performance generally lacks such investigation of hotels at the establishment level. The purpose of the current study is to investigate the volatility of operating profit (risk) of different types of hotel assets. Using relative standard deviations of historical performance (gross operating profit [GOP]) to measure hotel risk, we examine various property characteristics and the extent to which they affect the volatility of GOP at the unit level from 2015 through 2020 of over 3,000 U.S. hotel properties. We find that different types of hotels have carried different levels of risk. Specifically, we find significant differences in risk based on hotel brand affiliation status, class, property type, location type, region in which the hotels are located, age of the hotels, size of the hotels, and their occupancy and average daily rate levels. This study provides practitioners and researchers with an understanding regarding the relationships between the risk of different types of hotels, and provides practitioners with information regarding risk and a benchmarking methodology that may be applied to evaluate risk to aid hotel investment decisions. Furthermore, we provide researchers with information regarding various hotel characteristics that may lead to relatively greater/lesser risk.

ОСНОВНЫЕ ПОДХОДЫ ОЦЕНКИ РЫНОЧНОЙ СТОИМОСТИ ИМУЩЕСТВА

The article discusses the main approaches to assessing the market value of property: the method of comparison with similar objects on the market, the income approach, as an assessment of potential income from property, and the cost approach, as an assessment of the costs of restoring or replacing property. The phasing of bankruptcy procedures, the methodology and principles ensuring the process of crisis management and bankruptcy are summarized. The main stages of comparative analysis and the need to adjust the cost of each of the compared objects are outlined. An income approach to assessing the value of property, an asset or a business is presented, which is based on forecasting future cash flows. It is noted that this valuation method is often used when valuing commercial real estate, businesses, investments and other income-generating assets. A feature of the income approach is considered, which consists in calculating net current cost income using a discounted rate that reflects the minimum acceptable return of the investor or a capitalized rate, depending on the risks and characteristics of the asset. The cost approach is based on determining the value of an asset based on its cost to restore or replace it. The method is used in valuing real estate, equipment and other assets using replacement cost estimates that take into account depreciation and obsolescence.

Rewarding a Long-Term Investment Strategy: REITs

The initial structure of real estate investment trusts (REITs) was predicated on real estate as a long-hold asset that would benefit from an ownership structure that fosters property portfolios held and managed for the long-term. Using a sample of publicly traded U.S. REITs from 1995 to 2018, we find that REIT performance is positively associated with previous-year property holding period. The results support the original broad policy goals associated with the REIT structure to allow for broad ownership of commercial real estate assets held for the long-term. The results further show that REITs adopting long-term investment and management strategies suggested by the enabling laws allowing the REIT structure are more profitable with better shareholder returns. The benefits related to portfolio construction and management shown in this study are complementary to studies related to property location and other portfolio construction strategies. Our findings are attributed to enhanced operational efficiency, property-level cash flow, and managerial effort.

Tokenization—The Future of Real Estate Investment?

This article examines the mechanisms now available to tokenize real asset ownership and create active secondary markets in tokenized or fractionalized units. Real estate is lumpy and illiquid, and tokenization would allow participation in the market by a broader group of investors and the ability to build diversified portfolios with modest sums of money. However, two developments are necessary for the digital tokenization of single real estate assets: (1) an expressed demand for the fractionalization of single real estate assets, and (2) market participants need to be comfortable with blockchain, the digital underpinning of tokenization. Further, in many land markets fractionalization requires an intermediate structure to be established because the direct ownership of land cannot be shared amongst many, increasing the cost of tokenization. Larger assets already held in fund structures may eventually be tokenized successfully; there may also be an alternative market for tokenized residential, social impact, or community assets where investment regulation and risk/return are not the main drivers of behavior. The mass market for the tokenization of single commercial real estate assets, however, may be some way down the road. <b>Key Findings</b> ▪ The author examines the mechanisms now available to tokenize real asset ownership. ▪ The author presents new survey-based evidence of real estate tokenization deals. ▪ The author concludes that an intermediate structure is likely to be both necessary and convenient when fractionalizing a single asset. For that reason, funds are more natural targets for tokenization.

The Rank-Size Rule and Challenges in Diversifying Commercial Real Estate Portfolios

The strategy of geographically diversifying a portfolio of commercial real estate assets is an intuitive approach for risk management. However, due to high concentrations of these assets in major metropolitan areas, investors may face additional constraints in the portfolio optimization process. The rank-size rule, a log-linear relationship between city rank and size, provides one of the greatest empirical regularities in regional science. As such, it serves as a possible theoretical guide to the weights given to properties by location in a commercial real estate portfolio. This paper sets forth some ideas relating to the concentration side of portfolio variance and the limiting effect that large concentrations may have on the ability to diversify risk. Two variants of the rank-size relationship – the Zipf distribution and the parabolic fractal distribution – are fitted to a variety of datasets to provide a sense of the degree of concentration in the commercial real estate industry. These empirical findings suggest the presence of limitations to geographical diversification that have varying degrees of severity across different property types or sectors of the commercial real estate market.

Tokenized Securities and Commercial Real Estate

The following research investigates the application of security tokenization to commercial real estate assets. Primary research through interviews was conducted to uncover some of the most salient use cases and blockchain benefits for the space. The report explores three domains of blockchain application to real estate: (1) the application of blockchain to securities issuance and trading, (2) the application of blockchain to the real estate investment value chain, and (3) the application of blockchain to the representation of the physical assets themselves. Overall, we find that the value creation provided by tokenization can come in several layers, with some standalone benefits emerging by applying tokenization to each of the three domains in isolation. However, significant synergies can arise from combining these layers. As integration increases, additional features become possible. Our conclusion offers a general framework that can be used to perform future research on the tokenization of other types of assets and their related securities.

Capitalization rates and transaction activity in international office markets: A global perspective

Based on a sample of office markets in 33 cities across 16 countries for the period 2007–2015, this paper explores variations in commercial real estate transaction activity and asset pricing in international office markets. It is argued that there are complex interactions and feedback relationships between asset prices, transaction activity, market conditions and local institutional structures in real estate markets. Commercial real estate pricing is modelled using data on capitalization rates. The relationships found are consistent with previous research in terms of the importance of drivers such as government bond yields, yield spreads and real estate rents. Consistent with information network effects, it is also found that larger and more mature markets tend to have lower cap rates and, thus, higher asset prices. The results for transaction activity are less clear cut. Results from econometric analysis of turnover rates suggest that the same explanatory factors do not determine transaction activity to the same extent as cap rates. When purged of possible joint determinants, there is no evidence to support the view that cap rates affect market turnover rates or that turnover rates affect cap rates.

A New Method to Estimate Risk and Return of Commercial Real Estate Assets from Cash Flows

We estimate the abnormal performance of real estate assets from cash flows to strengthen the position that open-end core real estate funds earn high (albeit levered) returns. We propose the use of detailed cash flows histories from the date of asset purchase (inception date) to the date of sale (liquidation date) plus the actual sale price, as well as appraised market values during the interim to determine a value for Jensen's alpha and beta for each investment made by an open-end core real estate fund. We examine how these Jensen's alphas are affected by (1) the rate of return from sector leverage, (2) the rate of return from incremental leverage, and (3) the rate of return from excess risk taking, and how the Jensen's alphas we estimate may overstate the “true” deal-level alpha. We offer an explanation that hinges on the observation that institutional investors prefer diversification over concentration of ownership due to their concern with minimizing portfolio risk.

Energy Factors, Leasing Structure and the Market Price of Office Buildings in the U.S.

This paper presents an empirical analysis of the relation between energy factor markets, leasing structures, and the transaction prices of office buildings in the U.S. We employ a large sample of 15,133 office building transactions between 2001 and 2010. In addition to building characteristics, we also include information on the operating expenses, net operating income, and market capitalization rates at sale to estimate an asset-pricing model for commercial office real estate assets. A further set of important controls in our analysis is the forward/futures contract prices for electricity and natural gas. We also include weather metrics for each building’s location and sale date. Our final set of controls includes information on the dominant contractual leasing structure of the buildings. Our empirical results suggest that Energy Star labels do not explain additional variance in property prices once the key asset-pricing factors of expenses, income and market capitalization rates are included. By contrast, energy-factor market prices, the shape of the energy forward price curves, and weather metrics are consistently significant determinants of office building transaction prices, suggesting that commercial office building prices are exposed to shocks in these markets.

'REITarization' Effects on Global Real Estate Markets

The creation of real estate investment trusts (REITs) facilitates the conversion of illiquid commercial real estate assets into liquid tradable securities in the public market space. The process increases liquidity and generates significant growth in cross-border real estate transactions. This study uses a large sample of proprietary commercial real estate transactions data from more than 73 countries within the sample period of 2001 through 2015. We investigate the impact of REIT creation on international real estate capital flows. Using non-REIT countries as the control group, we show that REITs open a new channel to attract institutional investments in real estate. New investments in real estate by both local institutional investors and foreign investors increase in REIT countries relative to non-REIT countries. The results imply that there is no substitution effects of REITs on private real estate market activities. We also find no evidence of “crowding out” effects on local investors, as we do not observe significant changes in the outflows of real estate capital. The relatively large increases in domestic real estate investments have caused declines in the shares of cross-border real estate capital flows in the post-REIT regime.

Big Data in Real Estate? From Manual Appraisal to Automated Valuation

Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry.

What sells in a crisis? Determinants of sale probability over a cycle and through a crash

PurposeThe purpose of this paper is to understand what affects the liquidity of individual commercial real estate assets over the course of the economic cycle by exploring a range of variables and a number of time periods to identify key determinants of sale probability.Design/methodology/approachAnalyzing 12,000 UK commercial real estate transactions (2003 to 2013) the authors use an innovative sampling technique akin to a perpetual inventory approach to generate a sample of held assets for each 12 month interval. Next, the authors use probit models to test how market, owner and property factors affect sale probability in different market environments.FindingsThe types of properties that are most likely to sell changes between strong and weak markets. Office and retail assets were more likely to sell than industrial both overall and in better market conditions, but were less likely to sell than industrial properties during the downturn from mid-2007 to mid-2009. Assets located in the City of London more likely to sell in both strong and weak markets. The behavior of different groups of owners changed over time, and this indicates that the type of owner might have implications for the liquidity of individual assets over and above their physical and locational attributes.Practical implicationsVariation in sale probability over time and across assets has implications for real estate investment management both in terms of asset selection and the ability to rebalance portfolios over the course of the cycle. Results also suggest that sample selection may be an issue for commercial real estate price indices around the globe and imply that indices based on a limited group of owners/sellers might be susceptible to further biases when tracking market performance through time.Originality/valueThe study differs from the existing literature on sale probability as the authors analyzed samples of transactions drawn from all investor types, a significant advantage over studies based on data restricted to samples of domestic institutional investors. As well, information on country of origin for buyers and sellers allows us to explore the influence of foreign ownership on the probability of sale. Finally, the authors not only analyze all transactions together, but the authors also look at transactions in five distinct periods that correspond with different phases of the UK commercial real estate cycle. This paper considers the UK real estate market, but it is likely that many of the findings hold for other major commercial real estate markets.

Do ‘foreigners’ pay more? The effects of investor type and nationality on office transaction prices in New York City

Are commercial real estate prices in metropolitan New York affected by the type or nationality of the investors involved in a transaction? Previous research has highlighted differences in pricing between in-state and out-of-state investors and between different types of investors, but there are few extant studies that consider the influence of nationality on pricing at a micro-level. Foreign investors might pay more than domestic investors for commercial real estate assets because of a lack of information or experience in the market concerned. However, they might use local partners or brokers to mitigate such problems. To explore these issues, we use data provided by Real Capital Analytics on over 3000 office transactions in the New York metro area from 2001 to 2015. We use hedonic regression techniques and propensity score matching to examine whether pricing differs across investor groups after controlling for asset attributes. We find that foreign investors pay more than domestic investors at acquisition, but receive more when selling assets. Tests suggest that it is unmeasured aspects of quality that explain any apparent overpayment for offices in this location, not information issues.

Shuffling Through the Bargain Bin: Real Estate Holdings of Public Firms

Using a novel and detailed transaction-level data set of commercial real estate assets, we construct real estate asset portfolios for a comprehensive set of public firms between 2000 and 2013. We find that bank loan spreads incorporate information not only on the alternative uses of a borrower's real estate portfolio, but also on the number of that portfolio's potential buyers. Using surges of foreign investor demand from countries with increased policy uncertainty and also local land-supply elasticity information, we identify plausibly exogenous shocks to commercial real estate prices. We find that, after a region experiences large foreign investor demand, loan spreads become less sensitive to collateral value of real estate holdings.

Characteristics of Depreciation in Commercial and Multifamily Property: An Investment Perspective

AbstractThis article reports empirical evidence on the nature and magnitude of real depreciation in commercial and multifamily investment properties in the United States. The article is based on a much larger and more comprehensive database than prior studies of depreciation in such properties, and it is based on actual transaction prices rather than appraisal estimates of property or building structure values. The article puts forth an “investment perspective” on depreciation, which differs from the tax policy perspective that has dominated the previous literature in the United States. From the perspective of the fundamentals of investment performance, depreciation is measured as a fraction of total property value, not just structure value, and it is oriented toward cash flow and market value metrics of investment performance such as internal rate of return and holding period return. Depreciation from this perspective includes all three age‐related sources of long‐term secular decline in real value: physical, functional and economic obsolescence of the building structure. The analysis based on 107,805 transaction price observations finds an overall average depreciation rate of 1.5%/year, ranging from 1.82%/year for properties with new buildings to 1.12%/year for properties with 50‐year‐old buildings. Apartment properties depreciate slightly faster than nonresidential commercial properties. Depreciation is caused almost entirely by decline in the property's current real income, only secondarily by increase in the capitalization rate (“cap rate creep”). Depreciation rates vary considerably across metropolitan areas, with areas characterized by space market supply constraints exhibiting notably less depreciation. This is particularly true when the supply constraints are caused by physical land scarcity as distinct from regulatory constraints. Commercial real estate asset market pricing, as indicated by transaction cap rates, is importantly related to depreciation differences across metro areas.

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