Property Size and Risk: Why Bigger is Not Always Better

Ziering, Barry; McIntosh, Willard

doi:10.1080/10835547.1999.12089578

Cited by 29 publications

(13 citation statements)

References 2 publications

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“…Z≥1, 2 or 3). The finding is consistent with Kallberg, Liu, and Greig (1996) and Ziering and McIntosh (1999) who argue from a portfolio standpoint: very large assets exhibit higher volatility and are more highly correlated with other asset classes, leading to value loss. Extremely large assets in the locality are too specific and thus prone to atypicality discount, as reported in Blal and Graf (2013).…”

Section: Largestsupporting

confidence: 89%

“…This is reflected in the literature: much has been documented about consumers' desire to "stand out" (Bagwell and Bernheim, 1996;Corneo and Jeanne, 1997;Ellingsen and Johannesson, 2008) but our knowledge about the financial prudence of investing in conspicuous assets is fragmented despite substantial evidence on the demand for such assets (Ackert and Church 2006;Esrig, Hudgins and Carreta 2011). Some studies even stress negative outcomes of investing in conspicuous assets (Malmendier and Tate 2005;Barr 2010a;Barr 2010b;Bènabou and Tirole 2009;Capozza, Israelsen, and Thomson 2005;Blal and Graf 2013;Ziering and McIntosh 1999) primarily owing to their "atypical" attributes.…”

Section: Separating Equilibriummentioning

confidence: 99%

“…Some studies suggest that large real estate assets perform better in a portfolio context (Pai and Geltner 2007;Plazzi , Torous and Valkanov 2011;Esrig et al 2011) while Kallberg, Liu, and Greig (1996) and Ziering and McIntosh (1999) report the opposite. Esrig et al (2011) provide an explanation for the better performance of large assets: when rents fall, the bad impacts of the down market are offset by improved occupancy rate (as lower quality tenants move up).…”

Section: Sources Of Conspicuity Extreme Physical Attributesmentioning

confidence: 99%

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Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions

Das

Smith

Gallimore

2017

J Real Estate Finan Econ

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We show that conventional hedonic models for commercial real estate prices ignore the utility investors derive from a building's extreme attributes. Analyzing geo-enriched data on nearly 4,800 hotel transactions in the United States, we find that the relative positioning of an asset's attributes-particularly at the extremes-has a significant impact on transaction prices. We also detect separating equilibria for extreme attributes across the premium and discount hotel segments. Extreme attributes "stand out" and are value enhancing in premium hotel segments. In contrast, extreme attributes are value diminishing in the discount hotel segment. The relative degree to which the asset's attributes are extreme is important. Being a locally largest asset has a negative effect on price, however the negative effect is more than offset if the hotel is among the largest hotels nationally. The results suggest that locally extreme assets, unless also nationally extreme, are considered atypical and trade at a discount.

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Section: Largestsupporting

confidence: 89%

Section: Separating Equilibriummentioning

confidence: 99%

Section: Sources Of Conspicuity Extreme Physical Attributesmentioning

confidence: 99%

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Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions

Das

Smith

Gallimore

2017

J Real Estate Finan Econ

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“…Some studies examined private-equity real estate from a portfolio's perspective. Ziering and McIntosh (1999) found that larger properties had higher risk-return profile and lagging performance during recovery phase than small properties across a broad spectrum of property sizes. Shilton and Stanley (1995) found that investors concentrated their holdings within a small core of counties, while Graff and Young (1996) and Cheng and Liang (2000) showed that diversification across geographic locations and property types generated no significantly superior efficiency than naively diversified portfolios.…”

Section: Literature Reviewmentioning

confidence: 90%

Private-equity commercial real estate, timberland, and farmland: market integration and information transition dynamics

Mei

Yao

2020

Can. J. For. Res.

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Using data from National Council of Real Estate Investment Fiduciaries (NCREIF), we examine market integration of commercial real estate and timberland/farmland assets via the Fama-MacBeth two-step approach under the intertemporal capital asset pricing framework. In addition, we study the information transition dynamics between those markets via the vector error correction model (VECM). We find evidence of market segregation and one-way information flow from the timberland/farmland market to the commercial real estate market. We conclude that commercial real estate and timberland/farmland assets are driven by different market fundamentals and that lagged timberland/farmland returns can help predict current commercial real estate returns. The only exception is during market downturns when commercial real estate and timberland/farmland markets are somewhat integrated and driven by some factors that are not specified in this study.

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“…Investment in property is important in both consumption and investment decisions (Henderson and Ioannides (1987)). Ziering and McIntosh (2000) argue that housing size is important in determining the risk and return of housing and conclude that the largest class of housing provides investors with the highest return and the greatest volatility. However, Flavin and Nakagawa (2008) document that investing in larger houses does not reduce risk, while Kallberg et al (1996) show that smaller property offers impactful diversification benefits for investment portfolios with high return aspirations.…”

Section: Illustrationmentioning

confidence: 99%

Stochastic Dominance and Omega Ratio: Measures to Examine Market Efficiency, Arbitrage Opportunity, and Anomaly

2017

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Both stochastic dominance and Omegaratio can be used to examine whether the market is efficient, whether there is any arbitrage opportunity in the market and whether there is any anomaly in the market. In this paper, we first study the relationship between stochastic dominance and the Omega ratio. We find that second-order stochastic dominance (SD) and/or second-order risk-seeking SD (RSD) alone for any two prospects is not sufficient to imply Omega ratio dominance insofar that the Omega ratio of one asset is always greater than that of the other one. We extend the theory of risk measures by proving that the preference of second-order SD implies the preference of the corresponding Omega ratios only when the return threshold is less than the mean of the higher return asset. On the other hand, the preference of the second-order RSD implies the preference of the corresponding Omega ratios only when the return threshold is larger than the mean of the smaller return asset. Nonetheless, first-order SD does imply Omega ratio dominance. Thereafter, we apply the theory developed in this paper to examine the relationship between property size and property investment in the Hong Kong real estate market. We conclude that the Hong Kong real estate market is not efficient and there are expected arbitrage opportunities and anomalies in the Hong Kong real estate market. Our findings are useful for investors and policy makers in real estate.

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Property Size and Risk: Why Bigger is Not Always Better

Cited by 29 publications

References 2 publications

Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions

Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions

Private-equity commercial real estate, timberland, and farmland: market integration and information transition dynamics

Stochastic Dominance and Omega Ratio: Measures to Examine Market Efficiency, Arbitrage Opportunity, and Anomaly

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