Measurement and drivers of time to transact UK commercial real estate investments

Abstract In stark contrast with liquid asset returns, commercial real estate idiosyncratic return means and variances do not scale with the holding period, even after accounting for all cash flow-relevant events. This puzzling phenomenon survives controlling for vintage effects, systematic risk heterogeneity, and a host of other explanations. To explain the findings, I derive an equilibrium search-based asset-pricing model that, when calibrated, provides an excellent fit to transactions data. A structural model of transaction risk seems crucial to understanding real estate price dynamics. These insights extend to other highly illiquid asset classes, such as private equity and residential real estate.

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Wildcat Capital Investors: Real Estate Private Equity

Kellogg School of Management Cases ◽

10.1108/case.kellogg.2016.000413 ◽

2017 ◽

pp. 1-12

Author(s):

Craig Furfine

Keyword(s):

Real Estate ◽

Cash Flow ◽

Private Equity ◽

Financial Models ◽

Cash Flows ◽

Commercial Real Estate ◽

Office Building ◽

Equity Structure ◽

Financial Model ◽

Real Estate Investments

Wildcat Capital Investors is a small real estate private equity company. Its MBA intern, Jessica Zaski, is asked to develop a financial model for the purchase of Financial Commons, a 90,000 square foot office building in suburban Chicago. By simple metrics, the property seems to be a good value, but with credit conditions tight, Jessica must consider whether outside investors would be comfortable with the risks of investing in the midst of a severe commercial real estate downturn. Wildcat is designed to give students exposure to both the quantitative and qualitative aspects of investing in commercial real estate through a private equity structure. Beyond the numbers, the case allows for a discussion of the process of finding suitable real estate investments. The importance of the simultaneous negotiations that Wildcat must have with the seller, the lender, and the outside investor can be emphasized.By working through the financial models, students will take a given set of assumptions and analyze the cash flows expected to be received by the equity partners of Financial Commons. With a given deal structure, the students can then model the cash flow to both outside equity investors and Wildcat, learning the mechanics of private equity. The model will allow students to investigate how the variations in the underlying assumptions affect returns to the property and to the investors.

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Understanding real estate investments through big data goggles

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-06-2018-0042 ◽

2019 ◽

Vol 12 (4) ◽

pp. 661-686 ◽

Cited By ~ 1

Author(s):

Marcelo Cajias

Keyword(s):

Big Data ◽

Real Estate ◽

Methodological Approach ◽

Investment Strategy ◽

Commercial Real Estate ◽

Hedonic Models ◽

Bond Yields ◽

Content Type ◽

Real Estate Investments ◽

Implicit Prices

Purpose This paper aims to develop a conceptual understanding and a methodological approach for calculating residential net initial yields for both a buy-to-hold and rental investment strategy from hedonic models. Design/methodology/approach The markets modelled comprehend of dwellings for rent and sell in Germany. For each of them, two regression models are estimated to extract implicit prices and rents for an artificial identical dwelling and estimate the willingness to pay for the same asset from both a buy-to-hold and rental investment strategy. Findings The 3,381 estimated net initial yields in the 161 German markets showed a spatial pattern with the biggest and most attractive cities showing the lowest yields and a self-adjusting process in the markets surrounding the top cities. The net initial yields over time show that prices have increased stronger than rents, leading to rock bottom yields for residential assets and a significant premium in comparison to government bond yields. The approach responds to the spatial hierarchy of markets in Germany, meaning that the level of the estimated yields is accurate and achievable from an investment perspective. Practical implications The investment case in residential markets is certainly unique as net initial yields are scarce, especially due to the relatively low number of investment comparables. The paper sheds light on this problem from a conceptual and methodological perspective and confirms that investment yields are deducible by making usage of hedonic models and big data. Originality/value In the era of digitalization and big data, residential assets are mostly brought to the market via digital multiple listing systems. Transparency is an essential barrier when assessing the pricing conditions of markets and deriving investment decisions. Although international brokers do provide detailed investment comparables on – mostly commercial – real estate markets, the residential sector remains a puzzle when it comes to investment yields. The paper sheds light on this problem.

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