Real Estate Research

Summary:

This is the working paper about which I am currently most excited. The genesis is my nearly decade-long puzzlement at why buyer-side brokerage is so prevalent in various markets for expensive and highly illiquid assets. This is most notably the case with residential & commercial real estate, but also extends to art & musical instruments, M&A, private equity, etc. Much of the literature presumes that such intermediation provides buyers with information about the asset, or helps them find sellers. I couldn’t completely buy into that logic: Zillow has been around for a couple of decades and buyers use brokers as intensively now as they did twenty years ago despite there being an order of magnitude more information available to buyers for free via the internet. The other answer provided by academics (and the popular press) is that the industry is collusive. That, however, doesn’t explain why such intermediation exists across other countries and markets. Two observations, and an inspiring conversation with Brent Ambrose, paved the way to an answer that deviates from the standard thinking.

• In teaching and interacting with commercial real estate professionals for well over a decade, I’ve come to appreciate that the highest bidder doesn’t always get the deal. What professionals in these highly illiquid markets co-prioritize with price is certainty of execution.

• My own solo paper, “Asset-Level Risk and Return in Real Estate Investments”, published in the Review of Financial Studies in 2021, taught me (and hopefully others) that the vagaries of the transaction process, i.e., search and bargaining, contribute to a great deal of price risk.

The main idea is not a major leap from these two observations: What if intermediation helped to mitigate execution (and transaction) risk because brokers are part of a reputational network and can “vouch” for their clients or work to bring their clients back to the table if negotiations start to break down? This further clicks into place when one considers that brokers are typically compensated only if there is a transaction. In other words, perhaps they work for the transaction and not, as may be commonly thought, for their client. Knowing full well that attacking this problem would require a deep knowledge of game theory, I managed to recruit Brendan Daley to partner with me on formalizing this intuition.

We proceeded as follows. Imagine the seller of a property who is entertaining overtures from several prospective buyers. The seller must select one of the buyers and then attempt to transact with them. The catch is that the buyers’ overtures may not be committal and consummating a transaction takes time (e.g., because it involves a due diligence period). In particular, imagine a situation where a selected buyer may renege on any promises made to an unwitting seller during the selection stage and force the seller into a renegotiation. This is what buying a property through Craig’s List might be like. To model this, Brendan and I created a novel search market framework – one in which there is an explicit buyer selection stage followed by a settlement stage. In the Craig’s List setting of the model, a rational seller would randomly choose an arriving buyer because any price promises would simply be cheap talk. The seller would then have to bargain with their chosen buyer without knowing the buyer's true valuation. The bargaining solution to this amounts to giving the buyer a chance to make a “low ball” offer to the seller, which will be rejected half the time, or transact for sure with the seller at an average price that is optimally chosen by the seller.1  We show that, relative to a standard search model equilibrium in which sellers know buyers’ valuations when they bargain, one expects prices to be depressed, assets to be inefficiently allocated, and a significant proportion of transactions to fail. Because of this, sellers would be willing to pay to learn more about prospective buyers’ valuations.

Having demonstrated that information about buyers' intentions can be very valuable to sellers, we next consider two information revealing solutions to this seller problem: The first is certification, as might be provided by a buyer broker who credibly vouches for their clients.2 The second is soliciting committed bids, as in an auction, but with the twist that the seller doesn’t know how many buyers will show up to bid. Both solutions require contractual commitments which are absent in the Craig’s List setting but which are, in practice, adopted by markets. For instance, intermediation using real estate brokers is prevalent in the U.S., but selling through auctions is prevalent in Australia. The question is, if sellers choose the settlement mechanism, under what circumstances will they opt for certification through brokers over auctions? Our model’s answer is that an equilibrium with brokers will tend to prevail when there isn’t sufficient competition between bidders, and this will be the case when supply is large relative to demand (i.e., few participating buyers) or when buyers’ private valuations vary widely. We also establish that broker certification is most efficient when the brokers’ services are pre-paid (so that buyers do not hesitate to use them)—this has been the prevalent type of brokerage contract in the US, at least until the recent National Association of Realtors settlement. Finally, in crudely calibrating the model parameters to those of the residential US housing market we indeed confirm that a broker certification equilibrium would be prevalent when supply and demand is in balance.

           This work, three years in the making, has opened up a host of new and original research questions. Something on which I will likely continue to work for several more years.

[1] In a recent AER article, Peski (2022) provides microfoundations for this bargaining solution (which can also be consistent with Myerson's 1984 EMA paper). Under symmetric information the solution reduces to standard Nash (1950) bargaining which is standard in search models.

[2] Brokers work within reputational networks. If a broker falsely vouches for the buyer they represent, they will suffer reputational harm (see works by, Fulghieri & Chemmanur, 1994 JF; 1994 RFS, and Lizzeri, 1999 Rand).

SUMMARY: An asset’s risk, as perceived by a lender, will be reflected in loan contract terms. With mortgage loans, these terms include the interest-only period, amortization schedule, mortgage rate, loan-to-value ratio (LTV), etc. What we do is employ a sophisticated option pricing model to back out the implied volatility (iVol) of the underlying asset—in this case, a commercial property—given the loan terms, the property’s income yield, and the current state of the lending market (AAA mortgage bond illiquidity and the treasury yield curve). We then show that iVol, which we interpret as the lender’s perceived property risk, explains two thirds of the variation in the loan LTV: Properties that are more (less) risky are associated with lower (higher) LTVs. This is consistent with the Leland (1994) model of optimal capital structure. When we include other determinants of optimal capital structure from the Leland model, the explanatory power rises to nearly 80%. After controlling for fundamentals that should theoretically drive optimal LTV, the residuals show little systematic patterns, suggesting that when lenders offer higher LTV CRE loans it is not because they are willing to take on more risk (they would otherwise charge higher spreads), but because they perceive the lending environment to be less risky. This seems to be especially true during the run-up to the Great Recession, suggesting that increase in credit extension in that period resulted from a systematic mis-perception of risk across lenders. An important policy lesson here is that regulators ought to perhaps rethink the practice of letting regulated financial institutions assess the riskiness of their own lending portfolios. Correspondingly, our approach to backing out perceived risk, if adopted by regulators, can be employed to detect whether lenders are systematically perceiving less risk in a given sector.

SUMMARY: we explore how information from leasing markets can be used to construct a ‘forward curve’ for the services rendered by the leased capital. In other words, if one views a lease as a bundle of forward claims on capital benefit flows, then one should hypothetically be able to invert a collection of leases to deduce the underlying forward curve. One challenge is that leased capital (e.g., commercial real estate, airplanes, ships, manufacturing equipment, and medical equipment) is heterogeneous in quality, which is not as much the case with, say, bars of aluminum. We get around this challenge by assuming that the quality distribution of the stock of leased capital is constant through time and that quality of benefit flow is entirely subsumed by price (all other contract provisions being equal). By further assuming that the term structure of a median-quality asset follows a VAR(1) process, we are able to recast the term structure inference problem as an extension to classic Kalman filtering. We do this for roughly 13,000 office leases in Manhattan and in Boston (obtained from CompStak and JLL). A couple of interesting observations emerge from this exercise. First, unlike typical commodity forwards, it is the long-term part of the term structure that is most volatile. This suggests that frictions in real estate markets limit the extent to which near-term lease rates react to news. It also suggests that one ought to look at longer term leases to impute market information about CRE leasing markets.  Second, the term structure of leased office space is generally upward sloping but concave. This may reflect the tension between expected growth in lease rates and depreciation/obsolescence of aging buildings.  Importantly, the positive slope of the term structure implies that locking in a long-term lease (or buying a property) only to turn around and lease it short-term is, on average, a money-losing proposition. An immediate implication is that the co-working concept, when executed purely as a space market strategy, is not on its own profitable in the long-term. To make co-working profitable, it would have to involve additional provision of services (i.e., amenities) or intensification of usage, which suggests that co-working better resembles a hotel than an office investment. We provide several quantitative analyses to back up this intuition and find that achieving a co-working Sharpe Ratio commensurate with public equities would require 40% more revenues beyond what a standard short-term lease would deliver.

ABSTRACT: Despite extensive empirical evidence of the environmental benefits of green buildings and the increasing urgency to reduce carbon emissions in cities, there has been limited widespread adoption of energy retrofit investments in existing buildings. In this paper, we empirically model financial returns to energy retrofit investments for more than 3600 multifamily and commercial buildings in New York City, using a comprehensive database of energy audits and renovation work extracted from city records using a natural language processing algorithm. Based on auditor cost and savings estimates, the median internal rate of return for adopted energy conservation measures is 21% for multifamily buildings and 25% for office properties. Logistic regression modeling demonstrates adoption rates are higher for office buildings than multifamily, and in both cases adopter buildings tend to be larger, higher value, and less energy efficient prior to retrofit implementation. The economically significant magnitudes of returns to adopted energy conservation measures raise important questions about why many property owners choose not to adopt. As such, we discuss incentive and regulatory mechanisms that can overcome financial and informational barriers to the adoption of energy efficiency measures.

SUMMARY: Private equity real estate (PERE) refers to professionally managed pooled investments in the real estate market available only to institutions (e.g., pension funds), private accredited investors, and high-net-worth individuals. In the ownership structure of PERE funds, general partners (GPs) serve as the active fund managers who raise an extensive amount of external capital from limited partners (LPs) to acquire and operate commercial real estate properties. Debt financing, namely the use of leverage, is prevalent in real estate investments and even more so in the setting of PERE funds. Though much empirical research is devoted to PERE fund performance, few studies directly investigate the role of financial leverage in PERE funds.

In an ideal friction-free setting, leverage creates no value and is essentially part of a zero-sum game of rights and privileges between various asset stakeholders. In practice, however, leverage seems far from irrelevant due to the existence of market frictions that could lead to value creation (or destruction) by its use. Financial economic theories indicate that leverage can amplify skill (or the lack thereof), reallocate cash flow rights, and shift incentives in the presence of market frictions. With PERE, existing work provides mixed or little evidence that leverage is employed to amplify skill and consistently hints that its use shifts the balance of benefits toward fund sponsors over their limited partners.

Based on data from Preqin and StepStone, a typical closed-end PERE fund targets roughly 65% debt to the value of total assets under management. Funds managing more risky real estate tend to use more leverage, and there is little evidence that fund terms are adjusted to reflect potential conflicts of interest posed by more intensive use of leverage. Rather, stylized facts raise concerns that the scope for conflict of interest may have increased over the past 10 years. Among these concerns is an increase in strategic longer-term use of subscription facilities. The bulk of evidence in the literature points to robust underperformance of high-leverage funds on a net-of-fee risk-adjusted basis. In other words, there is little evidence supporting the notion that leverage is employed to enhance skilled management and to benefit LPs. This suggests that a significant portion of PERE investors are not optimizing risk-return tradeoffs in allocating investments to high-leverage PERE funds. More work is needed to refine these findings and, more importantly, understand the source of market frictions behind them.

SUMMARY: For a "layperson's" description of what this paper is about, please see the short article linked here.

This paper seeks to explain various puzzling market features of real estate returns in repeat sales data. I demonstrate that the latter exhibit anomalous scaling behavior and argue this is because repeat sales data suffer from selection bias when private valuations are persistent: Short holding periods mostly reflect investors who were lucky twice because they bought from someone who didn’t value the asset highly, and were soon after able to chance upon an investor who valued the asset more than they did. Meanwhile, long holding periods mostly reflect the experience of investors who were not as lucky because they were among the highest valuation buyers. It took them a long time to sell because it takes a long time for their private valuation to regress to the mean, by which time if they sell it would often be to someone who values the asset no more than they originally did. I show that a calibrated partial equilibrium search model incorporating these intuitions can explain a large number commercial real estate return characteristics. The model also qualitatively applies to residential real estate and private equity transactions. 

SUMMARY: Private equity real estate investors have the option of investing in the asset class through two common structures: the open-ended “evergreen” fund and the closed-end fixed-life fund.  Industry group NCREIF indexes select open-ended “core” funds invested in the U.S. The market index, NFI-ODCE, reported outstanding net asset value (“NAV”) totaling $178 billion at 12/31/17, across 24 core funds. Open-ended core funds typically offer some form of a limited quarterly redemption to investors to satisfy liquidity requests, either with capital from new investors or asset sales proceeds.  However, the timeliness of redemptions from open ended funds can vary as investor demand shifts. Redemption queues have historically formed when investor demand for liquidity exceeds both incoming capital from new investors and proceeds available from property dispositions.  Depending on the severity of liquidity needs, investors can wait in the que for a quarterly redemption or can seek liquidity in the secondary market.    

Closed-end funds, on the other hand, do not typically provide a redemption option to investors.  These funds are more opportunistic in their investment strategy, with a shorter investment term than open-end funds.  Rather than the “buy and hold” approach employed by core funds, in which the majority of an investor’s return is generated through operating cash flow (approximately 69% during a the 25-year period ending 6/30/17 according to a 2017 NAREIT analysis), these funds typically seek to purchase a property, improve it, and sell it – with much of the return generated by value appreciation.  In these instances, the objective is to generate additional return to investors by upgrading the condition of the property through redevelopment, repositioning, leasing, repurposing, or some other strategy that is often capital intensive.  Therefore, such funds have a fixed term with an expiration date that serves as a liquidation target for the fund manager. It is not unusual for a closed-end real estate fund to have a series of extension options available to the fund manager that provide the ability to extend the fund’s termination date.  

There was approximately $500 billion of NAV held in closed-end real estate funds as of 12/31/16 according to Landmark’s analysis of statistics from Preqin and Burgiss.  Closed-end funds grew in popularity in the early 2000s as investors increased their allocation to the real estate sector.  During that time closed-end real estate funds saw annual fundraising increase from $19 billion in 2000 up to a peak of $145 billion in 2008.  Closed end private real estate funds remain a major part of the market with over $274 billion raised since 2016, according to fundraising statistics reported by Preqin.  

Through its real estate and quantitative research platform, Landmark analyzed legal terms of a sample of closed-end real estate funds to determine a fund’s average initial legal term and realization behavior.  The analysis began by filtering Preqin’s database of 1,359 funds to include only funds with both (A) commitments of at least $300 million and (B) information available regarding the terms of their legal life.   For the resulting filtered set of 352 funds (which we refer to as the Preqin data set), Preqin provides general information on the length of a fund’s legal term, extension options, as well as fund performance. Landmark cross-referenced this data against its own extensive fund portfolio data and randomly selected a sample of 100 funds (which we refer to as the “performance data set”) for which it could validate information and fill any information gaps regarding investment performance and the terms of the fund extension options. 

In this paper we aim to (1) share conclusions of this analysis, (2) provide perspective and observations on the behavior of private real estate funds as they approach their legal termination, and (3) raise questions for fund managers and investors to consider when faced with the challenge of weighing fund liquidity decisions with value maximization strategies.

ABSTRACT: Commercial real estate (CRE) is a major institutional asset class to which the banking sector has considerable exposure. Because CRE prices tend to be smoothed it is hard to infer their relationship with fundamentals. This is compounded by the presence of complicated underlying dynamics. For instance, inflation acts to increase discount rates but may also be associated with higher rental revenues. Thus it is difficult to sign the impact of inflation on CRE prices, especially given that the dynamics of macro fundamentals periodically undergo regime changes. Similar considerations apply to real economic growth and, by extension, interest rates. We estimate a model consistent with rational expectations where monetary policy regimes impact asset/macro fundamentals and are anticipated in prices. We find that real estate fundamentals and prices vary with macro fundamentals and are highly sensitive to potential regime changes in monetary policy. Correspondingly, information in real estate prices improves the identification of monetary policy model parameters. Our model allows us to assess the impact of policy regime changes, quantify sources of systematic risk in real estate, and price mortgages (which are sensitive to the joint dynamics of interest rates and real estate prices).

ABSTRACT: Capital expenditures data is critical in accurately calculating commercial real estate (CRE) property return. For example, price indices and related benchmarks that only rely on transaction information may not accurately reflect price appreciation returns. Capital expenditure details are also important in understanding the benefits to investing in various property improvements, in predicting operational risk, and in assessing the impact of changes to a structure on neighboring properties as well as the local economy. Unfortunately, few data sources capture capital expenditures. We explore a statistical solution to these issues by studying the relationship between permitting data, acquired from BuildFax via county-level sources, and known outlays reported in the NCREIF property-level dataset. Our model is able to predict CapEx out of sample and captures significant time-series and cross-sectional variation. We demonstrate the model’s utility by applying its out of sample predictions to correcting a repeat sales index which, in the absence of adjustment for capital investment, results in a 2% bias per year in true capital gains.