The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific log price trends in deviation from the common trend. Moreover, we assume that the error terms potentially have a heavy tailed (t) distribution to effectively deal with outliers. We apply the hierarchical repeat sales model on commercial properties in the Philadelphia/Baltimore region and on residential properties in a small part of Amsterdam. The results show that the hierarchical repeat sales model provides reliable indices on a very detailed level based on a small number of observations. The estimated degrees of freedom for the t-distribution is small, largely rejecting the commonly made assumption of normality of the error term.     

Is There One Best Way to Sell a Company? Auctions Versus Negotiations and Controlled Sales1

For managements and boards that have decided that the value‐maximizing choice is to sell their companies, the board must then address another important question: what is the best way to sell the company? Should they use a wide‐ranging auction that seeks to attract the largest number of bidders, exclusive negotiation with a single bidder, or a “controlled sale” with a limited group of potential buyers? In a much cited 1996 article in the American Economic Review called “Auctions Versus Negotiations,” economists Jeremy Bulow and Paul Klemperer argued that there is “no merit in arguments that negotiation should be restricted to one or a few bidders to allow the seller to maintain control of the negotiating process.” But in their series of studies of the corporate M&A sales process over the past five years, the authors of this article have come to a very different conclusion. Contrary to the conventional wisdom, wide‐ranging auctions that seek the greatest number of bidders are far from the dominant approach. Roughly half of the large M&A deals investigated by the authors were accomplished through negotiations with single bidders. At the same time, full‐fledged auctions accounted for only about half of the deals involving multiple bidders, while the other half were classified as controlled sales aimed at a small number of carefully selected potential buyers. In their attempt to explain this preference for negotiations and controlled sales over auctions in M&A sales, the authors draw extensive parallels with the market for initial public offerings. As in the “bookbuilding” approach that now dominates the IPO process in virtually all global capital markets, the decision to limit the number of bidders through either negotiations or controlled sales appears to have the advantage of eliciting more aggressive bids from the “most qualified” buyers. Or, to put this another way, auctions appear to have the effect of discouraging such buyers from participating in the process.     

Residential Mortgage Default: The Roles of House Price Volatility, Euphoria and the Borrower’s Put Option

House price volatility; lender and borrower perception of price trends, loan and property features; and the borrower’s put option are integrated in a model of residential mortgage default. These dimensions of the default problem have, to our knowledge, not previously been considered altogether within the same investigation framework. We rely on a sample of individual mortgage loans for 20 counties in Florida, over the period 2001 through 2008, third quarter, with housing price performance obtained from repeat sales analysis of individual transactions. The results from the analysis strongly confirm the significance of the borrower’s put as an operative factor in default. At the same time, the results provide convincing evidence that the experience in Florida is in part driven by lenders and purchasers exhibiting euphoric behavior such that in markets with higher price appreciation there is a willingness to accept recent prior performance as an indicator of future risk. This connection illustrates a familiar moral hazard in the housing market due to the limited information about future prices.     

The Choice of Methodology for Computing Housing Price Indexes: Comparisons of Temporal Aggregation and Sample Definition

Housing transactions are executed and recorded daily, but are routinely pooled into longer time periods for the measurement and analysis of housing price trends. We utilize an unusually rich data set, covering essentially all arm's length housing sales in Sweden for a dozen years, in an attempt to understand the effect of temporal aggregation upon estimates of housing prices and their volatilities. This rich data set also provides a unique opportunity to compare the results using the conventional weighted repeat sales model (WRS) to those based on a research strategy which incorporates all available information on house sales. The results indicate the clear importance of temporal disaggregation in the estimation of housing prices and volatilities—regardless of the model employed.The appropriately disaggregated model is then used as a benchmark to compare estimates of the course of housing prices produced by the two models during the twelve year period 1981–1993. These results indicate that much of the difference between estimates of price movements can be attributed to the data limitations which are inherent in the repeat sales approach. The results, thus, suggest caution in the interpretation of government-produced price indices or those produced by private firms based on the repeated sales model.     

Adjusting for Non-Linear Age Effects in the Repeat Sales Index

A true constant quality real estate price index should measure the general change in price level free from any change in quality over time. In recent years, the repeat-sales method has been widely used to construct constant quality property price indices. Since buildings depreciate over time, a simple repeat-sales index would underestimate the growth in property prices. The major problem of controlling the effects of age constant in a repeat-sales model arises from the exact multicollinearity between the age variable and the time dummy variables. In this study, we derive a solution that is theoretically sound and practical by allowing the age effects to be non-linear. In case of leasehold properties, we further incorporated interest rates into the model because the effects of age on real estate prices depend theoretically on interest rates. A sample of residential units in Hong Kong sold more than once from Quarter 2 of 1991 to Quarter 1 of 2001 (more than 11,000 repeat sales pairs) are used for the empirical analysis.     

The Effects of Attributes on the Repeat Sales Pattern of Residential Property in Hong Kong

There has been copious research work on the development of house price models and the construction of house price indices. However, results in some studies revealed that the accuracy of such indices could be subject to selection bias when using only information from a sample of sold properties to estimate value movements for the entire housing stock. In particular, estimated house price appreciation is usually systematically higher among properties that change hands more frequently. It therefore suggests that the determination of important factors affecting the transaction frequency or intensity of a housing unit should be a more fundamental research question. This paper examines the possible factors that determine the popularity of residential unit by means of a repeated sales pattern. The Poisson regression model and event history analysis techniques are employed to assess the effect of attributes on transaction frequency and intensity. The event history analyses technique can take account of transaction-specific as well as time-dependent covariates, and therefore is recommended for analyzing repeated sales data in a real estate market. All transaction records during the period 1993–2000 from the Land Registry of one of the most popular residential estates in Hong Kong were used to illustrate the method. Unlike a response to favorable transaction price, good quality units do not necessarily inherently display a high transaction frequency. Rather, units of average quality are more likely to be transactionally active.     

Repeat Sales Regression on Heterogeneous Properties

This paper proposes a generalized repeat sales regression (GRSR) that uses repeat sales from the entire market, in which properties may have heterogeneous value appreciation processes, to estimate price indices for not only the entire market, but also submarkets or customized portfolios of properties that only have small numbers of value observations. Monte Carlo simulations provide strong evidence that the GRSR indices more accurately measure the index for the entire market as well as individual property value appreciation than conventional RSR indices. This paper also proposes a Chi-square test to detect the heterogeneity in property value appreciation across submarkets/portfolios, and use simulations to show that the test is powerful in small samples. This paper finally illustrates the application of the GRSR using a historical dataset of the Chicago housing market from 1970 to 1986.     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

Golf courses and residential house prices: An empirical examination

The importance of location is well known in the literature on real estate valuation. Our study extends this body of literature by being the first to examine empirically the effect on the selling price of single-family residential properties when they abut a golf course. We determine the incremental effect on the sales price of houses on the golf course by fitting a standard hedonic pricing model to a sample of 717 sales transactions drawn from a sub-urban area of a large city. We employ a matched-pair research design to hold constant the price effects of other location factors on these golf course properties. Our results indicate that a golf course location adds 7.6 percent to a property's sales price. We believe this finding is of interest to developers in their design of a golf course subdivision and to appraisers who wish to make location-specific value adjustments of golf course properties.     

Strategic Sequential Bidding for Government Land Auction Sales – Evidence from Singapore

This paper studies the extent to which equilibrium land auction prices are pushed up sequentially due to strategic bidding behaviors in government land auction sales. Using a unique dataset that covers the universe of tendering prices submitted by all developers for all residential land auction sales in Singapore, we find that a tenderer’s bids are significantly higher where there was a previous land parcel sold within two years and located within four kilometers. The identified price margin decreases with time and geographic distance. Tracking sequential bids submitted by the same developer over time, we find that the incumbent winner of a previous auction is more likely to participate in subsequent nearby land sales as compared to the second-highest bidder of the same auction. However, it does not necessarily win the subsequent sites. We argue that the incumbent deliberately bids up the subsequent land prices to gain pricing advantages to their own parcels.     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

Green Design and the Market for Commercial Office Space

This paper considers the relationship between energy-efficient design and the leasing/sales markets for commercial real estate. An economic model is provided that considers lease rates and occupancy in simultaneous equilibrium. The behavior of both is predicted to be influenced by efficient design attributes. Selling price is determined by both rents and occupancy; therefore the impact of efficient design on commercial sales activity should be distributed through the leasing market. The model is tested empirically using a national sample of sales and leasing data for class A office buildings. The evidence indicates that “green” buildings achieve superior rents and sustain significantly higher occupancy. The improved performance in the rental market is reflected in a significant premium for the selling price of Energy Star-labeled and LEED-certified properties.     

Frothy Housing Markets and Local Stock-Price Movements

Prior empirical research finds habitat effects manifest in stock pricing among firms that share headquarters cities. We empirically investigate whether trends in residential real estate prices affect headquarters-city stock pricing phenomena for companies across U.S. metro areas for 1989?C2004. Specifically, we hypothesize that stocks of firms headquartered in ??hot?? residential real estate markets experience higher returns compared to stocks of firms from ??cold?? markets. We also hypothesize that stocks of firms headquartered in hot real estate markets display stronger return comovement with same-city stocks. We find support for these hypotheses during the 1999?C2004 sample period which coincides with the start of the housing bubble of the 2000?s; we find mixed results in earlier periods. Our findings indicate that city-specific home price patterns conditionally affect stock pricing of local firms, suggesting that investor behavior is influenced by localized shocks to household real estate wealth.     

Foreclosure,REO, and Market Sales in Residential Real Estate

Assessment of lender and third-party bidder acquired properties at foreclosure auction is provided. Properties acquired at foreclosure auction by third-party bidders transact at a discount to lender dispositions of real estate owned (REO) properties. The discount reflects a reduction in costs associated with lender owned (REO) dispositions and uncertainty faced by third-party bidders. Moreover, there is a ranking in transaction prices among initial purchases by third-party bidders at foreclosure auction, REO sales, non-distressed property sales and the subsequent sales of third-party bidder acquired properties. Third-party bidder auction prices are below REO sale prices, which are below non-distressed property sale prices, which are below the subsequent sale prices of third-party bidder acquired properties. The price spacing by cohort is logical, intuitive and economically justified in a market with rational participants. Implications are also apparent for the measurement of price changes, net sale proceeds and returns to residential real estate.     

The Paradox of Financial Fire Sales: The Role of Arbitrage Capital in Determining Liquidity

How can fire sales for financial assets happen when the economy contains well‐capitalized but nonspecialist investors? Our explanation combines rational expectations equilibrium and “lemons” models. When specialist (informed) market participants are liquidity‐constrained, prices become less informative. This creates an adverse selection problem, decreasing the supply of high‐quality assets, and lowering valuations by nonspecialist (uninformed) investors, who become unwilling to supply capital to support the price. In normal times, arbitrage capital can “multiply” itself by making uninformed capital function as informed capital, but in a crisis, this stabilizing mechanism fails.     

WHY SOCIETY NEEDS "IRRATIONAL EXUBERANCE"—AND WHAT THIS MEANS FOR VALUATIONS AND MONETARY POLICY

Despite all the talk of a New Economy and the revolutionary import of the Internet, this article suggests that there is nothing really new under the sun. When Christopher Columbus was building the ships for his expeditions to the East Indies, only the vaguest estimates could be made of their potential value. At this stage, nobody knew if Columbus would be able to manage the crossing, when the ships would return, and what cargo they would eventually carry. In this sense, Columbus's venture bears a striking resemblance to many of today's Internet stocks. This paper raises and attempts to answer a number of interesting questions. For example, how do risky ventures with very high fixed (startup) costs but very low expected variable costs raise the capital necessary to fund the fixed costs? What role should government (and, in particular, monetary) policy play in encouraging (or discouraging) funding for such ventures? And how does one establish the value of such ventures when there is little or no revenue and, in some cases, no clearly defined product? The answer to the first question is investor enthusiasm–or, in Alan Greenspan's terms, “irrational exuberance.” Irrational exuberance plays a very important economic role in giving entrepreneurs access to the cheap financing necessary to fund ventures with heavy startup costs. Indeed, “irrational exuberance” may be the best solution for financing large fixed costs, not only because it is a private (as opposed to a government‐financed) solution, but because it gives investors direct access to the risks and rewards of promising investment opportunities. Before the recent “democratization” of capital markets, such ventures would have been funded by large corporations if not government agencies. As for the third question, this paper suggests there is only one useful way to estimate the value of ventures without revenues or products. The author provides a back‐of‐the‐envelope, “Fermitype” valuation method that is based on the principle of “human capital arbitrage.” For those Internet startups that lure top executives away from established firms with large grants of stock options but relatively low salaries, there is a “breakeven level” for the future stock price that can be calculated using a fairly modest amount of information about the executive's past and current compensation plans. For outside investors, such movements of human capital provide what is perhaps the most reliable basis for estimating the value of the firm.     

Aggregation Bias in Repeat-Sales Indices

The repeat-sales methodology has become a standard approach for estimating real estate price indices. This article examines the underlying assumptions inherent in the repeat sales model and provides an empirical test for both included and omitted variables as sources of aggregation bias. The results indicate that virtually all price indices may be biased, the degree of bias being dependent upon the number of variables examined and the instability of their parameters over time.     

The Effect of Listing Price Strategy on Transaction Selling Prices

While true underlying home values are expected to be randomly distributed, actual residential listing prices tend to be highly clustered. Particularly, more than 75 % of the homes in our sample are associated with a round or “just below” round asking price. This study provides a theoretical and empirical examination of how the thousands digit in a home’s asking price is related to the final transaction price relative to its true underlying value. Our findings suggest that, on average, homes listed using a “just below” pricing strategy are associated with the greatest discount negotiated relative to the asking price. However, the higher initial degree of list overpricing reflected in “just below” pricing compared with other strategies more than offsets the greater discount. Therefore, “just below” is the most effective pricing strategy for the seller in terms of a greater dollar yield relative to value. These empirical findings have economic significance and are robust across both “buyer” and “seller” housing markets, new versus existing homes, and across multiple home price ranges.     

SIX CHALLENGES IN DESIGNING EQUITY-BASED PAY

The past two decades have seen a dramatic increase in the equitybased pay of U.S. corporate executives, an increase that has been driven almost entirely by the explosion of stock option grants. When properly designed, equity‐based pay can raise corporate productivity and shareholder value by helping companies attract, motivate, and retain talented managers. But there are good reasons to question whether the current forms of U.S. equity pay are optimal. In many cases, substantial stock and option payoffs to top executives–particularly those who cashed out much of their holdings near the top of the market–appear to have come at the expense of their shareholders, generating considerable skepticism about not just executive pay practices, but the overall quality of U.S. corporate governance. At the same time, many companies that have experienced sharp stock price declines are now struggling with the problem of retaining employees holding lots of deep‐underwater options. This article discusses the design of equity‐based pay plans that aim to motivate sustainable, or long‐run, value creation. As a first step, the author recommends the use of longer vesting periods and other requirements on executive stock and option holdings, both to limit managers' ability to “time” the market and to reduce their incentives to take shortsighted actions that increase near‐term earnings at the expense of longer‐term cash flow. Besides requiring “more permanent” holdings, the author also proposes a change in how stock options are issued. In place of popular “fixed value” plans that adjust the number of options awarded each year to reflect changes in the share price (and that effectively reward management for poor performance by granting more options when the price falls, and fewer when it rises), the author recommends the use of “fixed number” plans that avoid this unintended distortion of incentives. As the author also notes, there is considerable confusion about the real economic cost of options relative to stock. Part of the confusion stems, of course, from current GAAP accounting, which allows companies to report the issuance of at‐the‐money options as costless and so creates a bias against stock and other forms of compensation. But, coming on top of the “opportunity cost” of executive stock options to the company's shareholders, there is another, potentially significant cost of options (and, to a lesser extent, stock) that arises from the propensity of executives and employees to place a lower value on company stock and options than well‐diversified outside investors. The author's conclusion is that grants of (slow‐vesting) stock are likely to have at least three significant advantages over employee stock options:

• ? they are more highly valued by executives and employees (per dollar of cost to shareholders);
• ? they continue to provide reasonably strong ownership incentives and retention power, regardless of whether the stock price rises or falls, because they don't go underwater; and
• ? the value of such grants is much more transparent to stockholders, employees, and the press.

     

What Drives Stock Price Behavior Following Extreme One-Day Returns

We identify samples of losers and winners by selecting daily stock price returns in excess of 10% (sign ignored) and determine whether these samples over‐ or underreact. We then identify “informed” events, which correspond to announcements in the Wall Street Journal(WSJ), and “uninformed” events, which are not explained in the WSJ. For winners, there is overreaction in response to uninformed events but no overreaction on average in response to informed events. This finding suggests the degree of overreaction to new information depends on whether the cause of the extreme stock price change is publicly released.