C. F. Sirmans

Spatial Modeling With Spatially Varying Coefficient Processes

In many applications, the objective is to build regression models to explain a response variable over a region of interest under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coefficients are assumed to be constant over the region. However, in some applications, coefficients are expected to vary at the local or subregional level. Here we focus on the local case. Although parametric modeling of the spatial surface for the coefficient is possible, here we argue that it is more natural and flexible to view the surface as a realization from a spatial process. We show how such modeling can be formalized in the context of Gaussian responses providing attractive interpretation in terms of both random effects and explaining residuals. We also offer extensions to generalized linear models and to spatio-temporal setting. We illustrate both static and dynamic modeling with a dataset that attempts to explain (log) selling price of single-family houses.

Nonstationary Multivariate Process Modeling through Spatially Varying Coregionalization

Models for the analysis of multivariate spatial data are receiving increased attention these days. In many applications it will be preferable to work with multivariate spatial processes to specify such models. A critical specification in providing these models is the cross covariance function. Constructive approaches for developing valid cross-covariance functions offer the most practical strategy for doing this. These approaches include separability, kernel convolution or moving average methods, and convolution of covariance functions. We review these approaches but take as our main focus the computationally manageable class referred to as the linear model of coregionalization (LMC). We introduce a fully Bayesian development of the LMC. We offer clarification of the connection between joint and conditional approaches to fitting such models including prior specifications. However, to substantially enhance the usefulness of such modelling we propose the notion of a spatially varying LMC (SVLMC) providing a very rich class of multivariate nonstationary processes with simple interpretation. We illustrate the use of our proposed SVLMC with application to more than 600 commercial property transactions in three quite different real estate markets, Chicago, Dallas and San Diego. Bivariate nonstationary process inodels are developed for income from and selling price of the property.

ESTIMATING BARGAINING POWER IN THE MARKET FOR EXISTING HOMES

Although bargaining is common in markets for heterogeneous goods, it has largely been ignored in the hedonic literature. In a break from that tradition, we establish sufficient conditions that permit one to identify the effect of buyer and seller bargaining on hedonic models. Our model is estimated using a previously overlooked feature of the American Housing Survey that permits us to observe characteristics of both buyers and sellers. Results suggest that household wealth, gender, and other demographic traits influence bargaining power. In addition, variation in bargaining power arising from the presence of school-age children accounts for anomalous seasonal patterns reported in various widely cited indices of quality-adjusted house prices.

A method for spatial–temporal forecasting with an application to real estate prices

Abstract Using 5243 housing price observations during 1984–92 from Baton Rouge, this manuscript demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the errors. Specifically, the spatial–temporal autoregression with 14 variables produced 46.9% less SSE than a 12-variable regression using simple indicator variables for time. More impressively, the spatial–temporal regression with 14 variables displayed 8% lower SSE than a regression using 211 variables attempting to control for the housing characteristics, time, and space via continuous and indicator variables. One-step ahead forecasts document the utility of the proposed spatial–temporal model. In addition, the manuscript illustrates techniques for rapidly computing the estimates based upon an interesting decomposition for modeling spatial and temporal effects. The decomposition maximizes the use of sparsity in some of the matrices and consequently accelerates computations. In fact, the model uses the frequent transactions in the housing market to help simplify computations. The techniques employed also have applications to other dimensions and metrics.

Price adjustment process for rental office space

Abstract This paper analyzes the price adjustment process for rental office space in 17 cities across the United States over the time period 1960 to 1975. The results confirm much of what economic theory suggests. Landlords react to fluctuations in demand by building up or drawing down inventories of unlet or vacant office space. Other things equal, higher levels of vacant office space mean that landlords lower their rents and reduce the difference between desired and actual vacancies. Empirical evidence is also presented on the normal vacancy rate across different cities.

Board Independence, Ownership Structure and Performance: Evidence from Real Estate Investment Trusts

For Real Estate Investment Trusts (REITs), mandatory distribution of income limits free cash flow. But, restrictions on source of income and asset structure result in widely dispersed stock ownership, which makes external monitoring through the takeover market less likely. As such, alternative monitoring mechanisms, including external directors, must be in place to discourage deviant managerial behavior. Using a simultaneous equation system, we conclude that while independent directors enhance REIT performance, the effect is weak. Higher CEO stock ownership and control through tenure and chairmanship of the board reduce the representation by outside directors, and adversely affect REIT performance. Institutional ownership or blockownership fails to serve as alternate disciplining mechanism to (inadequate) monitoring by outside board members, although their presence seems to enhance performance.

An analysis of gains to acquiring firm's shareholders: The special case of REITs

Abstract This study uses capital market data to measure the effects of REIT mergers on the wealth of the acquiring trusts shareholders. A significant increase in shareholder wealth is detected. This differs from the findings of other acquisition studies. The primary source of the value gain seems to be improved management of the acquired trusts assets.

Investing in International Real Estate Stocks: A Review of the Literature

This paper summarises the various findings of studies completed on the benefits of international diversification using real estate stocks. With the increased availability of data and analytical tools, the quantity and variety of work over the past decade have increased dramatically in this area. The first two sections of the paper chronologically review the studies, focusing on how the diversification benefits are analysed: in a mixed-asset portfolio context or a real-estate-only portfolio context. The third section highlights work that uses alternative analyses to the traditional mean-variance framework to examine the international real estate stock as an investment alternative. Almost all of the studies reach the same conclusion: diversification gains are possible but are often reduced if currency risk is included in the analysis.

The Information Content of Method of Payment in Mergers: Evidence from Real Estate Investment Trusts (REITs)

We provide evidence on the information content of the method of payment in mergers by examining shareholder returns in a sample of REIT mergers over the period 1994-1998. When the target firm is publicly held, we find that transactions are always stock-financed, and that acquiring firm shareholders sustain small negative returns around the announcement date. When the target is privately held, cash financing, mixed (stock and cash) financing, and placement of blocks of acquirer stock with target owners are more prevalent. Acquirer returns are positive in stock-financed mergers when the target is private, which is consistent with both the information signaling and monitoring by blockholders hypotheses. Further analysis supports the information signaling hypothesis as the dominant explanation. The effects of other explanatory variables are similar whether the target is public or private. Most significantly, acquiring shareholder returns are negatively related to the acquirers size, but positively related to the acquirers use of the UPREIT organizational structure. The positive wealth effects of the UPREIT structure are not fully explained as the capitalization of tax benefits. Copyright 2001 by the American Real Estate and Urban Ecopnomics Assocaition.

Information, search, and house prices

Abstract Different groups of homebuyers have varying levels of information about the housing market as well as different search costs. Is the housing market efficient enough to ‘protect’ less informed buyers from paying more for comparable houses? And are search cost differentials large enough to induce significantly different prices across types of buyers? We examine these questions using a unique data base that allows us to compare house price across various groups of homebuyers. Surprisingly, the results reveal no systematic price differentials across types of buyers in the market.