Statistics for Spatial Data.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

On robust estimation of the location parameter

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A User’s Guide

We construct measures of the annual cost of single-family housing for 46 metropolitan areas in the United States over the last 25 years and compare them with local rents and incomes as a way of judging the level of housing prices. Conventional metrics like the growth rate of house prices, the price-to-rent ratio, and the price-to-income ratio can be misleading because they fail to account both for the time series pattern of real long-term interest rates and predictable differences in the long-run growth rates of house prices across local markets. These factors are especially important in recent years because house prices are theoretically more sensitive to interest rates when rates are already low, and more sensitive still in those cities where the long-run rate of house price growth is high. During the 1980s, our measures show that houses looked most overvalued in many of the same cities that subsequently experienced the largest house price declines. We find that from the trough of 1995 to 2004, the cost of owning rose somewhat relative to the cost of renting, but not, in most cities, to levels that made houses look overvalued.

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Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions

We maintain that the appropriate definition of submarkets depends on the use to which they will be put. For mass appraisal purposes, submarkets should be defined so that the accuracy of hedonic predictions will be optimized. Thus we test whether out-of-sample hedonic value predictions can be improved when a large urban housing market is divided into submarkets and we explore the effects of alternative definitions of submarkets on the accuracy of predictions. We compare a set of submarkets based on small geographical areas defined by real estate appraisers with a set of statistically generated submarkets consisting of dwellings that are similar but not necessarily contiguous. The empirical analysis uses a transactions database from Auckland, New Zealand. Price predictions are found to be most accurate when based on the housing market segmentation used by appraisers. We conclude that housing submarkets matter, and location plays the major role in explaining why they matter.

Steven C. Bourassa

Papers

Do Housing Submarkets Really Matter

Defining Housing Submarkets

Do housing submarkets really matter

The Aesthetics of Landscape

The aesthetics of landscape