Keeping a Home: Changing Mortgage Markets and Regional Economic Distress

Abstract

In the relatively affluent post World War II period, homeownership in the United States has risen steadily to include an increasing segment of the working class. Although the extension of homeownership has helped to attenuate some of the inequalities of class and to integrate the more affluent segments of the working class into the social and political mainstream, economic and political events are threatening this historic trend. Dependent upon long term financing, homeownership has two components: keeping as well as buying a home. High and persistent rates of unemployment in old industrial areas have meant the loss of stable incomes for skilled workers, and with it, rising rates of home mortgage delinquency and foreclosure. To examine the threat to continued homeownership, this paper locates homeownership in its structural context, at the intersection of major changes in financial institutions (deregulation) as well as local economic and industrial change.

Our research suggests that regional economic difficulties are being exaccerbated by national deregulatory trends. We found that deregulation has facilitated a general movement from a local to a nonlocal mortgage market. This includes the growth and privatization of the secondary mortgage market and the repositioning of lenders in the local market. We found that delinquent homeowners with locally held and serviced mortgages were better able to negotiate forebearance with their lenders for reasons ranging from the ability to exert public pressure to the economic self-interest of local lenders. Except for FHA mortgages, delinquent homeowners with nonlocal mortgages were penalized. For blue collar workers, the loss of a home more than the loss of a job removes an important class buffer thus reversing a post World War II trend.     

Computing Optimal Beams in Two and Three Dimensions

The problem of computing an optimal beam among weighted regions (called the optimal beam problem) arises in several applied areas such as radiation therapy, stereotactic brain surgery, medical surgery, geological exploration, manufacturing, and environmental engineering. In this paper, we present computational geometry techniques that enable us to develop efficient algorithms for solving various optimal beam problems among weighted regions in two and three dimensional spaces. In particular, we consider two types of problems: the covering problems (seeking an optimal beam to contain a specified target region), and the piercing problems (seeking an optimal beam of a fixed shape to pierce the target region). We investigate several versions of these problems, with a variety of beam shapes and target region shapes in 2-D and 3-D. Our algorithms are based on interesting combinations of computational geometry techniques and optimization methods, and transform the optimal beam problems to solving a collection of instances of certain special non-linear optimization problems. Our approach makes use of interesting geometric observations, such as utilizing some new features of Minkowski sums.     

Moving beyond dualism: A dialogue with Western European and American Indian views of spirituality, nature, and science

Western European and Native American approaches to spirituality, nature, and science have implications for policy decisions now and into the Twenty First century. This essay explicates traditional American Indian and Western European philosophic and spiritual views of nature in order to promote dialogue and inform choices that are crucial regarding the future of the biosphere. A concept of nature, then, is more than just an explanation of how living things interact with one another. It also serves as a reference point for deciphering the meaning of existence itself. Jeremy Rifkin     

Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability

In quantitative uncertainty analysis, it is essential to define rigorously the endpoint or target of the assessment. Two distinctly different approaches using Monte Carlo methods are discussed: (1) the end point is a fixed but unknown value (e.g., the maximally exposed individual, the average individual, or a specific individual) or (2) the end point is an unknown distribution of values (e.g., the variability of exposures among unspecified individuals in the population). In the first case, values are sampled at random from distributions representing various "degrees of belief" about the unknown "fixed" values of the parameters to produce a distribution of model results. The distribution of model results represents a subjective confidence statement about the true but unknown assessment end point. The important input parameters are those that contribute most to the spread in the distribution of the model results. In the second case, Monte Carlo calculations are performed in two dimensions producing numerous alternative representations of the true but unknown distribution. These alternative distributions permit subject confidence statements to be made from two perspectives: (1) for the individual exposure occurring at a specified fractile of the distribution or (2) for the fractile of the distribution associated with a specified level of individual exposure. The relative importance of input parameters will depend on the fractile or exposure level of interest. The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state-of-the-art in assessment modeling.     

Multinomial and conditional logit discrete-choice models in demography

Although discrete-choice statistical techniques have been used with increasing regularity in demographic analyses, McFadden's conditional logit model is less well known and seldom used. Conditional logit models are appropriate when the choice among alternatives is modeled as a function of the characteristics of the alternatives, rather than (or in addition to) the characteristics of the individual making the choice. We argue that this feature of conditional logit makes it more appropriate for estimating behavioral models. In this article, the conditional logit model is presented and compared with the more familiar multinomial logit model. The difference between the two techniques is illustrated with an analysis of the choice of marital and welfare status by divorced or separated women.     

Interest rate policies and inflation in interdependent economies: Recent policy dilemmas

This paper examines Federal Reserve Board policies that are premised on a negative short-run association of interest rate movements and the rate of inflation. In particular, econometric evidence is provided, supporting the view that tighter monetary policy appears to raise inflation rates in the short run. Conversely, it is demonstrated that easier monetary policy does not necessarily raise inflation rates in the short run. In the case of uncoordinated monetary restrictiveness, interest rate competition among major countries can produce higher inflation and lower growth than was originally intended.     

The Simple Bind and Discontinuous Change

The author argues that families usually respond to internal and external demands for change in terms of leaps or transformations to new and different adaptive structural arrangements. Such transformations unavoidably have stress and disruption as a prelude. Transformations are precipitated by paradoxical injunctions called “simple binds”. The presence of “double binds” lead to the disqualification of, and failure of transformations. Symptomatic behaviour of family members can be seen as a product of failed transformations.     

Structural probability bounds for the strong Pareto law

By using the structural density function (Fraser 1979, Ch. 7) of the parameters of a Pareto distribution, the structural distribution function of the strong Pareto law is derived. Its fractiles have been evaluated numerically for special cases, and the results are displayed through graphs from which structural one-sided probability bounds may be found. It is shown that these graphs may also be used to find structural tolerance bounds for the Pareto distribution.     

Estimation of gene/haplotype frequencies in genetic marker systems based on phenotype data

Two equivalent methods (gene counting and maximum likelihood) for estimating gene frequencies in a general genetic marker system based on observed phenotype data are derived. Under the maximum likelihood approach, an expression is given for the estimated covariance matrix from which estimated standard errors of the estimators can be found. In addition, consideration is given to the problem of estimating gene frequencies when there are available several independent population data sets.