The Economics of the Apartment Market in the 1990s

This paper examines fundamental and investment demand for rental apartments in the 1990s. Demographic and economic trends fuel the demand for rental housing. While rental demand in the U.S. as a whole will be somewhat weak in the 1990s, demand will be strong for areas with high in-migration, due to the younger age characteristics of movers, and the high costs of homeownership in many regions. Apartments represent one of the few real estate product classes in which demand will outpace supply in the 1990s. This impending supply-demand imbalance will result in substantial increases in real rents and investment values in select apartment markets across the country. This report proceeds to describe some of the major financial, economic and demographic conditions that will create attractive investment opportunities for institutional-grade apartment investments in the 1990s.

Housing Tenure, Uncertainty, and Taxation

Modern empirical work on the choice between renting and owning focuses on the concept of the "user cost" of housing, which integrates into a single measure the various components of housing costs. The standard approach implicitly assumes that households know the user cost of housing with certainty. However, the ex post user cost measure exhibits substantial variability over time, and it is highly unlikely that individuals believe themselves able to forecast these fluctuations with certainty. In this paper, we construct and estimate a model of the tenure choice that explicitly allows for the effects of uncertainty. The results suggest that previous work which ignored uncertainty may have overstated the effects of the income tax system upon the tenure choice.

Computational methods for the identification of spatially varying stiffness and damping in beams

A numerical approximation scheme for the estimation of functional parameters in Euler-Bernoulli models for the transverse vibration of flexible beams with tip bodies is developed. The method permits the identification of spatially varying flexural stiffness and Voigt-Kelvin viscoelastic damping coefficients which appear in the hybrid system of ordinary and partial differential equations and boundary conditions describing the dynamics of such structures. An inverse problem is formulated as a least squares fit to data subject to constraints in the form of a vector system of abstract first order evolution equations. Spline-based finite element approximations are used to finite dimensionalize the problem. Theoretical convergence results are given and numerical studies carried out on both conventional (serial) and vector computers are discussed

Approximation techniques for parameter estimation and feedback control for distributed models of large flexible structures

Approximation ideas are discussed that can be used in parameter estimation and feedback control for Euler-Bernoulli models of elastic systems. Focusing on parameter estimation problems, ways by which one can obtain convergence results for cubic spline based schemes for hybrid models involving an elastic cantilevered beam with tip mass and base acceleration are outlined. Sample numerical findings are also presented

Altruistic Behavior and Habit Formation

This paper examines whether altruistic behavior is habit forming. We take advantage of a data set that includes a rich set of information concerning individuals’ donations of cash and time as adults as well as information about whether they were involved with charitable activities when they were young. The basic premise is that if altruistic behavior when young is a good predictor of such behavior in adulthood, then this is consistent with the notion that altruistic behavior is habit forming. Using U.S. data, we examine both donations of money and time, and find that engaging in charitable behavior when young is a strong predictor of adult altruistic behavior, ceteris paribus. A major issue in the interpretation of this result is that the correlation between youthful and adult altruistic behavior may be due to some third variable that affects both. While it is impossible to rule out such a possibility, we are able to control for family influences that likely could affect lifetime attitudes toward altruism. We find that, even taking this factor into account, altruistic behavior as a youth plays a significant role in explaining adult behavior. This result applies to donations of money and time to a variety of types of non-profit organizations.altruistic behavior, donations, nonprofit fundraising

Mortgage Credit Availability and Residential Construction

macroeconomics,mortgage credit, homebuilding

Missed opportunities: Module design to meet the learning and access needs of practitioners - A work based learning pilot in the rehabilitation setting

It is with great pleasure that this report is presented as a result of an exciting project that truly exemplified partnership working. For a Higher Education Institution to come together with an NHS organisation to negotiate and tailor an education initiative in direct response to the needs of both the organisation and its staff is a very positive direction of travel. The project has been possible through the enthusiasm and commitment of its partners, their contribution of resources including time and funding, and the support of others who have played a part in enabling it to happen. The willingness of the students taking part in the pilot module should be recognised as much of what we have learnt from the process and the evaluation of it, will more directly benefit future students rather than the participating students themselves. As with any pilot, there are risks and where challenges have not been foreseen they have been addressed along the way, flexibly and promptly. Whilst a relatively small project, it has generated much interest from others interested in work based learning approaches and potential students from across the health care professions wanting to take part in future courses. On behalf of the Project Team, I hope you find the report useful and encourage you to make contact if you require further information, wish to explore work based learning opportunities (uni-discipline or multi-professional) here at the University or would like to discuss research or evaluation

An approximation theory for the identification of nonlinear distributed parameter systems

An abstract approximation framework for the identification of nonlinear distributed parameter systems is developed. Inverse problems for nonlinear systems governed by strongly maximal monotone operators (satisfying a mild continuous dependence condition with respect to the unknown parameters to be identified) are treated. Convergence of Galerkin approximations and the corresponding solutions of finite dimensional approximating identification problems to a solution of the original finite dimensional identification problem is demonstrated using the theory of nonlinear evolution systems and a nonlinear analog of the Trotter-Kato approximation result for semigroups of bounded linear operators. The nonlinear theory developed here is shown to subsume an existing linear theory as a special case. It is also shown to be applicable to a broad class of nonlinear elliptic operators and the corresponding nonlinear parabolic partial differential equations to which they lead. An application of the theory to a quasilinear model for heat conduction or mass transfer is discussed

Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Histology imaging is an essential diagnosis method to finalize the grade and stage of cancer of different tissues, especially for breast cancer diagnosis. Specialists often disagree on the final diagnosis on biopsy tissue due to the complex morphological variety. Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is computationally infeasible currently. Besides, inconsistent discriminative features often distribute over the whole histology image, which incurs challenges in patch-based CNN classification method. In this paper, we propose a novel architecture for automatic classification of high resolution histology images. First, an adapted residual network is employed to explore hierarchical features without attenuation. Second, we develop a robust deep fusion network to utilize the spatial relationship between patches and learn to correct the prediction bias generated from inconsistent discriminative feature distribution. The proposed method is evaluated using 10-fold cross-validation on 400 high resolution breast histology images with balanced labels and reports 95% accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class classification (carcinoma and non-carcinoma), which substantially outperforms previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding