Найти книгу: "Housing Markets and Planning Policy"

Housing systems in many countries are now more market-oriented than ever before. This is particularly true of the UK, where there is heightened interest in the ability of the market to deliver new housing, as well as considerable debate among housing academics and policy makers over the extent to which policy instruments can be used to steer market processes. This increased market orientation means a greater understanding of market economics is needed. The challenges of providing affordable housing, while simultaneously addressing the problems of low demand housing in some areas, together with the revitalisation of neighbourhoods in need of renewal, also underline the need for a better understanding of the structure and operation of housing markets at local and neighbourhood level. This timely contribution to the field addresses the main housing and planning policy challenges in the UK today. It does so by examining the structure and operation of the urban housing system and then exploring both conceptual and empirical analyses of the workings of the market. The authors then consider the lessons for policy makers, discussing the limitations of the policy framework and considering the strategies for integrating market information into the analysis undertaken in practice. Housing Markets & Planning Policy is an invaluable advanced text for students of land economy, land management, urban planning, housing and urban studies. The authors provide a uniquely detailed analysis of an important policy area that builds on a strong theoretical basis drawn from housing economics. With the challenges posed by the instability of the housing market, it will be of particular interest to academic researchers, policy-makers and housing and planning practitioners.

Brian Tracy shares the most important principles for sales success he has discovered in 30 years of training more than a half million sales professionals in 23 countries. Based on Tracy’s detailed discussions with top salespeople and his keen observation of their methods, as well as his own experiences as a record-breaking salesman, these guidelines address both the inner game of selling—the mental component—and the outer game of selling—the methods and techniques of actually making the sale. Concise and action-oriented, Be a Sales Superstar is a handbook for busy sales professionals, providing key ideas and techniques that will immediately increase your effectiveness and boost your results. Brian Tracy shows you how to: • Get more and better appointments, easier; • Build high rapport in the first few minutes; • Make better, more effective sales presentations • Close more sales faster than ever before Apply Tracy’s 21 great ways to be a superstar salesperson, and your success in selling will become unlimited.

A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.