An econometric analysis of U.K. regional real estate markets

This thesis presents an analysis of UK national and regional property price dynamics with the focus on changes in the time-series properties of real estate prices and their forecastability. The main research questions addressed are the following. First, have UK regional property prices experienced episodes of explosive dynamics in the past and if so, can these episodes be explained by movements in economic fundamentals. Second, considering the substantial instability of UK real estate markets over the last few decades, which are the best econometric models for predicting future house price dynamics and which economic variables are the most important drivers of property prices movements

Exuberance in the U.K. Regional Housing Markets

We combine the estimation of a structural model with inference based on recently developed recursive unit root tests to analyse the behaviour of regional real estate markets in the U.K. over the last four decades. We find two episodes, the late 1980s and the early and mid-2000s, when all regional house prices experienced explosive dynamics above and beyond factors such as housing supply relative to demographics, income, regional spillovers and credit availability. This is the first econometric analysis to provide evidence that would endorse the view that ‘bubbles’, with a particular spatial pattern, are a feature of UK regional housing markets

Exuberance in the U.K. Regional Housing Markets

We combine the estimation of a structural model with inference based on recently developed recursive unit root tests to analyse the behaviour of regional real estate markets in the U.K. over the last four decades. We find two episodes, the late 1980s and the early and mid-2000s, when all regional house prices experienced explosive dynamics above and beyond factors such as housing supply relative to demographics, income, regional spillovers and credit availability. This is the first econometric analysis to provide evidence that would endorse the view that ‘bubbles’, with a particular spatial pattern, are a feature of UK regional housing markets

Episodes of exuberance in housing markets:in search of the smoking gun

After a prolonged period characterized by rapid real appreciation in house prices, there is now broad recognition of the severe correction in housing markets that followed as one of the causes of the 2008-09 global recession. We investigate the time series characteristics of three relevant price indicators of the housing market --real house prices, price-to-income, and price-to-rent ratios-- for the U.S. and 21 other countries during the period 1975Q1-2013Q2 (see Mack and Martínez-García (2011)) for evidence of explosive behavior as a plausible explanation for the boom and bust. The empirical detection of explosive behavior in house prices provides a precise timeline as well as empirical content to the narrative connecting the evolution of housing markets to the global recession; our rich cross-country dataset offers a novel international perspective. For testing and detection, we adopt a pair of novel techniques based on a right-tail variation of the standard Augmented Dickey-Fuller (ADF) test --the supremum ADF (SADF) (Phillips et al. (2011)) and the generalized SADF (GSADF) (Phillips et al. (2012) and Phillips et al. (2013))-- where the alternative hypothesis is of a mildly explosive process (even periodically collapsing with the GSADF test) behavior within sample. Statistically significant periods of exuberance are found in most countries, with our empirical estimates suggesting an unprecedented synchronization across countries preceding the global recession. The boom in housing begins during the late 90s in the U.S. spreading to most countries by the early 2000s, until it bursts for most during 2007-08 as the impact on economic activity was being felt. In this regard, our findings corroborate the narrative of the 2008-09 global recession. In this paper, we also discuss more generally the use of these procedures to monitor international housing markets and as a warning signal

Episodes of exuberance in housing markets:in search of the smoking gun

In this paper, we examine changes in the time series properties of three widely used housing market indicators (real house prices, price-to-income ratios, and price-to-rent ratios) for a large set of countries to detect episodes of explosive dynamics. Dating such episodes of exuberance in housing markets provides a timeline as well as empirical content to the narrative connecting housing exuberance to the global 2008 - 09 recession. For our empirical analysis, we employ two recursive univariate unit root tests recently developed by Phillips et al. (2011) and Phillips et al. (2015). We also propose a novel extension of the test developed by Phillips et al. (2015) to a panel setting in order to exploit the large cross-sectional dimension of our international dataset. Statistically significant periods of exuberance are found in most countries. Moreover, we find strong evidence of the emergence of an unprecedented period of exuberance in the early 2000s that eventually collapsed around 2006 - 07, preceding the 2008 - 09 global recession. We examine whether macro and financial variables help to predict (in-sample) episodes of exuberance in housing markets. Long-term interest rates, credit growth and global economic conditions are found to be among the best predictors. We conclude that global factors explain (partly) the synchronization of exuberance episodes that we detect in the data in the 2000s

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Dynamic Linear Models with Adaptive Discounting

Dynamic linear models with discounting are state-space models that are sufficiently flexible interpretable, and computationally efficient. As such they are increasingly applied in economics and finance. Successful modeling and forecasting with such models depends on an appropriate choice of the discount factor. In this work we develop an adaptive approach to sequentially estimate this parameter, which relies on the minimisation of the one-step-ahead forecast error. Simulated data and an in-depth empirical application to the problem of forecasting quarterly UK house prices shows that our approach can achieve significant improvement in forecast accuracy at a computational cost that is orders of magnitude smaller than approaches based on sequential Monte Carlo. We also conduct an extensive evaluation of diverse forecast combination methods on the task of predicting UK house prices. Our results indicate that a recent density combination method can substantially improve forecast accuracy

Survey: What’s new in forecasting software?

When a company is looking into purchasing forecasting software, it inevitably faces the kid-in-a-candy shop situation: There are lots of options, but the forecasting needs may well be quite specific, and the budget is typically constrained. Never has there been a larger choice of forecasting software solutions on the market than now. In addition to new startups that supply very specific machine learning (ML) and artificial intelligence (AI) algorithms, established business intelligence software companies are adding more and more predictive time-series analysis tools to their product lines. As a result, it is difficult to choose the most appropriate product to meet prospective users’ needs. With this years’ biennial forecasting software survey, we aim to provide some insights on the recent development in this industry sector