scholarly journals International Real Estate Review

2015 ◽  
Vol 18 (4) ◽  
pp. 455-471
Author(s):  
William Miles ◽  
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Portfolio Management ◽  
House Prices ◽  
Time Series Models ◽  
Fiscal Policies ◽  
Home Prices ◽  
The Past ◽  
The Uk

Since the recent turmoil in UK housing, there has been controversy over whether house prices in the past decade have entered a bubble. While there are numerous techniques employed to investigate the presence of bubbles, testing the significance of breaks in the dynamics of prices has been utilized in other research to detect such bubbles. This is important in itself, as changing parameters in housing time series models make forecasting and portfolio management more difficult. We examine thirteen regions of the UK as well as the national home prices. The results indicate that while there were some breaks over the 2000s, more regions (and the UK as a whole) experienced breaks over the late 1980s and early 1990s. These results indicate that while there have been large price swings over the past decade, the late 1980s/early 1990s, which followed sharp changes in housing, monetary and fiscal policies, appear to be the larger boom-bust episode.

Consumption and Housing Wealth in the UK

2003 ◽  
Vol 186 ◽  
pp. 53-56 ◽  
Author(s):  
Ray Barrell ◽  
Amanda Choy ◽  
Rebecca Riley
Keyword(s):  
Housing Market ◽  
Asset Prices ◽  
House Prices ◽  
Pension Funds ◽  
Housing Wealth ◽  
The Past ◽  
Equilibrium Ratio ◽  
Net Assets ◽  
The Uk ◽  
The Relationship

Consumption behaviour in the UK is frequently seen as different from that in other countries. The relationship between the housing market and consumption is discussed at length in HM Treasury (2003). The housing market, which has been particularly cyclically volatile in the past 30 years, has contributed to cycles in consumption through its impact on housing wealth. Increased house prices increase the value of assets held, and impact on consumption, making the economy more cyclical. There is a clear relationship between the level of real financial plus housing net wealth as a proportion of income and the savings ratio (excluding adjustment for changes in net equity of households in pension funds), as can be seen from chart 1, where we plot the stock of total net assets over the flow of income to indicate just how much ‘cover’ the personal sector has on its current commitments. When wealth rises, for instance because real asset prices have risen, then individuals find themselves with more assets than they need and increase their consumption in order to return their assets to their equilibrium ratio to income. Clearly this process is not instantaneous, but cycles in wealth driven by house prices could have contributed to the cyclical nature of overall demand in the UK in the past 30 years.

scholarly journals AUTOREGRESSIVE PROCESSES AND GRAIN YIELD FORECASTING IN THE NORTHERN KAZAKHSTAN

2018 ◽  
pp. 98-105
Author(s):  
T. A. Kussaiynov ◽  
A. A. Bulasheva ◽  
Zh. O. Zhakupova
Keyword(s):  
Time Series ◽  
Grain Yield ◽  
Crop Yields ◽  
Linear Trend ◽  
Statistical Properties ◽  
Time Series Models ◽  
Autoregressive Processes ◽  
Agricultural Economy ◽  
The Past ◽  
The North

Time series models are one of the most commonly used forecasting tools in the agricultural economy. In this case, the future values of the variable are function of the past values of the same variable. In other words, there are autoregressive processes. The dynamic of grain yields in the North-Kazakhstan and Kostanay regions of Kazakhstan demonstrate very similar statistical properties. In both cases, there is a positive linear trend, the cyclical development of the process is clearly discernible. Serious attention should also be given to the existence of a cycle in the dynamics of the dispersion level of crop yields. These stochastic features of the indicator should be taken into account in agricultural forecasting.

Forward-looking financial risk management and the housing market in the United Kingdom: is there a role for sentiment indicators?

2020 ◽  
Vol 21 (5) ◽  
pp. 659-678
Author(s):  
Frederik Kunze ◽  
Tobias Basse ◽  
Miguel Rodriguez Gonzalez ◽  
Günter Vornholz
Keyword(s):  
Time Series ◽  
Housing Market ◽  
House Prices ◽  
Market Survey ◽  
Content Type ◽  
Time Series Econometrics ◽  
Property Prices ◽  
The Uk ◽  
Toda And Yamamoto ◽  
Forward Looking

Purpose In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK. Design/methodology/approach By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically. Findings The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK. Practical implications Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK. Originality/value To authors’ knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets.

scholarly journals Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Harry Joe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Probability Generating Function ◽  
Likelihood Estimation ◽  
Model Fit ◽  
Likelihood Inference ◽  
Series Data ◽  
Time Series Models ◽  
Autoregressive Time Series ◽  
The Past

For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

LIBOR, base rate spreads and the UK housing market

2015 ◽  
Vol 8 (1) ◽  
pp. 118-134 ◽  
Author(s):  
Martin Hinch ◽  
Jim Berry ◽  
William McGreal ◽  
Terry Grissom
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
House Prices ◽  
House Price ◽  
Base Rate ◽  
Percentage Change ◽  
Content Type ◽  
Change Models ◽  
The Uk ◽  
The Impact

Purpose – The purpose of this paper is to analyse how London Interbank Offered Rate Index (LIBOR) and the spread between LIBOR and the base rate of interest as set by the Bank of England (BoE) influences the variation in house prices in the UK. Design/methodology/approach – This paper uses monthly data over a long time series, since 1986, to investigate the relationships between house price and LIBOR. Data are drawn from several different sources to include housing, financial and macro-economic variables. The time series is sub-divided into a series of splines based on stages in the economic and property market cycle. Both value-based and percentage change models are developed. Findings – The results show that BoE base/LIBOR margin variable has a strong positive and significant effect on house price; however, the percentage change model infers a weaker and inverse relationship. The spline analysis re-emphasised the significance of the BoE base/LIBOR margin variable. Where variation between base rates and LIBOR is reduced, a significant positive effect can be observed in the average house price; however, where significant variation exists, the BoE base/LIBOR margin has little effect and LIBOR itself becomes a significant driver. Research limitations/implications – The results highlight that the predictive qualities of the BoE base/LIBOR margin, as the contribution of this margin to the explanation of house price, exceeds both the base rate and LIBOR variables individually. Also highlighted is the contribution of unemployment to the explanation of house price. In both the value and percentage change models, unemployment is shown as a negative and highly significant contributor. Originality/value – Previous papers have demonstrated the important linkage between house price and interest rates, the originality in this paper lies in examining the impact of LIBOR and the spreads between LIBOR and base rate as key variables influencing variation in UK house prices.

scholarly journals Financialization, real estate and COVID-19 in the UK

2020 ◽  
Author(s):  
Grace Blakeley
Keyword(s):  
Monetary Policy ◽  
Real Estate ◽  
Financial Markets ◽  
Housing Market ◽  
Negative Impact ◽  
House Prices ◽  
Wealth Inequality ◽  
The Uk

Abstract In the UK, financialization has transformed many areas of the economy, including the housing market. The deregulation of financial markets that took place from the 1980s onwards, combined with the privatization of social housing, has transformed UK real estate from an ordinary good, insulated to some extent from consumer and financial markets, into a valuable financial asset. The financialization of real estate has had a largely negative impact on the UK’s housing market, the wider economy and individual communities; wealth inequality, financial instability, gentrification and homelessness have all increased as the role of the financial sector in UK property has increased. The financial crisis only accelerated many of these trends as distressed real estate was bought up by investors in its wake, and as loose monetary policy pushed up house prices in the period after the crisis. The COVID-19 pandemic is only likely to exacerbate these issues; the UK is sleepwalking into a potential evictions crisis, and ongoing loose monetary policy is likely to prevent a significant and necessary correction in house prices over the long term.

scholarly journals TRACKING THE MUTANT: FORECASTING AND NOWCASTING COVID-19 IN THE UK IN 2021

2021 ◽  
Vol 256 ◽  
pp. 110-126
Author(s):  
Andrew Harvey ◽  
Paul Kattuman ◽  
Craig Thamotheram
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Reproduction Number ◽  
Time Series Models ◽  
Daily Number ◽  
New Class ◽  
Bivariate Models ◽  
The Uk ◽  
Different Sources

A new class of time series models is used to track the progress of the COVID-19 epidemic in the UK in early 2021. Models are fitted to England and the regions, as well as to the UK as a whole. The growth rate of the daily number of cases and the instantaneous reproduction number are computed regularly and compared with those produced by SAGE. The results from figures published each day are compared with results based on figures by specimen date, which may be more accurate but are subject to substantial revisions. It is then shown how data from the two different sources can be combined in bivariate models.

The UK Economy

1995 ◽  
Vol 154 ◽  
pp. 7-26 ◽  
Author(s):  
Garry Young
Keyword(s):  
House Prices ◽  
Aggregate Demand ◽  
Public Spending ◽  
Output Growth ◽  
Positive Contribution ◽  
Negative Contribution ◽  
On Demand ◽  
The Past ◽  
Rate Of Growth ◽  
The Uk

There are now widespread fears that the slowdown in the rate of growth of the UK economy might worsen and turn into another recession, Those holding this view can point to a variety of evidence to support their claim. In particular, retail sales in September were 0.4 per cent lower then a year earlier, house prices were 1.8 per cent lower than a year earlier, manufacturing output, which had grown by over 4 per cent in 1994, was only 0.6 per cent higher in September than it had been a year earlier. In addition, they can emphasise that stockbuilding cannot be expected to make the same positive contribution to output growth that it has made over the past year and is likely to make a negative contribution when excessive stocks are run down. They can also argue that any reduction in public spending relative to existing plans, to make way for reductions in taxation, will tend to reduce aggregate demand since cuts in spending have a larger effect on demand than equivalent cuts in taxation because the latter affect saving as well as spending.

scholarly journals Distance and diversification

2016 ◽  
Vol 9 (2) ◽  
pp. 183-192
Author(s):  
Stephen Lee
Keyword(s):  
Real Estate ◽  
Portfolio Management ◽  
Spatial Diversity ◽  
Geographical Distance ◽  
Content Type ◽  
Economic Distance ◽  
Office Markets ◽  
The Uk ◽  
City Of London ◽  
The City

Purpose The purpose of this paper is to examine whether geographical distance or economic distance offers greater diversification benefits in the UK office market. Design/methodology/approach The real estate investment data for this study come from the Investment Property Databank analysis “UK Quarterly Key Centres Q2 2015”. The author measures the geographical distance between the City of London and 27 local authorities (LAs) by road distance. The author used the market size and employment structure of the LAs relative to the City of London to calculate economic distance. Findings The results show that LAs that are classified on their economic distance show significant negative office rental growth correlations with the City of London. In contrast, geographical distance shows no relationship. Results are consistent for the overall sample period and for various periods. Practical implications Spatial diversity is a fundamental tenet of real estate portfolio management and the results here show that it is better to diversify by across office markets in the UK using the economic attributes of LAs rather than the physical distance between locations. Originality/value This is one of only two papers to explicitly examine whether economic distance or geographical distance leads to significantly lower rental growth coefficients between locations in office markets and the first in the UK.

scholarly journals International Real Estate Review

2020 ◽  
Vol 23 (3) ◽  
pp. 397-416
Author(s):  
William Miles ◽  
Keyword(s):  
House Prices ◽  
Data Set ◽  
Nonlinear Adjustment ◽  
Stable Relationship ◽  
Home Prices ◽  
Long Run ◽  
Income Ratio ◽  
The Uk ◽  
First Time ◽  
The Relationship

Asset prices and fundamentals can move apart, as is the case during bubble episodes. However, they should exhibit a stable relationship in the long run. For UK housing, previous studies have investigated whether house prices share a long run relationship with income. Results thus far have not yet found such stability in the interaction of the two variables. These previous papers have imposed linear adjustment on the relationship. Nonlinear adjustment, however, has been shown to be a feature in a number of housing market relationships. In this study, we utilize a data set that consists of home prices relative to first time buyer income for the UK and its twelve constituent regions, which gives us a direct measure of affordability. We test for the stationarity of the home price/first time buyer income ratio with linear tests, and, as in past studies, fail to find a long run relationship. However, we then employ a nonlinear test, and find a stationary relationship for the UK and seven of the twelve regions. In particular, the regions closest to London appear most clearly to have a stationary relationship between home prices and income.

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