Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.
Purpose-This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies.
There was a notable housing price inflation in aggregate/local levels in Turkey during the last few years. Although the country’s economic fundamentals remain strong, the probability of a housing bubble is a heated debate among market participants. This timely investigation brings greater clarity to whether the Turkish housing market is in a bubble. The study uses a multi-strand approach to dissect the bubble over the period of Jan. 2010 - Dec. 2014. First, monthly/annual price-to-income and monthly price-to-rent ratios are examined for the national Turkish as well as regional Istanbul, Izmir and Ankara housing markets. Second, an extended CASE and SHILLER (2003) model is applied assessing the interdependence between housing prices and a series of explanatory variables. Lastly, the Right Tail Augmented Dickey-Fuller (Rtadf) test is performed to support the overall analysis. This study finds that neither affordability ratios nor regression estimates support the existence of the bubble in Turkey.
Purpose – The paper aims to discuss the major and auxiliary types of cycles found in the literature. Design/methodology/approach – The existence of cycles within economy and its sub-sectors has been studied for a number of years. In the wake of the recent cyclical downturn, interest in cycles has increased. To mitigate future risks, scholars and investors seek new insights for a better understanding of the cyclical phenomenon. The paper presents systematic review of the existing copious cyclical literature. It then discusses general characteristics and the key forces that produce these cycles. Findings – The study finds four major and eight auxiliary cycles. It suggests that each cycle has its own distinct empirical periodicity and theoretical underpinnings. The longer the cycles are the greater controversy which surrounds them. Practical implications – Cycles are monumental to a proper understanding of complex property market dynamics. Their existence implies that economies, whilst not deterministic, have a rhythm. Cyclical awareness can therefore advance property market participants. Originality/value – The paper uncovers four major and eight auxiliary types of cycles and argues their importance.
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