Arvydas Jadevicius

A “family of cycles” – major and auxiliary business cycles

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.

ARIMA modelling of Lithuanian house price index

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. Design/methodology/approach - – The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context. Findings - – As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes. Research limitations/implications - – Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market. Originality/value - – Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.

UK REITs don’t like Mondays

Purpose The purpose of this paper is to examine whether Real Estate Investment Trusts (REITs) returns on the different days of the week differ from each other. Design/methodology/approach It uses European Public Real Estate Association (EPRA)/National Association of Real Estate Investment Trusts (NAREIT) UK index daily closing values (GBP) and its two sub-indices FTSE EPRA/NAREIT UK REITs and non-REITs as dependent variables. It employs Kruskal-Wallis tests and dummy-variable regression to test the hypothesis. Findings The overall findings provide evidence that return anomalies exist in the UK REITs. Practical implications Thought significant, the absolute returns differences are modest for investors to gain superior returns in UK REITs. However, by recognising the day-of-the-week effect, investors can buy/sell UK REITs more effectively. Originality/value This research brings updated evidence of the contested calendar anomalies issues in REITs.

Is there a Housing Bubble in Turkey

Abstract 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.

Macro-determinants of the Lithuanian housing market: a test for Granger causality

ABSTRACT This research examines the macro-determinants of the Lithuanian housing market. The study employs the Granger causality test to assess the interdependence of a number of macro-variables and the national housing price index. Regardless of the limitations involved with this methodology, the empirical findings suggest that the Lithuanian housing market relates to growth in building activity, interest rates, inflation, and employment. Considering all of these results, the research highlights useful policy implications for property market participants. Investors and developers can employ this information to guide their investment decisions. Likewise, the government and central bank could use this updated knowledge to drive a successful macroeconomic program.

Property market modelling and forecasting: simple vs complex models

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 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.

Two Centuries of Farmland Prices in England

The dissemination of robust real estate data helps to improve market efficiency. Sound investment analysis requires long series to form a long-term view but, whilst this has been available for the commercial sector, the same has not been true for agricultural land. Comparable series on the long-term market position of the farmland in England are fragmented. The current paper navigates the methodological complexities involved in rectifying the land price information deficiency. The study employs chain-linking approach to construct a long-term farmland series for England. It then adjusts series for inflation to examine real land market returns. The constructed 200 year series of English farm land prices performance tightens analysis and enhances decision making.

Two centuries of farmland prices in England

Abstract The dissemination of robust asset price data can help improve market efficiency, resource allocation and investment analysis. Land prices influence housing affordability, food security and the carbon infrastructure. Yet price and return histories for farmland in England are fragmented. To provide perspective, a long farmland price series is needed to improve transparency and bring the asset class into line with commercial and residential real estate. After reviewing the historical backdrop and considering methodology, this research uses a chain-linking approach to construct a long-term farmland price series for England. It then adjusts the series for inflation to examine real land prices. The resulting two-century English farmland prices series contributes to farmland market analysis. Notwithstanding some concerns with long-run chain component heterogeneity, the combined series helps us to understand English average farmland price dynamics. As measured by the geometric mean, English land price real capital returns have been positive over more than two centuries. Farmland real price growth was 0.33 per cent annually from 1781 to 2013 and 0.71 per cent from 1801 to 2013. The series contributes to an understanding of land price dynamics.

How long is UK property cycle

Purpose The purpose of this paper is to assess the duration of the UK commercial property cycles, their volatility and persistence to gauge future market direction. Design/methodology/approach The study employs a novel approach to dissect cycles in a form of a three-step algorithm. First, the Hodrick-Prescott de-trends the selected variables. Second, volatility (measured by the variance) screens periods of atypical fluctuations in the series. Finally, the series is regressed against its past values to assess the level of persistence. The sequential steps screen the length of the cycles in UK commercial property market to facilitate interpretation. Findings The estimates suggest that UK commercial property market follows an eight-year cycle. Combined modelling results indicate that the current market trend is likely to change over the coming year. The modelling suggests increasing probability of a market correction in late 2016/early 2017. Practical implications This updated appreciation of the UK commercial property cycle duration allows for better market timing and investment decision making. Originality/value The paper adds additional evidence on the contested issue of UK commercial property cycle duration.

Century of research on property cycles: a literature review

The existence of cycles in building and property, has grown to have significant importance in the UK and internationally; whereas property markets have been characterised by boom and bust cycles with a negative impact on the national economies. As a result, property cycles became a popular research topic amongst property professionals and scholars, with a greater understanding of the cyclical behaviour of the property market being seen as a major guide to the financial success (failure) of property investments. consequently, considerable literature has accumulated over the years on the subject. This paper provides a review of this literature, mostly written in the UK and US, with international insights on the subject. This paper reviews research on the subject chronologically over a one hundred-year period. The study is designed to provide readers with a historical overview of Property cycles research by emphasising the underlying theme which dominated a particular period of this research, as well as indicating methods, data analysis techniques employed and outcomes of these studies. Its ai is to put more clarity on the subject, as well as help to navigate anyone interested in Property cycles through a considerable amount of research which has accumulated over the last century.