Andrius Grybauskas and Vaida Pilinkiene
The purpose of this paper is to investigate whether real estate investment trusts (REITs) have any significant cost-efficiency advantages over real estate operating companies…
Abstract
Purpose
The purpose of this paper is to investigate whether real estate investment trusts (REITs) have any significant cost-efficiency advantages over real estate operating companies (REOCs).
Design/methodology/approach
The data for listed companies were extracted from the Bloomberg terminal. The authors analyzed financial ratios and conducted a non-parametric data envelope analysis (DEA) for 534 firms in the USA, Canada and some EU member states.
Findings
The results suggest that REITs were much more cost-efficient than REOCs by all the parameters in the DEA model during the entire three-year period under consideration. Although the debt-to-equity levels were similar, REOCs were more relying on short-term than long-term maturities, which made them more vulnerable against market corrections or shocks. Being larger in asset size did not necessarily guarantee greater economies of scale. Both – the cases of increasing economies of scale and diseconomies – were detected. The time period 2015–2017 showed the general trend of decreasing efficiency.
Originality/value
Very few papers on the topic of REITs have attempted to find out whether a different firm structure displays any differences in efficiency. Because the question of REITs and sustainable growth of the real estate market has become a prominent issue, this research can help EU countries to consider the option of adopting a REIT system. If this system were successfully implemented, the EU member states could benefit from a more sustainable and more rapid growth of their real estate markets.
Details
Keywords
Alina Stundziene, Vaida Pilinkienė and Andrius Grybauskas
This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.
Abstract
Purpose
This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.
Design/methodology/approach
The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations.
Findings
The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle.
Practical implications
Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary.
Originality/value
This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.
Details
Keywords
Alina Stundziene, Vaida Pilinkiene and Andrius Grybauskas
This paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.
Abstract
Purpose
This paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.
Design/methodology/approach
The econometric analysis includes stationarity test, Granger causality test, correlation analysis, autoregressive distributed lag models and cointegration analysis using ARDL bounds testing.
Findings
The econometric modelling reveals that the housing price in Lithuania correlates with quarterly changes in the gross domestic product and approves that the cycles of the real estate market are related to the economic cycles. Economic stimulus measures should mainly focus on stabilizing the economics, preserving the cash and deposits of households, as well as consumer spending in the case of economic shock.
Originality Value
This study is beneficial for policy makers to make decisions to maintain stability in the housing market in the event of any economic shock.