The purpose of this paper is to validate and quantify the effect of key macroeconomic drivers on London house prices using annual data over the period 1983–2016.
Abstract
Purpose
The purpose of this paper is to validate and quantify the effect of key macroeconomic drivers on London house prices using annual data over the period 1983–2016.
Design/methodology/approach
Within this context, the authors estimate alternative error-correction and partial-adjustment models (PAMs), which have been widely used in the empirical literature in modelling the slow adjustments of house prices to demand and supply shocks.
Findings
The results verify the existence of a strong long-term relationship between London house prices and key macroeconomic variables, such as UK GDP, London population and housing completions. A key finding of the study relevant to the debate on the causes of the housing affordability crisis is that the results provide little evidence in support of the argument that user demand, which is captured in the author’s model by Greater London population, may have had a diminished role in driving house price inflation in London.
Practical implications
The practical and policy implications of the results are that increased homebuilding activity in London will undoubtedly help limit house price increases. Also, any potential reduction of immigration and economic growth due to Brexit will also have a similar effect.
Originality/value
The originality of this research lies in the use of annual data that may better capture the long-term effect of macroeconomic drivers on house prices and the estimation of such effects through both error-correction and partial-adjustment models.
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Dario Pontiggia and Petros Stavrou Sivitanides
The purpose of this paper is to assess whether the rapid accumulation of bank deposits before the global financial crisis and their subsequent drastic reduction was the main…
Abstract
Purpose
The purpose of this paper is to assess whether the rapid accumulation of bank deposits before the global financial crisis and their subsequent drastic reduction was the main driving force of the Cyprus house price cycle over the period 2006–2015.
Design/methodology/approach
To this aim we estimate a three-equation model in which house prices are determined by housing loans, among other factors, and housing loans are determined by bank deposits. All equations are estimated using partial adjustment model specifications.
Findings
Our findings indicate that housing loans, which capture the effect of credit availability on housing demand, had the smallest effect on house prices, thus providing little support to our proposition of a deposits-driven cycle in house prices.
Research limitations/implications
The main limitation of the study is the use of the housing loan stock instead of the actual volume of housing loans in each period due to lack of such data. As a result our econometric estimates may not accurately capture the magnitude of the effect of housing loans on house prices.
Practical implications
The study has important practical implications for policy makers as it highlights the importance of availability of credit in supporting effective demand for housing during periods of economic growth. Furthermore, it highlights the key role of house price increases in combination with the collateral effect in driving the house price cycle.
Originality/value
This is among the few studies internationally and the first study in Cyprus that attempts to link econometrically the credit and house price cycles that were caused by the global financial crisis.
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Philippos Nikiforou, Thomas Dimopoulos and Petros Sivitanides
The purpose of this study is to investigate how the degree of overpricing (DOP) and other variables are associated with the time on the market (TOM) and the final selling price…
Abstract
Purpose
The purpose of this study is to investigate how the degree of overpricing (DOP) and other variables are associated with the time on the market (TOM) and the final selling price (SP) for residential properties in the Paphos urban area.
Design/methodology/approach
The hedonic pricing model was used to examine the association of TOM and SP with various factors. The association of the independent variable of DOP and other independent variables with the two dependent variables of TOM and SP were investigated via ordinary least squares (OLS) regression models. In the first set of models the dependent variable was TOM and in the second set of models the dependent variable was SP. A sample of N = 538 completed transactions from Q1 2008 to Q2 2019 was used to estimate the optimum DOP that a seller must apply on the current market value of a property in order to achieve highest SP price in the shortest TOM.
Findings
The results of this study also suggest that the degree of overpricing in thin and less transparent markets is higher than that in transparent markets with high property transaction volumes. In mature markets like the USA and the UK where the actual sold prices are published, the DOP is around 1.5% which is much lower than the 11% DOP identified in this study.
Practical implications
It was found that buyers are willing to pay more for the same house in a bigger plot than a bigger house in the same plot. The outcome is that smaller houses sell faster at a higher price per square meter than larger houses. Smaller houses are more affordable than larger houses.
Social implications
There is a large pool of buyers for smaller houses than bigger houses. Higher demand for smaller houses results in a higher price per square meter for smaller houses than the price per square meter for bigger houses. Respectively the TOM for smaller houses is shorter than the TOM for bigger houses.
Originality/value
The database used is unique, from an estate agent located in Paphos that managed to sell more than 27,000 properties in 20 years. This data set is the most accurate information for Cyprus' property transactions.
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Franz Fuerst, Patrick McAllister and Petros Sivitanides
The purpose of this paper is to investigate the effect of the crisis on the pricing of asset quality attributes. This paper uses sales transaction data to examine whether flight…
Abstract
Purpose
The purpose of this paper is to investigate the effect of the crisis on the pricing of asset quality attributes. This paper uses sales transaction data to examine whether flight from risk phenomena took place in the US office market during the financial crisis of 2007-2009.
Design/methodology/approach
Hedonic regression procedures are used to test the hypothesis that the spread between the pricing of low-quality and high-quality characteristics increased during the crisis period compared to the pre-crisis period.
Findings
The results of the hedonic regression models suggest that the price spread between Class A and other properties grew significantly during the downturn.
Research limitations/implications
Our results are consistent with the hypothesis of an increased price spread following a market downturn between Class A and non-Class A offices. The evidence suggests that the relationships between the returns on Class A and non-Class A assets changed during the period of market stress or crisis.
Practical implications
These findings have implications for real estate portfolio construction. If regime switches can be predicted and/or responded to rapidly, portfolios may be rebalanced. In crisis periods, portfolios might be reweighted towards Class A properties and in positive market periods, the reweighting would be towards non-Class A assets.
Social implications
The global financial crisis has demonstrated that real estate markets play a crucial role in modern economies and that negative developments in these markets have the potential to spillover and create contagion for the larger economy, thereby affecting jobs, incomes and ultimately people’s livelihoods.
Originality/value
This is one of the first studies that address the flight to quality phenomenon in commercial real estate markets during periods of financial crisis and market turmoil.
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Justine Wang, Alla Koblyakova, Piyush Tiwari and John S. Croucher
This paper aims to explore principal drivers affecting prices in the Australian housing market, aiming to detect the presence of housing bubbles within it. The data set analyzed…
Abstract
Purpose
This paper aims to explore principal drivers affecting prices in the Australian housing market, aiming to detect the presence of housing bubbles within it. The data set analyzed covers the past two decades, thereby including the period of the most recent housing boom between 2012 and 2015.
Design/methodology/approach
The paper describes the application of combined enhanced rigorous econometric frameworks, such as ordinary least square (OLS), Granger causality and the Vector Error Correction Model (VECM) framework, to provide an in-depth understanding of house price dynamics and bubbles in Australia.
Findings
The empirical results presented reveal that Australian house prices are driven primarily by four key factors: mortgage interest rates, consumer sentiment, the Australian S&P/ASX 200 stock market index and unemployment rates. It finds that these four key drivers have long-term equilibrium in relation to house prices, and any short-term disequilibrium always self-corrects over the long term because of economic forces. The existence of long-term equilibrium in the housing market suggests it is unlikely to be in a bubble (Diba and Grossman, 1988; Flood and Hodrick, 1986).
Originality/value
The foremost contribution of this paper is that it is the first rigorous study of housing bubbles in Australia at the national level. Additionally, the data set renders the study of particular interest because it incorporates an analysis of the most recent housing boom (2012-2015). The policy implications from the study arise from the discussion of how best to balance monetary policy, fiscal policy and macroeconomic policy to optimize the steady and stable growth of the Australian housing market, and from its reconsideration of affordability schemes and related policies designed to incentivize construction and the involvement of complementary industries associated with property.