scholarly journals Volume effects in the London housing market

2018 ◽  
Vol 11 (3) ◽  
pp. 586-602 ◽  
Author(s):  
Steve Cook ◽  
Duncan Watson
Keyword(s):  
Housing Market ◽  
House Price ◽  
Housing Markets ◽  
Price Volatility ◽  
Financial Assets ◽  
Asymmetric Adjustment ◽  
Related Factors ◽  
Content Type ◽  
Alternative Housing ◽  
Volume Effects

Purpose This paper aims to extend existing research in relation to both the importance of volume effects within housing markets and the specific behaviour of the London housing market. A detailed borough-level examination is undertaken of the relationships between volume, house prices and house price volatility. Support for alternative housing market theories, the degree of heterogeneity in house price behaviour across boroughs and the extent to which housing displays differing properties to other financial assets are examined. Design/methodology/approach Correlation analyses, causality testing and volatility modelling are undertaken in extended forms which synthesise and extend approaches within the housing, economics and finance literatures. The various modelling and testing techniques are supplemented via the use of alternative variable transformations to evaluate housing market behaviour in detail. Findings Novel findings are provided concerning both volume effects within housing markets generally and the specific properties of London housing market. Evidence concerning bubbles, the volatility-reducing effects of volume, the importance of geographical and price-related factors underlying the relationship between volume and both house price growth and volatility and the presence of asymmetric adjustment in the London housing market are all provided. The extent and nature of the support available for alternative housing market theories are evaluated. Originality/value The volatility-reducing effects of volume within housing markets, along with volume effects and the presence of asymmetric adjustment within the London housing market are examined for the first time. New empirical evidence on the support for alternative housing market theories and the differing empirical characteristics of housing relative to other financial assets are presented.

scholarly journals Volatility clustering, risk-return relationship and asymmetric adjustment in the Finnish housing market

2020 ◽  
Vol 13 (4) ◽  
pp. 661-688 ◽  
Author(s):  
Josephine Dufitinema
Keyword(s):  
Housing Market ◽  
Portfolio Management ◽  
House Price ◽  
Significant Risk ◽  
Price Volatility ◽  
Asymmetric Adjustment ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Risk Return ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types. Design/methodology/approach Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas. Findings Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types. Research limitations/implications The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study. Originality/value To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

Housing market networks in China's major cities: a conditional causality approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lu Yang ◽  
Nannan Yuan ◽  
Shichao Hu
Keyword(s):  
Housing Market ◽  
Granger Causality ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Scale Free ◽  
Second Tier ◽  
The Difference ◽  
Secondary Housing Market ◽  
Market Networks

PurposeTo explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.Design/methodology/approachAlthough housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.FindingsWe discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.Originality/valueBy excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

An examination of the risk-return relation in the Australian housing market

2017 ◽  
Vol 10 (3) ◽  
pp. 431-449 ◽  
Author(s):  
Chyi Lin Lee
Keyword(s):  
Housing Market ◽  
Investment Decision ◽  
Housing Markets ◽  
Price Volatility ◽  
Asset Markets ◽  
Risk Level ◽  
Risk And Return ◽  
Content Type ◽  
Volatility Clustering ◽  
Risk Return

Purpose Extensive studies have investigated the relation between risk and return in the stock and major asset markets, whereas little studies have been done for housing, particularly the Australian housing market. This study aims to determine the relationship between housing risk and housing return in Australia. Design/methodology/approach The analysis of this study involves two stages. The first stage is to estimate the presence of volatility clustering effects. Thereafter, the relation between risk and return in the Australian housing market is assessed by using a component generalised autoregressive conditional heteroscedasticity-in-mean (C-CARCH-M) model. Findings The empirical results show that there is a strong positive risk-return relationship in all Australian housing markets. Specifically, comparable results are also evident in all housing markets in various Australian capital cities, reflecting that Australian home buyers, in general, are risk reverse and require a premium for higher risk level. This could be attributed the unique characteristics of the Australian housing market. In addition, there is evidence to suggest that a stronger volatility clustering effect than previously documented in the daily case. Practical implications The findings enable more informed and practical investment decision-making regarding the relation between housing return and housing risk. Originality/value This paper is the first study to offer empirical evidence of the risk-return relationship in the Australian housing market. Besides, this is the first housing price volatility study that utilizes daily data.

Long-range dependence in the returns and volatility of the Finnish housing market

2019 ◽  
Vol 13 (1) ◽  
pp. 29-54 ◽  
Author(s):  
Josephine Dufitinema ◽  
Seppo Pynnönen
Keyword(s):  
Housing Market ◽  
Long Range ◽  
Portfolio Management ◽  
Long Memory ◽  
Moving Average ◽  
House Price ◽  
Price Volatility ◽  
Long Range Dependence ◽  
Content Type ◽  
Fractional Differencing

Purpose The purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types. Design/methodology/approach For each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d, q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns. Findings A higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour. Research limitations/implications Investigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study. Originality/value To the best of the authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.

The search and matching process in the housing market

2019 ◽  
Vol 12 (3) ◽  
pp. 380-391
Author(s):  
Gaetano Lisi
Keyword(s):  
Housing Market ◽  
Price Dispersion ◽  
House Price ◽  
Price Volatility ◽  
Housing Tenure ◽  
Search Process ◽  
Search And Matching ◽  
Tenure Status ◽  
Content Type ◽  
Matching Process

Purpose This paper aims to study the phenomenon known as “house price dispersion”, one of the most important distinctive features of housing markets. House price dispersion refers to the phenomenon of selling two houses with very similar attributes and in near locations at the same time but at very different prices. Design/methodology/approach This theoretical paper makes use of a search and matching model of the housing market. The search and matching models are the benchmark models of the “matching” markets, such as the labour market and the housing market, where trade is a decentralised, uncoordinated and time-consuming economic activity. Findings Unlike the previous related literature that attributes to the heterogeneity of buyers and sellers a significant part of the price volatility, in this paper, the house price dispersion depends on the housing tenure status of home-seekers in the house search process. Indeed, in the presence of different housing tenure status of home-seekers, the house search process leads to different types of matching. In turn, this implies different surpluses (the sum of the net gains of the parties involved in the trade), and eventually, different surpluses produce different prices of equilibrium. Research limitations/implications An interesting research agenda for future works would be an extension of the model to study the effect of “online housing search” on the house search and matching process, and thus, on the house price dispersion. Practical implications The main practical implication of this work is that the house price dispersion is an inherent phenomenon in the house search and matching process. Originality/value None of the existing and related works of research have considered how to take advantage of the search and matching approach to deal with the phenomenon known as “house price dispersion”, without relying on the ex ante heterogeneity of the parties but looking at the “core” of the house search and matching process.

Heterogeneous housing markets: structural implications for pricing and risk

2014 ◽  
Vol 7 (3) ◽  
pp. 383-396 ◽  
Author(s):  
Trond A. Borgersen
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Value Added ◽  
Price Risk ◽  
Content Type ◽  
Price Response ◽  
Market Framework ◽  
House Price Risk

Purpose – The purpose of this paper is to compare the structure of risk and the structure of pricing in housing markets where the interaction between segments is taken into account with the structures that come about in a housing market approach that ignores this interplay. Knowing how most empirical assessments of whether housing markets are in or out of equilibrium is related to macroeconomic variables and is ignoring the interplay between segments our aim is to highlight the extent to which a homogeneous market framework underestimates pricing and risk in real housing markets. Design/methodology/approach – Framed in terms of a linearized housing market with two segments, the author derives expressions for house prices and house price risk in three scenarios. The author compares the structure of pricing and the structure of risk in a homogeneous housing market with those of two distinct heterogeneous housing markets where segments are linked as well analyzing as how prices and risk responds to shocks. Findings – The author derives expressions for market segment prices and for the house price index in three distinct housing market scenarios and shows how heterogeneous housing market frameworks produce both expressions for house prices and for house price risk, as well as a response in both risk and prices to shocks to demand, that deviate from those of a homogeneous housing market framework. While significantly underestimating house price risk a homogeneous framework might also be taken by surprise of the price response accompanying shocks to demand. Originality/value – The authors' simplistic expressions for house prices and house price risk provides a framework for bringing two distinct theoretical housing market camps onto the same playing field. The approach shows the value added of taking the interplay between market segments into account when analyzing housing market developments.

scholarly journals Land price dynamics in a large Australian urban housing market

2014 ◽  
Vol 7 (1) ◽  
pp. 42-60 ◽  
Author(s):  
Greg Costello
Keyword(s):  
Housing Market ◽  
Price Change ◽  
House Price ◽  
Housing Markets ◽  
Urban Housing ◽  
Price Dynamics ◽  
Data Set ◽  
Content Type ◽  
Hedonic Price Analysis ◽  
Spatial Disaggregation

Purpose – Housing is a composite asset comprising land and improved components varying as proportions of total value over space and time. Theory suggests land and improvements (structures) are unique goods responding differently to economic stimuli. This paper aims to test the expectation of different overall house price changes in response to variation in land and improved components. Design/methodology/approach – House price dynamics are decomposed to analyse the influence of land and structure components for the city of Perth, Australia both at aggregate level and for spatially defined housing sub-regions, sample period 1995-2010. Findings – Values of land and improvements on that land evolve differently over time and are significantly influenced by the magnitude of land leverage. The study extends previous research through extensive spatial disaggregation of a larger more detailed data set than previously used in studies of this type confirming significant variation in land leverage ratios, overall price change and growth rates for land and improvements in sub-regional markets defined by spatial criteria. Research limitations/implications – The results suggest an important role for policy development with respect to housing affordability and supply side regulation of land in large urban housing markets. Practical implications – The results suggest important implications for hedonic price analysis of housing markets. The inclusion of land leverage variables in hedonic regression could remove coefficient bias associated with omitted location amenity variables. Originality/value – The paper adapts methodology from previous studies but extends previous literature through detailed analysis of a large Australian housing market (Perth) enabling extensive spatial disaggregation of the sample and providing greater insight to spatial variation of land leverage than in previous studies.

scholarly journals Speculative housing markets and rent control: insights from nonlinear economic dynamics

2021 ◽  
Author(s):  
Noemi Schmitt ◽  
Frank Westerhoff
Keyword(s):  
Housing Market ◽  
Housing Stock ◽  
House Price ◽  
Housing Markets ◽  
Market Model ◽  
Market Dynamics ◽  
Rent Control ◽  
Forecasting Accuracy ◽  
Expectation Formation ◽  
Rent Level

AbstractWe propose a novel housing market model to explore the effectiveness of rent control. Our model reveals that the expectation formation and learning behavior of boundedly rational homebuyers, switching between extrapolative and regressive expectation rules subject to their past forecasting accuracy, may create endogenous housing market dynamics. We show that policymakers may use rent control to reduce the rent level, although such policies may have undesirable effects on the house price and the housing stock. However, we are also able to prove that well-designed rent control may help policymakers to stabilize housing market dynamics, even without creating housing market distortions.

A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syed Ali Raza ◽  
Nida Shah ◽  
Muhammad Tahir Suleman ◽  
Md Al Mamun
Keyword(s):  
Financial Crisis ◽  
Housing Market ◽  
Market Efficiency ◽  
House Price ◽  
Fluctuation Analysis ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Run ◽  
Before And After

Purpose This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period. Design/methodology/approach This study uses the MF-DFA to analyze house price fluctuations. Findings The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result. Originality/value The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

Sign in / Sign up

Export Citation Format

Share Document