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.

Rent or buy, what are the odds? Analysing the price-to-rent ratio for housing types within the Northern Ireland housing market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniel Lo ◽  
Michael James McCord ◽  
John McCord ◽  
Peadar Thomas Davis ◽  
Martin Haran
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Main Property ◽  
Property Market ◽  
Important Indicator ◽  
Content Type ◽  
Rental Market ◽  
Price Series ◽  
The Relationship

Purpose The price-to-rent ratio is often regarded as an important indicator for measuring housing market imbalance and inefficiency. A central question is the extent to which house prices and rents form part of the same market and thus whether they respond similarly to parallel stimulus. If they are close proxies dynamically, then this provides valuable market intelligence, particularly where causal relationships are evident. Therefore, this paper aims to examine the relationship between market and rental pricing to uncover the price switching dynamics of residential real estate property types and whether the deviation between market rents and prices are integrated over both the long- and short-term. Design/methodology/approach This paper uses cointegration, Wald exogeneity tests and Granger causality models to determine the existence, if any, of cointegration and lead-lag relationships between prices and rents within the Belfast property market, as well as the price-to-rent ratios amongst its five main property sub-markets over the time period M4, 2014 to M12 2018. Findings The findings provide some novel insights in relation to the pricing dynamics within Belfast. Housing and rental prices are cointegrated suggesting that they tend to move in tandem in the long run. It is further evident that in the short-run, the price series Granger-causes that of rents inferring that sales price information unidirectionally diffuse to the rental market. Further, the findings on price-to-rent ratios reveal that the detached sector appears to Granger-cause those of other property types except apartments in both the short- and long-term, suggesting possible spill-over of pricing signals from the top-end to the lower strata of the market. Originality/value The importance of understanding the relationship between house prices and rental market performance has gathered momentum. Although the house price-rent ratio is widely used as an indicator of over and undervaluation in the housing market, surprisingly little is known about the theoretical relationship between the price-rent ratio across property types and their respective inter-relationships.

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.

A look into German housing markets: A bubble call?

2018 ◽  
Vol 21 (4) ◽  
pp. 289-301
Author(s):  
Jan R. Kim ◽  
Gieyoung Lim
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Present Value ◽  
Present Value Model ◽  
Price Bubbles ◽  
Steep Rise ◽  
Value Model ◽  
German Housing Market

The steep rise in German house prices in recent years raises the question of whether a speculative bubble has already emerged. Using a modified present-value model, we estimate the size of speculative house price bubbles in the German housing market. We do not find evidence for positive bubble accumulation in recent years, and interpret the current bullish run as reflecting the correction of house prices that have been undervalued for more than 10 years. With house prices close to their fair values as of 2018:Q1, our answer to the question is, ‘Not yet, but it is likely soon’.

scholarly journals Club convergence of house prices: Evidence from China’s ten key cities

2015 ◽  
Vol 29 (24) ◽  
pp. 1550181 ◽  
Author(s):  
Hao Meng ◽  
Wen-Jie Xie ◽  
Wei-Xing Zhou
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Economic Recession ◽  
Housing Markets ◽  
Extensive Study ◽  
Ripple Effect ◽  
Chinese Stock Market ◽  
Price Growth ◽  
Chinese Housing Market

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a marked fall during the global financial tsunami and China’s economy has also slowed down by about 2%–3% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than 10 years. However, the structure and dynamics of the Chinese housing market are less studied. Here, we perform an extensive study of the Chinese housing market by analyzing 10 representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5% of the house price growth, indicating very high systemic risk in the Chinese housing market. The 10 key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices among different cities.

scholarly journals A dynamic model for housing price spillovers with an evidence from the US and the UK markets

2018 ◽  
Vol 2 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Alper Ozun ◽  
Hasan Murat Ertugrul ◽  
Yener Coskun
Keyword(s):  
New York ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Real Estate Markets ◽  
Country Level ◽  
The Us

Purpose The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies. Design/methodology/approach The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method. Findings The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach. Research limitations/implications One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets. Practical implications The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City. Social implications The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice. Originality/value The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

Time-frequency linkages of international housing markets and macroeconomic drivers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hardik Marfatia
Keyword(s):  
Housing Market ◽  
Time Domain ◽  
House Prices ◽  
Housing Markets ◽  
Income Growth ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Time Frequency ◽  
Direction Of Causation ◽  
Over Time

Purpose The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships. Design/methodology/approach The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables. Findings Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis. Research limitations/implications The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important. Practical implications Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time. Social implications The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study. Originality/value The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.

Assessing Dutch housing cycle and near-term market prospects

2020 ◽  
Vol 13 (2) ◽  
pp. 257-270
Author(s):  
Arvydas Jadevicius ◽  
Peter van Gool
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Future Market ◽  
Price Inflation ◽  
Content Type ◽  
Dutch Housing ◽  
The Usa ◽  
Near Term ◽  
The Uk

Purpose This study is a practice undertaking examining three main concerns that currently dominate Dutch housing market debate: how long is the cycle, will the current house price inflation continue and is housing market in a bubble. With national house prices reaching record highs across all major cities, future market prospects became a topic of significant debate among policymakers, investors and the populace. Design/methodology/approach A triangulation of well-established academic methods is used to perform investigation. The models include Hodrick-Prescott (HP) filter, volatility autoregressive conditional heteroskedasticity (ARCH approximation) and right tail augmented Dickey–Fuller (Rtadf) test (bubble screening technique). Findings Interestingly, over the years from 1985 to 2019 research period, filtering extracts only one Dutch national housing cycle. This is a somewhat distinct characteristic compared to other advanced Western economies (inter alia the UK and the USA) where markets tend to experience 8- to 10-year gyrations. Volatility and Rtadf test suggest that current house prices in most Dutch cities are in excess of historical averages and statistical thresholds. House price levels in Almere, Amsterdam, The Hague, Groningen, Rotterdam and Utrecht are of particular concern. Originality/value Retail investors should therefore be cautious as they are entering the market at the time of elevated housing values. For institutional investors, those investing in long-term, housing in key Dutch metropolitan areas, even if values decline, is still an attractive investment conduit.

Regional economic divergence and house prices: a comparison of Germany and the UK

2019 ◽  
Vol 12 (4) ◽  
pp. 722-735
Author(s):  
Benedikt Blaseio ◽  
Colin Jones
Keyword(s):  
Human Capital ◽  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Annual Data ◽  
Content Type ◽  
Economic Divergence ◽  
Price Trends ◽  
The Uk

Purpose Increasing regional wealth disparities have been explained by the role of agglomeration economies and the concentration of skilled mobile human capital. This paper aims to draw out the role of the housing market by considering the differential experience of Germany and the UK. Design/methodology/approach The empirical analysis is based on the comparison of regional house price trends in Germany and UK-based annual data from 1991 to 2015. Findings Regional house price inequality is found to have increased in both countries with the spatial concentration of skilled human capital. However, the main conclusion is that there are differential paths to regional house price inequality explained by the parameters of each country’s housing market. Originality/value The research is the first to compare and explain differential regional house price trends across countries.

scholarly journals House prices and neighbourhood amenities: beyond the norm?

2018 ◽  
Vol 11 (2) ◽  
pp. 263-289 ◽  
Author(s):  
Michael James McCord ◽  
Peadar Thomas Davis ◽  
Paul Bidanset ◽  
William McCluskey ◽  
John McCord ◽  
...  
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Ordinary Least Squares ◽  
Content Type ◽  
Conditional Quantiles ◽  
Price Distribution ◽  
Regression Techniques ◽  
High Level ◽  
The Impact

Purpose Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets. Design/methodology/approach Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales transactions within the Belfast Housing market over 2014. Findings The findings emerging from this research demonstrate that housing and market characteristics are valued differently across the quantile values and that conditional quantiles are asymmetrical. Pertinently, the findings demonstrate that ordinary least squares (OLS) coefficient estimates have a tendency to over or under specify the marginal mean conditional pricing effects because of their inability to adequately capture and comprehend the complex spatial relationships which exist across the pricing distribution. Originality value Numerous studies have used OLS regression to measure the impact of key housing market externalities on house prices, providing a single estimate. This paper uses a QR approach to examine the impact of local amenities on house prices across the house price distribution.

House price overvaluation in Hong Kong

2017 ◽  
Vol 10 (2) ◽  
pp. 282-304 ◽  
Author(s):  
Philip Arestis ◽  
Ana Rosa Gonzalez-Martinez ◽  
Lu-kui Jia
Keyword(s):  
Hong Kong ◽  
Housing Market ◽  
Design Methodology ◽  
House Prices ◽  
House Price ◽  
Theoretical Framework ◽  
Second Step ◽  
Content Type

Purpose The purpose of this paper is twofold. First, the authors investigate the main drivers of house prices in the Hong Kong housing market. Second, further research is undertaken to confirm the existence of house price overvaluation, which has driven the market into a bubble episode. Design/methodology/approach First, the authors propose a theoretical framework to identify the fundamentals of the market. In the second step, they decompose house prices into fundamentals, frictions and bubble episodes for a better understanding of the evolution of house prices during the period 1996(Q3)-2013(Q3). Findings The results of this paper suggest an eventual possible correction of up to 46 per cent of house prices with respect to their 2013(Q3) level. Originality/value The originality of this paper is to use the procedure developed by Glindro and Delloro (2010) to analyse the Hong Kong housing market. The contribution of this paper also modifies the original Glindro and Delloro’s (2010) approach by including the Christiano and Fitzgerald (2003) filter to decompose house prices.

Sign in / Sign up

Export Citation Format

Share Document