scholarly journals HOUSING PRICE DIFFUSION PATTERN OF AUSTRALIA'S STATE CAPITAL CITIES

2007 ◽  
Vol 11 (4) ◽  
pp. 227-242 ◽  
Author(s):  
Zhen Qiang Luo ◽  
Chunlu Liu ◽  
David Picken
Keyword(s):  
House Prices ◽  
House Price ◽  
Housing Price ◽  
Ripple Effect ◽  
Correction Model ◽  
Capital Cities ◽  
Diffusion Pattern ◽  
State Capital ◽  
Regional Housing Market ◽  
Spatial Locations

The ripple effect of house prices within metropolitan areas has recently been recognised by researchers. However, it is very difficult to formulate and measure this effect using conventional house price theories particularly in consideration of the spatial locations of cities. Based on econometrics principles of the cointegration test and the error correction model, this research develops an innovative approach to quantitatively examine the diffusion patterns of house prices in mega‐cities of a country. Taking Australia's eight capital cities as an example, the proposed approach is validated in terms of an empirical study. The results show that a 1–1–2–4 diffusion pattern exists within these cities. Sydney is on the top tier with Melbourne in the second; Perth and Adelaide are in the third level and the other four cities lie on the bottom. This research may be applied to predict the regional housing market behavior in a country. Būsto kainų pasiskirstymo struktūra Australijos valstijų sostinėse Santrauka Neseniai mokslininkai nustatė, kad didmiesčiuose būsto kainos veikia vienos kitas (angl. ripple effect). Tačiau ši poveiki itin sunku suformuluoti ir išmatuoti pasitelkus įprastas būsto kainų teorijas, ypač įvertinant teritorini miestu išsidėstymą. Remiantis ekonometrijos principais, tokiais kaip kointegracijos analize ir klaidu taisymo modelis, šiame tyrime sukurtas novatoriškas būdas, kaip kiekybiškai tyrinėti būsto kainų pasiskirstymo struktūras šalies didmiesčiuose. Kaip pavyzdį pasirinkus aštuonias Australijos valstijų sostines, siūlomas būdas patvirtinamas empiriniu tyrimu. Rezultatai rodo, kad šiu miestu pasiskirstymo struktūra yra 1–1–2–4. Sidnėjus užima aukščiausia pakopa, o Melburnas yra antrasis. Pertui ir Adelaidei tenka trečioji pakopa, o kiti keturi miestai yra žemiausiai. Šiuo tyrimu galima remtis prognozuojant regionines būsto rinkos elgsena kitose šalyse.

Regional housing price dependency in the UK: A dynamic network approach

Urban Studies ◽  
2020 ◽  
pp. 004209802094348
Author(s):  
Dayong Zhang ◽  
Qiang Ji ◽  
Wan-Li Zhao ◽  
Nicholas J Horsewood
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Price ◽  
Dynamic Network ◽  
Network Approach ◽  
Market Interactions ◽  
Partial Correlations ◽  
Regional Housing Market ◽  
The Uk

The cross-regional dependency in the UK housing market is analysed using regional house price indices. In this article, a network approach based on partial correlations is proposed, along with rolling-window analysis to consider potential time-varying dependency. The results show that house prices in the outer South East region have the strongest influence on regional housing market interactions in the UK. This influence is stronger when the markets are highly interconnected, whereas the house prices in London have the strongest influence when the UK regional housing markets are relatively less connected.

scholarly journals IDENTIFYING HOUSE PRICE DIFFUSION PATTERNS AMONG AUSTRALIAN STATE CAPITAL CITIES

2008 ◽  
Vol 12 (4) ◽  
pp. 237-250 ◽  
Author(s):  
Chunlu Liu ◽  
Zhen Qiang Luo ◽  
Le Ma ◽  
David Picken
Keyword(s):  
House Price ◽  
Impulse Response Function ◽  
Spatial Dimension ◽  
Ripple Effect ◽  
Price Changes ◽  
Capital Cities ◽  
Vector Autoregression Model ◽  
Australian State ◽  
State Capital

Prior research supports the proposition that house price diffusion shows a ripple effect along the spatial dimension. That is, house price changes in one region would reflect in subsequent house price changes in other regions, showing certain linkages among regions. Using the vector autoregression model and the impulse response function, this study investigates house price diffusion among Australia's state capital cities, examining the response of one market to the innovation of other markets and determining the lagged terms for the maximum absolute value of the other markets’ responses. The results show that the most important sub‐national markets in Australia do not point to Sydney, rather towards Canberra and Hobart, while the Darwin market plays a role of buffer. The safest markets are Sydney and Melbourne. This study helps to predict house price movement trends in eight capital cities. Santrauka Ankstesnių tyrimų duomenimis, nekilnojamojo turto kainų kitimas sukelia bangų efektą atsižvelgiant į erdvinį matmenį. Tai yra nekilnojamojo turto kainų kitimus viename regione rodytų paskesnis nekilnojamojo turto kainų kitimas kituose regionuose. Taip ryškėja tam tikri glaudūs ryšiai tarp regionų. Taikant vektorinį autoregresinį modelį ir impulso perdavimo funkciją, šioje studijoje tiriama nekilnojamojo turto kainų kitimas tarp pagrindinių Australijos miestų, nagrinėjant vienos rinkos reakciją į kitų rinkų naujoves bei nustatant uždelstus terminus kitų rinkų reakcijų maksimaliai absoliutinei vertei. Rezultatai rodo, kad svarbiausios Australijos vidaus rinkos nėra orientuotos į Sidnėjų, bet labiau į Kanberą ir Hobartą. Darvino rinka atlieka buferio vaidmenį. Saugiausios rinkos yra Sidnėjus ir Melburnas. Ši studija padeda numatyti nekilnojamojo turto kainų judėjimo tendencijas aštuoniuose pagrindiniuose Australijos miestuose.

International Real Estate Review

2009 ◽  
Vol 12 (3) ◽  
pp. 193-220
Author(s):  
Karol Jan Borowiecki ◽  
Keyword(s):  
Real Estate ◽  
Positive Impact ◽  
House Prices ◽  
House Price ◽  
Housing Price ◽  
Quality Improvements ◽  
Vector Autoregressive ◽  
Price Growth ◽  
Price Determinants ◽  
A Minor

This paper studies the Swiss housing price determinants. The Swiss housing economy is reproduced by employing a macro- series from the last seventeen years and constructing a vector-autoregressive model. Conditional on a comparatively broad set of fundamental determinants considered, i.e. wealth, banking, demographic and real estate specific variables, the following findings are made: 1) real house price growth and construction activity dynamics are most sensitive to changes in population and construction prices, whereas real GDP, in contrary to common empirical findings in other countries, turns out to have only a minor impact in the short-term, 2) exogenous house price shocks have no long-term impacts on housing supply and vice versa, and 3) despite the recent substantial price increases, worries of overvaluation are unfounded. Furthermore, based on a self-constructed quality index, evidence is provided for a positive impact of quality improvements in supplied dwellings on house prices.

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 House Prices and Macroprudential Policies

2020 ◽  
Vol 20 (291) ◽  
Author(s):  
Bhupal Singh
Keyword(s):  
Strong Influence ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Price ◽  
Macroprudential Policies ◽  
Regulatory Changes ◽  
Downward Pressure ◽  
Level Data ◽  
Housing Assets

This paper examines the efficacy of macroprudential policies in addressing housing prices in a developing country while underscoring the importance of fundamental factors. The estimated models using city-level data for India suggest a strong influence of fundamental factors in driving housing prices. There is compelling evidence of the effectiveness of macroprudential tools viz., Loan-to-value (LTV) ratio, risk weights, and provisioning requirements, in influencing housing price movements. A granular analysis suggests an even stronger impact on housing prices of a change in the regulatory LTV ratio for large-sized vis-à-vis small-sized mortgages, which buttresses their potency in fighting house price speculations. A tightening of the risk weights on the housing assets of banks causes significant downward pressure on house prices. Similarly, regulatory changes in standard asset provisioning on housing loans also influence house prices.

scholarly journals The House Price Dynamics and the Macro-Economy: an Empirical Perspective of UAE

2019 ◽  
Vol 2 (1) ◽  
pp. 28-33
Author(s):  
Yahya Hamad Al Zaabi ◽  
Genanew Bekele
Keyword(s):  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Price ◽  
Future Research ◽  
Hedonic Price ◽  
Policy Makers ◽  
Empirical Perspective ◽  
House Condition ◽  
Internal And External Factors

Objective: The paper aims to examine house price drivers in Dubai, addressing the effect of internal and external factors afecting house prices   Design/methedology/approach: Using the Hedonic price model, the study examined the implications of house size (space), the availability of bathrooms, bedrooms, waterfronts, and pool and cell phone towers within residential area as auxiliary determinant factors to housing price within developed cities by using the Hedonic Modelling. Also, study highlight the effect of the green strategies that been followed by developer on the housing prices.   Findings: The study is expected to reveal results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution.   Research limitations/implications: The data used in this study could be limited, and depends on information to be provided by the Dubai Land Department. There is a room for future research to include more data (such as on other house attributes such as house condition, plot numbers and configuration).

scholarly journals International Real Estate Review

2015 ◽  
Vol 18 (4) ◽  
pp. 503-521
Author(s):  
Le Ma ◽  
◽  
Chunlu Liu ◽  
Keyword(s):  
Spatial Heterogeneity ◽  
House Prices ◽  
House Price ◽  
Urban Housing ◽  
Price Convergence ◽  
Capital Cities ◽  
Regression Methods ◽  
Long Run ◽  
Spatio Temporal ◽  
Long Run Equilibrium

In order to explore the long-run equilibrium in the house prices of different cities, studies on house price convergence have been conducted by a number of researchers. However, the majority of previous studies have neglected the effects of spatial heterogeneity and autocorrelation on house prices. This research improves on the investigation of house price convergence by developing a spatio-temporal autoregressive model based on a framework of panel regression methods. Both spatial heterogeneity and autocorrelation of house prices in different cities are taken into account. Geographical distance and the scale of development of the urban housing market are used to construct temporal varying spatial measurements. The spatio-temporal model is then applied to investigate the long-run equilibrium in the house prices of Australian capital cities. The results confirm that house prices in Sydney approach a steady state in the long run, whereas house prices in Brisbane, Canberra, Melbourne and Perth are able to do with lower confidence. However, little evidence supports the existence of long-run equilibrium in the house prices of Adelaide, Darwin and Hobart.

scholarly journals SPATIO-TEMPORAL ANALYSIS OF HOUSE PRICE CONVERGENCE BASED ON A DEMOGRAPHICAL DISTANCE

2013 ◽  
Vol 17 (3) ◽  
pp. 263-277 ◽  
Author(s):  
Le Ma ◽  
Chunlu Liu
Keyword(s):  
Spatial Heterogeneity ◽  
Price Level ◽  
House Prices ◽  
House Price ◽  
Temporal Analysis ◽  
Capital Cities ◽  
Spatial Factors ◽  
Spatio Temporal ◽  
Geographical Locations ◽  
Spatial And Temporal Issues

Convergences of house prices have been studied for over three decades, but yet have been confirmed because of spatial heterogeneity and autocorrelations in house prices. A spatio-temporal approach was recently proposed to address the spatial and temporal issues related to house prices. However, most previous studies placed the focus on the spatial heterogeneity and autocorrelations from geographical locations, which neglected other spatial factors. In order to overcome this shortfall, this research argued a demographical distance, constructed by demographical structure and housing market scales, to investigate the house price convergences in Australian capital cities. The results confirmed the house price levels in Canberra, Brisbane and Perth converged to the house price level in Sydney.

scholarly journals Amsterdam house price ripple effects in The Netherlands

2017 ◽  
Vol 10 (3) ◽  
pp. 331-345 ◽  
Author(s):  
Alfred Larm Teye ◽  
Michel Knoppel ◽  
Jan de Haan ◽  
Marja G. Elsinga
Keyword(s):  
The Netherlands ◽  
Granger Causality ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Wealth Effects ◽  
Ripple Effect ◽  
Content Type ◽  
Long Run ◽  
Lag Effect

Purpose This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam. Design/methodology/approach The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks. Findings The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. Research limitations/implications The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation. Originality/value This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

scholarly journals House Price Forecasting from Investment Perspectives

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1009
Author(s):  
Song Shi ◽  
Vince Mangioni ◽  
Xin Janet Ge ◽  
Shanaka Herath ◽  
Fethi Rabhi ◽  
...  
Keyword(s):  
Interest Rates ◽  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Bank Lending ◽  
Market Dynamics ◽  
Correction Model ◽  
Investment Theory ◽  
Investment Demand ◽  
Long Run

Housing market dynamics have primarily shifted from consumption- to investment-driven in many countries, including Australia. Building on investment theory, we investigated market dynamics by placing investment demand at the center using the error correction model (ECM). We found that house prices, rents, and interest rates are cointegrated in the long run under the present value investment framework. Other economic factors such as population growth, unemployment, migration, construction activities, and bank lending were also important determinants of the housing market dynamics. Our forecasting results show that the Sydney housing market will continue to grow with no significant price decline in the foreseeable future.

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