Search results

The purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.

This study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.

The empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.

This study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.

The findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

Housing prices in the UK offer an inspiring, yet a complex and under-explored research area. The purpose of this paper is to investigate the critical factors that affect UK’s housing prices.

The authors utilize the recently developed nonlinear ARDL approach of Shin et al. (2014) over the period 1969–2016.

The authors find that both the long-run and short-run impact of the price-to-rent (PTR) ratio and credit-to-GDP ratio on house prices (HP) is asymmetric whilst ambiguous results are established for mortgage rates, industrial production and equities. Apart from the novel framework of analysis, this study also establishes a positive association between HP and the PTR ratio which suggests a speculative behaviour and could imply the formation of a housing bubble.

It is the first study for the UK housing market that explores the underlying fundamental relationships by looking at nonlinearities hence, allowing HP to be tied by asymmetric relationships in the long as well as in the short run. Modelling the inherent nonlinearities enhances significantly the understanding of UK housing market which can prove useful for policymaking and forecasting purposes.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

The paper sets out a conceptualisation of the housing cycle centring on households' desire to upgrade their housing consumption.

The paper begins by studying house price trends and cycles in OECD countries since 2000 to identify housing cycle patterns. It then assesses existing theories partly in relation to these patterns. It then proposes a new conceptualisation of the housing cycle.

The paper finds the central role of supply lags in housing cycles is not warranted. Instead, a demand cycle generated by upgrading desires better explains an initial boom followed by a slow recovery.

The paper challenges existing orthodoxy on housing cycle dynamics and proposes an alternative perspective.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

This study examines the long term effects of macroeconomic fundamentals on apartment price dynamics in major metropolitan areas in Sweden and Germany.

The main approach is panel cointegration analysis that allows to overcome certain data restrictions such as spatial heterogeneity, cross-sectional dependence, and non-stationary, but cointegrated data. The Swedish dataset includes three cities over a period of 23 years, while the German dataset includes seven cities for 29 years. Analysis of apartment price dynamics include population, disposable income, mortgage interest rate, and apartment stock as underlying macroeconomic variables in the model.

The empirical results indicate that apartment prices react more strongly on changes in fundamental factors in major Swedish cities than in German ones despite quite similar development of these macroeconomic variables in the long run in both countries. On one hand, overreactions in apartment price dynamics might be considered as the evidence of the price bubble building in Sweden. On the other hand, these two countries differ in institutional arrangements of the housing markets, and these differences might contribute to the size of apartment price elasticities from changes in fundamentals. These arrangements include various banking sector policies, such as mortgage financing and valuation approaches, as well as different government regulations of the housing market as, for example, rent control.

In distinction to the previous studies carried out on Swedish and German data for single-family houses, this study focuses on the apartment segment of the market and examines apartment price elasticities from a long term perspective. In addition, the results from this study highlight the differences between the two countries at the city level in an integrated long run equilibrium framework.

This paper aims to empirically examine the relationship between macroeconomic factors and housing prices in Malaysia from 1991 to 2016.

This study was based on annual macroeconomic data from 1991 to 2016. For the model that was constructed, house prices were treated as the dependent variable and other variables, namely, the interest rate, the gross domestic product (GDP) and the consumer price index (CPI) as explanatory or regression variables. All these data come from the annual publication of the World Bank’s statistical report. In the meantime, data on house prices have been obtained through the publication of the National Property Information Centre in the form of an index. In the study, the bounds testing approach for cointegration was used because the Engel Granger cointegration tests can be used only if all the series are stationary at first difference. And it is against this background the author could not use it because our variables are a combination of series integrated of orders I (0) and I (1).

The results of this study revealed a positive equilibrium relationship in the long run between interest rate, CPI and housing prices and negative for the GDP. In addition, in the short run, housing prices were shown to have a positive relationship with GDP and CPI, but no relationship with the interest rate, based on the assumption that “all other things being equal”.

The data on economic factors (GDP, RI and CPI) used in this study are secondary and not from the Malaysian Department of Statistics and Economic Studies. Furthermore, the result of this research only reflects the Malaysian reality, and therefore, cannot be generalised to the entire housing market worldwide.

The result of this study can be used for housing valuation by Malaysian property market players, investors and especially policymakers.

This study’s findings can help Malaysian policymakers to limit the high price fluctuation in the housing industry and help Malaysian citizens to buy their own homes at a reasonable price.

The contributions of this study are structured around two points. The first is the contribution to the body of knowledge, where the results of the study will contribute to the growing number of literature in the housing sector. Moreover, the second contribution of this document is the strength of its recommendation to policymakers. This is because it analyses only the main macroeconomic determinants (IR, GDP and CPI) that are essential to better influence the housing sector as quickly as possible, as opposed to those that use many variables that could lead to possible specification errors.

This paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

The housing market in South Africa has the potential to drive economic growth and attract foreign investment, but it can be affected by various risk factors. This paper aims to conduct an empirical analysis of the effect of country risk components on the housing market in South Africa.

Linear and nonlinear autoregressive distributed lag (ARDL) models were used to evaluate the effects of the economic, financial and political risk factors of country risk on the prices of different segments of houses based on 276 monthly time-series data from January1995 to December 2015.

First, the results established that the three housing indices were more sensitive to political risk in the long run. Second, short run results showed that the three housing indices were largely influenced by their own preceding adjustments in the short run albeit minimal influences from political risk. Third, large housing segments indicated a higher magnitude of the country risk effect in South Africa.

This paper concluded that the response of housing prices to changes in the country risk components differed across the three segments of the housing market in South Africa. Consequently, this study presented the first comparison of the reactions of different housing segments to different components country risk.