scholarly journals Are there irrational bubbles under the high residential housing prices in China’s major cities?

2020 ◽  
Vol 67 (1) ◽  
pp. 1-26
Author(s):  
Philip Arestis ◽  
Sixia Zhang
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
Theoretical Framework ◽  
Real Estate Market ◽  
Property Market ◽  
Residential Housing ◽  
Real Estate Sector ◽  
Long Run ◽  
Short Run

House prices in the main cities of China have been rising to historically high levels. Unsustainable growth of housing prices might cause financial crises and damage the whole economy. This research aims to detect whether bubbles dominate China?s real estate market. It begins by systematically analysing the features of China?s real estate sector, followed by proposing a theoretical framework to identify the fundamentals of house prices and decompose house prices into cyclical and bubble components. It then applies the vector error correction model and other econometric techniques to testify the theoretical framework with data of seven Chinese cities from 2008M01 to 2017M12. The main findings of this research include the following four parts. Firstly, the residential housing market of Shanghai was exposed to the irrational bubble issue, but the rest six cities examined were at safe positions. Secondly, both long-run and short-run relationships between economic fundamentals and house prices have been verified. Thirdly, economic regulations also have significant effects on house prices. Finally, this research suggests that the root cause of the high housing prices in major cities in China is due to the excessive capital injection into the residential property market.

scholarly journals International Real Estate Review

2007 ◽  
Vol 10 (2) ◽  
pp. 113-130
Author(s):  
Benoit Julien ◽  
◽  
Paul Lanoie ◽  
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
House Prices ◽  
Real Estate Market ◽  
Data Set ◽  
Long Run ◽  
Before And After ◽  
Short Run ◽  
Noise Barrier ◽  
The Impact

This paper provides the first study on the impact of noise barriers on the price of adjacent houses based on a repeat sale analysis (RSA). RSA allows us to empirically examine the differential between the prices of houses sold before and after an event that may have affected their value, and after other relevant variables such as the evolution of the real estate market and major renovations performed on the house are controlled. This paper focuses on the neighborhood of Laval, a suburb of Montreal, where a large noise barrier was built in 1990 along a highway. The data set contains transaction information on 134 houses that were sold at least twice from 1980–2000. The empirical result will show that the noise barrier induced a decrease of 6% in the house prices in our sample in the short run, while it had a stronger negative impact of 11% in the long run.

scholarly journals COVID-19 Infects Real Estate Markets: Short and Mid-Run Effects on Housing Prices in Campania Region (Italy)

2020 ◽  
Vol 9 (7) ◽  
pp. 114 ◽  
Author(s):  
Vincenzo Del Giudice ◽  
Pierfrancesco De Paola ◽  
Francesco Paolo Del Giudice
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Campania Region ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Short Run ◽  
Predator Model ◽  
Real Estate Prices

The COVID-19 (also called “SARS-CoV-2”) pandemic is causing a dramatic reduction in consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be added an increase in unemployment, which will further depress consumption. The real estate market, as for other productive and commercial sectors, in the short and mid-run, will not tend to move independently from the context of the aforementioned economic variables. The effect of pandemics or health emergencies on housing markets is an unexplored topic in international literature. For this reason, firstly, the few specific studies found are reported and, by analogy, studies on the effects of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently, beginning from the real estate dynamics and economic indicators of the Campania region before the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment, personal and household income, real estate judicial execution, real estate dynamics). Finally, a real estate pricing model is developed, evaluating the short and mid-run COVID-19 effects on housing prices. To predict possible changes in the mid-run of real estate judicial execution and real estate dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in the mid-run (late 2020–early 2021).

scholarly journals Untangling the nonlinear “knots” of UK’s housing prices

2019 ◽  
Vol 46 (5) ◽  
pp. 1083-1103
Author(s):  
Constantinos Alexiou ◽  
Sofoklis Vogiazas
Keyword(s):  
Housing Market ◽  
House Prices ◽  
Housing Prices ◽  
Positive Association ◽  
Research Area ◽  
Content Type ◽  
Nonlinear Ardl ◽  
Long Run ◽  
Short Run ◽  
The Uk

Purpose 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. Design/methodology/approach The authors utilize the recently developed nonlinear ARDL approach of Shin et al. (2014) over the period 1969–2016. Findings 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. Originality/value 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.

scholarly journals Homeownership Rate and Other House Price Determinants Impacts on New House Prices in Colombia

2020 ◽  
pp. 35-50
Author(s):  
Hector Botello-Peñaloza
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Real Estate Market ◽  
The Real Estate ◽  
Homeownership Rate ◽  
Colombian Government ◽  
Price Determinants ◽  
Price Instability

Homeownership remains a preferred form of tenancy in different parts of the world. The attractions of security, stability, investment potential and a sense of pride outweigh the fear of price instability. For this reason, the Colombian government has encouraged in recent years, various demand policies that have sought to promote the increase in the number of homeowners. However, these ideas could have a severe impact on prices in the real estate market. Therefore, this study seeks to examine the effect of homeownership rate on new house prices in an emerging country with low real estate ownership, credit restrictions and average per capita income. The study uses panel data model to examine the influence of housing tenancy and other variables on the variation of housing prices in Colombia. Data were obtained from various sources including the Central Bank of Colombia, Financial Superintendence of Colombia, and National Administrative Department of Statistics of Colombia. The results show that homeownership rates have a positive effect on the price of new homes, which supports the hypothesis of the research. The population growth of the cities is the factor that is most relevant when explaining the price variations.

Housing Prices and Money Demand: Empirical Evidence in Selected MENA Countries

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Nadia Mbazia ◽  
Mouldi Djelassi
Keyword(s):  
Money Demand ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Structural Features ◽  
Demand Model ◽  
Mena Countries ◽  
Long Run ◽  
Short Run ◽  
Dynamic Relationships

Abstract This paper examines the links between housing and money empirically in a money demand framework for a panel of five Middle East and North Africa (MENA) countries using quarterly data from 2007Q3 to 2014Q4 with the inclusion of house prices as a variable representing the developments in housing markets. We applied the Pool Mean Group Estimation technique to estimate the long-run and short-run dynamic relationships in money demand model. Empirical results provide the evidence that higher house prices lead to a rise in M2 demand in long-run and short-run estimations. This finding may explain the importance influence of the house price developments on monetary policy in MENA countries. The results confirm that the cross-country heterogeneity of money holdings is also connected with structural features of the housing market.

Spatial Deployment of the Chinese Property Market (2010–2016)

2019 ◽  
Vol 55 (03) ◽  
pp. 1950006
Author(s):  
ELFIE SWERTS
Keyword(s):  
Real Estate ◽  
Direct Investment ◽  
Multinational Firms ◽  
Spatial Location ◽  
Real Estate Market ◽  
Property Market ◽  
Real Estate Investment ◽  
The Real Estate ◽  
Real Estate Sector ◽  
New Perspective

Real estate activities and companies in China have grown considerably since the major reforms of the late 1970s. This paper examines the spatial deployment of firms linked to the Chinese real estate market in Chinese cities in 2010, 2013 and 2016. It provides a first mapping of multinational firms specialized in the real estate sector. It describes the patterns of ownership networks built by financial links both between foreign multinational firms and Chinese firms and among multinational firms themselves. It therefore provides a new understanding about the penetration of both foreign direct investment (FDI) and Hong Kong’s role in the Chinese real estate market. This paper provides a comparison of the spatial location logics of these firms according to their Chinese or foreign origin and offers a new perspective on the geography of real estate investment by analyzing financial links between the Chinese and foreign cities involved.

scholarly journals The Real Estate Market and Financial Stability

2020 ◽  
Vol 5 (6) ◽  
pp. 1270-1283
Author(s):  
My-Linh Thi Nguyen ◽  
Toan Ngoc Bui
Keyword(s):  
Real Estate ◽  
Financial Stability ◽  
Real Estate Market ◽  
Data Series ◽  
Stock Market Volatility ◽  
The Real ◽  
The Real Estate ◽  
Long Run ◽  
The Real Estate Market ◽  
Short Run

This paper investigates the relationship between the real estate market (REM) and financial stability in Vietnam. Financial stability is measured using stock market volatility. The research is performed in Vietnam, a developing country whose stock and real estate markets are considered to be nascent, so the data series is very short. To solve this problem, the autoregressive distributed lag (ARDL) approach, which generates more valid results than its counterparts, is adopted. Furthermore, the ARDL approach is appropriate for a model with non-stationary data series and especially allows the analysis of the impact between data series in the short run and the long run. The results reveal the positive relationship between the real estate market and stock market volatility. However, this correlation only exists in the short run, which is a difference between Vietnam and developed countries. The paper also obtains an unprecedented finding confirming that the global financial crisis exerted a negative impact on the REM in Vietnam in the short run and the long run.

scholarly journals Effects Of Exchange Rate Volatility On Real Estate Prices In Developing Economies, A Case Of Ghana

2019 ◽  
Vol 6 (11) ◽  
pp. 268-287
Author(s):  
John Kwame Adu Jack ◽  
Frimpong Okyere ◽  
Emmanuel K. S. Amoah
Keyword(s):  
Exchange Rate ◽  
Real Estate ◽  
Developing Economies ◽  
Negative Impact ◽  
House Prices ◽  
Housing Prices ◽  
Exchange Rate Volatility ◽  
Bounds Testing ◽  
Long Run ◽  
Real Estate Prices

This study aims to find out whether exchange rate volatility affects real estate domestic house prices in Ghana. To this end, a 32 years secondary data from World Development Indicators (WDI) and data from Real Estate Developers in Ghana are employed for the study. The study employs Autoregressive distributed lags (ARDL) bounds testing of cointegration t o test the null hypothesis that exchange rate volatility has n o impact on real estate housing prices. The study finds that real estate price is cointegrated with remittances, exchange rate and inflation. The long run equilibrium is stable and significant. Exchange rates d o not cause changes in real estate prices in both short and long run. Similarly past prices of real estate d o not have impact on current house prices.  Rather, remittances positively cause real estate prices. Inflation on its part has a negative impact on real estate prices. It is therefore concluded that, volatility in the exchange rate between the cedi and other trading currencies does not predict changes in real estate prices.

Artificial intelligence algorithms to predict Italian real estate market prices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luca Rampini ◽  
Fulvio Re Cecconi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Content Type ◽  
Two Cities ◽  
Artificial Neural

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

scholarly journals Real estate boom in Chile and fundamentals on house prices

2020 ◽  
Vol 6 (1) ◽  
pp. 1-26
Author(s):  
C. Aguilera Alvial
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Structural Breaks ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Integration Test ◽  
The Real Estate Market

This article studies the fundamentals of housing prices based on the Real Index of Housing Prices (IRPV), given that in recent times in Chile there has been a sustained increase in price levels and seeks to find evidence on the existence of a possible speculative bubble in the real estate market. Following the methodology of various Chilean and international authors, the Engle & Granger Co-integration methodology was applied. Furthermore, the results of the previous methodology were compared using the Johansen Co-integration test. Then a method to find structural breaks is applied. As a result, evidence is found to not reject the existence of a bubble in the real estate market. It is found that only interest rates co-integrate in the long term with the evolution of house prices, while the other fundamentals present a spurious relationship.

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