ABSTRACT
The UK's city centre apartment markets have been affected by the coronavirus pandemic and a building safety crisis in ways not experienced by its suburban and rural housing markets. Sellers and estate agents have encountered falling demand and prices, elevated safety concerns, reluctant lenders and changes in buyers' preferences. Against this backdrop, we investigated the narratives and images used to sell what have sometimes appeared to be 'less sellable' homes. Analysing the textual and visual content of 100 adverts for city centre flats, we explored the possible effects of the pandemic on property advertising, the positioning within adverts of building safety and, noting growing interest in sustainability, the presence of sustainability messages. Findings suggest that the core narratives used to sell city centre flats remain largely unchanged from those deployed to first market the concept of 'city living' to UK buyers in the late 1990s. Messages about building safety and sustainability appear uncommon. The implications of the findings are considered.
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Big Data has transformed the workings of real estate firms by improving the efficiency, cutting costs and by enhancing decision making. It helps them to become more agile for improved customer satisfaction and experiences. In the past, real estate businesses had to follow traditional methods by following past trends and professional expertise to make major decisions. Big Data has become much easier to access accurate real data, make conclusions and to even predict future prices of properties. This research uses machine learning algorithms for the appraisal of property prices in New York City. The methods are applied to the data sample of about 80,000 properties, which have sufficient information about each property and its demographic aspects. By further analysis and modelling, it is observed that model with Feature Engineering has performed much better that the model in which Random Forest was implemented. The conclusions drawn from the empirical study would be beneficial for real estate agents and people who are looking forward to invest in New York properties and understand the variation of property prices of New York in the post covid era in comparison to the pre covid era. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Purpose: This paper aims to examine the housing market responses to two outbreaks of respiratory diseases in Hong Kong during the Information Era – the 2003 SARS and COVID-19 outbreaks. Design/methodology/approach: The authors first investigate the aggregate housing price changes during SARS and COVID-19. Next, the authors conduct a battery of univariate analyses pertaining to the relationship between district-level housing price movements and geographic and demographic patterns during the pandemic periods. Finally, to shed light on the housing price dynamics at the micro level, the authors conduct an estate-level analysis with the data of 234 residential estates from 2003 to 2020, focusing on the impacts of SARS and COVID-19 on the idiosyncratic volatility of residential estates. Findings: Overall, SARS and COVID-19 outbreaks are negatively associated with housing prices. However, unlike SARS, the impact of COVID-19 on housing prices was moderate and transient. The geographic imbalances of the epidemic-induced underperformance are observed at the district and estate levels. Finally, the estate-level analysis presented in this paper indicates that the average idiosyncratic volatility of residential estates is 1.5% higher during the SARS period but 3.7% lower during the COVID-19 period. Lower volatility during COVID-19 is likely explained by household learning from the SARS period. Practical implications: Regulators and investors could resort to efficient information disclosure to attenuate idiosyncratic volatility's adverse impact on housing market returns. Originality/value: To the best of the authors' knowledge, the authors are among the first to examine housing market responses to the 2003 SARS and COVID-19 outbreaks using the Hong Kong housing market as a laboratory. © 2023, Emerald Publishing Limited.
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In the past, there have been several major and minor economic crises in global society. The financial crisis in 2008 was one of the biggest economic crises since the Great Depression in 1928. The crisis was a direct result of the decline in liquidity in global financial markets that arose in the United States as a result of the collapse of the US housing market. The Covid-19 pandemic crisis stunned all aspects of society and saw dramatic effects on society's socio-economic spectrum. The paper analyzes the effects of selected crises on the profitability of sales. The research analyzed data from companies that belong to the TOP 100 construction companies operating in Slovakia and their activities began before 2008. The data used in the survey were obtained from the annual reports of selected companies and publicly available economic portals. The aim of the paper is to compare the profitability of sales and results of selected construction companies in three periods, namely during the financial crisis in 2008, in 2014, which can be specified as a transitional period, or the market stabilized after the financial crisis and the crisis caused by the Covid-19 pandemic. The survey will result in conclusions and future recommendations that will help eliminate the adverse effects of future crises on the activities of construction companies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Previous studies extensively examined the role of accessibility to metro in shaping house prices but largely overlooked the contribution of accessibility by metro. In addition, limited studies examined the moderating effect of COVID-19 on the price effects of to-metro and by-metro accessibility. Based on multilevel hedonic price and quantile regression models, this study scrutinizes the association between to-metro accessibility, by-metro accessibility, and house prices in Chengdu, China, and examines the moderating role of COVID-19 in this association. We show that by-metro accessibility significantly influences house prices. COVID-19 significantly influences the value of to-metro accessibility but marginally affects that of by-metro accessibility. The value of to-metro accessibility is disproportionately affected by the pandemic. Specifically, small or low-priced houses are less affected than big or high-priced houses. In other words, the flattening of the to-metro price gradient is more discernible for big or high-priced houses. The changing preference of residents has also been verified by the decreases in house transaction volume in metro-adjacent areas. © 2022 Elsevier Ltd
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The COVID-19 outbreak caused a slowdown in the Indonesian economy, as it did in many other impacted nations. Consequently, the housing market in Indonesia, along with other industries, deteriorated. Other post-pandemic issues displace the property industry's priorities in Indonesia. Determining a fair property price is a problem occurring because of the economic slowdown. Property sellers expected their property selling prices to be the same before the pandemic or even increase, but property agents hoped the properties would be selling fast, creating a sense of distrust between the seller and the property agents. This work aims to develop a machine learning-based prediction model for real estate agents to use in determining property prices, with the expectation that the resulting predictions will be more accurate and supported by the data, increasing seller and buyer confidence. Following the suggestion from previous studies, several supervised algorithms such as Linear Regression, Decision Tree, and Random Forest were used to develop the model. Training data were collected from five property agents in Surabaya and as well as web scraping from the online home sales portals. Findings from the study show that Random Forest performs best in predicting with the highest coefficient of determination and lowest error. Using evaluation measures such as Mean Absolute Percent Error (MAPE), the error was calculated to be 23%, which is acceptable for prediction. © 2022 Science Press. All rights reserved.
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Purpose>This paper aims to document the economic importance of the housing sector, as measured by its contribution to gross domestic product (GDP), which is not fully recognized. In response to the joint economic and health crises caused by the COVID-19 pandemic, there is an opportunity for emerging market countries to develop and implement inclusive housing strategies that stimulate the economy and improve community health outcomes. However, so far housing does not feature prominently in the recovery plans of many emerging market countries.Design/methodology/approach>This paper uses national account data and informal housing estimates for 11 emerging market economies to estimate the contribution of housing investments and housing services to the GDP of these countries.Findings>This paper finds that the combined contribution of housing investments and housing services represents between 6.9% and 18.5% of GDP, averaging 13.1% in the countries with information about both. This puts the housing sector roughly on par with other key sectors such as manufacturing. In addition, if the informal housing sector is undercounted in the official national account figures used in this analysis by 50% or 100%, for example, then the true averages of housing investments and housing services’ contribution to GDP would increase to 14.3% or 16.1% of GDP, respectively.Research limitations/implications>Further efforts to improve data collection about housing investments and consumption, particularly imputed rent for owner occupiers and informal activity require national government to conduct regular household and housing surveys. Researcher can help make these surveys more robust and leverage new data sources such as scraped housing price and rent data to complement traditional surveys. Better data are needed in order to capture housing contribution to the economy.Practical implications>The size of the housing sector and its impact in terms of employment and community resilience indicate the potential of inclusive housing investments to both serve short-term economic stimulus and increase long-term community resilience.Originality/value>The role of housing in the economy is often limited to housing investment, despite the importance of housing services and well-documented methodologies to include them. This analysis highlights the importance of housing to the economy of emerging market countries (in addition to all the non-GDP related impact of housing on welfare) and indicate data limitation that need to be addressed to further strengthen the case for focusing on housing as part of economic recovery plans.
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The Covid-19 pandemic has caused numerous variations in the global economies with repercussions in all sectors. Once the emergency phase has finished, the entire worldwide population has changed its lifestyle and has had to adapt to live with the pandemic. In particular, the several modifications that have occurred in the job market and in schools and universities have determined a necessary reorganization of domestic spaces. The present study represents the first phase of a wider research aimed at verifying the transformation in the Italian residential market demand resulted by the Covid-19. The analysis carried out in this work has been performed at the municipal level, by considering the data published by the National Institute of Statistics collected for the 15th General Census of the population and housing in 2011. The dataset collected has been processed through an advanced econometric technique in order to identify the functional relationships between the residential average unit market value and the main architectural, socio-demographic and territorial factors. Further developments of this research will concern the application of the same methodological approach proposed to data detected by the National Institute of Statistics for the 16th Census scheduled for 2021. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This paper explores changes in social behavior since the start of the COVID-19 pandemic, which are characterized by reduction in relocation, mobility, and community engagement, and how the correlations between regional housing markets are affected by these changes. Because changes in mobility and engagement are the most apparent in large cities, the present study calculates the independence indicator of regional housing markets in the 50 largest metropolitan statistical areas (MSAs) in the United States and determines their relationship with Mobility and Engagement Index values. The empirical results show that as mobility and community engagement decline in a certain area, housing market fluctuations become more independent, indicating correlations between regional housing markets in the US might decrease after the COVID-19 outbreak. This paper also finds that there are more MSAs having significantly decreased in volatility since the outbreak of the pandemic. This paper provides evidence indicating that housing markets may be impacted differently by the COVID-19 pandemic than other asset markets, particularly stock markets. Changes in mobility and engagement can be used as an indicator to assess whether the correlation between regional housing markets would decline, which means that, compared with financial instruments, more factors from real aspects need to be considered when determining the changes in real estate affected by the epidemic.
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The article defines a list of tools and mechanisms of foreign housing markets, which can be adapted to Russian practice. This will help resolve several issues related to both improving the efficiency of the market and its financing. As part of the study, a system of interaction between the subjects of the national housing market was formed, considering the introduction of a crowdfunding platform into it, on which this interaction will be built. The COVID-19 pandemic has shown that it is information technology that can make it possible to ensure adaptation of subjects to crisis phenomena and minimize the risks associated with this. In addition, an accumulative system is included that allows you to form an initial payment on a mortgage loan and receive additional income within this system. The necessity of including a guarantee fund, mortgage banks and a state organization engaged in insurance of a mortgage loan in the subject composition of the Russian housing market is revealed. Each of these subjects allows to bring the interaction of market participants closer to the maximum effect. The study focuses on the need to strengthen the role and increase the volume of project financing, which will contribute to an increase in the volume of housing construction. Separately, the problem of the presence of dilapidated and dilapidated housing and at the same time illiquid housing is noted, the solution to which is in the experience of Asian countries and can be adapted in Russia. © 2021 ACM.
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Mortgage is an important part in the housing market, it cause a clear and strong relationship between monetary policies and housing market. The release of growing commodity causes the diversification of housing investment. Actually, the Financial Tsunami was cause by subprime-mortgage crisis in U.S. in 2007. A lot of models have been proposed to predict housing price over the course of decades of research, but some new models should be proposed to suitable the situation in the post era of financial tsunami and COVID-19. The subprime-mortgage crisis damaged the housing market of San Francisco, directly. The housing market in a port is different from in a city, shipping is an important factor to urban development, the paper build the system dynamic model of Kaohsiung housing market, which is a port the same as San Francisco, to predict Kaohsiung housing price. This study aims to extract the related factors to the housing prices of Kaohsiung by correlation analysis, find out the critical factors of the housing prices of Kaohsiung by regression analysis. © 2021 IEEE.