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This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The effects of COVID-19 cases on housing prices are formally investigated by using a two-way fixed effects panel regression, where county-specific factors, time-specific factors, and mobility measures of individuals are controlled for. The benchmark results show evidence for negative and significant effects of COVID-19 cases on housing prices, robust to the consideration of several permutation tests, where the negative effects are more evident in counties with higher poverty rates. Exclusion tests further suggest that U.S. counties in the state of California or the month of May 2020 are more responsible for the empirical results, although the results based on other counties and months are still in line with the benchmark results.
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In 2016, the city of Shanghai increased the minimum down payment rate requirement for purchasing various types of properties. We study the treatment effect of this major policy change on Shanghai's housing market by employing panel data from March 2009 to December 2021. Since the observed data are either in the form of no treatment or under the treatment but before and after the outbreak of COVID-19, we use the panel data approach suggested by Hsiao et al. (J Appl Econ, 27(5):705-740, 2012) to estimate the treatment effects and a time-series approach to disentangle the treatment effects and the effects of the pandemic. The results suggest that the average treatment effect on the housing price index of Shanghai over 36 months after the treatment is -8.17%. For time periods after the outbreak of the pandemic, we find no significant impact of the pandemic on the real estate price indices between 2020 and 2021.
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PurposeIndia is one of those countries that are severely affected by the COVID-19 pandemic. With the upsurge in the cases, the country recorded high unemployment rates, economic uncertainties and slugging growth rates. This adversely affected the real estate sector in India. As the relation of the housing market with the gross domestic product is quite lasting thus, the decline in housing prices has severely impacted the economic growth of the nation. Hence, the purpose of this paper is to gauge the asymmetric impact of COVID-19 shocks on housing prices in India.Design/methodology/approachStudies revealed the symmetric impact of macroeconomic variables, and contingencies on housing prices dominate the literature. However, the assumption of linearity fails to apprehend the asymmetric dynamics of the housing sector. Thus, the author uses a nonlinear autoregressive distributed lag model to address this limitation and test the existence of short- and long-run asymmetry.FindingsThe findings revealed the long- and short-run asymmetric impact of the COVID-19 outbreak and the peak of the COVID-19 on housing prices. The results indicate that the peak of COVID-19 had a greater impact on housing prices in comparison to the outbreak of COVID-19. This can be explained as prices will revert to normal at a speed of 0.978% with the decline in the number of COVID-19 cases. Whereas the housing prices rise at a rate of 0.714 as a result of government intervention to deal with the ill effects of the COVID-19 outbreak. Moreover, it can be inferred that both the outbreak and peak of COVID-19 will lead to a minimal decline in housing prices, while with the decline in the number of cases and reduction in the impact of the outbreak of COVID, the housing prices will rise at an increasing rate.Originality/valueTo the best of the authors' knowledge, this is the first study to understand the impact of the outbreak and peak of COVID-19 on the housing prices separately.
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PurposeBy considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks.Design/methodology/approachA quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness.FindingsPredictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables;the pandemic and rent have the highest effect on the indices;The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles;the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles;the results for RPPI, NRPPI and ORPPI are consistent and robust.Research limitations/implicationsThe results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study.Practical implicationsThe COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables.Social implicationsEmerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets.Originality/valueThe QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.
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PurposeThis study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.Design/methodology/approachThis study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.FindingsThe regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.Originality/valuePrevious literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.
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This study examines the impacts of the COVID-19 pandemic on house prices over time for the Fargo-Moorhead-West Fargo MSA of North Dakota and Minnesota. We examine overall trends by estimating an OLS hedonic model and dig deeper into the heterogeneity of price trends across the house price distribution using an unconditional quantile hedonic price model. We find that house prices increased in the MSA by about 2.5 percent during a period when an executive order closing non-essential businesses was in effect and another 1 percent during the period after the executive order expired. Moreover, we find that the price increase occurring during the period when the executive order was in place was concentrated in the lower priced portion of the house price distribution, while the price impact after the executive order expired was more widespread. Combined with data on listings, sales, and average time on market, our results suggest that the price effects during the executive order period were primarily the result of a decrease in housing supply, while the post order price effects reflect a combination of the supply decrease and an increase in housing demand.
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PurposeThis study aims to determine the relationship between the banking industry and home financing by conducting a regression analysis between the mortgage loan interest rates and the number of housing sales, and based on the results of the analysis, this paper proposes a new and alternative interest-free home financing model by directing the savings of the people in pension funds into real estate investment funds (housing fund), specifically established to provide a bank loan-free home financing solution. Diminishing Musharakah (partnership) is also integrated into the model from an interest-free and saving economy perspective. The model developed also provides opportunities to increase the size of the real estate investment funds and provide alternative investment tools to pension funds.Design/methodology/approachWhile the global financial crisis resulted from the mortgage crisis in the USA in very recent history, the world has been experiencing the evolution of a new health crisis, COVID-19, a pandemic that has been heavily affecting the global economy in the past two years. The housing sector is among one of the major industries that may be affected by this new global crisis because of the high dependency of the current home financing models on the banking industry, which is carrying the burden of the pandemic. The rapid increase in global debt volume, housing prices, inflation and interest rates are observed as bad signs that may increase the risks of the housing industry. A potential decrease in purchasing power because of high inflation rates may decrease the welfare of people and reduce the income level. While the total debt keeps increasing worldwide, and central banks are considering increasing the interest rates, any potential default in the repayment of the mortgage loans may trigger a new mortgage crisis as the bank loan-dependent financing system of the housing industry lacks alternatives. Thus, a relationship analysis between the banking and housing sectors is required to figure out the dependency of home financing on the banking industry, and a new sustainable home financing model is needed to protect the housing industry and the homebuyers from a negative effect of a new possible financial crisis.FindingsThe results of the analysis exhibit that there is a strong negative relationship between the mortgage loan interest rates and the total home sales. As a result, the new model is suggested and this new model is tested in an emerging country, Turkey, with the real housing sector and economic data where the interest rates are high and the home prices are booming. The results exhibit that the new interest-free home financing model provides a more economic financing solution compared with the high financing costs of bank loans.Research limitations/implicationsThe model proposed in this study is unique, and there is no such system that has integrated the pension funds, the real estate investment funds and diminishing partnership in one ecosystem. It is expected that the model may decrease the dependency of home financing on the banking industry and decrease the risks of the housing sector in the case a new financial crisis occurs.Social implicationsWhile providing a sustainable and alternative interest-free home financing tool, the model also provides individuals who do not prefer to use any bank loan because of religious or other concerns an opportunity to purchase their houses.Originality/valueThe model proposed in this study is a unique and original model that aims to provide a bank loan-free, sustainable home financing solution by integrating the pension funds, real estate investment funds and diminishing partnership in one ecosystem.
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PurposeThis paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.Design/methodology/approachThe econometric analysis includes stationarity test, Granger causality test, correlation analysis, autoregressive distributed lag models and cointegration analysis using ARDL bounds testing.FindingsThe econometric modelling reveals that the housing price in Lithuania correlates with quarterly changes in the gross domestic product and approves that the cycles of the real estate market are related to the economic cycles. Economic stimulus measures should mainly focus on stabilizing the economics, preserving the cash and deposits of households, as well as consumer spending in the case of economic shock.Originality ValueThis study is beneficial for policy makers to make decisions to maintain stability in the housing market in the event of any economic shock.
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PurposeThis study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.Design/methodology/approachThis study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla's and Amazon's new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.FindingsThe results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla's relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon's relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.Originality/valuePrevious literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.
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This paper investigates the effect of COVID-19 on both housing prices and housing price gradients in China using transaction level data from 60 Chinese cities. After using a difference-in-differences (DID) specification to disentangle the confounding effects of China's annual Spring Festival, we find that housing prices decreased by two percent immediately after the COVID-19 outbreak but gradually recovered by September 2020. Moreover, our findings suggest that COVID-19 flattens the horizontal housing price gradient, reduces the price premium for living in tall buildings, and changes the vertical gradient within residential buildings. This is likely explained by the changing household preferences towards low-density areas associated with lower infection risk.
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This paper makes use of residential housing prices across US states, along with COVID-19 confirmed cases and deaths, to explore the impact of COVID-19 on housing prices. The work also investigates the role of the vaccination program on those prices. The panel estimates document the negative impact of COVID-19 metrics on housing prices, but when the vaccination program is underway, this impact disappears. The results are robust across US regions as well. © 2022, Global Social Science Institute. All rights reserved.
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The COVID-19 pandemic has affected businesses, supply chains, financial and real estate markets around the world. This case study takes a detailed look at the impacts that the COVID-19 pandemic has had on the real estate markets throughout North America (United States and Canada). An emphasis was made to look at different aspects of real estate markets, including how current conditions relate to previous housing bubbles and financial crises (e.g., the 2008 housing crisis), and what impacts will the current market situation have on lower to middle income classes. This case study also examined the condition of the real estate market as impacted by the COVID-19 pandemic and an outlook of how the current real estate markets may impact society and finances throughout North America into the future.
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Purpose: A notable observation in the literature of financial markets is the debate on market contagion and causality. During periods of financial distress, global financial markets experience record low market prices partly due to the spread of fear. It was therefore necessary to investigate market contagions using causality relationships during periods of financial distress. Methodology: A unit root test, Granger causality and Test for equality of means was used as the blueprint. The sample periods where December 1, 2007 to June 30, 2009 and January 1, 2020 to December 31, 2021. Findings: Contrary to the perceptions that prevails in most stock markets during distress, there was little empirical evidence to support market contagions. Although very few markets are indeed related. Originality/Value: The implications of this study extends the efficient market hypothesis concept to market efficiency during periods of financial distress. It is evident that financial markets display greater efficiencies during periods of financial distress. This study is the first to investigate market contagion during periods of distress as per author's knowledge.
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The property market in Central European Region countries share a number of common features among which privatization, restitution of property, massive regulation or underdeveloped financial market all of which contributed on persisting property market imbalances and continuous dynamic changes. These changes have recently been significantly exacerbated by the presence of the Covid-19 pandemic, the war in Ukraine and a significant increase in energy prices (heating of apartments and houses, production of building materials, etc.). It is currently difficult for investors and people looking for their own housing to predict the future development of housing prices and housing affordability. This article analyses the housing market trends in this region taking the example of the Czech Republic using unique primary statistical data. It offers a deeper insight into the trends present on this market, identifies significant determinants of housing prices and evaluates changes in housing affordability. Our research reveals why the property market trends may contribute to opening inequality scissors and thus economic stability. This research is based on primary statistical data mined by EVAL software which allows to gather information about the development of the real estate market from real estate advertising.
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In this article, we analyse the effects of the COVID-19 crisis on the French residential property markets. More precisely, we explore whether household demand for residential properties has been impacted by this crisis. Based on data on property transactions recorded between 2016 and 2021, we compare the evolution of prices before and after the crisis. The comparison is done between municipalities within urban areas on one hand, between urban areas on the other. Within urban areas, we show that the less dense municipalities that are farthest from the centre are also those where prices have risen the most. This reflects the desire among households for more spacious properties on the outskirts of urban centres. The results of the analysis of the evolution of prices between urban areas suggest, in line with urban economics theory, that a change in dynamics has occurred in favour of the least productive agglomerations. © 2022, Institut National de la Statistique et des Etudes Economiques. All rights reserved.
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Purpose>The purpose of this study is to explain the potential long-term impacts of working from home on housing wealth inequality in large cities of advanced economies.Design/methodology/approach>This study is descriptive research and It supports the arguments by providing some emerging evidence from property markets in developed countries.Findings>The authors argue that due to the unique nature of the COVID-19 crisis, it will have a different and long-term impact on housing wealth inequality. Changes in the working arrangements of many professionals will change the housing demand dynamic across different suburbs and may lead to a reduction of the housing wealth gap in the long term. In this paper, the authors propose five mechanisms that may impact housing wealth inequality.Research limitations/implications>Long-term data is required to test the proposed conceptual model in this study and the effect of the COVID-19 pandemic on housing wealth across and within suburbs of large cities.Practical implications>Policymakers and regulators may benefit from the discussions and suggestions provided in this study and consider the proposed avenues on how new changes in the working environment (remote working) may result in a reduction of housing wealth inequality.Originality/value>This study presents a new perspective about the potential long-term impacts of working from home that is posed by the COVID-19 pandemic on housing wealth inequality in large cities of developed economies.
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Understanding the impact of housing supply on housing price inflation is a particularly important issue from a policy-maker's perspective. Notwithstanding the impact of the great financial crisis (GFC) in 2007/08, the past 25 years has seen a significant increase in housing prices across a number of western economies. More recently, across countries, a common characteristic observed in housing markets appears to be the increase in price inflation in the aftermath of the Covid-19 pandemic. A key question which arises is whether housing price inflation can be assuaged somewhat by greater levels of housing supply? In this paper, we seek to quantify the impact of additional supply on price inflation in the Irish property market. While residential property markets in many countries experienced substantial swings in activity since the early 1990s, the Irish market has demonstrated particular volatility. Given such a high degree of volatility, it is plausible that the relationship between housing prices and its fundamental drivers could have changed over time. Crucially, therefore, we address this question using both a multiple breakpoint model and a Markov switching model to allow for the presence of structural changes in the Irish residential market over the period 1981–2019. Our results indicate a complex relationship between additional supply and house prices, with the impact varying over time. © 2022 Board of Trustees of the University of Illinois
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This study investigates the interaction between the accessibility of various urban public facilities and the price of urban space by analysing the influence of urban hospitals and rail accessibility on housing prices. In recent years, with the development of social civilisation and the influence of COVID-19, people have become increasingly interested in the quality of hospitals in their living environment. This makes medical convenience (hospital accessibility) a crucial element in determining housing prices. At the same time, people regard rail as one of the important means to access hospitals. Therefore, demonstrating the intrinsic value of accessibility to hospitals and rail in residential areas is essential. As a point of reference, this paper presents an empirical analysis of Fuzhou, Fujian Province, China, a city in a developing nation with relatively widespread access to hospitals during a significant rail construction period. The study demonstrates the interaction between hospital and rail accessibility and their moderate influence on housing prices, which is geographically heterogeneous. The study also determines the optimal metric model for assessing geographical interaction based on the significance and stability of the interaction in geographic space. It concludes with a discussion of the findings and social recommendations. Copyright © 2022 Chen, Lin, Cao, Han, You, Shyr, Lu and Huang.
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Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographically neural network weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13.0% in the evaluation on the validation dataset. It is a practical and powerful way to assess house prices, and we believe our model could be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.