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COVID-19 has generated many problems and some opportunities in the housing market. The potential role of privately-owned short-term lets meeting specialist family violence crisis accommodation demand is one such opportunity. This paper engages with an important and increasing practice in the Australian context, of the utilisation of private housing stock as a component part of a public housing crisis response system, in this case explored in relation to domestic and family violence. In seeking to gain insights into the feasibility of this practice, this article will first frame mixed public/private accommodation provision as potentially overlapping relations between a thin territory of insufficient crisis infrastructure and a thick territory of commodified short-term let infrastructure. Second, this paper situates the potential of this intersection of mixed private/public responses in terms of riskscapes by unpacking how risk is perceived within these contested territories. The findings highlight tensions between both real and perceived understandings of safety, housing, wellbeing, economic and political risks. While there was some support for utilising short-term lets for crisis accommodation, barriers were revealed to adding thickness to the crisis accommodation space. Given increasing homelessness in Australia, diversifying crisis models could offer increased violence-prevention infrastructure to support women.
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Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.
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The COVID-19 pandemic and resulting economic crisis exposed the U.S. rental housing market to extraordinary stress. Policymakers at the federal, state, and local levels established eviction moratoria and a number of additional direct and indirect renter-supportive measures in a bid to prevent a surge in evictions and associated public health risks. This article assesses the net efficacy of these interventions, analyzing changes in eviction filing patterns in 2020–2021 in thirty-one cities across the country. We find that eviction filings were dramatically reduced over this period. The largest reductions were in places that previously experienced highest eviction filing rates, particularly majority-Black and low-income neighborhoods. Although these changes did not ameliorate racial, gender, and income inequalities in relative risk of eviction, they did significantly reduce rates across the board, resulting in especially large absolute gains in previously high-risk communities.
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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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Italian cities have been touched by two major events, the 2008 and 2012 crises and the Covid-19 pandemic in 2020 and 2021. The research aimed to verify whether, and in what way, Italian cities have embarked on a path of transformation, outlining their possible trajectories of change in the intervening decade. The cities considered were the metropolitan cities to which the legislature has assigned the role of territorial reference for areas of a regional nature. The research examined real estate market values for their ability to represent a city's degree of attractiveness in synthetic form. The other variables used made it possible to detect trends in the determinants of the real estate market: economic growth, demographic development and changes in the territorial capital endowment. Concerning the research objectives, cluster analysis appeared to be the most suitable tool to represent changes by aggregating cities according to common patterns. The survey considered the reactions of the different cities in the two five-year periods related to each exogenous shock and, overall, in the decade under review for a long-term reading of the trends. The conclusions reached by the survey show how, between 2012 and 2017, there was a concentration of wealth and population in the major centers and in particular in the city of Milan, characterized by rising property values against a generalized decline in the Italian market. In the second five-year period from 2017 to 2022, the pattern is reproduced with similar intensity, despite a vast debate on the crisis of large cities and their sustainability in the face of the pandemic. An overall ten-year view from 2012 to 2022 of metropolitan cities shows trends with a sufficiently solid and stable character. In the case of Milan, the expression of a clear-cut process of concentration on which the pandemic has had no effect, is counterbalanced by a second cluster of peripheral metropolitan cities that are suffering from processes that penalize their development prospects, while the third cluster of cities is distinguished by a profile that combines opportunities for growth and critical aspects in demographic and economic terms. © 2022, Dei Tipografia del Genio Civile. All rights reserved.
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While official statistics provide lagged and aggregate information on the housing market, extensive information is available publicly on real-estate websites. By web-scraping them for the UK on a daily basis, this paper extracts a large database from which we build timely and highly granular indicators. One originality of the dataset is to focus on the supply side of the housing market, allowing to compute innovative indicators reflecting the sellers' perspective such as the number of new listings posted or how prices fluctuate over time for existing listings. Matching listing prices in our dataset with transacted prices from the notarial database, using machine learning, also measures the negotiation margin of buyers. During the Covid-19 crisis, these indicators demonstrate the freezing of the market and the "wait-and-see" behaviour of sellers. They also show that listing prices after the lockdown experienced a continued decline in London but increased in other regions. [ FROM AUTHOR] Copyright of Journal of Housing Economics is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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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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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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We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding logs data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lockdown on March 12, and the re-opening on April 20. We observe changes on the date of the lockdown in transaction volumes, sell-prediction spreads, exploitative bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that about half of the total fall in prices had already occurred before the lockdown was implemented. The re-opening completely reverses the lockdown effect on prices. We show that voluntary behavioral changes, as well as lockdown and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.
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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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‘Patient capital' is presented by many policymakers as a panacea to address domestic (and sometimes city-level) gaps in financing urban development, particularly housing, that emerged in the post-2008 credit crunch. In this article, we analyse the complexities of patient investors' entry into residential markets in London and their response to the first major, and unexpected, crisis of demand: the COVID-19 pandemic and immediate falls in market demand. We focus on how patient capital and the firms invested in the professionalised rental market, build to rent (BTR), have responded. We highlight three main responses: (1) advancing their lobbying efforts to secure a more supportive political environment;(2) protecting their income streams by offering new payment plans and adaptability to prevent void rates;(3) turning to a ‘reserve army' of renters backed by the state – so-called Key Workers (KWs). We argue these demonstrate a continual and co-evolutionary dimension to policy promoting patient capital and the need for patient planning to govern patient investment in housing systems. Our findings are in ‘real-time' and highlight the importance of structural uncertainties and the breakdown of long-term assumptions in shaping investment decisions. © The Author(s) 2022.
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Research has shown high levels of housing precarity among government-assisted refugees (GARs) connected to difficult housing markets, limited social benefits, and other social and structural barriers to positive settlement (Lumley-Sapanski, 2021). The COVID-19 pandemic has likely exacerbated this precarity. Research to date demonstrates the negative consequences of the COVID-19 pandemic for refugees and low-income households, including both health-related issues and economic challenges, that may exacerbate their ability to obtain affordable, suitable housing (Jones & Grigsby-Toussaint, 2020;Shields & Alrob, 2020). In this context, we examined Syrian government-assisted refugees' experiences during the pandemic, asking: how the COVID-19 pandemic has impacted Syrian refugees' experiences of housing stability. To examine this issue, we interviewed 38 families in Calgary, London, and Fredericton. Using a qualitative descriptive methodology for analysis and interpretation (Thorne et al., 1997), we found the liminality of settling as a GAR has been compounded by isolation, further economic loss, and new anxieties during the pandemic. Ultimately, for many participants, the pandemic has thwarted their housing stability goals and decreased their likelihood of improving their housing conditions. Based on our findings, we discuss potential policy and practice relevant solutions to the challenges faced by refugees in Canada during the pandemic and likely beyond.
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This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from January 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use partial least squares (PLS) to extract a recursive look-ahead-bias-free local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.
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The study used Google search query data on real estate interest for several countries in the Baltic area. The dynamics of public interest in housing have been compared to the dynamics of the COVID-19 infections in Lithuania, Latvia, Poland, and Sweden. This study uses the Vector autoregressive (VAR) model to forecast such time series. VAR is a multivariate linear time series model in which the endogenous variables in the system are lagged functions of the values of all endogenous variables. The increase in COVID-19 infections negatively affected society's interest in housing. The study used Google Trends and R software. [ FROM AUTHOR]
ABSTRACT
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.
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The COVID-19 outbreak magnified territorial inequalities and increased vulnerability among low-income groups. Inhabitants in informal settlements are structurally disadvantaged in coping with communicative diseases such as the COVID-19 pandemic. Despite that, the pandemic has been accompanied by the proliferation of informal settlements. This study explores how the pandemic caused the squatting on new land with the case of "Los Hornos" in suburban Buenos Aires. We used a random forest algorithm and Google Earth Engine to estimate the rapid growth of a new informal settlement from a series of satellite images from early 2020. We also conducted semi-structured interviews with inhabitants to investigate the link between squatting and COVID-19. The study revealed that squatting on new land during the pandemic was mainly due to economic difficulties, overcrowding in existing informal settlements in the metropolitan center, and speculation in the informal housing market. This case is an example of how the most vulnerable groups bore the brunt of the pandemic, how the households in the existing informal settlement were behaving similar to those in the formal housing market (i.e., away from the urban centers), and how the outbreak had also been an opportunity for collective action of squatting a new land to materialize.
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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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In 2020, governments worldwide enforced lockdowns to contain the spread of COVID-19, severely impeding aspects of daily life such as work, school, and tourism. Consequently, numerous economic activities were affected. Before the COVID-19 outbreak, city-center housing markets in areas surrounding popular tourist attractions performed better than did suburban housing markets because of the output of the tourism industry. This study examines the changes in the performance of city-center and suburban housing markets in regions with popular tourist attractions after the lockdown. Specifically, the dynamics of city-center and suburban housing markets in Hangzhou, where West Lake is located, and the changes in the information transfer between these housing markets after the lockdown are explored. Transaction data from January 1, 2019 to September 30, 2020 are used to perform analysis, in which adjusted housing prices and asking prices are employed to measure market performance and sellers' pricing strategies, and transaction volume and time on the market are used to measure market liquidity and transaction frequency. The results reveal that the effects of lockdowns differ between city-center and suburban housing markets. After the lockdown, a substantial structural change is observed in the suburban housing market; the volatility risk of housing prices decreases substantially, causing an increase in transaction premiums. Housing prices and transaction volume increase in the city-center housing market after the lockdown; this is possibly because of the influence from the overall housing market booms. In addition, because sellers raise their asking prices and the transaction time is extended, the sellers in the city-center housing market are particularly influenced by the disposition effect. This leads to a reversal in the lead-lag relationship between the city center and suburban housing markets in terms of informativeness. Specifically, before the lockdown, the city-center market transfers information to the suburban market, but after the lockdown, the suburban market transfers information to the city-center market. The COVID-19 pandemic has changed the world in many aspects; this paper finds that it will also change the development pattern of the real estate market in different locations.
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The COVID-19 pandemic has brought numerous changes to the various segments of the economy. After the unexpected initial shock of the pandemic in Croatia and the consequent lockdown in March 2020, there were some "black" predictions that stable and positive trends in the housing market in Croatia will be reversed. However, the stable growth of the housing prices on the national market has continued in 2020, as well as in 2021. In this paper, we analyse housing market trends of Northern and Pannonian Croatia. This part of Croatia includes all continental counties but excludes the City of Zagreb. In our analysis, we use data which cover the period from 2010 to 2021. Using the hedonic modeling analytic frame, we are assessing the effects of the COVID-19 pandemic, but also of some other market shocks on housing prices and the volume of transactions in the observed part of Croatia. Through the calculation of the hedonic index of asked and realized housing prices, we conclude that the housing prices in Northern and Pannonian Croatia have increased in the recent period Also, we did not find the evidence that the pandemic was statistically significant determinant of the housing price movements. However, the effect of the government's measure of subsidizing housing loans, which has been implemented in the last five observed years, could be assessed as statistically significant.