Stock Comovement and Financial Flexibility

We develop a dynamic model of corporate investment and financing, in which shocks to the value of collateralizable assets generate variation in firms' debt capacity. We show that the degree of similarity among firms' financial flexibility forecasts cross-sectional variation in return correlation. We test the implications of the model with firm-level data in two empirical analyses using i) an instrumental variable approach based on shocks to the value of collateralizable corporate assets and ii) the outbreak of the COVID-19 crisis as an event study. We find that firms in the same percentile of the cross-sectional distribution of financial flexibility have 62% higher correlation in stock-return residuals than firms 50 percentiles apart.

House prices, airport location proximity, air traffic volume and the COVID-19 effect

Although house prices and airports are influenced by distinct factors that shape their evolutions, they are also intrinsically connected through the natural and built environment. Standard theory suggests that air-traffic noise and proximity to key economic hubs such as airports are of prime importance to house prices and the housing market. This study contributes to understanding the link between the housing market, airport location proximity and air traffic. The research investigates this association across four key urban areas within New Zealand proximal to an international airport: Auckland, Wellington, Christchurch and Queenstown. Applying a generalized least squares (GLS) regression approach, the analysis reveals that house prices, air-traffic activity and proximity to airports within New Zealand demonstrate a statistically significant effect, and that air traffic volume has a positive effect on house prices. Moreover, the findings reveal a ‘U'-shape relationship between distance to the airport and house prices, suggesting that airport noise and pollution adversely affect house prices, with this effect diminishing with distance, indicating that economic influences and employment may also serve as a positive externality. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Is this time the same? Housing market performance during SARS and COVID-19*

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.

U.S. House Prices by Census Division: Persistence, Trends and Structural Breaks

This paper uses fractional integration methods to examine persistence, trends and structural breaks in United States house prices, more specifically the monthly Federal Housing Finance Agency House Price Index for census divisions, and the United States as a whole over the period from January 1991 to August 2022. The full sample estimates imply that the order of integration of the series is above one in all cases, and is particularly high for the aggregate series, implying high levels of persistence. However, when the possibility of structural breaks is taken into account, segmented trends are detected. The subsample estimates of the fractional differencing parameter tend to be lower, with mean reversion occurring in a number of cases. This means that shocks in the series are expected to be transitory in these subsamples, disappearing in the long run by themselves. In addition, the time trend coefficient is at its highest in the last subsample, which in most cases starts around May 2020 coincident with the beginning of the coronavirus pandemic. The results provide clear evidence of differences between census divisions, which implies that appropriate housing policies should be designed at the local (rather than at the federal) level. © 2023, The Author(s).

The Effect of Proximity to Universities on House Prices after the COVID Outbreak

COVID-19 has made virtual interactions an integral part of learning modes. This made it possible for college students to live further away from school than before, which might change the house price neighboring universities. This article studies the effect of proximity to school on house prices after the COVID-19 outbreak using a non-parametric difference-in-differences approach with property-level transaction data surrounding 128 universities in the U.S. The results show that house prices within 0.5 miles of universities experienced a maximum decrease of approximately 7% after three months of the outbreak. The effects vary for universities that implemented different teaching modes of in-person, hybrid, and online. Since house prices are important indicators for local economic conditions, the results help local homeowners, investors, and governments in their decision-making processes. © 2023 by the author.

Stuck at home: Housing demand during the COVID-19 pandemic.

The COVID-19 pandemic induced an increase in both the amount of time that households spend at home and the share of expenditures allocated to at-home consumption. These changes coincided with a period of rapidly rising house prices. We interpret these facts as the result of stay-at-home shocks that increase demand for goods consumed at home as well as the homes that those goods are consumed in. We first test the hypothesis empirically using US cross-county panel data and instrumental variables regressions. We find that counties where households spent more time at home experienced faster increases in house prices. We then study various pandemic shocks using a heterogeneous agent model with general equilibrium in housing markets. Stay-at-home shocks explain around half of the increase in model house prices in 2020. Lower mortgage rates explain around one third of the price rise, while unemployment shocks and fiscal stimulus have relatively small effects on house prices. We find that young households and first-time home buyers account for much of the increase in housing demand during the pandemic, but they are largely crowded out of the housing market by the equilibrium rise in house prices.

COVID-19 moderates the association between to-metro and by-metro accessibility and house prices

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

Synchronisation among short-term rental markets, co-movements and cycles in 39 European cities

This paper presents new evidence of the short-term rental market's prices and transactions from a daily time -series perspective in 39 European cities from 2015 to 2020. It uses Airbnb micro datasets to build time-series cycles by extracting the original observations containing total bookings (rent transactions), rental units sup-ply, and asking rent, with a daily periodicity. The cycles show the periods in which short-rental activity was more relevant for each city, and the level of rents across Europe. The paper provides empirical evidence of a long-term relationship among the city variables (tested via mean and variance). Causality supporting co-movements across cities was found by estimating a short-term naive market equilibrium model using the vector error correction model approach, supporting the hypothesis that the short-term rental market performs according to housing -market principles. Short-run elasticities among rents and contracts across the 39 cities show causal evidence of co-movements among rents and the supply and demand of properties. The market adjustment on the supply side estimates new units responding to changes in prices within 15 lags (days) and longer (350 lags) from the demand side, equivalent to eight to nine months. Evidence of the pandemic's limited effect on housing supply and prices' positive effect is also provided. A robust negative weekend impact on prices was found, suggesting stronger market relevance on weekdays.

The remote work revolution: Impact on real estate values and the urban environment: 2023 AREUEA Presidential Address

The covid‐19 pandemic induced a major shift in the prevalence of remote and hybrid work arrangements. This review article studies the effects of this remote work revolution for residential and commercial real estate values and for the future of cities. It also discusses consequences for productivity, innovation, local public finance, and the climate. The last part of the article discusses policy interventions.

POST-PANDEMIC ANALYSIS OF HOUSE PRICE PREDICTION IN SURABAYA: A MACHINE LEARNING APPROACH

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.

The remote work revolution: Impact on real estate values and the urban environment

The covid-19 pandemic induced a major shift in the prevalence of remote and hybrid work arrangements. This review article studies the effects of this remote work revolution for residential and commercial real estate values and for the future of cities. It also discusses consequences for productivity, innovation, local public finance, and the climate. The last part of the article discusses policy interventions. © 2023 American Real Estate and Urban Economics Association.

Housing market volatility under COVID-19: Diverging response of demand in luxury and low-end housing markets

The ongoing pandemic has led to substantial volatility in residential housing markets. However, relatively little is known about whether the volatility is dominated by housing demand or supply, and how different priced markets contribute to the volatility. This article first examines the temporal effect of COVID-19 on house prices, housing demand, and supply in Los Angeles, and second explores the effect heterogeneity in luxury and low-end housing markets within the city. For identification, the article employs a revised difference-in-differences (DID) method that controls more rigorously for unobservables and improves on the traditional DID with smaller prior trends. Using individual level data, the result first shows that, in response to the outbreak, house prices, demand, and supply all decreased in March to May 2020 and increased in July and August 2020, with demand dominating the process. Second, the heterogeneity exploration identifies diverging COVID-19 impacts in higher-and lower priced markets. Particularly, the decline in overall price and demand before June originates mainly from the lower-priced market while the higher-priced one experienced limited changes in demand. After July, higher priced markets led housing market's surge in price, demand, and supply, whereas the lower-priced market has not fully recovered from decreases in house prices and housing demand. Finally, a larger price decline in lower-priced markets is found to be associated with higher service shares and lower homeownership rates. The results not only facilitate market participants in their decision making but also aid local governments in formulating policies and allocating subsidies to mitigate the effects of the outbreak.

The Impact of Monetary Policy on Real Estate Market during Covid-19 Crisis Using a Dsge Model of a Small Open Economy

Using a new Keynesian small open economy dynamic stochastic general equilibrium model (NK SOE DSGE) with the housing sector, this paper evaluates the impact of housing collateral and changes in openness of economy on the business cycle in the Czech economy. We devote special attention to the setting of the loan to value (LTV) ratio, which we believe plays an important role as a regulator of the monetary transmission mechanism. Moreover, we try to simulate the effects of a reduction in the openness of the economy in the context of an incoming pandemic crisis. The impacts alternative LTV level and openness level setting are quantified by simulating the responses of monetary shock on key macroeconomic variables. Our simulations are based on an estimated DSGE model. Our approach allows a better understanding of the response of the real economy to monetary tightening mitigated by different levels of LTV, and allows a comparison of how these effects change in an environment of altered economic openness. Our results show that higher loan to value ratios strengthen the effect of the monetary transmission mechanism to consumption and output. In contrast, changes in the openness of the economy showed no significant changes in the dynamics of monetary transmission to real variables.

Government and Private Household Debt Relief during COVID-19

We study the suspension of household debt payments (debt forbearance) during the COVID-19 pandemic. Between March 2020 and May 2021, more than 70 million consumers with loans worth $2.3 trillion entered forbearance, missing $86 billion of their payments. This debt relief can help explain the absence of consumer defaults relative to the evolution of economic fundamentals. Borrowers??? self-selection is a powerful force in determining forbearance rates: relief flows to households suffering pandemic-induced shocks that would otherwise have faced debt distress. Moreover, 55 percent of forbearance is provided to less creditworthy borrowers with above median income and higher debt balances???that is, those excluded from income-based policies, such as the stimulus check program. A fifth of borrowers in forbearance continued making full payments, suggesting that forbearance acts as a credit line. By May 2021, about 60 percent of borrowers had already exited forbearance while more financially vulnerable and lower income borrowers were still in forbearance with an accumulated debt overhang of about $60 billion. Exploiting a discontinuity in mortgage eligibility under the CARES Act, we estimate that implicit government debt relief subsidies increase the rate of forbearance by about a third. Government relief is provided through private intermediaries, with shadow banks less likely to provide forbearance than traditional banks.

Impact of Brexit on G7 properties

In this article, we adopted the Full Information Maximum Likelihood (FIML) Markov-switching model of Yoon to examine the contribution of the UK housing business cycle to the common G7 housing business cycle between housing price and GDP seeking to access the impact of Brexit on G7 properties. Taking a sample of G7 countries we investigated a period of over 50 years, using quarterly data from 1970:II to 2020:IV. Our findings demonstrate that UK GDP is a significant variable contributing to the G7 GDP growth, and furthermore that the UK housing price is a significant variable to the G7 housing prices. Considering common international housing business cycle, we found that the UK is not a significant variable for determining the common international housing business cycle between housing price and the real growth of output in the G7 countries. Finally, applying a FIML Markov-switching model to the G7 countries, we found a common international housing business cycle during the oil shock periods of the 1970s, the financial crisis in 2008, and COVID-19 pandemic. These findings are the first empirical evidence of the comparison of COVID-19 pandemic and other crises in terms of common international housing business cycle, thus providing significant input for policymakers.

Housing market volatility under COVID-19: Diverging response of demand in luxury and low-end housing markets.

The ongoing pandemic has led to substantial volatility in residential housing markets. However, relatively little is known about whether the volatility is dominated by housing demand or supply, and how different priced markets contribute to the volatility. This article first examines the temporal effect of COVID-19 on house prices, housing demand, and supply in Los Angeles, and second explores the effect heterogeneity in luxury and low-end housing markets within the city. For identification, the article employs a revised difference-in-differences (DID) method that controls more rigorously for unobservables and improves on the traditional DID with smaller prior trends. Using individual level data, the result first shows that, in response to the outbreak, house prices, demand, and supply all decreased in March to May 2020 and increased in July and August 2020, with demand dominating the process. Second, the heterogeneity exploration identifies diverging COVID-19 impacts in higher- and lower- priced markets. Particularly, the decline in overall price and demand before June originates mainly from the lower-priced market while the higher-priced one experienced limited changes in demand. After July, higher-priced markets led housing market's surge in price, demand, and supply, whereas the lower-priced market has not fully recovered from decreases in house prices and housing demand. Finally, a larger price decline in lower-priced markets is found to be associated with higher service shares and lower homeownership rates. The results not only facilitate market participants in their decision making but also aid local governments in formulating policies and allocating subsidies to mitigate the effects of the outbreak.

A regional renaissance? The shifting geography of internal migration under COVID-19

Since the beginning of the COVID-19 pandemic there has been interest in the migration of city-dwellers to regional areas to escape lockdowns and movement restrictions, yet evidence of a ‘regional renaissance’ in Australia remains anecdotal. This paper aims to quantify the current and future dynamics of the COVID-19 pandemic on the levels, patterns and drivers of migration to and from Australian cities and regions. Results show a 7% drop in the rate of migration between Greater Capital City Statistical Areas in 2020 compared to the previous year, with record net gains in regional areas facilitated by a 9% decrease in departures rather than an increase in arrivals from cities. Our forecasts suggest that net gains to regions will slow down after 2022, but remain higher than pre-pandemic levels in New South Wales, Queensland, South Australia, and Tasmania because of a sustained increase in arrivals. Regional Western Australia and the Northern Territory will continue to record net losses, while net gains to regional Victoria are predicted to be lower than pre-COVID-19 because of an increase in departures. These patterns have important implications for population projections and policies regarding the attraction and retention of internal migrants to Australia's regions. [ FROM AUTHOR] Copyright of Australian Geographer is the property of Routledge 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.)

An alternative interest-free home financing model

Purpose This 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/approach While 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. Findings The 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/implications The 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 implications While 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/value The 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.

Bayesian Inference in Census-House Dataset

As one of the most popular probabilistic programming tools, PyMC3 can solve inference problems in many scientific fields. In this paper, we used PyMC3 to build a Bayesian model for the census-house dataset to predict the correspondence between the U.S. population and house prices, and evaluated it using the dataset to determine the validity and accuracy of the established model. Through the evaluation of this dataset, the Bayesian model established in this paper can predict the theoretical data of house prices with high accuracy in the absence of COVID-19, which has implications for the study of the current property prices that have increased significantly because of COVID-19 and the due prices of similar large assets, researchers can predict the house prices in the absence of COVID-19, and then based on the current house prices calculate the difference and thus study the impact of COVID-19 in terms of house prices as well as the impact of similar asset prices. © 2021 IEEE.

Hedonic Modeling for Predicting House Prices during CoVid19 Pandemic in the Philippines

House projects kept changing in the Philippines, because of the changing needs and demands of the market valuation. Predicting the housing prices will be the focus of the study especially during the CoVid-19 pandemic. As the Consumer Price Index (CPI in the Philippines increased to 123.40 points in September from 123.30 points in August of 2020 [8]. The researcher will use Hedonic Regression Model techniques for analyzing its statistical technique for investigating and modeling the relationship between variables that will be applied in the study. In the data collection, the regression analysis will be the basis for collecting the data such as its historical data, observational study, and design experiment of housing prices before the CoVid19 pandemic came in the Philippines. The results and conclusion of the different machine algorithms will be used showing that regression analysis is the best technique for prediction of prices for housing in the Philippines during the Pandemic. The study will apply parameters that can calculate house valuations within Metro Manila. © 2021 ACM.