Journal of Housing Economics最新文献

A heated debate has emerged drawing a connection between housing affordability and home-sharing platforms such as Airbnb. Despite first regulatory efforts by municipalities, the impact on rents and house prices has been examined insufficiently in scientific literature, especially with regards to Europe. Therefore, this paper addresses this gap by analyzing data on Airbnb listings for 25 European cities between 2010 and 2019. Using fixed effects and dynamic panel regressions, we show that home-sharing has significantly contributed to a rise in rents and house prices in European cities. While these effects are mainly concentrated in city centers, we also document effects in other urban districts. Finally, recent home-sharing regulations are not associated significantly with housing affordability.

This paper generalizes the simple two-city work-from-home model of Brueckner et al. (2022) by adding a group of non-remote workers, who must live in the city where they work. The results show that the main qualitative conclusions of BKL regarding the intercity effects of WFH are unaffected by this modification, with WFH yielding the same aggregate population and employment changes in the two cities and the same house-price and wage effects as in the simpler model. Even though the aggregate population effects are the same, the population relocation of non-remote workers is in the opposite direction to that of remote workers, which matches the direction in BKL. These conclusions are useful because they establish the robustness of BKL’s highly parsimonious model. The paper also contains material surveying other theoretical research on WFH as well as empirical work in the area, including BKL’s empirical findings in support of their model.

This paper uses a survey of over 2500 rental property owners in ten cities across the United States to determine the impact of the COVID-19 pandemic on landlords’ rent collection and business behavior. Our findings show that yearly rent collection was down significantly in 2020 relative to 2019—both within and across rental markets—and that an increasing number of owners have a large share of their portfolio behind on rent. Small owners and owners of color faced the highest exposure to deep tenant arrears in 2020, challenges they were also more likely to face prior to pandemic. Our findings show that owner business practices changed dramatically in 2020, with a higher share of landlords granting tenants rent extensions or forgiving back rent during the pandemic relative to prior. However, many owners also disinvested in their rental properties through deferred maintenance, missed mortgage payments, and property sale listings. Landlords of color pursued disinvestment strategies during the pandemic at an elevated rate compared to white landlords. Owners of properties in neighborhoods with more non-white residents were both more likely to experience decreased rent collection and more likely to pursue evictions and rental late fees holding constant rental payment rates, implying the pandemic has disproportionately affected renters in communities of color. Overall, our findings highlight the strain the pandemic has placed on the housing stock, which has implications for the long-term viability and affordability of many of these units. More concerningly, our results show that households of color—which have been disproportionately affected by the pandemic in other domains—were more likely to face punitive measures from landlords in both 2019 and 2020, suggesting the pandemic has exacerbated existing racial inequality in housing markets.

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.

US homeowners receive income tax deductions for mortgage interest payments and state and local property taxes, pay no income tax on their home's imputed rental income, and may exclude most of the capital gains earned from a home sale. This paper characterizes the geographic distribution of the tax expenditures from these tax preferences using a new method that exploits household-level microdata from the 2019 Census Public Use Microdata Sample to simulate homeownership tax expenditures at the Public Use Microdata Area level. I estimate that in the 2018 tax year, $226.05 billion in taxable revenue was lost to the mortgage interest deduction ($28.20 billion), the property tax deduction ($9.51 billion), the exclusion of net imputed rental income ($134.82 billion), and the partial exclusion of housing-related capital gains ($53.52 billion). Large metropolitan areas and neighborhoods with high housing prices receive subsidies in excess of the cost of funding homeowner tax preferences, while the burden of homeowner tax preferences falls heavily on rural areas. If federal income tax law reverted to what existed just prior to the 2017 Tax Cuts and Jobs Act, these geographic disparities would be exacerbated.

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.

Early in the COVID-19 pandemic, policymakers initiated a forbearance program—that allowed borrowers to pause their mortgage payments—to prevent a large-scale foreclosure crisis. Using detailed loan-level performance data, we study forbearance take-up and subsequent performance among two distinct group of mortgage borrowers: single borrowers versus coborrowers. We provide stylized facts that compared to coborrowers, single borrowers have lower incomes, lower credit scores, higher loan-to-value ratios and higher debt-to-income ratios and are hence more financially vulnerable. We find that single borrowers are more apt to elect forbearance, all else constant. We further find that forbearance had a stronger positive effect on helping single borrowers avoid or recover and exit delinquency than coborrowers.

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.

I estimate the “simple mechanical effect” of permanent supportive housing and Housing First as studied in the At Home/Chez Soi and HUD-VASH experiments on point-in-time counts of homelessness (HUD definition). The simple mechanical effect is the effect that would occur in the absence of any behavioral responses aside from those in the experiments. The estimates of the simple mechanical effects overlap the confidence intervals in Corinth's (2017) regression study of the total effect. This finding suggests that the net effect of behavioral responses outside the experiments is small. The essay illustrates how useful inferences about aggregate-level phenomena can be derived from individual-level data.

I am grateful to Emmy Tiderington and Yi-Ping Tseng for helpful comments and information, and to an anonymous referee.

We assess the role of state-level renter protection regulations on the pricing, performance and risk of multifamily housing. We construct a renter protection score (RPS) to measure the extent of renter protection in each state. Using a proprietary property-level dataset from loans backed by commercial mortgage backed securities (CMBS) and census tract socioeconomic variables, we study the role of RPS on initial capitalization (cap) rates, annual net operating income (NOI) and annual loan delinquency rates of multifamily housing. We find that, contrary to conventional wisdom that renter protection is ‘bad’ for investors, multifamily housing in high RPS states is associated higher annual NOI and NOI growth and lower delinquency rates. We also show that better tenant protection is associated with lower initial cap rates. The results point to investors perceiving properties in more regulated states as lower risk due to reduced income volatility. For institutional investors, higher levels of renter protection are, therefore, not ‘bad’ but are instead associated with lower cash flow volatility and better income growth prospects.