Housing
Paper Session
Sunday, Jan. 8, 2023 1:00 PM - 3:00 PM (CST)
- Chair: Erik Johnson, University of Alabama
The Covid Shock to Retail Markets: The Persistence of Retail Effects and the Implications for the Future of Cities
Abstract
This paper uses granular data on tens of millions of credit and debit customers and their transactions to estimate the persistence of changes to the demand and supply for retail across space due to COVID-19. For individual customers, we measure changes in residential location choice, the use of online products, and patterns in offline trip choices. For neighborhoods, we measure the entry and exit of establishments across space and how this varied across neighborhoods with more exposure to the growth of work-from-home and online retail. Initial results show that some shocks to retail markets, such as changes in online spending behavior, are highly persistent. These changes have important implications for the spatial structure of cities even in an endemic COVID-19 future.Cyclical Volatility in Home Sales and Prices: Supply or Demand?
Abstract
Both home sales and home prices are cyclical, generally rising together during boomsand falling during busts. Using individual home listing data and models of housing search, this paper investigates the relative importance of supply and demand factors in driving these fluctuations. We find that demand – i.e., the number of actively searching buyers – explains the large majority of the variation in housing market dynamics. The supply of homes for sale, in contrast, can explain very little of the variation in home sales or prices. We highlight the important role market tightness and congestion play in in this result and contrast our findings with previous work that does not take congestion into account. We also consider two implications of our results. First, we show that, despite common reports of low inventory, the extraordinary performance of the housing market during the COVID-19 era was almost entirely due to a surge in demand. To keep house price growth steady at its pre-pandemic levels, new for-sale listings would have had to expand by over 30 percent (an increase far outside typical yearly changes, or one that could be plausibly achieved in the near term through new construction). Second, we estimate that housing demand is very sensitive to changes in mortgage rates, even more so than comparable estimates for home sales. Directly estimating the effect of interest rates on demand corrects for the bias caused by search frictions and variable lags between agreement and sale dates that attenuates the estimated relationship between interest rates and sales. This suggests that policies that affect housing demand through mortgage rates can influence the cyclicality of the housing market, while any interventions on the supply side will have little effect in the near term.
The Effects of Federal “Redlining” Maps: A Novel Estimation Strategy
Abstract
This paper investigates the causal effects of the Home Owners’ Loan Corporation (HOLC) maps and the neighborhood grades they assigned to summarize lending risk in the second half of the 1930s. In particular, we estimate the effects of different grades on homeownership rates, property values and shares of African-Americans between 1940 and 2010. To measure the short and long-term effects of the HOLC mapping intervention, we propose a new estimation strategy. Spatial discontinuity designs, often used in the literature on this topic, suffer from endogeneity concerns: multiple authors documented socioeconomic differences on opposite sides of boundaries traced by the agency, indicating that the HOLC did not assign border locations and grades randomly. Instead, we exploit an exogenous population threshold that determined which cities were mapped and a machine learning algorithm drawing HOLC maps in control cities. Using the grades predicted by the machine learning model, we apply a grouped difference-in-differences design to measure the causal effects of the HOLC intervention. The causal effects are identified by differences between neighborhoods in treated cities and areas in control cities that would have received the same grade but were not mapped.This empirical strategy is possible thanks to a new spatial dataset we constructed geocoding full-count Census records between 1910 and 1940.
For the year 1940, we find a substantial reduction in property values and homeownership rates in areas with the lowest grade, along with an increase in the share of African American residents. We also find sizable house value reductions in the second-to-lowest grade areas.
Such negative effects on property values persisted until the early 1980s. Our results illustrate that institutional practices can coordinate individual discriminatory choices and amplify their consequences.
Mismeasuring Risk: The Welfare Effects of Flood Risk Information
Abstract
Rapidly improving data and models are giving homeowners more information about their disaster risk while also increasing insurance premiums for the highest risk homes. In this paper, I study the economic consequences of using better flood risk models to more accurately identify and price flood insurance for high-risk homes. I estimate my results with administrative flood insurance policy data and a novel survey measuring flood insurance demand, risk perceptions, and objective risk. To identify the effects of risk information, I use variation created by outdated elevation data and risk models that caused high-risk homes to be misclassified as low-risk. My findings show that flood risk classification provides valuable information not only for insurers, but also for homeowners. Misclassifying high-risk homes as low-risk causes owners to underestimate their current and future flood risk, invest less in risk-reducing adaptation, and buy less flood insurance despite substantially lower premiums. Embedding these estimates in a sufficient statistics model with dynamic risk and endogenous risk beliefs and adaptation, I find that identifying and pricing the estimated six million high-risk homes outside the floodplain would increase social welfare by $138 billion.The Great Reshuffile: Residential Sorting During the Covid Pandemic and Its Welfare Implications
Abstract
Using individual micro data , we document a sudden but continuing shift of residential demand towards the suburbs as well as less densely populated MSAs during the COVID-19 pandemic. Furthermore, the shift was driven disproportionately by the movement of the high income population. As a result, housing costs rose more in the destination locations and less in the origin locations. Job losses were milder and unemployment rates increased by less during the height of the pandemic for low-skilled workers in the destination locations than they did in the origin locations. The equilibrium response in housing costs due to spatial sorting reduced the rent burden faced by the average person, especially that faced by the average low-income person. Spatial sorting also led to a higher job growth in lower-income labor markets and mildly mitigated unemployment rates faced by the average low-income person during the pandemic peak. Welfare exercises accounting for these migration-induced spatial changes in rents and employment opportunities indicate that the pandemic-era spatial sorting raised the welfare of low-income population by more than that of the high-income population, mainly due to the mitigating effect of out-migration from high-density high-cost locations on housing cost.JEL Classifications
- R2 - Household Analysis