Journal of Housing Economics最新文献

This is the Introduction for the joint special issue of Land Economics (LE) and the Journal of Housing Economics (JHE) focused on property value applications using Zillow's ZTRAX data. ZTRAX is a real estate–focused database with over 400 million public records spanning nearly all U.S. counties across more than 30 years. The spatially explicit data on deed transfers, sale prices, and property characteristics has provided the basis for analyses on themes such as disaster risks, including wildfires, flooding, and chemical accidents; natural resources, including water quality, farmland, and coal; and land uses, such as open space, national parks, and critical habitat designation, among others.

A substantial increase in electricity demand has triggered a rising investment in energy infrastructure in the US over the last decade. This paper examines the capitalization effects of electricity transmission lines (TMLs) on nearby farmland values and housing prices in the Midwest from 2015 to 2019, based on 16,026 parcel-level farmland sales data from FarmlandFinder and 1,905,280 housing transaction data from the Zillow Transaction and Assessment Dataset database. Our estimation results reaffirm the disamenity effects of TMLs on housing property values and find that the disamenity effects are larger on houses in urban areas than in rural areas. Nearby TMLs generate premiums for surrounding farmland, which contrasts with the disamenity evidence due to aesthetics (either visual or audible) in the literature. We further show that farmland parcels within 0–2 km of TMLs in high-wind areas are approximately 3.10% more expensive than comparable parcels in low-wind areas. Our paper provides novel and contrarian evidence on the effects of TMLs on property values amid rising investment in energy infrastructure.

We examine the extent to which the increased prevalence of work from home (WFH) due to the COVID-19 pandemic has made green amenities more desirable.² Specifically, we focus on ten metropolitan statistical areas (MSAs) in the United States (Baltimore, Chicago, Cleveland, Los Angeles, Minneapolis/St. Paul, New York City, Philadelphia, Pittsburgh, St. Louis, and Tampa) and use a hedonic pricing approach to identify changes in the implicit prices of yard space and park proximity. We use a combination of data sources, including the Zillow Transaction and Assessment Database (ZTRAX), Open Street Maps, Longitudinal Employer Household Dynamics Origin–Destination Employment Statistics (LODES), National Land Cover Database (NLCD), and the Environmental Protection Agency’s EnviroAtlas to study the interactions between the exposure of a given neighborhood to WFH shock and private yard space, as well as proximity to the nearest park. Our findings suggest that home buyers in all the MSAs, except Minneapolis and Pittsburgh, assigned a greater value to private green amenities during the post-COVID period. However, for the MSAs of Los Angeles, New York, Philadelphia, and Pittsburgh, the marginal willingness to pay (MWTP) for private green spaces decreased in the post-COVID period in the areas with large WFH shocks. No significant change in the MWTP for proximity to public green spaces is noted in the post-COVID period for most MSAs. An event study of yard space reveals that most MSAs experienced an increase in the hedonic price of yard space in the post-COVID period. In the pre-COVID period, for many MSAs, the hedonic price of yard space was decreasing over the years, and this trend reversed in the post-COVID period. These results suggest that the preferred amenity bundles of people living in major cities in the U.S. have shifted as a result of changes in their commutes and work habits.

Causal studies on the effect of air quality on house prices, specifically focusing on a large metropolitan area, are rare and difficult to obtain because of potential endogeneity from residential sorting. In this study, we use the Aliso Canyon gas leak as a natural experiment to examine the effect of air quality on housing prices of Los Angeles City. Using a spatial difference-in-differences model, we estimate that houses within the 5-mile radius of the gas well experienced a 8.6% discount in price during the leak, and an additional 13.1% discount after the well was sealed and the air quality was restored. The decrease in price lasted beyond 18 months, with houses closer to the leak suffering higher discounts. We conclude that air quality degradation has a direct and longer term negative impact on house prices.

This paper studies how information frictions in the securitization market amplify the response of mortgage credit supply to house price shocks. Securitization prices and quantities endogenously result from an optimal contracting problem between investors and banks. Banks are better informed than investors about the quality of mortgages they originate, leading to adverse selection in securitization. Investors use the quantity sold as a screening device to induce banks to reveal truthful information. We find that adverse selection amplifies the response of a bank’s mortgage credit to house price shocks. The degree of amplification is also a function of the technological differences in managing portfolios between banks and investors. The model is informative on how information frictions can induce large fluctuations in mortgage credit supply.

The dual nature of property as both a consumption and investment good presents a challenge for household portfolios. Prior theoretical literature predicts a constraint imposed by property on investment decisions, and empirical studies support this notion. However, previous research often overlooks investigating the heterogeneity of this constraint and fails to differentiate between owner-occupied and investment property. Building on a stochastic control model, we analyse the UK’s Wealth and Assets Survey panel and find that along the distribution of how household allocate their wealth a one percentage point (pp) increase in the share of owner-occupied property in the total portfolio is associated with a 0.07 pp decrease in the share of stocks in liquid assets. However, this association varies significantly based on the value of the owner-occupied property share. For low values of the owner-occupied property share, the association with stockholdings is negligible. As the share of owner-occupied property increases, the negative association with stockholdings becomes more pronounced: when the owner-occupied property share reaches 90%, a further 1 pp increase corresponds to a 0.14 pp decrease in the share of stocks in liquid assets. By contrast, buy-to-let property shows no significant relationship with stockholdings, supporting the idea that the constraint on portfolio decisions is primarily driven by the role of property as a consumption good.

Standard measures of vertical inequality suggest that assessments are regressive in the sense that high-priced properties are often assessed at lower rates than low-priced properties. We show that some of this regressivity is due to the regression-based estimation procedures used by many jurisdictions to calculate assessments. A review of existing measures of assessment regressivity suggests that severe biases associated with regression-based procedures make them much less useful than the traditional price-related differential (PRD) as a measure of vertical inequality. To supplement existing measures of vertical inequality, we propose approaches using Gini coefficients, Suits index, and kernel density tests to provide information on the relationship between the assessment and sale price distributions. We compute the measures using data on sales prices and assessments for 48 large central city counties. The results suggest that the PRD remains a useful approach for measuring vertical inequality due to its simplicity and familiarity, while distribution-based procedures are helpful because they are not as sensitive to small numbers of very high-priced sales. Together, the approaches provide a more complete picture of how assessment rates vary across the full distribution of sales prices.

Concerns about crowding out of long-term rentals have led many cities to impose limits on the days that properties can be let via Airbnb or similar platforms in a given year. However, so far, there has been little research into the effect of such measures. Using micro-level data on long-term rental and Airbnb contracts for Sydney, Australia, we first estimate how much more landlords can earn on Airbnb compared to the long-term rental market. We use three different methods to control for differences in the quality of Airbnb and long-term rentals (hedonic regression, switching model, and matching approach) and find that, on average the weekly Airbnb rent is nearly double that in the long-term market. This Airbnb rent premium reflects the higher costs borne by Airbnb landlords. Consistent with a spatial equilibrium, landlords require a higher Airbnb rent premium in postcodes with a higher average time since the last review (a proxy for the Airbnb vacancy rate). Similarly, we find that Airbnb rent premia and time-since-last-review are lower in cheaper postcodes. It is therefore important to recognize that the impact of day restrictions is likely to be felt more in the cheaper rather than more touristic areas of a city.

We explore long-term patterns of the house price-income relationship across the 70 largest U.S. metropolitan areas. In line with a standard spatial equilibrium model, our empirical findings indicate that regional house price-income ratios are typically not stable, even over the long run. In contrast, panel regression models that relate house prices to aggregate personal income and allow for regional heterogeneity yield stationary long-term relationships in most areas. The house price-income relationship varies significantly across locations, underscoring the importance of using estimation techniques that allow for spatial heterogeneity. The substantial regional differences are closely related to the elasticity of housing supply.

A typical U.S. homebuyer's understanding of whether a property faces flood risk is based on whether the property is located inside the National Flood Insurance Program's (NFIP) Special Flood Hazard Area (SFHA). The SFHA boundary, however, may bias homebuyers’ perceptions of flood risk relative to unobserved true risk because the SFHA is an incomplete, and sometimes inaccurate, representation of flood hazards. Using a national dataset of property transactions, flood hazard data from the First Street Foundation, state-level measures of flood disclosure laws, and a spatially restricted triple-difference design, we distinguish capitalization effects of policy-driven (SFHA) versus quasi-objective (First Street) indicators of flood hazard. We identify these effects by assessing thousands of local spatial interactions between property SFHA designations, measures of quasi-objective flood hazard, and being in a state that mandates flood history disclosures. Being inside the SFHA generates a risk discount, but the signal is muted relative to underlying hazard exposure. Further, the SFHA signal can result in inefficient discounts for properties erroneously mapped in the SFHA. Disclosure requirements about flood history accentuate price effects for hazardous properties and result in more adequate risk internalization. In the absence of disclosures, however, we find either no or a weak risk signal for houses outside the SFHA that face flood hazard. Our results highlight potential benefits of updating SFHA boundaries to include all houses that may experience flooding, and argue in favor of requiring flood-related disclosures from sellers to improve the market's ability to internalize flood risk.