Neighborhood Amenities
Paper Session
Friday, Jan. 5, 2024 8:00 AM - 10:00 AM (CST)
- Chair: Sophie Calder-Wang, University of Pennsylvania
Pricing Neighborhood Amenities: A Proxy-Based Approach
Abstract
Understanding how housing markets price neighborhood amenities is key to unpacking socioeconomic disparities. Yet prior approaches to amenity pricing have suffered from the key confounder of unmeasured neighborhood quality, often leading to wrong-signed estimates. In this paper, we develop a novel proxy-based method that allows us to more accurately estimate a wide set of housing amenities in the midst of unobserved neighborhood quality. Using detailed migration data, we construct an innovative measure of locational desirability–Geographic PageRank–to use as the proxy variable for quality. We show that this new approach can successfully correct the “wrong-signed” problem in the amenities valuation literature when applied to a standard measure of environmental air quality. The estimated amenity prices will be a key input to evaluating the returns to investment in local public goods or environmental policies, including their roles in reducing housing disparities.Code to implement the methods proposed in the paper is available in the Stata package, which is available on github \url{https://github.com/ZhongShusheng/proxy_stata_package}
The Historic Roots of Neighborhood Heterogeneity
Abstract
This paper sheds light on historic railroad placement as a predictor of contemporary segregation. Employing a digitized map of Texas railroads circa 1911 to compare census block groups separated by tracks in 2018, I first document discrete changes in house prices, income, and racial composition at the railroad boundary. I then use spatial difference-in-differences to estimate an unconditional house price premium of 21% to live on the high amenity side of the tracks. Hedonic estimates of the model predict the house price premium is more likely explained by differences in income, racial demographics and test scores; and less likely driven by differences in private consumption amenities such as restaurants and bars. To mitigate the effects of unobserved neighborhood quality attributes, I estimate the model on samples progressively close to the railroad boundary on either side. In doing so I find new evidence that neighborhood racial demographics are a stronger predictor of the house price premium than income, school quality, and access to private consumption amenities.The Price of Quietness: Behavioural Responses to Road Traffic Noise during COVID-19
Abstract
Using the outbreak of COVID-19 in Singapore as a quasi-natural experiment, we investigate tenants' changing responses to road traffic noise in the rental housing market, using 46,980 transaction records between 2006 and 2022. Our difference-in-differences estimates show that road traffic noise decreases housing rents by 3.8% immediately after the pandemic outbreak and further declines by 12.7% in the subsequent year—equivalent to 186.7 US dollars per month. The results are robust to parallel trend analysis, permutation placebo tests, and tests using alternative distance thresholds or distance to the nearest main road. Then, we adopt a machine learning text analysis of 10,425 rental housing advertisements, showing that tenants' preference for quietness increases by approximately 10% from 2019 into 2020. The new work-from-home business model and rising traffic from delivery services can explain for this pattern. To the best of our knowledge, this is the first paper using a large volume of transaction records to quantify city dwellers' willingness to pay for quietness in the COVID-19 context. Our results have policy implications for other nations and post-pandemic era on the interaction among urban planning, transport networks, and human settlements, and shed light on the pathway to achieve sustainable development goals.Discussant(s)
Erika Moszkowski
,
Federal Reserve Board
Nicolai Kuminoff
,
Arizona State University
Kwan Ok Lee
,
National University of Singapore
Rebecca Taylor
,
University of Sydney
JEL Classifications
- R1 - General Regional Economics
- R2 - Household Analysis