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
AbstractUnderstanding 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.
Local Segregation of Neighborhoods and Amenities
AbstractThis 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
AbstractUsing the outbreak of COVID-19 in Singapore as a quasi-natural experiment, we investigate tenants’ changing behaviroual responses to traffic noise in the rental housing market, using 46,980 transaction records between 2006 and 2022. Our difference-in-differences (DiD) estimates show that traffic noise decreases housing rents by 3.8% in the immediate first year after the pandemic outbreak. The estimate rockets to 12.7% in the subsequent year, which is equivalent to 186.7 US dollars per month. Our results are robust to parallel trend analysis, permutation placebo tests, and robustness tests using alternative distance thresholds or linear distance to the nearest major road. Then, we adopt a machine learning text analysis of 10,425 rental housing advertisements, showing that tenants’ preference for quietness significantly increases during this time period. The new work-from-home business model and rising traffic from delivery services can partially explain this pattern. To the best of our knowledge, this is the first paper using large volume of transaction records to quantify people's willingness to pay for quietness in the COVID-19 context. Our results have policy implications on the interaction between urban planning and human wellbeing and shed light on the post-pandemic urban design in promoting health living.
- R1 - General Regional Economics
- R2 - Household Analysis