Conference Agenda

This paper examines the effect of information sharing agreements between Multiple Listing Service (MLS) platforms on the economic outcomes of listings. We present a theoretical model involving search and matching frictions that predicts that properties will trade at a price discount in the absence of information sharing. To test the model's prediction, we capitalize on a temporary suspension in the sharing agreements between MLS platforms in South Florida. We find that listings by a broker that operates in an area where brokers from the other MLS platform also operate significantly (and hence a reliance on brokers from the other MLS platform), trade at a discount during the suspension period. The results also reveal both heterogeneous and liquidity effects. Overall, we highlight the role of sharing agreements in housing prices.

This paper investigates the impacts of Real Estate Investment Trust (REIT) acquisitions on local land prices and the residential market in Japan. REIT acquisitions fell significantly in Japan after 2007 due to the global credit crunch, which was orthogonal to the domestic real estate market fundamentals. Exploiting this variation, we present casual evidence that REIT acquisition has a positive effect on local land prices in general but the effects are heterogeneous across different market segments within a city. Specifically, we find a greater post-crisis decline in the price of large land parcels (mainly used for developing multi-family homes (MFHs)) in regions with higher pre-crisis REIT acquisition intensity, while the prices for small-sized lands, which are used for developing single-family homes (SFHs) and rarely appear in REIT portfolios, were not affected. We do not find the liquidity of large land parcels to have significantly changed, reflecting an inelastic land supply. However, falling land prices increased construction starts of MFHs relative to that of SFHs.

This study examines the effect of smart city development on urban air pollution from the perspective of smart city policy implementation and smart city development framework. We manually collect data on 166 global leading smart cities from 2005 to 2020 and find evidence of a positive effect of smart city development on air pollution reduction. First, using the principal components analysis (PCA) technique, we synthesise thirty-five indicators that broadly represent the smartness of a city and generate a smart city index. Secondly, using the staggered difference-in-difference (DID) method, we find that cities experience a reduction in PM2.5 and PM10 in response to the establishment of smart city policy. Third, by using the multilevel model which accounts for country effects, we find a negative relationship between our synthesised smartness score (Smart city index) and urban air pollution. We further investigate the smart city framework and break it down into six key dimensions, including smart economy, smart governance, smart people, smart mobility, smart living, and smart environment. This allows us to test the mechanism via which smart city policy impacts urban air quality. Specifically, we find that all six dimensions strengthen the reduction effects on PM2.5 and PM10. Furthermore, our results support a moderation role of institutional quality in the relationship between smart city policy and air pollution, suggesting that smart city policy formulation and implementation along with a higher level of institutional quality produces a stronger effect on air pollution reduction. Finally, our results reveal that the average cities’ PM2.5 and PM10 reduced by 4.1% and 3.8% respectively in response to the establishment of smart city policy.

Short-term rental market in Hong Kong witnessed a dramatic change from boom to bust, which poses a critical question to hosts: how to survive. Extent studies acknowledge the importance of both internal and external factors but lack an integrative lens to explore how they jointly affect survival. This study aims to fill this gap by identifying how the impacts of strategic proximity vary with market conditions. The empirical analysis was conducted with survival analysis based on 17,375 Airbnb units operated by multiunit hosts. When operationalizing strategic proximity, we quantify aesthetic styles by probabilistic distributions predicted by a pre-trained deep learning model. The findings suggest that functional and geographic proximity improves survival regardless of market conditions, while aesthetic proximity is encouraged in a growing market but discouraged in a declining market. This study contributes to survival and hospitality literature by embracing the interaction between strategic choice and environmental conditions. The findings also provide guidance for short-term rental practitioners and policymakers.