Global Study on Subway Network and Urban Vibrancy

How does the construction of new subway stations affect amenity mix in surrounding neighborhoods and how do changes in neighborhood amenities impact real estate prices?

TOPIC:

The objective of this research proposal is to look specifically at transit development across cities and ask how new subway station openings determine changes in vibrancy, and later, real estate prices along the route. Currently, six cities:  Beijing (China), São Paulo (Brazil), New York (US), Santiago (Chile), and Madrid (Spain) are under analysis. 

 

BACKGROUND:

  

There is a long lineage of analysis studying the impact of transportation on real estate prices. However, few studies exist that tie transit development to proximity and density of consumer amenities and in turn, vibrancy, to real estate prices. Preliminary literature reviews, however, have helped craft the evolution and best-practice parameters for this study.

 

METHODOLOGY:

 

Step 1:

Results from the proposal will be established utilizing the difference-in-difference-in-difference (DDD) methodology combined with the propensity score matching (to reduce bias) empirical methods to compare pre and post-differences between amenity mixes and real estate prices in 5 and 10-minute walking distances before and after phase I subway stations were completed. Assuming the data collected is sufficient for grouping complete and incomplete stations, we intend to take advantage of the natural experiment provided by the construction of a planned subway station and the incomplete status of a planned station in each city. This research will compare a completed station (treatment group) and an incomplete station (control group) at a time period (potentially three years prior) to the start of revenue service and then potentially three years after the start of revenue service. We will draw the 400m and 800m buffer around each station and collect amenity data such as a number of restaurants, cafes, libraries, salons. Using two relatively close proximity stations will allow us to granular differences in semi-similar spaces.

In terms of the DDD, the dependent variable will include the real average (potentially quarterly) prices of real estate assets within the circular buffer around each transit station. Independent variable coefficients will need to include indicators for the specific buffers after the start of revenue service and the potential number of new amenity openings or an increase in tax revenues for new jobs within the buffer.

 

In addition to analyzing treatment and not treatment stations against each other, it may also be valuable to understand the impact of development on the three constructed stations to understand how these three located were comparatively impacted by new amenities and real estate process – providing a complimentary addendum to this research.

Step 2:

Step 3:

 

Team

Siqi Zheng

MIT Sustainable Urbanization Lab, Department of Urban Studies and Planning, 

Center for Real Estate​

Adriano Borges Costa

MIT Sustainable Urbanization Lab, Department of Urban Studies and Planning, 

Center for Real Estate​

Camila Ramos, MCP

MIT Sustainable Urbanization Lab, Department of Urban Studies and Planning, 

Center for Real Estate​

Tabea Sonnenschein

MIT Sustainable Urbanization Lab, Department of Urban Studies and Planning, 

Center for Real Estate​

Kristopher Steele, MCP/MSRED

MIT Sustainable Urbanization Lab, Department of Urban Studies and Planning, 

Center for Real Estate​

MIT Sustainable Urbanization Lab

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