Urban Transportation +
Consumption Vibrancy

Beijing’s new subway neighborhoods: new F&B services contribute to increasing property rental values.


Subways provide convenient access in cities, especially for people living or working close to a station. As there is limited evidence on the changes in businesses following subway investments, we ask the question:

How do new subway stations impact restaurants and how does this influence property values?

To understand the connection between new subway stations and home value appreciation influenced by new restaurants, we focus on how Beijing's expanding subways for the past decade affects neighborhoods across the city.

Beijing's Subway System


A timelapse of Beijing's expanding subway system.

Source: Hat600 [GFDL or CC BY-SA 4.0] from Wikimedia Commons

Beijing has experienced massive subway construction and investment for the past decade. From 2007 to 2013, 12 new subway lines were put into use, increasing the total number of subway stations from 70 to 274.


By the end of 2013, with a revenue length of 476 km and a typical daily ridership exceeding 10 million, 

Beijing boasts the second longest and the busiest urban subway system in the world.

We used dianping.com data (China's Yelp) to measure how new subway stations affect the demand + supply of restaurants in the surrounding neighborhood.

The spatial distribution of restaurants in Beijing using dianping.com data


The Transit - Retail Amenities Connection

Increasing consumption vibrancy: quantity, diversity, and consumer demand of restaurants.


To understand the transit-retail amenities connection between 2004 to 2013, we used supply (number and diversity of cuisines) and demand (consumer volume proxied by customer reviews) data from 86,456 restaurants in Beijing. 

We looked at 154 new subway stations, and defined a subway neighborhood as a 400 meter buffer around a station. 400 meters was chosen because it is a widely accepted distance for walkability.

A new subway station increases the quantity, diversity of cuisines and consumer demand of nearby restaurants, mainly within 800 meters from the station.


The increased number of restaurants following a new subway station is accompanied by a rise in the diversity of cuisine choices. 

A neighborhood's restaurant diversity and number of new restaurants increases significantly in the inner city while the effect is insignificant in the outer city. 


Following the opening of a new subway station, the annual number of restaurant openings in inner-city neighborhoods is 2.19x more than in outer-city neighborhoods.

The number of fast food services increases more than sit-down restaurants following the opening of new stations, especially within 800m of the station.


We used WoAiWoJia data (a major real estate broker) to measure how restaurants contribute to property rental values in the face of new subway stations.

The Multiplier Effect

The appreciation of local home values is connected to restaurants brought about by new subway stations.


To understand the transit-retail amenities-property value connection between 2006 to 2013, we gathered 273,000 rental transactions in Beijing, of which 13,640 units were located in subway neighborhoods.

We looked at 167 new subway stations, and also investigated these effects at a subway neighborhood level using 0-400m and 400-800m buffers from the station.


When restaurant activity (new openings and diversity) is included in the analysis, rent appreciation increases about 48% in the 0-400m buffer -- from 16.1% to 23.8% after the openign of a station.

This means changes in neighborhood restaurant activities capture 20-40% of the home value appreciation following the construction of a subway station.

Understanding how public investment in transit influences private markets (eg. restaurants), which in turn impacts property values, is important as these interactions reshape urban spatial structure and contribute to quality of life.


Siqi Zheng
MIT China Future City Lab, Department of Urban Studies and Planning, 
Center for Real Estate

Xiaoke Hu
Tsinghua University

Jianghao Wang
Chinese Academy of Sciences

Rui Wang
University of California, Los Angeles

Yangfei Xu
Tsinghua University, University of California Berkeley

Xiaonan Zhang
Tsinghua University

Web and Visuals

Kai Ying Lau

MIT China Future City Lab


Icons from free-icon-rainbow.com




The material on this website can be used freely. We just ask that it is duly credited as a project by MIT China Future City Lab, and a PDF is sent to cfclab@mit.edu.