Huawei was founded in Shenzhen, China in 1987. Huawei spent 30 years to become the largest Telecomm Manufacturer, yet Ericsson had spent more than 100 years in development history and they are no. 3 in market share. I am wondering, what is the magic of Shenzhen, a city that has incubated so many big companies that drive the economy of China?
Shenzhen is the so-called Silicon Valley of China. In the 1980s, the country’s national congress set Shenzhen as the first Special Economic Zone of China. Since then, Shenzhen has been the driving force of “Trickle Down” policy which “allows some population to get rich first so that they can lead the economy and create jobs, and last to achieve common prosperity.” Shenzhen homes 13 million people, almost double of the Bay Area in the west coast. In order to better understand the economy and the living situation of the people, I scraped more than 20k pieces of information on the housing marketing. In the following, I am presenting the findings and in the end comparing that with the Bay Area.
First, Data Collection
The website: Lianjia.com (The most widely used housing website)
Scraped Information includes the name, address, price, advantages, house plan, house areas, release date, tenancy term, leasing requirements, parking space, electricity, and distance to the bus station using Ocoparse.
Second, scraping process:
Third, visualize the data
The graph above shows the rental cost per squared meter. Blue means the lower end of the price spectrum, whereas the red shows the higher end of the spectrum. The size of the circle means the number of rentals on the market. The larger the circle, the denser the number is. As you can see the price radiance from the map, the price of rental close to Hong Kong (located south of Shenzhen) is higher than that away from.
Next, the rent and the correlated number of houses on the market. The bars are the number of records and the red line represents the unit cost per square meter in Chinese Yuan. For example, the 2nd bar from the left shows that Futian district 2550 units available for rent for an average of ￥130/m2($1.75/sqft).
Then, let’s take a closer look at the data for subdistricts (aka 2nd-degree districts. A district comprises of multiple subdistricts).
The following graph shows the cost per square meter vs. the size of an apartment in square meters. It is apparent that smaller apartment costs more to rent at the unit price level. A micro-apartment which is smaller than 2o sq.m. has the highest unit cost.
Homes that are 8-15 sq.m have the most expensive rental cost per unit. Whereas the homes between 70 to 110 sq.m have a much lower rental cost per unit. Rental cost bounces back with the homes larger than 110 sq.m.
Let’s look at the correlation between rent and walking distance to the subway station. The correlation coefficient value is -0.49 which means the distance to the subway station has a relatively strong relationship with the rental cost. The rental cost decreases as the distance to a subway station increases.
Huawei headquarter is located in Longgang District, of where has been historically treated as outside of the center of the city. The rent per unit cost is only 55 Yuan per square meter. So a 50 square meter apartment will cost 50 * 55 = 2750 Yuan, which is the Imperial system equates to 538 square feet for 400 dollars.
However, if you work for most tech companies including Tencent and Baidu and all the startup companies in Nanshan district and make an income of 8,000 Yuan a month and rent a 50 square meter apartment. The apartment monthly rent would be 50*130 = 6500 Yuan. So you cannot afford it and have to find a roommate. If you are a 3-5 year software developer, you probably make 20,000 Yuan a month. Then a 50 square meter apartment looks quite affordable in Nanshan district.
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Author: Ashley Ng
Ashley is a data enthusiast and passionate blogger with hands-on experience in web scraping. She focuses on capturing web data and analyzing in a way that empowers companies and businesses with actionable insights. Read her blog here to discover practical tips and applications on web data extraction
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