Find the Key Factor Influences the Rent with GIS

Taipei, the largest city and also the capital of Taiwan, has one of the highest housing prices in the world. According to official government data, the ratio for housing prices to income in Taipei is 15, which means an average adult in Taipei must work a least for fifteen years to own a dwelling, and this ratio doesn’t even count the money spent on daily lives. The same index in London is 8.5 while in New York is 6.1.

Despite the housing price is so unaffordable in Taipei, this crowded metropolis still attracts a lot young people to move in. Because as the most developed and richest area in Taiwan, Taipei offers abundant job opportunities and numerous educational institutions that other cities can hardly compare.

Combining these reasons, most non-native young people in Taipei turn into the renting market. But unfortunately, they usually need to find a place in a short time and have no background knowledge to evaluate whether the rent is reasonable, some landlords can rent their houses at a very high level but has no coordinating quality.

According to the existing problem mentioned above, it will significantly help the young people in Taipei if a rent estimation model could be established. Therefore, the goal of this case study is to discover the dominating factors that influence the rent in Taipei.

If such factors can be discovered and summarized, we could provide a guideline for those who are seeking to rent a house in Taipei, helping them to estimate a reasonable budget based on their needs and also to find a suitable house quickly.

Based on previous studies and related experiences, we assume that the rent of a house is majorly influenced by factors from two different aspects: the internal and the external. The internal aspect of a house focuses on the features of itself, such as the size, the decoration, and the equipment inside. On the other hand, the external aspect concerns the environmental factors, including its location and the accessibility to different services.

In this study, we selected several variables into the internal aspect like the size, the amount of furniture, and the type of the house. And for the external aspect, two main factors were considered for measuring the quality of life: the distance to public transportation and the distance to the nearest park.

For acquiring enough house renting data in a short time, we collected data from the biggest real estate online platform in Taiwan and then geocoded these data by its address. Since the internal variables are provided comprehensively by the source, we listed them as independent variables in the regression analysis after checking its correlation.

▲ Fig.1 The distribution of bus stops and parks in this area
And for the external variables, we overlaid the point data of house for renting with point data of bus stops and polygon data of parks. The Proximity Analysis (Buffer and Near) and Spatial Join were selected to calculate the variables we want to use in the regression model. With SuperGIS Desktop, we successfully attained the proximity to bus stops and found the nearest park for every house. Finally, these environmental variables are also putted into the same regression model for discovering their influence to the rent.
▲ Fig.2 Using Proximity Analysis to calculate the facilities in the neighborhood
▲ Fig.3 The table generated after Proximity Analysis
The final result of the analysis shows that several variables have statistical significance to the rent, including two internal variables and one external variable. The size and the type of the house will all considerably influence the rent. For every meter square living space, tenants should pay 7.1 US dollars a month. And when the house type is a suite, tenants will pay extra 40 US dollars per month than renting a room.

Finally, for the external variables, we found that the accessibility to one bus stop will increase the rent by 0.5 US dollar.
Now the people who are seeking to rent a house in Taipei have more detailed information about how these factors will change the rent and can hence make better decisions.

The study is an excellent example of how real estate research can utilize GIS technologies in a rather basic and simple way. We used buffer analysis to calculate external variables with SuperGIS Desktop, including the distance to the nearest park and the accessibility of public transportation. But with more spatial data, we can analyze environmental factors more deliberately by using spatial analysis and network analysis of SuperGIS extensions. The researchers of real estate companies can integrate, manage, and analyze various spatial data by using SuperGIS Desktop and its extensions. Furthermore, if we use GIS server software like SuperGIS Server, we can enable other users to conduct such analysis online, making the information more accessible and transparent.
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