Does the infrastructure of Amsterdam make a difference?

Do factors like distance to/from tram stops, metro stops, central station, swimming pools, religious sites, parks, Schiphol airport, sport facilities influence the prices of AirBnB listings in Amsterdam? And does the effect of the several infrastructure components on AirBnB pricing differ depending on whether there is a holiday/weekend or not?

Motivation

Infrastructure is the key to developing a successful tourism destination. Tourism industry stimulates investments in new infrastructure, most of which improves the living conditions of local residents as well as tourists.The reason for diving into this research is that the distances to transportation hubs and busy areas are important considerations for tourists when choosing an accommodation. Also other infrastructure components like swimming pools, sport facilities, religious sites or parks can be factors which improve the living conditions of local residents as well as tourists. These components are for some people of vital importance in choosing a place to stay. While AirBnB plays an important role in the tourism industry of today, infrastructure components might influence prices of AirBnB listings accordingly. Moreover, the holiday season and weekends can be contributing factors to prices of AirBnB listings because the demand for accommodations increases. Therefore questions like: ‘Does the distance to/from Amsterdam Central Station or the distance to/from Schiphol Airport in relation to the location of the AirBnB listing influence the prices of that AirBnB listing?’ and ‘does the effect of the several infrastructure components on AirBnB pricing differ depending on whether there is a holiday/weekend or not?’ could be asked. This code is written in order to find answers to these questions which focuses on AirBnB listings in Amsterdam.

Method

The research method that is used in this project is a regression analysis. This method is used to predict the influence of the independent variables ‘Distance to/from tram stops’, ‘Distance to/from metro stops’, ‘Distance to/from Amsterdam Central Station’, ‘Distance to/from swimming pools’, ‘Distance to/from religious sites’, ‘Distance to/from parks’, ‘Distance to/from Schiphol Airport’, ‘Distance to/from sport facilities’ and number of ‘tram stops’, ‘metro stops’, ‘swimming pools’, ‘religious sites’, ‘parks’ and ‘sport facilities’ within a radius of 500 meter on the dependent variable ‘Prices of AirBnB listings’. Furthermore the moderators ‘Holidays in NL’ and ‘Weekend’ will be included and analyzed. According to this model, the research questions can be answered.

Moderator: Holidays in NL and Weekend. The relationship between the infrastructure of Amsterdam and price of AirBnB may be influenced by whether it is holiday in the Netherlands or weekend.

Results

According to the plots it can be found that schiphol_dist is positively correlated with price, while the rest of the independent variables are negatively correlated with price. Since the value of dist is particularly large, the log() function is used when relationships are not linear, it allows to process the value for visualization again in order to prevent the influence of the huge value on the model. Based on further visualization, it can be found that price around 175 has a significant inflection point that changes the direction.

We also established multiple regression model “model_log_dist_num_wkend_hol” to find the possible interaction factors (weekend and holidays) would affect the result. The adjusted R2 is 0.173, which means the multiple regression model is a good fit, that proves the effectiveness of the model.

Conclusions

As a conclusion, it can be stated that every distance-variable (Schiphol Airport, Central Station, tram stops, metro stops, swimming pools, religious sites, parks, sport facilities) has a significant effect on the prices of AirBnB listings in Amsterdam, whereas the interaction effect of holiday and weekend show no significant effect on the model. This explains that distances from and to important infrastructure sites play an important role in the price setting of AirBnB listings. The positive inflection point with regards to the distance to/from schiphol airport could be due to the fact of noise pollution of the airport. It can be concluded that residents and tourists of AirBnB listings do not want to be as close to the infrastructure (in particular for Schiphol) while also staying not too far away. Hence, the positive inflection point at a price of 175. In the distance plots of cs, tram, metro and religion sites, a small increase at first is followed by a negative relationship between the distances and the prices of AirBnB. This can also be related to the fact of noises nearby the AirBnB listings due to trams/cs/metros while at the same time being mobile and staying close to the infrastructure sites. Parks, swimming pools and sport facilities show an immediate negative relationship between the distances of these infrastructure sites and the prices of AirBnB listings. This indicates the importance of these infrastructures sites nearby the AirBnB listings. All in all, the closer the infrastructure sites, the higher the prices of AirBnB listings (except for Schiphol airport). This shows that tourists and residents value the infrastructure sites to be

Authors: Francisco Riaño Martinez, Jan van der Doe, Kamila Majdlenová, Pomme Verhagen, Yuetong Bi