🤖 Neural Network and Chill

Team Members:
Bosco Lee
Rachel Lui
Syaiful Iylia

📒Project Description

Our project aims to shed some light on the residential real estate landscape by delving into an online residential leasing and sales platform - Idealista, and further looking into the macro economic trends of Spain’s residential property rental and sales market from other relevant websites. What made us curious about this data was the volatile and unpredictable nature of the housing market, and we felt that Spain was an ideal location to analyse given the ease of API access provided by Idealista, along with the high number of cities and data available in those cities for us to analyse. Using the insights obtained from the data analysis, our objective is to empower individuals, especially members of the DS105 course, with the knowledge necessary to make informed decisions in the Spanish residential real estate market. Whether you are an investor seeking profitable opportunities or an individual looking for a place to call home, our findings and recommendations will aid in navigating this complex market.

To achieve this, we have conducted a comprehensive comparative analysis of real estate prices in 10 cities of Spain, considering both the sale and rental markets. This dual analysis provides a holistic view of the Spanish real estate landscape, uncovering key insights into the factors influencing property prices across different cities and their relative standing in relation to other variables.

By studying the role of location, we aimed to uncover how geographical variations influence real estate prices across different regions in Spain. We conducted intercity and intracity analyses and explored the correlation between location factors and property prices. Understanding these location-based factors provided a foundation for comprehending the pricing patterns observed. In addition to location, we also investigated other relevant variables that influence real estate prices by conducting analysis on rental property data. This part encompasses economic indicators and demographic factors, investigating the relationship of them playing a role in shaping property prices. By analysing the relationships between these variables and real estate prices, we sought to identify patterns and correlations that would deepen our understanding of the pricing dynamics.

We have aspired to provide detailed analysis and visual representations of the data, allowing for a clear and concise presentation of our findings. We have mainly used Numpy, Pandas, matplotlib, Geopandas and plotnine to clean and wrangle the data, in order to produce specific outputs and graphs that align with our project objective. Additionally, we have explored the underlying factors contributing to the observed correlations between prices and relevant variables. This understanding will help us offer meaningful suggestions and insights to assist potential investors or renters in making sound decisions.