Final Project

Master Thesis of our Masters in Business Analytics. The focus of this project is the city of Madrid, Spain.

Title in Spanish: Predicción del precio de la vivienda mediante el uso de métodos estadísticos espaciales.

Title in English: Prediction of house prices through the use of spatial statistical methods.


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Abstract

The housing market offers great opportunities to those who know how to take advantage of them. In this document we propose a spatial statistical study of the price of housing aimed both at obtaining an accurate prediction and at ensuring that said prediction is based on a solid and stable statistical model throughout the space; guaranteeing acceptable results even when trying to predict based on the data of a new house, totally unrelated to those used in the modeling phase. Thanks to these techniques, we intend to offer a different point of view, basing our study on the influence that the price of a house suffers due to the value of its neighboring homes, and thus proposing a new framework of work and research little explored compared to other more traditional statistical models used for this topic.

Statistical Models used

• Generalized Linear Model (GLM)
• Spatial Generalized Linear Model (GLM)
• Spatial AutoRegressive (SAR)

Data Sources

• Idealista API: allowed us to get 50 property records per GET request, up to 100 requests per month. (Requested from Idealista Labs). Requested in February 2020.

• ALL-JSON-FILES.csv: all JSON data collected from the API, united into one single file.

• Datos_abiertos_Red_de_Metros.zip: Metro Madrid Shapefile. Source: https://data-crtm.opendata.arcgis.com/datasets/m4-accesos?geometry=-3.730%2C40.417%2C-3.669%2C40.429. (Powered by CRTM). Downloaded in June 2020.

• Madrid_Postal_Codes.zip: Postal Codes of Madrid Shapefile. Source: https://www.madrid.org/nomecalles/DescargaBDTCorte.icm. (Centro Regional de Información Cartográfica. Comunidad de Madrid). Downloaded in June 2020.

• Salud_Farmacias.zip: Pharmacies in Madrid Shapefile. Source: https://www.madrid.org/nomecalles/DescargaBDTCorte.icm. (Centro Regional de Información Cartográfica. Comunidad de Madrid). Downloaded in June 2020.

• Educacion_Centros.zip: Public Education Centres in Madrid Shapefile. Source: https://www.madrid.org/nomecalles/DescargaBDTCorte.icm. (Centro Regional de Información Cartográfica. Comunidad de Madrid). Downloaded in June 2020.

Data Cleaning & Transformation

Data downloaded over time (API requests) as JSON files with Idealista_API.R, then unified all records and deleted duplicates (by propertyCode), and finally converted to DataFrame with JSON2DF.ipynb on Google Colab.

Made sure that all datatypes are appropriate (e.g. size = number, district = character).

Dashboard Data Sources

Data sources which are especifically used for the Shiny Dashboard.

• final_df.rds: DataFrame of consolidated data.

• final_df_scaled.rds: scaled/normalized DataFrame of consolidated data.

• final_model.rds: Final statistical model.

• leaflet_data.Rda: DataFrame with all visual data for the interactive leaflet.

• cod_postal_analysis.Rda: DataFrame of Madrid postal data with average property price per area.

Contributors

• Andrés David DELGADO MOSQUERA

• Iván Carlos BARRIO HERREROS

• David Jo Konstantin TOFAN