Python and Geospatial tools create powerful and compelling data visualizations that supports Real Estate Analysis

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Photo by Franki Chamaki on Unsplash

Data visualization is a staple — a data preprocessing step — for data scientists developing machine models, and a necessity for business stakeholders with the desire to learn more about their business space.

Unfortunately, many industries today still rely on excel charting, either due to legacy technology or are unwitting of the benefits of powerful Python based visualizations (using Altair, Plotly, Matplotlib and Seaborn libraries) and other related softwares (e.g. tableau, geospatial and charting visualization).

The Real Estate industry is not indifferent to this sentiment.

In this post, we seek to explore a subset of the wide range of tools available for such visualization tasks, with 3 application genres in the real estate space. The dataset used is focused on transaction level data in Singapore’s private residential industry i.e. data containing known purchase price with stated property characteristics. …


Keith Tan

Keith is a Data Scientist at PropertyQuants, building time-series based machine- and deep- learning models for real estate valuation. He loves handling data.

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