Real Estate Investment and Development: Data Visualization as a tool

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

Point- And Aggregate- Level Visualizations

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Fig 1. Barchart on 10 most and least expensive private residential projects in Singapore. Image by author.
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Fig 2. Scatterplot on transacted prices for 3 chosen projects in Singapore. Image by author.
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Fig 3. Radar chart for project attribute comparison. Image by author.

Geospatial Visualization

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Fig 4. Buffer arcs to segment Singapore’s different Central Region zones (silhouette). Image by author.
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Fig 5. Buffer arcs to segment Singapore’s different Central Region zones (map). Image by author.
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Fig 6. Residential property capital appreciation across OCR, RCR, CCR between 2012 to 2019, SIngapore. Image by author.
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Fig 7. Residential property capital appreciation across districts between 2009 to 2014, SIngapore. Image by author.
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Fig 8. Distance of buildings from CCR, Singapore. Image by author.

Time Series Visualizations

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Fig 9. Residential Property Price Index for selected districts, Singapore, 2008–2019. Image by author.

Beyond That, What Else?

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Fig 10. Gif of an interactive chart using Plotly, on house prices in Berlin. Image from Elizabeth Ter.
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Fig 11. Interactive chart on 2014 US population, by city. Image from Plotly.
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Fig 12. Bubble Chart on real estate investment flow and GDP. Image from JLL

Great! Where can I start learning these?

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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