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Insights from the Visualizations to complement the Story by ‘Raw data’

1.Areas with High levels of Distress had a high percentage of vacant and poor condition housing pointing towards the inference that housing is costed out by a person in distress and other priorities take over.
2.Areas of higher densities also have higher vacancy rates which points at the condition of housing. This is confirmed by the illustration with the condition of housing.
3.It can also be seen that areas with poor housing conditions also have a high percentage of subsidized housing. This gives us a sense of how even with subsidies, people are not able to afford a house in good condition.
4.A seemingly linear relationship does exist between the amount of vacant houses in the Hill District and the condition of those houses.
5.The effect of these vacant houses on the median sale price of houses in the area is also visible given that neighborhoods with higher vacancy rates selling houses at lower prices and those with lower vacancy rates selling high.
6.The pre-conception that Owner Occupied housing is generally in a better condition than rentals and higher valued was not supported by the visualizations. Houses with low owner occupancy were sold both at higher and lower ends of the spectrum. This led this data visualization to be inconclusive. 

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