Summary
Our goal for this project was to find what factors were most important in determining the housing prices in Los Angeles cities. Using Multiple Linear Regression, we created a model which predicted housing prices at an 80% accuracy rate. The model included variables relating to the city's GDP per capita, proportion of minorities, total population, proportion of registered voters and proportion of registered voters who are democrats.
In our findings, Counties’ GDP per Capita and the percentage of registered Democrat voters being less than 31.25% are highly correlated to Housing Prices in counties across California. There was no correlation to more diverse counties having an effect on Housing Prices. In more Democratic populated areas, housing prices are higher compared to less Democratic populated areas. As well as counties with lower proportions of registered voters tend to have higher housing prices.
In our findings, Counties’ GDP per Capita and the percentage of registered Democrat voters being less than 31.25% are highly correlated to Housing Prices in counties across California. There was no correlation to more diverse counties having an effect on Housing Prices. In more Democratic populated areas, housing prices are higher compared to less Democratic populated areas. As well as counties with lower proportions of registered voters tend to have higher housing prices.
