It would depend on your specific problem statement, if you do not want to consider this as a time series data i.e. do not want to take year into account you would simply consider the correlation values between the home price and homeless population versus home price and domestic violence; whichever value will be high in magnitude (positively or negatively correlated) will be strongly correlated than the other

data_df['Home Price'].corr(data_df['Homeless pop'])

data_df['Home Price'].corr(data_df['Domestic Violence rate'])

If you want to consider time factor ; then you would have to convert the date column into datetime column and then consider three different time series

1. Year and home price
2. Year and homeless pop
3. Year and domestic violence rate

And then you can use granger causality test for causality or cross correlation to see the correlation between time series. You can refer to this post as well -

https://towardsdatascience.com/computing-cross-correlation-between-geophysical-time-series-488642be7bf0#:~:text=Cross%2Dcorrelation%20is%20an%20established,inference%20on%20the%20seismic%20data.

Please consider voting and accepting an appropriate answer.

It would depend on your specific problem statement, if you do not want to consider this as a time series data i.e. do not want to take year into account you would simply consider the correlation values between the home price and homeless population versus home price and domestic violence; whichever value will be high in magnitude (positively or negatively correlated) will be strongly correlated than the other

data_df['Home Price'].corr(data_df['Homeless pop'])

data_df['Home Price'].corr(data_df['Domestic Violence rate'])

If you want to consider time factor ; then you would have to convert the date column into datetime column and then consider three different time series

1. Year and home price
2. Year and homeless pop
3. Year and domestic violence rate

And then you can use granger causality test for causality or cross correlation to see the correlation between time series. You can refer to this post as well -

https://towardsdatascience.com/computing-cross-correlation-between-geophysical-time-series-488642be7bf0#:~:text=Cross%2Dcorrelation%20is%20an%20established,inference%20on%20the%20seismic%20data.

It would depend on your specific problem statement, if you do not want to consider this as a time series data i.e. do not want to take year into account you would simply consider the correlation values between the home price and homeless population versus home price and domestic violence; whichever value will be high in magnitude (positively or negatively correlated) will be strongly correlated than the other

data_df['Home Price'].corr(data_df['Homeless pop'])

data_df['Home Price'].corr(data_df['Domestic Violence rate'])

If you want to consider time factor ; then you would have to convert the date column into datetime column and then consider three different time series

1. Year and home price
2. Year and homeless pop
3. Year and domestic violence rate

And then you can use granger causality test for causality or cross correlation to see the correlation between time series. You can refer to this post as well -

https://towardsdatascience.com/computing-cross-correlation-between-geophysical-time-series-488642be7bf0#:~:text=Cross%2Dcorrelation%20is%20an%20established,inference%20on%20the%20seismic%20data.

It would depend on your specific problem statement, if you do not want to consider this as a time series data i.e. do not want to take year into account you would simply consider the correlation values between the home price and homeless population versus home price and domestic violence; whichever value will be high in magnitude (positively or negatively correlated) will be strongly correlated than the other

data_df['Home Price'].corr(data_df['Homeless pop'])

data_df['Home Price'].corr(data_df['Domestic Violence rate'])

If you want to consider time factor ; then you would have to convert the date column into datetime column and then consider three different time series

1. Year and home price
2. Year and homeless pop
3. Year and domestic violence rate

And then you can use granger causality test for causality or cross correlation to see the correlation between time series. You can refer to this post as well -

https://towardsdatascience.com/computing-cross-correlation-between-geophysical-time-series-488642be7bf0#:~:text=Cross%2Dcorrelation%20is%20an%20established,inference%20on%20the%20seismic%20data.

Please consider voting and accepting an appropriate answer.