Essentially, I am looking at a set of housing data from 2000-2017 and I am examining 'affordability' by year through quantile regression. The Y variable (affordability) is a ratio of what an affordable mortgage payment would be (based on median income that year) to an estimate of what an actual mortgage payment for an individual house would be (based on interest rates and that home's actual sale price). The X is year, and the data is binned by year because this is a class project and I was told to avoid trying to do a real time series. So I use this code in R to run the regression:
QR <- rq(data$Ratio ~ data$Year, tau=seq(0.1, 0.9, by=0.1)) sumQR <- summary(QR) sumQR plot(sumQR)
And I get a series of plots that look like this (sorry they're each so small):
What I (think) I have is a plot for each year that shows the effect of being in that year, relative to the baseline (which I think is the year 2000), on the 'affordability' of houses at every 10th quantile of house price. So, for example, in 2006, the effect of being in 2006 vs 2000 is greater for cheap homes than expensive ones. In 2016, the effect of being in 2016 vs 2000 is greater for expensive homes than cheap ones. So my questions:
- Am I interpreting the plots for individual years correctly?
- How do I interpret the plot for Intercept? Is it just the plot for the year 2000, because that's being treated as the baseline?