  Urban transportation and land use models have used theory and statistical
modeling methods to develop model systems that are useful in planning
applications. Machine learning methods have been considered too 'black box',
lacking interpretability, and their use has been limited within the land use
and transportation modeling literature. We present a use case in which
predictive accuracy is of primary importance, and compare the use of random
forest regression to multiple regression using ordinary least squares, to
predict rents per square foot in the San Francisco Bay Area using a large
volume of rental listings scraped from the Craigslist website. We find that we
are able to obtain useful predictions from both models using almost exclusively
local accessibility variables, though the predictive accuracy of the random
forest model is substantially higher.
