Real estate pricing data has traditionally been under lock-and-key, but with the rise of short-term rentals and their openly available pricing and demand data, we can use this information as a proxy to the overall housing market.
Understanding local real estate markets through short-term rentals requires knowing where the short-term listings are and understanding the demand for these listings.
Since short-term rentals data is public across a variety of apps, one could manually go through all of these apps and copy-paste the data into a spreadsheet for later analysis. However, many companies are increasingly resorting to automating this task using data scraping, to automatically collect relevant short-term rentals data.
You'll want to first target a location or city you're interested in and get back the basic listing details, typically including location and price. You can then go a step further for each listing and look up details, reviews and get the future availability calendar from certain sites.
Once you have a location in mind, you'll want to scrape all of the short-term rentals nearby.
After you've built up a list of listings in a location, you may want to get details about them, including photos, descriptions and more.
In addition to looking up listing details, you can also get the future availability of listings to see which ones are more in demand compared to others - as well as how the future prices fluctuate over the year.
You may also be interested in more "soft" data about local markets, including reviews and information about hosts (see who is a new vs. tenured host in different markets).