Real Estate Predictions With Web Scraping

Real Estate Predictions Enhanced by Web Scraping Techniques

By Julius Černiauskas, CEO at Oxylabs.io

Real estate, while a relatively stable investment, has to be evaluated based on numerous external and internal factors. Some of them might have an immediately obvious impact on pricing – such as construction or last renovation date. They are also relatively easy to find.

There are factors, however, that may have a significant impact on the value of a property but are either harder to calculate or difficult to acquire. Crime rates, for example, have a propensity to affect the desirability of properties by a large margin. Getting accurate data, however, can be challenging in many countries.

On the other hand, data such as the density of nearby businesses might be easier to collect. Yet, it’s not always clear how these factors might affect property valuations. It’s obvious that it somehow matters, but it isn’t exactly as clear how much.

Web scraping can attenuate these issues by some margin. Automated data collection online can deliver fairly accurate data on most external factors or provide a window into how these factors might affect properties over time.

Web scraping without the technical fluff

The process can be loosely defined as automated online data collection from publicly available sources. At first glance, it seems as if there are so many limitations placed on public web scraping that it can rarely be useful. Most of what seems interesting or valuable should be hidden behind a login screen.

Surprisingly, while the statement is true, there’s still lots of valuable information remaining. Real estate is one of the few industries where there’s little data hidden behind logins. Scraping can be remarkably useful to anyone who wants to support traditional analysis methods.

To better imagine how automated online data collection works, think of a browser that does all the actions without any user input. Instead of, however, simply displaying data, it downloads the entire page into local storage.

Such a process repeats itself until some desired amount of URLs has been traveled through. After that, usually, some search and parse function is initiated to extract the desired data from the content. Finally, all the valuable information is exported to an easy to parse file format (e.g., CSV, JSON) and the rest is either sent to a database or completely scrapped.

Data is then, usually, used by analysts to develop actionable insights for the business. In the case of the real estate industry, a team would be able to predict how impactful some factors might be on property valuations.

What should we look for?

Now, a slightly different issue that reveals itself. There is so much data available that it can be hard to choose what seems valuable. After all, whatever is found on the internet is likely indirectly related to property value. In other words, it won’t be immediately apparent which data sources indicate what.

Frequently, businesses opt for the first idea that comes to mind. These are usually competitor data or comments and feedback left online by customers. Both options aren’t entirely bad ideas, but they’re not as valuable to real estate.

One of the easiest ways to get started with scraping in real estate is to start collecting crime data, assuming it is not available officially. Data about crime rates is available from numerous sources. The best one will always be the one your lawyer tells you is okay to scrape.

Over time there should be enough data collected to get an accurate weekly, monthly, and yearly crime rate gauge in cities or even parts of them. Crime rates will have a clear negative impact on property value. Such data, however, would be inaccessible to any competitors, which would make more accurate predictions possible.

Another route is to scrape data about all businesses within a specific distance from a property of interest. Most importantly, anonymized reviews could be scraped as an indicator of the quality of that region.

Businesses that have a high rating, regardless of their type, will usually be located in more upscale neighborhoods. Types of businesses and industries can also be considered as impactful. Banks, for example, usually nest in financial districts, which would indicate a better property value.

Additionally, web scraping can be used to evaluate the future of specific regions more accurately. Job growth, for example, is a good indicator of prosperity, which, in turn, increases property prices. Other factors, such as investments in infrastructure or number of active permits, can be used to prop up calculations as well.

Finally, a lot of the classic scraping methods are available. Property listings, competitor monitoring, foreclosures, and many more data points can be collected. The only differentiating factor for web scraping is whether the data acquired is useful. If there is some potential it can be, there’s no reason to avoid scraping.

Conclusion

Real estate businesses have an incredible opportunity to get involved with web scraping. Such data can be immensely beneficial in various ways, but, primarily, for improving the accuracy of property valuations. In other words, scraping improves the ROI of real estate investments.

Additionally, secondary signals that may be important to a real estate company can be extracted. Web scraping lends itself perfectly to market trends analysis, real estate economics, and other deeper research topics. It can provide all the necessary information. All it takes is drive and willingness to use it.