Mikhail Sisin Co-founder of cloud-based web scraping and data extraction platform Diggernaut. Over 10 years of experience in data extraction, ETL, AI, and ML.

Web scraping as a tool for the participants of the real estate market

2 min read

In today’s world, information plays a key role. As Nathan Rothschild said, “Who owns the information, he owns the world,” and indeed it is. The one who is quicker and can gather data most efficient way gets analytics first and can use it effectively in their business.

It is no secret that the real estate market participants are continually using analytics to various issues arising in the course of conducting their business, for example, to assess the value of the property. After all, to make an assessment the correct way you must take into account many factors such as the age of the object, its state, area, and other attributes; the number of which can be within hundreds of different aspects.

Moreover, to make a correct analysis, you should take into account not only the parameters of a particular object, but also nearby ones located in the same area, city, or state. Additionally considered are various environmental factors not directly related to the object, but related to the geographical area of the object. For example, this may be the level of the crime situation or the environment. This is a huge amount of information that is impossible to collect manually. A company that collects data accurately has better success than a company that doesn’t follow the protocol of collecting data and may end up with less or inadequate information.

One possibility is to obtain information needed to use specialized resources that provide access to the information that has been collected. The disadvantage of this method is, as a rule, the high price of subscriptions and being limited to a strict set of data.  For example, if a service provides limited data (e.g., only taxes and sales), the analysis is also limited and incomplete due to the lack of other factors, such as crime statistics. A company needs to have a full collection of data on a broad array of factors to obtain a full analysis. Companies that lack information are not giving a clear overall picture to their customers or clients as compared to a company that collects many different factors and gives an unbiased report.

Another solution to this problem is web scraping. Web scraping – is the process of automated data collection from various web resources, such as websites. In this case, the advantage is that you define where and what information to collect and how to form data sets. You can even configure different sets of data and then combine them for a particular attribute, such as geolocation. The downside of this method is that to write a scraper, someone should have some expertise in programming, but the majority of real estate professionals have little to no programming experience.  They are experts in their field of real estate, but not in programming complex scripts. It does make web scraping a difficult task for real estate professionals. However, various scraping services provide cloud solutions to host scrapers and specialized tools for their configuration. These tools usually do not require any specialized technical knowledge, and thus negates the web scraping fear for any industry, including the real estate market. On average, the development of scraper in such systems takes from 30 minutes to 2 hours and once completed, it should be able to run indefinitely until the user stops the process.

The information collected can be used to analyze and resolve any problems that arise; as well as for machine learning that could predict property assessment. As is known, machine learning often requires large amounts of data to train the algorithms, and the problem is solved entirely by using automated web scraping tools.

You can try to use this tool for your business. Sign up for free at Diggernaut and start using a special Excavator app for Google (™) Chrome

Mikhail Sisin Co-founder of cloud-based web scraping and data extraction platform Diggernaut. Over 10 years of experience in data extraction, ETL, AI, and ML.

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