There was a time when real estate dealings were discrete, paper-based operations done on a one to one basis. With the rise of the internet and every industry finding its way into it, real estate began to realize its true potential on the web. There is no denying the fact that the internet is the most useful tool at a seller’s disposal.
With a large number of potential buyers online, realtors find the internet an excellent source to advertise property listings, hereby automating the whole process. Statistics suggest that 40% of buyer’s inquiries stem from internet advertisements and nine out of ten people use the internet to search for property. Moreover, the same property can be enlisted on numerous sites to increase traffic and the corresponding chance of a sale.
This implies endless opportunities for a realtor. But harnessing relevant data out of big data to a non-technical realtor is like looking for a needle in a haystack. The web has a staggering amount of information leading to a plethora of choices and comparisons can lead to significant confusion, making it difficult to fathom and make sense of.
Web Scraping in real estate to the rescue
Web scraping is the process of sorting through overwhelming amounts of data, refine the user’s searches and provide a list of relevant information. In a realtor’s case, it is the go-to tool for organized property listings. Scraping the web provides parameters which the realtor can further study to determine sales and prospective buyers. Parameters extracted by web scraping are:
Size
Property type
Location
Sale price
Size
Amenities
Monthly rental price
Parking spaces
Agent contact
This information is displayed in form of a spreadsheet, allowing the realtor to make comparisons of relevant parameters.
1. Property value tracking
Let’s assume you decide to sell your property. Scraping the web for the value of similar properties can aid you in setting a good value on your own. This allows users searching for such properties to get fair deals, and on the other, you getting a profitable one.
2. Making the right investment
Obtaining real estate data is hard, as result of which most investors make financial investments blindly. With web scraping, an investor can make decisions based on qualitative and relevant empirical data, rather than outdated or incomplete information. Aggregating property data from real estate listing websites is essential for investment analysis.
3. Rental Yield
Rental yield is the most important factor to be considered before investing in property. By scraping data from real estate websites, you can determine which properties have the best rental yield for any suburb. Moreover, scraping answers which property types (house, apartment, 1 bedroom,
2 bedrooms) are more preferred in a particular area and yield the best return on investment.
4. Track vacancy rates
A vacant investment property is risky. To minimize this risk, it is imperative to analyze property data and suburbs which have higher rental listings.
The above parameters are the most relevant decoded by web scraping through numerous websites online. Having the above details at your fingertips improves a realtor’s efficacy at decision making, better communication and faster and profitable sales. The role of web scraping in retail is just getting started, its potential is however limitless.
Searching for a web scraper for your real estate needs? Contact us at Datahut, your big data experts.