Big Data Is Tapping into Real Estate

 

                                     

In the real estate industry,  there is a clear sign that data analytics is playing more actively. For example, real estate enterprises are getting to know people’s housing needs from different classes and making changes to accommodate their various housing demands by using data mining techniques. The transaction price, listing price, quantity and other key indicators can help to give consultants a relatively accurate estimated price.
However, it’s not the end point where the real estate industry has reached for now. As an insightful insider in the real estate industry, you must extract more valuable information from the market to explore the sales leads and grow your business. Thus, you should be clear what’s your goal and why you do so with these data.

 

Know more about your customers

It’s unlikely to meet all of housing needs from a customer, but we can explore some commonness among most customers.    To learn more about customers’ requirements, we need to dig into customers’ behaviors by training the history customer records data or the statistics.


The data sets normally come from different ways. For some enterprises, they prefer crawling the social media platforms which shows the habbits, behaviors, preference, emotional data, and they can easily extract the data by using an automatic web crawler tool - Octoparse which can help to collect data from most websites. The real-time data transmitted from the mobile devices, like GPS, remote sensors , is also an essential part of the data sets.
However, the data collected or crawled above is unstructured or semi-structured, that means we can’t accurately analyze customers’ demands by using the traditional statistical method. To explore and find out the messy patterns and underlying associations among these data, classification or clustering algorithm is proposed to be applied in customers requirements clustering. For example, as the nature of the job, habits, living condition, and even the purchasing and web browsing history records can all be listed as worth mining, we could use cloud-based techniques to filter, compute and refine the customers  groups as different clusters, including high-quality customers, prospective customers, generalized customers according to their characters based on a variety of dimensions.



Describe customer profiles

To locate the prospective customers, enterprises must truly understand what customers’ demands are and filter out those unlikely buyers. To refine our data sets, for example, we can inspect the customers personal information, consumption patterns, and other possible pointcuts to classify the customer characters into price-orientation, quality-orientation, etc. Optionally, we can also inspect their housing affordability, surrounding, and health condition to classify the customers into groups which puts more weight on property management, landscaping, etc.
Thus, we may draw many characteristic generalities from customers’ varieties of behaviors, and evaluate the customers’ characters to complete a characteristic profile of each customer group. These profiles can help estate agents furtherand accurately pinpoint their target customers.



Accurate match with customers’ demands

After we complete a unified profile for each clustered customer group respectively, we are able to find out the underlying associations among the characteristic profiles and customer clusters. Then, we can match the characters with customers’ demands. For example, when facilities, floor, quality demand, surroundings, atmosphere are concerned, then we need to match these multiple dimensional key indicators with our customers demands and categorize customers based on user requirements, thus pinpointing our target prospective customers accurately.

 


Even though, the three steps above may help estate agents to get close to their prospective sales leads, however, we’d better not forget about always doing a deep research or synthetic data analysis on the general situation in the estate industry, since the landing supply and demand, housing regulations, financial markets, history trending, and even public opinions can also have impact on the housing market. Currently, although big data has been applied in many aspects of real estate industry, it is still in the state of infancy, we can expect more on what big data can bring to the real estate industry in the future.

 

 

Check out some case studies below to get started on collecting real estate industry information.

Web Crawling Case Study | Crawling real estate data from Zillow

Scrape Real Estate Data (Example:www.realtor.com)