How Real Estate Data Aggregators Can Optimize Operations with Automation
The real estate industry is no stranger to data. It’s one of the most data-driven industries in the market. With so much information to collect and process, there’s a constant heat on data aggregators for delivering extensive and accurate data.
Furthermore, today, the prime focus of real estate data aggregation has shifted toward collecting data that is ready for actionable insights.
However, the inefficiency of manual data aggregation becomes a hurdle in supplying quality data in bulk. This is where automation of data aggregation works as a boon for the real estate industry. This blog post will guide you with five data aggregation tasks that you can automate for a quick and streamlined workflow:
Tasks That Real Estate Aggregators Should Automate and Why?
Real estate data aggregators face challenges in collecting and extracting property information from various sources such as government departments, online platforms, and social media.
This is because the data is often unstructured and difficult to interpret. Herein, manually handling the identification of source type and standardization of data can be time-consuming, costly, and prone to errors.
To address and combat these challenges, real estate data aggregators can automate tasks such as data collection, extraction, and validation. By using automated bots and spiders, these tasks can be performed more efficiently and accurately.
For instance, suppose you are a real estate data research and information service provider who wants customer records from multiple property documents across different states/countries. Herein, using automated bots/spiders can help simplify your task, allowing you to quickly identify potential customers for targeted marketing campaigns.
Real estate document processing involves two stages. First is understanding the technical terms and illegible handwritten texts, examining different forms and invoices, etc. The next step is to accurately organize and transfer the extracted information to a database.
Herein, solely relying on manual data processing makes it difficult to thoroughly read through large volumes of data. Plus, the legal language used in real estate documents requires data aggregators to be extra cautious during the processing stages
This is where automation can help data aggregators to effectively carry out large-volume data processing and improve operational efficiencies.
Property data enrichment
This involves improving the quality and accuracy of data. It is particularly important in the real estate industry as the data is in continuous flux. While the physical characteristics of a property may remain the same, its value keeps fluctuating depending on the development aspects or the change in ownership.
To enrich real estate data, data aggregators need to find reliable data sources and carry out data pre-processing involving steps like data matching, purging, and appending to fix the missing details. This is all to improve the accuracy and consistency of the data before enriching it further.
However, performing these tasks manually for thousands of records within tight deadlines is almost impossible. Therefore, automating the data enrichment process is more of a necessity for real estate data aggregators. Automating these tasks can help businesses save time and ensure the accuracy and reliability of their data.
Verification and validation for the title and legal checks
Real estate companies regularly need to verify and validate their records to ensure that the information provided to the customers is all accurate and complete.
However, it is a fact that approx 30% of data decay occurs due to the presence of outdated and unnecessary data every year. And if a buyer discovers any data inaccuracies during the title and legal checks, it can cause a problem for both sellers and agents alike. For this reason, most real estate firms prefer using data from aggregators with advanced data validation solutions.
The main goal of data verification and validation is to carefully examine all the relevant information regarding the property, seller, and financing for any errors. This helps to ensure compliance with legal obligations, mitigates financial risks, and improves overall customer experience.
Cleansing data to avoid irregularities
When it comes to cleaning property records, solely relying on manual processing can be challenging and ineffective.
With large volumes of data and constantly changing requirements, manually correcting spelling errors, filling in missing information, and fixing formatting issues can result in duplicate records and the need for even more cleansing.
For effective property database management, cleansing also accounts for data validation and verification of any erroneous entries. Meanwhile, it is equally important for real estate database providers to maintain integrity constraints and ensure that the data attributes are accurate and consistent.
However, these requirements can take a toll on aggregators, manually trying to remove redundant, obsolete, and trivial (ROT) data from the database. Herein, automating real estate data cleansing can be of great help.
How to Automate Data Aggregation Tasks?
Automating core data aggregation tasks can help the easy and efficient management of real estate data. But how to implement automation in data aggregation? Here are a few easy ways using which real estate aggregators can automate and speed up their core tasks:
Business rules and scripts
In any given process, there are rules to ensure that everything runs smoothly. The same goes for the process of data extraction, cleansing, and enrichment – everything follows certain pre-defined rules. Using business rules and scripts can help define and establish a standard logic that applies to any situation within a particular context.
However, when programming these rules, make sure to cover all the potential events. You can develop macros, codes, and procedural logic to automate certain tasks. For example, a macro can help data aggregators extract property information from designated locations and web sources.
And since property data is often subject to inaccuracies, another macro can be used to clean the data and make it usable.
In addition to this, a code can be programmed to validate the extracted data against authentic sources and a macro can be used to populate the validated data into databases. Meanwhile, what’s important to note here is that programmable business rules are never static. Hence they should be regularly updated as the processes evolve.
Using custom bots and crawlers
Custom bots and crawlers are gradually transforming the ways of collecting, authenticating, and standardizing property data. Bots are like virtual assistants that collect information by asking a series of contextual questions from website visitors.
For instance, if you want to collect information about property prices across a state, you can install a crawler that browses relevant websites to fetch the required data. This is why crawlers are a major part of automated data collection and enrichment.
By constantly discovering and browsing through multiple sources, crawlers simplify the process of data validation and enrichment.
Traditional methods of real estate data aggregation further become a challenge when it comes to collecting property information such as surrounding conditions, landmarks, and locality status.
These challenges can be efficiently addressed with the help of AI which uses techniques like cluster analysis and computer vision to group and analyze real estate data.
One such example of AI technologies is Natural Language Processing (NLP) which can be used to obtain information on the best properties. NLP uses the text responses of the audience to detect relevant keywords and enable search engines to provide top listings. Moreover, NLP can also be used to validate metadata using features such as the number of rooms, dimensions, surface area, and more.
Real estate data aggregators deal with a vast amount of data regularly. However, the manual labor required to collect and analyze it can be time-consuming and prone to errors.
Automation can be a valuable tool for data aggregators looking to improve their services and stay ahead of the curve.
By automating certain tasks (data collection, cleansing, validation, enrichment, and processing), aggregators can offer more accurate, reliable, and timely information to their clients, helping them make informed decisions.
However, if you are not well-equipped with all the required resources or lack expertise in the same, you can always choose to outsource data services to a third-party service provider.