Real Estate Data Aggregators: How can real estate data aggregators improve operations with automation? The real estate industry uses a lot of data. It’s a highly data-driven industry. Data aggregators are under pressure to gather and provide large amounts of accurate information due to the overwhelming amount of data available. Today, real estate data aggregation is mainly focused on collecting data that can be used to make informed decisions.
However, manual data aggregation is inefficient, making it difficult to provide large amounts of quality data. Automation of data aggregation is a great help for the real estate industry. In this blog post, you will learn about five data aggregation tasks you can automate to make your workflow faster and more efficient.
Tasks That Real Estate Aggregators Should Automate and Why?
1. Data collection
Real estate data aggregators need help gathering and organizing property information from different sources like government departments, online platforms, and social media. The data could be more structured and easier to understand. Doing the identification and standardization of data manually can take a lot of time, be expensive, and have a higher chance of mistakes.
Real estate data aggregators can automate tasks like collecting, extracting, and validating data to tackle these challenges. Automated bots and spiders can help perform tasks faster and with greater accuracy.
Imagine you work for a company that collects real estate data. Your job is to gather customer information from various property documents in different states or countries. Automated bots or spiders can simplify your task by quickly identifying potential customers for targeted marketing campaigns.
2. Document processing
Real estate document processing has two stages. First, you need to understand technical terms and decipher illegible handwritten texts. Additionally, you should examine various forms and invoices. Now, we need to accurately organize and transfer the extracted information to a database. Relying only on manual data processing makes reading large amounts of data hard. In addition, the legal language in real estate documents makes data aggregators need to be very careful during processing.
Automation can assist data aggregators in efficiently processing large amounts of data and enhancing operational efficiencies.
3. Property data enrichment
It means making data better and more accurate. Data is constantly changing in real estate, so it’s essential. The value of a property can change even if its physical characteristics stay the same. It can happen due to development or when ownership changes.
Data aggregators enhance real estate data by sourcing reliable information and performing data pre-processing tasks such as matching, purging, and appending to address missing details. We are doing this to make the data more accurate and consistent before adding more information.
Doing these tasks manually for thousands of records within tight deadlines is nearly impossible. Automating data enrichment is essential for real estate data aggregators. Automating tasks can save businesses time and ensure data accuracy and reliability.
4. Verification and validation for the title and legal checks
Real estate companies often check and confirm their records to ensure the information given to customers is accurate and complete.
Approximately 30% of data decay happens yearly because of outdated and unnecessary data. If a buyer finds any data inaccuracies during the title and legal checks, it can be a problem for both sellers and agents. To simplify things, real estate firms often rely on data from aggregators that have reliable data validation solutions.
Data verification and validation aim to check for any mistakes in the critical information about the property, seller, and financing. It helps ensure we follow the law, reduce financial risks, and improve customer experience.
5. Cleansing data to avoid irregularities
Relying only on manual processing for cleaning property records can be complicated and inefficient. When dealing with lots of data and changing requirements, fixing spelling errors, adding missing information, and resolving formatting problems by hand can lead to duplicate records and the need for more cleansing.
To manage property databases effectively, cleansing includes validating and verifying incorrect entries. Real estate database providers must ensure their data is accurate and consistent by maintaining integrity constraints.
Aggregators often need help with meeting these requirements because they have to manually remove redundant, obsolete, and trivial (ROT) data from the database. Automating real estate data cleansing can be very helpful.
How to Automate Data Aggregation Tasks?
Automating basic data aggregation tasks can make managing real estate data more manageable and more efficient. How can data aggregation be automated? Real estate aggregators can automate and speed up their core tasks with these easy methods:
1. Business rules and scripts
All processes have rules for smooth operation. Data extraction, cleansing, and enrichment all have set rules. Business rules and scripts help maintain consistent logic in specific contexts.
When writing these rules, make sure to include every possible event. You can automate tasks using macros, codes, and procedural logic. A macro can help collect property information from certain locations and websites. A macro can be used to clean and prepare property data for accuracy.
You can use a code to verify the accuracy and reliability of the data. You can use a macro to input the verified data into databases. Remember, programmable business rules are always changing. It’s important to update them regularly as processes change.
2. Using custom bots and crawlers
Bots and crawlers are changing how we collect and organize property data. Bots are virtual assistants that gather information by asking visitors questions on websites. If you want to gather property price information for a whole state, you can use a crawler to browse websites and get the data you need. Crawlers are essential for automated data collection and enrichment. Crawlers make data validation and enrichment easier by finding and browsing through multiple sources.
3. Applying AI/ML
Collecting property information like surrounding conditions, landmarks, and locality status can be difficult using traditional real estate data aggregation methods. AI can effectively tackle these challenges using cluster analysis and computer vision to group and analyze real estate data.
Natural Language Processing (NLP) is an example of AI technology that helps gather information about the best properties. NLP uses audience text responses to find essential keywords and help search engines show top results. In addition, NLP can be used to check metadata by looking at features like room count, size, and surface area.
Conclusion
Real estate data aggregators handle large amounts of data regularly. Collecting and analyzing data manually can take a lot of time and may lead to mistakes.
Automation is helpful for data aggregators who want to enhance their services and stay ahead. Aggregators can provide better information to clients by automating tasks like data collection, cleansing, validation, enrichment, and processing. It helps clients make informed decisions with accurate, reliable, and timely information. You can outsource data services to a third-party provider if you need the necessary resources or expertise.
