Handling Unstructured Data in Real Estate: PDFs, Emails & More

July 9, 2024
8 min

According to different estimates, at least 80% of data in the world is unstructured. Imagine how many powerful insights lie beyond the database tables in scanned documents, handwritten text, video, audio, webpages, emails, surveys, etc.

But how to deal with unstructured data? Can AI analyze unstructured data? How to avoid the risks unstructured data causes? We will answer all those questions, but let’s start with the most important one: what is unstructured data?

What is Unstructured Data?

Unstructured data is information that does not have a clear structure and default format or organization. Simply put, unstructured data can’t be fit into a table, sorted into rows and columns, or labeled. That’s why unstructured data management and analytics are challenging and resource-consuming since machines cannot fully understand or categorize them.

Examples of unstructured data are:

  • Images, videos, audio, and multimedia;
  • Emails and voicemails;
  • Webpages;
  • Social media;
  • Open question surveys;
  • Business documents;
  • IoT and GPS data;
  • CCTV footage;
  • Customer feedback.

All those types of unstructured data can be sorted into human-generated and machine-generated.

Human-generated unstructured dataMachine-generated unstructured data
Text data (social media posts, emails, blog posts, books, articles, and other written content);

Audio data (voice recordings, voicemail messages, podcasts);

Image data (photographs, drawings and illustrations, infographics, memes);

User Interactions (web browsing history, search engine queries).
Log files;

Sensor data (IoT sensor data, satellite imagery, and telemetry data);

Network traffic data (network packet captures, firewall logs, VPN logs, Network flow data);

Multimedia data (CCTV camera footage);

Scientific Data (particle physics experiment data, astronomical observatory data).

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Unstructured Data in Big Data

Big data implies a massive volume of structured, semi-structured, and unstructured data. Unstructured data holds 80% of big data. To better understand the scale of unstructured big data, consider that more than 50% of enterprises manage at least 5PB of data (5 x 1,024 terabytes), and this amount is growing relentlessly. Data Never Sleeps has shown its latest research results, and the numbers are overwhelming.

How many data are produced on the Internet every minute

Data Never Sleeps Research 2023

Unstructured big data sounds distant and out-of-touch now, but look at how full your cloud storage is, how thick your photo albums and yearbooks are, and how often you accept cookies on every website you visit. Those are only a few examples of unstructured data around you. What about whole industries?

Examples of Unstructured Data in Real Estate

Real estate makes a tremendous contribution to the rapid accumulation of unstructured data in big data. For example, Zillow alone receives around 40,000 listings monthly, filled with text, visual, and video content. That’s a tremendous amount of unstructured data that keeps growing.

Read also: How to Create a Real Estate Marketplace Like Zillow, Redfin, CREXi, and LoopNet

Examples of unstructured data in real estate are:

  • Property descriptions. The written descriptions of properties are presented as web pages and considered unstructured data since they are typically free-form statements. Some parts of unstructured data in property descriptions can be categorized into default for property marketplace filters.
  • Photos and videos. The visual media used to showcase properties, including images, 360-degree views, and video tours, can contain valuable information about the property’s condition, layout, and features. Visuals are the most difficult to process.
  • Floor plans are graphical representations of a property’s layout, which can be in various formats (e.g., PDF, JPEG, CAD) and contain unstructured data about the property’s dimensions, room layout, and features. Interactive floor plans are even more difficult to deal with than static 2D pictures.
  • Virtual tours. Interactive 3D models or virtual walkthroughs of properties are another example of unstructured data in real estate. Computer vision enables 2D image understanding, but multi-dimensional interactive visuals are still challenging for machines.
  • Maps and GIS data: These are geospatial data about properties, including boundaries, zoning information, and proximity to amenities, presented in a format impossible to structurize due to the diversity of landscapes and constant changes. Coordinates are only structured data that can be extracted from maps and satellite imagery.
  • Aerial imagery: Satellite or aerial images of properties can provide information about the property’s surroundings or potential environmental concerns. However, aerial images are difficult to analyze mainly due to their quality issues and the landscape’s diversity.
  • Email correspondence: Emails are human-language texts with links, tags, and file attachments. The only structured data available are the sender, receiver, and topic; the rest is a complete blind spot for traditional data management systems.
  • Handwritten texts, notes, and documents:
    • handwritten notes taken by agents or inspectors during property visits;
    • construction documents with sketches and notes;
    • legal documents like POAs, and that tremendous amount of handwritten;
    • archived documents concluded before digitalization, such as data about property rights.
  • Customer feedback and reviews: Customer feedback about their experiences with real estate agents or property management companies on websites like Yelp, Google, or Zillow is usually a flow of thoughts with no structure.
  • Various business documents include appraisal reports, property inspection reports, lease agreements, mortgage documents, building permits and code violations, invoices, etc. Although official documents must be in specific form according to legal requirements, the information they contain is considered unstructured data because of pictures, signs, and free-form text.

How to Deal with Unstructured Data?

Business documents have the lion’s share among the examples of unstructured data in real estate. Real estate is one of the most paper-heavy industries, alongside finance and healthcare. That’s why unstructured data management has been a long-standing problem here.

A survey of more than 300 IT leaders shows that nearly 68% of them spend over 30% of their budget on data storage, management, and protection. Intelligent Data Processing, using the latest AI technologies, can lighten this financial burden and convert unstructured data into partially structured data.

What is Intelligent Document Processing

Let’s see how it works using the example of a complex framework we built at Ascendix for IDP.

  • AI Document Classification.
    The first step of unstructured data management is categorizing the pile of files for more convenient storage, faster search, and further bulk processing.
  • Structured Data Extraction from Unstructured Data.
    AI-powered tools can convert or extract structured data from unstructured data. For example, Optical Character Recognition scans the images, identifies the text, and converts it into editable digital text. Computer Vision and Object Detection technologies analyze visuals, including complex and dynamic ones, identify specific features and objects, describe them, and then convert the output into structured data. When it comes to free-form text as an example of unstructured data, Natural Language Processing can understand and marginally structurize it.
  • Data Processing.
    Extracted and validated structured data can be automatically inputted into company software, ERP, or CRM.
  • Unstructured data analytics.
    Machine Learning and Deep Learning are technologies that enable unstructured data analysis, and tools like Tableau provide a visual representation of gathered results.

Still can’t see the big picture? Let’s break down the process of creating a lease abstract with the help of AI. Commercial lease agreements are among the longest and most complex documents in real estate. An AI lease abstraction tool can transform tens of pages of unstructured data into an organized summary and save up to 25% of the time spent on commercial lease agreement processing. Extracted structured data are presented as neat tables, which are easier to comprehend and more efficient to store.

AI tool for commercial lease agreements abstraction

Interface of AI Lease Abstraction Tool | AscendixTech

Ascendix’s robust framework can help optimize unstructured data processing. Use AI to extract valuable data from a sea of files, abstract complex documents into concise summaries, process data from scanned and handwritten documents, categorize them effortlessly and make the most of unstructured data.

Automate Your Real Estate Document Processing

Ascendix has built an AI framework specifically trained to abstract, summarize and unlock insights from real estate documents.

Risks and Perks of Unstructured Data in Real Estate

Unstructured data management covers the processing, collecting, storage, and analysis of unstructured data. Since unstructured data are not presented in a pre-defined format and usually do not have a straightforward structure, all related processes are associated with challenges. Unstructured data is something in between a Pandora’s box and a treasure chest—it can have both benefits and risks.

The potential risks of unstructured data in real estate, if not managed effectively, can result in:

  • Privacy and Security Concerns: Improper unstructured data storage can expose sensitive information and increase vulnerability to cyber threats.
  • Regulatory Compliance Issues: Unstructured data can make it difficult for real estate companies to demonstrate compliance with regulations, which can lead to fines, penalties, and reputational damage.
  • Operational Inefficiencies: Lack of integration between data sources can result in incomplete market views, hindered team collaboration, and poor decisions. Also, business automation systems do not fully understand unstructured data, so many skip what doesn’t fit into a pattern. It results in increased manual workload as employees struggle to fill in the gaps left by traditional automated systems.
  • Difficulty in Tracking and Reporting: Unstructured data cannot be fit into the table, so they cannot serve as a basis for charts or diagrams.
  • Data Inaccuracy and Inconsistency: Unstructured data contains much information that reflects the industry’s current state. Unfortunately, this info can’t be easily extracted, so many facts, metrics, and statistics are outdated. Incorrect information can lead to misinformed decisions, market distortions, and missed opportunities.

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In contrast, efficient unstructured data management with AI tools will significantly enhance:

  • Due Diligence: Quickly analyze hundreds of files to identify potential risks, opportunities, and frauds.
  • Market Analysis: Extract data from multiple property reports to spot market trends.
  • Property Management: Unstructured data analysis can be used to automate some property management tasks. For example, an AI chatbot receives a photo of a leaking roof and automatically sends it to the relevant repair services. Meanwhile, the IDP tool sorts out and routes invoices, photos, reports, and other documents as soon as they get into the inbox.
  • Automated Document Review: Use AI-powered document review to reduce the time and cost of reviewing contracts, leases, and other documents.
  • Reduced Errors: Use data validation and verification to reduce errors and improve the accuracy of transactions.
  • Sustainable Future: Environmental impact has been a hot topic at many proptech conferences, and GreenTech is gaining momentum. Since IoT scanners and satellite imagery are examples of unstructured data, their deep analysis will help achieve sustainability goals.
  • Client Satisfaction and User Experience: AI lease abstraction tools and Contract AI facilitate deal closure and bring a clear understanding of all terms and conditions. Meanwhile, AI search and AI recommendation systems leverage unstructured data about user interactions and make house hunting more convenient and personalized, so many top property marketplaces have already adopted AI.

The list of risks and opportunities does not end here, so do not hesitate to open this mystery box called unstructured data with the help of AI and a trustworthy partner.

Ascendix – Reliable Partner for AI Adoption

Ascendix has been an industry leader since 1996 and has earned the trust of JLL, Colliers, and Savills, as well as over 300 other clients worldwide. Over the years, we have gained a breadth of experience in property technology development, AI integration, AI-powered solution creation, and business automation.

How Can Ascendix Help to Deal with Unstructured Data?

We’ve developed our own document abstraction framework tailored specifically for real estate businesses. This framework can become a solid basis for your custom solution, and it is faster and cheaper than beginning from scratch. Also, the solution built on Ascendix’s real estate industry-focused framework will surpass any generic tool implementation or customization.

Do you have an exciting idea? Contact us to discuss its realization and further integration with your company software.

Already have an AI tool integrated? We can audit the system and suggest improvements.

Intelligent Document Processing can optimize your business processes weighed down with terabytes of unstructured data. Schedule a free consultation, and our experts will help you choose a suitable AI solution.

FAQ

What type of data is unstructured?

Any type of information that cannot be transformed into a table because of its lack of structure and organization is considered unstructured data. Images, emails, social media, videos, search history, GPS data, books, property descriptions, PDFs, and scanned documents are perfect examples of unstructured data. 

What is an example of unstructured data?

Books, audio, video, images, data about user interactions, cash, cookies, and social media are examples of unstructured data in everyday life. Focusing on the real estate industry, examples of unstructured data are property listings, contracts, floor plans, and 3D tours, clients’ feedback and reviews, etc. 

Why is unstructured data difficult to analyze?

Unstructured data cannot be labeled or sorted into rows and columns, so machines have difficulty understanding and analyzing it. Also, it’s difficult to optimize unstructured data for charts and diagrams or any other visual representations. 

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