Metadata Extraction from Rental Agreements Using AI

Jan 14, 2022

According to this PolicyAdvice report, the demand for rental properties has been experiencing growth in recent times. To keep up with this expected growth, metadata extraction from the rental agreements would play a crucial role in ensuring a quick review of rental agreements.

Before going into the details of that, let’s take a step back to understand the current state of reviewing rental agreements.

A rental agreement is a legal contract between the owner and the renter of a property, containing information about the parties and properties involved with the terms and conditions of the rental.


This document requires attention to detail when reading. Most times, the service of a lawyer is employed to understand the details of the agreement and the potential risks involved. Reviewing rental agreements, evidently, is a core task in the rental business.

Unfortunately, the common practice involves manually scanning these rental agreements. For large companies that deal with thousands of contracts every day, this method is not scalable. Important clauses can get buried in this high volume of documents and can go unnoticed during the review process.

This method is slow, expensive, error-prone, and exposes clients to unwanted legal risks.

AI and its use in Rental Agreements

Today, AI is used to automate several repetitive tasks and cut down on errors. The rental business is not left out of this innovation.

UBIAI uses artificial intelligence (AI) and natural language processing (NLP) to help identify and extract metadata from documents such as rental agreements and contracts. These metadata, such as:

– the start date,

– renewal and termination options,

– lease duration,

– the security deposit fee

are what experts look for when reviewing and scanning a rental agreement.

Automatically scanning rental contracts and extracting this information can help legal professionals save time, save costs, reduce errors, perform contract reviews more efficiently, and enables them to focus on more intelligent and complex tasks.

How it works

This process of metadata extraction from rental agreements involves several simple steps.

  1.  First, the user (which could be a lawyer or you) would upload the relevant documents and contract for extraction into the software.
  2.  Next, you define the metadata you are interested in extracting by annotating them in the uploaded documents.
  3. Afterward, you can either train the AI model on the UBIAI platform to automatically identify these metadata or you can export the annotated data to train the model outside of the platform.
  4.  Finally, an API key is provided to you, which you would use to deploying and running predictions on your trained model.

With UBIAI, none of these steps require any AI or programming skills.

Next, we look at each of these steps in detail.

  1. Scanning and uploading the rental contract

As mentioned before, the first step involves uploading the contract for extraction into the software.

UBIAI tool supports several file formats, including images and PDF documents. You can also convert scanned rental agreements and other physical documents into digital files that can be processed by a computer using UBIAI’s OCR (Optical Character Recognition) technology.

It is as simple as uploading the documents and letting UBIAI handle the rest.

  1. Annotating the text with your metadata of interest

Now that you have your documents uploaded, the next thing on your mind is metadata extraction from your rental agreements. But how does the system know which metadata you’re interested in?

While UBIAI comes with several custom metadata types like names, numbers, dates, you would want something much specialized for your use case. Like the lessee’s full name, lessor’s full name, amount of rent, start and end date of the contract, duration of the lease, etc.

To accomplish this, you start by annotating some documents with examples of your new metadata types. With support for multiple languages, you can use UBIAI’s annotation tool to annotate your documents quickly and track progress easily.


After annotating, you then train your AI model on these examples to learn to identify the important metadata.

  1. Metadata extraction from rental agreements

Once you’ve annotated a few examples, you can use the Model-Assisted labeling feature to help you with labeling these metadata from your lease agreement.

At first, the model will require you to review the extracted metadata for errors and correct them accordingly. The model then learns from its mistakes. Eventually, it gets better and begins to operate well without supervision.

In cases of high failure rates, you can annotate additional documents with more examples to help the AI model understand your metadata better.

  1. Beyond the metadata extraction

Once you have achieved a satisfactory result, you have the option to download the labeled data in several popular formats for training the model

Lastly, you may decide to take a step further by training an AI model directly on UBIAI to detect anomalies and red flags in rental agreements. Using the already extracted metadata, performing these advanced tasks is much easier.



With increasing demands for rental properties, reviewing rental agreements would have to become faster.

UBIAI uses artificial intelligence to automatically extract relevant metadata from rental agreements and contracts. With this, companies can review contracts more rapidly and can keep up with the increasing demands.

With UBIAI, you can digitalize your documents, annotate your data, train and deploy tour AI models, on a single platform without any IT or AI experience.