Ready for a wild ride into the world of Chat GPT use cases for commercial real estate?

I call it “wild” because many use cases you will learn about are still in their infancy and lack variety regarding providers offering ready-made solutions. 

So we will navigate a bit through uncharted territory.

However, even without ready-made solutions and a series of sophisticated prompts, you can still benefit greatly from them and change how you do business in commercial real estate. 

Sounds interesting? I invite you to read to the end.


Identifying the ChatGPT Use Cases for Commercial Real Estate

Generally, all use cases for real estate agents I discussed in my past article can also be applied to commercial real estate. 

However, I don’t want to bore you by repeating them here. 

Instead, I identified eight use cases specifically relevant to commercial real estate.

And the criteria I used to rank them are based on their potential impact on the overall success and profitability of various commercial real estate businesses.

So the most important use cases are those with a larger effect on decision-making, financial performance, and project success.

The least important ones are those focusing on improving operational efficiency and support.

I chose these criteria since commercial real estate is more “ROI-orientied” than other real estate niches.

I know all are in a way, but commercial real estate is slightly more.


8 Powerful Chat GPT Use Cases for Commercial Real Estate

So let’s start with the eight Chat GPT use cases for commercial real estate you will find below, ordered from high to low importance regarding the effect on decision-making, financial performance, and project success.

1) Commercial Real Estate Market Analysis and Trend Prediction 

By analyzing large datasets, Chat GPT can help you identify market trends, pricing patterns, and potential investment opportunities in commercial real estate.  

But how would you go about using large datasets in Chat GPT?

First, they shouldn’t be too large at the time of this writing because of Chat GPTs token limit.

However, as a text-based AI model, it can work with several formats to help with market analysis and trend prediction in commercial real estate.

Here are the formats that are viable for me to work with:

  • CSV (Comma Separated Values): It can process CSV files, provided the data is structured and organized. It then can filter, sort, and extract insights from the dataset to identify trends and patterns. By the way, most real estate tools offer CSV or JSON formats when you export your data. 
  • Excel Spreadsheets: You can’t just copy and paste an Excel file into ChatGPT. However, again you can convert the file into a CSV or other text-based format, which then can be analyzed for insights.
  • JSON (JavaScript Object Notation): As mentioned above, Chat GPT can parse and process JSON data, extracting relevant information and helping analyze trends and patterns.
  • APIs (Application Programming Interfaces): Chat GPT cannot directly interact with APIs. But, you can use other tools or programming languages to gather data from APIs and provide the data in a text-based format like JSON or CSV. So you could write code that gets data via your relevant real estate tool’s API and then use OpenAI’s API to do whatever AI task you need.
  • PDF Reports: If the text from the PDF reports is extracted and provided Chat GPT can analyze the content and help identify trends and insights. 
  • Web Scraping: Chat GPT cannot directly perform web scraping (yet). However, if you use other tools or programming languages to extract data from websites and provide it in a structured, text-based format like CSV or JSON, the data can be analyzed.

So you can feed Chat GPT with tons of data, like historical sales records, rental rates, zoning regulations, and economic indicators (in the proper formats). 

It then crunches the numbers and – voila! – serves up insights that help you stay one step ahead of the competition. 

For example, it might identify a booming neighborhood with high demand for office spaces or an industrial area ripe for redevelopment.

Chat GPT can also analyze news articles, social media chatter, and industry reports to help you understand the latest trends and shifts in commercial real estate.  

How can you implement this in practice, and are there any ready-made solutions on the market?

While specific providers for Chat GPT applications in commercial real estate market analysis are still emerging, you would have to use a combination of manually getting and exporting the relevant data sets and then using the proper prompts in Chat GPT.

The other option would be to use your or someone else’s coding skills to create a custom solution via OpenAI’s API. 


2) Analyzing and Summarizing Lease Agreements

Chat GPT can be trained to extract, analyze, and summarize crucial information from lease agreements, such as rent escalation clauses, lease terms, and tenant improvement allowances. 

This can help brokers and property managers easily understand and compare lease terms across multiple properties.

Some may like the task, but most will feel bogged down by analyzing and summarizing lease agreements. Chat GPT can help with this tedious task.

For example, imagine you have a stack of lease agreements to go through. 

Instead of spending countless hours poring over the details, you can hand over the documents to your Chat GPT-powered solution. 

It’ll quickly identify key information like rent escalation clauses, lease terms, and tenant improvement allowances, presenting you with a tidy summary of each agreement. That’s a great time-saver.

How can you implement this in practice, and are there any ready-made solutions on the market?

While there might not be providers exclusively focused on lease agreement analysis using GPT, there are two generic providers that at least offer GPT integration into their solutions.

It’s Summize and ContractKen.

The other two options would be similar to the earlier use case: a series of smart prompts or coding your custom solution with OpenAI’s API.


3) Site Selection Analysis

Chat GPT can assist in the site selection process by evaluating and comparing factors like zoning, transportation accessibility, local demographics, and surrounding businesses.

This can provide valuable insights for developers and investors looking to make informed decisions about potential commercial real estate projects.

By doing the site selection analysis, Chat GPT can consider crucial elements such as zoning, transportation accessibility, local demographics, and surrounding businesses to provide valuable insights into potential locations.

For instance, imagine you have a list of potential sites for a new office complex. 

Instead of manually assessing each site, your Chat GPT-powered tool can swiftly analyze available data and rank the locations based on the factors most important to you. 

How can you implement this in practice, and are there any ready-made solutions on the market?

There are no providers solely dedicated to GPT-based site selection analysis (yet). 

However, there are solutions with other AI models on the market you may consider. It’s Cherre or Reonomy.

Similar to the previous use case, the alternatives include utilizing a sequence of intelligent prompts or developing your tailored solution using OpenAI’s API.


4) Commercial Property Valuation

My article about AI appraisals discussed how AI can help with property valuations.

And Chat GPT can also assist in this, especially in estimating the value of commercial properties.

This can be useful for investors, brokers, and property owners during commercial real estate asset negotiation, acquisition, or disposition.

Commercial Property Valuation using Chat GPT involves leveraging the AI model’s natural language processing capabilities to analyze market data and property-specific information to estimate the value of commercial properties. 

Here’s a general overview of how it works:

1) You provide the necessary details about the property, such as location, size, age, condition, recent renovations, and other crucial factors. You may also include information about comparable properties and market trends.

2) Chat GPT’s AI model processes the given information, possibly comparing it to historical data or other relevant market trends to estimate the property’s value. 

This analysis can help account for supply and demand, market sentiment, and potential future developments.

3) Then, based on the analysis, Chat GPT generates a suggested valuation for the property. 

This valuation can be a starting point for investors, brokers, and property owners during negotiations, acquisitions, or dispositions of commercial real estate assets.

While this can help give you valuable insights, you always want to cross-check its estimates with professional appraisals or other trusted valuation methods to ensure accuracy.

How can you implement this in practice, and are there any ready-made solutions on the market?

To use Chat GPT for this task, you still need to approach it manually with a series of intelligent prompts and gather the information it needs for the valuation from other sources.


Currently, no specific providers offer dedicated GPT-based commercial property valuation services.

Another approach would be again coding a custom solution with the help of OpenAI’s API.

However, as I already covered in the initially mentioned article, there are AI solutions that can do AI appraisals, not with GPT, but with other AI models.

For instance, it’s bowery and Quantarium.


5) Retail Tenant Mix Analysis

You may know how the perfect retail tenant mix can make a shopping center thrive.

Chat GPT lets you discover the ideal combination of retail tenants for your commercial space.

How so? 

Imagine Chat GPT as your retail mixologist, blending consumer behavior patterns, foot traffic data, and local demographics to concoct the perfect tenant portfolio. 

It would go like this…and it’s similar to the commercial property valuation approach.

You first provide data like consumer preferences, neighboring businesses, and the demographics of your target audience.

With the right advanced series of prompts, Chat GPT then processes this information to identify patterns and trends, which ultimately helps you target and attract the right tenants.

Finally, it will list recommended retail tenant types to create a balanced and attractive tenant portfolio for your commercial property.

How can you implement this in practice, and are there any ready-made solutions on the market?

I will repeat myself here again. 

But there isn’t yet a specific provider dedicated to GPT-powered retail tenant mix analysis. 

The approach would be again manually with a series of prompts, copying and pasting data, or your custom coding solution via OpenAI’s API that can parse data about your consumer preferences, etc., to the AI model. 

6) Investment Portfolio Analysis

As the final Chat GPT use case for commercial real estate, I want to mention the option to evaluate the performance of commercial real estate investment portfolios by assessing risk, return, and diversification factors. 

Chat GPT, in this case, can also provide valuable insights for investors looking to optimize their commercial real estate holdings.

The principle of how it can work is the same as earlier.

You feed Chat GPT data about your investment portfolio, such as property types, financial performance metrics, and locations.

Then comes the essential part of having a series of well-crafted prompts ready so it can evaluate your portfolio’s performance and identify areas for improvement.

Ready-made providers are not using (Chat) GPT, but other AI models are TheAnalystPro and Skyline.

In my article about real estate ai companies, I discussed these software providers more in-depth. 


How Are the Chat GPT Uses Cases for Commercial Real Estate Relevant to Marketing?

Now you may wonder how these use cases play into real estate marketing. And they are incredibly relevant to marketing in various ways. 

Let’s dive into how these use cases can enhance your commercial real estate lead generation and marketing efforts and give you a competitive edge:

1) Market Analysis

By understanding market trends and pricing patterns, you can fine-tune your marketing strategies to target the right audience and promote properties in high-demand areas. 

2) Analyzing and Summarizing Lease Agreements

Chat GPT can help you present lease terms clearly and concisely, making it easier for potential tenants to understand the benefits of your property offerings.

 This transparency can boost your credibility, foster trust, and ultimately drive more referral leads and, more so, sales. So you can consider it more as a “real estate sales conversion booster.” 

3) Site Selection Analysis

By evaluating various factors like zoning, transportation accessibility, and local demographics, Chat GPT can help you identify prime locations for your marketing campaigns. 

Armed with this knowledge, you can maximize the impact of your real estate lead generation effortstargeting the right prospects and capturing their attention.

4) Commercial Property Valuation

You can set competitive prices and effectively promote your properties by providing accurate valuations. 

Chat GPT can assist with this, enabling you to attract and retain tenants and investors who appreciate fair pricing. So this would be the “pricing” part of the 4 Ps in marketing.

5) Retail Tenant Mix Analysis

Knowing the ideal tenant mix for your commercial spaces allows you to target specific retail segments in your marketing campaigns.  

Chat GPT can analyze consumer behavior patterns, foot traffic data, and local demographics to help you tailor your marketing efforts for maximum impact.

This will again help you with more precise real estate targeting.

6) Investment Portfolio Analysis 

If you have to generate real estate investor leads to gain partners, Chat GPT can help you showcase the performance of your commercial real estate investment portfolio, emphasizing strong returns and diversification. 

This can increase your lead conversion rates and attract potential investors.

This article has been reviewed by our editorial team. It has been approved for publication in accordance with our editorial policy.

Tobias Schnellbacher