Appraiser-Approved AI-Powered Market Analyses + My AI Tech Stack
The June 2025 Edition of ChatCRE
Welcome to the June 2025 edition of ChatCRE - Your monthly dose of updates on AI tools, and practical ways to put them to work in commercial real estate.
TL;DR
You can now build customized versions of ChatGPT using its most powerful models to execute specific CRE tasks.
Learn how CRE appraiser Justin Gohn is creating comprehensive market analyses for evaluating properties using ChatGPT Deep Research.
“What AI Tools Am I Using?” - See the exact AI tools I’m using right now, and the features that made me hit “subscribe”.
P.S. I’m currently booking online AI presentations & training sessions for Q3 & Q4, and in-person presentations for Q4 only (travel dates for Q3 are booked solid.) If you’re interested in having me present to your team, company, or association on how to use AI in CRE, you can find more information here, or just send me an email and we’ll connect.
Quick Update: ChatGPT Custom GPTs Can Now Use Any ChatGPT Model
If you read my original post on building Custom GPTs with ChatGPT, you already know they’re an excellent way to build customized AI assistants to execute laser-focused CRE tasks. Not only can you provide them instructions to do specific tasks, you can upload information and examples to their knowledge base so it provides the same consistent output, based on your examples, every time you use them.
Think:
Draft the marketing copy for this property (a’la Real Estate Writer Pro)
Summarize this lease agreement or analyze this OM and extract the relevant deal info
Write a letter to our tenants about (insert topic) based off of this template
Create a press release about this transaction based on these deal points, in the style, tone and structure of the press releases in your knowledge
The useful examples go on and on. The main limitation of these Custom GPTs? Until recently, Custom GPTs build with ChatGPT could only use model 4.0. Don’t get me wrong, 4.0 is an amazing model, but it’s limited. Great for simple data extraction, writing, and many other simple tasks. But it’s not great at complex, multi-step tasks.
Think: Review this lease and extract the deal data, now construct the rent table, now ask me for our commissionable rate and billing terms for the deal, calculate the total commission, now draft our commission invoice with all of the above based on this example.
4.0 might fall short with these multi-step tasks like the one described above because it’s not a “reasoning” model, and it has a short context window (meaning it can process less information). So if you wanted to build a complex AI assistant for a task like the one described above, you’d probably be need to build them them on some other platform, such as creating a Project on Claude (similar concept as a Custom GPT, just with a different LLM) which let you use Claude’s most advance models for any Project. Doing this means more subscriptions, more back and forth, more confusion, etc.
The Update: That bottleneck is now gone. OpenAI now lets you assign any ChatGPT model, including o3, to every Custom GPT you spin up. Toggle the model, and you’re instantly able to create more complex Custom GPTs.
Why this matters
More brainpower, same interface. o3’s larger context window and stronger reasoning means it can provide more in-depth analyses, and complete more complex tasks with your Custom GPTs.
Real Example: This week we created a Custom GPT at Aspire to stress test our pitch deck drafts. Feed the GPT a property pitch deck and the comp research, and it will:
Compare the comps to our market summary, strategy & pricing guidance, and tell us if anything seems out of alignment, or needs more justification in order to explain to an owner why we’re suggesting our pricing or strategy (if ChatGPT picks up on any perceived discrepancies, an owner would probably question them as well.)
Search the internet to verify any stats mentioned in the deck and provide a secondary source for the information (a double check before we quote stats where multiple sources have conflicting information).
Review our target list of buyers/tenants and search the internet for specific businesses/investors that are actively expanding or acquiring in our market (this would work better with Deep Research, but it’s still useful)
Return a list of any and all grammar, spelling or labeling mistakes.
You’re probably thinking “wow Topher, pretty boring use-case”. That’s kind of the point, these GPTs can be built to do the boring work that sucks up time. This boring use-case will save our brokerage hours taking our pitches from initial draft to presentation-ready.
What to do next:
Remember that complex CRE Custom GPT you were trying to build but gave up on? Try it again and flip the model to o3, you might be amazed at what tasks you can put on auto-pilot by using this model your own GPTs. Even simple GPTs I’ve built in the past such as Real Estate Writer Pro now get much better outputs.
In case you have no idea how to switch models with ChatGPT, here’s a quick run-thru. It’s easy, don’t blink or you might miss it.
Need a refresher on building Custom GPTs from scratch? Check out this writeup I did in a recent edition of ChatCRE, it has a link to a step-by-step video showing you how to create your own Custom GPTs. You can build GPTs with the same walk-thru, now you just have the option to use any model you want.
Appraiser-Approved, AI-Powered Market Analyses with Notebook LM + ChatGPT Deep Research
Use-Case by Justin C. Gohn, MAI, SRA: Commercial Real Estate Appraiser
If you’ve spent much time on LinkedIn lately, you’ll see CRE professionals doing some WILD things to evaluate, underwrite, and run due diligence on properties. Let’s be serious: This could be a dangerous use-case. AI is powerful, but it’s still AI, is this technology actually in a place where it can be used for detailed property analyses? So I thought to myself, “How would an actual appraiser use AI?” Luckily, I recently connected with Justin C. Gohn, who is exploring the frontier of how AI can take the minutia out of property evaluations. Below is a detailed walk-through on how Justin is doing just that.
The AI-Powered 6 Step Market Analyses:
Justin’s approach in this example is simple, but effective. He combines Notebook LM for extracting information from his Appraisal Institute approved frameworks, feeds that to ChatGPT o3 to create his prompt, and feeds the prompt to ChatGPT Deep Research to deliver a detailed, actionable, verifiable market analyses reports he can use to evaluate a property.
Justin was kind enough to record a video explaining the process that you can watch here, but I’ve also detailed the process and provided the Deep Research prompt below.
Justin’s AI-Powered Market Analyses Process
Extract the Playbook: Justin has a robust database of Appraisal Institute frameworks loaded into Notebook LM. When he wants to use AI to run an analyses framework, he selects the source, and asks it for the information needed to execute (in this case, the 6-step market analyses framework). If you don’t know what Notebook LM is, check out this recent edition of ChatCRE to get the rundown and learn how you can use it for CRE.
Draft an Deep Research-Ready Prompt with o3. Justin opens ChatGPT o3, pastes the framework he got from Notebook LM, and tells it: “Draft a Deep Research prompt to run this six-step analysis on [PROPERTY ADDRESS].” o3 returns a multi-page prompt, custom-tailored prompt written to get the best output possible from Deep Research, including citation rules, specific data requests, the works.
Pro Tip: You can also use o3 to create instructions for your complex Custom GPTs but telling it what you want the GPT to do.
Run Deep Research. Justin switches ChatGPT to Deep Research mode, pastes the prompt from o3, hits “enter”, and let’s it rip. Deep Research scours the internet for zoning codes, relevant publicly available comp details, STR data, new projects in the pipeline—whatever the prompt demands—and assembles the Market Analyses based on the 6-step framework, complete with citations for all sources.
The Final Output (The Market Analyses)
Justin pointed this workflow at a Comfort Inn outside Philadelphia. Deep Research nailed the property analyses & found the 2019 renovation date, cited the Concord Township zoning ordinance with relevant zoning details, and mapped out comp details from ten competitors—including one still under construction, and generated a detailed demand analyses. The final doc ran roughly 50 pages. According to Justin, the only debate among appraisers he’s shown this to is Step 6: The Capture-Rate Assumption (which is essentially the share of qualified demand that will choose the subject property over competitors), which is something appraisers would likely still disagree over even if it were created by a human, and is exactly where human judgment belongs.
All of this was done without Justin even needing to write the prompt. Quick and simple - Request the framework from Notebook, have o3 write the prompt, run deep research, get the market analyses.
You may not have access to all the Appraisal Institute playbooks that Justin does, but if you’d like to give this a whirl, you can find the Deep Research prompt Justin used at the bottom of this newsletter.
Bonus: “What AI Tools Am I Actually Using Right Now?”
This is a question I get pretty frequently on social media and during my presentations. The truth is that it’s always changing. There are platforms I use every day, platforms I use once in a while, and platforms I’m just battle-testing because I know it’s something I’d have used in my previous roles in CRE. But here’s a quick run down of what I’m currently subscribed to and why I use them, along with some tools I subscribe to once in a while, and some tools that others in the field swear by. A detailed rundown of everything I use these tools for would be too much for one newsletter, but I’ll be covering them all in the months ahead.
Here’s my Current AI Tech Stack:
LLMs (Large Language Models):
ChatGPT Teams — $60/mo
Why I use it:
o3 model excels at reasoning and CRE-specific Deep Research. You CAN use Deep Research on most major LLM’s. ChatGPT’s version covers less sources, but tends to find the “right” sources. It goes deep, not wide.
4o image generator is so good, I’ve canceled any subscriptions to other AI image generators
Easy to build Custom GPTs, and share them with team members or VAs (and again, you can now use them with any ChatGPT model)
The memory feature enables it to recall high-level details about you and your work
ChatGPT can now connect to your email and share drive. Admittedly, I don’t use this much yet, but many like this feature.
You can learn to create Custom GPTs for commercial real estate here, and learn to use the ChatGPT 4o image generator for commercial real estate use-cases here.
Claude Max — $100/mo
Why I use it:
Claude Projects are top-tier if you want to create AI-assistants to complete complex, multi-step tasks. That said, I’m rethinking this subscription now that Custom GPTs can use any model.
The reason I subscribe to Max is that Claude has pretty low usage caps on it’s high-end models - Meaning if you use it too much, you’re downgraded to the lower-tier models. With the Max subscription, I don’t have to worry about this.
This is anecdotal, but I still find Claude to be the best LLM for writing, or creating marketing copy/content.
Similar to how ChatGPT let’s you connect to your Outlook email and Microsoft share drive, Claude enables you to connect it to your Google Drive. Personally, I’ve found this integration to work best compared to other AI platforms that enable you to do the same. In my honest opinion, this feature doesn’t work as well as you’d want it to on any platform.
Google Gemini Pro (+ Notebook LM Pro) — $19/mo
Why I use it:
The key reason I still subscribe to Gemini is it provides you with Notebook LM Pro, which allows you to give your Notebooks up to 300 sources instead of 50.
I’m using Notebook LM constantly, but you can check out many ways to use Notebook LM for commercial real estate here.
Gemini has some other great things going for it:
The OCR functionality (ability to read scanned documents), is best-in-class
Gemini’s Deep Research is the farthest reaching Deep Research feature I’ve used (it can scan over 1,000 sources, it goes wide, but less deep)
Many use Gemini’s latest model 2.5 Pro as their LLM model of choice. Personally, I don’t, but it’s very good.
Perplexity — Free
Why I use it:
Perplexity is literally AI BUILT for searching the internet. All major LLM’s can now do that now, but I still find Perplexity to be the best, most reliable tool for quick AI-powered internet searches where I want to trust the results.
More reliable than ChatGPT’s internet search, and surprisingly better output than Google (based on my usage). Great for market, property, owner or tenant research.
Genspark AI — $25/mo
Why I use it:
Genspark is a “multi-agent” that can do many things that you’d otherwise need multiple AI subscriptions for, under one hood. One of those uses being that you can chat with most of the powerful models I’ve listed above (Gemini 2.5, Claude 4, ChatGPT o3), all from one subscription.
You can check out some of the MANY potential ways to use Genspark for commercial real estate here.
The key feature that’s kept me subscribed to Genspark: AI Sheets. Here’s the 2 ways I’ve kept using it:
1. Web scraping:
AI sheets enables you to scrape the internet for specific information (contact info for local businesses, for example), and map the info to specific columns in a spreadsheet, along with the source where it found the information. You can also tell it to verify the information against a second source, or even give it a confidence score for the accuracy of the information. It really is pretty powerful.
2. Upload, verify, and build onto YOUR existing spreadsheets:
Upload a document of any type to AI sheets, have it extract the information to a spreadsheet, and then continue to search the internet for additional information, based on the intel in your original documents. You can also have it review the information you uploaded from your existing documents and verify that information against publicly available data sources.
This is an extremely powerful tool for building tenant & property databases, or really databases of any CRE information that’s publicly available.
Genspark can also create beautiful presentations based on the data you give it, or the data it collects, and a fine-tuned prompt. We prefer Gamma for designing marketing collateral.
Productivity:
You’ll notice I provide a lot less context for these tools. I’m not just being lazy, these are much more specific tools, not LLM that have a wide range of features and use-cases.
Superhuman for Email Management — $40/mo
Why I use it:
Superhuman cuts my email management time in half, easily. Keyboard shortcuts let me respond, forward, archive, or set up reminders about emails without my hands leaving my keyboard to touch my mouse. Doesn’t sound like it would save you much time, but it does.
You can also draft emails using AI, create re-usable email snippets (templates) for emails that come up over and over again, and create split inboxes to separate your emails by marketing emails, calendar requests, invoices/ receipts, etc.
I did a slightly more detailed writeup on why I love Superhuman and some of it’s AI features earlier this year, you can read it here.
Monday.com for Automated Project Management - Pricing Based on Team Size and Usage
Why I use it:
All of the projects our team executes daily run through our automated project management system in Monday.com
Someone fills out a form with a request, it goes in the system, a team member is automatically notified, they upload a draft and the next person is automatically notified to review and approve, after approval next person on the team is automatically notified, on and on until the project is complete.
Nothing gets forgotten about, everyone on the team knows when it’s their turn to take the wheel.
You can also send emails to a project board on Monday, which is great if you want to use Superhuman to fly through your emails, and forward tasks you need to take care of to a to-do list in Monday.
There’s some great AI features on Monday as well (think, upload an invoice, use AI to extract the invoice number and map it somewhere else in the system), but for our team, that’s the icing on the cake. It’s all about automated project management.
Fireflies AI for Meeting Notes — $18/mo
Why I use it:
There’s 100+ AI notetakers out there. The key reason I like Fireflies: It can connect to a lot of other platforms. If you have an AI-notetaker that doesn’t connect to other platforms, your meeting notes will just sit there, probably unused.
With Fireflies, I can connect it to Monday.com to add tasks I’ve committed to to my to-do lists, or create automations based on the information in the transcripts using the Relay App (described below).
Relay App for AI-Powered Automations - Pricing Based On Usage
Why I use it:
Relay connects the tools you use every day so you can use AI to automate repetitive tasks with those tools.
Think of it this way: If I need to execute repetitive tasks frequently, I’ll build a Custom GPT to do most of the work. If I need to do that task everyday or every week, I’ll build an automation to do the work using Relay so I never even need to open the Custom GPT.
Automations really enable you to integrate your tech stack to get more work and more value out of the platforms you’re already paying for. Here’s a quick example of how you could use Relay to build your own comp database in something like Monday.com every time you get a signed lease.
Marketing:
Gamma For Presentation & Pitch Deck Design — $10/user
Why I use it:
Copy and paste information (such as a property description & relevant details) into Gamma, it will create a stunning marketing document, presentation or website based on that information, using your branded colors & fonts in seconds.
Once you’ve made your templates, they’re extremely easy to edit/update, and then continue building on additional slides when you need using AI.
You can get more insight on using Gamma for commercial real estate here.
Canva AI for Custom Graphics - $15/User
If design is a regular part of your life, or if you have a designer on your team, I can’t recommend Canva’s AI-powered design features enough.
Design tasks that used to take an hour are now a button click: Things like removing a person from a photo, grabbing text from a photo so you can edit it, removing the background, or expanding a photo to fit certain dimensions all take seconds in Canva.
Full transparency, I don’t really use Canva’s AI tools a lot, but I work with people that do and it makes their life easier. Here’s an example I recorded for a presentation with a group that wanted to use Canva to advertise events at their properties. (Warning, this is a long video as I needed to talk it through during the presentation.)
Focusee for Screen Recordings — $70 per Device for Lifetime Access
Three people in the past week have asked me what platform I use to create tutorial videos for this newsletter and social media, the answer is Focusee.
You can record your screen to easily produce tutorial/ training videos, with or without yourself as a talking head. Focusee will automatically “zoom in” to wherever your mouse clicked on in the video so that viewers can easily see what’s happening on your screen in the video. Clipping the video, speeding things up, editing your zooms, improving audio quality, adding “spotlights”, and other standard tutorial video edits are all simple to do in Focusee.
Opus Clips Pro for Social Media Videos — $174/yr
This one is a layup: Take any long-form video, and Opus Pro will break it up into 10+ highly engaging short form video clips for social media, complete with subtitles in your branded fonts. It can also re-structure the video to fit dimensions for different platforms.
It’s hard to explain how much time this saves until you actually try it, it’s extremely simple to use and definitely something you can train a good VA to do for you.
Honorable Mentions (Tools I Subscribe to or Use Once in a While):
Runway ML for Making Videos from Images — $15/mo when needed
Upload property photos and turn them into drone-style aerial video clips in under a minute (really, there are A LOT of different ways to use Runway ML for videos, but this is an immediate CRE use-case.)
InVideo AI for CRE Video Ads — $20/mo as needed
Give InVideo AI the copy from a property brochure, add in the video clips you just made with Runway ML, and create a custom-tailored marketing video for your listing with an AI-narrator in under 10 minutes. Just like Runway ML, there are many ways you could leverage this technology in or outside of CRE.
Bardeen for Web Scraping— Free (paid tiers exist)
Bardeen is another automation platform with some useful AI features, but I use it mostly for point and click web scraping
Think: Click on a person’s LinkedIn profile, automatically extract their name, title, contact info, profile URL, and save them to a Google Sheet (or your CRM of choice), and draft an email to them using a specific template - All from a button click.
TurboScribe for Video or Audio Transcription — Free (paid tiers exist)
Upload a video or audio file, get a transcript in under a minute.
YouTube Summary by Glasp — Free
Free Chrome extension that automatically adds transcriptions to all Youtube videos. This works so well I honestly forgot about it and thought it was just part of Youtube now.
Peer-Recommended AI Platforms Built for CRE
AI platforms I don’t currently have a need for, but colleagues and friends swear by:
Terrakotta for AI-Powered Prospecting - $250/mo
Use AI to search for property owners, find their phone numbers, and power dial through the phone numbers until you connect with the owner. I recommend reading my full writeup on the pros and cons of Terrakotta here before you test it out.
Henry AI for Automated OM & BOV Generation - Starts at $500/mo
Upload financials and property photos, and use AI to generate your brochure, offering memorandum, or BOV. Henry will generate consistent, fully customized marketing collateral based on your approved design templates every time (something general AI-platforms like Gamma can’t do). I recommend reading my full writeup on when Henry AI might make sense for your team, and how it differs from platforms like Gamma and Genspark here.
DealGround for Your Automated Deal Database — Standard: Free, Pro: $75/mo per seat
This is a new platform I’m excited to go deeper on in next month’s edition of ChatCRE. You receive OM’s all day, but have no easily accessible way to access all the deal info. DealGround has a potential solution for this. Forward your OM’s to DealGround via email, upload them yourself, or add them with their Chrome extension, DealGround will automatically use AI to extract the deal info, and add it to your private OM database. You can then use DealGround’s map-based database to search through and filter your deals using any of the data points extracted from the OM, and soon you’ll be able to overlay transaction history for each property based on title information. It becomes your internal on-market deal/property database.
I haven’t done a deep dive on DealGround yet, but it’s coming in next month’s edition of ChatCRE.
This was a long newsletter..
Be honest, did you enjoy reading about 3 interrelated AI topics in 1 monthly dose, or should I start breaking this up into a weekly newsletter? Feedback is much appreciated!
That’s It, That’s All.
That’s it for the June 2025 edition of ChatCRE. I’d love to hear your thoughts on this edition—what you found valuable, what you could do without, or any topics you’d like me to cover in future newsletters. Feel free to comment below, send me an email, or reach out on X and LinkedIn.
If you found this newsletter helpful, please consider sharing it with a colleague or friend who could benefit from enhancing their CRE operations, marketing, or pipeline with AI.
P.S. I’m currently booking online AI presentations & training sessions for Q3 & Q4, and in-person presentations for Q4 only (travel dates for Q3 are booked solid.) If you’re interested in having me present to your team, company, or association on how to use AI in CRE, you can find more information here, or just send me an email and we’ll connect.
And for those of you who made it to the end..
Here is Justin Gohn’s Deep Research Prompt for Market Analyses:
The Prompt:
Context and Objective:
You are an experienced commercial real estate analyst from a top-tier firm (e.g., Newmark, JLL, CBRE, Cushman & Wakefield, Colliers, Avison Young) preparing a comprehensive, expert-level market analysis report. Your subject property is the Comfort Inn & Suites Glen Mills – West Chester, located at 1110 Baltimore Pike, Glen Mills, PA 19342. Using the six-step process outlined in The Appraisal of Real Estate, 15th Edition, produce a rigorous, well-referenced, data-driven report. Cite authoritative sources (e.g., STR, CoStar, local market reports, county or municipal data, state tourism authorities) wherever possible. Your analysis should be sufficiently detailed to serve as a Level C or D study if needed, reflecting a deep dive into local, regional, and property-specific nuances. Use clear subheadings for each of the six steps.
1. Property Productivity Analysis (Determine the Product)
Physical Attributes
Provide a detailed description of the subject property’s size, room mix, brand affiliation, amenities (e.g., pool, fitness center, breakfast area), overall condition, design, age, recent renovations, and any unique features (e.g., meeting space, special event facilities).
Include insights on how these features position the property within its competitive set.
Legal and Regulatory Attributes
Summarize relevant zoning regulations, building codes, and any notable public or private restrictions (e.g., easements, deed restrictions, franchisor requirements).
Note any local ordinances that could affect hotel operations, expansions, or renovations.
Location Attributes (Situs)
Examine the broader neighborhood, traffic patterns, visibility, and access to major highways (e.g., US-1, Route 202, or I-95) and local demand generators (e.g., corporate offices, universities, tourist sites, medical centers).
Identify economic drivers in Glen Mills / West Chester that influence hotel demand, including corporate headquarters, business parks, and leisure attractions.
Outline strengths and weaknesses stemming from location, proximity to demand drivers, and local real estate trends.
Identify competing hotels (by brand, location, chain scale) within the market and explain how they compare in terms of price point, service level, amenities, and brand recognition.
Note any special consumer preferences or traveler profiles that may impact how the subject property competes.
Consumer Profile Concepts
Discuss the demographic, psychographic, and behavioral traits of likely guests (e.g., average length of stay, seasonality patterns, rate sensitivity, loyalty program usage).
Differentiate between the broader market area for hotel demand and the narrower area for selecting sales comparables, if applicable.
3. Demand Analysis (Measure Demand)
Current Demand Estimate
Use the latest data (e.g., from STR, CoStar, local CVB reports) to quantify current hotel demand, occupancy levels, and average daily rates in the Glen Mills–West Chester area.
Evaluate macroeconomic factors (e.g., employment trends, GDP growth) and local factors (e.g., corporate expansions, tourism initiatives, new institutions) that drive room nights.
Historic Trends & Inferred Demand Projection
Examine historical occupancy, ADR (average daily rate), and RevPAR trends for both the subject and the competitive set (if available).
Project near-term demand using past absorption rates and broader economic indicators, citing relevant secondary data.
Fundamental Demand Forecast
If possible, conduct a more detailed analysis by breaking down major demand drivers (population growth, new business developments, local event expansions, infrastructure improvements).
Incorporate any new corporate relocations, expansions, or tourism campaigns that might influence hotel demand in the next 3–5 years.
Comment on potential shifts in traveler behavior (remote work, bleisure travel) and how they might affect the subject property.
4. Supply Analysis (Measure Competition)
Existing Competitive Supply
Provide an inventory of existing hotels that directly compete with Comfort Inn & Suites Glen Mills, including brand affiliations, opening dates, and relevant performance metrics (if available).
Discuss their strengths, weaknesses, and unique selling points.
Pipeline Analysis
Identify hotels under construction or in advanced planning stages within the market area.
Evaluate potential future supply additions (proposed hotels, announced redevelopments, brand conversions) that could affect market equilibrium.
Attributes of Competing Properties
Compare the subject property with competitive supply on location, facility quality, room mix, guest experience, brand loyalty programs, and overall market positioning.
Examine barriers to entry (land availability, construction costs, local planning restrictions) that could influence future supply.
5. Residual Demand Analysis (Compare Supply and Demand)
Current Equilibrium
Compare the existing market demand (from Step 3) with the existing supply (from Step 4).
Discuss whether the market is undersupplied, oversupplied, or roughly balanced at present.
Assess the present market cycle stage (recovery, expansion, hypersupply, recession) for the hospitality sector locally.
Projected Market Balance
Forecast how the balance between supply and demand may shift, considering both new pipeline properties and anticipated changes in demand drivers.
If applicable, quantify any residual demand or shortfall using a fundamental approach (e.g., how many additional rooms the market can absorb) or an inferred approach (based on trends and qualitative data).
6. Subject Capture Analysis (Marketability Analysis Conclusions)
Competitive Positioning
Compare the subject property’s attributes (Step 1) and the competitive context (Steps 2–5) to estimate how well Comfort Inn & Suites Glen Mills can capture future demand.
Highlight the hotel’s unique value propositions and potential vulnerabilities.
Projected Performance
Based on occupancy and ADR trends, forecast likely performance metrics (occupancy rate, ADR, RevPAR) for the subject property over the next few years.
Include both a base-case scenario and potential upside or downside scenarios (e.g., effects of new competition, changes in local demand drivers).
Absorption Rate & Stabilization Timeline
Estimate the time horizon for the property to achieve stabilized occupancy under typical market conditions, factoring in any renovations, rebranding, or market fluctuations.
Potential Alternate Use or Repositioning
Briefly discuss if the property could be repositioned or converted to meet alternative lodging or extended-stay demand.
Note any highest and best use considerations that could emerge from market changes.
Conclusion & Recommendations
Summarize key findings from the six-step analysis.
Present clear, concise recommendations for ownership or stakeholders, backed by the data and trends revealed in your analysis.
Research Expectations and Sourcing
Throughout your report, cite relevant data sources and justify your assumptions with quantitative metrics, market reports, local economic development information, and reputable third-party research (e.g., STR, CoStar, local economic development agencies, county records, state tourism boards).
Offer specific links or references (if publicly available) or describe where data was obtained (if proprietary).
Wherever feasible, include graphs, tables, or charts to illustrate market trends, competitive sets, pipeline analysis, and demand forecasts.