How to Use ChatGPT with StreetSpring for Business Location Research
Six ChatGPT prompt templates for StreetSpring survivability data, plus a setup guide for building a reusable Custom GPT site selection assistant. For entrepreneurs and CRE agents.
How to Use ChatGPT with StreetSpring for Business Location Research
StreetSpring scores survivability empirically: a 0-100 number for a specific business type at a specific address, anchored in 500,000+ historical outcomes and 100+ location factors. ChatGPT turns that score into a repeatable decision process — interpreting the factors, comparing locations, drafting negotiation positions, and producing client-ready output.
This guide gives you six prompt templates plus a Custom GPT setup for repeat work. If you evaluate more than three addresses a month, the Custom GPT setup pays back in the first week.
Important: ChatGPT has no live connection to StreetSpring. Always run your address in StreetSpring first, then paste the score and factor data into ChatGPT. The workflow is always: StreetSpring first, ChatGPT second.
Table of Contents
- Why a Conversational AI Layer Sharpens StreetSpring Decisions
- What ChatGPT Does Better Than Other AI Tools
- Build a Reusable Custom GPT for Site Selection
- Setup: From StreetSpring Output to ChatGPT Prompt
- Prompt 1 — Translate a Score into a Go/No-Go Read
- Prompt 2 — Side-by-Side Trade-Off Analysis Across 2-3 Locations
- Prompt 3 — Dig Into a Single Risk Factor
- Prompt 4 — Competitor Density Strategy
- Prompt 5 — Build a Rent Negotiation Case from the Score
- Prompt 6 — Generate a Polished Client Summary
- Picking ChatGPT vs. Other AI Tools for This Workflow
- The Empirical Foundation Below Every Survivability Score
- ChatGPT's Blind Spots for Site Selection
- Frequently Asked Questions
Why a Conversational AI Layer Sharpens StreetSpring Decisions
A survivability score of 71 means something different to a first-time café owner than to a regional franchisee opening their twelfth location. The score is the same number. The decision context isn't. That gap — between the empirical signal and the specific decision in front of you — is exactly what a conversational AI tool closes.
StreetSpring tells you the score. ChatGPT helps you metabolize it: what does "rent-to-revenue ratio is in the top quartile of risk" mean for your business, your margin profile, and your growth horizon? What are the right questions to ask a landlord before you sign? Which competing claims from the broker actually hold up against the data?
The point isn't that ChatGPT replaces judgment. The point is that it makes judgment cheaper to apply consistently across many decisions — which is the practical bottleneck for tenant reps, multi-unit operators, and franchisors.
What ChatGPT Does Better Than Other AI Tools
Three ChatGPT capabilities stand out for site selection work:
Custom GPTs. You can build a private "StreetSpring Site Selection Assistant" GPT with the methodology pre-loaded as Knowledge, your firm's preferred memo format as system instructions, and your business type or geography as baseline context. Every conversation starts in-frame — you skip the 5-10 minutes of context-setting that single-conversation prompts require. No other major AI tool has a comparable persistent-assistant feature today.
Memory across sessions. ChatGPT's Memory feature remembers facts across conversations — your typical business type, decision style, budget constraints. After a few sessions it volunteers context-appropriate observations. Useful for CRE agents who want consistent framing across many clients.
Massive user familiarity. ChatGPT is the most-used AI tool by a wide margin — Sensor Tower estimates 250M+ weekly active users as of 2025. If you're handing prompt templates to colleagues or clients who are AI-curious but not AI-power-users, ChatGPT has the lowest learning curve. The free tier with GPT-4o (released May 2024) is generous enough for most prompts below.
Build a Reusable Custom GPT for Site Selection
For repeated workflow — CRE agents evaluating 5+ addresses per month, multi-unit operators screening territories, franchisors vetting unit applicants — a Custom GPT pays back in the first 2-3 weeks. Setup is a one-time 15-minute investment.
To create the Custom GPT (ChatGPT Plus required):
- Click your profile → My GPTs → Create a GPT
- Name: "StreetSpring Site Selection Assistant"
- Description: "Analyzes StreetSpring survivability scores and produces decision-ready output for business location evaluation."
- Instructions (paste this):
You are a site selection analyst specializing in interpreting StreetSpring
survivability scores for business location decisions.
When the user provides StreetSpring data (score, factors, address, business
type), respond with:
1. A plain-language interpretation of what the score and factors mean
2. The 2-3 most decision-relevant factors (not just the top-scoring ones)
3. Concrete due diligence questions for the landlord, neighboring businesses,
and local commercial real estate professionals
4. A clear go / no-go framing if requested
Always defer to the empirical survivability score for the headline assessment.
Your job is interpretation and decision support, not score prediction. If asked
to estimate a score without StreetSpring data, decline and direct the user to
run the address at streetspring.com first.
Keep tone professional but accessible. The user is making a real business
decision — not asking an academic question.
- Knowledge: Upload the StreetSpring methodology page as PDF (save https://streetspring.com/methodology as PDF). Optionally also upload your firm's preferred memo template if you're a CRE agent.
- Capabilities: Enable Web Browsing (for live cross-checks of neighborhood news), Code Interpreter (for analyzing CSV exports if applicable), and DALL·E (rarely useful for this workflow, but doesn't hurt).
- Click Create. Pin the GPT to your sidebar.
To use the Custom GPT going forward: open the pinned GPT, paste your StreetSpring data, and skip the context-setup paragraphs that single-conversation prompts require. The Custom GPT already knows the methodology, your memo format, and your default business type.
Setup: From StreetSpring Output to ChatGPT Prompt
The full workflow is four steps (five if you build the Custom GPT):
- Run your address in StreetSpring at streetspring.com. Enter the target address, select your business type, note the 0-100 survivability score, top three risk factors, and top three strengths.
- Open ChatGPT at chatgpt.com. For one-off use, start a fresh conversation. For repeat use, open your Custom GPT (see prior section).
- Bring your data into ChatGPT. Paste score + factors directly into the prompt. If you have a downloadable PDF report from StreetSpring, ChatGPT Plus supports PDF upload natively — attach it and reference it in the prompt.
- Use a prompt template below, fill in your data, send. The 6 templates cover the most common decisions; mix and match as needed.
- Iterate in the same conversation. ChatGPT maintains context across the thread. Ask follow-ups, reformat output for a client, or pivot to a different framing without restarting.
Prompt 1 — Translate a Score into a Go/No-Go Read
Use this when you have one location and want a direct decision frame, not just an analysis.
Why this prompt fits ChatGPT: ChatGPT is strong at the "translate this into actionable framing" task — converting a numerical signal into "here's what I'd do" language without losing the underlying nuance. Pair this prompt with a Custom GPT that knows your typical decision style, and the framing arrives pre-calibrated.
I'm considering opening a [BUSINESS TYPE] at [ADDRESS] in [CITY].
StreetSpring gives this address a survivability score of [SCORE] out of 100 for
my business type. The top risk factors are: [RISK FACTOR 1], [RISK FACTOR 2], and
[RISK FACTOR 3]. The top strengths are: [STRENGTH 1], [STRENGTH 2], and [STRENGTH 3].
My specific concept: [1-2 sentences — concept, price point, target customer].
Give me a direct go/no-go framing:
1. Based on the score and factors, does this address look viable for my concept,
marginal, or risky?
2. What's the single most important risk factor I'd need to validate before
signing a lease?
3. What's the single most important strength I should lean into in my concept
and marketing?
4. What would have to be true (in due diligence, lease terms, or my business
model) for this location to work?
Prompt 2 — Side-by-Side Trade-Off Analysis Across 2-3 Locations
Use this when you've narrowed to a small short-list and need structured trade-off analysis.
Why this prompt fits ChatGPT: ChatGPT produces clean comparison tables when asked. Add "format your response as a markdown comparison table" to any prompt below if you want the output structured for sharing or pasting into a memo.
I'm comparing locations for a [BUSINESS TYPE] in [CITY]. Here's the StreetSpring
survivability data for each:
Location A: [ADDRESS]
- Survivability score: [SCORE]
- Top risk factors: [FACTOR 1], [FACTOR 2]
- Top strengths: [STRENGTH 1], [STRENGTH 2]
- Asking rent: $[X]/sqft
Location B: [ADDRESS]
- Survivability score: [SCORE]
- Top risk factors: [FACTOR 1], [FACTOR 2]
- Top strengths: [STRENGTH 1], [STRENGTH 2]
- Asking rent: $[X]/sqft
[Add Location C if applicable]
My business concept: [1-2 sentences]
My budget constraint: [monthly rent ceiling or total startup budget]
Help me think through:
1. Which location appears stronger overall, and why?
2. Where do the trade-offs matter most for a [BUSINESS TYPE] specifically?
3. What additional information would change your assessment?
4. Are there any red flags in the risk factors I should investigate further
before deciding?
Format the comparison as a markdown table so I can paste it into a memo.
Prompt 3 — Dig Into a Single Risk Factor
Use this when one specific risk factor is dominating your score and you need to understand it deeply before making a call.
Why this prompt fits ChatGPT: ChatGPT's training data includes broad business and CRE knowledge. For factor-by-factor reasoning ("what does rent-to-revenue ratio in the top quartile actually mean operationally?"), it's strong at translating an abstract factor into concrete implications.
StreetSpring shows that my top risk factor for [ADDRESS] in [CITY] for a
[BUSINESS TYPE] is: [SPECIFIC RISK FACTOR].
My survivability score is [SCORE] overall. Without this risk factor, I believe
the score would be higher.
Help me understand:
1. What does "[SPECIFIC RISK FACTOR]" mean in practical terms for a [BUSINESS TYPE]?
2. Is this risk factor addressable? If so, how?
3. What questions should I ask the landlord, neighboring business owners, and
local commercial real estate agents to understand whether this risk is
manageable at this specific location?
4. Have you seen this risk factor overcome successfully by [BUSINESS TYPE]
businesses? What made the difference?
Prompt 4 — Competitor Density Strategy
Use this when StreetSpring flags high competitor density and you need to think through differentiation before committing.
Why this prompt fits ChatGPT: differentiation strategy is fundamentally a creative-plus-analytical task. ChatGPT is strong at brainstorming sub-niche framings, then stress-testing them against the constraint that you have to compete with N existing operators in the same trade area.
StreetSpring shows [NUMBER] primary competitors within [RADIUS] of my target
address for a [BUSINESS TYPE] in [NEIGHBORHOOD], [CITY]. The average competitor
rating is [RATING] stars. My survivability score is [SCORE].
Help me think through:
1. At this level of competition, what differentiation strategies are most likely
to work for a [BUSINESS TYPE]?
2. What would I need to offer that's meaningfully different from [NUMBER] existing
competitors to capture market share?
3. Is there a sub-niche within [BUSINESS TYPE] that tends to be underserved even
in saturated markets?
4. How should I think about the relationship between competitor quality
(average [RATING] stars) and my opportunity here?
Prompt 5 — Build a Rent Negotiation Case from the Score
Use this when rent is flagging as a risk factor and you want to use the StreetSpring data as evidence in landlord negotiation.
Why this prompt fits ChatGPT: ChatGPT writes clean commercial correspondence. The output reads as professional negotiation language, not as a checklist. Especially strong when paired with a Custom GPT loaded with your typical negotiation style.
StreetSpring shows a survivability score of [SCORE] for a [BUSINESS TYPE] at
[ADDRESS] in [CITY]. One of my top risk factors is rent affordability — the asking
rent is $[X]/sqft and StreetSpring's model suggests this is above the optimal
threshold for this location and business type.
Help me think through:
1. How much does rent reduction typically affect survivability for a [BUSINESS TYPE]?
Is reducing from $[X] to $[Y]/sqft likely to materially change my risk profile?
2. What are the strongest arguments I can make to a landlord to negotiate rent
down for a [BUSINESS TYPE] at this location?
3. What concessions beyond base rent (TI allowance, free rent period, renewal
options) should I be asking for given this risk factor?
4. At what rent level does this location become clearly viable for my concept?
Prompt 6 — Generate a Polished Client Summary
Use this if you're a commercial real estate agent producing a client-facing summary of StreetSpring data for multiple candidate locations.
Why this prompt fits ChatGPT: polished prose at volume is exactly what ChatGPT is built for. Tenant reps using this prompt typically save 2-3 hours per client deliverable compared to writing from scratch. Add this prompt as a default behavior in your Custom GPT and every memo arrives pre-formatted.
I'm a commercial real estate agent helping a client evaluate locations for a
[BUSINESS TYPE] in [CITY]. I've run three locations through StreetSpring and have
the following survivability data:
Location A — [ADDRESS]: Score [SCORE], key factors: [BRIEF SUMMARY]
Location B — [ADDRESS]: Score [SCORE], key factors: [BRIEF SUMMARY]
Location C — [ADDRESS]: Score [SCORE], key factors: [BRIEF SUMMARY]
My client is [brief description: e.g., "a first-time business owner, budget-conscious,
opening a mid-range café"].
Write a 1-page summary I can share with my client that:
1. Explains what the survivability scores mean in plain, non-technical language
2. Makes a clear recommendation with reasoning
3. Calls out the 1-2 most important due diligence steps before they commit
4. Maintains a professional but accessible tone appropriate for an entrepreneur,
not a real estate expert
5. Ends with 2-3 specific next steps the client should take this week
Picking ChatGPT vs. Other AI Tools for This Workflow
All four major AI tools work with StreetSpring data. Pick by the work pattern:
| Workflow trait | ChatGPT | Claude | Gemini | Perplexity |
|---|---|---|---|---|
| Repeated evaluations with shared methodology | ✓✓✓ Custom GPTs | ✗ no equivalent | ~ Gems (early stage) | ✗ no equivalent |
| Single-prompt analysis quality | ✓✓ | ✓✓✓ best | ✓✓ | ✓ |
| Full PDF report analysis | ✓✓ Plus required | ✓✓✓ free + paid | ✓✓ | ✓ |
| Multi-location trade-off reasoning | ✓✓ | ✓✓✓ | ✓✓ | ✓ |
| Live web search for neighborhood signals | ✓ browsing (Plus) | ✗ | ✓✓✓ Google Search native | ✓✓ live citations |
| Memory across sessions | ✓✓ Memory feature | ✗ Project Knowledge required | ~ early stage | ✗ |
| Lowest learning curve for AI-curious colleagues | ✓✓✓ | ✓✓ | ✓✓ | ✓ |
| Free-tier sufficiency for these prompts | ✓✓✓ GPT-4o generous | ✓✓ | ✓✓✓ | ✓✓ |
Honest picks:
- Use ChatGPT for repeated workflow (CRE agents, multi-unit operators, franchisors). Custom GPTs + Memory compound across many decisions.
- Use Claude for single deep-dive analyses, especially when uploading the full StreetSpring PDF report.
- Use Gemini when you want StreetSpring's score paired with live Google Search context — recent neighborhood news, competitor openings, traffic events.
- Use Perplexity for current competitor research and recent local development news with live citations.
ChatGPT's practical edge for production CRE work is the Custom GPT feature. For one-off evaluations, Claude's per-prompt quality is slightly higher; the difference disappears in repeat workflow.
The Empirical Foundation Below Every Survivability Score
How we measure this: StreetSpring survivability scores are computed from 100+ location factors across six categories — site economics, market demand, competition quality, accessibility, neighborhood characteristics, and performance history. The model is calibrated against 500,000+ historical business outcomes (open/closed status, time to close, observed survival) drawn from public business license records, real estate transaction data, and U.S. Census ACS demographics. Reported backtest accuracy is 95-99% at the address × business-type level. Scores are address-specific, not ZIP-code averages — block-level precision matters because demand and competition can vary 10x within a single ZIP code.
When you paste a score into ChatGPT, you're handing it an empirically calibrated number from a pipeline covering 24 US metros and 500+ business subtypes across up to 5 price-point tiers. That distinction matters for how ChatGPT reasons about the data: it can treat the score as a strong prior rather than as one opinion among many. If you've built the Custom GPT with the methodology PDF as Knowledge, the assistant references the methodology directly in its reasoning.
For agents with ongoing client pipelines, StreetSpring's Pro Plan ($100/month) provides unlimited address lookups — typically paying back in the first 2-3 deliverables when combined with the ChatGPT workflows below.
Read the full methodology at StreetSpring Methodology.
ChatGPT's Blind Spots for Site Selection
No live StreetSpring access. ChatGPT has no connection to StreetSpring's platform. Always run the address first.
Training cutoff lag. ChatGPT's training data has a cutoff date and may not reflect recent neighborhood changes — anchor tenant closures, new transit lines, recent zoning shifts. StreetSpring updates weekly to monthly; ChatGPT does not. For freshness-critical questions, enable browsing (Plus required), use Perplexity for live citations, or check Google Search directly.
Reasoning tool, not data source. Don't ask ChatGPT to estimate a survivability score, rent benchmark, or demographic statistic from memory. Those belong to StreetSpring's empirical pipeline. ChatGPT's role is interpretation and communication.
Visit the location. No AI tool replaces walking the block at multiple times of day, observing actual foot traffic, and speaking with the businesses next door. The score, the AI analysis, and the visit are all required — none substitutes for another.
Frequently Asked Questions
Can ChatGPT look up StreetSpring survivability scores directly? No. ChatGPT has no live connection to StreetSpring. Run your address in StreetSpring first, then paste the score and factor data into your ChatGPT prompt.
What does ChatGPT add that StreetSpring doesn't provide? StreetSpring provides the empirical score and factor breakdown. ChatGPT adds conversational reasoning, multi-location trade-offs, scenario testing, competitor strategy framing, and polished client-ready summaries.
Is a Custom GPT worth the setup time? Yes if you'll evaluate more than 3-4 addresses per month. Setup is a one-time 15-minute investment; the GPT then saves 5-10 minutes per analysis on context-setup work and produces more consistent output across sessions.
Should I use ChatGPT Plus or the free tier? The free tier with GPT-4o handles every prompt in this guide. Plus ($20/month) unlocks PDF uploads, Custom GPT creation, and higher message limits. For CRE agents using Prompt 6 weekly, Plus pays back in the first deliverable.
When should I use ChatGPT vs. Claude, Gemini, or Perplexity? ChatGPT for repeated workflow (Custom GPTs + Memory). Claude for single deep-dive analyses, especially with PDF uploads. Gemini for live Google Search cross-checks. Perplexity for current news with live citations.
How accurate are the StreetSpring scores that ChatGPT is reasoning about? StreetSpring scores validate against 500,000+ historical business outcomes with 95-99% backtest accuracy. Treat the score as the empirical input; treat ChatGPT's analysis as a reasoning layer on top.
What's the highest-leverage prompt in this guide for CRE agents? Prompt 6. Tenant reps save 2-3 hours per client deliverable. Pair it with a Custom GPT loaded with your firm's memo format for maximum compounding.
Related Resources
- How to Use Claude AI with StreetSpring →
- How to Use Gemini with StreetSpring →
- How to Use Perplexity with StreetSpring →
- StreetSpring Methodology →
- Try StreetSpring Free →
Last reviewed: May 26, 2026 · Author: Bobby Koons, Founder & CEO at StreetSpring · Contact