This article will give you tips on how to analyze responses from a tenants survey about rent affordability using streamlined AI and survey analysis approaches.
Choosing the right tools for analyzing rent affordability survey responses
How you analyze your tenants’ survey data about rent affordability really depends on the type and structure of the responses you’ve collected.
Quantitative data: If you asked things like “What percentage of your income goes to rent?” or provided fixed-choice options, these are simple to summarize. You can count responses and chart results in Excel, Google Sheets, or similar tools.
Qualitative data: However, if your survey includes open-ended questions or dynamic follow-ups—like “Describe your biggest challenges paying rent”—sifting through the answers manually just isn’t practical. Reviewing hundreds of stories by hand kills productivity and invites bias. This is where AI tools, like GPT-based solutions, make all the difference, surfacing patterns and themes across qualitative feedback with speed and depth that’s hard to match by hand.
There are two main approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can export your qualitative survey data, then copy and paste it into ChatGPT, Claude, or another language model and begin a conversation to search for insights.
Downsides? Handling data this way isn’t very convenient for large sample sizes. Formatting data for the AI, breaking things into manageable chunks, and keeping track of previous questions can quickly become a headache if your dataset is big—or if you want to collaborate with others.
Privacy and compliance often require extra care when using a general-purpose public AI for handling respondent data, especially if the answers are sensitive or personally identifiable.
All-in-one tool like Specific
Purpose-built AI Survey tools like Specific combine collection and AI-powered analysis from the start. When you collect responses, the system can probe deeper by dynamically asking follow-up questions, which almost always leads to richer, more actionable data. See how AI follow-up questions work in detail in the context of rent surveys.
During analysis, Specific summarizes open-ended responses, highlights key recurring themes, and allows you to chat with the survey data using natural language. No spreadsheet wrangling, no manual coding, and no need to export data. You can even define the context the AI uses—giving you more control and better results.
If you’re interested in seeing how this approach can work for your rent affordability survey, you can check out AI-powered survey analysis for tenants surveys and compare it with a more generic workflow. The speed from raw feedback to strategic insight is a game changer, especially when the stakes are high: for example, renters in England now spend on average 36.3% of their gross income on rent (well above the 30% affordability threshold) and all signs point to this trend continuing[1].
Useful prompts that you can use to analyze tenants rent affordability surveys
Whether you’re using ChatGPT, Specific, or any other AI tool, you can get more out of your tenants survey analysis by using the right prompts. Here are some proven ones for rent affordability surveys:
Prompt for core ideas: If you want the top recurring topics from all your responses, use this. It’s the default for Specific and is robust for big qualitative data sets:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
Give the AI more context: Always tell the AI what the survey is about, your goals, or any important background. This leads to dramatically better results. Here's how you could do it:
This is a survey of UK tenants about rent affordability. Our goal is to understand why so many struggle to afford rent, what sacrifices they make, and what they think landlords or policymakers should know. Please extract core ideas as above.
Ask for detail on a topic: Once you have your initial list of core ideas/themes, dig deeper by asking things like:
Tell me more about “difficulty saving for a deposit”
Prompt for specific topic: Quickly test whether something important is mentioned in your responses:
Did anyone talk about housing benefit? Include quotes.
Prompt for personas: If you want insight into segments within your sample:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: To surface the most pressing frustrations and struggles:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for Motivations & Drivers: Useful for understanding “the why” behind answers:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Prompt for sentiment analysis: Understand whether the general mood is hopeful, fearful, angry, or something else:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
For more inspiration—especially at the survey creation stage—check out our deep-dive on the best questions to ask tenants about rent affordability.
How Specific summarizes and analyzes qualitative survey data
Open-ended questions with (or without) follow-ups: Specific instantly generates a clear summary for every open-ended question, plus a separate summary for any follow-up responses branching off from that same question.
Choices with follow-ups: When tenants select a choice and trigger a follow-up question (like “Why did you say that?”), you’ll see a breakdown by choice, with a summary of all relevant follow-up responses for each option. This makes comparing groups (for example, people spending under 30% of their income on rent vs. over 50%) straightforward and actionable.
NPS questions: If you include a Net Promoter Score question (e.g., “How likely are you to recommend renting in your city?”), Specific creates a collection of all follow-ups for detractors, passives, and promoters, so you always see pain points and bright spots segmented by group.
You can absolutely do the same thing in ChatGPT, but keeping everything organized across multiple types of questions is slower and involves more manual copy-pasting, filtering, and conversational prompts. If creating customized surveys for renters about affordability is new to you, the step-by-step guide to building a tenants rent affordability survey will help you get started fast.
Working with AI context size limits on tenant survey data
One of the main technical challenges with AI-powered survey analysis is context size: GPT models can only “see” so much data at once. If you’ve collected hundreds (or thousands) of tenants’ survey responses, not everything will fit into a single chat or API call.
Filtering: You can focus your analysis on a subset of your data. Maybe you only want surveys from single parents in London or renters allocating over 50% of income to housing (like nearly a third of tenants in Tampa Bay[5]). In Specific, you choose just those conversations before sending them to the AI to keep within context constraints.
Cropping: Instead of analyzing every question, send only your top-priority questions (e.g., open-ends or NPS follow-ups) to the AI for summary. This increases the number of surveys you can analyze and ensures you’re distilling insights from the parts that matter most.
Both filtering and cropping are standard parts of Specific’s AI response analysis workflow, so you tackle the context size problem head-on. For an example of filtering and cropping in action, check out the AI survey response analysis guide.
Collaborative features for analyzing tenants survey responses
Analyzing rent affordability survey responses in a team is tough—comments get lost in gigantic spreadsheets, and it’s hard to keep track of who’s asking what (and why). That’s why collaborative features truly change how you work with survey data, especially with sensitive or complex topics like rent affordability for tenants.
AI-powered chat that fits teamwork: In Specific, you can chat with the AI about tenants’ responses—just like you would in ChatGPT. But you can run multiple chat threads, each with their own filters and focus (like “responses from London tenants” or “pain points for renters over 50”). Each thread clearly shows who started it, which helps teams divide analysis and see which colleague found what.
Message history that’s truly collaborative: As you and your colleagues comment or explore threads, avatars show who said what, creating visible accountability and letting you pick up the thread where someone else left off.
Filters that let teams review their segment: If one person wants to look at single mothers under housing pressure and someone else wants to study young professionals in price surges, each can create a dedicated analysis chat. The filters are saved automatically, so segment-based insights are visible and reproducible to the team.
Collaboration feels natural (not a battle with sheets or docs), so you can focus more on supporting your tenants and less on mechanics or project management. If you’re aiming to build or analyze a custom tenants survey, the AI survey generator for rent affordability can accelerate your workflow.
Create your tenants survey about rent affordability now
Make your tenants’ voices heard and turn every survey response into actionable insight—get started quickly, benefit from dynamic follow-up questions, and analyze every story with ease using AI.