Property management teams screen dozens, sometimes hundreds, of rental inquiries every week. Most of that work is manual: reading emails, asking for documents, checking applications against criteria, deciding who gets a showing. For a two-person leasing team, this work eats 15 hours a week or more. It is not skilled work. It is sorting work, and sorting is exactly what agents are good at.
The stack described below handles the entire intake funnel for eleven dollars a month in new tool costs. Typeform, Make, HubSpot, OpenAI, and Google Workspace. Five components, connected without custom code. Here is what each one does.
The Workflow
Typeform sits at the front of the funnel. When a prospective tenant submits an inquiry through your listing page or rental form, Typeform captures their responses: income range, desired move-in date, number of occupants, pets, prior evictions, current landlord contact. The form is designed around your qualifying criteria, not generic demographics. Anyone who submits receives an immediate confirmation. Anyone who does not meet minimum criteria receives an automated decline. Human attention is reserved from the first interaction.
Make (formerly Integromat) is the router. When a Typeform submission arrives, Make checks it against your rules and routes it. Qualified applicants flow into HubSpot as new contacts with their form data attached. Borderline submissions get flagged for manual review. Disqualified applications close automatically with a templated response. The routing logic lives in Make, which means you can adjust criteria without a developer.
HubSpot manages the pipeline from that point. Each qualified applicant becomes a contact with a deal attached: stage set to Inquiry, submission data in the contact notes, follow-up tasks auto-created for your leasing agent. The CRM becomes the single source of truth for who is in the funnel, at what stage, and what action is next. No inbox triage. No spreadsheet updates.
OpenAI is the scoring layer. For each application that clears the Typeform gate, Make sends the full responses to OpenAI with a prompt tuned to your criteria. The model scores the application against your thresholds, flags anything worth a second look, and writes a one-paragraph assessment. That assessment lands automatically in the HubSpot contact record. Your leasing agent reads a structured judgment, not a stack of raw answers.
Google Workspace closes the loop. Approved applicants receive a templated email with next steps: document requests, showing availability, lease timeline. Responses thread back through Gmail and are logged in the CRM contact. Nothing lives in personal inboxes. The paper trail is clean from day one.
What Changes
The math is straightforward. Typeform costs four dollars a month on the basic plan. Make costs nine dollars. HubSpot, OpenAI, and Google Workspace are either free at this volume or already part of your existing stack. Total new spend: eleven dollars a month.
What actually changes is where your team's attention goes. A leasing agent who was reading 60 inquiries a week to find 12 viable applicants now opens a CRM dashboard with 12 scored contacts already ranked. Showings get scheduled faster. Qualified tenants do not fall through while someone works through their inbox. Vacancy cycles get shorter because response time drops.
At scale, the savings compound. If you manage 30 units and turn five of them each year, cutting three days from each vacancy cycle pays for this entire stack roughly 40 times over in recovered rent alone.
What It Takes to Build This
This stack took two days to configure from scratch. The Typeform logic, the Make routing rules, the HubSpot pipeline stages, the OpenAI scoring prompt, the Gmail response templates. Everything connected without writing a single line of custom code.
The prerequisite was clarity about the qualifying criteria before starting. If you cannot answer the question "what makes an applicant worth a showing" in three or four sentences, the automation will not help you, because you are still making those decisions manually at the end. The stack automates execution, not judgment. Get the judgment written down first.
That pattern holds across every workflow like this. The tools are inexpensive. The configuration is a few days of focused work. The value is not in the technology. It is in taking work that currently lives in someone's head and inbox and giving it a structure that runs without them.
Montebelle