Use AI to Pre-Qualify Your Rental Applicants
Yes, AI can pre-qualify rental applicants using your specific criteria. This system scores applicants automatically, flagging the best candidates for agents.
The system connects to your application source, like your website or a listing service, and runs each applicant through a series of checks. This includes income verification, credit history patterns, and custom red flags that you define. Complexity depends on the number of data sources and the uniqueness of your screening rules.
We built a pre-qualification system for a regional property management company with 12 leasing agents. They processed 800 applications per month, and agents spent 20 minutes on each one. The system now scores and sorts every new applicant in under 30 seconds, reducing manual review time by over 90%.
What Problem Does This Solve?
Most property management platforms like AppFolio or Yardi have built-in screening, but it is rigid. They run a credit check and a background check, but they cannot interpret nuance. An applicant with a 650 credit score but zero late payments in three years is often flagged the same as someone with a 650 score and recent collections. The rules are binary and lack the context your experienced agents use.
A property manager in Austin handling 500 units receives 40 applications for a single property. Their PMS flags 15 as "denied" based on a credit score below 620. An agent must then manually review the other 25. They find one has 5x the rent in income but a medical collection from two years ago. Another has a perfect credit score but an unstable job history with four jobs in 12 months. The PMS cannot distinguish these cases, so agents waste hours sifting through "qualified" but unsuitable applicants.
These off-the-shelf systems use simple if-then logic. They check if `credit_score > 620` and if `income > 3 * rent`. They cannot build a holistic profile by weighting factors, like penalizing recent evictions more heavily than a low credit score from student loans. This forces leasing agents to become manual data analysts for every single application, defeating the purpose of the initial screening.
How Does It Work?
We connect directly to your application intake source via API or a webhook. Using a Python script, we extract over 15 distinct data points from each application, including employment history, income verification documents, and rental history. We use the Claude API to parse unstructured text from applicant notes or uploaded PDFs, converting them into structured data stored in a Supabase database.
The core logic is a scoring model built in Python. Instead of rigid rules, we define a weighted scoring system based on your ideal tenant profile, trained on 12 months of your past application data. For income, we use Plaid's API to verify bank statements directly, confirming stated income with 99% accuracy. This avoids manual document review and catches fraud. The final output is a 0-100 score with a summary of positive and negative factors.
The scoring logic is packaged into a FastAPI application and deployed on AWS Lambda. When a new application arrives, the API is triggered and returns a score in under 200ms. We write this score and a summary back into a custom field in your existing property management software, like RentManager or Entrata. Agents see the score directly on their dashboard without needing a new login. The entire system runs for under $30/month in hosting costs.
We build a simple dashboard on Vercel that monitors the system's performance. It tracks the distribution of scores and flags any application that takes longer than one second to process. During a 3-week build cycle, we achieved 95% agreement between the AI's top-20% recommendations and the leasing agents' final decisions, validating the model's effectiveness before full rollout.
What Are the Key Benefits?
First Scores in 10 Business Days
From kickoff to live scoring takes just two weeks. Your agents start seeing pre-qualified applicants immediately, not after a quarter-long software rollout.
Pay for the Build, Not by the Door
A one-time engagement cost followed by minimal monthly AWS hosting fees. No per-unit, per-user, or per-application subscription fees that penalize growth.
You Get the Keys to the Code
We deliver the complete Python codebase in your private GitHub repository. You own the intellectual property and can modify it with any developer in the future.
Alerts Before Your Agents Notice a Problem
The system includes health checks that ping a monitoring service every five minutes. If an API connection fails, we get an alert and fix it, often before your team starts their day.
Writes Scores Directly into Your PMS
Integrates with AppFolio, Buildium, and Yardi via their APIs. Your team sees scores and summaries inside the tool they already use every day.
What Does the Process Look Like?
System & Criteria Audit (Week 1)
You provide API access to your application source and property management software. We review your current screening criteria and 100 historical applications to define the model's logic.
Core System Build (Week 2)
We build the data extraction pipeline, scoring engine, and API. You receive a link to a staging environment where you can test sample applications and see the scores they generate.
Integration & Live Testing (Week 3)
We connect the API to your live system in a shadow mode. It scores incoming applicants while agents follow the old process. You receive a report comparing the AI scores to your team's decisions.
Go-Live & Monitoring (Week 4+)
After successful testing, the system goes fully live. We provide a runbook with documentation and monitor performance for 30 days. You have direct access to the engineer who built it for support.
Frequently Asked Questions
- How much does a custom pre-qualification system cost?
- The final price depends on the number of application sources and the complexity of your screening rules. A single-source system is a straightforward build. Integrating with multiple state-specific legal databases or parsing non-standard documents increases scope. We provide a fixed-price quote after our initial discovery call, so you know the full cost upfront.
- What happens if the system incorrectly denies a good applicant?
- The AI system provides a score and a recommendation, not a final decision. It flags the top 20% of applicants for priority review and provides clear reasons for low scores. Your leasing agents always make the final call. This keeps you compliant with Fair Housing laws and ensures a human is in the loop for edge cases the model may misinterpret.
- How is this different from the screening built into AppFolio?
- AppFolio's screening is a pass/fail check on credit and background. We build a custom decisioning model that reflects your specific business logic. It can weigh 15+ factors, understand that high income can offset a mediocre credit score, and rank applicants instead of just approving or denying them. This focuses agent time on the best fits.
- Is this system compliant with Fair Housing laws?
- Yes. We build the logic based on your existing, compliant screening criteria. The system automates your rules consistently, reducing the risk of human bias or error. We do not use demographic data as inputs. The final decision is always made by a human agent, which is a key requirement for compliance. We provide a full audit trail of every score generated.
- What if we change our screening criteria in the future?
- The scoring logic is documented in your code repository. A small change, like adjusting the minimum income-to-rent ratio from 3x to 3.5x, is a single line of code. We can make these adjustments for you on an hourly basis, or your own technical staff can do it. The runbook we provide details how to modify these core parameters.
- What technical resources do we need on our end?
- None. Syntora handles the entire build, deployment, and maintenance. All you need to provide is API access to your existing systems. We can host the system on our AWS account or yours, whichever you prefer. After the initial monitoring period, we offer an optional support plan for ongoing changes, but no internal technical team is required to operate the system.
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