Is HelloData market intelligence, or does it set rents for me?

HelloData is strictly market intelligence — rent comps, unit-mix, concession data, and submarket benchmarks observed from the listing market. It is a tool for reading what the market is doing, not for setting, coordinating, or recommending your rents. Every automation we describe uses HelloData to surface observed-market context (a comp set, a concession trend, a loss-to-lease estimate) and explicitly leaves all pricing and operating decisions to your team. We deliberately do not build rent-setting or rent-coordination automations on top of HelloData; the value is in giving your analysts and asset managers a current, structured market read to inform decisions they make.

Does HelloData have a documented API I can build automations on?

Yes. HelloData provides a documented, developer-accessible API that returns structured JSON for rent comps, unit-mix, concessions, and submarket benchmarks. You authenticate with an API key and can call it directly from n8n, Make, Python, or any HTTP client — this is a genuine native-api integration, not a scrape or an export-only workaround. Available data and market coverage depend on your HelloData plan, so confirm your modules and geographies with HelloData before scoping a workflow.

How does HelloData improve a multifamily underwriting model?

The most scrutinized input in any multifamily model is the rent comp, and assembling it manually from listing sites and broker surveys is slow and quickly stale. HelloData lets an automation pull a current, floorplan-level comp set the moment a target enters your pipeline, normalize advertised rents to effective rents by netting out concessions, and write those inputs straight into the revenue section of your model. Your analyst starts from observed market data instead of spending an afternoon building comps by hand, and the revenue side of the pro forma stays anchored to what the market is actually advertising.

Can HelloData monitor the market around the properties I already own?

Yes — this is one of the strongest use cases. You define a comp set around each owned property and schedule HelloData to re-pull it (monthly is typical), so you can observe how the competitive market moves: when comp effective rents shift, when concessions start appearing on a competitor's lease-up, or when local supply changes. The automation surfaces these movements to your asset managers as context for their own renewal and operating decisions. Again, this is market observation and benchmarking — the workflow reports what the market did; it does not set or recommend your rents.

What is effective rent and why does HelloData normalize to it?

Advertised (asking) rent is the headline number on a listing; effective rent is what a tenant actually pays once concessions — free-rent periods, waived fees, specials — are netted out. In a concession-heavy market, underwriting to asking rents systematically overstates revenue. HelloData observes advertised concessions across a comp set and normalizes asking rents into effective rents, giving your model the honest number. That difference is frequently the line between a deal that pencils and one that disappoints in its first year of operations.

How is HelloData different from a property-data source like ATTOM or a data-fabric like Cherre?

They cover different layers. ATTOM provides nationwide property, ownership, tax, and transaction data — it tells you who owns an asset and what it last traded for. Cherre is a data-unification fabric that connects and normalizes many sources into one queryable layer. HelloData is specifically a multifamily rent-comp and market-survey source: effective rents, unit mix, and concessions observed from the listing market. In a complete stack they are complementary — ATTOM and Cherre for ownership and unified data, HelloData for the competitive rent picture that drives multifamily revenue assumptions.

Do I need a developer to integrate HelloData with my tools?

Not necessarily. HelloData's API returns standard JSON and authenticates with a single API key header, both compatible with n8n's HTTP Request node without custom code. An operations professional comfortable with n8n or Make can build comp-enrichment and monitoring workflows themselves. The main effort is mapping HelloData's response structure into your underwriting model or asset register, which calls for reading the API documentation and basic JSON-path work. For larger builds — portfolio-wide monitoring, custom benchmarking reports, or batch comp pulls — a developer will accelerate the work.

HelloData Integration

Data & Market Intelligence

What It Does

HelloData is a multifamily market-data platform that turns the public listing market into structured rent-comp intelligence. By continuously reading apartment listings across the country, it produces automated rent comparables, unit-mix breakdowns, concession data, and submarket benchmarks — the same competitive-market picture an analyst would assemble by hand from a dozen ILS sites and brokerage surveys, refreshed automatically.

Why Real Estate Professionals Use It

Multifamily underwriting and asset management both live or die on the quality of the rent comp. When you underwrite an acquisition, your projected revenue is only as credible as the comparable set behind it — and assembling that set manually from Apartments.com, Zillow, and a broker's market survey is slow, inconsistent, and out of date by the time it reaches the model. HelloData replaces that ritual with an API call: query the subject property and receive a structured comp set with effective rents, unit mix, and concession data you can drop straight into your pro forma.

Visit HelloData

AI Solutions for Data

Ebook

CTO's Guide to AI Implementation

Technical framework for CTOs evaluating AI solutions with Build vs Buy analysis

Solution

Property Data Enrichment

Auto-enrich contacts with property ownership, tax records, and market data

Solution

Universal CRM Sync Hub

Keep HubSpot, Follow Up Boss, Salesforce, and spreadsheets in perfect sync

Key Features for Real Estate

Discover how HelloData powers real estate automation workflows

1 of 6

Feature

Automated Rent Comparables

Generate a comparable set of competing multifamily communities for a subject property, with advertised and effective rents, sourced and refreshed from live listing data rather than a one-time manual survey.

Real Estate Impact

The rent comp is the single most scrutinized input in a multifamily underwriting model. Pulling a current, structured comp set via API removes hours of manual ILS shopping and keeps the revenue side of your pro forma anchored to what the market is actually advertising today.

See how we automate HelloData

Get a personalized plan for your tech stack

Get Your Free AI Roadmap

Automation Workflows

Ready-to-deploy workflows powered by HelloData + NextAutomation

Workflow 1 of 4

Intermediate

New Multifamily Target → Auto Rent-Comp + Unit-Mix Into Underwriting Model

When a new multifamily acquisition target enters your pipeline, this workflow queries HelloData for a comparable set and unit-mix breakdown around the subject property, normalizes asking rents to effective rents, and writes the comp inputs directly into the revenue section of your underwriting model — so the analyst opens a deal that already has a current, structured comp set attached.

Trigger: New multifamily deal created in your pipeline (webhook from a deal-intake form, Airtable row created, or CRM stage change to 'Screening')

Workflow Steps

1n8n receives the subject property address and asset type from the trigger source

2n8n calls the HelloData comps endpoint with the address to retrieve a comparable set with advertised rents and unit mix

3n8n requests concession data for the comp set and normalizes advertised rents to effective rents

4AI node groups comps by floorplan and matches each to the subject property's unit types for like-for-like comparison

+3 more steps

Every new multifamily target arrives with a current, floorplan-level rent-comp set already in the model. Analysts start from observed market data instead of spending an afternoon assembling comps by hand, increasing screening throughput without adding headcount.

Triggers & Actions

Connect HelloData to your workflows with powerful triggers and actions

Triggers

Actions

Fetch Rent Comparables for a Property

Query HelloData with a subject property address to retrieve a comparable set of competing multifamily communities with advertised rents and basic property attributes.

When a new multifamily target enters the pipeline, pull a current comp set so the underwriting model starts from observed market rents rather than a manually assembled survey.

Fetch Unit-Mix and Floorplan Data

Retrieve the unit-mix and floorplan-level detail for the comp set — bed/bath configuration, square footage, and per-floorplan rent — for like-for-like comparison.

Match each comp's floorplans to the subject asset's unit types so the model compares studios to studios and two-beds to two-beds instead of a blended average.

Fetch Concession Data

Retrieve advertised concessions across the comp set — free-rent periods, waived fees, specials — and the prevalence of concessions in the surrounding submarket.

Monitor when concessions begin appearing across an owned asset's comp set as an early, observational read on softening demand to factor into renewal planning.

Fetch Submarket Benchmarks

Retrieve aggregated submarket-level benchmarks — typical effective rents by unit type, concession rates, and supply trends — for a target geography.

Assemble an objective submarket backdrop for an investment-committee memo, positioning a prospective acquisition against current market benchmarks.

Compute Effective Rents

Net observed concessions out of advertised asking rents to return effective rents for the comp set, reflecting what tenants actually pay.

Underwrite to effective rather than advertised rents in a concession-heavy submarket so projected revenue reflects real market conditions, not headline asking numbers.

Refresh a Saved Comp Set

Re-pull a previously defined comp set for an owned property to retrieve the latest market data and observe period-over-period change.

Run a monthly refresh of each owned property's comp set and surface month-over-month movement in market effective rents and concession prevalence to the asset-management team.

Setup Guide

Get started in approximately 45 minutes for the API test and first comp-enrichment workflow; 2-3 hours for the full owned-asset monitoring and benchmarking suite

Prerequisites

HelloData account with API access (contact HelloData at hellodata.ai to confirm API access and the comp/benchmark modules included in your plan)
HelloData API key (provisioned in your HelloData account after activation)
n8n instance (cloud or self-hosted) with the HTTP Request node — no dedicated HelloData node is required; all calls are standard REST
A destination for comp data: an underwriting model (Google Sheets), a deal/asset register (Airtable), or your CRM
Basic familiarity with JSON and REST API key authentication

Activate HelloData and Retrieve Your API Key

Sign in to your HelloData account at hellodata.ai and confirm your plan includes API access to the comp, unit-mix, and benchmark data you need. Navigate to the API or developer section of your account and copy your API key — you will pass it as an authentication header on every request.

Confirm with HelloData which markets and data modules your plan covers before building. Comp depth varies by geography; testing in a market you know well makes it easy to sanity-check the data the API returns.

Test the API with a Single Property Comp Pull

Before building any workflow, confirm your key works. In n8n, add an HTTP Request node, set the method and the HelloData comps endpoint, pass a known subject property address, and add your API key in the Authorization header along with `Accept: application/json`. Execute the node and confirm you receive a structured comp set in the response.

Store the HelloData API key as an n8n credential (Header Auth) so you can reuse it across every workflow without re-entering it, and so it never appears in plain text inside individual nodes.

Map the Comp Response to Your Model Fields

HelloData returns structured comp, unit-mix, and concession data. In n8n, add a Set or Code node after the HTTP Request to flatten the response into the fields your model expects — comp name, unit type, advertised rent, effective rent, and concession flag — and map those to the revenue section of your underwriting model or the asset record.

Build a reusable 'HelloData Comp Pull' sub-workflow that takes an address and returns a flat, floorplan-grouped object. Reference it from every acquisition and asset-management workflow so the mapping logic lives in one place.

Build the New-Target Comp Enrichment Workflow

Create your first end-to-end flow: trigger (deal-intake form or CRM webhook) → HTTP Request (HelloData comps) → HTTP Request (concessions) → Code node (normalize to effective rents and group by floorplan) → Sheets/Airtable node (write comp set into the model). Run it against a real multifamily address in a covered market and verify the comp inputs populate correctly.

Add an error branch after the comp request. If HelloData returns thin or no comps for an address (common in low-density or uncovered submarkets), route the deal to a 'Needs Manual Comp Review' flag instead of writing an empty comp set into the model.

Schedule the Owned-Asset and Benchmarking Workflows

For recurring monitoring (monthly owned-asset comp refresh, weekly loss-to-lease context digest, quarterly submarket benchmarking), add a Cron Trigger at the start of each workflow and configure the cadence. Activate the workflows and review the first few runs to confirm the comp sets, thresholds, and report formatting are correct before relying on the alerts.

Log a short run summary to Slack or a sheet at the end of each scheduled run (e.g., 'Portfolio comp refresh: 14 assets updated, 3 flagged Market Moved'). It makes it easy to catch a workflow silently returning empty data if coverage or the API changes.

Frequently Asked Questions

Common questions about HelloData integration

Related Integrations

Explore other tools that work great with your workflow

Buildium

Payments & Accounting

Property-management and accounting platform — automate rent rolls, owner statements, and maintenance across a mixed or commercial-leaning portfolio.

Cherre

AI & Automation

CRE data-of-record platform — connect, normalize, and enrich property, ownership, tax, and market data across every source via one API layer.

ATTOM Data

AI & Automation

Nationwide property, parcel, ownership, and tax data via a documented REST API — enrich your deal pipeline and source off-market targets.

AppFolio

Payments & Accounting

Property-management and accounting platform for multifamily and commercial portfolios — automate via the AppFolio Stack partner API once your firm is approved.

Placer.ai