Skip to main content

Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o

Workflow preview

Workflow preview
100%
Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o preview
Open on n8n.io

1. Workflow Overview

How It Works This workflow automates weekly capital expenditure (CAPEX) forecasting for property portfolios using a multi agent AI architecture. It targets property managers, asset managers, and fa...

Best for

  • Engineering automation workflows
  • AI RAG automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.scheduletrigger, n8n-nodes-base.googlesheets, n8n-nodes-base.merge, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.splitout, @n8n/n8n-nodes-langchain.agenttool

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Cheng Siong Chin.

Original n8n.io source

1.1 Workflow description

Title
Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o
Workflow name
Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o

How It Works

This workflow automates weekly capital expenditure (CAPEX) forecasting for property portfolios using a multi-agent AI architecture. It targets property managers, asset managers, and facilities finance teams who need data-driven maintenance budgeting without manual spreadsheet analysis. Three Google Sheets sources, namely: maintenance records, property data, and tenant feedback, are merged into a unified dataset. A Main Prediction Agent orchestrates three specialist sub-agents: a CAPEX Prioritizer that ranks spending needs, an ROI Simulator that models return scenarios, and a Quote Requester that fetches vendor estimates. Each agent is backed by dedicated AI models, memory, and tools including a Calculator and Financial Modeling Tool. Structured predictions are parsed, split by category, formatted, saved back to Google Sheets, and pushed to an external budgeting system via POST, delivering a fully automated, auditable CAPEX planning pipeline every week.

Setup Steps

  1. Connect Google Sheets credentials to all three read nodes and the Save Predictions node.
  2. Set correct Sheet IDs for maintenance, property, and tenant feedback tabs.
  3. Add Claude or OpenAI API credentials to all Chat Model nodes.
  4. Configure the Financial Modeling Tool with your cost rate assumptions.
  5. Replace the POST placeholder URL in Update Budgeting System with your actual endpoint.

Prerequisites

  • Google Sheets account with populated maintenance, property, and tenant data
  • Claude or OpenAI API credentials
  • External budgeting system with a POST-compatible API endpoint

Use Cases

  • Weekly CAPEX forecasting for multi-property real estate portfolios
  • Automated ROI modelling for planned renovations or equipment replacement

Customization

Add more data sources (e.g., IoT sensors, ERP exports).

Benefits

Eliminates manual CAPEX spreadsheet work with autonomous AI forecasting.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Weekly Maintenance Analysis

Type / Role
n8n-nodes-base.scheduleTrigger - scheduleTrigger
Config choices
Version 1.3

Block 2 - Get Maintenance Data

Type / Role
n8n-nodes-base.googleSheets - googleSheets
Config choices
Version 4.7

Block 3 - Get Property Data

Type / Role
n8n-nodes-base.googleSheets - googleSheets
Config choices
Version 4.7

Block 4 - Get Tenant Feedback

Type / Role
n8n-nodes-base.googleSheets - googleSheets
Config choices
Version 4.7

Block 5 - Combine All Data

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.2

Block 6 - Main Prediction Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 3.1

Block 7 - Main Agent Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 8 - Prediction Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 9 - Split Predictions

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 10 - CAPEX Prioritizer Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 11 - CAPEX Agent Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 12 - ROI Simulator Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 13 - ROI Agent Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 14 - Quote Requester Agent

Type / Role
@n8n/n8n-nodes-langchain.agentTool - agentTool
Config choices
Version 3

Block 15 - Quote Agent Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 16 - Calculator Tool

Type / Role
@n8n/n8n-nodes-langchain.toolCalculator - toolCalculator
Config choices
Version 1

Block 17 - Financial Modeling Tool

Type / Role
@n8n/n8n-nodes-langchain.toolCode - toolCode
Config choices
Version 1.3

Block 18 - Format Results

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 19 - Save Predictions

Type / Role
n8n-nodes-base.googleSheets - googleSheets
Config choices
Version 4.7

Block 20 - Update Budgeting System

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.4

Block 21 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 22 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 23 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 24 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Showing the first 24 of 26 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o
Complexity advanced
Nodes 26
Categories Engineering, AI RAG
Author Cheng Siong Chin
Published 13 Apr 2026

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/15027/15027.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o do?

How It Works This workflow automates weekly capital expenditure (CAPEX) forecasting for property portfolios using a multi agent AI architecture. It targets property managers, asset managers, and fa...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.